ggpubr/0000755000176200001440000000000014371467262011554 5ustar liggesusersggpubr/NAMESPACE0000644000176200001440000000661714370060431012767 0ustar liggesusers# Generated by roxygen2: do not edit by hand S3method(drawDetails,splitText) S3method(print,ggscatterhist) S3method(print,ggsummarystats) S3method(print,ggsummarystats_list) export("%>%") export(add_summary) export(annotate_figure) export(as_ggplot) export(as_npc) export(as_npcx) export(as_npcy) export(background_image) export(bgcolor) export(border) export(change_palette) export(clean_table_theme) export(clean_theme) export(colnames_style) export(color_palette) export(compare_means) export(create_aes) export(desc_statby) export(facet) export(fill_palette) export(font) export(geom_bracket) export(geom_exec) export(geom_pwc) export(geom_signif) export(get_breaks) export(get_coord) export(get_legend) export(get_palette) export(get_summary_stats) export(ggadd) export(ggadjust_pvalue) export(ggarrange) export(ggballoonplot) export(ggbarplot) export(ggboxplot) export(ggdensity) export(ggdonutchart) export(ggdotchart) export(ggdotplot) export(ggecdf) export(ggerrorplot) export(ggexport) export(gghistogram) export(ggline) export(ggmaplot) export(ggpaired) export(ggpar) export(ggparagraph) export(ggpie) export(ggpubr_options) export(ggqqplot) export(ggscatter) export(ggscatterhist) export(ggstripchart) export(ggsummarystats) export(ggsummarytable) export(ggtext) export(ggtexttable) export(ggviolin) export(gradient_color) export(gradient_fill) export(grids) export(group_by) export(labs_pubr) export(mean_ci) export(mean_range) export(mean_sd) export(mean_se_) export(median_hilow_) export(median_iqr) export(median_mad) export(median_q1q3) export(median_range) export(mutate) export(npc_to_data_coord) export(rotate) export(rotate_x_text) export(rotate_y_text) export(rownames_style) export(rremove) export(set_palette) export(show_line_types) export(show_point_shapes) export(stat_anova_test) export(stat_bracket) export(stat_central_tendency) export(stat_chull) export(stat_compare_means) export(stat_conf_ellipse) export(stat_cor) export(stat_friedman_test) export(stat_kruskal_test) export(stat_mean) export(stat_overlay_normal_density) export(stat_pvalue_manual) export(stat_pwc) export(stat_regline_equation) export(stat_stars) export(stat_welch_anova_test) export(tab_add_border) export(tab_add_footnote) export(tab_add_hline) export(tab_add_title) export(tab_add_vline) export(tab_cell_crossout) export(tab_ncol) export(tab_nrow) export(table_cell_bg) export(table_cell_font) export(tbody_add_border) export(tbody_style) export(text_grob) export(thead_add_border) export(theme_classic2) export(theme_cleveland) export(theme_pubclean) export(theme_pubr) export(theme_transparent) export(ttheme) export(xscale) export(yscale) import(ggplot2) importFrom(dplyr,arrange) importFrom(dplyr,desc) importFrom(dplyr,distinct) importFrom(dplyr,do) importFrom(dplyr,everything) importFrom(dplyr,group_by) importFrom(dplyr,group_nest) importFrom(dplyr,mutate) importFrom(dplyr,mutate_if) importFrom(dplyr,pull) importFrom(dplyr,select) importFrom(dplyr,summarise) importFrom(ggsignif,geom_signif) importFrom(glue,glue) importFrom(grid,drawDetails) importFrom(magrittr,"%>%") importFrom(purrr,map) importFrom(purrr,map2) importFrom(rlang,"!!!") importFrom(rlang,"!!") importFrom(rlang,.data) importFrom(rlang,syms) importFrom(rstatix,df_arrange) importFrom(rstatix,df_group_by) importFrom(rstatix,df_nest_by) importFrom(rstatix,df_select) importFrom(rstatix,df_split_by) importFrom(rstatix,get_summary_stats) importFrom(tibble,as_tibble) importFrom(tidyr,unite) ggpubr/tools/0000755000176200001440000000000014332653662012712 5ustar liggesusersggpubr/tools/README-ordered-bar-plots-1.png0000644000176200001440000015023114334142172020027 0ustar liggesusersPNG  IHDR@MnEiCCPkCGColorSpaceGenericRGB8U]hU>sg#$Sl4t? % V46nI6"dΘ83OEP|1Ŀ (>/ % (>P苦;3ie|{g蹪X-2s=+WQ+]L6O w[C{_F qb Uvz?Zb1@/zcs>~if,ӈUSjF 1_Mjbuݠpamhmçϙ>a\+5%QKFkm}ۖ?ޚD\!~6,-7SثŜvķ5Z;[rmS5{yDyH}r9|-ăFAJjI.[/]mK 7KRDrYQO-Q||6 (0 MXd(@h2_f<:”_δ*d>e\c?~,7?& ك^2Iq2"y@g|U\ 8eXIfMM*i@6G:@IDATx$E& Q""I0AgEEѿDEE}U<I% E$,yI|Kjizޝ{W;==]Uߪ:uI- " " " CD`UE@@4tUHR:U` @j" " " CG@U ," " " Hm@D@D@D`H*WE@D@D@$   @CW* # h\6 " " "0t$ ]" " " S@D@ `a_?ɓlfhl.\hVyaʔ)}fD`b'|b2SD`B@P9ߗ-T{6k%D&MbOs-|˺m-]\ɓ'_W%k9S" y$Q9RGWRuV^rL 2-_/ɋDfSy_, Ph*N@6@U>:D' hװ'" " "A@PAg?M4_J!" v "0Pzq'͙3ƪB͛gsXN" hC&`}# y{k_v_k='|r\cmᆶ+_J?9.L@HI#@`ܹvی3`uw ;lG}>6˗~vu׍=vmݞz)w3 s׾6VE4Bξk^{}ӟB?yc:3mu׵o}[}YfUW]._v{OSO=wxs}نSC~zOVZɖ[nUlڎ iUqE-Yge3wӎ=X 즛n-v뭷ڕW^.׏~#e]lUVi>h('hK.xȲ=v_}Wb/=[{oc) mE&UqE mY;;aw=϶n;[k'?I!E#C9${7'96׿>_K_}߰N:i\hRhC/~6{l?a D@0ն:W_#lm'79,a;KHw^.`tw.Ʋ.;0{_ w!h3v7oo| Ӡ]~/JGL g.-o}[2v?^Kg wv=yv饗уV]uՠEYm\a²XĀ\}a4fm")}' hW 8^ lFa@J~7/&lboq_r\.xv5״wz^g?Oo7H|aK4V?yǘ!%WUqGx=Yϲ7M۬Ynb-lvk-KӦMq}FQG4'k=ϵӧG>Q_'>aks̙viVtD` hא7X;s똹; vpAJ%a_״mF/}'=QN # "0$ c@_<}dZqW8eXD@ @n) ~K,a_|q?T" "04&54UAE` oe2Y.^{8[j)&czZkޜ~ 4&էʓ?v_UOR{\FXe q;nq>~izjIIh?ٞ "PM@P5#]!" 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Amk 7Ƞ #jk3Spd{挾]A4 TP( #iMAc̘(r H')tDfu"mGvHJ9ŮOv6 :ҥ2nSN'/ԩCoذ&Osʢ=([_-[RŊ=DK,yJ|}&Lwԉ+Jr֬YrJ{jըI{Op[}[] nIwO g0̉AdEr+8Y#BBXc?Ծ@e0"WamI'ZZI,'Y4"yVL/`N R'{8vt26`{Z!i=((tzO1#w/RN&aVvIw(?_3``6*yL8KK}(ދׇN~kAӭA_P-(^6Ļfă}@+Ruk>ۦdMkS6T{f9^"mu)h68ݸqO巺|۬3x@9,k6Z碌Lw (kU~?L[[JA'AiR2NlU/l(e8f.*mi|uqaKڄ#(!(ab U9b%wZ<' !孟Y 9&̘Dx< HwclM䓌/r":Pc"@* ߇74SBsRic[;](O̷ B}4j(zuz:.ӧZ9&tԉ1Җ qK$1Ɏ; ޠ| ag}r_k~4XPE@*v("P =;zyL; )<dN\Dv(|u:묔ᆪm)wH/~~O't 26oޜ2pt:t(S~wIRAͷpiѣGi̙oɓS8뮻ړO>)P/>(g 3RW^yf̘aݐm)|'^n Fk֬y.X@,7w-/(Vu;t󩧞}'k=| /`wߕoG}l?ܴiKA:߬:+)9E ;[: m۶pP ꫯ9t2~Gsu =z0 ÇҔrW 3[oM`Eܣ;_0 ^NRũ/u~ي ; 4iRz2PIPͷ]{rp< &hMDd9tPZ,eLyfh-ZnYi0b#a?7bYO^z,;|bfǵ$c k ތ6S:ެ\tEދ=pbSsRX/{뭷.}> uyFl`̻OBŸŞX?曒V;w#'pp@NMrE L7XOo oIB/i3As~É G3Xdel.uX> .K)X|2pad֭['9&1glL.] t0l|Ax7u`brȽϻu\;w5`f\7d1°'M*Hr yAǿG9~B|dpo{eBM7lrx8`Ar݋/8gqhR:̱|um5Dے(v$!+P6&jY4.~A'6B8RpѩjdYͷ `'\(": E@PD@ӦVE@pϠ_PEIT;9m:hE@PP E@PD@ӦVE@pϠ_PEIT;9m:hE@PP GFO6MݛV\I6SZZJ;vѣGKV H I8 ={vB&"H Hp7O?MW_}׌~*9E@#a^m\Pj@˖-'2/{9IاOjժ;>Snʔ)j*j۶-qdK :/{W^r`nJXmڴ)5o<~w-M43FLk׮"+Tڮ"4p aye`J@>PE@hP ҥK%e!q 3Xu0 wn3E"pA_[n1V$8|xF#쥒"/T iGPE@!&hz(" BZQE@P|"(@P/E@PE `z(" BZQE@P|?lv&IENDB`ggpubr/README.md0000644000176200001440000003162114334142204013017 0ustar liggesusers [![R-CMD-check](https://github.com/kassambara/ggpubr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/kassambara/ggpubr/actions/workflows/R-CMD-check.yaml) [![CRAN\_Status\_Badge](https://www.r-pkg.org/badges/version/ggpubr)](https://cran.r-project.org/package=ggpubr) [![Downloads](https://cranlogs.r-pkg.org/badges/ggpubr)](https://cran.r-project.org/package=ggpubr) [![Total Downloads](https://cranlogs.r-pkg.org/badges/grand-total/ggpubr?color=orange)](https://cran.r-project.org/package=ggpubr) ggpubr: ‘ggplot2’ Based Publication Ready Plots =============================================== [ggplot2, by Hadley Wickham,](https://ggplot2.tidyverse.org/) is an excellent and flexible package for elegant data visualization in R. However the default generated plots requires some formatting before we can send them for publication. Furthermore, to customize a ggplot, the syntax is opaque and this raises the level of difficulty for researchers with no advanced R programming skills. The ‘ggpubr’ package provides some easy-to-use functions for creating and customizing ‘ggplot2’- based publication ready plots. Find out more at https://rpkgs.datanovia.com/ggpubr/. Installation and loading ------------------------ - Install from [CRAN](https://cran.r-project.org/package=ggpubr) as follow: ``` r install.packages("ggpubr") ``` - Or, install the latest version from [GitHub](https://github.com/kassambara/ggpubr) as follow: ``` r # Install if(!require(devtools)) install.packages("devtools") devtools::install_github("kassambara/ggpubr") ``` Distribution ------------ ``` r library(ggpubr) #> Loading required package: ggplot2 # Create some data format # ::::::::::::::::::::::::::::::::::::::::::::::::::: set.seed(1234) wdata = data.frame( sex = factor(rep(c("F", "M"), each=200)), weight = c(rnorm(200, 55), rnorm(200, 58))) head(wdata, 4) #> sex weight #> 1 F 53.79293 #> 2 F 55.27743 #> 3 F 56.08444 #> 4 F 52.65430 # Density plot with mean lines and marginal rug # ::::::::::::::::::::::::::::::::::::::::::::::::::: # Change outline and fill colors by groups ("sex") # Use custom palette ggdensity(wdata, x = "weight", add = "mean", rug = TRUE, color = "sex", fill = "sex", palette = c("#00AFBB", "#E7B800")) ``` ![](tools/README-ggpubr-1.png) ``` r # Histogram plot with mean lines and marginal rug # ::::::::::::::::::::::::::::::::::::::::::::::::::: # Change outline and fill colors by groups ("sex") # Use custom color palette gghistogram(wdata, x = "weight", add = "mean", rug = TRUE, color = "sex", fill = "sex", palette = c("#00AFBB", "#E7B800")) ``` ![](tools/README-ggpubr-2.png) Box plots and violin plots -------------------------- ``` r # Load data data("ToothGrowth") df <- ToothGrowth head(df, 4) #> len supp dose #> 1 4.2 VC 0.5 #> 2 11.5 VC 0.5 #> 3 7.3 VC 0.5 #> 4 5.8 VC 0.5 # Box plots with jittered points # ::::::::::::::::::::::::::::::::::::::::::::::::::: # Change outline colors by groups: dose # Use custom color palette # Add jitter points and change the shape by groups p <- ggboxplot(df, x = "dose", y = "len", color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"), add = "jitter", shape = "dose") p ``` ![](tools/README-ggpubr-box-plot-dot-plots-strip-charts-1.png) ``` r # Add p-values comparing groups # Specify the comparisons you want my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") ) p + stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value stat_compare_means(label.y = 50) # Add global p-value ``` ![](tools/README-ggpubr-box-plot-dot-plots-strip-charts-2.png) ``` r # Violin plots with box plots inside # ::::::::::::::::::::::::::::::::::::::::::::::::::: # Change fill color by groups: dose # add boxplot with white fill color ggviolin(df, x = "dose", y = "len", fill = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07"), add = "boxplot", add.params = list(fill = "white"))+ stat_compare_means(comparisons = my_comparisons, label = "p.signif")+ # Add significance levels stat_compare_means(label.y = 50) # Add global the p-value ``` ![](tools/README-ggpubr-box-plot-dot-plots-strip-charts-3.png) Bar plots --------- ### Demo data set Load and prepare data: ``` r # Load data data("mtcars") dfm <- mtcars # Convert the cyl variable to a factor dfm$cyl <- as.factor(dfm$cyl) # Add the name colums dfm$name <- rownames(dfm) # Inspect the data head(dfm[, c("name", "wt", "mpg", "cyl")]) #> name wt mpg cyl #> Mazda RX4 Mazda RX4 2.620 21.0 6 #> Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 6 #> Datsun 710 Datsun 710 2.320 22.8 4 #> Hornet 4 Drive Hornet 4 Drive 3.215 21.4 6 #> Hornet Sportabout Hornet Sportabout 3.440 18.7 8 #> Valiant Valiant 3.460 18.1 6 ``` ### Ordered bar plots Change the fill color by the grouping variable “cyl”. Sorting will be done globally, but not by groups. ``` r ggbarplot(dfm, x = "name", y = "mpg", fill = "cyl", # change fill color by cyl color = "white", # Set bar border colors to white palette = "jco", # jco journal color palett. see ?ggpar sort.val = "desc", # Sort the value in dscending order sort.by.groups = FALSE, # Don't sort inside each group x.text.angle = 90 # Rotate vertically x axis texts ) ``` ![](tools/README-ordered-bar-plots-1.png) Sort bars inside each group. Use the argument **sort.by.groups = TRUE**. ``` r ggbarplot(dfm, x = "name", y = "mpg", fill = "cyl", # change fill color by cyl color = "white", # Set bar border colors to white palette = "jco", # jco journal color palett. see ?ggpar sort.val = "asc", # Sort the value in dscending order sort.by.groups = TRUE, # Sort inside each group x.text.angle = 90 # Rotate vertically x axis texts ) ``` ![](tools/README-ordered-bar-plots-by-groups-1.png) ### Deviation graphs The deviation graph shows the deviation of quantitatives values to a reference value. In the R code below, we’ll plot the mpg z-score from the mtcars dataset. Calculate the z-score of the mpg data: ``` r # Calculate the z-score of the mpg data dfm$mpg_z <- (dfm$mpg -mean(dfm$mpg))/sd(dfm$mpg) dfm$mpg_grp <- factor(ifelse(dfm$mpg_z < 0, "low", "high"), levels = c("low", "high")) # Inspect the data head(dfm[, c("name", "wt", "mpg", "mpg_z", "mpg_grp", "cyl")]) #> name wt mpg mpg_z mpg_grp cyl #> Mazda RX4 Mazda RX4 2.620 21.0 0.1508848 high 6 #> Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 0.1508848 high 6 #> Datsun 710 Datsun 710 2.320 22.8 0.4495434 high 4 #> Hornet 4 Drive Hornet 4 Drive 3.215 21.4 0.2172534 high 6 #> Hornet Sportabout Hornet Sportabout 3.440 18.7 -0.2307345 low 8 #> Valiant Valiant 3.460 18.1 -0.3302874 low 6 ``` Create an ordered barplot, colored according to the level of mpg: ``` r ggbarplot(dfm, x = "name", y = "mpg_z", fill = "mpg_grp", # change fill color by mpg_level color = "white", # Set bar border colors to white palette = "jco", # jco journal color palett. see ?ggpar sort.val = "asc", # Sort the value in ascending order sort.by.groups = FALSE, # Don't sort inside each group x.text.angle = 90, # Rotate vertically x axis texts ylab = "MPG z-score", xlab = FALSE, legend.title = "MPG Group" ) ``` ![](tools/README-deviation-graphs-1.png) Rotate the plot: use rotate = TRUE and sort.val = “desc” ``` r ggbarplot(dfm, x = "name", y = "mpg_z", fill = "mpg_grp", # change fill color by mpg_level color = "white", # Set bar border colors to white palette = "jco", # jco journal color palett. see ?ggpar sort.val = "desc", # Sort the value in descending order sort.by.groups = FALSE, # Don't sort inside each group x.text.angle = 90, # Rotate vertically x axis texts ylab = "MPG z-score", legend.title = "MPG Group", rotate = TRUE, ggtheme = theme_minimal() ) ``` ![](tools/README-deviation-graphs-horizontal-1.png) Dot charts ---------- ### Lollipop chart Lollipop chart is an alternative to bar plots, when you have a large set of values to visualize. Lollipop chart colored by the grouping variable “cyl”: ``` r ggdotchart(dfm, x = "name", y = "mpg", color = "cyl", # Color by groups palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette sorting = "ascending", # Sort value in descending order add = "segments", # Add segments from y = 0 to dots ggtheme = theme_pubr() # ggplot2 theme ) ``` ![](tools/README-lollipop-chart-1.png) - Sort in decending order. **sorting = “descending”**. - Rotate the plot vertically, using **rotate = TRUE**. - Sort the mpg value inside each group by using **group = “cyl”**. - Set **dot.size** to 6. - Add mpg values as label. **label = “mpg”** or **label = round(dfm$mpg)**. ``` r ggdotchart(dfm, x = "name", y = "mpg", color = "cyl", # Color by groups palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette sorting = "descending", # Sort value in descending order add = "segments", # Add segments from y = 0 to dots rotate = TRUE, # Rotate vertically group = "cyl", # Order by groups dot.size = 6, # Large dot size label = round(dfm$mpg), # Add mpg values as dot labels font.label = list(color = "white", size = 9, vjust = 0.5), # Adjust label parameters ggtheme = theme_pubr() # ggplot2 theme ) ``` ![](tools/README-lollipop-chart-rotate-1.png) Deviation graph: - Use y = “mpg\_z” - Change segment color and size: add.params = list(color = “lightgray”, size = 2) ``` r ggdotchart(dfm, x = "name", y = "mpg_z", color = "cyl", # Color by groups palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette sorting = "descending", # Sort value in descending order add = "segments", # Add segments from y = 0 to dots add.params = list(color = "lightgray", size = 2), # Change segment color and size group = "cyl", # Order by groups dot.size = 6, # Large dot size label = round(dfm$mpg_z,1), # Add mpg values as dot labels font.label = list(color = "white", size = 9, vjust = 0.5), # Adjust label parameters ggtheme = theme_pubr() # ggplot2 theme )+ geom_hline(yintercept = 0, linetype = 2, color = "lightgray") ``` ![](tools/README-lollipop-chart-deviation-1.png) ### Cleveland’s dot plot Color y text by groups. Use y.text.col = TRUE. ``` r ggdotchart(dfm, x = "name", y = "mpg", color = "cyl", # Color by groups palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette sorting = "descending", # Sort value in descending order rotate = TRUE, # Rotate vertically dot.size = 2, # Large dot size y.text.col = TRUE, # Color y text by groups ggtheme = theme_pubr() # ggplot2 theme )+ theme_cleveland() # Add dashed grids ``` ![](tools/README-cleveland-dot-plots-1.png) More ---- Find out more at https://rpkgs.datanovia.com/ggpubr/. Blog posts ---------- - A. Kassambara. 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(Lb^nJ\"T%]B8輍01a5qo,8ҋ/T4)F 4K0޼ fk&dp\Pnrp>rNv.M򔏹mdn;1([VBpݶΜVw|J*AjIڒ6o fGWa?PCy"k~/^maz`M\hT%r,{9_ZAwD1Ft6X!L2#] ̿m@6 }PxT/&@Im>cY5fA4a2mLn%ozʩ\;59PĒ㹄2>l)_Bڒ8\t&:7κ@^q஡[m_n4I!~oqOh f;᳗|n-9v6[s%lV6c :-yrx/}pHP qs5Šń̏di_Pw6U]=FvdVy7Yj,˰`Wm#uو{[IK{|lIy@.˞:u9Նvi5vL&; YZsg[I-~ 6PNRV(i ̧b%Xee> O-Ϭ )?(Aec4kÌ=s6ⵂLW5 4N>| ^)Ndie˥!MɻC 7lJc> +,jK)s&YL0lcP~5b,͟8fdK_ݖҘߟuUӼN9eVmb8t˱0߱N`/4 bz7~VFM۲?pAb@iTշzGk|A&^|hs:K/j?U aF)֮w#8W*uT$Ȩ"a&(7@TF0u٧b]N.ʀaN ݽ _H`qK ,^TZ=`i#ߩ"&LKe!ږ8m/;5$N -!wZ'D:9_'Hb!5AIB߾z /C4Ewax/Pd <J%E#K~;i@B&׌K V 2 & `y` < 5 & `x` \%in\% c('A', 'B')")}. } }} \item{repel}{a logical value, whether to use ggrepel to avoid overplotting text labels or not.} \item{label.rectangle}{logical value. If TRUE, add rectangle underneath the text, making it easier to read.} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to \code{\link[ggplot2]{geom_violin}}, \code{\link{ggpar}} and \code{\link{facet}}.} } \description{ Create a violin plot with error bars. Violin plots are similar to box plots, except that they also show the kernel probability density of the data at different values. } \details{ The plot can be easily customized using the function ggpar(). Read ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels and position: legend = "right" \item plot orientation : orientation = c("vertical", "horizontal", "reverse") } } \examples{ # Load data data("ToothGrowth") df <- ToothGrowth # Basic plot # +++++++++++++++++++++++++++ ggviolin(df, x = "dose", y = "len") # Change the plot orientation: horizontal ggviolin(df, "dose", "len", orientation = "horiz") # Add summary statistics # ++++++++++++++++++++++++++ # Draw quantiles ggviolin(df, "dose", "len", add = "none", draw_quantiles = 0.5) # Add box plot ggviolin(df, x = "dose", y = "len", add = "boxplot") ggviolin(df, x = "dose", y = "len", add = "dotplot") # Add jitter points and # change point shape by groups ("dose") ggviolin(df, x = "dose", y = "len", add = "jitter", shape = "dose") # Add mean_sd + jittered points ggviolin(df, x = "dose", y = "len", add = c("jitter", "mean_sd")) # Change error.plot to "crossbar" ggviolin(df, x = "dose", y = "len", add = "mean_sd", error.plot = "crossbar") # Change colors # +++++++++++++++++++++++++++ # Change outline and fill colors ggviolin(df, "dose", "len", color = "black", fill = "gray") # Change outline colors by groups: dose # Use custom color palette and add boxplot ggviolin(df, "dose", "len", color = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07"), add = "boxplot") # Change fill color by groups: dose # add boxplot with white fill color ggviolin(df, "dose", "len", fill = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07"), add = "boxplot", add.params = list(fill = "white")) # Plot with multiple groups # +++++++++++++++++++++ # fill or color box plot by a second group : "supp" ggviolin(df, "dose", "len", color = "supp", palette = c("#00AFBB", "#E7B800"), add = "boxplot") } \seealso{ \code{\link{ggpar}} } ggpubr/man/gene_citation.Rd0000644000176200001440000000253514333713444015425 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gene_citation.R \docType{data} \name{gene_citation} \alias{gene_citation} \title{Gene Citation Index} \format{ A data frame with 66 rows and 2 columns. \describe{ \item{\code{gene}}{gene names} \item{\code{citation_index}}{mean citation index} } } \usage{ data("gene_citation") } \description{ Contains the mean citation index of 66 genes obtained by assessing PubMed abstracts and annotations using two key words i) Gene name + b cell differentiation and ii) Gene name + plasma cell differentiation. } \examples{ data(gene_citation) # Some key genes of interest to be highlighted key.gns <- c("MYC", "PRDM1", "CD69", "IRF4", "CASP3", "BCL2L1", "MYB", "BACH2", "BIM1", "PTEN", "KRAS", "FOXP1", "IGF1R", "KLF4", "CDK6", "CCND2", "IGF1", "TNFAIP3", "SMAD3", "SMAD7", "BMPR2", "RB1", "IGF2R", "ARNT") # Density distribution ggdensity(gene_citation, x = "citation_index", y = "..count..", xlab = "Number of citation", ylab = "Number of genes", fill = "lightgray", color = "black", label = "gene", label.select = key.gns, repel = TRUE, font.label = list(color= "citation_index"), xticks.by = 20, # Break x ticks by 20 gradient.cols = c("blue", "red"), legend = "bottom", legend.title = "" # Hide legend title ) } ggpubr/man/stat_friedman_test.Rd0000644000176200001440000002061314367517301016472 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stat_friedman_test.R \name{stat_friedman_test} \alias{stat_friedman_test} \title{Add Friedman Test P-values to a GGPlot} \usage{ stat_friedman_test( mapping = NULL, data = NULL, wid = NULL, group.by = NULL, label = "{method}, p = {p.format}", label.x.npc = "left", label.y.npc = "top", label.x = NULL, label.y = NULL, step.increase = 0.1, p.adjust.method = "holm", significance = list(), geom = "text", position = "identity", na.rm = FALSE, show.legend = FALSE, inherit.aes = TRUE, parse = FALSE, ... ) } \arguments{ \item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and \code{inherit.aes = TRUE} (the default), it is combined with the default mapping at the top level of the plot. You must supply \code{mapping} if there is no plot mapping.} \item{data}{The data to be displayed in this layer. There are three options: If \code{NULL}, the default, the data is inherited from the plot data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}. A \code{data.frame}, or other object, will override the plot data. All objects will be fortified to produce a data frame. See \code{\link[ggplot2:fortify]{fortify()}} for which variables will be created. A \code{function} will be called with a single argument, the plot data. The return value must be a \code{data.frame}, and will be used as the layer data. A \code{function} can be created from a \code{formula} (e.g. \code{~ head(.x, 10)}).} \item{wid}{(factor) column name containing individuals/subjects identifier. Should be unique per individual. Required only for repeated measure tests (\code{"one_way_repeated", "two_way_repeated", "friedman_test", etc}).} \item{group.by}{(optional) character vector specifying the grouping variable; it should be used only for grouped plots. Possible values are : \itemize{ \item \code{"x.var"}: Group by the x-axis variable and perform the test between legend groups. In other words, the p-value is compute between legend groups at each x position \item \code{"legend.var"}: Group by the legend variable and perform the test between x-axis groups. In other words, the test is performed between the x-groups for each legend level. }} \item{label}{the column containing the label (e.g.: label = "p" or label = "p.adj"), where \code{p} is the p-value. Can be also an expression that can be formatted by the \code{\link[glue]{glue}()} package. For example, when specifying label = "t-test, p = \{p\}", the expression \{p\} will be replaced by its value.} \item{label.x.npc, label.y.npc}{can be \code{numeric} or \code{character} vector of the same length as the number of groups and/or panels. If too short they will be recycled. \itemize{ \item If \code{numeric}, value should be between 0 and 1. Coordinates to be used for positioning the label, expressed in "normalized parent coordinates". \item If \code{character}, allowed values include: i) one of c('right', 'left', 'center', 'centre', 'middle') for x-axis; ii) and one of c( 'bottom', 'top', 'center', 'centre', 'middle') for y-axis.}} \item{label.x, label.y}{\code{numeric} Coordinates (in data units) to be used for absolute positioning of the label. If too short they will be recycled.} \item{step.increase}{numeric vector with the increase in fraction of total height for every additional comparison to minimize overlap.} \item{p.adjust.method}{method for adjusting p values (see \code{\link[stats]{p.adjust}}). Has impact only in a situation, where multiple pairwise tests are performed; or when there are multiple grouping variables. Allowed values include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p value (not recommended), use p.adjust.method = "none".} \item{significance}{a list of arguments specifying the signifcance cutpoints and symbols. For example, \code{significance <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, Inf), symbols = c("****", "***", "**", "*", "ns"))}. In other words, we use the following convention for symbols indicating statistical significance: \itemize{ \item \code{ns}: p > 0.05 \item \code{*}: p <= 0.05 \item \code{**}: p <= 0.01 \item \code{***}: p <= 0.001 \item \code{****}: p <= 0.0001 }} \item{geom}{The geometric object to use to display the data, either as a \code{ggproto} \code{Geom} subclass or as a string naming the geom stripped of the \code{geom_} prefix (e.g. \code{"point"} rather than \code{"geom_point"})} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{na.rm}{If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.} \item{show.legend}{logical. Should this layer be included in the legends? \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and \code{TRUE} always includes. It can also be a named logical vector to finely select the aesthetics to display.} \item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. \code{\link[ggplot2:borders]{borders()}}.} \item{parse}{If TRUE, the labels will be parsed into expressions and displayed as described in \code{?plotmath}.} \item{...}{other arguments passed to the function \code{geom_bracket()} or \code{geom_text()}} } \description{ Add automatically Friedman test p-values to a ggplot, such as box blots, dot plots and stripcharts. } \section{Computed variables}{ \itemize{ \item{statistic}: the value of the test statistic (Chi-squared). \item{df}: the degrees of freedom of the approximate chi-squared distribution of the test statistic. \item{p}: p-value. \item{p.adj}: Adjusted p-values. \item{p.signif}: P-value significance. \item{p.adj.signif}: Adjusted p-value significance. \item{p.format}: Formated p-value. \item{p.adj.format}: Formated adjusted p-value. \item{n}: number of samples. } } \examples{ # Data preparation #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% # Transform `dose` into factor variable df <- ToothGrowth df$dose <- as.factor(df$dose) df$id <- as.factor(c(rep(1:10, 3), rep(11:20, 3))) # Add a random grouping variable set.seed(123) df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20))) df$len <- ifelse(df$group == "grp2", df$len+2, df$len) df$len <- ifelse(df$group == "grp3", df$len+7, df$len) head(df, 3) # Basic boxplot #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% # Create a basic boxplot # Add 5\% and 10\% space to the plot bottom and the top, respectively bxp <- ggboxplot(df, x = "dose", y = "len") + scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) # Add the p-value to the boxplot bxp + stat_friedman_test(aes(wid = id)) # Change the label position # Using coordinates in data units bxp + stat_friedman_test(aes(wid = id), label.x = "1", label.y = 10, hjust = 0) # Format the p-value differently custom_p_format <- function(p) { rstatix::p_format(p, accuracy = 0.0001, digits = 3, leading.zero = FALSE) } bxp + stat_friedman_test( aes(wid = id), label = "Friedman test, italic(p) = {custom_p_format(p)}{p.signif}" ) # Show a detailed label in italic bxp + stat_friedman_test(aes(wid = id), label = "as_detailed_italic") # Faceted plots #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% # Create a ggplot facet df$id <- rep(1:10,6) bxp <- ggboxplot(df, x = "dose", y = "len", facet.by = "supp") + scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) # Add p-values bxp + stat_friedman_test(aes(wid = id)) # Grouped plots #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% df$id <- rep(1:10,6) bxp <- ggboxplot(df, x = "dose", y = "len", color = "supp", palette = "jco") # For each legend group, computes tests within x variable groups bxp + stat_friedman_test(aes(wid = id, group = supp, color = supp), within = "x") # For each x-position, computes tests within legend variable groups bxp + stat_friedman_test( aes(wid = id, group = supp, color = supp), within = "group", label = "p = {p.format}" ) } ggpubr/man/background_image.Rd0000644000176200001440000000160314333713444016071 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/background_image.R \name{background_image} \alias{background_image} \title{Add Background Image to ggplot2} \usage{ background_image(raster.img) } \arguments{ \item{raster.img}{raster object to display, as returned by the function \code{readPNG()}[in \code{png} package] and \code{readJPEG()} [in \code{jpeg} package].} } \description{ Add background image to ggplot2. } \examples{ \dontrun{ install.packages("png") # Import the image img.file <- system.file(file.path("images", "background-image.png"), package = "ggpubr") img <- png::readPNG(img.file) # Plot with background image ggplot(iris, aes(Species, Sepal.Length))+ background_image(img)+ geom_boxplot(aes(fill = Species), color = "white")+ fill_palette("jco") } } \author{ Alboukadel Kassambara } ggpubr/man/annotate_figure.Rd0000644000176200001440000000442714333713444015771 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/annotate_figure.R \name{annotate_figure} \alias{annotate_figure} \title{Annotate Arranged Figure} \usage{ annotate_figure( p, top = NULL, bottom = NULL, left = NULL, right = NULL, fig.lab = NULL, fig.lab.pos = c("top.left", "top", "top.right", "bottom.left", "bottom", "bottom.right"), fig.lab.size, fig.lab.face ) } \arguments{ \item{p}{(arranged) ggplots.} \item{top, bottom, left, right}{optional string, or grob.} \item{fig.lab}{figure label (e.g.: "Figure 1").} \item{fig.lab.pos}{position of the figure label, can be one of "top.left", "top", "top.right", "bottom.left", "bottom", "bottom.right". Default is "top.left".} \item{fig.lab.size}{optional size of the figure label.} \item{fig.lab.face}{optional font face of the figure label. Allowed values include: "plain", "bold", "italic", "bold.italic".} } \description{ Annotate figures including: i) ggplots, ii) arranged ggplots from \code{\link{ggarrange}()}, \code{\link[gridExtra:arrangeGrob]{grid.arrange}()} and \code{\link[cowplot]{plot_grid}()}. } \examples{ data("ToothGrowth") df <- ToothGrowth df$dose <- as.factor(df$dose) # Create some plots # :::::::::::::::::::::::::::::::::::::::::::::::::: # Box plot bxp <- ggboxplot(df, x = "dose", y = "len", color = "dose", palette = "jco") # Dot plot dp <- ggdotplot(df, x = "dose", y = "len", color = "dose", palette = "jco") # Density plot dens <- ggdensity(df, x = "len", fill = "dose", palette = "jco") # Arrange and annotate # :::::::::::::::::::::::::::::::::::::::::::::::::: figure <- ggarrange(bxp, dp, dens, ncol = 2, nrow = 2) annotate_figure(figure, top = text_grob("Visualizing Tooth Growth", color = "red", face = "bold", size = 14), bottom = text_grob("Data source: \n ToothGrowth data set", color = "blue", hjust = 1, x = 1, face = "italic", size = 10), left = text_grob("Figure arranged using ggpubr", color = "green", rot = 90), right = text_grob(bquote("Superscript: ("*kg~NH[3]~ha^-1~yr^-1*")"), rot = 90), fig.lab = "Figure 1", fig.lab.face = "bold" ) } \seealso{ \code{\link{ggarrange}()} } \author{ Alboukadel Kassambara \email{alboukadel.kassambara@gmail.com} } ggpubr/man/desc_statby.Rd0000644000176200001440000000273314337240740015117 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/desc_statby.R \name{desc_statby} \alias{desc_statby} \title{Descriptive statistics by groups} \usage{ desc_statby(data, measure.var, grps, ci = 0.95) } \arguments{ \item{data}{a data frame.} \item{measure.var}{the name of a column containing the variable to be summarized.} \item{grps}{a character vector containing grouping variables; e.g.: grps = c("grp1", "grp2")} \item{ci}{the percent range of the confidence interval (default is 0.95).} } \value{ A data frame containing descriptive statistics, such as: \itemize{ \item \strong{length}: the number of elements in each group \item \strong{min}: minimum \item \strong{max}: maximum \item \strong{median}: median \item \strong{mean}: mean \item \strong{iqr}: interquartile range \item \strong{mad}: median absolute deviation (see ?MAD) \item \strong{sd}: standard deviation of the sample \item \strong{se}: standard error of the mean. It's calculated as the sample standard deviation divided by the root of the sample size. \item \strong{ci}: confidence interval of the mean \item \strong{range}: the range = max - min \item \strong{cv}: coefficient of variation, sd/mean \item \strong{var}: variance, sd^2 } } \description{ Computes descriptive statistics by groups for a measure variable. } \examples{ # Load data data("ToothGrowth") # Descriptive statistics res <- desc_statby(ToothGrowth, measure.var = "len", grps = c("dose", "supp")) head(res[, 1:10]) } ggpubr/man/npc_to_data_coord.Rd0000644000176200001440000000137514333713444016257 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/npc_to_data_coord.R \name{npc_to_data_coord} \alias{npc_to_data_coord} \title{Convert NPC to Data Coordinates} \usage{ npc_to_data_coord(npc, data.ranges) } \arguments{ \item{npc}{a numeric vector. Each value should be in [0-1]} \item{data.ranges}{a numeric vector of length 2 containing the data ranges (minimum and the maximum)} } \value{ a numeric vector representing data coordinates. } \description{ Convert NPC (Normalized Parent Coordinates) into data coordinates. } \examples{ npc_to_data_coord(npc = c(0.2, 0.95), data.ranges = c(1, 20)) as_npc(c("top", "right")) \%>\% npc_to_data_coord(data.ranges = c(1, 20)) } \seealso{ \code{\link{as_npc}}, \code{\link{get_coord}}. } ggpubr/man/axis_scale.Rd0000644000176200001440000000147714332553656014742 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/axis_scale.R \name{axis_scale} \alias{axis_scale} \alias{xscale} \alias{yscale} \title{Change Axis Scale: log2, log10 and more} \usage{ xscale(.scale, .format = FALSE) yscale(.scale, .format = FALSE) } \arguments{ \item{.scale}{axis scale. Allowed values are one of c("none", "log2", "log10", "sqrt", "percent", "dollar", "scientific"); e.g.: .scale="log2".} \item{.format}{ogical value. If TRUE, axis tick mark labels will be formatted when .scale = "log2" or "log10".} } \description{ Change axis scale. \itemize{ \item \code{xscale}: change x axis scale. \item \code{yscale}: change y axis scale. } } \examples{ # Basic scatter plots data(cars) p <- ggscatter(cars, x = "speed", y = "dist") p # Set log scale p + yscale("log2", .format = TRUE) } ggpubr/man/gene_expression.Rd0000644000176200001440000000174114336762430016012 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gene_expression.R \docType{data} \name{gene_expression} \alias{gene_expression} \title{Gene Expression Data} \format{ A data frame with 1305 rows and 5 columns. \describe{ \item{\code{bcr_patient_barcode}}{sample ID} \item{\code{dataset}}{cance type} \item{\code{GATA3}}{GATA3 gene expression} \item{\code{PTEN}}{PTEN gene expression}\item{\code{XBP1}}{XBP1 gene expression.}} } \usage{ data("gene_expression") } \description{ Gene expression data extracted from TCGA using the `RTCGA` and `RTCGA.mRNA` R packages. It contains the mRNA expression for 3 genes - GATA3, PTEN and XBP1- from 3 different datasets: Breast invasive carcinoma (BRCA), Ovarian serous cystadenocarcinoma (OV) and Lung squamous cell carcinoma (LUSC) } \examples{ data(gene_expression) ggboxplot(gene_expression, x = "dataset", y = c("GATA3", "PTEN", "XBP1"), combine = TRUE, ylab = "Expression", color = "dataset", palette = "jco") } ggpubr/man/ggpubr_args.Rd0000644000176200001440000000641314333713444015116 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggpubr_args.R \name{ggpubr_args} \alias{ggpubr_args} \title{ggpubr General Arguments Description} \arguments{ \item{data}{a data frame} \item{x}{character string containing the name of x variable.} \item{y}{character vector containing one or more variables to plot} \item{combine}{logical value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, create a multi-panel plot by combining the plot of y variables.} \item{merge}{logical or character value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, merge multiple y variables in the same plotting area. Allowed values include also "asis" (TRUE) and "flip". If merge = "flip", then y variables are used as x tick labels and the x variable is used as grouping variable.} \item{color}{outline color.} \item{fill}{fill color.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{linetype}{line types.} \item{size}{Numeric value (e.g.: size = 1). change the size of points and outlines.} \item{select}{character vector specifying which items to display.} \item{remove}{character vector specifying which items to remove from the plot.} \item{order}{character vector specifying the order of items.} \item{add}{character vector for adding another plot element (e.g.: dot plot or error bars). Allowed values are one or the combination of: "none", "dotplot", "jitter", "boxplot", "point", "mean", "mean_se", "mean_sd", "mean_ci", "mean_range", "median", "median_iqr", "median_mad", "median_range"; see ?desc_statby for more details.} \item{add.params}{parameters (color, shape, size, fill, linetype) for the argument 'add'; e.g.: add.params = list(color = "red").} \item{error.plot}{plot type used to visualize error. Allowed values are one of c("pointrange", "linerange", "crossbar", "errorbar", "upper_errorbar", "lower_errorbar", "upper_pointrange", "lower_pointrange", "upper_linerange", "lower_linerange"). Default value is "pointrange" or "errorbar". Used only when add != "none" and add contains one "mean_*" or "med_*" where "*" = sd, se, ....} \item{font.label}{a list which can contain the combination of the following elements: the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of labels. For example font.label = list(size = 14, face = "bold", color ="red"). To specify only the size and the style, use font.label = list(size = 14, face = "plain").} \item{title}{plot main title.} \item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to hide xlab.} \item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to hide ylab.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} } \description{ ggpubr General Arguments Description } ggpubr/man/stat_overlay_normal_density.Rd0000644000176200001440000000641714333713444020443 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stat_overlay_normal_density.R \name{stat_overlay_normal_density} \alias{stat_overlay_normal_density} \title{Overlay Normal Density Plot} \usage{ stat_overlay_normal_density( mapping = NULL, data = NULL, geom = "line", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... ) } \arguments{ \item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and \code{inherit.aes = TRUE} (the default), it is combined with the default mapping at the top level of the plot. You must supply \code{mapping} if there is no plot mapping.} \item{data}{The data to be displayed in this layer. There are three options: If \code{NULL}, the default, the data is inherited from the plot data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}. A \code{data.frame}, or other object, will override the plot data. All objects will be fortified to produce a data frame. See \code{\link[ggplot2:fortify]{fortify()}} for which variables will be created. A \code{function} will be called with a single argument, the plot data. The return value must be a \code{data.frame}, and will be used as the layer data. A \code{function} can be created from a \code{formula} (e.g. \code{~ head(.x, 10)}).} \item{geom}{The geometric object to use to display the data, either as a \code{ggproto} \code{Geom} subclass or as a string naming the geom stripped of the \code{geom_} prefix (e.g. \code{"point"} rather than \code{"geom_point"})} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{na.rm}{If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.} \item{show.legend}{logical. Should this layer be included in the legends? \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and \code{TRUE} always includes. It can also be a named logical vector to finely select the aesthetics to display.} \item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. \code{\link[ggplot2:borders]{borders()}}.} \item{...}{other arguments to pass to \code{\link[ggplot2:geom_path]{geom_line}}.} } \description{ Overlay normal density plot (with the same mean and SD) to the density distribution of 'x'. This is useful for visually inspecting the degree of deviance from normality. } \examples{ # Simpledensity plot data("mtcars") ggdensity(mtcars, x = "mpg", fill = "red") + scale_x_continuous(limits = c(-1, 50)) + stat_overlay_normal_density(color = "red", linetype = "dashed") # Color by groups data(iris) ggdensity(iris, "Sepal.Length", color = "Species") + stat_overlay_normal_density(aes(color = Species), linetype = "dashed") # Facet ggdensity(iris, "Sepal.Length", facet.by = "Species") + stat_overlay_normal_density(color = "red", linetype = "dashed") } \seealso{ \code{\link{ggdensity}} } ggpubr/man/text_grob.Rd0000644000176200001440000000417014333713444014607 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/text_grob.R \name{text_grob} \alias{text_grob} \title{Create a Text Graphical object} \usage{ text_grob( label, just = "centre", hjust = NULL, vjust = NULL, rot = 0, color = "black", face = "plain", size = NULL, lineheight = NULL, family = NULL, ... ) } \arguments{ \item{label}{A character or \link{expression} vector. Other objects are coerced by \code{\link{as.graphicsAnnot}}.} \item{just}{The justification of the text relative to its (x, y) location. If there are two values, the first value specifies horizontal justification and the second value specifies vertical justification. Possible string values are: \code{"left"}, \code{"right"}, \code{"centre"}, \code{"center"}, \code{"bottom"}, and \code{"top"}. For numeric values, 0 means left (bottom) alignment and 1 means right (top) alignment. } \item{hjust}{A numeric vector specifying horizontal justification. If specified, overrides the \code{just} setting.} \item{vjust}{A numeric vector specifying vertical justification. If specified, overrides the \code{just} setting.} \item{rot}{The angle to rotate the text.} \item{color}{text font color.} \item{face}{font face. Allowed values include one of \code{"plain", "bold", "italic", "bold.italic"}.} \item{size}{font size (e.g.: size = 12)} \item{lineheight}{line height (e.g.: lineheight = 2).} \item{family}{font family.} \item{...}{other arguments passed to \link[grid:grid.text]{textGrob}.} } \value{ a text grob. } \description{ Create easily a customized text grob (graphical object). Wrapper around \code{\link[grid:grid.text]{textGrob}}. } \examples{ text <- paste("iris data set gives the measurements in cm", "of the variables sepal length and width", "and petal length and width, respectively,", "for 50 flowers from each of 3 species of iris.", "The species are Iris setosa, versicolor, and virginica.", sep = "\n") # Create a text grob tgrob <- text_grob(text, face = "italic", color = "steelblue") # Draw the text as_ggplot(tgrob) } ggpubr/man/stat_mean.Rd0000644000176200001440000000562414333713444014572 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stat_mean.R \name{stat_mean} \alias{stat_mean} \title{Draw group mean points} \usage{ stat_mean( mapping = NULL, data = NULL, geom = "point", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... ) } \arguments{ \item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and \code{inherit.aes = TRUE} (the default), it is combined with the default mapping at the top level of the plot. You must supply \code{mapping} if there is no plot mapping.} \item{data}{The data to be displayed in this layer. There are three options: If \code{NULL}, the default, the data is inherited from the plot data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}. A \code{data.frame}, or other object, will override the plot data. All objects will be fortified to produce a data frame. See \code{\link[ggplot2:fortify]{fortify()}} for which variables will be created. A \code{function} will be called with a single argument, the plot data. The return value must be a \code{data.frame}, and will be used as the layer data. A \code{function} can be created from a \code{formula} (e.g. \code{~ head(.x, 10)}).} \item{geom}{The geometric object to use to display the data, either as a \code{ggproto} \code{Geom} subclass or as a string naming the geom stripped of the \code{geom_} prefix (e.g. \code{"point"} rather than \code{"geom_point"})} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{na.rm}{If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.} \item{show.legend}{logical. Should this layer be included in the legends? \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and \code{TRUE} always includes. It can also be a named logical vector to finely select the aesthetics to display.} \item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. \code{\link[ggplot2:borders]{borders()}}.} \item{...}{other arguments to pass to \code{\link[ggplot2]{geom_point}}.} } \description{ Draw the mean point of each group. } \examples{ # Load data data("mtcars") df <- mtcars df$cyl <- as.factor(df$cyl) # Scatter plot with ellipses and group mean points ggscatter(df, x = "wt", y = "mpg", color = "cyl", shape = "cyl", ellipse = TRUE)+ stat_mean(aes(color = cyl, shape = cyl), size = 4) } \seealso{ \code{\link{stat_conf_ellipse}}, \code{\link{stat_chull}} and \code{\link{ggscatter}} } ggpubr/man/create_aes.Rd0000644000176200001440000000144314333664263014711 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/create_aes.R \name{create_aes} \alias{create_aes} \title{Create Aes Mapping from a List} \usage{ create_aes(.list, parse = TRUE) } \arguments{ \item{.list}{a list of aesthetic arguments; for example .list = list(x = "dose", y = "len", color = "dose").} \item{parse}{logical. If TRUE, parse the input as an expression.} } \description{ Create aes mapping to make programming easy with ggplot2. } \examples{ # Simple aes creation create_aes(list(x = "Sepal.Length", y = "Petal.Length" )) # Parse an expression x <- "log2(Sepal.Length)" y <- "log2(Petal.Length)" create_aes(list(x = x, y = y ), parse = TRUE) # Create a ggplot mapping <- create_aes(list(x = x, y = y ), parse = TRUE) ggplot(iris, mapping) + geom_point() } ggpubr/man/stat_chull.Rd0000644000176200001440000000616314332576562014767 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stat_chull.R \name{stat_chull} \alias{stat_chull} \title{Plot convex hull of a set of points} \usage{ stat_chull( mapping = NULL, data = NULL, geom = "path", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... ) } \arguments{ \item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and \code{inherit.aes = TRUE} (the default), it is combined with the default mapping at the top level of the plot. You must supply \code{mapping} if there is no plot mapping.} \item{data}{The data to be displayed in this layer. There are three options: If \code{NULL}, the default, the data is inherited from the plot data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}. A \code{data.frame}, or other object, will override the plot data. All objects will be fortified to produce a data frame. See \code{\link[ggplot2:fortify]{fortify()}} for which variables will be created. A \code{function} will be called with a single argument, the plot data. The return value must be a \code{data.frame}, and will be used as the layer data. A \code{function} can be created from a \code{formula} (e.g. \code{~ head(.x, 10)}).} \item{geom}{The geometric object to use to display the data, either as a \code{ggproto} \code{Geom} subclass or as a string naming the geom stripped of the \code{geom_} prefix (e.g. \code{"point"} rather than \code{"geom_point"})} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{na.rm}{If \code{FALSE}, the default, missing values are removed with a warning. If \code{TRUE}, missing values are silently removed.} \item{show.legend}{logical. Should this layer be included in the legends? \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and \code{TRUE} always includes. It can also be a named logical vector to finely select the aesthetics to display.} \item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. \code{\link[ggplot2:borders]{borders()}}.} \item{...}{Other arguments passed on to \code{\link[ggplot2:layer]{layer()}}. These are often aesthetics, used to set an aesthetic to a fixed value, like \code{colour = "red"} or \code{size = 3}. They may also be parameters to the paired geom/stat.} } \description{ Plot convex hull of a set of points. } \examples{ # Load data data("mtcars") df <- mtcars df$cyl <- as.factor(df$cyl) # scatter plot with convex hull ggscatter(df, x = "wt", y = "mpg", color = "cyl")+ stat_chull(aes(color = cyl)) ggscatter(df, x = "wt", y = "mpg", color = "cyl")+ stat_chull(aes(color = cyl, fill = cyl), alpha = 0.1, geom = "polygon") } \seealso{ \code{\link{ggpar}}, \code{\link{ggscatter}} } ggpubr/man/geom_pwc.Rd0000644000176200001440000003313514343207174014414 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/geom_pwc.R \name{stat_pwc} \alias{stat_pwc} \alias{geom_pwc} \title{Add Pairwise Comparisons P-values to a GGPlot} \usage{ stat_pwc( mapping = NULL, data = NULL, method = "wilcox_test", method.args = list(), ref.group = NULL, label = "p.format", y.position = NULL, group.by = NULL, dodge = 0.8, bracket.nudge.y = 0.05, bracket.shorten = 0, bracket.group.by = c("x.var", "legend.var"), step.increase = 0.12, tip.length = 0.03, size = 0.3, label.size = 3.88, family = "", vjust = 0, hjust = 0.5, p.adjust.method = "holm", p.adjust.by = c("group", "panel"), symnum.args = list(), hide.ns = FALSE, remove.bracket = FALSE, position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, parse = FALSE, ... ) geom_pwc( mapping = NULL, data = NULL, stat = "pwc", method = "wilcox_test", method.args = list(), ref.group = NULL, label = "p.format", y.position = NULL, group.by = NULL, dodge = 0.8, stack = FALSE, step.increase = 0.12, tip.length = 0.03, bracket.nudge.y = 0.05, bracket.shorten = 0, bracket.group.by = c("x.var", "legend.var"), size = 0.3, label.size = 3.88, family = "", vjust = 0, hjust = 0.5, p.adjust.method = "holm", p.adjust.by = c("group", "panel"), symnum.args = list(), hide.ns = FALSE, remove.bracket = FALSE, position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, parse = FALSE, ... ) } \arguments{ \item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and \code{inherit.aes = TRUE} (the default), it is combined with the default mapping at the top level of the plot. You must supply \code{mapping} if there is no plot mapping.} \item{data}{The data to be displayed in this layer. There are three options: If \code{NULL}, the default, the data is inherited from the plot data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}. A \code{data.frame}, or other object, will override the plot data. All objects will be fortified to produce a data frame. See \code{\link[ggplot2:fortify]{fortify()}} for which variables will be created. A \code{function} will be called with a single argument, the plot data. The return value must be a \code{data.frame}, and will be used as the layer data. A \code{function} can be created from a \code{formula} (e.g. \code{~ head(.x, 10)}).} \item{method}{a character string indicating which method to be used for pairwise comparisons. Default is \code{"wilcox_test"}. Allowed methods include pairwise comparisons methods implemented in the \code{rstatix} R package. These methods are: \code{"wilcox_test", "t_test", "sign_test", "dunn_test", "emmeans_test", "tukey_hsd", "games_howell_test"}.} \item{method.args}{a list of additional arguments used for the test method. For example one might use \code{method.args = list(alternative = "greater")} for wilcoxon test.} \item{ref.group}{a character string or a numeric value specifying the reference group. If specified, for a given grouping variable, each of the group levels will be compared to the reference group (i.e. control group). \code{ref.group} can be also \code{"all"}. In this case, each of the grouping variable levels is compared to all (i.e. basemean). Allowed values can be: \itemize{ \item \strong{numeric value}: specifying the rank of the reference group. For example, use \code{ref.group = 1} when the first group is the reference; use \code{ref.group = 2} when the second group is the reference, and so on. This works for all situations, including i) when comparisons are performed between x-axis groups and ii) when comparisons are performed between legend groups. \item \strong{character value}: For example, you can use \code{ref.group = "ctrl"} instead of using the numeric rank value of the "ctrl" group. \item \strong{"all"}: In this case, each of the grouping variable levels is compared to all (i.e. basemean). }} \item{label}{character string specifying label. Can be: \itemize{ \item the column containing the label (e.g.: \code{label = "p"} or \code{label = "p.adj"}), where \code{p} is the p-value. Other possible values are \code{"p.signif", "p.adj.signif", "p.format", "p.adj.format"}. \item an expression that can be formatted by the \code{\link[glue]{glue}()} package. For example, when specifying \code{label = "Wilcoxon, p = \{p\}"}, the expression \{p\} will be replaced by its value. \item a combination of plotmath expressions and glue expressions. You may want some of the statistical parameter in italic; for example:\code{label = "Wilcoxon, italic(p)= {p}"}}.} \item{y.position}{numeric vector with the y positions of the brackets} \item{group.by}{(optional) character vector specifying the grouping variable; it should be used only for grouped plots. Possible values are : \itemize{ \item \code{"x.var"}: Group by the x-axis variable and perform the test between legend groups. In other words, the p-value is compute between legend groups at each x position \item \code{"legend.var"}: Group by the legend variable and perform the test between x-axis groups. In other words, the test is performed between the x-groups for each legend level. }} \item{dodge}{dodge width for grouped ggplot/test. Default is 0.8. It's used to dodge the brackets position when \code{group.by = "legend.var"}.} \item{bracket.nudge.y}{Vertical adjustment to nudge brackets by (in fraction of the total height). Useful to move up or move down the bracket. If positive value, brackets will be moved up; if negative value, brackets are moved down.} \item{bracket.shorten}{a small numeric value in [0-1] for shortening the width of bracket.} \item{bracket.group.by}{(optional); a variable name for grouping brackets before adding step.increase. Useful for grouped plots. Possible values include \code{"x.var"} and \code{"legend.var"}.} \item{step.increase}{numeric vector with the increase in fraction of total height for every additional comparison to minimize overlap.} \item{tip.length}{numeric vector with the fraction of total height that the bar goes down to indicate the precise column/} \item{size}{change the width of the lines of the bracket} \item{label.size}{change the size of the label text} \item{family}{change the font used for the text} \item{vjust}{move the text up or down relative to the bracket.} \item{hjust}{move the text left or right relative to the bracket.} \item{p.adjust.method}{method for adjusting p values (see \code{\link[stats]{p.adjust}}). Has impact only in a situation, where multiple pairwise tests are performed; or when there are multiple grouping variables. Ignored when the specified method is \code{"tukey_hsd"} or \code{"games_howell_test"} because they come with internal p adjustment method. Allowed values include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p value (not recommended), use p.adjust.method = "none".} \item{p.adjust.by}{possible value is one of \code{c("group", "panel")}. Default is \code{"group"}: for a grouped data, if pairwise test is performed, then the p-values are adjusted for each group level independently. P-values are adjusted by panel when \code{p.adjust.by = "panel"}.} \item{symnum.args}{a list of arguments to pass to the function \code{\link[stats]{symnum}} for symbolic number coding of p-values. For example, \code{symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, Inf), symbols = c("****", "***", "**", "*", "ns"))}. In other words, we use the following convention for symbols indicating statistical significance: \itemize{ \item \code{ns}: p > 0.05 \item \code{*}: p <= 0.05 \item \code{**}: p <= 0.01 \item \code{***}: p <= 0.001 \item \code{****}: p <= 0.0001 }} \item{hide.ns}{can be logical value (\code{TRUE} or \code{FALSE}) or a character vector (\code{"p.adj"} or \code{"p"}).} \item{remove.bracket}{logical, if \code{TRUE}, brackets are removed from the plot. \itemize{ \item Case when logical value. If TRUE, hide ns symbol when displaying significance levels. Filter is done by checking the column \code{p.adj.signif}, \code{p.signif}, \code{p.adj} and \code{p}. \item Case when character value. Possible values are "p" or "p.adj", for filtering out non significant. }} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{na.rm}{If \code{FALSE} (the default), removes missing values with a warning. If \code{TRUE} silently removes missing values.} \item{show.legend}{logical. Should this layer be included in the legends? \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and \code{TRUE} always includes. It can also be a named logical vector to finely select the aesthetics to display.} \item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. \code{\link[ggplot2:borders]{borders()}}.} \item{parse}{logical for parsing plotmath expression.} \item{...}{other arguments passed on to \code{\link{layer}}. These are often aesthetics, used to set an aesthetic to a fixed value, like \code{color = "red"} or \code{size = 3}. They may also be parameters to the paired geom/stat.} \item{stat}{The statistical transformation to use on the data for this layer, either as a \code{ggproto} \code{Geom} subclass or as a string naming the stat stripped of the \code{stat_} prefix (e.g. \code{"count"} rather than \code{"stat_count"})} \item{stack}{logical value. Default is FALSE; should be set to TRUE for stacked bar plots or line plots. If TRUE, then the brackets are automatically removed and the \code{dodge} value is set to zero.} } \description{ add pairwise comparison p-values to a ggplot such as box plots, dot plots and stripcharts. } \details{ \bold{Notes on adjusted p-values and facet}. When using the ggplot facet functions, the p-values are computed and adjusted by panel, without taking into account the other panels. This is by design in ggplot2. In this case, when there is only one computed p-value by panel, then using `label = "p"` or `label = "p.adj"` will give the same results using `geom_pwc()`. Again, p-value computation and adjustment in a given facet panel is done independently to the other panels. One might want to adjust the p-values of all the facet panels together. There are two solutions for that: \itemize{ \item Using \code{\link{ggadjust_pvalue}(p)} after creating the plot \code{p} \item or adding the adjusted p-value manually using \code{\link{stat_pvalue_manual}()}. Read more at: \itemize{ \item \href{https://www.datanovia.com/en/blog/how-to-add-p-values-to-ggplot-facets/}{How to Add P-values to GGPLOT Facets} \item \href{https://www.datanovia.com/en/blog/add-p-values-to-ggplot-facets-with-different-scales/}{Add P-values to GGPLOT Facets with Different Scales} } } } \examples{ df <- ToothGrowth df$dose <- factor(df$dose) # Data preparation #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% # Transform `dose` into factor variable df <- ToothGrowth df$dose <- as.factor(df$dose) # Add a random grouping variable df$group <- factor(rep(c("grp1", "grp2"), 30)) head(df, 3) # Two groups by x position #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% # Create a box plot # Add 10\% spaces between the p-value labels and the plot border bxp <- ggboxplot( df, x = "dose", y = "len", color = "supp", palette = c("#00AFBB", "#E7B800") ) + scale_y_continuous(expand = expansion(mult = c(0.05, 0.10))) # Add p-values onto the box plots # label can be "p.format" or "p.adj.format" bxp + geom_pwc( aes(group = supp), tip.length = 0, method = "t_test", label = "p.format" ) # Show adjusted p-values and significance levels # Hide ns (non-significant) bxp + geom_pwc( aes(group = supp), tip.length = 0, method = "t_test", label = "{p.adj.format}{p.adj.signif}", p.adjust.method = "bonferroni", p.adjust.by = "panel", hide.ns = TRUE ) # Complex cases #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% # 1. Add p-values of OJ vs VC at each dose group bxp.complex <- bxp + geom_pwc( aes(group = supp), tip.length = 0, method = "t_test", label = "p.adj.format", p.adjust.method = "bonferroni", p.adjust.by = "panel" ) # 2. Add pairwise comparisons between dose levels # Nudge up the brackets by 20\% of the total height bxp.complex <- bxp.complex + geom_pwc( method = "t_test", label = "p.adj.format", p.adjust.method = "bonferroni", bracket.nudge.y = 0.2 ) # 3. Display the plot bxp.complex # Three groups by x position #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% # Simple plots #_____________________________________ # Box plots with p-values bxp <- ggboxplot( df, x = "supp", y = "len", fill = "dose", palette = "npg" ) bxp + geom_pwc( aes(group = dose), tip.length = 0, method = "t_test", label = "p.adj.format", bracket.nudge.y = -0.08 ) + scale_y_continuous(expand = expansion(mult = c(0, 0.1))) # Bar plots with p-values bp <- ggbarplot( df, x = "supp", y = "len", fill = "dose", palette = "npg", add = "mean_sd", position = position_dodge(0.8) ) bp + geom_pwc( aes(group = dose), tip.length = 0, method = "t_test", label = "p.adj.format", bracket.nudge.y = -0.08 ) + scale_y_continuous(expand = expansion(mult = c(0, 0.1))) } \seealso{ \code{\link{ggadjust_pvalue}} } ggpubr/man/ggdensity.Rd0000644000176200001440000001371514367732422014620 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggdensity.R \name{ggdensity} \alias{ggdensity} \title{Density plot} \usage{ ggdensity( data, x, y = "density", combine = FALSE, merge = FALSE, color = "black", fill = NA, palette = NULL, size = NULL, linetype = "solid", alpha = 0.5, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, add = c("none", "mean", "median"), add.params = list(linetype = "dashed"), rug = FALSE, label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, ggtheme = theme_pubr(), ... ) } \arguments{ \item{data}{a data frame} \item{x}{variable to be drawn.} \item{y}{one of "density" or "count".} \item{combine}{logical value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, create a multi-panel plot by combining the plot of y variables.} \item{merge}{logical or character value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, merge multiple y variables in the same plotting area. Allowed values include also "asis" (TRUE) and "flip". If merge = "flip", then y variables are used as x tick labels and the x variable is used as grouping variable.} \item{color, fill}{density line color and fill color.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{size}{Numeric value (e.g.: size = 1). change the size of points and outlines.} \item{linetype}{line type. See \code{\link{show_line_types}}.} \item{alpha}{numeric value specifying fill color transparency. Value should be in [0, 1], where 0 is full transparency and 1 is no transparency.} \item{title}{plot main title.} \item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to hide xlab.} \item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to hide ylab.} \item{facet.by}{character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.} \item{panel.labs}{a list of one or two character vectors to modify facet panel labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies the labels for the "sex" variable. For two grouping variables, you can use for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", "Lev", "Lev2") ).} \item{short.panel.labs}{logical value. Default is TRUE. If TRUE, create short labels for panels by omitting variable names; in other words panels will be labelled only by variable grouping levels.} \item{add}{allowed values are one of "mean" or "median" (for adding mean or median line, respectively).} \item{add.params}{parameters (color, size, linetype) for the argument 'add'; e.g.: add.params = list(color = "red").} \item{rug}{logical value. If TRUE, add marginal rug.} \item{label}{the name of the column containing point labels. Can be also a character vector with length = nrow(data).} \item{font.label}{a list which can contain the combination of the following elements: the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of labels. For example font.label = list(size = 14, face = "bold", color ="red"). To specify only the size and the style, use font.label = list(size = 14, face = "plain").} \item{label.select}{can be of two formats: \itemize{ \item a character vector specifying some labels to show. \item a list containing one or the combination of the following components: \itemize{ \item \code{top.up} and \code{top.down}: to display the labels of the top up/down points. For example, \code{label.select = list(top.up = 10, top.down = 4)}. \item \code{criteria}: to filter, for example, by x and y variabes values, use this: \code{label.select = list(criteria = "`y` > 2 & `y` < 5 & `x` \%in\% c('A', 'B')")}. } }} \item{repel}{a logical value, whether to use ggrepel to avoid overplotting text labels or not.} \item{label.rectangle}{logical value. If TRUE, add rectangle underneath the text, making it easier to read.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to \code{\link[ggplot2]{geom_density}} and \code{\link{ggpar}}.} } \description{ Create a density plot. } \details{ The plot can be easily customized using the function ggpar(). Read ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels and position: legend = "right" \item plot orientation : orientation = c("vertical", "horizontal", "reverse") } } \examples{ # Create some data format set.seed(1234) wdata = data.frame( sex = factor(rep(c("F", "M"), each=200)), weight = c(rnorm(200, 55), rnorm(200, 58))) head(wdata, 4) # Basic density plot # Add mean line and marginal rug ggdensity(wdata, x = "weight", fill = "lightgray", add = "mean", rug = TRUE) # Change outline colors by groups ("sex") # Use custom palette ggdensity(wdata, x = "weight", add = "mean", rug = TRUE, color = "sex", palette = c("#00AFBB", "#E7B800")) # Change outline and fill colors by groups ("sex") # Use custom palette ggdensity(wdata, x = "weight", add = "mean", rug = TRUE, color = "sex", fill = "sex", palette = c("#00AFBB", "#E7B800")) } \seealso{ \code{\link{gghistogram}} and \code{\link{ggpar}}. } ggpubr/man/as_ggplot.Rd0000644000176200001440000000170614343207174014572 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/as_ggplot.R \name{as_ggplot} \alias{as_ggplot} \title{Storing grid.arrange() arrangeGrob() and plots} \usage{ as_ggplot(x) } \arguments{ \item{x}{an object of class gtable or grob as returned by the functions \code{\link[gridExtra:arrangeGrob]{arrangeGrob}()} and \code{\link[gridExtra:arrangeGrob]{grid.arrange}()}.} } \value{ an object of class ggplot. } \description{ Transform the output of \code{\link[gridExtra:arrangeGrob]{arrangeGrob}()} and \code{\link[gridExtra:arrangeGrob]{grid.arrange}()} to a an object of class ggplot. } \examples{ # Creat some plots bxp <- ggboxplot(iris, x = "Species", y = "Sepal.Length") vp <- ggviolin(iris, x = "Species", y = "Sepal.Length", add = "mean_sd") # Arrange the plots in one page # Returns a gtable (grob) object library(gridExtra) gt <- arrangeGrob(bxp, vp, ncol = 2) # Transform to a ggplot and print as_ggplot(gt) } ggpubr/man/ggadjust_pvalue.Rd0000644000176200001440000000731614343207174016002 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggadjust_pvalue.R \name{ggadjust_pvalue} \alias{ggadjust_pvalue} \title{Adjust p-values Displayed on a GGPlot} \usage{ ggadjust_pvalue( p, layer = NULL, p.adjust.method = "holm", label = "p.adj", hide.ns = NULL, symnum.args = list(), output = c("plot", "stat_test") ) } \arguments{ \item{p}{a ggplot} \item{layer}{An integer indicating the statistical layer rank in the ggplot (in the order added to the plot).} \item{p.adjust.method}{method for adjusting p values (see \code{\link[stats]{p.adjust}}). Has impact only in a situation, where multiple pairwise tests are performed; or when there are multiple grouping variables. Ignored when the specified method is \code{"tukey_hsd"} or \code{"games_howell_test"} because they come with internal p adjustment method. Allowed values include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p value (not recommended), use p.adjust.method = "none".} \item{label}{character string specifying label. Can be: \itemize{ \item the column containing the label (e.g.: \code{label = "p"} or \code{label = "p.adj"}), where \code{p} is the p-value. Other possible values are \code{"p.signif", "p.adj.signif", "p.format", "p.adj.format"}. \item an expression that can be formatted by the \code{\link[glue]{glue}()} package. For example, when specifying \code{label = "Wilcoxon, p = \{p\}"}, the expression \{p\} will be replaced by its value. \item a combination of plotmath expressions and glue expressions. You may want some of the statistical parameter in italic; for example:\code{label = "Wilcoxon, italic(p)= {p}"}}.} \item{hide.ns}{can be logical value (\code{TRUE} or \code{FALSE}) or a character vector (\code{"p.adj"} or \code{"p"}).} \item{symnum.args}{a list of arguments to pass to the function \code{\link[stats]{symnum}} for symbolic number coding of p-values. For example, \code{symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, Inf), symbols = c("****", "***", "**", "*", "ns"))}. In other words, we use the following convention for symbols indicating statistical significance: \itemize{ \item \code{ns}: p > 0.05 \item \code{*}: p <= 0.05 \item \code{**}: p <= 0.01 \item \code{***}: p <= 0.001 \item \code{****}: p <= 0.0001 }} \item{output}{character. Possible values are one of \code{c("plot", "stat_test")}. Default is "plot".} } \description{ Adjust p-values produced by \code{\link{geom_pwc}()} on a ggplot. This is mainly useful when using facet, where p-values are generally computed and adjusted by panel without taking into account the other panels. In this case, one might want to adjust after the p-values of all panels together. } \examples{ # Data preparation #::::::::::::::::::::::::::::::::::::::: df <- ToothGrowth df$dose <- as.factor(df$dose) # Add a random grouping variable df$group <- factor(rep(c("grp1", "grp2"), 30)) head(df, 3) # Boxplot: Two groups by panel #::::::::::::::::::::::::::::::::::::::: # Create a box plot bxp <- ggboxplot( df, x = "supp", y = "len", fill = "#00AFBB", facet.by = "dose" ) # Make facet and add p-values bxp <- bxp + geom_pwc(method = "t_test") bxp # Adjust all p-values together after ggadjust_pvalue( bxp, p.adjust.method = "bonferroni", label = "{p.adj.format}{p.adj.signif}", hide.ns = TRUE ) # Boxplot: Three groups by panel #::::::::::::::::::::::::::::::::::::::: # Create a box plot bxp <- ggboxplot( df, x = "dose", y = "len", fill = "#00AFBB", facet.by = "supp" ) # Make facet and add p-values bxp <- bxp + geom_pwc(method = "t_test") bxp # Adjust all p-values together after ggadjust_pvalue( bxp, p.adjust.method = "bonferroni", label = "{p.adj.format}{p.adj.signif}" ) } ggpubr/man/stat_kruskal_test.Rd0000644000176200001440000001757614367517301016377 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stat_kruskal_test.R \name{stat_kruskal_test} \alias{stat_kruskal_test} \title{Add Kruskal-Wallis Test P-values to a GGPlot} \usage{ stat_kruskal_test( mapping = NULL, data = NULL, group.by = NULL, label = "{method}, p = {p.format}", label.x.npc = "left", label.y.npc = "top", label.x = NULL, label.y = NULL, step.increase = 0.1, p.adjust.method = "holm", significance = list(), geom = "text", position = "identity", na.rm = FALSE, show.legend = FALSE, inherit.aes = TRUE, parse = FALSE, ... ) } \arguments{ \item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and \code{inherit.aes = TRUE} (the default), it is combined with the default mapping at the top level of the plot. You must supply \code{mapping} if there is no plot mapping.} \item{data}{The data to be displayed in this layer. There are three options: If \code{NULL}, the default, the data is inherited from the plot data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}. A \code{data.frame}, or other object, will override the plot data. All objects will be fortified to produce a data frame. See \code{\link[ggplot2:fortify]{fortify()}} for which variables will be created. A \code{function} will be called with a single argument, the plot data. The return value must be a \code{data.frame}, and will be used as the layer data. A \code{function} can be created from a \code{formula} (e.g. \code{~ head(.x, 10)}).} \item{group.by}{(optional) character vector specifying the grouping variable; it should be used only for grouped plots. Possible values are : \itemize{ \item \code{"x.var"}: Group by the x-axis variable and perform the test between legend groups. In other words, the p-value is compute between legend groups at each x position \item \code{"legend.var"}: Group by the legend variable and perform the test between x-axis groups. In other words, the test is performed between the x-groups for each legend level. }} \item{label}{the column containing the label (e.g.: label = "p" or label = "p.adj"), where \code{p} is the p-value. Can be also an expression that can be formatted by the \code{\link[glue]{glue}()} package. For example, when specifying label = "t-test, p = \{p\}", the expression \{p\} will be replaced by its value.} \item{label.x.npc, label.y.npc}{can be \code{numeric} or \code{character} vector of the same length as the number of groups and/or panels. If too short they will be recycled. \itemize{ \item If \code{numeric}, value should be between 0 and 1. Coordinates to be used for positioning the label, expressed in "normalized parent coordinates". \item If \code{character}, allowed values include: i) one of c('right', 'left', 'center', 'centre', 'middle') for x-axis; ii) and one of c( 'bottom', 'top', 'center', 'centre', 'middle') for y-axis.}} \item{label.x, label.y}{\code{numeric} Coordinates (in data units) to be used for absolute positioning of the label. If too short they will be recycled.} \item{step.increase}{numeric vector with the increase in fraction of total height for every additional comparison to minimize overlap.} \item{p.adjust.method}{method for adjusting p values (see \code{\link[stats]{p.adjust}}). Has impact only in a situation, where multiple pairwise tests are performed; or when there are multiple grouping variables. Allowed values include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p value (not recommended), use p.adjust.method = "none".} \item{significance}{a list of arguments specifying the signifcance cutpoints and symbols. For example, \code{significance <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, Inf), symbols = c("****", "***", "**", "*", "ns"))}. In other words, we use the following convention for symbols indicating statistical significance: \itemize{ \item \code{ns}: p > 0.05 \item \code{*}: p <= 0.05 \item \code{**}: p <= 0.01 \item \code{***}: p <= 0.001 \item \code{****}: p <= 0.0001 }} \item{geom}{The geometric object to use to display the data, either as a \code{ggproto} \code{Geom} subclass or as a string naming the geom stripped of the \code{geom_} prefix (e.g. \code{"point"} rather than \code{"geom_point"})} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{na.rm}{If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.} \item{show.legend}{logical. Should this layer be included in the legends? \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and \code{TRUE} always includes. It can also be a named logical vector to finely select the aesthetics to display.} \item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. \code{\link[ggplot2:borders]{borders()}}.} \item{parse}{If TRUE, the labels will be parsed into expressions and displayed as described in \code{?plotmath}.} \item{...}{other arguments passed to the function \code{geom_bracket()} or \code{geom_text()}} } \description{ Add Kruskal-Wallis test p-values to a ggplot, such as box blots, dot plots and stripcharts. } \section{Computed variables}{ \itemize{ \item{statistic}: the Kruskal-Wallis rank sum chi-squared statistic used to compute the p-value. \item{p}: p-value. \item{p.adj}: Adjusted p-values. \item{p.signif}: P-value significance. \item{p.adj.signif}: Adjusted p-value significance. \item{p.format}: Formated p-value. \item{p.adj.format}: Formated adjusted p-value. \item{n}: number of samples. } } \examples{ # Data preparation #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% # Transform `dose` into factor variable df <- ToothGrowth df$dose <- as.factor(df$dose) # Add a random grouping variable set.seed(123) df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20))) df$len <- ifelse(df$group == "grp2", df$len+2, df$len) df$len <- ifelse(df$group == "grp3", df$len+7, df$len) head(df, 3) # Basic boxplot #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% # Create a basic boxplot # Add 5\% and 10\% space to the plot bottom and the top, respectively bxp <- ggboxplot(df, x = "dose", y = "len") + scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) # Add the p-value to the boxplot bxp + stat_kruskal_test() # Change the label position # Using coordinates in data units bxp + stat_kruskal_test(label.x = "1", label.y = 10, hjust = 0) # Format the p-value differently custom_p_format <- function(p) { rstatix::p_format(p, accuracy = 0.0001, digits = 3, leading.zero = FALSE) } bxp + stat_kruskal_test( label = "Kruskal-Wallis, italic(p) = {custom_p_format(p)}{p.signif}" ) # Show a detailed label in italic bxp + stat_kruskal_test(label = "as_detailed_italic") # Faceted plots #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% # Create a ggplot facet bxp <- ggboxplot(df, x = "dose", y = "len", facet.by = "supp") + scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) # Add p-values bxp + stat_kruskal_test() # Grouped plots #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% bxp2 <- ggboxplot(df, x = "group", y = "len", color = "dose", palette = "npg") # For each x-position, computes tests between legend groups bxp2 + stat_kruskal_test(aes(group = dose), label = "p = {p.format}{p.signif}") # For each legend group, computes tests between x variable groups bxp2 + stat_kruskal_test(aes(group = dose, color = dose), group.by = "legend.var") } ggpubr/man/stat_conf_ellipse.Rd0000644000176200001440000000706714333713444016317 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stat_conf_ellipse.R \name{stat_conf_ellipse} \alias{stat_conf_ellipse} \title{Plot confidence ellipses.} \usage{ stat_conf_ellipse( mapping = NULL, data = NULL, geom = "path", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, level = 0.95, npoint = 100, bary = TRUE, ... ) } \arguments{ \item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and \code{inherit.aes = TRUE} (the default), it is combined with the default mapping at the top level of the plot. You must supply \code{mapping} if there is no plot mapping.} \item{data}{The data to be displayed in this layer. There are three options: If \code{NULL}, the default, the data is inherited from the plot data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}. A \code{data.frame}, or other object, will override the plot data. All objects will be fortified to produce a data frame. See \code{\link[ggplot2:fortify]{fortify()}} for which variables will be created. A \code{function} will be called with a single argument, the plot data. The return value must be a \code{data.frame}, and will be used as the layer data. A \code{function} can be created from a \code{formula} (e.g. \code{~ head(.x, 10)}).} \item{geom}{The geometric object to use to display the data, either as a \code{ggproto} \code{Geom} subclass or as a string naming the geom stripped of the \code{geom_} prefix (e.g. \code{"point"} rather than \code{"geom_point"})} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{na.rm}{If \code{FALSE}, the default, missing values are removed with a warning. If \code{TRUE}, missing values are silently removed.} \item{show.legend}{logical. Should this layer be included in the legends? \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and \code{TRUE} always includes. It can also be a named logical vector to finely select the aesthetics to display.} \item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. \code{\link[ggplot2:borders]{borders()}}.} \item{level}{confidence level used to construct the ellipses. By default, 0.95.} \item{npoint}{number of points used to draw the ellipses.} \item{bary}{logical value. If TRUE, the coordinates of the ellipse around the barycentre of individuals are calculated.} \item{...}{Other arguments passed on to \code{\link[ggplot2:layer]{layer()}}. These are often aesthetics, used to set an aesthetic to a fixed value, like \code{colour = "red"} or \code{size = 3}. They may also be parameters to the paired geom/stat.} } \description{ Plot confidence ellipses around barycenters. The method for computing confidence ellipses has been modified from \code{FactoMineR::coord.ellipse()}. } \examples{ # Load data data("mtcars") df <- mtcars df$cyl <- as.factor(df$cyl) # scatter plot with confidence ellipses ggscatter(df, x = "wt", y = "mpg", color = "cyl")+ stat_conf_ellipse(aes(color = cyl)) ggscatter(df, x = "wt", y = "mpg", color = "cyl")+ stat_conf_ellipse(aes(color = cyl, fill = cyl), alpha = 0.1, geom = "polygon") } \seealso{ \code{\link{stat_conf_ellipse}} } ggpubr/man/facet.Rd0000644000176200001440000000630214333713444013673 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/facet.R \name{facet} \alias{facet} \title{Facet a ggplot into Multiple Panels} \usage{ facet( p, facet.by, nrow = NULL, ncol = NULL, scales = "fixed", short.panel.labs = TRUE, labeller = "label_value", panel.labs = NULL, panel.labs.background = list(color = NULL, fill = NULL), panel.labs.font = list(face = NULL, color = NULL, size = NULL, angle = NULL), panel.labs.font.x = panel.labs.font, panel.labs.font.y = panel.labs.font, strip.position = "top", ... ) } \arguments{ \item{p}{a ggplot} \item{facet.by}{character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.} \item{nrow, ncol}{Number of rows and columns in the panel. Used only when the data is faceted by one grouping variable.} \item{scales}{should axis scales of panels be fixed ("fixed", the default), free ("free"), or free in one dimension ("free_x", "free_y").} \item{short.panel.labs}{logical value. Default is TRUE. If TRUE, create short labels for panels by omitting variable names; in other words panels will be labelled only by variable grouping levels.} \item{labeller}{Character vector. An alternative to the argument \code{short.panel.labs}. Possible values are one of "label_both" (panel labelled by both grouping variable names and levels) and "label_value" (panel labelled with only grouping levels).} \item{panel.labs}{a list of one or two character vectors to modify facet panel labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies the labels for the "sex" variable. For two grouping variables, you can use for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", "Lev", "Lev2") ).} \item{panel.labs.background}{a list to customize the background of panel labels. Should contain the combination of the following elements: \itemize{ \item \code{color, linetype, size}: background line color, type and size \item \code{fill}: background fill color. } For example, panel.labs.background = list(color = "blue", fill = "pink", linetype = "dashed", size = 0.5).} \item{panel.labs.font}{a list of aestheics indicating the size (e.g.: 14), the face/style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") and the orientation angle (e.g.: 45) of panel labels.} \item{panel.labs.font.x, panel.labs.font.y}{same as panel.labs.font but for only x and y direction, respectively.} \item{strip.position}{(used only in \code{facet_wrap()}). By default, the labels are displayed on the top of the plot. Using \code{strip.position} it is possible to place the labels on either of the four sides by setting \code{strip.position = c("top", "bottom", "left", "right")}} \item{...}{not used} } \description{ Create multi-panel plots of a data set grouped by one or two grouping variables. Wrapper around \code{\link[ggplot2]{facet_wrap}} } \examples{ p <- ggboxplot(ToothGrowth, x = "dose", y = "len", color = "supp") print(p) facet(p, facet.by = "supp") # Customize facet(p + theme_bw(), facet.by = "supp", short.panel.labs = FALSE, # Allow long labels in panels panel.labs.background = list(fill = "steelblue", color = "steelblue") ) } ggpubr/man/ggtexttable.Rd0000644000176200001440000003237314350533257015133 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggtexttable.R \name{ggtexttable} \alias{ggtexttable} \alias{ttheme} \alias{colnames_style} \alias{rownames_style} \alias{tbody_style} \alias{table_cell_font} \alias{table_cell_bg} \alias{tab_cell_crossout} \alias{tab_ncol} \alias{tab_nrow} \alias{tab_add_hline} \alias{tab_add_vline} \alias{tab_add_border} \alias{tbody_add_border} \alias{thead_add_border} \alias{tab_add_title} \alias{tab_add_footnote} \title{Draw a Textual Table} \usage{ ggtexttable( x, rows = rownames(x), cols = colnames(x), vp = NULL, theme = ttheme(), ... ) ttheme( base_style = "default", base_size = 11, base_colour = "black", padding = unit(c(4, 4), "mm"), colnames.style = colnames_style(size = base_size), rownames.style = rownames_style(size = base_size), tbody.style = tbody_style(size = base_size) ) colnames_style( color = "black", face = "bold", size = 12, fill = "grey80", linewidth = 1, linecolor = "white", parse = FALSE, ... ) rownames_style( color = "black", face = "italic", size = 12, fill = NA, linewidth = 1, linecolor = "white", parse = FALSE, ... ) tbody_style( color = "black", face = "plain", size = 12, fill = c("grey95", "grey90"), linewidth = 1, linecolor = "white", parse = FALSE, ... ) table_cell_font(tab, row, column, face = NULL, size = NULL, color = NULL) table_cell_bg( tab, row, column, fill = NULL, color = NULL, linewidth = NULL, alpha = NULL ) tab_cell_crossout( tab, row, column, linetype = 1, linewidth = 1, linecolor = "black", reduce.size.by = 0 ) tab_ncol(tab) tab_nrow(tab) tab_add_hline( tab, at.row = 2:tab_nrow(tab), row.side = c("bottom", "top"), from.column = 1, to.column = tab_ncol(tab), linetype = 1, linewidth = 1, linecolor = "black" ) tab_add_vline( tab, at.column = 2:tab_ncol(tab), column.side = c("left", "right"), from.row = 1, to.row = tab_nrow(tab), linetype = 1, linewidth = 1, linecolor = "black" ) tab_add_border( tab, from.row = 2, to.row = tab_nrow(tab), from.column = 1, to.column = tab_ncol(tab), linetype = 1, linewidth = 1, linecolor = "black" ) tbody_add_border( tab, from.row = 2, to.row = tab_nrow(tab), from.column = 1, to.column = tab_ncol(tab), linetype = 1, linewidth = 1, linecolor = "black" ) thead_add_border( tab, from.row = 1, to.row = 1, from.column = 1, to.column = tab_ncol(tab), linetype = 1, linewidth = 1, linecolor = "black" ) tab_add_title( tab, text, face = NULL, size = NULL, color = NULL, family = NULL, padding = unit(1.5, "line"), just = "left", hjust = NULL, vjust = NULL ) tab_add_footnote( tab, text, face = NULL, size = NULL, color = NULL, family = NULL, padding = unit(1.5, "line"), just = "right", hjust = NULL, vjust = NULL ) } \arguments{ \item{x}{a \code{data.frame} or \code{matrix}.} \item{rows}{optional vector to specify row names} \item{cols}{optional vector to specify column names} \item{vp}{optional viewport} \item{theme}{a list, as returned by the function \code{ttheme()}, defining the parameters of the table theme. Allowed values include one of \code{ttheme()} and \code{ttheme_clean()}.} \item{...}{extra parameters for text justification, e.g.: hjust and x. Default is "centre" for the body and header, and "right" for the row names. Left justification: \code{hjust = 0, x = 0.1}. Right justification: \code{hjust = 1, x = 0.9}.} \item{base_style}{character string the table style/theme. The available themes are illustrated in the \href{https://rpkgs.datanovia.com/ggpubr/files/ggtexttable-theme.pdf}{ggtexttable-theme.pdf} file. Allowed values include one of \code{c("default", "blank", "classic", "minimal", "light", "lBlack", "lBlue", "lRed", "lGreen", "lViolet", "lCyan", "lOrange", "lBlackWhite", "lBlueWhite", "lRedWhite", "lGreenWhite", "lVioletWhite", "lCyanWhite", "lOrangeWhite", "mBlack", "mBlue", "mRed", "mGreen", "mViolet", "mCyan", "mOrange", "mBlackWhite", "mBlueWhite", "mRedWhite", "mGreenWhite", "mVioletWhite", "mCyanWhite", "mOrangeWhite" )}. Note that, l = "light"; m = "medium".} \item{base_size}{default font size} \item{base_colour}{default font colour} \item{padding}{length-2 unit vector specifying the horizontal and vertical padding of text within each cell} \item{colnames.style}{a list, as returned by the function \code{colnames_style()}, defining the style of the table column names. Considered only when \code{base_size = "default"}.} \item{rownames.style}{a list, as returned by the function \code{rownames_style()}, defining the style of the table row names. Considered only when \code{base_size = "default"}.} \item{tbody.style}{a list, as returned by the function \code{tbody_style()}, defining the style of the table body. Considered only when \code{base_size = "default"}.} \item{color, face, size}{text font color, face and size, respectively. Allowed values for face include c("plain", "bold", "italic", "bold.italic").} \item{fill}{background color.} \item{linewidth, linecolor}{line width and color, respectively.} \item{parse}{logical, default behaviour for parsing text as plotmath} \item{tab}{an object from \code{ggtexttable} or from \code{gridExtra::tableGrob()}.} \item{row, column}{an integer specifying the row and the column numbers for the cell of interest.} \item{alpha}{numeric value specifying fill color transparency. Value should be in [0, 1], where 0 is full transparency and 1 is no transparency.} \item{linetype}{line type} \item{reduce.size.by}{Numeric value in [0, 1] to reduce the size by.} \item{at.row}{a numeric vector of row indexes; for example \code{at.row = c(1, 2)}.} \item{row.side}{row side to which the horinzotal line should be added. Can be one of \code{c("bottom", "top")}.} \item{from.column}{integer indicating the column from which to start drawing the horizontal line.} \item{to.column}{integer indicating the column to which the horizontal line should end.} \item{at.column}{a numeric vector of column indexes; for example \code{at.column = c(1, 2)}.} \item{column.side}{column side to which the vertical line should be added. Can be one of \code{c("left", "right")}.} \item{from.row}{integer indicating the row from which to start drawing the horizontal line.} \item{to.row}{integer indicating the row to which the vertical line should end.} \item{text}{text to be added as title or footnote.} \item{family}{font family} \item{just}{The justification of the text relative to its (x, y) location. If there are two values, the first value specifies horizontal justification and the second value specifies vertical justification. Possible string values are: "left", "right", "centre", "center", "bottom", and "top". For numeric values, 0 means left (bottom) alignment and 1 means right (top) alignment.} \item{hjust}{A numeric vector specifying horizontal justification. If specified, overrides the just setting.} \item{vjust}{A numeric vector specifying vertical justification. If specified, overrides the just setting.} } \value{ an object of class ggplot. } \description{ Draw a textual table. \itemize{ \item \code{ggtexttable()}: draw a textual table. \item \code{ttheme()}: customize table theme. \item \code{rownames_style(), colnames_style(), tbody_style()}: helper functions to customize the table row names, column names and body. \item \code{table_cell_font()}: access to a table cell for changing the text font (size and face). \item \code{table_cell_bg()}: access to a table cell for changing the background (fill, color, linewidth). \item \code{tab_cell_crossout()}: cross out a table cell. \item \code{tab_ncol(), tab_nrow()}: returns, respectively, the number of columns and rows in a ggtexttable. \item \code{tab_add_hline()}: Creates horizontal lines or separators at the top or the bottom side of a given specified row. \item \code{tab_add_vline()}: Creates vertical lines or separators at the right or the left side of a given specified column. \item \code{tab_add_border(), tbody_add_border(), thead_add_border()}: Add borders to table; tbody is for table body and thead is for table head. \item \code{tab_add_title(),tab_add_footnote()}: Add title, subtitle and footnote to a table. } } \examples{ # data df <- head(iris) # Default table # Remove row names using rows = NULL ggtexttable(df, rows = NULL) # Text justification for individual cells/rows/columns (#335) # First column is left justified i.e., hjust = 0 , x = 0.1 # Remaining columns are right justified i.e., hjust = 1 , x = 0.9 table_theme <- ttheme( tbody.style = tbody_style( hjust = as.vector(matrix(c(0, 1, 1, 1, 1), ncol = 5, nrow = nrow(df), byrow = TRUE)), x = as.vector(matrix(c(.1, .9, .9,.9, .9), ncol = 5, nrow = nrow(df), byrow = TRUE)) ) ) ggtexttable(df, rows = NULL, theme = table_theme) # Blank theme ggtexttable(df, rows = NULL, theme = ttheme("blank")) # light theme ggtexttable(df, rows = NULL, theme = ttheme("light")) # Column names border only ggtexttable(df, rows = NULL, theme = ttheme("blank")) \%>\% tab_add_hline(at.row = 1:2, row.side = "top", linewidth = 2) # classic theme ggtexttable(df, rows = NULL, theme = ttheme("classic")) # minimal theme ggtexttable(df, rows = NULL, theme = ttheme("minimal")) # Medium blue (mBlue) theme ggtexttable(df, rows = NULL, theme = ttheme("mBlue")) # Customize the table as you want ggtexttable(df, rows = NULL, theme = ttheme( colnames.style = colnames_style(color = "white", fill = "#8cc257"), tbody.style = tbody_style(color = "black", fill = c("#e8f3de", "#d3e8bb")) ) ) # Use RColorBrewer palette # Provide as many fill color as there are rows in the table body, here nrow = 6 ggtexttable(df, theme = ttheme( colnames.style = colnames_style(fill = "white"), tbody.style = tbody_style(fill = get_palette("RdBu", 6)) ) ) # Text justification #:::::::::::::::::::::::::::::::::::::::::::::: # Default is "centre" for the body and header, and "right" for the row names. # Left justification: hjust=0, x=0.1 # Right justification: hjust=1, x=0.9 tbody.style = tbody_style(color = "black", fill = c("#e8f3de", "#d3e8bb"), hjust=1, x=0.9) ggtexttable(head(iris), rows = NULL, theme = ttheme( colnames.style = colnames_style(color = "white", fill = "#8cc257"), tbody.style = tbody.style ) ) # Access and modify the font and # the background of table cells # ::::::::::::::::::::::::::::::::::::::::::::: tab <- ggtexttable(head(iris), rows = NULL, theme = ttheme("classic")) tab <- table_cell_font(tab, row = 3, column = 2, face = "bold") tab <- table_cell_bg(tab, row = 4, column = 3, linewidth = 5, fill="darkolivegreen1", color = "darkolivegreen4") tab # Change table cells background and font for column 3, # Spaning from row 2 to the last row in the data tab <- ggtexttable(df, rows = NULL, theme = ttheme("classic")) tab \%>\% table_cell_bg(row = 2:tab_nrow(tab), column = 3, fill = "darkblue") \%>\% table_cell_font(row = 2:tab_nrow(tab), column = 3, face = "italic", color = "white") # Add separators and borders # ::::::::::::::::::::::::::::::::::::::::::::::::::: # Table with blank theme tab <- ggtexttable(df, theme = ttheme("blank"), rows = NULL) # Add horizontal and vertical lines tab \%>\% tab_add_hline(at.row = c(1, 2), row.side = "top", linewidth = 3, linetype = 1) \%>\% tab_add_hline(at.row = c(7), row.side = "bottom", linewidth = 3, linetype = 1) \%>\% tab_add_vline(at.column = 2:tab_ncol(tab), column.side = "left", from.row = 2, linetype = 2) # Add borders to table body and header # Cross out some cells tab \%>\% tbody_add_border() \%>\% thead_add_border() \%>\% tab_cell_crossout( row = c(2, 4), column = 3, linecolor = "red", reduce.size.by = 0.6 ) # Add titles andd footnote # ::::::::::::::::::::::::::::::::::::::::::::::::::: # Add titles and footnote # Wrap subtitle into multiple lines using strwrap() main.title <- "Edgar Anderson's Iris Data" subtitle <- paste0( "This famous (Fisher's or Anderson's) iris data set gives the measurements", " in centimeters of the variables sepal length and width and petal length and width,", " respectively, for 50 flowers from each of 3 species of iris.", " The species are Iris setosa, versicolor, and virginica." ) \%>\% strwrap(width = 80) \%>\% paste(collapse = "\n") tab <- ggtexttable(head(iris), theme = ttheme("light")) tab \%>\% tab_add_title(text = subtitle, face = "plain", size = 10) \%>\% tab_add_title(text = main.title, face = "bold", padding = unit(0.1, "line")) \%>\% tab_add_footnote(text = "*Table created using ggpubr", size = 10, face = "italic") # Combine density plot and summary table #::::::::::::::::::::::::::::::::::::: # Density plot of "Sepal.Length" density.p <- ggdensity(iris, x = "Sepal.Length", fill = "Species", palette = "jco") # Draw the summary table of Sepal.Length # Descriptive statistics by groups stable <- desc_statby(iris, measure.var = "Sepal.Length", grps = "Species") stable <- stable[, c("Species", "length", "mean", "sd")] stable.p <- ggtexttable(stable, rows = NULL, theme = ttheme("mOrange")) # Arrange the plots on the same page ggarrange(density.p, stable.p, ncol = 1, nrow = 2, heights = c(1, 0.5)) } ggpubr/man/geom_bracket.Rd0000644000176200001440000001525214333713444015237 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/geom_bracket.R \name{stat_bracket} \alias{stat_bracket} \alias{geom_bracket} \title{Add Brackets with Labels to a GGPlot} \usage{ stat_bracket( mapping = NULL, data = NULL, position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, label = NULL, type = c("text", "expression"), y.position = NULL, xmin = NULL, xmax = NULL, step.increase = 0, step.group.by = NULL, tip.length = 0.03, bracket.nudge.y = 0, bracket.shorten = 0, size = 0.3, label.size = 3.88, family = "", vjust = 0, ... ) geom_bracket( mapping = NULL, data = NULL, stat = "bracket", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, label = NULL, type = c("text", "expression"), y.position = NULL, xmin = NULL, xmax = NULL, step.increase = 0, step.group.by = NULL, tip.length = 0.03, bracket.nudge.y = 0, bracket.shorten = 0, size = 0.3, label.size = 3.88, family = "", vjust = 0, coord.flip = FALSE, ... ) } \arguments{ \item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and \code{inherit.aes = TRUE} (the default), it is combined with the default mapping at the top level of the plot. You must supply \code{mapping} if there is no plot mapping.} \item{data}{The data to be displayed in this layer. There are three options: If \code{NULL}, the default, the data is inherited from the plot data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}. A \code{data.frame}, or other object, will override the plot data. All objects will be fortified to produce a data frame. See \code{\link[ggplot2:fortify]{fortify()}} for which variables will be created. A \code{function} will be called with a single argument, the plot data. The return value must be a \code{data.frame}, and will be used as the layer data. A \code{function} can be created from a \code{formula} (e.g. \code{~ head(.x, 10)}).} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{na.rm}{If \code{FALSE} (the default), removes missing values with a warning. If \code{TRUE} silently removes missing values.} \item{show.legend}{logical. Should this layer be included in the legends? \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and \code{TRUE} always includes. It can also be a named logical vector to finely select the aesthetics to display.} \item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. \code{\link[ggplot2:borders]{borders()}}.} \item{label}{character vector with alternative label, if not null test is ignored} \item{type}{the label type. Can be one of "text" and "expression" (for parsing plotmath expression).} \item{y.position}{numeric vector with the y positions of the brackets} \item{xmin}{numeric vector with the positions of the left sides of the brackets} \item{xmax}{numeric vector with the positions of the right sides of the brackets} \item{step.increase}{numeric vector with the increase in fraction of total height for every additional comparison to minimize overlap.} \item{step.group.by}{a variable name for grouping brackets before adding step.increase. Useful to group bracket by facet panel.} \item{tip.length}{numeric vector with the fraction of total height that the bar goes down to indicate the precise column} \item{bracket.nudge.y}{Vertical adjustment to nudge brackets by. Useful to move up or move down the bracket. If positive value, brackets will be moved up; if negative value, brackets are moved down.} \item{bracket.shorten}{a small numeric value in [0-1] for shortening the with of bracket.} \item{size}{change the width of the lines of the bracket} \item{label.size}{change the size of the label text} \item{family}{change the font used for the text} \item{vjust}{move the text up or down relative to the bracket} \item{...}{other arguments passed on to \code{\link{layer}}. These are often aesthetics, used to set an aesthetic to a fixed value, like \code{color = "red"} or \code{size = 3}. They may also be parameters to the paired geom/stat.} \item{stat}{The statistical transformation to use on the data for this layer, either as a \code{ggproto} \code{Geom} subclass or as a string naming the stat stripped of the \code{stat_} prefix (e.g. \code{"count"} rather than \code{"stat_count"})} \item{coord.flip}{logical. If \code{TRUE}, flip x and y coordinates so that horizontal becomes vertical, and vertical, horizontal. When adding the p-values to a horizontal ggplot (generated using \code{\link[ggplot2]{coord_flip}()}), you need to specify the option \code{coord.flip = TRUE}.} } \description{ add brackets with label annotation to a ggplot. Helpers for adding p-value or significance levels to a plot. } \examples{ df <- ToothGrowth df$dose <- factor(df$dose) # Add bracket with labels ggboxplot(df, x = "dose", y = "len") + geom_bracket( xmin = "0.5", xmax = "1", y.position = 30, label = "t-test, p < 0.05" ) # Customize bracket tip.length tip.length ggboxplot(df, x = "dose", y = "len") + geom_bracket( xmin = "0.5", xmax = "1", y.position = 30, label = "t-test, p < 0.05", tip.length = c(0.2, 0.02) ) #Using plotmath expression ggboxplot(df, x = "dose", y = "len") + geom_bracket( xmin = "0.5", xmax = "1", y.position = 30, label = "list(~italic(p)<=0.001)", type = "expression", tip.length = c(0.2, 0.02) ) # Specify multiple brackets manually ggboxplot(df, x = "dose", y = "len") + geom_bracket( xmin = c("0.5", "1"), xmax = c("1", "2"), y.position = c(30, 35), label = c("***", "**"), tip.length = 0.01 ) # Compute statistical tests and add p-values stat.test <- compare_means(len ~ dose, ToothGrowth, method = "t.test") ggboxplot(df, x = "dose", y = "len") + geom_bracket( aes(xmin = group1, xmax = group2, label = signif(p, 2)), data = stat.test, y.position = 35 ) # Increase step length between brackets ggboxplot(df, x = "dose", y = "len") + geom_bracket( aes(xmin = group1, xmax = group2, label = signif(p, 2)), data = stat.test, y.position = 35, step.increase = 0.1 ) # Or specify the positions of each comparison ggboxplot(df, x = "dose", y = "len") + geom_bracket( aes(xmin = group1, xmax = group2, label = signif(p, 2)), data = stat.test, y.position = c(32, 35, 38) ) } ggpubr/man/stat_stars.Rd0000644000176200001440000000561114332576562015011 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stat_stars.R \name{stat_stars} \alias{stat_stars} \title{Add Stars to a Scatter Plot} \usage{ stat_stars( mapping = NULL, data = NULL, geom = "segment", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... ) } \arguments{ \item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and \code{inherit.aes = TRUE} (the default), it is combined with the default mapping at the top level of the plot. You must supply \code{mapping} if there is no plot mapping.} \item{data}{The data to be displayed in this layer. There are three options: If \code{NULL}, the default, the data is inherited from the plot data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}. A \code{data.frame}, or other object, will override the plot data. All objects will be fortified to produce a data frame. See \code{\link[ggplot2:fortify]{fortify()}} for which variables will be created. A \code{function} will be called with a single argument, the plot data. The return value must be a \code{data.frame}, and will be used as the layer data. A \code{function} can be created from a \code{formula} (e.g. \code{~ head(.x, 10)}).} \item{geom}{The geometric object to use to display the data, either as a \code{ggproto} \code{Geom} subclass or as a string naming the geom stripped of the \code{geom_} prefix (e.g. \code{"point"} rather than \code{"geom_point"})} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{na.rm}{If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.} \item{show.legend}{logical. Should this layer be included in the legends? \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and \code{TRUE} always includes. It can also be a named logical vector to finely select the aesthetics to display.} \item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. \code{\link[ggplot2:borders]{borders()}}.} \item{...}{other arguments to pass to \code{\link[ggplot2]{geom_segment}}.} } \description{ Create a star plot by drawing segments from group centroid to each points. } \examples{ # Load data data("mtcars") df <- mtcars df$cyl <- as.factor(df$cyl) # Scatter plot with ellipses and group mean points ggscatter(df, x = "wt", y = "mpg", color = "cyl", shape = "cyl", mean.point = TRUE, ellipse = TRUE)+ stat_stars(aes(color = cyl)) } \seealso{ \code{\link{ggscatter}} } ggpubr/man/stat_compare_means.Rd0000644000176200001440000001760614334200505016454 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stat_compare_means.R \name{stat_compare_means} \alias{stat_compare_means} \title{Add Mean Comparison P-values to a ggplot} \usage{ stat_compare_means( mapping = NULL, data = NULL, method = NULL, paired = FALSE, method.args = list(), ref.group = NULL, comparisons = NULL, hide.ns = FALSE, label.sep = ", ", label = NULL, label.x.npc = "left", label.y.npc = "top", label.x = NULL, label.y = NULL, vjust = 0, tip.length = 0.03, bracket.size = 0.3, step.increase = 0, symnum.args = list(), geom = "text", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... ) } \arguments{ \item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and \code{inherit.aes = TRUE} (the default), it is combined with the default mapping at the top level of the plot. You must supply \code{mapping} if there is no plot mapping.} \item{data}{The data to be displayed in this layer. There are three options: If \code{NULL}, the default, the data is inherited from the plot data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}. A \code{data.frame}, or other object, will override the plot data. All objects will be fortified to produce a data frame. See \code{\link[ggplot2:fortify]{fortify()}} for which variables will be created. A \code{function} will be called with a single argument, the plot data. The return value must be a \code{data.frame}, and will be used as the layer data. A \code{function} can be created from a \code{formula} (e.g. \code{~ head(.x, 10)}).} \item{method}{a character string indicating which method to be used for comparing means.} \item{paired}{a logical indicating whether you want a paired test. Used only in \code{\link[stats]{t.test}} and in \link[stats]{wilcox.test}.} \item{method.args}{a list of additional arguments used for the test method. For example one might use \code{method.args = list(alternative = "greater")} for wilcoxon test.} \item{ref.group}{a character string specifying the reference group. If specified, for a given grouping variable, each of the group levels will be compared to the reference group (i.e. control group). \code{ref.group} can be also \code{".all."}. In this case, each of the grouping variable levels is compared to all (i.e. basemean).} \item{comparisons}{A list of length-2 vectors. The entries in the vector are either the names of 2 values on the x-axis or the 2 integers that correspond to the index of the groups of interest, to be compared.} \item{hide.ns}{logical value. If TRUE, hide ns symbol when displaying significance levels.} \item{label.sep}{a character string to separate the terms. Default is ", ", to separate the correlation coefficient and the p.value.} \item{label}{character string specifying label type. Allowed values include "p.signif" (shows the significance levels), "p.format" (shows the formatted p value).} \item{label.x.npc, label.y.npc}{can be \code{numeric} or \code{character} vector of the same length as the number of groups and/or panels. If too short they will be recycled. \itemize{ \item If \code{numeric}, value should be between 0 and 1. Coordinates to be used for positioning the label, expressed in "normalized parent coordinates". \item If \code{character}, allowed values include: i) one of c('right', 'left', 'center', 'centre', 'middle') for x-axis; ii) and one of c( 'bottom', 'top', 'center', 'centre', 'middle') for y-axis.}} \item{label.x, label.y}{\code{numeric} Coordinates (in data units) to be used for absolute positioning of the label. If too short they will be recycled.} \item{vjust}{move the text up or down relative to the bracket.} \item{tip.length}{numeric vector with the fraction of total height that the bar goes down to indicate the precise column. Default is 0.03. Can be of same length as the number of comparisons to adjust specifically the tip lenth of each comparison. For example tip.length = c(0.01, 0.03). If too short they will be recycled.} \item{bracket.size}{Width of the lines of the bracket.} \item{step.increase}{numeric vector with the increase in fraction of total height for every additional comparison to minimize overlap.} \item{symnum.args}{a list of arguments to pass to the function \code{\link[stats]{symnum}} for symbolic number coding of p-values. For example, \code{symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, Inf), symbols = c("****", "***", "**", "*", "ns"))}. In other words, we use the following convention for symbols indicating statistical significance: \itemize{ \item \code{ns}: p > 0.05 \item \code{*}: p <= 0.05 \item \code{**}: p <= 0.01 \item \code{***}: p <= 0.001 \item \code{****}: p <= 0.0001 }} \item{geom}{The geometric object to use to display the data, either as a \code{ggproto} \code{Geom} subclass or as a string naming the geom stripped of the \code{geom_} prefix (e.g. \code{"point"} rather than \code{"geom_point"})} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{na.rm}{If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.} \item{show.legend}{logical. Should this layer be included in the legends? \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and \code{TRUE} always includes. It can also be a named logical vector to finely select the aesthetics to display.} \item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. \code{\link[ggplot2:borders]{borders()}}.} \item{...}{other arguments to pass to \code{\link[ggplot2]{geom_text}} or \code{\link[ggplot2:geom_text]{geom_label}}.} } \description{ Add mean comparison p-values to a ggplot, such as box blots, dot plots and stripcharts. } \examples{ # Load data data("ToothGrowth") head(ToothGrowth) # Two independent groups #::::::::::::::::::::::::::::::::::::::::::::::::: p <- ggboxplot(ToothGrowth, x = "supp", y = "len", color = "supp", palette = "npg", add = "jitter") # Add p-value p + stat_compare_means() # Change method p + stat_compare_means(method = "t.test") # Paired samples #::::::::::::::::::::::::::::::::::::::::::::::::: ggpaired(ToothGrowth, x = "supp", y = "len", color = "supp", line.color = "gray", line.size = 0.4, palette = "npg")+ stat_compare_means(paired = TRUE) # More than two groups #::::::::::::::::::::::::::::::::::::::::::::::::: # Pairwise comparisons: Specify the comparisons you want my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") ) ggboxplot(ToothGrowth, x = "dose", y = "len", color = "dose", palette = "npg")+ # Add pairwise comparisons p-value stat_compare_means(comparisons = my_comparisons, label.y = c(29, 35, 40))+ stat_compare_means(label.y = 45) # Add global Anova p-value # Multiple pairwise test against a reference group ggboxplot(ToothGrowth, x = "dose", y = "len", color = "dose", palette = "npg")+ stat_compare_means(method = "anova", label.y = 40)+ # Add global p-value stat_compare_means(aes(label = after_stat(p.signif)), method = "t.test", ref.group = "0.5") # Multiple grouping variables #::::::::::::::::::::::::::::::::::::::::::::::::: # Box plot facetted by "dose" p <- ggboxplot(ToothGrowth, x = "supp", y = "len", color = "supp", palette = "npg", add = "jitter", facet.by = "dose", short.panel.labs = FALSE) # Use only p.format as label. Remove method name. p + stat_compare_means( aes(label = paste0("p = ", after_stat(p.format))) ) } \seealso{ \code{\link{compare_means}} } ggpubr/man/ggbarplot.Rd0000644000176200001440000002161314367732422014600 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggbarplot.R \name{ggbarplot} \alias{ggbarplot} \title{Bar plot} \usage{ ggbarplot( data, x, y, combine = FALSE, merge = FALSE, color = "black", fill = "white", palette = NULL, size = NULL, width = NULL, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, select = NULL, remove = NULL, order = NULL, add = "none", add.params = list(), error.plot = "errorbar", label = FALSE, lab.col = "black", lab.size = 4, lab.pos = c("out", "in"), lab.vjust = NULL, lab.hjust = NULL, lab.nb.digits = NULL, sort.val = c("none", "desc", "asc"), sort.by.groups = TRUE, top = Inf, position = position_stack(), ggtheme = theme_pubr(), ... ) } \arguments{ \item{data}{a data frame} \item{x, y}{x and y variables for drawing.} \item{combine}{logical value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, create a multi-panel plot by combining the plot of y variables.} \item{merge}{logical or character value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, merge multiple y variables in the same plotting area. Allowed values include also "asis" (TRUE) and "flip". If merge = "flip", then y variables are used as x tick labels and the x variable is used as grouping variable.} \item{color, fill}{outline and fill colors.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{size}{Numeric value (e.g.: size = 1). change the size of points and outlines.} \item{width}{numeric value between 0 and 1 specifying box width.} \item{title}{plot main title.} \item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to hide xlab.} \item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to hide ylab.} \item{facet.by}{character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.} \item{panel.labs}{a list of one or two character vectors to modify facet panel labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies the labels for the "sex" variable. For two grouping variables, you can use for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", "Lev", "Lev2") ).} \item{short.panel.labs}{logical value. Default is TRUE. If TRUE, create short labels for panels by omitting variable names; in other words panels will be labelled only by variable grouping levels.} \item{select}{character vector specifying which items to display.} \item{remove}{character vector specifying which items to remove from the plot.} \item{order}{character vector specifying the order of items.} \item{add}{character vector for adding another plot element (e.g.: dot plot or error bars). Allowed values are one or the combination of: "none", "dotplot", "jitter", "boxplot", "point", "mean", "mean_se", "mean_sd", "mean_ci", "mean_range", "median", "median_iqr", "median_hilow", "median_q1q3", "median_mad", "median_range"; see ?desc_statby for more details.} \item{add.params}{parameters (color, shape, size, fill, linetype) for the argument 'add'; e.g.: add.params = list(color = "red").} \item{error.plot}{plot type used to visualize error. Allowed values are one of c("pointrange", "linerange", "crossbar", "errorbar", "upper_errorbar", "lower_errorbar", "upper_pointrange", "lower_pointrange", "upper_linerange", "lower_linerange"). Default value is "pointrange" or "errorbar". Used only when add != "none" and add contains one "mean_*" or "med_*" where "*" = sd, se, ....} \item{label}{specify whether to add labels on the bar plot. Allowed values are: \itemize{ \item \strong{logical value}: If TRUE, y values is added as labels on the bar plot \item \strong{character vector}: Used as text labels; must be the same length as y. }} \item{lab.col, lab.size}{text color and size for labels.} \item{lab.pos}{character specifying the position for labels. Allowed values are "out" (for outside) or "in" (for inside). Ignored when lab.vjust != NULL.} \item{lab.vjust}{numeric, vertical justification of labels. Provide negative value (e.g.: -0.4) to put labels outside the bars or positive value to put labels inside (e.g.: 2).} \item{lab.hjust}{numeric, horizontal justification of labels.} \item{lab.nb.digits}{integer indicating the number of decimal places (round) to be used.} \item{sort.val}{a string specifying whether the value should be sorted. Allowed values are "none" (no sorting), "asc" (for ascending) or "desc" (for descending).} \item{sort.by.groups}{logical value. If TRUE the data are sorted by groups. Used only when sort.val != "none".} \item{top}{a numeric value specifying the number of top elements to be shown.} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to be passed to ggpar().} } \description{ Create a bar plot. } \details{ The plot can be easily customized using the function ggpar(). Read ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels and position: legend = "right" \item plot orientation : orientation = c("vertical", "horizontal", "reverse") } } \examples{ # Data df <- data.frame(dose=c("D0.5", "D1", "D2"), len=c(4.2, 10, 29.5)) print(df) # Basic plot with label outsite # +++++++++++++++++++++++++++ ggbarplot(df, x = "dose", y = "len", label = TRUE, label.pos = "out") # Change width ggbarplot(df, x = "dose", y = "len", width = 0.5) # Change the plot orientation: horizontal ggbarplot(df, "dose", "len", orientation = "horiz") # Change the default order of items ggbarplot(df, "dose", "len", order = c("D2", "D1", "D0.5")) # Change colors # +++++++++++++++++++++++++++ # Change fill and outline color # add labels inside bars ggbarplot(df, "dose", "len", fill = "steelblue", color = "steelblue", label = TRUE, lab.pos = "in", lab.col = "white") # Change colors by groups: dose # Use custom color palette ggbarplot(df, "dose", "len", color = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07")) # Change fill and outline colors by groups ggbarplot(df, "dose", "len", fill = "dose", color = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07")) # Plot with multiple groups # +++++++++++++++++++++ # Create some data df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3), dose=rep(c("D0.5", "D1", "D2"),2), len=c(6.8, 15, 33, 4.2, 10, 29.5)) print(df2) # Plot "len" by "dose" and change color by a second group: "supp" # Add labels inside bars ggbarplot(df2, "dose", "len", fill = "supp", color = "supp", palette = "Paired", label = TRUE, lab.col = "white", lab.pos = "in") # Change position: Interleaved (dodged) bar plot ggbarplot(df2, "dose", "len", fill = "supp", color = "supp", palette = "Paired", label = TRUE, position = position_dodge(0.9)) # Add points and errors # ++++++++++++++++++++++++++ # Data: ToothGrowth data set we'll be used. df3 <- ToothGrowth head(df3, 10) # It can be seen that for each group we have # different values ggbarplot(df3, x = "dose", y = "len") # Visualize the mean of each group ggbarplot(df3, x = "dose", y = "len", add = "mean") # Add error bars: mean_se # (other values include: mean_sd, mean_ci, median_iqr, ....) # Add labels ggbarplot(df3, x = "dose", y = "len", add = "mean_se", label = TRUE, lab.vjust = -1.6) # Use only "upper_errorbar" ggbarplot(df3, x = "dose", y = "len", add = "mean_se", error.plot = "upper_errorbar") # Change error.plot to "pointrange" ggbarplot(df3, x = "dose", y = "len", add = "mean_se", error.plot = "pointrange") # Add jitter points and errors (mean_se) ggbarplot(df3, x = "dose", y = "len", add = c("mean_se", "jitter")) # Add dot and errors (mean_se) ggbarplot(df3, x = "dose", y = "len", add = c("mean_se", "dotplot")) # Multiple groups with error bars and jitter point ggbarplot(df3, x = "dose", y = "len", color = "supp", add = "mean_se", palette = c("#00AFBB", "#E7B800"), position = position_dodge()) } \seealso{ \code{\link{ggpar}}, \code{\link{ggline}} } ggpubr/man/ggpubr-package.Rd0000644000176200001440000000434414343207174015473 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggpubr-package.R \docType{package} \name{ggpubr-package} \alias{ggpubr} \alias{ggpubr-package} \title{ggpubr: 'ggplot2' Based Publication Ready Plots} \description{ The 'ggplot2' package is excellent and flexible for elegant data visualization in R. However the default generated plots requires some formatting before we can send them for publication. Furthermore, to customize a 'ggplot', the syntax is opaque and this raises the level of difficulty for researchers with no advanced R programming skills. 'ggpubr' provides some easy-to-use functions for creating and customizing 'ggplot2'- based publication ready plots. } \details{ General resources: \itemize{ \item \href{https://rpkgs.datanovia.com/ggpubr/}{ggpubr documentation} \item \href{https://www.datanovia.com/en/blog/tag/ggpubr/}{ggpubr tutorials} } } \section{Package options}{ \describe{ \item{ggpubr.parse_aes}{logical indicating whether to parse or not aesthetics variables names. Default is \code{TRUE}. For example, if you want \code{ggpubr} to handle non-standard column names, like \code{A-A}, without parsing, then set this option to \code{FALSE} using \code{options(ggpubr.parse_aes = FALSE)}.} \item{ggpubr.null_device}{A function that creates an appropriate null device. These include: \code{\link[cowplot:pdf_null_device]{cowplot::pdf_null_device}}, \code{\link[cowplot:png_null_device]{cowplot::png_null_device}}, \code{\link[cowplot:cairo_null_device]{cowplot::cairo_null_device}} and \code{\link[cowplot:agg_null_device]{cowplot::agg_null_device}}. Default is \code{\link[cowplot:pdf_null_device]{cowplot::pdf_null_device}}. This is used in function like \code{\link{as_ggplot}()}, which needs to open a graphics device to render ggplot objects into grid graphics objects. This function is used to open null device for avoiding the display of unnecessary blank page when calling \code{\link{ggarrange}()} or \code{\link{as_ggplot}()}} } } \seealso{ Useful links: \itemize{ \item \url{https://rpkgs.datanovia.com/ggpubr/} \item Report bugs at \url{https://github.com/kassambara/ggpubr/issues} } } \author{ \strong{Maintainer}: Alboukadel Kassambara \email{alboukadel.kassambara@gmail.com} } \keyword{internal} ggpubr/man/stat_welch_anova_test.Rd0000644000176200001440000002002214367517301017165 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stat_welch_anova_test.R \name{stat_welch_anova_test} \alias{stat_welch_anova_test} \title{Add Welch One-Way ANOVA Test P-values to a GGPlot} \usage{ stat_welch_anova_test( mapping = NULL, data = NULL, group.by = NULL, label = "{method}, p = {p.format}", label.x.npc = "left", label.y.npc = "top", label.x = NULL, label.y = NULL, step.increase = 0.1, p.adjust.method = "holm", significance = list(), geom = "text", position = "identity", na.rm = FALSE, show.legend = FALSE, inherit.aes = TRUE, parse = FALSE, ... ) } \arguments{ \item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and \code{inherit.aes = TRUE} (the default), it is combined with the default mapping at the top level of the plot. You must supply \code{mapping} if there is no plot mapping.} \item{data}{The data to be displayed in this layer. There are three options: If \code{NULL}, the default, the data is inherited from the plot data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}. A \code{data.frame}, or other object, will override the plot data. All objects will be fortified to produce a data frame. See \code{\link[ggplot2:fortify]{fortify()}} for which variables will be created. A \code{function} will be called with a single argument, the plot data. The return value must be a \code{data.frame}, and will be used as the layer data. A \code{function} can be created from a \code{formula} (e.g. \code{~ head(.x, 10)}).} \item{group.by}{(optional) character vector specifying the grouping variable; it should be used only for grouped plots. Possible values are : \itemize{ \item \code{"x.var"}: Group by the x-axis variable and perform the test between legend groups. In other words, the p-value is compute between legend groups at each x position \item \code{"legend.var"}: Group by the legend variable and perform the test between x-axis groups. In other words, the test is performed between the x-groups for each legend level. }} \item{label}{the column containing the label (e.g.: label = "p" or label = "p.adj"), where \code{p} is the p-value. Can be also an expression that can be formatted by the \code{\link[glue]{glue}()} package. For example, when specifying label = "t-test, p = \{p\}", the expression \{p\} will be replaced by its value.} \item{label.x.npc, label.y.npc}{can be \code{numeric} or \code{character} vector of the same length as the number of groups and/or panels. If too short they will be recycled. \itemize{ \item If \code{numeric}, value should be between 0 and 1. Coordinates to be used for positioning the label, expressed in "normalized parent coordinates". \item If \code{character}, allowed values include: i) one of c('right', 'left', 'center', 'centre', 'middle') for x-axis; ii) and one of c( 'bottom', 'top', 'center', 'centre', 'middle') for y-axis.}} \item{label.x, label.y}{\code{numeric} Coordinates (in data units) to be used for absolute positioning of the label. If too short they will be recycled.} \item{step.increase}{numeric vector with the increase in fraction of total height for every additional comparison to minimize overlap.} \item{p.adjust.method}{method for adjusting p values (see \code{\link[stats]{p.adjust}}). Has impact only in a situation, where multiple pairwise tests are performed; or when there are multiple grouping variables. Allowed values include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p value (not recommended), use p.adjust.method = "none".} \item{significance}{a list of arguments specifying the signifcance cutpoints and symbols. For example, \code{significance <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, Inf), symbols = c("****", "***", "**", "*", "ns"))}. In other words, we use the following convention for symbols indicating statistical significance: \itemize{ \item \code{ns}: p > 0.05 \item \code{*}: p <= 0.05 \item \code{**}: p <= 0.01 \item \code{***}: p <= 0.001 \item \code{****}: p <= 0.0001 }} \item{geom}{The geometric object to use to display the data, either as a \code{ggproto} \code{Geom} subclass or as a string naming the geom stripped of the \code{geom_} prefix (e.g. \code{"point"} rather than \code{"geom_point"})} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{na.rm}{If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.} \item{show.legend}{logical. Should this layer be included in the legends? \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and \code{TRUE} always includes. It can also be a named logical vector to finely select the aesthetics to display.} \item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. \code{\link[ggplot2:borders]{borders()}}.} \item{parse}{If TRUE, the labels will be parsed into expressions and displayed as described in \code{?plotmath}.} \item{...}{other arguments passed to the function \code{geom_bracket()} or \code{geom_text()}} } \description{ Add Welch one-way ANOVA test p-values to a ggplot, such as box blots, dot plots and stripcharts. } \section{Computed variables}{ \itemize{ \item{statistic}: the value of the test statistic (F-value) \item{DFn}: Degrees of Freedom in the numerator (i.e. DF effect) \item{DFd}: Degrees of Freedom in the denominator (i.e., DF error) \item{p}: p-value. \item{p.adj}: Adjusted p-values. \item{p.signif}: P-value significance. \item{p.adj.signif}: Adjusted p-value significance. \item{p.format}: Formated p-value. \item{p.adj.format}: Formated adjusted p-value. \item{n}: number of samples. } } \examples{ # Data preparation #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% # Transform `dose` into factor variable df <- ToothGrowth df$dose <- as.factor(df$dose) # Add a random grouping variable set.seed(123) df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20))) df$len <- ifelse(df$group == "grp2", df$len+2, df$len) df$len <- ifelse(df$group == "grp3", df$len+7, df$len) head(df, 3) # Basic boxplot #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% # Create a basic boxplot # Add 5\% and 10\% space to the plot bottom and the top, respectively bxp <- ggboxplot(df, x = "dose", y = "len") + scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) # Add the p-value to the boxplot bxp + stat_welch_anova_test() # Change the label position # Using coordinates in data units bxp + stat_welch_anova_test(label.x = "1", label.y = 10, hjust = 0) # Format the p-value differently custom_p_format <- function(p) { rstatix::p_format(p, accuracy = 0.0001, digits = 3, leading.zero = FALSE) } bxp + stat_welch_anova_test( label = "Welch Anova, italic(p) = {custom_p_format(p)}{p.signif}" ) # Show a detailed label in italic bxp + stat_welch_anova_test(label = "as_detailed_italic") # Faceted plots #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% # Create a ggplot facet bxp <- ggboxplot(df, x = "dose", y = "len", facet.by = "supp") + scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) # Add p-values bxp + stat_welch_anova_test() # Grouped plots #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% bxp2 <- ggboxplot(df, x = "group", y = "len", color = "dose", palette = "npg") # For each x-position, computes tests between legend groups bxp2 + stat_welch_anova_test(aes(group = dose), label = "p = {p.format}{p.signif}") # For each legend group, computes tests between x variable groups bxp2 + stat_welch_anova_test(aes(group = dose, color = dose), group.by = "legend.var") } ggpubr/man/show_line_types.Rd0000644000176200001440000000057414332553656016037 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/show_line_types.R \name{show_line_types} \alias{show_line_types} \title{Line types available in R} \usage{ show_line_types() } \value{ a ggplot. } \description{ Show line types available in R. } \examples{ show_line_types()+ theme_minimal() } \seealso{ \code{\link{ggpar}} and \code{\link{ggline}}. } ggpubr/man/bgcolor.Rd0000644000176200001440000000100614332553656014242 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/bgcolor.R \name{bgcolor} \alias{bgcolor} \title{Change ggplot Panel Background Color} \usage{ bgcolor(color) } \arguments{ \item{color}{background color.} } \description{ Change ggplot panel background color. } \examples{ # Load data data("ToothGrowth") df <- ToothGrowth # Basic plot p <- ggboxplot(df, x = "dose", y = "len") p # Change panel background color p + bgcolor("#BFD5E3")+ border("#BFD5E3") } \seealso{ \link{border}(). } ggpubr/man/ggline.Rd0000644000176200001440000002173514367732422014071 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggline.R \name{ggline} \alias{ggline} \title{Line plot} \usage{ ggline( data, x, y, group = 1, numeric.x.axis = FALSE, combine = FALSE, merge = FALSE, color = "black", palette = NULL, linetype = "solid", plot_type = c("b", "l", "p"), size = 0.5, shape = 19, stroke = NULL, point.size = size, point.color = color, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, select = NULL, remove = NULL, order = NULL, add = "none", add.params = list(), error.plot = "errorbar", label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, show.line.label = FALSE, position = "identity", ggtheme = theme_pubr(), ... ) } \arguments{ \item{data}{a data frame} \item{x, y}{x and y variables for drawing.} \item{group}{grouping variable to connect points by line. Allowed values are 1 (for one line, one group) or a character vector specifying the name of the grouping variable (case of multiple lines).} \item{numeric.x.axis}{logical. If TRUE, x axis will be treated as numeric. Default is FALSE.} \item{combine}{logical value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, create a multi-panel plot by combining the plot of y variables.} \item{merge}{logical or character value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, merge multiple y variables in the same plotting area. Allowed values include also "asis" (TRUE) and "flip". If merge = "flip", then y variables are used as x tick labels and the x variable is used as grouping variable.} \item{color}{line colors.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{linetype}{line type.} \item{plot_type}{plot type. Allowed values are one of "b" for both line and point; "l" for line only; and "p" for point only. Default is "b".} \item{size}{Numeric value (e.g.: size = 1). change the size of points and outlines.} \item{shape}{point shapes.} \item{stroke}{point stroke. Used only for shapes 21-24 to control the thickness of points border.} \item{point.size}{point size.} \item{point.color}{point color.} \item{title}{plot main title.} \item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to hide xlab.} \item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to hide ylab.} \item{facet.by}{character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.} \item{panel.labs}{a list of one or two character vectors to modify facet panel labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies the labels for the "sex" variable. For two grouping variables, you can use for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", "Lev", "Lev2") ).} \item{short.panel.labs}{logical value. Default is TRUE. If TRUE, create short labels for panels by omitting variable names; in other words panels will be labelled only by variable grouping levels.} \item{select}{character vector specifying which items to display.} \item{remove}{character vector specifying which items to remove from the plot.} \item{order}{character vector specifying the order of items.} \item{add}{character vector for adding another plot element (e.g.: dot plot or error bars). Allowed values are one or the combination of: "none", "dotplot", "jitter", "boxplot", "point", "mean", "mean_se", "mean_sd", "mean_ci", "mean_range", "median", "median_iqr", "median_hilow", "median_q1q3", "median_mad", "median_range"; see ?desc_statby for more details.} \item{add.params}{parameters (color, shape, size, fill, linetype) for the argument 'add'; e.g.: add.params = list(color = "red").} \item{error.plot}{plot type used to visualize error. Allowed values are one of c("pointrange", "linerange", "crossbar", "errorbar", "upper_errorbar", "lower_errorbar", "upper_pointrange", "lower_pointrange", "upper_linerange", "lower_linerange"). Default value is "pointrange" or "errorbar". Used only when add != "none" and add contains one "mean_*" or "med_*" where "*" = sd, se, ....} \item{label}{the name of the column containing point labels. Can be also a character vector with length = nrow(data).} \item{font.label}{a list which can contain the combination of the following elements: the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of labels. For example font.label = list(size = 14, face = "bold", color ="red"). To specify only the size and the style, use font.label = list(size = 14, face = "plain").} \item{label.select}{can be of two formats: \itemize{ \item a character vector specifying some labels to show. \item a list containing one or the combination of the following components: \itemize{ \item \code{top.up} and \code{top.down}: to display the labels of the top up/down points. For example, \code{label.select = list(top.up = 10, top.down = 4)}. \item \code{criteria}: to filter, for example, by x and y variabes values, use this: \code{label.select = list(criteria = "`y` > 2 & `y` < 5 & `x` \%in\% c('A', 'B')")}. } }} \item{repel}{a logical value, whether to use ggrepel to avoid overplotting text labels or not.} \item{label.rectangle}{logical value. If TRUE, add rectangle underneath the text, making it easier to read.} \item{show.line.label}{logical value. If TRUE, shows line labels.} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to geom_dotplot.} } \description{ Create a line plot. } \details{ The plot can be easily customized using the function ggpar(). Read ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels and position: legend = "right" \item plot orientation : orientation = c("vertical", "horizontal", "reverse") } } \examples{ # Data df <- data.frame(dose=c("D0.5", "D1", "D2"), len=c(4.2, 10, 29.5)) print(df) # Basic plot # +++++++++++++++++++++++++++ ggline(df, x = "dose", y = "len") # Plot with multiple groups # +++++++++++++++++++++ # Create some data df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3), dose=rep(c("D0.5", "D1", "D2"),2), len=c(6.8, 15, 33, 4.2, 10, 29.5)) print(df2) # Plot "len" by "dose" and # Change line types and point shapes by a second groups: "supp" ggline(df2, "dose", "len", linetype = "supp", shape = "supp") # Change colors # +++++++++++++++++++++ # Change color by group: "supp" # Use custom color palette ggline(df2, "dose", "len", linetype = "supp", shape = "supp", color = "supp", palette = c("#00AFBB", "#E7B800")) # Add points and errors # ++++++++++++++++++++++++++ # Data: ToothGrowth data set we'll be used. df3 <- ToothGrowth head(df3, 10) # It can be seen that for each group we have # different values ggline(df3, x = "dose", y = "len") # Visualize the mean of each group ggline(df3, x = "dose", y = "len", add = "mean") # Add error bars: mean_se # (other values include: mean_sd, mean_ci, median_iqr, ....) # Add labels ggline(df3, x = "dose", y = "len", add = "mean_se") # Change error.plot to "pointrange" ggline(df3, x = "dose", y = "len", add = "mean_se", error.plot = "pointrange") # Add jitter points and errors (mean_se) ggline(df3, x = "dose", y = "len", add = c("mean_se", "jitter")) # Add dot and errors (mean_se) ggline(df3, x = "dose", y = "len", add = c("mean_se", "dotplot"), color = "steelblue") # Add violin and errors (mean_se) ggline(df3, x = "dose", y = "len", add = c("mean_se", "violin"), color = "steelblue") # Multiple groups with error bars # ++++++++++++++++++++++ ggline(df3, x = "dose", y = "len", color = "supp", add = "mean_se", palette = c("#00AFBB", "#E7B800")) # Add jitter ggline(df3, x = "dose", y = "len", color = "supp", add = c("mean_se", "jitter"), palette = c("#00AFBB", "#E7B800")) # Add dot plot ggline(df3, x = "dose", y = "len", color = "supp", add = c("mean_se", "dotplot"), palette = c("#00AFBB", "#E7B800")) } \seealso{ \code{\link{ggpar}}, \code{\link{ggbarplot}} } ggpubr/man/gghistogram.Rd0000644000176200001440000001554414367732422015140 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gghistogram.R \name{gghistogram} \alias{gghistogram} \title{Histogram plot} \usage{ gghistogram( data, x, y = "count", combine = FALSE, merge = FALSE, weight = NULL, color = "black", fill = NA, palette = NULL, size = NULL, linetype = "solid", alpha = 0.5, bins = NULL, binwidth = NULL, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, add = c("none", "mean", "median"), add.params = list(linetype = "dashed"), rug = FALSE, add_density = FALSE, label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, position = position_identity(), ggtheme = theme_pubr(), ... ) } \arguments{ \item{data}{a data frame} \item{x}{variable to be drawn.} \item{y}{one of "density" or "count".} \item{combine}{logical value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, create a multi-panel plot by combining the plot of y variables.} \item{merge}{logical or character value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, merge multiple y variables in the same plotting area. Allowed values include also "asis" (TRUE) and "flip". If merge = "flip", then y variables are used as x tick labels and the x variable is used as grouping variable.} \item{weight}{a variable name available in the input data for creating a weighted histogram.} \item{color, fill}{histogram line color and fill color.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{size}{Numeric value (e.g.: size = 1). change the size of points and outlines.} \item{linetype}{line type. See \code{\link{show_line_types}}.} \item{alpha}{numeric value specifying fill color transparency. Value should be in [0, 1], where 0 is full transparency and 1 is no transparency.} \item{bins}{Number of bins. Defaults to 30.} \item{binwidth}{numeric value specifying bin width. use value between 0 and 1 when you have a strong dense dotplot. For example binwidth = 0.2.} \item{title}{plot main title.} \item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to hide xlab.} \item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to hide ylab.} \item{facet.by}{character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.} \item{panel.labs}{a list of one or two character vectors to modify facet panel labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies the labels for the "sex" variable. For two grouping variables, you can use for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", "Lev", "Lev2") ).} \item{short.panel.labs}{logical value. Default is TRUE. If TRUE, create short labels for panels by omitting variable names; in other words panels will be labelled only by variable grouping levels.} \item{add}{allowed values are one of "mean" or "median" (for adding mean or median line, respectively).} \item{add.params}{parameters (color, size, linetype) for the argument 'add'; e.g.: add.params = list(color = "red").} \item{rug}{logical value. If TRUE, add marginal rug.} \item{add_density}{logical value. If TRUE, add density curves.} \item{label}{the name of the column containing point labels. Can be also a character vector with length = nrow(data).} \item{font.label}{a list which can contain the combination of the following elements: the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of labels. For example font.label = list(size = 14, face = "bold", color ="red"). To specify only the size and the style, use font.label = list(size = 14, face = "plain").} \item{label.select}{can be of two formats: \itemize{ \item a character vector specifying some labels to show. \item a list containing one or the combination of the following components: \itemize{ \item \code{top.up} and \code{top.down}: to display the labels of the top up/down points. For example, \code{label.select = list(top.up = 10, top.down = 4)}. \item \code{criteria}: to filter, for example, by x and y variabes values, use this: \code{label.select = list(criteria = "`y` > 2 & `y` < 5 & `x` \%in\% c('A', 'B')")}. } }} \item{repel}{a logical value, whether to use ggrepel to avoid overplotting text labels or not.} \item{label.rectangle}{logical value. If TRUE, add rectangle underneath the text, making it easier to read.} \item{position}{Position adjustment, either as a string, or the result of a call to a position adjustment function. Allowed values include "identity", "stack", "dodge".} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to \code{\link[ggplot2]{geom_histogram}} and \code{\link{ggpar}}.} } \description{ Create a histogram plot. } \details{ The plot can be easily customized using the function ggpar(). Read ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels and position: legend = "right" \item plot orientation : orientation = c("vertical", "horizontal", "reverse") } } \examples{ # Create some data format set.seed(1234) wdata = data.frame( sex = factor(rep(c("F", "M"), each=200)), weight = c(rnorm(200, 55), rnorm(200, 58))) head(wdata, 4) # Basic density plot # Add mean line and marginal rug gghistogram(wdata, x = "weight", fill = "lightgray", add = "mean", rug = TRUE) # Change outline colors by groups ("sex") # Use custom color palette gghistogram(wdata, x = "weight", add = "mean", rug = TRUE, color = "sex", palette = c("#00AFBB", "#E7B800")) # Change outline and fill colors by groups ("sex") # Use custom color palette gghistogram(wdata, x = "weight", add = "mean", rug = TRUE, color = "sex", fill = "sex", palette = c("#00AFBB", "#E7B800")) # Combine histogram and density plots gghistogram(wdata, x = "weight", add = "mean", rug = TRUE, fill = "sex", palette = c("#00AFBB", "#E7B800"), add_density = TRUE) # Weighted histogram gghistogram(iris, x = "Sepal.Length", weight = "Petal.Length") } \seealso{ \code{\link{ggdensity}} and \code{\link{ggpar}} } ggpubr/man/ggqqplot.Rd0000644000176200001440000000775014367732422014463 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggqqplot.R \name{ggqqplot} \alias{ggqqplot} \title{QQ Plots} \usage{ ggqqplot( data, x, combine = FALSE, merge = FALSE, color = "black", palette = NULL, size = NULL, shape = NULL, add = c("qqline", "none"), add.params = list(linetype = "solid"), conf.int = TRUE, conf.int.level = 0.95, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, ggtheme = theme_pubr(), ... ) } \arguments{ \item{data}{a data frame} \item{x}{variable to be drawn.} \item{combine}{logical value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, create a multi-panel plot by combining the plot of y variables.} \item{merge}{logical or character value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, merge multiple y variables in the same plotting area. Allowed values include also "asis" (TRUE) and "flip". If merge = "flip", then y variables are used as x tick labels and the x variable is used as grouping variable.} \item{color}{point color.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{size}{point size.} \item{shape}{point shape.} \item{add}{character vector. Allowed values are one of "none" and "qqline" (for adding qqline).} \item{add.params}{parameters (color, size, linetype) for the argument 'add'; e.g.: add.params = list(color = "red").} \item{conf.int}{logical value. If TRUE, confidence interval is added.} \item{conf.int.level}{the confidence level. Default value is 0.95.} \item{title}{plot main title.} \item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to hide xlab.} \item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to hide ylab.} \item{facet.by}{character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.} \item{panel.labs}{a list of one or two character vectors to modify facet panel labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies the labels for the "sex" variable. For two grouping variables, you can use for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", "Lev", "Lev2") ).} \item{short.panel.labs}{logical value. Default is TRUE. If TRUE, create short labels for panels by omitting variable names; in other words panels will be labelled only by variable grouping levels.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to \code{\link{ggpar}}.} } \description{ Quantile-Quantile plot. } \details{ The plot can be easily customized using the function ggpar(). Read ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels and position: legend = "right" \item plot orientation : orientation = c("vertical", "horizontal", "reverse") } } \examples{ # Create some data format set.seed(1234) wdata = data.frame( sex = factor(rep(c("F", "M"), each=200)), weight = c(rnorm(200, 55), rnorm(200, 58))) head(wdata, 4) # Basic QQ plot ggqqplot(wdata, x = "weight") # Change colors and shape by groups ("sex") # Use custom palette ggqqplot(wdata, x = "weight", color = "sex", palette = c("#00AFBB", "#E7B800")) } \seealso{ \code{\link{ggpar}} } ggpubr/man/show_point_shapes.Rd0000644000176200001440000000061214332553656016351 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/show_point_shapes.R \name{show_point_shapes} \alias{show_point_shapes} \title{Point shapes available in R} \usage{ show_point_shapes() } \value{ a ggplot. } \description{ Show point shapes available in R. } \examples{ show_point_shapes()+ theme_minimal() } \seealso{ \code{\link{ggpar}} and \code{\link{ggline}}. } ggpubr/man/ggsummarystats.Rd0000644000176200001440000001240414367517301015704 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggsummarystats.R \name{ggsummarytable} \alias{ggsummarytable} \alias{ggsummarystats} \alias{print.ggsummarystats} \alias{print.ggsummarystats_list} \title{GGPLOT with Summary Stats Table Under the Plot} \usage{ ggsummarytable( data, x, y, digits = 0, size = 3, color = "black", palette = NULL, facet.by = NULL, labeller = "label_value", position = "identity", ggtheme = theme_pubr(), ... ) ggsummarystats( data, x, y, summaries = c("n", "median", "iqr"), ggfunc = ggboxplot, color = "black", fill = "white", palette = NULL, facet.by = NULL, free.panels = FALSE, labeller = "label_value", heights = c(0.8, 0.2), digits = 0, table.font.size = 3, ggtheme = theme_pubr(), ... ) \method{print}{ggsummarystats}(x, heights = c(0.8, 0.2), ...) \method{print}{ggsummarystats_list}(x, heights = c(0.8, 0.2), legend = NULL, ...) } \arguments{ \item{data}{a data frame} \item{x}{a list of \code{ggsummarystats}.} \item{y}{character vector containing one or more variables to plot} \item{digits}{integer indicating the number of decimal places (round) to be used.} \item{size}{Numeric value (e.g.: size = 1). change the size of points and outlines.} \item{color}{outline color.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{facet.by}{character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.} \item{labeller}{Character vector. An alternative to the argument \code{short.panel.labs}. Possible values are one of "label_both" (panel labelled by both grouping variable names and levels) and "label_value" (panel labelled with only grouping levels).} \item{position}{Position adjustment, either as a string, or the result of a call to a position adjustment function.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments passed to the function \code{\link{ggpar}()}, \code{\link{facet}()} or \code{\link{ggarrange}()} when printing the plot.} \item{summaries}{summary stats to display in the table. Possible values are those returned by the function \code{\link[rstatix]{get_summary_stats}()}, including: \code{"n", "min", "max", "median", "q1", "q2", "q3", "mad", "mean", "sd", "se", "ci"}.} \item{ggfunc}{a ggpubr function, including: ggboxplot, ggviolin, ggdotplot, ggbarplot, ggline, etc. Can be any other ggplot function that accepts the following arguments \code{data, x, color, fill, palette, ggtheme, facet.by}.} \item{fill}{fill color.} \item{free.panels}{logical. If TRUE, create free plot panels when the argument \code{facet.by} is specified.} \item{heights}{a numeric vector of length 2, specifying the heights of the main and the summary table, respectively.} \item{table.font.size}{the summary table font size.} \item{legend}{character specifying legend position. Allowed values are one of c("top", "bottom", "left", "right", "none"). To remove the legend use legend = "none".} } \description{ Create a ggplot with summary stats (n, median, mean, iqr) table under the plot. Read more: \href{https://www.datanovia.com/en/blog/how-to-create-a-beautiful-plots-in-r-with-summary-statistics-labels/}{How to Create a Beautiful Plots in R with Summary Statistics Labels}. } \section{Functions}{ \itemize{ \item \code{ggsummarytable()}: Create a table of summary stats \item \code{ggsummarystats()}: Create a ggplot with a summary stat table under the plot. }} \examples{ # Data preparation #:::::::::::::::::::::::::::::::::::::::::::::::: data("ToothGrowth") df <- ToothGrowth df$dose <- as.factor(df$dose) # Add random QC column set.seed(123) qc <- rep(c("pass", "fail"), 30) df$qc <- as.factor(sample(qc, 60)) # Inspect the data head(df) # Basic summary stats #:::::::::::::::::::::::::::::::::::::::::::::::: # Compute summary statistics summary.stats <- df \%>\% group_by(dose) \%>\% get_summary_stats(type = "common") summary.stats # Visualize summary table ggsummarytable( summary.stats, x = "dose", y = c("n", "median", "iqr"), ggtheme = theme_bw() ) # Create plots with summary table under the plot #:::::::::::::::::::::::::::::::::::::::::::::::: # Basic plot ggsummarystats( df, x = "dose", y = "len", ggfunc = ggboxplot, add = "jitter" ) # Color by groups ggsummarystats( df, x = "dose", y = "len", ggfunc = ggboxplot, add = "jitter", color = "dose", palette = "npg" ) # Create a barplot ggsummarystats( df, x = "dose", y = "len", ggfunc = ggbarplot, add = c("jitter", "median_iqr"), color = "dose", palette = "npg" ) # Facet #:::::::::::::::::::::::::::::::::::::::::::::::: # Specify free.panels = TRUE for free panels ggsummarystats( df, x = "dose", y = "len", ggfunc = ggboxplot, add = "jitter", color = "dose", palette = "npg", facet.by = c("supp", "qc"), labeller = "label_both" ) } ggpubr/man/as_npc.Rd0000644000176200001440000000444714367517301014065 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/as_npc.R \name{as_npc} \alias{as_npc} \alias{as_npcx} \alias{as_npcy} \title{Convert Character Coordinates into Normalized Parent Coordinates (NPC)} \usage{ as_npc( value, group = 1L, step = 0.1, margin.npc = 0.05, axis = c("xy", "x", "y") ) as_npcx(value, group = 1L, step = 0.1, margin.npc = 0.05) as_npcy(value, group = 1L, step = 0.1, margin.npc = 0.05) } \arguments{ \item{value}{numeric (in [0-1]) or character vector of coordinates. If character, should be one of \code{c('right', 'left', 'bottom', 'top', 'center', 'centre', 'middle')}.} \item{group}{integer ggplot's group id. Used to shift coordinates to avoid overlaps.} \item{step}{numeric value in [0-1]. The step size for shifting coordinates in npc units. Considered as horizontal step for x-axis and vertical step for y-axis. For y-axis, the step value can be negative to reverse the order of groups.} \item{margin.npc}{numeric [0-1] The margin added towards the nearest plotting area edge when converting character coordinates into npc.} \item{axis}{the concerned axis . Should be one of \code{c("xy", "x", "y")}.} } \value{ A numeric vector with values in the range [0-1] representing npc coordinates. } \description{ Convert character coordinates to npc units and shift postions to avoid overlaps when grouping is active. If numeric validate npc values. } \details{ the \code{as_npc()} function is an adaptation from \code{ggpmisc::compute_npc()}. } \section{Functions}{ \itemize{ \item \code{as_npc()}: converts x or y coordinate values into npc. Input values should be numeric or one of the following values \code{c('right', 'left', 'bottom', 'top', 'center', 'centre', 'middle')}. \item \code{as_npcx()}: converts x coordinate values into npc. Input values should be numeric or one of the following values \code{c('right', 'left', 'center', 'centre', 'middle')}. Wrapper around \code{as_npc(axis = "x")}. \item \code{as_npcy()}: converts y coordinate values into npc. Input values should be numeric or one of the following values \code{c( 'bottom', 'top', 'center', 'centre', 'middle')}. Wrapper around \code{as_npc(axis = "y")}. }} \examples{ as_npc(c("left", "right")) as_npc(c("top", "right")) } \seealso{ \code{\link{npc_to_data_coord}}, \code{\link{get_coord}}. } ggpubr/man/rremove.Rd0000644000176200001440000000257114333713444014274 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rremove.R \name{rremove} \alias{rremove} \title{Remove a ggplot Component} \usage{ rremove(object) } \arguments{ \item{object}{character string specifying the plot components. Allowed values include: \itemize{ \item \code{"grid"} for both x and y grids \item \code{"x.grid"} for x axis grids \item \code{"y.grid"} for y axis grids \item \code{"axis"} for both x and y axes \item \code{"x.axis"} for x axis \item \code{"y.axis"} for y axis \item \code{"xlab", or "x.title"} for x axis label \item \code{"ylab", or "y.title"} for y axis label \item \code{"xylab", "xy.title" or "axis.title"} for both x and y axis labels \item \code{"x.text"} for x axis texts (x axis tick labels) \item \code{"y.text"} for y axis texts (y axis tick labels) \item \code{"xy.text"} or \code{"axis.text"} for both x and y axis texts \item \code{"ticks"} for both x and y ticks \item \code{"x.ticks"} for x ticks \item \code{"y.ticks"} for y ticks \item \code{"legend.title"} for the legend title \item \code{"legend"} for the legend }} } \description{ Remove a specific component from a ggplot. } \examples{ # Load data data("ToothGrowth") # Basic plot p <- ggboxplot(ToothGrowth, x = "dose", y = "len", ggtheme = theme_gray()) p # Remove all grids p + rremove("grid") # Remove only x grids p + rremove("x.grid") } ggpubr/man/compare_means.Rd0000644000176200001440000001132714333713444015425 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/compare_means.R \name{compare_means} \alias{compare_means} \title{Comparison of Means} \usage{ compare_means( formula, data, method = "wilcox.test", paired = FALSE, group.by = NULL, ref.group = NULL, symnum.args = list(), p.adjust.method = "holm", ... ) } \arguments{ \item{formula}{a formula of the form \code{x ~ group} where \code{x} is a numeric variable giving the data values and \code{group} is a factor with one or multiple levels giving the corresponding groups. For example, \code{formula = TP53 ~ cancer_group}. It's also possible to perform the test for multiple response variables at the same time. For example, \code{formula = c(TP53, PTEN) ~ cancer_group}.} \item{data}{a data.frame containing the variables in the formula.} \item{method}{the type of test. Default is \link[stats]{wilcox.test}. Allowed values include: \itemize{ \item \code{\link[stats]{t.test}} (parametric) and \code{\link[stats]{wilcox.test}} (non-parametric). Perform comparison between two groups of samples. If the grouping variable contains more than two levels, then a pairwise comparison is performed. \item \code{\link[stats]{anova}} (parametric) and \code{\link[stats]{kruskal.test}} (non-parametric). Perform one-way ANOVA test comparing multiple groups. }} \item{paired}{a logical indicating whether you want a paired test. Used only in \code{\link[stats]{t.test}} and in \link[stats]{wilcox.test}.} \item{group.by}{a character vector containing the name of grouping variables.} \item{ref.group}{a character string specifying the reference group. If specified, for a given grouping variable, each of the group levels will be compared to the reference group (i.e. control group). \code{ref.group} can be also \code{".all."}. In this case, each of the grouping variable levels is compared to all (i.e. basemean).} \item{symnum.args}{a list of arguments to pass to the function \code{\link[stats]{symnum}} for symbolic number coding of p-values. For example, \code{symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, Inf), symbols = c("****", "***", "**", "*", "ns"))}. In other words, we use the following convention for symbols indicating statistical significance: \itemize{ \item \code{ns}: p > 0.05 \item \code{*}: p <= 0.05 \item \code{**}: p <= 0.01 \item \code{***}: p <= 0.001 \item \code{****}: p <= 0.0001 }} \item{p.adjust.method}{method for adjusting p values (see \code{\link[stats]{p.adjust}}). Has impact only in a situation, where multiple pairwise tests are performed; or when there are multiple grouping variables. Allowed values include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p value (not recommended), use p.adjust.method = "none". Note that, when the \code{formula} contains multiple variables, the p-value adjustment is done independently for each variable.} \item{...}{Other arguments to be passed to the test function.} } \value{ return a data frame with the following columns: \itemize{ \item \code{.y.}: the y variable used in the test. \item \code{group1,group2}: the compared groups in the pairwise tests. Available only when \code{method = "t.test"} or \code{method = "wilcox.test"}. \item \code{p}: the p-value. \item \code{p.adj}: the adjusted p-value. Default for \code{p.adjust.method = "holm"}. \item \code{p.format}: the formatted p-value. \item \code{p.signif}: the significance level. \item \code{method}: the statistical test used to compare groups. } } \description{ Performs one or multiple mean comparisons. } \examples{ # Load data #::::::::::::::::::::::::::::::::::::::: data("ToothGrowth") df <- ToothGrowth # One-sample test #::::::::::::::::::::::::::::::::::::::::: compare_means(len ~ 1, df, mu = 0) # Two-samples unpaired test #::::::::::::::::::::::::::::::::::::::::: compare_means(len ~ supp, df) # Two-samples paired test #::::::::::::::::::::::::::::::::::::::::: compare_means(len ~ supp, df, paired = TRUE) # Compare supp levels after grouping the data by "dose" #:::::::::::::::::::::::::::::::::::::::: compare_means(len ~ supp, df, group.by = "dose") # pairwise comparisons #:::::::::::::::::::::::::::::::::::::::: # As dose contains more thant two levels ==> # pairwise test is automatically performed. compare_means(len ~ dose, df) # Comparison against reference group #:::::::::::::::::::::::::::::::::::::::: compare_means(len ~ dose, df, ref.group = "0.5") # Comparison against all #:::::::::::::::::::::::::::::::::::::::: compare_means(len ~ dose, df, ref.group = ".all.") # Anova and kruskal.test #:::::::::::::::::::::::::::::::::::::::: compare_means(len ~ dose, df, method = "anova") compare_means(len ~ dose, df, method = "kruskal.test") } ggpubr/man/ggscatter.Rd0000644000176200001440000002305414367733325014606 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggscatter.R \name{ggscatter} \alias{ggscatter} \title{Scatter plot} \usage{ ggscatter( data, x, y, combine = FALSE, merge = FALSE, color = "black", fill = "lightgray", palette = NULL, shape = 19, size = 2, point = TRUE, rug = FALSE, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, add = c("none", "reg.line", "loess"), add.params = list(), conf.int = FALSE, conf.int.level = 0.95, fullrange = FALSE, ellipse = FALSE, ellipse.level = 0.95, ellipse.type = "norm", ellipse.alpha = 0.1, ellipse.border.remove = FALSE, mean.point = FALSE, mean.point.size = ifelse(is.numeric(size), 2 * size, size), star.plot = FALSE, star.plot.lty = 1, star.plot.lwd = NULL, label = NULL, font.label = c(12, "plain"), font.family = "", label.select = NULL, repel = FALSE, label.rectangle = FALSE, parse = FALSE, cor.coef = FALSE, cor.coeff.args = list(), cor.method = "pearson", cor.coef.coord = c(NULL, NULL), cor.coef.size = 4, ggp = NULL, show.legend.text = NA, ggtheme = theme_pubr(), ... ) } \arguments{ \item{data}{a data frame} \item{x}{x variables for drawing.} \item{y}{y variables for drawing.} \item{combine}{logical value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, create a multi-panel plot by combining the plot of y variables.} \item{merge}{logical or character value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, merge multiple y variables in the same plotting area. Allowed values include also "asis" (TRUE) and "flip". If merge = "flip", then y variables are used as x tick labels and the x variable is used as grouping variable.} \item{color, fill}{point colors.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{shape}{point shape. See \code{\link{show_point_shapes}}.} \item{size}{Numeric value (e.g.: size = 1). change the size of points and outlines.} \item{point}{logical value. If TRUE, show points.} \item{rug}{logical value. If TRUE, add marginal rug.} \item{title}{plot main title.} \item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to hide xlab.} \item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to hide ylab.} \item{facet.by}{character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.} \item{panel.labs}{a list of one or two character vectors to modify facet panel labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies the labels for the "sex" variable. For two grouping variables, you can use for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", "Lev", "Lev2") ).} \item{short.panel.labs}{logical value. Default is TRUE. If TRUE, create short labels for panels by omitting variable names; in other words panels will be labelled only by variable grouping levels.} \item{add}{allowed values are one of "none", "reg.line" (for adding linear regression line) or "loess" (for adding local regression fitting).} \item{add.params}{parameters (color, size, linetype) for the argument 'add'; e.g.: add.params = list(color = "red").} \item{conf.int}{logical value. If TRUE, adds confidence interval.} \item{conf.int.level}{Level controlling confidence region. Default is 95\%. Used only when add != "none" and conf.int = TRUE.} \item{fullrange}{should the fit span the full range of the plot, or just the data. Used only when add != "none".} \item{ellipse}{logical value. If TRUE, draws ellipses around points.} \item{ellipse.level}{the size of the concentration ellipse in normal probability.} \item{ellipse.type}{Character specifying frame type. Possible values are \code{"convex"}, \code{"confidence"} or types supported by \code{\link[ggplot2]{stat_ellipse}()} including one of \code{c("t", "norm", "euclid")} for plotting concentration ellipses. \itemize{ \item \code{"convex"}: plot convex hull of a set o points. \item \code{"confidence"}: plot confidence ellipses arround group mean points as \code{FactoMineR::coord.ellipse()}. \item \code{"t"}: assumes a multivariate t-distribution. \item \code{"norm"}: assumes a multivariate normal distribution. \item \code{"euclid"}: draws a circle with the radius equal to level, representing the euclidean distance from the center. This ellipse probably won't appear circular unless \code{\link[ggplot2]{coord_fixed}()} is applied.}} \item{ellipse.alpha}{Alpha for ellipse specifying the transparency level of fill color. Use alpha = 0 for no fill color.} \item{ellipse.border.remove}{logical value. If TRUE, remove ellipse border lines.} \item{mean.point}{logical value. If TRUE, group mean points are added to the plot.} \item{mean.point.size}{numeric value specifying the size of mean points.} \item{star.plot}{logical value. If TRUE, a star plot is generated.} \item{star.plot.lty, star.plot.lwd}{line type and line width (size) for star plot, respectively.} \item{label}{the name of the column containing point labels. Can be also a character vector with length = nrow(data).} \item{font.label}{a vector of length 3 indicating respectively the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of point labels. For example \emph{font.label = c(14, "bold", "red")}. To specify only the size and the style, use font.label = c(14, "plain").} \item{font.family}{character vector specifying font family.} \item{label.select}{character vector specifying some labels to show.} \item{repel}{a logical value, whether to use ggrepel to avoid overplotting text labels or not.} \item{label.rectangle}{logical value. If TRUE, add rectangle underneath the text, making it easier to read.} \item{parse}{If \code{TRUE}, the labels will be parsed into expressions and displayed as described in \code{?plotmath}.} \item{cor.coef}{logical value. If TRUE, correlation coefficient with the p-value will be added to the plot.} \item{cor.coeff.args}{a list of arguments to pass to the function \code{\link{stat_cor}} for customizing the displayed correlation coefficients. For example: \code{cor.coeff.args = list(method = "pearson", label.x.npc = "right", label.y.npc = "top")}.} \item{cor.method}{method for computing correlation coefficient. Allowed values are one of "pearson", "kendall", or "spearman".} \item{cor.coef.coord}{numeric vector, of length 2, specifying the x and y coordinates of the correlation coefficient. Default values are NULL.} \item{cor.coef.size}{correlation coefficient text font size.} \item{ggp}{a ggplot. If not NULL, points are added to an existing plot.} \item{show.legend.text}{logical. Should text be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to \code{\link[ggplot2]{geom_point}} and \code{\link{ggpar}}.} } \description{ Create a scatter plot. } \details{ The plot can be easily customized using the function ggpar(). Read ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels and position: legend = "right" \item plot orientation : orientation = c("vertical", "horizontal", "reverse") } } \examples{ # Load data data("mtcars") df <- mtcars df$cyl <- as.factor(df$cyl) head(df[, c("wt", "mpg", "cyl")], 3) # Basic plot # +++++++++++++++++++++++++++ ggscatter(df, x = "wt", y = "mpg", color = "black", shape = 21, size = 3, # Points color, shape and size add = "reg.line", # Add regressin line add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line conf.int = TRUE, # Add confidence interval cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n") ) # loess method: local regression fitting ggscatter(df, x = "wt", y = "mpg", add = "loess", conf.int = TRUE) # Control point size by continuous variable values ("qsec") ggscatter(df, x = "wt", y = "mpg", color = "#00AFBB", size = "qsec") # Change colors # +++++++++++++++++++++++++++ # Use custom color palette # Add marginal rug ggscatter(df, x = "wt", y = "mpg", color = "cyl", palette = c("#00AFBB", "#E7B800", "#FC4E07") ) # Add group ellipses and mean points # Add stars # +++++++++++++++++++ ggscatter(df, x = "wt", y = "mpg", color = "cyl", shape = "cyl", palette = c("#00AFBB", "#E7B800", "#FC4E07"), ellipse = TRUE, mean.point = TRUE, star.plot = TRUE) # Textual annotation # +++++++++++++++++ df$name <- rownames(df) ggscatter(df, x = "wt", y = "mpg", color = "cyl", palette = c("#00AFBB", "#E7B800", "#FC4E07"), label = "name", repel = TRUE) } \seealso{ \code{\link{stat_cor}}, \code{\link{stat_stars}}, \code{\link{stat_conf_ellipse}} and \code{\link{ggpar}}. } ggpubr/man/stat_pvalue_manual.Rd0000644000176200001440000001522014333713444016474 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stat_pvalue_manual.R \name{stat_pvalue_manual} \alias{stat_pvalue_manual} \title{Add Manually P-values to a ggplot} \usage{ stat_pvalue_manual( data, label = NULL, y.position = "y.position", xmin = "group1", xmax = "group2", x = NULL, size = 3.88, label.size = size, bracket.size = 0.3, bracket.nudge.y = 0, bracket.shorten = 0, color = "black", linetype = 1, tip.length = 0.03, remove.bracket = FALSE, step.increase = 0, step.group.by = NULL, hide.ns = FALSE, vjust = 0, coord.flip = FALSE, position = "identity", ... ) } \arguments{ \item{data}{a data frame containing statitistical test results. The expected default format should contain the following columns: \code{group1 | group2 | p | y.position | etc}. \code{group1} and \code{group2} are the groups that have been compared. \code{p} is the resulting p-value. \code{y.position} is the y coordinates of the p-values in the plot.} \item{label}{the column containing the label (e.g.: label = "p" or label = "p.adj"), where \code{p} is the p-value. Can be also an expression that can be formatted by the \code{\link[glue]{glue}()} package. For example, when specifying label = "t-test, p = \{p\}", the expression \{p\} will be replaced by its value.} \item{y.position}{column containing the coordinates (in data units) to be used for absolute positioning of the label. Default value is "y.position". Can be also a numeric vector.} \item{xmin}{column containing the position of the left sides of the brackets. Default value is "group1".} \item{xmax}{(optional) column containing the position of the right sides of the brackets. Default value is "group2". If NULL, the p-values are plotted as a simple text.} \item{x}{x position of the p-value. Should be used only when you want plot the p-value as text (without brackets).} \item{size, label.size}{size of label text.} \item{bracket.size}{Width of the lines of the bracket.} \item{bracket.nudge.y}{Vertical adjustment to nudge brackets by. Useful to move up or move down the bracket. If positive value, brackets will be moved up; if negative value, brackets are moved down.} \item{bracket.shorten}{a small numeric value in [0-1] for shortening the with of bracket.} \item{color}{text and line color. Can be variable name in the data for coloring by groups.} \item{linetype}{linetype. Can be variable name in the data for changing linetype by groups.} \item{tip.length}{numeric vector with the fraction of total height that the bar goes down to indicate the precise column. Default is 0.03.} \item{remove.bracket}{logical, if \code{TRUE}, brackets are removed from the plot. Considered only in the situation, where comparisons are performed against reference group or against "all".} \item{step.increase}{numeric vector with the increase in fraction of total height for every additional comparison to minimize overlap.} \item{step.group.by}{a variable name for grouping brackets before adding step.increase. Useful to group bracket by facet panel.} \item{hide.ns}{can be logical value or a character vector. \itemize{ \item Case when logical value. If TRUE, hide ns symbol when displaying significance levels. Filter is done by checking the column \code{p.adj.signif}, \code{p.signif}, \code{p.adj} and \code{p}. \item Case when character value. Possible values are "p" or "p.adj", for filtering out non significant. }} \item{vjust}{move the text up or down relative to the bracket. Can be also a column name available in the data.} \item{coord.flip}{logical. If \code{TRUE}, flip x and y coordinates so that horizontal becomes vertical, and vertical, horizontal. When adding the p-values to a horizontal ggplot (generated using \code{\link[ggplot2]{coord_flip}()}), you need to specify the option \code{coord.flip = TRUE}.} \item{position}{position adjustment, either as a string, or the result of a call to a position adjustment function.} \item{...}{other arguments passed to the function \code{geom_bracket()} or \code{geom_text()}} } \description{ Add manually p-values to a ggplot, such as box blots, dot plots and stripcharts. Frequently asked questions are available on \href{https://www.datanovia.com/en/blog/tag/ggpubr/}{Datanovia ggpubr FAQ page}, for example: \itemize{ \item \href{https://www.datanovia.com/en/blog/how-to-add-p-values-onto-basic-ggplots/}{How to Add P-Values onto Basic GGPLOTS} \item \href{https://www.datanovia.com/en/blog/ggpubr-how-to-add-adjusted-p-values-to-a-multi-panel-ggplot/}{How to Add Adjusted P-values to a Multi-Panel GGPlot} \item \href{https://www.datanovia.com/en/blog/how-to-add-p-values-to-ggplot-facets/}{How to Add P-values to GGPLOT Facets} \item \href{https://www.datanovia.com/en/blog/ggpubr-how-to-add-p-values-generated-elsewhere-to-a-ggplot/}{How to Add P-Values Generated Elsewhere to a GGPLOT} \item \href{https://www.datanovia.com/en/blog/how-to-add-p-values-onto-a-grouped-ggplot-using-the-ggpubr-r-package/}{How to Add P-Values onto a Grouped GGPLOT using the GGPUBR R Package} \item \href{https://www.datanovia.com/en/blog/how-to-create-stacked-bar-plots-with-error-bars-and-p-values/}{How to Create Stacked Bar Plots with Error Bars and P-values} \item \href{https://www.datanovia.com/en/blog/how-to-add-p-values-onto-horizontal-ggplots/}{How to Add P-Values onto Horizontal GGPLOTS} } } \examples{ # T-test stat.test <- compare_means( len ~ dose, data = ToothGrowth, method = "t.test" ) stat.test # Create a simple box plot p <- ggboxplot(ToothGrowth, x = "dose", y = "len") p # Perform a t-test between groups stat.test <- compare_means( len ~ dose, data = ToothGrowth, method = "t.test" ) stat.test # Add manually p-values from stat.test data # First specify the y.position of each comparison stat.test <- stat.test \%>\% mutate(y.position = c(29, 35, 39)) p + stat_pvalue_manual(stat.test, label = "p.adj") # Customize the label with glue expression # (https://github.com/tidyverse/glue) p + stat_pvalue_manual(stat.test, label = "p = {p.adj}") # Grouped bar plots #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% ToothGrowth$dose <- as.factor(ToothGrowth$dose) # Comparisons against reference stat.test <- compare_means( len ~ dose, data = ToothGrowth, group.by = "supp", method = "t.test", ref.group = "0.5" ) stat.test # Plot bp <- ggbarplot(ToothGrowth, x = "supp", y = "len", fill = "dose", palette = "jco", add = "mean_sd", add.params = list(group = "dose"), position = position_dodge(0.8)) bp + stat_pvalue_manual( stat.test, x = "supp", y.position = 33, label = "p.signif", position = position_dodge(0.8) ) } \seealso{ \code{\link{stat_compare_means}} } ggpubr/man/gradient_color.Rd0000644000176200001440000000247014332553656015614 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gradient_color.R \name{gradient_color} \alias{gradient_color} \alias{gradient_fill} \title{Set Gradient Color} \usage{ gradient_color(palette) gradient_fill(palette) } \arguments{ \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty". Can be also a numeric vector; in this case a basic color palette is created using the function \link[grDevices]{palette}.} } \description{ Change gradient color. \itemize{ \item \code{gradient_color()}: Change gradient color. \item \code{gradient_fill()}: Change gradient fill. } } \examples{ df <- mtcars p <- ggscatter(df, x = "wt", y = "mpg", color = "mpg") # Change gradient color # Use one custom color p + gradient_color("red") # Two colors p + gradient_color(c("blue", "red")) # Three colors p + gradient_color(c("blue", "white", "red")) # Use RColorBrewer palette p + gradient_color("RdYlBu") # Use ggsci color palette p + gradient_color("npg") } \seealso{ \link{set_palette}. } ggpubr/man/ggstripchart.Rd0000644000176200001440000002012314367732422015313 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggstripchart.R \name{ggstripchart} \alias{ggstripchart} \title{Stripcharts} \usage{ ggstripchart( data, x, y, combine = FALSE, merge = FALSE, color = "black", fill = "white", palette = NULL, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, shape = 19, size = NULL, select = NULL, remove = NULL, order = NULL, add = "mean_se", add.params = list(), error.plot = "pointrange", label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, jitter = 0.2, position = position_jitter(jitter, seed = 123), ggtheme = theme_pubr(), ... ) } \arguments{ \item{data}{a data frame} \item{x}{character string containing the name of x variable.} \item{y}{character vector containing one or more variables to plot} \item{combine}{logical value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, create a multi-panel plot by combining the plot of y variables.} \item{merge}{logical or character value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, merge multiple y variables in the same plotting area. Allowed values include also "asis" (TRUE) and "flip". If merge = "flip", then y variables are used as x tick labels and the x variable is used as grouping variable.} \item{color}{outline color.} \item{fill}{fill color.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{title}{plot main title.} \item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to hide xlab.} \item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to hide ylab.} \item{facet.by}{character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.} \item{panel.labs}{a list of one or two character vectors to modify facet panel labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies the labels for the "sex" variable. For two grouping variables, you can use for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", "Lev", "Lev2") ).} \item{short.panel.labs}{logical value. Default is TRUE. If TRUE, create short labels for panels by omitting variable names; in other words panels will be labelled only by variable grouping levels.} \item{shape}{point shape} \item{size}{Numeric value (e.g.: size = 1). change the size of points and outlines.} \item{select}{character vector specifying which items to display.} \item{remove}{character vector specifying which items to remove from the plot.} \item{order}{character vector specifying the order of items.} \item{add}{character vector for adding another plot element (e.g.: dot plot or error bars). Allowed values are one or the combination of: "none", "dotplot", "jitter", "boxplot", "point", "mean", "mean_se", "mean_sd", "mean_ci", "mean_range", "median", "median_iqr", "median_hilow", "median_q1q3", "median_mad", "median_range"; see ?desc_statby for more details.} \item{add.params}{parameters (color, shape, size, fill, linetype) for the argument 'add'; e.g.: add.params = list(color = "red").} \item{error.plot}{plot type used to visualize error. Allowed values are one of c("pointrange", "linerange", "crossbar", "errorbar", "upper_errorbar", "lower_errorbar", "upper_pointrange", "lower_pointrange", "upper_linerange", "lower_linerange"). Default value is "pointrange" or "errorbar". Used only when add != "none" and add contains one "mean_*" or "med_*" where "*" = sd, se, ....} \item{label}{the name of the column containing point labels. Can be also a character vector with length = nrow(data).} \item{font.label}{a list which can contain the combination of the following elements: the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of labels. For example font.label = list(size = 14, face = "bold", color ="red"). To specify only the size and the style, use font.label = list(size = 14, face = "plain").} \item{label.select}{can be of two formats: \itemize{ \item a character vector specifying some labels to show. \item a list containing one or the combination of the following components: \itemize{ \item \code{top.up} and \code{top.down}: to display the labels of the top up/down points. For example, \code{label.select = list(top.up = 10, top.down = 4)}. \item \code{criteria}: to filter, for example, by x and y variabes values, use this: \code{label.select = list(criteria = "`y` > 2 & `y` < 5 & `x` \%in\% c('A', 'B')")}. } }} \item{repel}{a logical value, whether to use ggrepel to avoid overplotting text labels or not.} \item{label.rectangle}{logical value. If TRUE, add rectangle underneath the text, making it easier to read.} \item{jitter}{the amount of jitter.} \item{position}{position adjustment, either as a string, or the result of a call to a position adjustment function. Used to adjust position for multiple groups.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to \code{\link[ggplot2]{geom_jitter}}, \code{\link{ggpar}} and \code{\link{facet}}.} } \description{ Create a stripchart, also known as one dimensional scatter plots. These plots are suitable compared to box plots when sample sizes are small. } \details{ The plot can be easily customized using the function ggpar(). Read ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels and position: legend = "right" \item plot orientation : orientation = c("vertical", "horizontal", "reverse") } } \examples{ # Load data data("ToothGrowth") df <- ToothGrowth # Basic plot with summary statistics: mean_se # +++++++++++++++++++++++++++ # Change point shapes by groups: "dose" ggstripchart(df, x = "dose", y = "len", shape = "dose", size = 3, add = "mean_se") # Use mean_sd # Change error.plot to "crossbar" ggstripchart(df, x = "dose", y = "len", shape = "dose", size = 3, add = "mean_sd", add.params = list(width = 0.5), error.plot = "crossbar") # Add summary statistics # ++++++++++++++++++++++++++ # Add box plot ggstripchart(df, x = "dose", y = "len", shape = "dose", add = "boxplot") # Add violin + mean_sd ggstripchart(df, x = "dose", y = "len", shape = "dose", add = c("violin", "mean_sd")) # Change colors # +++++++++++++++++++++++++++ # Change colors by groups: dose # Use custom color palette ggstripchart(df, "dose", "len", shape = "dose", color = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07"), add = "mean_sd") # Plot with multiple groups # +++++++++++++++++++++ # Change shape and color by a second group : "supp" ggstripchart(df, "dose", "len", shape = "supp", color = "supp", palette = c("#00AFBB", "#E7B800")) # Adjust point position ggstripchart(df, "dose", "len", shape = "supp", color = "supp", palette = c("#00AFBB", "#E7B800"), position = position_dodge(0.8) ) # You can also use position_jitterdodge() # but fill aesthetic is required ggstripchart(df, "dose", "len", shape = "supp", color = "supp", palette = c("#00AFBB", "#E7B800"), position = position_jitterdodge() ) # Add boxplot ggstripchart(df, "dose", "len", shape = "supp", color = "supp", palette = c("#00AFBB", "#E7B800"), add = "boxplot", add.params = list(color = "black") ) } \seealso{ \code{\link{ggpar}}, \code{\link{ggviolin}}, \code{\link{ggdotplot}} and \code{\link{ggboxplot}}. } ggpubr/man/geom_signif.Rd0000644000176200001440000000045214332553656015105 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils-geom-signif.R \name{geom_signif} \alias{geom_signif} \title{geom_signif exported from ggsignif package} \description{ See \code{ggsignif::\link[ggsignif:stat_signif]{geom_signif}} for details. } \keyword{internal} ggpubr/man/ggdotplot.Rd0000644000176200001440000001611514367732422014623 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggdotplot.R \name{ggdotplot} \alias{ggdotplot} \title{Dot plot} \usage{ ggdotplot( data, x, y, combine = FALSE, merge = FALSE, color = "black", fill = "lightgray", palette = NULL, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, size = NULL, binwidth = NULL, select = NULL, remove = NULL, order = NULL, add = "mean_se", add.params = list(), error.plot = "pointrange", label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, ggtheme = theme_pubr(), ... ) } \arguments{ \item{data}{a data frame} \item{x}{character string containing the name of x variable.} \item{y}{character vector containing one or more variables to plot} \item{combine}{logical value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, create a multi-panel plot by combining the plot of y variables.} \item{merge}{logical or character value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, merge multiple y variables in the same plotting area. Allowed values include also "asis" (TRUE) and "flip". If merge = "flip", then y variables are used as x tick labels and the x variable is used as grouping variable.} \item{color}{outline color.} \item{fill}{fill color.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{title}{plot main title.} \item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to hide xlab.} \item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to hide ylab.} \item{facet.by}{character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.} \item{panel.labs}{a list of one or two character vectors to modify facet panel labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies the labels for the "sex" variable. For two grouping variables, you can use for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", "Lev", "Lev2") ).} \item{short.panel.labs}{logical value. Default is TRUE. If TRUE, create short labels for panels by omitting variable names; in other words panels will be labelled only by variable grouping levels.} \item{size}{Numeric value (e.g.: size = 1). change the size of points and outlines.} \item{binwidth}{numeric value specifying bin width. use value between 0 and 1 when you have a strong dense dotplot. For example binwidth = 0.2.} \item{select}{character vector specifying which items to display.} \item{remove}{character vector specifying which items to remove from the plot.} \item{order}{character vector specifying the order of items.} \item{add}{character vector for adding another plot element (e.g.: dot plot or error bars). Allowed values are one or the combination of: "none", "dotplot", "jitter", "boxplot", "point", "mean", "mean_se", "mean_sd", "mean_ci", "mean_range", "median", "median_iqr", "median_hilow", "median_q1q3", "median_mad", "median_range"; see ?desc_statby for more details.} \item{add.params}{parameters (color, shape, size, fill, linetype) for the argument 'add'; e.g.: add.params = list(color = "red").} \item{error.plot}{plot type used to visualize error. Allowed values are one of c("pointrange", "linerange", "crossbar", "errorbar", "upper_errorbar", "lower_errorbar", "upper_pointrange", "lower_pointrange", "upper_linerange", "lower_linerange"). Default value is "pointrange" or "errorbar". Used only when add != "none" and add contains one "mean_*" or "med_*" where "*" = sd, se, ....} \item{label}{the name of the column containing point labels. Can be also a character vector with length = nrow(data).} \item{font.label}{a list which can contain the combination of the following elements: the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of labels. For example font.label = list(size = 14, face = "bold", color ="red"). To specify only the size and the style, use font.label = list(size = 14, face = "plain").} \item{label.select}{can be of two formats: \itemize{ \item a character vector specifying some labels to show. \item a list containing one or the combination of the following components: \itemize{ \item \code{top.up} and \code{top.down}: to display the labels of the top up/down points. For example, \code{label.select = list(top.up = 10, top.down = 4)}. \item \code{criteria}: to filter, for example, by x and y variabes values, use this: \code{label.select = list(criteria = "`y` > 2 & `y` < 5 & `x` \%in\% c('A', 'B')")}. } }} \item{repel}{a logical value, whether to use ggrepel to avoid overplotting text labels or not.} \item{label.rectangle}{logical value. If TRUE, add rectangle underneath the text, making it easier to read.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to \code{\link[ggplot2]{geom_dotplot}}, \code{\link{ggpar}} and \code{\link{facet}}.} } \description{ Create a dot plot. } \details{ The plot can be easily customized using the function ggpar(). Read ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels and position: legend = "right" \item plot orientation : orientation = c("vertical", "horizontal", "reverse") } } \examples{ # Load data data("ToothGrowth") df <- ToothGrowth # Basic plot with summary statistics : mean_sd # +++++++++++++++++++++++++++ ggdotplot(df, x = "dose", y = "len", add = "mean_sd") # Change error.plot to "crossbar" ggdotplot(df, x = "dose", y = "len", add = "mean_sd", add.params = list(width = 0.5), error.plot = "crossbar") # Add box plot ggdotplot(df, x = "dose", y = "len", add = "boxplot") # Add violin + mean_sd ggdotplot(df, x = "dose", y = "len", add = c("violin", "mean_sd")) # Change colors # +++++++++++++++++++++++++++ # Change fill and outline colors by groups: dose # Use custom color palette ggdotplot(df, "dose", "len", add = "boxplot", color = "dose", fill = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07")) # Plot with multiple groups # +++++++++++++++++++++ # Change color by a second group : "supp" ggdotplot(df, "dose", "len", fill = "supp", color = "supp", palette = c("#00AFBB", "#E7B800")) } \seealso{ \code{\link{ggpar}}, \code{\link{ggviolin}}, \code{\link{ggboxplot}} and \code{\link{ggstripchart}}. } ggpubr/man/ggmaplot.Rd0000644000176200001440000001265514367732422014437 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggmaplot.R \name{ggmaplot} \alias{ggmaplot} \title{MA-plot from means and log fold changes} \usage{ ggmaplot( data, fdr = 0.05, fc = 1.5, genenames = NULL, detection_call = NULL, size = NULL, alpha = 1, seed = 42, font.label = c(12, "plain", "black"), label.rectangle = FALSE, palette = c("#B31B21", "#1465AC", "darkgray"), top = 15, select.top.method = c("padj", "fc"), label.select = NULL, main = NULL, xlab = "Log2 mean expression", ylab = "Log2 fold change", ggtheme = theme_classic(), ... ) } \arguments{ \item{data}{an object of class DESeqResults, get_diff, DE_Results, matrix or data frame containing the columns baseMean (or baseMeanLog2), log2FoldChange, and padj. Rows are genes. Two possible formats are accepted for the input data: \itemize{ \item 1/ \code{baseMean | log2FoldChange | padj}. This is a typical output from DESeq2 pipeline. Here, we'll use log2(baseMean) as the x-axis variable. \item 2/ \code{baseMeanLog2 | log2FoldChange | padj}. Here, baseMeanLog2 is assumed to be the mean of logged values; so we'll use it as the x-axis variable without any transformation. This is the real A in MA plot. In other words, it is the average of two log-scales values: \code{A = (log2(x) + log2(y))/2 = log2(xy)*1/2} } Terminology: \itemize{ \item baseMean: the mean expression of genes in the two groups. \item log2FoldChange: the log2 fold changes of group 2 compared to group 1 \item padj: the adjusted p-value of the used statiscal test. }} \item{fdr}{Accepted false discovery rate for considering genes as differentially expressed.} \item{fc}{the fold change threshold. Only genes with a fold change >= fc and padj <= fdr are considered as significantly differentially expressed.} \item{genenames}{a character vector of length nrow(data) specifying gene names corresponding to each row. Used for point labels.} \item{detection_call}{a numeric vector with length = nrow(data), specifying if the genes is expressed (value = 1) or not (value = 0). For example detection_call = c(1, 1, 0, 1, 0, 1). Default is NULL. If detection_call column is available in data, it will be used.} \item{size}{points size.} \item{alpha}{numeric value betwenn 0 an 1 specifying point alpha for controlling transparency. For example, use alpha = 0.5.} \item{seed}{Random seed passed to \code{set.seed}. if \code{NA}, set.seed will not be called. Default is 42 for reproducibility.} \item{font.label}{a vector of length 3 indicating respectively the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of point labels. For example \emph{font.label = c(14, "bold", "red")}.} \item{label.rectangle}{logical value. If TRUE, add rectangle underneath the text, making it easier to read.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{top}{the number of top genes to be shown on the plot. Use top = 0 to hide to gene labels.} \item{select.top.method}{methods to be used for selecting top genes. Allowed values include "padj" and "fc" for selecting by adjusted p values or fold changes, respectively.} \item{label.select}{character vector specifying some labels to show.} \item{main}{plot main title.} \item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to hide xlab.} \item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to hide ylab.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to \code{\link{ggpar}}.} } \value{ returns a ggplot. } \description{ Make MA-plot which is a scatter plot of log2 fold changes (M, on the y-axis) versus the average expression signal (A, on the x-axis). \code{M = log2(x/y)} and \code{A = (log2(x) + log2(y))/2 = log2(xy)*1/2}, where x and y are respectively the mean of the two groups being compared. } \examples{ data(diff_express) # Default plot ggmaplot(diff_express, main = expression("Group 1" \%->\% "Group 2"), fdr = 0.05, fc = 2, size = 0.4, palette = c("#B31B21", "#1465AC", "darkgray"), genenames = as.vector(diff_express$name), legend = "top", top = 20, font.label = c("bold", 11), font.legend = "bold", font.main = "bold", ggtheme = ggplot2::theme_minimal()) # Add rectangle around labels ggmaplot(diff_express, main = expression("Group 1" \%->\% "Group 2"), fdr = 0.05, fc = 2, size = 0.4, palette = c("#B31B21", "#1465AC", "darkgray"), genenames = as.vector(diff_express$name), legend = "top", top = 20, font.label = c("bold", 11), label.rectangle = TRUE, font.legend = "bold", font.main = "bold", ggtheme = ggplot2::theme_minimal()) # Select specific genes to show # set top = 0, then specify genes using label.select argument ggmaplot(diff_express, main = expression("Group 1" \%->\% "Group 2"), fdr = 0.05, fc = 2, size = 0.4, genenames = as.vector(diff_express$name), ggtheme = ggplot2::theme_minimal(), top = 0, label.select = c("BUB1", "CD83") ) } ggpubr/man/ggparagraph.Rd0000644000176200001440000000326114333713444015075 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggparagraph.R \name{ggparagraph} \alias{ggparagraph} \alias{drawDetails.splitText} \title{Draw a Paragraph of Text} \usage{ ggparagraph( text, color = NULL, size = NULL, face = NULL, family = NULL, lineheight = NULL ) \method{drawDetails}{splitText}(x, recording) } \arguments{ \item{text}{the text to plot.} \item{color}{font color, example: color = "black"} \item{size}{font size, example: size = 12} \item{face}{font face. Allowed values are one of "plain", "italic", "bold", "bold.italic".} \item{family}{font family} \item{lineheight}{Line height, example: lineheight = 2.} \item{x}{a grid grob} \item{recording}{a logical value indicating whether a grob is being added to the display list or redrawn from the display list.} } \description{ Draw a paragraph o text. Splits a long text into multiple lines (by inserting line breaks) so that the output will fit within the current viewport. } \examples{ # Density plot density.p <- ggdensity(iris, x = "Sepal.Length", fill = "Species", palette = "jco") # Text plot text <- paste("iris data set gives the measurements in cm", "of the variables sepal length and width", "and petal length and width, respectively,", "for 50 flowers from each of 3 species of iris.", "The species are Iris setosa, versicolor, and virginica.", sep = " ") text.p <- ggparagraph(text, face = "italic", size = 12) # Arrange the plots on the same page ggarrange(density.p, text.p, ncol = 1, nrow = 2, heights = c(1, 0.3)) } \author{ Alboukadel Kassambara } ggpubr/man/reexports.Rd0000644000176200001440000000102014332553656014642 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/reexports.R \docType{import} \name{reexports} \alias{reexports} \alias{mutate} \alias{group_by} \alias{get_summary_stats} \title{Objects exported from other packages} \keyword{internal} \description{ These objects are imported from other packages. Follow the links below to see their documentation. \describe{ \item{dplyr}{\code{\link[dplyr]{group_by}}, \code{\link[dplyr]{mutate}}} \item{rstatix}{\code{\link[rstatix]{get_summary_stats}}} }} ggpubr/man/ggboxplot.Rd0000644000176200001440000002073714367732422014632 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggboxplot.R \name{ggboxplot} \alias{ggboxplot} \title{Box plot} \usage{ ggboxplot( data, x, y, combine = FALSE, merge = FALSE, color = "black", fill = "white", palette = NULL, title = NULL, xlab = NULL, ylab = NULL, bxp.errorbar = FALSE, bxp.errorbar.width = 0.4, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, linetype = "solid", size = NULL, width = 0.7, notch = FALSE, outlier.shape = 19, select = NULL, remove = NULL, order = NULL, add = "none", add.params = list(), error.plot = "pointrange", label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, ggtheme = theme_pubr(), ... ) } \arguments{ \item{data}{a data frame} \item{x}{character string containing the name of x variable.} \item{y}{character vector containing one or more variables to plot} \item{combine}{logical value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, create a multi-panel plot by combining the plot of y variables.} \item{merge}{logical or character value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, merge multiple y variables in the same plotting area. Allowed values include also "asis" (TRUE) and "flip". If merge = "flip", then y variables are used as x tick labels and the x variable is used as grouping variable.} \item{color}{outline color.} \item{fill}{fill color.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{title}{plot main title.} \item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to hide xlab.} \item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to hide ylab.} \item{bxp.errorbar}{logical value. If TRUE, shows error bars of box plots.} \item{bxp.errorbar.width}{numeric value specifying the width of box plot error bars. Default is 0.4.} \item{facet.by}{character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.} \item{panel.labs}{a list of one or two character vectors to modify facet panel labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies the labels for the "sex" variable. For two grouping variables, you can use for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", "Lev", "Lev2") ).} \item{short.panel.labs}{logical value. Default is TRUE. If TRUE, create short labels for panels by omitting variable names; in other words panels will be labelled only by variable grouping levels.} \item{linetype}{line types.} \item{size}{Numeric value (e.g.: size = 1). change the size of points and outlines.} \item{width}{numeric value between 0 and 1 specifying box width.} \item{notch}{If \code{FALSE} (default) make a standard box plot. If \code{TRUE}, make a notched box plot. Notches are used to compare groups; if the notches of two boxes do not overlap, this suggests that the medians are significantly different.} \item{outlier.shape}{point shape of outlier. Default is 19. To hide outlier, specify \code{outlier.shape = NA}. When jitter is added, then outliers will be automatically hidden.} \item{select}{character vector specifying which items to display.} \item{remove}{character vector specifying which items to remove from the plot.} \item{order}{character vector specifying the order of items.} \item{add}{character vector for adding another plot element (e.g.: dot plot or error bars). Allowed values are one or the combination of: "none", "dotplot", "jitter", "boxplot", "point", "mean", "mean_se", "mean_sd", "mean_ci", "mean_range", "median", "median_iqr", "median_hilow", "median_q1q3", "median_mad", "median_range"; see ?desc_statby for more details.} \item{add.params}{parameters (color, shape, size, fill, linetype) for the argument 'add'; e.g.: add.params = list(color = "red").} \item{error.plot}{plot type used to visualize error. Allowed values are one of c("pointrange", "linerange", "crossbar", "errorbar", "upper_errorbar", "lower_errorbar", "upper_pointrange", "lower_pointrange", "upper_linerange", "lower_linerange"). Default value is "pointrange" or "errorbar". Used only when add != "none" and add contains one "mean_*" or "med_*" where "*" = sd, se, ....} \item{label}{the name of the column containing point labels. Can be also a character vector with length = nrow(data).} \item{font.label}{a list which can contain the combination of the following elements: the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of labels. For example font.label = list(size = 14, face = "bold", color ="red"). To specify only the size and the style, use font.label = list(size = 14, face = "plain").} \item{label.select}{can be of two formats: \itemize{ \item a character vector specifying some labels to show. \item a list containing one or the combination of the following components: \itemize{ \item \code{top.up} and \code{top.down}: to display the labels of the top up/down points. For example, \code{label.select = list(top.up = 10, top.down = 4)}. \item \code{criteria}: to filter, for example, by x and y variabes values, use this: \code{label.select = list(criteria = "`y` > 2 & `y` < 5 & `x` \%in\% c('A', 'B')")}. } }} \item{repel}{a logical value, whether to use ggrepel to avoid overplotting text labels or not.} \item{label.rectangle}{logical value. If TRUE, add rectangle underneath the text, making it easier to read.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to \code{\link[ggplot2]{geom_boxplot}}, \code{\link{ggpar}} and \code{\link{facet}}.} } \description{ Create a box plot with points. Box plots display a group of numerical data through their quartiles. } \details{ The plot can be easily customized using the function ggpar(). Read ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels and position: legend = "right" \item plot orientation : orientation = c("vertical", "horizontal", "reverse") } } \section{Suggestions for the argument "add"}{ Suggested values are one of c("dotplot", "jitter"). } \examples{ # Load data data("ToothGrowth") df <- ToothGrowth # Basic plot # +++++++++++++++++++++++++++ # width: change box plots width ggboxplot(df, x = "dose", y = "len", width = 0.8) # Change orientation: horizontal ggboxplot(df, "dose", "len", orientation = "horizontal") # Notched box plot ggboxplot(df, x = "dose", y = "len", notch = TRUE) # Add dots # ++++++++++++++++++++++++++ ggboxplot(df, x = "dose", y = "len", add = "dotplot") # Add jitter points and change the shape by groups ggboxplot(df, x = "dose", y = "len", add = "jitter", shape = "dose") # Select and order items # ++++++++++++++++++++++++++++++ # Select which items to display: "0.5" and "2" ggboxplot(df, "dose", "len", select = c("0.5", "2")) # Change the default order of items ggboxplot(df, "dose", "len", order = c("2", "1", "0.5")) # Change colors # +++++++++++++++++++++++++++ # Change outline and fill colors ggboxplot(df, "dose", "len", color = "black", fill = "gray") # Change outline colors by groups: dose # Use custom color palette # Add jitter points and change the shape by groups ggboxplot(df, "dose", "len", color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"), add = "jitter", shape = "dose") # Change fill color by groups: dose ggboxplot(df, "dose", "len", fill = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07")) # Box plot with multiple groups # +++++++++++++++++++++ # fill or color box plot by a second group : "supp" ggboxplot(df, "dose", "len", color = "supp", palette = c("#00AFBB", "#E7B800")) } \seealso{ \code{\link{ggpar}}, \code{\link{ggviolin}}, \code{\link{ggdotplot}} and \code{\link{ggstripchart}}. } ggpubr/man/ggerrorplot.Rd0000644000176200001440000001474114367732422015171 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggerrorplot.R \name{ggerrorplot} \alias{ggerrorplot} \title{Visualizing Error} \usage{ ggerrorplot( data, x, y, desc_stat = "mean_se", numeric.x.axis = FALSE, combine = FALSE, merge = FALSE, color = "black", fill = "white", palette = NULL, size = NULL, width = NULL, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, select = NULL, remove = NULL, order = NULL, add = "none", add.params = list(), error.plot = "pointrange", ci = 0.95, position = position_dodge(), ggtheme = theme_pubr(), ... ) } \arguments{ \item{data}{a data frame} \item{x, y}{x and y variables for drawing.} \item{desc_stat}{descriptive statistics to be used for visualizing errors. Default value is "mean_se". Allowed values are one of , "mean", "mean_se", "mean_sd", "mean_ci", "mean_range", "median", "median_iqr", "median_hilow", "median_q1q3", "median_mad", "median_range"; see \code{\link{desc_statby}} for more details.} \item{numeric.x.axis}{logical. If TRUE, x axis will be treated as numeric. Default is FALSE.} \item{combine}{logical value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, create a multi-panel plot by combining the plot of y variables.} \item{merge}{logical or character value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, merge multiple y variables in the same plotting area. Allowed values include also "asis" (TRUE) and "flip". If merge = "flip", then y variables are used as x tick labels and the x variable is used as grouping variable.} \item{color, fill}{outline and fill colors.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{size}{Numeric value (e.g.: size = 1). change the size of points and outlines.} \item{width}{numeric value between 0 and 1 specifying box width.} \item{title}{plot main title.} \item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to hide xlab.} \item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to hide ylab.} \item{facet.by}{character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.} \item{panel.labs}{a list of one or two character vectors to modify facet panel labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies the labels for the "sex" variable. For two grouping variables, you can use for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", "Lev", "Lev2") ).} \item{short.panel.labs}{logical value. Default is TRUE. If TRUE, create short labels for panels by omitting variable names; in other words panels will be labelled only by variable grouping levels.} \item{select}{character vector specifying which items to display.} \item{remove}{character vector specifying which items to remove from the plot.} \item{order}{character vector specifying the order of items. Considered only when x axis is a factor variable.} \item{add}{character vector for adding another plot element (e.g.: dot plot or error bars). Allowed values are one or the combination of: "none", "dotplot", "jitter", "boxplot", "point", "mean", "mean_se", "mean_sd", "mean_ci", "mean_range", "median", "median_iqr", "median_hilow", "median_q1q3", "median_mad", "median_range"; see ?desc_statby for more details.} \item{add.params}{parameters (color, shape, size, fill, linetype) for the argument 'add'; e.g.: add.params = list(color = "red").} \item{error.plot}{plot type used to visualize error. Allowed values are one of c("pointrange", "linerange", "crossbar", "errorbar", "upper_errorbar", "lower_errorbar", "upper_pointrange", "lower_pointrange", "upper_linerange", "lower_linerange"). Default value is "pointrange" or "errorbar". Used only when add != "none" and add contains one "mean_*" or "med_*" where "*" = sd, se, ....} \item{ci}{the percent range of the confidence interval (default is 0.95).} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to be passed to ggpar().} } \description{ Visualizing error. } \details{ The plot can be easily customized using the function ggpar(). Read ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels and position: legend = "right" \item plot orientation : orientation = c("vertical", "horizontal", "reverse") } } \examples{ # Data: ToothGrowth data set we'll be used. df<- ToothGrowth head(df, 10) # Plot mean_se ggerrorplot(df, x = "dose", y = "len") # Change desc_stat to mean_sd # (other values include: mean_sd, mean_ci, median_iqr, ....) # Add labels ggerrorplot(df, x = "dose", y = "len", desc_stat = "mean_sd") # Change error.plot to "errorbar" and add mean point # Visualize the mean of each group ggerrorplot(df, x = "dose", y = "len", add = "mean", error.plot = "errorbar") # Horizontal plot ggerrorplot(df, x = "dose", y = "len", add = "mean", error.plot = "errorbar", orientation = "horizontal") # Change error.plot to "crossbar" ggerrorplot(df, x = "dose", y = "len", error.plot = "crossbar", width = 0.5) # Add jitter points and errors (mean_se) ggerrorplot(df, x = "dose", y = "len", add = "jitter") # Add dot and errors (mean_se) ggerrorplot(df, x = "dose", y = "len", add = "dotplot") # Multiple groups with error bars and jitter point ggerrorplot(df, x = "dose", y = "len", color = "supp", palette = "Paired", error.plot = "pointrange", position = position_dodge(0.5)) } \seealso{ \code{\link{ggpar}}, \code{\link{ggline}} } ggpubr/man/stat_regline_equation.Rd0000644000176200001440000001331614333713444017201 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stat_regline_equation.R \name{stat_regline_equation} \alias{stat_regline_equation} \title{Add Regression Line Equation and R-Square to a GGPLOT.} \usage{ stat_regline_equation( mapping = NULL, data = NULL, formula = y ~ x, label.x.npc = "left", label.y.npc = "top", label.x = NULL, label.y = NULL, output.type = "expression", geom = "text", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... ) } \arguments{ \item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and \code{inherit.aes = TRUE} (the default), it is combined with the default mapping at the top level of the plot. You must supply \code{mapping} if there is no plot mapping.} \item{data}{The data to be displayed in this layer. There are three options: If \code{NULL}, the default, the data is inherited from the plot data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}. A \code{data.frame}, or other object, will override the plot data. All objects will be fortified to produce a data frame. See \code{\link[ggplot2:fortify]{fortify()}} for which variables will be created. A \code{function} will be called with a single argument, the plot data. The return value must be a \code{data.frame}, and will be used as the layer data. A \code{function} can be created from a \code{formula} (e.g. \code{~ head(.x, 10)}).} \item{formula}{a formula object} \item{label.x.npc, label.y.npc}{can be \code{numeric} or \code{character} vector of the same length as the number of groups and/or panels. If too short they will be recycled. \itemize{ \item If \code{numeric}, value should be between 0 and 1. Coordinates to be used for positioning the label, expressed in "normalized parent coordinates". \item If \code{character}, allowed values include: i) one of c('right', 'left', 'center', 'centre', 'middle') for x-axis; ii) and one of c( 'bottom', 'top', 'center', 'centre', 'middle') for y-axis.} If too short they will be recycled.} \item{label.x, label.y}{\code{numeric} Coordinates (in data units) to be used for absolute positioning of the label. If too short they will be recycled.} \item{output.type}{character One of "expression", "latex" or "text".} \item{geom}{The geometric object to use to display the data, either as a \code{ggproto} \code{Geom} subclass or as a string naming the geom stripped of the \code{geom_} prefix (e.g. \code{"point"} rather than \code{"geom_point"})} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{na.rm}{If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.} \item{show.legend}{logical. Should this layer be included in the legends? \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and \code{TRUE} always includes. It can also be a named logical vector to finely select the aesthetics to display.} \item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. \code{\link[ggplot2:borders]{borders()}}.} \item{...}{other arguments to pass to \code{\link[ggplot2]{geom_text}} or \code{\link[ggplot2:geom_text]{geom_label}}.} } \description{ Add regression line equation and R^2 to a ggplot. Regression model is fitted using the function \code{\link[stats]{lm}}. } \section{Computed variables}{ \describe{ \item{x}{x position for left edge} \item{y}{y position near upper edge} \item{eq.label}{equation for the fitted polynomial as a character string to be parsed} \item{rr.label}{\eqn{R^2} of the fitted model as a character string to be parsed} \item{adj.rr.label}{Adjusted \eqn{R^2} of the fitted model as a character string to be parsed} \item{AIC.label}{AIC for the fitted model.} \item{BIC.label}{BIC for the fitted model.} \item{hjust}{Set to zero to override the default of the "text" geom.}} } \examples{ # Simple scatter plot with correlation coefficient and # regression line #:::::::::::::::::::::::::::::::::::::::::::::::::::: ggscatter(mtcars, x = "wt", y = "mpg", add = "reg.line") + stat_cor(label.x = 3, label.y = 34) + stat_regline_equation(label.x = 3, label.y = 32) # Groupped scatter plot #:::::::::::::::::::::::::::::::::::::::::::::::::::: ggscatter( iris, x = "Sepal.Length", y = "Sepal.Width", color = "Species", palette = "jco", add = "reg.line" ) + facet_wrap(~Species) + stat_cor(label.y = 4.4) + stat_regline_equation(label.y = 4.2) # Polynomial equation #:::::::::::::::::::::::::::::::::::::::::::::::::::: # Demo data set.seed(4321) x <- 1:100 y <- (x + x^2 + x^3) + rnorm(length(x), mean = 0, sd = mean(x^3) / 4) my.data <- data.frame(x, y, group = c("A", "B"), y2 = y * c(0.5,2), block = c("a", "a", "b", "b")) # Fit polynomial regression line and add labels formula <- y ~ poly(x, 3, raw = TRUE) p <- ggplot(my.data, aes(x, y2, color = group)) + geom_point() + stat_smooth(aes(fill = group, color = group), method = "lm", formula = formula) + stat_regline_equation( aes(label = paste(..eq.label.., ..adj.rr.label.., sep = "~~~~")), formula = formula ) + theme_bw() ggpar(p, palette = "jco") } \references{ the source code of the function \code{stat_regline_equation()} is inspired from the code of the function \code{stat_poly_eq()} (in ggpmisc package). } \seealso{ \code{\link{ggscatter}} } ggpubr/man/ggecdf.Rd0000644000176200001440000000726114367732422014041 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggecdf.R \name{ggecdf} \alias{ggecdf} \title{Empirical cumulative density function} \usage{ ggecdf( data, x, combine = FALSE, merge = FALSE, color = "black", palette = NULL, size = NULL, linetype = "solid", title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, ggtheme = theme_pubr(), ... ) } \arguments{ \item{data}{a data frame} \item{x}{variable to be drawn.} \item{combine}{logical value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, create a multi-panel plot by combining the plot of y variables.} \item{merge}{logical or character value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, merge multiple y variables in the same plotting area. Allowed values include also "asis" (TRUE) and "flip". If merge = "flip", then y variables are used as x tick labels and the x variable is used as grouping variable.} \item{color}{line and point color.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{size}{line and point size.} \item{linetype}{line type. See \code{\link{show_line_types}}.} \item{title}{plot main title.} \item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to hide xlab.} \item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to hide ylab.} \item{facet.by}{character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.} \item{panel.labs}{a list of one or two character vectors to modify facet panel labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies the labels for the "sex" variable. For two grouping variables, you can use for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", "Lev", "Lev2") ).} \item{short.panel.labs}{logical value. Default is TRUE. If TRUE, create short labels for panels by omitting variable names; in other words panels will be labelled only by variable grouping levels.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to \code{\link[ggplot2]{stat_ecdf}} and \code{\link{ggpar}}.} } \description{ Empirical Cumulative Density Function (ECDF). } \details{ The plot can be easily customized using the function ggpar(). Read ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels and position: legend = "right" \item plot orientation : orientation = c("vertical", "horizontal", "reverse") } } \examples{ # Create some data format set.seed(1234) wdata = data.frame( sex = factor(rep(c("F", "M"), each=200)), weight = c(rnorm(200, 55), rnorm(200, 58))) head(wdata, 4) # Basic ECDF plot ggecdf(wdata, x = "weight") # Change colors and linetype by groups ("sex") # Use custom palette ggecdf(wdata, x = "weight", color = "sex", linetype = "sex", palette = c("#00AFBB", "#E7B800")) } \seealso{ \code{\link{ggpar}} } ggpubr/man/ggarrange.Rd0000644000176200001440000001014414333713444014545 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggarrange.R \name{ggarrange} \alias{ggarrange} \title{Arrange Multiple ggplots} \usage{ ggarrange( ..., plotlist = NULL, ncol = NULL, nrow = NULL, labels = NULL, label.x = 0, label.y = 1, hjust = -0.5, vjust = 1.5, font.label = list(size = 14, color = "black", face = "bold", family = NULL), align = c("none", "h", "v", "hv"), widths = 1, heights = 1, legend = NULL, common.legend = FALSE, legend.grob = NULL ) } \arguments{ \item{...}{list of plots to be arranged into the grid. The plots can be either ggplot2 plot objects or arbitrary gtables.} \item{plotlist}{(optional) list of plots to display.} \item{ncol}{(optional) number of columns in the plot grid.} \item{nrow}{(optional) number of rows in the plot grid.} \item{labels}{(optional) list of labels to be added to the plots. You can also set labels="AUTO" to auto-generate upper-case labels or labels="auto" to auto-generate lower-case labels.} \item{label.x}{(optional) Single value or vector of x positions for plot labels, relative to each subplot. Defaults to 0 for all labels. (Each label is placed all the way to the left of each plot.)} \item{label.y}{(optional) Single value or vector of y positions for plot labels, relative to each subplot. Defaults to 1 for all labels. (Each label is placed all the way to the top of each plot.)} \item{hjust}{Adjusts the horizontal position of each label. More negative values move the label further to the right on the plot canvas. Can be a single value (applied to all labels) or a vector of values (one for each label). Default is -0.5.} \item{vjust}{Adjusts the vertical position of each label. More positive values move the label further down on the plot canvas. Can be a single value (applied to all labels) or a vector of values (one for each label). Default is 1.5.} \item{font.label}{a list of arguments for customizing labels. Allowed values are the combination of the following elements: size (e.g.: 14), face (e.g.: "plain", "bold", "italic", "bold.italic"), color (e.g.: "red") and family. For example font.label = list(size = 14, face = "bold", color ="red").} \item{align}{(optional) Specifies whether graphs in the grid should be horizontally ("h") or vertically ("v") aligned. Options are "none" (default), "hv" (align in both directions), "h", and "v".} \item{widths}{(optional) numerical vector of relative columns widths. For example, in a two-column grid, widths = c(2, 1) would make the first column twice as wide as the second column.} \item{heights}{same as \code{widths} but for column heights.} \item{legend}{character specifying legend position. Allowed values are one of c("top", "bottom", "left", "right", "none"). To remove the legend use legend = "none".} \item{common.legend}{logical value. Default is FALSE. If TRUE, a common unique legend will be created for arranged plots.} \item{legend.grob}{a legend grob as returned by the function \code{\link{get_legend}()}. If provided, it will be used as the common legend.} } \value{ return an object of class \code{ggarrange}, which is a ggplot or a list of ggplot. } \description{ Arrange multiple ggplots on the same page. Wrapper around \code{\link[cowplot]{plot_grid}()}. Can arrange multiple ggplots over multiple pages, compared to the standard \code{\link[cowplot]{plot_grid}()}. Can also create a common unique legend for multiple plots. } \examples{ data("ToothGrowth") df <- ToothGrowth df$dose <- as.factor(df$dose) # Create some plots # :::::::::::::::::::::::::::::::::::::::::::::::::: # Box plot bxp <- ggboxplot(df, x = "dose", y = "len", color = "dose", palette = "jco") # Dot plot dp <- ggdotplot(df, x = "dose", y = "len", color = "dose", palette = "jco") # Density plot dens <- ggdensity(df, x = "len", fill = "dose", palette = "jco") # Arrange # :::::::::::::::::::::::::::::::::::::::::::::::::: ggarrange(bxp, dp, dens, ncol = 2, nrow = 2) # Use a common legend for multiple plots ggarrange(bxp, dp, common.legend = TRUE) } \seealso{ \code{\link{annotate_figure}()} } \author{ Alboukadel Kassambara \email{alboukadel.kassambara@gmail.com} } ggpubr/man/ggadd.Rd0000644000176200001440000000757714340445172013674 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggadd.R \name{ggadd} \alias{ggadd} \title{Add Summary Statistics or a Geom onto a ggplot} \usage{ ggadd( p, add = NULL, color = "black", fill = "white", group = 1, width = 1, shape = 19, size = NULL, alpha = 1, jitter = 0.2, seed = 123, binwidth = NULL, dotsize = size, linetype = 1, show.legend = NA, error.plot = "pointrange", ci = 0.95, data = NULL, position = position_dodge(0.8), p_geom = "" ) } \arguments{ \item{p}{a ggplot} \item{add}{character vector specifying other plot elements to be added. Allowed values are one or the combination of: "none", "dotplot", "jitter", "boxplot", "point", "mean", "mean_se", "mean_sd", "mean_ci", "mean_range", "median", "median_iqr", "median_hilow", "median_q1q3", "median_mad", "median_range".} \item{color}{point or outline color.} \item{fill}{fill color. Used only when \code{error.plot = "crossbar"}.} \item{group}{grouping variable. Allowed values are 1 (for one group) or a character vector specifying the name of the grouping variable. Used only for adding statistical summary per group.} \item{width}{numeric value between 0 and 1 specifying bar or box width. Example width = 0.8. Used only when \code{error.plot} is one of c("crossbar", "errorbar").} \item{shape}{point shape. Allowed values can be displayed using the function \code{\link{show_point_shapes}()}.} \item{size}{numeric value in [0-1] specifying point and line size.} \item{alpha}{numeric value specifying fill color transparency. Value should be in [0, 1], where 0 is full transparency and 1 is no transparency.} \item{jitter}{a numeric value specifying the amount of jittering. Used only when \code{add} contains "jitter".} \item{seed}{A random seed to make the jitter reproducible. Default is `123`. Useful if you need to apply the same jitter twice, e.g., for a point and a corresponding label. The random seed is reset after jittering. If `NA`, the seed is initialized with a random value; this makes sure that two subsequent calls start with a different seed. Use NULL to use the current random seed and also avoid resetting (the behaviour of ggplot 2.2.1 and earlier).} \item{binwidth}{numeric value specifying bin width. use value between 0 and 1 when you have a strong dense dotplot. For example binwidth = 0.2. Used only when \code{add} contains "dotplot".} \item{dotsize}{as \code{size} but applied only to dotplot.} \item{linetype}{line type.} \item{show.legend}{logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.} \item{error.plot}{plot type used to visualize error. Allowed values are one of \code{c("pointrange", "linerange", "crossbar", "errorbar", "upper_errorbar", "lower_errorbar", "upper_pointrange", "lower_pointrange", "upper_linerange", "lower_linerange")}. Default value is "pointrange".} \item{ci}{the percent range of the confidence interval (default is 0.95).} \item{data}{a \code{data.frame} to be displayed. If \code{NULL}, the default, the data is inherited from the plot data as specified in the call to \link[ggplot2]{ggplot}.} \item{position}{position adjustment, either as a string, or the result of a call to a position adjustment function. Used to adjust position for multiple groups.} \item{p_geom}{the geometry of the main plot. Ex: p_geom = "geom_line". If NULL, the geometry is extracted from p. Used only by \link{ggline}().} } \description{ Add summary statistics or a geometry onto a ggplot. } \examples{ # Basic violin plot data("ToothGrowth") p <- ggviolin(ToothGrowth, x = "dose", y = "len", add = "none") # Add mean +/- SD and jitter points p \%>\% ggadd(c("mean_sd", "jitter"), color = "dose") # Add box plot p \%>\% ggadd(c("boxplot", "jitter"), color = "dose") } ggpubr/man/grids.Rd0000644000176200001440000000141114332553656013723 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/grids.R \name{grids} \alias{grids} \title{Add Grids to a ggplot} \usage{ grids(axis = c("xy", "x", "y"), color = "grey92", size = NULL, linetype = NULL) } \arguments{ \item{axis}{axis for which grid should be added. Allowed values include \code{c("xy", "x", "y")}.} \item{color}{grid line color.} \item{size}{numeric value specifying grid line size.} \item{linetype}{line type. An integer (0:8), a name (blank, solid, dashed, dotted, dotdash, longdash, twodash). Sess \code{\link{show_line_types}}.} } \description{ Add grids to ggplot. } \examples{ # Load data data("ToothGrowth") # Basic plot p <- ggboxplot(ToothGrowth, x = "dose", y = "len") p # Add border p + grids(linetype = "dashed") } ggpubr/man/rotate.Rd0000644000176200001440000000107214333713444014106 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rotate.R \name{rotate} \alias{rotate} \title{Rotate a ggplot Horizontally} \usage{ rotate(...) } \arguments{ \item{...}{other arguments to pass to \code{\link[ggplot2]{coord_flip}}.} } \description{ Rotate a ggplot to create horizontal plots. Wrapper around \code{\link[ggplot2]{coord_flip}}. } \examples{ # Load data data("ToothGrowth") df <- ToothGrowth # Basic plot p <- ggboxplot(df, x = "dose", y = "len", color = "dose", palette = "jco") p # Create horizontal plots p + rotate() } ggpubr/man/pipe.Rd0000644000176200001440000000040514332553656013552 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils-pipe.R \name{\%>\%} \alias{\%>\%} \title{Pipe operator} \usage{ lhs \%>\% rhs } \description{ See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. } \keyword{internal} ggpubr/man/get_legend.Rd0000644000176200001440000000157714332553656014725 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/get_legend.R \name{get_legend} \alias{get_legend} \title{Extract Legends from a ggplot object} \usage{ get_legend(p, position = NULL) } \arguments{ \item{p}{an object of class ggplot or a list of ggplots. If p is a list, only the first legend is returned.} \item{position}{character specifying legend position. Allowed values are one of c("top", "bottom", "left", "right", "none"). To remove the legend use legend = "none".} } \value{ an object of class gtable. } \description{ Extract the legend labels from a ggplot object. } \examples{ # Create a scatter plot p <- ggscatter(iris, x = "Sepal.Length", y = "Sepal.Width", color = "Species", palette = "jco", ggtheme = theme_minimal()) p # Extract the legend. Returns a gtable leg <- get_legend(p) # Convert to a ggplot and print as_ggplot(leg) } ggpubr/man/ggpaired.Rd0000644000176200001440000001126614367732422014404 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggpaired.R \name{ggpaired} \alias{ggpaired} \title{Plot Paired Data} \usage{ ggpaired( data, cond1, cond2, x = NULL, y = NULL, id = NULL, color = "black", fill = "white", palette = NULL, width = 0.5, point.size = 1.2, line.size = 0.5, line.color = "black", linetype = "solid", title = NULL, xlab = "Condition", ylab = "Value", facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, ggtheme = theme_pubr(), ... ) } \arguments{ \item{data}{a data frame} \item{cond1}{variable name corresponding to the first condition.} \item{cond2}{variable name corresponding to the second condition.} \item{x, y}{x and y variables, where x is a grouping variable and y contains values for each group. Considered only when \code{cond1} and \code{cond2} are missing.} \item{id}{variable name corresponding to paired samples' id. Used to connect paired points with lines.} \item{color}{points and box plot colors. To color by conditions, use color = "condition".} \item{fill}{box plot fill color. To change fill color by conditions, use fill = "condition".} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{width}{box plot width.} \item{point.size, line.size}{point and line size, respectively.} \item{line.color}{line color.} \item{linetype}{line type.} \item{title}{plot main title.} \item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to hide xlab.} \item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to hide ylab.} \item{facet.by}{character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.} \item{panel.labs}{a list of one or two character vectors to modify facet panel labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies the labels for the "sex" variable. For two grouping variables, you can use for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", "Lev", "Lev2") ).} \item{short.panel.labs}{logical value. Default is TRUE. If TRUE, create short labels for panels by omitting variable names; in other words panels will be labelled only by variable grouping levels.} \item{label}{the name of the column containing point labels. Can be also a character vector with length = nrow(data).} \item{font.label}{a list which can contain the combination of the following elements: the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of labels. For example font.label = list(size = 14, face = "bold", color ="red"). To specify only the size and the style, use font.label = list(size = 14, face = "plain").} \item{label.select}{can be of two formats: \itemize{ \item a character vector specifying some labels to show. \item a list containing one or the combination of the following components: \itemize{ \item \code{top.up} and \code{top.down}: to display the labels of the top up/down points. For example, \code{label.select = list(top.up = 10, top.down = 4)}. \item \code{criteria}: to filter, for example, by x and y variabes values, use this: \code{label.select = list(criteria = "`y` > 2 & `y` < 5 & `x` \%in\% c('A', 'B')")}. } }} \item{repel}{a logical value, whether to use ggrepel to avoid overplotting text labels or not.} \item{label.rectangle}{logical value. If TRUE, add rectangle underneath the text, making it easier to read.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to be passed to \link{ggpar}().} } \description{ Plot paired data. } \examples{ # Example 1 #:::::::::::::::::::::::::::::::::::::::::: before <-c(200.1, 190.9, 192.7, 213, 241.4, 196.9, 172.2, 185.5, 205.2, 193.7) after <-c(392.9, 393.2, 345.1, 393, 434, 427.9, 422, 383.9, 392.3, 352.2) d <- data.frame(before = before, after = after) ggpaired(d, cond1 = "before", cond2 = "after", fill = "condition", palette = "jco") # Example 2 #:::::::::::::::::::::::::::::::::::::::::: ggpaired(ToothGrowth, x = "supp", y = "len", color = "supp", line.color = "gray", line.size = 0.4, palette = "npg") } ggpubr/man/get_breaks.Rd0000644000176200001440000000315114333713444014716 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/get_breaks.R \name{get_breaks} \alias{get_breaks} \title{Easy Break Creation for Numeric Axes} \usage{ get_breaks(n = NULL, by = NULL, from = NULL, to = NULL) } \arguments{ \item{n}{number of breaks.} \item{by}{number: the step between breaks.} \item{from}{the starting value of breaks. By default, 0 is used for positive variables} \item{to}{the end values of breaks. This corresponds generally to the maximum limit of the axis.} } \value{ a break function } \description{ Creates breaks for numeric axes to be used in the functions \code{\link[ggplot2:scale_continuous]{scale_x_continuous}()} and \code{\link[ggplot2:scale_continuous]{scale_y_continuous}()}. Can be used to increase the number of x and y ticks by specifying the option \code{n}. It's also possible to control axis breaks by specifying a step between ticks. For example, if \code{by = 5}, a tick mark is shown on every 5. } \examples{ # Generate 5 breaks for a variable x get_breaks(n = 5)(x = 1:100) # Generate breaks using an increasing step get_breaks(by = 10)(x = 1:100) # Combine with ggplot scale_xx functions library(ggplot2) # Create a basic plot p <- ggscatter(mtcars, x = "wt", y = "mpg") p # Increase the number of ticks p + scale_x_continuous(breaks = get_breaks(n = 10)) + scale_y_continuous(breaks = get_breaks(n = 10)) # Set ticks according to a specific step, starting from 0 p + scale_x_continuous( breaks = get_breaks(by = 1.5, from = 0), limits = c(0, 6) ) + scale_y_continuous( breaks = get_breaks(by = 10, from = 0), limits = c(0, 40) ) } ggpubr/man/add_summary.Rd0000644000176200001440000001203114367517301015113 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/add_summary.R \name{add_summary} \alias{add_summary} \alias{mean_se_} \alias{mean_sd} \alias{mean_ci} \alias{mean_range} \alias{median_iqr} \alias{median_hilow_} \alias{median_q1q3} \alias{median_mad} \alias{median_range} \title{Add Summary Statistics onto a ggplot.} \usage{ add_summary( p, fun = "mean_se", error.plot = "pointrange", color = "black", fill = "white", group = 1, width = NULL, shape = 19, size = 1, linetype = 1, show.legend = NA, ci = 0.95, data = NULL, position = position_dodge(0.8) ) mean_se_(x, error.limit = "both") mean_sd(x, error.limit = "both") mean_ci(x, ci = 0.95, error.limit = "both") mean_range(x, error.limit = "both") median_iqr(x, error.limit = "both") median_hilow_(x, ci = 0.95, error.limit = "both") median_q1q3(x, error.limit = "both") median_mad(x, error.limit = "both") median_range(x, error.limit = "both") } \arguments{ \item{p}{a ggplot on which you want to add summary statistics.} \item{fun}{a function that is given the complete data and should return a data frame with variables ymin, y, and ymax. Allowed values are one of: "mean", "mean_se", "mean_sd", "mean_ci", "mean_range", "median", "median_iqr", "median_hilow", "median_q1q3", "median_mad", "median_range".} \item{error.plot}{plot type used to visualize error. Allowed values are one of \code{c("pointrange", "linerange", "crossbar", "errorbar", "upper_errorbar", "lower_errorbar", "upper_pointrange", "lower_pointrange", "upper_linerange", "lower_linerange")}. Default value is "pointrange".} \item{color}{point or outline color.} \item{fill}{fill color. Used only whne \code{error.plot = "crossbar"}.} \item{group}{grouping variable. Allowed values are 1 (for one group) or a character vector specifying the name of the grouping variable. Used only for adding statistical summary per group.} \item{width}{numeric value between 0 and 1 specifying bar or box width. Example width = 0.8. Used only when \code{error.plot} is one of c("crossbar", "errorbar").} \item{shape}{point shape. Allowed values can be displayed using the function \code{\link{show_point_shapes}()}.} \item{size}{numeric value in [0-1] specifying point and line size.} \item{linetype}{line type.} \item{show.legend}{logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.} \item{ci}{the percent range of the confidence interval (default is 0.95).} \item{data}{a \code{data.frame} to be displayed. If \code{NULL}, the default, the data is inherited from the plot data as specified in the call to \link[ggplot2]{ggplot}.} \item{position}{position adjustment, either as a string, or the result of a call to a position adjustment function. Used to adjust position for multiple groups.} \item{x}{a numeric vector.} \item{error.limit}{allowed values are one of ("both", "lower", "upper", "none") specifying whether to plot the lower and/or the upper limits of error interval.} } \description{ add summary statistics onto a ggplot. } \section{Functions}{ \itemize{ \item \code{add_summary()}: add summary statistics onto a ggplot. \item \code{mean_se_()}: returns the \code{mean} and the error limits defined by the \code{standard error}. We used the name \code{mean_se_}() to avoid masking \code{\link[ggplot2]{mean_se}}(). \item \code{mean_sd()}: returns the \code{mean} and the error limits defined by the \code{standard deviation}. \item \code{mean_ci()}: returns the \code{mean} and the error limits defined by the \code{confidence interval}. \item \code{mean_range()}: returns the \code{mean} and the error limits defined by the \code{range = max - min}. \item \code{median_iqr()}: returns the \code{median} and the error limits defined by the \code{interquartile range}. \item \code{median_hilow_()}: computes the sample median and a selected pair of outer quantiles having equal tail areas. This function is a reformatted version of \code{Hmisc::smedian.hilow()}. The confidence limits are computed as follow: \code{lower.limits = (1-ci)/2} percentiles; \code{upper.limits = (1+ci)/2} percentiles. By default (\code{ci = 0.95}), the 2.5th and the 97.5th percentiles are used as the lower and the upper confidence limits, respectively. If you want to use the 25th and the 75th percentiles as the confidence limits, then specify \code{ci = 0.5} or use the function \code{median_q1q3()}. \item \code{median_q1q3()}: computes the sample median and, the 25th and 75th percentiles. Wrapper around the function \code{median_hilow_()} using \code{ci = 0.5}. \item \code{median_mad()}: returns the \code{median} and the error limits defined by the \code{median absolute deviation}. \item \code{median_range()}: returns the \code{median} and the error limits defined by the \code{range = max - min}. }} \examples{ # Basic violin plot p <- ggviolin(ToothGrowth, x = "dose", y = "len", add = "none") p # Add mean_sd add_summary(p, "mean_sd") } ggpubr/man/font.Rd0000644000176200001440000000463314333713444013564 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/font.R \name{font} \alias{font} \title{Change the Appearance of Titles and Axis Labels} \usage{ font(object, size = NULL, color = NULL, face = NULL, family = NULL, ...) } \arguments{ \item{object}{character string specifying the plot components. Allowed values include: \itemize{ \item \code{"title"} for the main title \item \code{"subtitle"} for the plot subtitle \item \code{"caption"} for the plot caption \item \code{"legend.title"} for the legend title \item \code{"legend.text"} for the legend text \item \code{"x", "xlab", or "x.title"} for x axis label \item \code{"y", "ylab", or "y.title"} for y axis label \item \code{"xy", "xylab", "xy.title" or "axis.title"} for both x and y axis labels \item \code{"x.text"} for x axis texts (x axis tick labels) \item \code{"y.text"} for y axis texts (y axis tick labels) \item \code{"xy.text"} or \code{"axis.text"} for both x and y axis texts }} \item{size}{numeric value specifying the font size, (e.g.: \code{size = 12}).} \item{color}{character string specifying the font color, (e.g.: \code{color = "red"}).} \item{face}{the font face or style. Allowed values include one of \code{"plain", "bold", "italic", "bold.italic"}, (e.g.: \code{face = "bold.italic"}).} \item{family}{the font family.} \item{...}{other arguments to pass to the function \code{\link[ggplot2:element]{element_text}()}.} } \description{ Change the appearance of the main title, subtitle, caption, axis labels and text, as well as the legend title and texts. Wrapper around \code{\link[ggplot2:element]{element_text}()}. } \examples{ # Load data data("ToothGrowth") # Basic plot p <- ggboxplot(ToothGrowth, x = "dose", y = "len", color = "dose", title = "Box Plot created with ggpubr", subtitle = "Length by dose", caption = "Source: ggpubr", xlab ="Dose (mg)", ylab = "Teeth length") p # Change the appearance of titles and labels p + font("title", size = 14, color = "red", face = "bold.italic")+ font("subtitle", size = 10, color = "orange")+ font("caption", size = 10, color = "orange")+ font("xlab", size = 12, color = "blue")+ font("ylab", size = 12, color = "#993333")+ font("xy.text", size = 12, color = "gray", face = "bold") # Change the appearance of legend title and texts p + font("legend.title", color = "blue", face = "bold")+ font("legend.text", color = "red") } ggpubr/man/theme_pubr.Rd0000644000176200001440000000447314333713444014752 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/theme_pubr.R \name{theme_pubr} \alias{theme_pubr} \alias{theme_pubclean} \alias{labs_pubr} \alias{theme_classic2} \alias{clean_theme} \alias{clean_table_theme} \title{Publication ready theme} \usage{ theme_pubr( base_size = 12, base_family = "", border = FALSE, margin = TRUE, legend = c("top", "bottom", "left", "right", "none"), x.text.angle = 0 ) theme_pubclean(base_size = 12, base_family = "", flip = FALSE) labs_pubr(base_size = 14, base_family = "") theme_classic2(base_size = 12, base_family = "") clean_theme() clean_table_theme() } \arguments{ \item{base_size}{base font size} \item{base_family}{base font family} \item{border}{logical value. Default is FALSE. If TRUE, add panel border.} \item{margin}{logical value. Default is TRUE. If FALSE, reduce plot margin.} \item{legend}{character specifying legend position. Allowed values are one of c("top", "bottom", "left", "right", "none"). Default is "top" side position. to remove the legend use legend = "none". Legend position can be also specified using a numeric vector c(x, y). In this case it is possible to position the legend inside the plotting area. x and y are the coordinates of the legend box. Their values should be between 0 and 1. c(0,0) corresponds to the "bottom left" and c(1,1) corresponds to the "top right" position. For instance use legend = c(0.8, 0.2).} \item{x.text.angle}{Rotation angle of x axis tick labels. Default value is 0. Use 90 for vertical text.} \item{flip}{logical. If TRUE, grid lines are added to y axis instead of x axis.} } \description{ \itemize{ \item \strong{theme_pubr()}: Create a publication ready theme \item \strong{theme_pubclean()}: a clean theme without axis lines, to direct more attention to the data. \item \strong{labs_pubr()}: Format only plot labels to a publication ready style \item \strong{theme_classic2()}: Create a classic theme with axis lines. \item \strong{clean_theme()}: Remove axis lines, ticks, texts and titles. \item \strong{clean_table_theme()}: Clean the the theme of a table, such as those created by \code{\link{ggsummarytable}()}}. } \examples{ p <- ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point(aes(color = gear)) # Default plot p # Use theme_pubr() p + theme_pubr() # Format labels p + labs_pubr() } ggpubr/man/ggpar.Rd0000644000176200001440000001465414367732422013726 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggpar.R \name{ggpar} \alias{ggpar} \title{Graphical parameters} \usage{ ggpar( p, palette = NULL, gradient.cols = NULL, main = NULL, submain = NULL, caption = NULL, xlab = NULL, ylab = NULL, title = NULL, subtitle = NULL, font.main = NULL, font.submain = NULL, font.x = NULL, font.y = NULL, font.caption = NULL, font.title = NULL, font.subtitle = NULL, font.family = "", xlim = NULL, ylim = NULL, xscale = c("none", "log2", "log10", "sqrt"), yscale = c("none", "log2", "log10", "sqrt"), format.scale = FALSE, legend = NULL, legend.title = NULL, font.legend = NULL, ticks = TRUE, tickslab = TRUE, font.tickslab = NULL, font.xtickslab = font.tickslab, font.ytickslab = font.tickslab, x.text.angle = NULL, y.text.angle = NULL, xtickslab.rt = x.text.angle, ytickslab.rt = y.text.angle, xticks.by = NULL, yticks.by = NULL, rotate = FALSE, orientation = c("vertical", "horizontal", "reverse"), ggtheme = NULL, ... ) } \arguments{ \item{p}{an object of class ggplot or a list of ggplots} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty". Can be also a numeric vector of length(groups); in this case a basic color palette is created using the function \link[grDevices]{palette}.} \item{gradient.cols}{vector of colors to use for n-colour gradient. Allowed values include brewer and ggsci color palettes.} \item{main}{plot main title.} \item{submain, subtitle}{plot subtitle.} \item{caption}{plot caption.} \item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to hide xlab.} \item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to hide ylab.} \item{title}{plot main title.} \item{font.main, font.submain, font.caption, font.x, font.y}{a vector of length 3 indicating respectively the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of main title, subtitle, caption, xlab and ylab, respectively. For example \emph{font.x = c(14, "bold", "red")}. Use font.x = 14, to change only font size; or use font.x = "bold", to change only font face.} \item{font.title, font.subtitle}{alias of font.submain and font.submain, respectively.} \item{font.family}{character vector specifying font family.} \item{xlim, ylim}{a numeric vector of length 2, specifying x and y axis limits (minimum and maximum), respectively. e.g.: ylim = c(0, 50).} \item{xscale, yscale}{x and y axis scale, respectively. Allowed values are one of c("none", "log2", "log10", "sqrt"); e.g.: yscale="log2".} \item{format.scale}{logical value. If TRUE, axis tick mark labels will be formatted when xscale or yscale = "log2" or "log10".} \item{legend}{character specifying legend position. Allowed values are one of c("top", "bottom", "left", "right", "none"). To remove the legend use legend = "none". Legend position can be also specified using a numeric vector c(x, y); see details section.} \item{legend.title}{legend title, e.g.: \code{legend.title = "Species"}. Can be also a list, \code{legend.title = list(color = "Species", linetype = "Species", shape = "Species")}.} \item{font.legend}{legend text font style; e.g.: font.legend = c(10, "plain", "black").} \item{ticks}{logical value. Default is TRUE. If FALSE, hide axis tick marks.} \item{tickslab}{logical value. Default is TRUE. If FALSE, hide axis tick labels.} \item{font.tickslab, font.xtickslab, font.ytickslab}{Font style (size, face, color) for tick labels, e.g.: c(14, "bold", "red").} \item{x.text.angle, y.text.angle}{Numeric value specifying the rotation angle of x and y axis tick labels, respectively. Default value is NULL. For vertical x axis texts use x.text.angle = 90.} \item{xtickslab.rt, ytickslab.rt}{Same as x.text.angle and y.text.angle, respectively. Will be deprecated in the near future.} \item{xticks.by, yticks.by}{numeric value controlling x and y axis breaks, respectively. For example, if yticks.by = 5, a tick mark is shown on every 5. Default value is NULL.} \item{rotate}{logical value. If TRUE, rotate the graph by setting the plot orientation to horizontal.} \item{orientation}{change the orientation of the plot. Allowed values are one of c( "vertical", "horizontal", "reverse"). Partial match is allowed.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{not used} } \description{ Graphical parameters } \examples{ # Load data data("ToothGrowth") df <- ToothGrowth # Basic box plot # +++++++++++++++++++++++++++ p <- ggboxplot(df, x = "dose", y = "len") # Change the plot orientation: horizontal ggpar(p, orientation = "horiz") # Change main title and axis labels # ++++++++++++++++++++++++++++ ggpar(p, main = "Plot of length \n by dose", xlab = "Dose (mg)", ylab = "Length") # Title font styles: 'plain', 'italic', 'bold', 'bold.italic' ggpar(p, main = "Length by dose", font.main = c(14,"bold.italic", "red"), font.x = c(14, "bold", "#2E9FDF"), font.y = c(14, "bold", "#E7B800")) # Hide axis labels ggpar(p, xlab = FALSE, ylab = FALSE) # Change colors # ++++++++++++++++++++++ # Change outline colors by groups: dose p2 <- ggboxplot(df, "dose", "len", color = "dose") p2 # Use custom color palette ggpar(p2, palette = c("#00AFBB", "#E7B800", "#FC4E07")) # Use brewer palette ggpar(p2, palette = "Dark2" ) # Use grey palette ggpar(p2, palette = "grey") # Use scientific journal palette from ggsci package ggpar(p2, palette = "npg") # nature # Axis ticks, limits, scales # +++++++++++++++++++++++++ # Axis ticks labels and rotation ggpar(p, font.tickslab = c(14,"bold", "#993333"), xtickslab.rt = 45, ytickslab.rt = 45) # Hide axis ticks and tick labels ggpar(p, ticks = FALSE, tickslab = FALSE) # Axis limits ggpar(p, ylim = c(0, 50)) # Axis scale ggpar(p, yscale = "log2") # Format axis scale ggpar(p, yscale = "log2", format.scale = TRUE) # Legends # ++++++++++++++++++ # Change legend position and title ggpar(p2, legend = "right", legend.title = "Dose (mg)", font.legend = c(10, "bold", "red")) } ggpubr/man/ggdotchart.Rd0000644000176200001440000001541114367732422014744 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggdotchart.R \name{ggdotchart} \alias{ggdotchart} \alias{theme_cleveland} \title{Cleveland's Dot Plots} \usage{ ggdotchart( data, x, y, group = NULL, combine = FALSE, color = "black", palette = NULL, shape = 19, size = NULL, dot.size = size, sorting = c("ascending", "descending", "none"), add = c("none", "segment"), add.params = list(), x.text.col = TRUE, rotate = FALSE, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, select = NULL, remove = NULL, order = NULL, label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, position = "identity", ggtheme = theme_pubr(), ... ) theme_cleveland(rotate = TRUE) } \arguments{ \item{data}{a data frame} \item{x, y}{x and y variables for drawing.} \item{group}{an optional column name indicating how the elements of x are grouped.} \item{combine}{logical value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, create a multi-panel plot by combining the plot of y variables.} \item{color, size}{points color and size.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{shape}{point shape. See \code{\link{show_point_shapes}}.} \item{dot.size}{numeric value specifying the dot size.} \item{sorting}{a character vector for sorting into ascending or descending order. Allowed values are one of "descending", "ascending" and "none". Partial match are allowed (e.g. sorting = "desc" or "asc"). Default is "descending".} \item{add}{character vector for adding another plot element (e.g.: dot plot or error bars). Allowed values are one or the combination of: "none", "dotplot", "jitter", "boxplot", "point", "mean", "mean_se", "mean_sd", "mean_ci", "mean_range", "median", "median_iqr", "median_hilow", "median_q1q3", "median_mad", "median_range"; see ?desc_statby for more details.} \item{add.params}{parameters (color, shape, size, fill, linetype) for the argument 'add'; e.g.: add.params = list(color = "red").} \item{x.text.col}{logical. If TRUE (default), x axis texts are colored by groups.} \item{rotate}{logical value. If TRUE, rotate the graph by setting the plot orientation to horizontal.} \item{title}{plot main title.} \item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to hide xlab.} \item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to hide ylab.} \item{facet.by}{character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.} \item{panel.labs}{a list of one or two character vectors to modify facet panel labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies the labels for the "sex" variable. For two grouping variables, you can use for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", "Lev", "Lev2") ).} \item{short.panel.labs}{logical value. Default is TRUE. If TRUE, create short labels for panels by omitting variable names; in other words panels will be labelled only by variable grouping levels.} \item{select}{character vector specifying which items to display.} \item{remove}{character vector specifying which items to remove from the plot.} \item{order}{character vector specifying the order of items.} \item{label}{the name of the column containing point labels.} \item{font.label}{a list which can contain the combination of the following elements: the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of labels. For example font.label = list(size = 14, face = "bold", color ="red"). To specify only the size and the style, use font.label = list(size = 14, face = "plain").} \item{label.select}{can be of two formats: \itemize{ \item a character vector specifying some labels to show. \item a list containing one or the combination of the following components: \itemize{ \item \code{top.up} and \code{top.down}: to display the labels of the top up/down points. For example, \code{label.select = list(top.up = 10, top.down = 4)}. \item \code{criteria}: to filter, for example, by x and y variabes values, use this: \code{label.select = list(criteria = "`y` > 2 & `y` < 5 & `x` \%in\% c('A', 'B')")}. } }} \item{repel}{a logical value, whether to use ggrepel to avoid overplotting text labels or not.} \item{label.rectangle}{logical value. If TRUE, add rectangle underneath the text, making it easier to read.} \item{position}{Position adjustment, either as a string, or the result of a call to a position adjustment function.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to \code{\link[ggplot2]{geom_point}} and \code{\link{ggpar}}.} } \description{ Draw a Cleveland dot plot. } \details{ The plot can be easily customized using the function ggpar(). Read ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels and position: legend = "right" \item plot orientation : orientation = c("vertical", "horizontal", "reverse") } } \examples{ # Load data data("mtcars") df <- mtcars df$cyl <- as.factor(df$cyl) df$name <- rownames(df) head(df[, c("wt", "mpg", "cyl")], 3) # Basic plot ggdotchart(df, x = "name", y ="mpg", ggtheme = theme_bw()) # Change colors by group cyl ggdotchart(df, x = "name", y = "mpg", group = "cyl", color = "cyl", palette = c('#999999','#E69F00','#56B4E9'), rotate = TRUE, sorting = "descending", ggtheme = theme_bw(), y.text.col = TRUE ) # Plot with multiple groups # +++++++++++++++++++++ # Create some data df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3), dose=rep(c("D0.5", "D1", "D2"),2), len=c(6.8, 15, 33, 4.2, 10, 29.5)) print(df2) ggdotchart(df2, x = "dose", y = "len", color = "supp", size = 3, add = "segment", add.params = list(color = "lightgray", size = 1.5), position = position_dodge(0.3), palette = "jco", ggtheme = theme_pubclean() ) } \seealso{ \code{\link{ggpar}} } ggpubr/man/stat_cor.Rd0000644000176200001440000001521014333713444014425 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stat_cor.R \name{stat_cor} \alias{stat_cor} \title{Add Correlation Coefficients with P-values to a Scatter Plot} \usage{ stat_cor( mapping = NULL, data = NULL, method = "pearson", alternative = "two.sided", cor.coef.name = c("R", "rho", "tau"), label.sep = ", ", label.x.npc = "left", label.y.npc = "top", label.x = NULL, label.y = NULL, output.type = "expression", digits = 2, r.digits = digits, p.digits = digits, r.accuracy = NULL, p.accuracy = NULL, geom = "text", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... ) } \arguments{ \item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and \code{inherit.aes = TRUE} (the default), it is combined with the default mapping at the top level of the plot. You must supply \code{mapping} if there is no plot mapping.} \item{data}{The data to be displayed in this layer. There are three options: If \code{NULL}, the default, the data is inherited from the plot data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}. A \code{data.frame}, or other object, will override the plot data. All objects will be fortified to produce a data frame. See \code{\link[ggplot2:fortify]{fortify()}} for which variables will be created. A \code{function} will be called with a single argument, the plot data. The return value must be a \code{data.frame}, and will be used as the layer data. A \code{function} can be created from a \code{formula} (e.g. \code{~ head(.x, 10)}).} \item{method}{a character string indicating which correlation coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman".} \item{alternative}{a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.} \item{cor.coef.name}{character. Can be one of \code{"R"} (pearson coef), \code{"rho"} (spearman coef) and \code{"tau"} (kendall coef). Uppercase and lowercase are allowed.} \item{label.sep}{a character string to separate the terms. Default is ", ", to separate the correlation coefficient and the p.value.} \item{label.x.npc, label.y.npc}{can be \code{numeric} or \code{character} vector of the same length as the number of groups and/or panels. If too short they will be recycled. \itemize{ \item If \code{numeric}, value should be between 0 and 1. Coordinates to be used for positioning the label, expressed in "normalized parent coordinates". \item If \code{character}, allowed values include: i) one of c('right', 'left', 'center', 'centre', 'middle') for x-axis; ii) and one of c( 'bottom', 'top', 'center', 'centre', 'middle') for y-axis.} If too short they will be recycled.} \item{label.x, label.y}{\code{numeric} Coordinates (in data units) to be used for absolute positioning of the label. If too short they will be recycled.} \item{output.type}{character One of "expression", "latex", "tex" or "text".} \item{digits, r.digits, p.digits}{integer indicating the number of decimal places (round) or significant digits (signif) to be used for the correlation coefficient and the p-value, respectively..} \item{r.accuracy}{a real value specifying the number of decimal places of precision for the correlation coefficient. Default is NULL. Use (e.g.) 0.01 to show 2 decimal places of precision. If specified, then \code{r.digits} is ignored.} \item{p.accuracy}{a real value specifying the number of decimal places of precision for the p-value. Default is NULL. Use (e.g.) 0.0001 to show 4 decimal places of precision. If specified, then \code{p.digits} is ignored.} \item{geom}{The geometric object to use to display the data, either as a \code{ggproto} \code{Geom} subclass or as a string naming the geom stripped of the \code{geom_} prefix (e.g. \code{"point"} rather than \code{"geom_point"})} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{na.rm}{If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.} \item{show.legend}{logical. Should this layer be included in the legends? \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and \code{TRUE} always includes. It can also be a named logical vector to finely select the aesthetics to display.} \item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. \code{\link[ggplot2:borders]{borders()}}.} \item{...}{other arguments to pass to \code{\link[ggplot2]{geom_text}} or \code{\link[ggplot2:geom_text]{geom_label}}.} } \description{ Add correlation coefficients with p-values to a scatter plot. Can be also used to add `R2`. } \section{Computed variables}{ \describe{ \item{r}{correlation coefficient} \item{rr}{correlation coefficient squared} \item{r.label}{formatted label for the correlation coefficient} \item{rr.label}{formatted label for the squared correlation coefficient} \item{p.label}{label for the p-value} \item{label}{default labeldisplayed by \code{stat_cor()}} } } \examples{ # Load data data("mtcars") df <- mtcars df$cyl <- as.factor(df$cyl) # Scatter plot with correlation coefficient #::::::::::::::::::::::::::::::::::::::::::::::::: sp <- ggscatter(df, x = "wt", y = "mpg", add = "reg.line", # Add regressin line add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line conf.int = TRUE # Add confidence interval ) # Add correlation coefficient sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) # Specify the number of decimal places of precision for p and r # Using 3 decimal places for the p-value and # 2 decimal places for the correlation coefficient (r) sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) # Show only the r.label but not the p.label sp + stat_cor(aes(label = ..r.label..), label.x = 3) # Use R2 instead of R ggscatter(df, x = "wt", y = "mpg", add = "reg.line") + stat_cor( aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~")), label.x = 3 ) # Color by groups and facet #:::::::::::::::::::::::::::::::::::::::::::::::::::: sp <- ggscatter(df, x = "wt", y = "mpg", color = "cyl", palette = "jco", add = "reg.line", conf.int = TRUE) sp + stat_cor(aes(color = cyl), label.x = 3) } \seealso{ \code{\link{ggscatter}} } ggpubr/man/rotate_axis_text.Rd0000644000176200001440000000205414333713444016177 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rotate_axis_text.R \name{rotate_axis_text} \alias{rotate_axis_text} \alias{rotate_x_text} \alias{rotate_y_text} \title{Rotate Axes Text} \usage{ rotate_x_text(angle = 90, hjust = NULL, vjust = NULL, ...) rotate_y_text(angle = 90, hjust = NULL, vjust = NULL, ...) } \arguments{ \item{angle}{numeric value specifying the rotation angle. Default is 90 for vertical x-axis text.} \item{hjust}{horizontal justification (in [0, 1]).} \item{vjust}{vertical justification (in [0, 1]).} \item{...}{other arguments to pass to the function \code{\link[ggplot2:element]{element_text}()}.} } \description{ Rotate the x-axis text (tick mark labels). \itemize{ \item \code{rotate_x_text}(): Rotate x axis text. \item \code{rotate_y_text}(): Rotate y axis text. } } \examples{ # Load data data("ToothGrowth") df <- ToothGrowth # Basic plot p <- ggboxplot(df, x = "dose", y = "len") p # Vertical x axis text p + rotate_x_text() # Set rotation angle to 45 p + rotate_x_text(45) p + rotate_y_text(45) } ggpubr/man/set_palette.Rd0000644000176200001440000000271014332553656015127 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/set_palette.R \name{set_palette} \alias{set_palette} \alias{change_palette} \alias{color_palette} \alias{fill_palette} \title{Set Color Palette} \usage{ set_palette(p, palette) change_palette(p, palette) color_palette(palette = NULL, ...) fill_palette(palette = NULL, ...) } \arguments{ \item{p}{a ggplot} \item{palette}{Color palette. Allowed values include: \itemize{ \item \strong{Grey color palettes}: "grey" or "gray"; \item \strong{RColorBrewer palettes}, see \code{\link[RColorBrewer:ColorBrewer]{brewer.pal}} and details section. Examples of palette names include: "RdBu", "Blues", "Dark2", "Set2", ...; \item \strong{Custom color palettes}. For example, palette = c("#00AFBB", "#E7B800", "#FC4E07"); \item \strong{ggsci scientific journal palettes}, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty". }} \item{...}{other arguments passed to ggplot2 scale_color_xxx() and scale_fill_xxx() functions.} } \description{ \itemize{ \item \code{change_palette(), set_palette()}: Change both color and fill palettes. \item \code{color_palette()}: change color palette only. \item \code{fill_palette()}: change fill palette only. } } \examples{ # Load data data("ToothGrowth") df <- ToothGrowth # Basic plot p <- ggboxplot(df, x = "dose", y = "len", color = "dose") p # Change the color palette set_palette(p, "jco") } \seealso{ \link{get_palette}. } ggpubr/man/geom_exec.Rd0000644000176200001440000000174514333713444014552 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/geom_exec.R \name{geom_exec} \alias{geom_exec} \title{Execute ggplot2 functions} \usage{ geom_exec(geomfunc = NULL, data = NULL, position = NULL, ...) } \arguments{ \item{geomfunc}{a ggplot2 function (e.g.: geom_point)} \item{data}{a data frame to be used for mapping} \item{position}{Position adjustment, either as a string, or the result of a call to a position adjustment function.} \item{...}{arguments accepted by the function} } \value{ return a plot if geomfunc!=Null or a list(option, mapping) if geomfunc = NULL. } \description{ A helper function used by ggpubr functions to execute any geom_* functions in ggplot2. Useful only when you want to call a geom_* function without carrying about the arguments to put in aes(). Basic users of ggpubr don't need this function. } \examples{ \dontrun{ ggplot() + geom_exec(geom_point, data = mtcars, x = "mpg", y = "wt", size = "cyl", color = "cyl") } } ggpubr/man/stat_anova_test.Rd0000644000176200001440000003050714333713444016013 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stat_anova_test.R \name{stat_anova_test} \alias{stat_anova_test} \title{Add Anova Test P-values to a GGPlot} \usage{ stat_anova_test( mapping = NULL, data = NULL, method = c("one_way", "one_way_repeated", "two_way", "two_way_repeated", "two_way_mixed"), wid = NULL, group.by = NULL, type = NULL, effect.size = "ges", error = NULL, correction = c("auto", "GG", "HF", "none"), label = "{method}, p = {p.format}", label.x.npc = "left", label.y.npc = "top", label.x = NULL, label.y = NULL, step.increase = 0.1, p.adjust.method = "holm", significance = list(), geom = "text", position = "identity", na.rm = FALSE, show.legend = FALSE, inherit.aes = TRUE, parse = FALSE, ... ) } \arguments{ \item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and \code{inherit.aes = TRUE} (the default), it is combined with the default mapping at the top level of the plot. You must supply \code{mapping} if there is no plot mapping.} \item{data}{The data to be displayed in this layer. There are three options: If \code{NULL}, the default, the data is inherited from the plot data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}. A \code{data.frame}, or other object, will override the plot data. All objects will be fortified to produce a data frame. See \code{\link[ggplot2:fortify]{fortify()}} for which variables will be created. A \code{function} will be called with a single argument, the plot data. The return value must be a \code{data.frame}, and will be used as the layer data. A \code{function} can be created from a \code{formula} (e.g. \code{~ head(.x, 10)}).} \item{method}{ANOVA test methods. Possible values are one of \code{c("one_way", "one_way_repeated", "two_way", "two_way_repeated", "two_way_mixed")}.} \item{wid}{(factor) column name containing individuals/subjects identifier. Should be unique per individual. Required only for repeated measure tests (\code{"one_way_repeated", "two_way_repeated", "friedman_test", etc}).} \item{group.by}{(optional) character vector specifying the grouping variable; it should be used only for grouped plots. Possible values are : \itemize{ \item \code{"x.var"}: Group by the x-axis variable and perform the test between legend groups. In other words, the p-value is compute between legend groups at each x position \item \code{"legend.var"}: Group by the legend variable and perform the test between x-axis groups. In other words, the test is performed between the x-groups for each legend level. }} \item{type}{the type of sums of squares for ANOVA. Allowed values are either 1, 2 or 3. \code{type = 2} is the default because this will yield identical ANOVA results as type = 1 when data are balanced but type = 2 will additionally yield various assumption tests where appropriate. When the data are unbalanced the \code{type = 3} is used by popular commercial softwares including SPSS.} \item{effect.size}{the effect size to compute and to show in the ANOVA results. Allowed values can be either "ges" (generalized eta squared) or "pes" (partial eta squared) or both. Default is "ges".} \item{error}{(optional) for a linear model, an lm model object from which the overall error sum of squares and degrees of freedom are to be calculated. Read more in \code{\link[car]{Anova}()} documentation.} \item{correction}{character. Used only in repeated measures ANOVA test to specify which correction of the degrees of freedom should be reported for the within-subject factors. Possible values are: \itemize{ \item{"GG"}: applies Greenhouse-Geisser correction to all within-subjects factors even if the assumption of sphericity is met (i.e., Mauchly's test is not significant, p > 0.05). \item{"HF"}: applies Hyunh-Feldt correction to all within-subjects factors even if the assumption of sphericity is met, \item{"none"}: returns the ANOVA table without any correction and \item{"auto"}: apply automatically GG correction to only within-subjects factors violating the sphericity assumption (i.e., Mauchly's test p-value is significant, p <= 0.05). }} \item{label}{character string specifying label. Can be: \itemize{ \item the column containing the label (e.g.: \code{label = "p"} or \code{label = "p.adj"}), where \code{p} is the p-value. Other possible values are \code{"p.signif", "p.adj.signif", "p.format", "p.adj.format"}. \item an expression that can be formatted by the \code{\link[glue]{glue}()} package. For example, when specifying \code{label = "Anova, p = \{p\}"}, the expression \{p\} will be replaced by its value. \item a combination of plotmath expressions and glue expressions. You may want some of the statistical parameter in italic; for example:\code{label = "Anova, italic(p) = {p}"}. \item a constant: \code{label = "as_italic"}: display statistical parameters in italic; \code{label = "as_detailed"}: detailed plain text; \code{label = "as_detailed_expression"} or \code{label = "as_detailed_italic"}: detailed plotmath expression. Statistical parameters will be displayed in italic.}.} \item{label.x.npc, label.y.npc}{can be \code{numeric} or \code{character} vector of the same length as the number of groups and/or panels. If too short they will be recycled. \itemize{ \item If \code{numeric}, value should be between 0 and 1. Coordinates to be used for positioning the label, expressed in "normalized parent coordinates". \item If \code{character}, allowed values include: i) one of c('right', 'left', 'center', 'centre', 'middle') for x-axis; ii) and one of c( 'bottom', 'top', 'center', 'centre', 'middle') for y-axis.}} \item{label.x, label.y}{\code{numeric} Coordinates (in data units) to be used for absolute positioning of the label. If too short they will be recycled.} \item{step.increase}{numeric value in with the increase in fraction of total height for every additional comparison to minimize overlap. The step value can be negative to reverse the order of groups.} \item{p.adjust.method}{method for adjusting p values (see \code{\link[stats]{p.adjust}}). Has impact only in a situation, where multiple pairwise tests are performed; or when there are multiple grouping variables. Allowed values include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p value (not recommended), use p.adjust.method = "none".} \item{significance}{a list of arguments specifying the signifcance cutpoints and symbols. For example, \code{significance <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, Inf), symbols = c("****", "***", "**", "*", "ns"))}. In other words, we use the following convention for symbols indicating statistical significance: \itemize{ \item \code{ns}: p > 0.05 \item \code{*}: p <= 0.05 \item \code{**}: p <= 0.01 \item \code{***}: p <= 0.001 \item \code{****}: p <= 0.0001 }} \item{geom}{The geometric object to use to display the data, either as a \code{ggproto} \code{Geom} subclass or as a string naming the geom stripped of the \code{geom_} prefix (e.g. \code{"point"} rather than \code{"geom_point"})} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{na.rm}{If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.} \item{show.legend}{logical. Should this layer be included in the legends? \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and \code{TRUE} always includes. It can also be a named logical vector to finely select the aesthetics to display.} \item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. \code{\link[ggplot2:borders]{borders()}}.} \item{parse}{If TRUE, the labels will be parsed into expressions and displayed as described in \code{?plotmath}.} \item{...}{other arguments to pass to \code{\link[ggplot2:geom_text]{geom_text}}, such as:\itemize{ \item \code{hjust}: horizontal justification of the text. Move the text left or right and \item \code{vjust}: vertical justification of the text. Move the text up or down. }} } \description{ Adds automatically one-way and two-way ANOVA test p-values to a ggplot, such as box blots, dot plots and stripcharts. } \section{Computed variables}{ \itemize{ \item{DFn}: Degrees of Freedom in the numerator (i.e. DF effect). \item{DFd}: Degrees of Freedom in the denominator (i.e., DF error). \item{ges}: Generalized Eta-Squared measure of effect size. Computed only when the option \code{effect.size = "ges"}. \item{pes}: Partial Eta-Squared measure of effect size. Computed only when the option \code{effect.size = "pes"}. \item{F}: F-value. \item{p}: p-value. \item{p.adj}: Adjusted p-values. \item{p.signif}: P-value significance. \item{p.adj.signif}: Adjusted p-value significance. \item{p.format}: Formated p-value. \item{p.adj.format}: Formated adjusted p-value. \item{n}: number of samples. } } \examples{ # Data preparation #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% # Transform `dose` into factor variable df <- ToothGrowth df$dose <- as.factor(df$dose) # Add individuals id df$id <- rep(1:10, 6) # Add a random grouping variable set.seed(123) df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20))) df$len <- ifelse(df$group == "grp2", df$len+2, df$len) df$len <- ifelse(df$group == "grp3", df$len+7, df$len) head(df, 3) # Basic boxplot #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% # Create a basic boxplot # Add 5\% and 10\% space to the plot bottom and the top, respectively bxp <- ggboxplot(df, x = "dose", y = "len") + scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) # Add the p-value to the boxplot bxp + stat_anova_test() \dontrun{ # Change the label position # Using coordinates in data units bxp + stat_anova_test(label.x = "1", label.y = 10, hjust = 0) } # Format the p-value differently custom_p_format <- function(p) { rstatix::p_format(p, accuracy = 0.0001, digits = 3, leading.zero = FALSE) } bxp + stat_anova_test( label = "Anova, italic(p) = {custom_p_format(p)}{p.signif}" ) # Show a detailed label in italic bxp + stat_anova_test(label = "as_detailed_italic") # Faceted plots #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% # Create a ggplot facet bxp <- ggboxplot(df, x = "dose", y = "len", facet.by = "supp") + scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) # Add p-values bxp + stat_anova_test() # Grouped plots #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% bxp2 <- ggboxplot(df, x = "group", y = "len", color = "dose", palette = "npg") # For each x-position, computes tests between legend groups bxp2 + stat_anova_test(aes(group = dose), label = "p = {p.format}{p.signif}") # For each legend group, computes tests between x variable groups bxp2 + stat_anova_test(aes(group = dose, color = dose), group.by = "legend.var") \dontrun{ # Two-way ANOVA: Independent measures #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% # Visualization: box plots with p-values # Two-way interaction p-values between x and legend (group) variables bxp3 <- ggboxplot( df, x = "supp", y = "len", color = "dose", palette = "jco" ) bxp3 + stat_anova_test(aes(group = dose), method = "two_way") # One-way repeatead measures ANOVA #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% df$id <- as.factor(c(rep(1:10, 3), rep(11:20, 3))) ggboxplot(df, x = "dose", y = "len") + stat_anova_test(method = "one_way_repeated", wid = "id") # Two-way repeatead measures ANOVA #\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\%\% df$id <- as.factor(rep(1:10, 6)) ggboxplot(df, x = "dose", y = "len", color = "supp", palette = "jco") + stat_anova_test(aes(group = supp), method = "two_way_repeated", wid = "id") # Grouped one-way repeated measures ANOVA ggboxplot(df, x = "dose", y = "len", color = "supp", palette = "jco") + stat_anova_test(aes(group = supp, color = supp), method = "one_way_repeated", wid = "id", group.by = "legend.var") } } ggpubr/man/ggexport.Rd0000644000176200001440000000341714333713444014454 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggexport.R \name{ggexport} \alias{ggexport} \title{Export ggplots} \usage{ ggexport( ..., plotlist = NULL, filename = NULL, ncol = NULL, nrow = NULL, width = 480, height = 480, pointsize = 12, res = NA, verbose = TRUE ) } \arguments{ \item{...}{list of plots to be arranged into the grid. The plots can be either ggplot2 plot objects, arbitrary gtables or an object of class \code{\link{ggarrange}}.} \item{plotlist}{(optional) list of plots to display.} \item{filename}{File name to create on disk.} \item{ncol}{(optional) number of columns in the plot grid.} \item{nrow}{(optional) number of rows in the plot grid.} \item{width, height}{plot width and height, respectively (example, width = 800, height = 800). Applied only to raster plots: "png", "jpeg", "jpg", "bmp" and "tiff".} \item{pointsize}{the default pointsize of plotted text (example, pointsize = 8). Used only for raster plots.} \item{res}{the resolution in ppi (example, res = 250). Used only for raster plots.} \item{verbose}{logical. If TRUE, show message.} } \description{ Export ggplots } \examples{ \dontrun{ require("magrittr") # Load data data("ToothGrowth") df <- ToothGrowth df$dose <- as.factor(df$dose) # Box plot bxp <- ggboxplot(df, x = "dose", y = "len", color = "dose", palette = "jco") # Dot plot dp <- ggdotplot(df, x = "dose", y = "len", color = "dose", palette = "jco") # Density plot dens <- ggdensity(df, x = "len", fill = "dose", palette = "jco") # Export to pdf ggarrange(bxp, dp, dens, ncol = 2) \%>\% ggexport(filename = "test.pdf") # Export to png ggarrange(bxp, dp, dens, ncol = 2) \%>\% ggexport(filename = "test.png") } } \author{ Alboukadel Kassambara } ggpubr/man/ggdonutchart.Rd0000644000176200001440000000665414337251252015312 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggdonutchart.R \name{ggdonutchart} \alias{ggdonutchart} \title{Donut chart} \usage{ ggdonutchart( data, x, label = x, lab.pos = c("out", "in"), lab.adjust = 0, lab.font = c(4, "plain", "black"), font.family = "", color = "black", fill = "white", palette = NULL, size = NULL, ggtheme = theme_pubr(), ... ) } \arguments{ \item{data}{a data frame} \item{x}{variable containing values for drawing.} \item{label}{variable specifying the label of each slice.} \item{lab.pos}{character specifying the position for labels. Allowed values are "out" (for outside) or "in" (for inside).} \item{lab.adjust}{numeric value, used to adjust label position when lab.pos = "in". Increase or decrease this value to see the effect.} \item{lab.font}{a vector of length 3 indicating respectively the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of label font. For example \emph{lab.font= c(4, "bold", "red")}.} \item{font.family}{character vector specifying font family.} \item{color, fill}{outline and fill colors.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{size}{Numeric value (e.g.: size = 1). change the size of points and outlines.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to be passed to ggpar().} } \description{ Create a donut chart. } \details{ The plot can be easily customized using the function ggpar(). Read ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels and position: legend = "right" \item plot orientation : orientation = c("vertical", "horizontal", "reverse") } } \examples{ # Data: Create some data # +++++++++++++++++++++++++++++++ df <- data.frame( group = c("Male", "Female", "Child"), value = c(25, 25, 50)) head(df) # Basic pie charts # ++++++++++++++++++++++++++++++++ ggdonutchart(df, "value", label = "group") # Change color # ++++++++++++++++++++++++++++++++ # Change fill color by group # set line color to white # Use custom color palette ggdonutchart(df, "value", label = "group", fill = "group", color = "white", palette = c("#00AFBB", "#E7B800", "#FC4E07") ) # Change label # ++++++++++++++++++++++++++++++++ # Show group names and value as labels labs <- paste0(df$group, " (", df$value, "\%)") ggdonutchart(df, "value", label = labs, fill = "group", color = "white", palette = c("#00AFBB", "#E7B800", "#FC4E07")) # Change the position and font color of labels ggdonutchart(df, "value", label = labs, lab.pos = "in", lab.font = "white", fill = "group", color = "white", palette = c("#00AFBB", "#E7B800", "#FC4E07")) } \seealso{ \code{\link{ggpar}}, \code{\link{ggpie}} } ggpubr/man/ggpie.Rd0000644000176200001440000000677114341741173013715 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggpie.R \name{ggpie} \alias{ggpie} \title{Pie chart} \usage{ ggpie( data, x, label = x, lab.pos = c("out", "in"), lab.adjust = 0, lab.font = c(4, "plain", "black"), font.family = "", color = "black", fill = "white", palette = NULL, size = NULL, ggtheme = theme_pubr(), ... ) } \arguments{ \item{data}{a data frame} \item{x}{variable containing values for drawing.} \item{label}{variable specifying the label of each slice.} \item{lab.pos}{character specifying the position for labels. Allowed values are "out" (for outside) or "in" (for inside).} \item{lab.adjust}{numeric value, used to adjust label position when lab.pos = "in". Increase or decrease this value to see the effect.} \item{lab.font}{a vector of length 3 indicating respectively the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of label font. For example \emph{lab.font= c(4, "bold", "red")}.} \item{font.family}{character vector specifying font family.} \item{color, fill}{outline and fill colors.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{size}{Numeric value (e.g.: size = 1). change the size of points and outlines.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to be passed to ggpar().} } \description{ Create a pie chart. } \details{ The plot can be easily customized using the function ggpar(). Read ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels and position: legend = "right" \item plot orientation : orientation = c("vertical", "horizontal", "reverse") } } \examples{ # Data: Create some data # +++++++++++++++++++++++++++++++ df <- data.frame( group = c("Male", "Female", "Child"), value = c(25, 25, 50)) head(df) # Basic pie charts # ++++++++++++++++++++++++++++++++ ggpie(df, "value", label = "group") # Reducing margins around the pie chart ggpie(df, "value", label = "group") + theme( plot.margin = unit(c(-.75,-.75,-.75,-.75),"cm")) # Change color # ++++++++++++++++++++++++++++++++ # Change fill color by group # set line color to white # Use custom color palette ggpie(df, "value", label = "group", fill = "group", color = "white", palette = c("#00AFBB", "#E7B800", "#FC4E07") ) # Change label # ++++++++++++++++++++++++++++++++ # Show group names and value as labels labs <- paste0(df$group, " (", df$value, "\%)") ggpie(df, "value", label = labs, fill = "group", color = "white", palette = c("#00AFBB", "#E7B800", "#FC4E07")) # Change the position and font color of labels ggpie(df, "value", label = labs, lab.pos = "in", lab.font = "white", fill = "group", color = "white", palette = c("#00AFBB", "#E7B800", "#FC4E07")) } \seealso{ \code{\link{ggpar}}, \code{\link{ggline}} } ggpubr/man/get_coord.Rd0000644000176200001440000000521214333713444014555 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/get_coord.R \name{get_coord} \alias{get_coord} \title{Checks and Returns Data Coordinates from Multiple Input Options} \usage{ get_coord( group = 1L, data.ranges = NULL, coord = NULL, npc = "left", step = 0.1, margin.npc = 0.05 ) } \arguments{ \item{group}{integer ggplot's group id. Used to shift coordinates to avoid overlaps.} \item{data.ranges}{a numeric vector of length 2 containing the data ranges (minimum and the maximum). Should be specified only when \code{coord = NULL} and \code{npc} is specified. Used to convert \code{npc} to data coordinates. Considered only when the argument \code{npc} is specified.} \item{coord}{data coordinates (i.e., either x or y coordinates).} \item{npc}{numeric (in [0-1]) or character vector of coordinates. If character, should be one of c('right', 'left', 'bottom', 'top', 'center', 'centre', 'middle'). Note that, the \code{data.ranges}, \code{step} and \code{margin.npc}, arguments are considered only when \code{npc} is specified. The option \code{npc} is ignored when the argument \code{coord} is specified.} \item{step}{numeric value in [0-1]. The step size for shifting coordinates in npc units. Considered as horizontal step for x-axis and vertical step for y-axis. For y-axis, the step value can be negative to reverse the order of groups.} \item{margin.npc}{numeric [0-1] The margin added towards the nearest plotting area edge when converting character coordinates into npc.} } \value{ a numeric vector representing data coordinates. } \description{ Checks and returns selected coordinates from multiple input options, which can be either data (x-y) coordinates or npc (normalized parent coordinates). Helper function internally used in \code{ggpubr} function to guess the type of coordinates specified by the user. For example, in the function \code{stat_cor()}, users can specify either the option \code{label.x} (data coordinates) or \code{label.x.npc} (npc coordinates); those coordinates are passed to \code{get_coord()}, which will make some checking and then return a unique coordinates for the label position. } \examples{ # If npc is specified, it is converted into data coordinates get_coord(data.ranges = c(2, 20), npc = "left") get_coord(data.ranges = c(2, 20), npc = 0.1) # When coord is specified, no transformation is performed # because this is assumed to be a data coordinate get_coord(coord = 5) # For grouped plots res_top <- get_coord( data.ranges = c(4.2, 36.4), group = c(1, 2, 3), npc = "top", step = -0.1, margin.npc = 0 ) res_top } \seealso{ \code{\link{as_npc}}, \code{\link{npc_to_data_coord}}. } ggpubr/man/ggscatterhist.Rd0000644000176200001440000001026714367733325015500 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggscatterhist.R \name{ggscatterhist} \alias{ggscatterhist} \alias{print.ggscatterhist} \title{Scatter Plot with Marginal Histograms} \usage{ ggscatterhist( data, x, y, group = NULL, color = "black", fill = NA, palette = NULL, shape = 19, size = 2, linetype = "solid", bins = 30, margin.plot = c("density", "histogram", "boxplot"), margin.params = list(), margin.ggtheme = theme_void(), margin.space = FALSE, main.plot.size = 2, margin.plot.size = 1, title = NULL, xlab = NULL, ylab = NULL, legend = "top", ggtheme = theme_pubr(), print = TRUE, ... ) \method{print}{ggscatterhist}( x, margin.space = FALSE, main.plot.size = 2, margin.plot.size = 1, title = NULL, legend = "top", ... ) } \arguments{ \item{data}{a data frame} \item{x}{an object of class \code{ggscatterhist}.} \item{y}{y variables for drawing.} \item{group}{a grouping variable. Change points color and shape by groups if the options \code{color} and \code{shape} are missing. Should be also specified when you want to create a marginal box plot that is grouped.} \item{color, fill}{point colors.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{shape}{point shape. See \code{\link{show_point_shapes}}.} \item{size}{Numeric value (e.g.: size = 1). change the size of points and outlines.} \item{linetype}{line type ("solid", "dashed", ...)} \item{bins}{Number of histogram bins. Defaults to 30. Pick a better value that fit to your data.} \item{margin.plot}{the type of the marginal plot. Default is "hist".} \item{margin.params}{parameters to be applied to the marginal plots.} \item{margin.ggtheme}{the theme of the marginal plot. Default is \code{\link[ggplot2:ggtheme]{theme_void}()}.} \item{margin.space}{logical value. If TRUE, adds space between the main plot and the marginal plot.} \item{main.plot.size}{the width of the main plot. Default is 2.} \item{margin.plot.size}{the width of the marginal plot. Default is 1.} \item{title}{plot main title.} \item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to hide xlab.} \item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to hide ylab.} \item{legend}{specify the legend position. Allowed values include: "top", "bottom", "left", "right".} \item{ggtheme}{the theme to be used for the scatter plot. Default is \code{\link{theme_pubr}()}.} \item{print}{logical value. If \code{TRUE} (default), print the plot.} \item{...}{other arguments passed to the function \code{\link{ggscatter}()}.} } \value{ an object of class \code{ggscatterhist}, which is list of ggplots, including the following elements: \itemize{\item sp: main scatter plot; \item xplot: marginal x-axis plot; \item yplot: marginal y-axis plot. }. User can modify each of plot before printing. } \description{ Create a scatter plot with marginal histograms, density plots or box plots. } \examples{ # Basic scatter plot with marginal density plot ggscatterhist(iris, x = "Sepal.Length", y = "Sepal.Width", color = "#00AFBB", margin.params = list(fill = "lightgray")) # Grouped data ggscatterhist( iris, x = "Sepal.Length", y = "Sepal.Width", color = "Species", size = 3, alpha = 0.6, palette = c("#00AFBB", "#E7B800", "#FC4E07"), margin.params = list(fill = "Species", color = "black", size = 0.2) ) # Use boxplot as marginal ggscatterhist( iris, x = "Sepal.Length", y = "Sepal.Width", color = "Species", size = 3, alpha = 0.6, palette = c("#00AFBB", "#E7B800", "#FC4E07"), margin.plot = "boxplot", ggtheme = theme_bw() ) # Add vertical and horizontal line to a ggscatterhist plots <- ggscatterhist(iris, x = "Sepal.Length", y = "Sepal.Width", print = FALSE) plots$sp <- plots$sp + geom_hline(yintercept = 3, linetype = "dashed", color = "blue") + geom_vline(xintercept = 6, linetype = "dashed", color = "red") plots } ggpubr/man/theme_transparent.Rd0000644000176200001440000000125314332553656016342 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/theme_transparent.R \name{theme_transparent} \alias{theme_transparent} \title{Create a ggplot with Transparent Background} \usage{ theme_transparent(base_size = 12, base_family = "") } \arguments{ \item{base_size}{base font size} \item{base_family}{base font family} } \description{ Create a ggplot with transparent background. } \examples{ # Create a scatter plot sp <- ggscatter(iris, x = "Sepal.Length", y = "Sepal.Width", color = "Species", palette = "jco", size = 3, alpha = 0.6) sp # Transparent theme sp + theme_transparent() } \seealso{ \code{\link{theme_pubr}} } ggpubr/man/ggpubr_options.Rd0000644000176200001440000000044414333716602015652 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggpubr_options.R \name{ggpubr_options} \alias{ggpubr_options} \title{Global Options for GGPubr} \usage{ ggpubr_options() } \description{ Displays allowed global options in ggpubr. } \examples{ ggpubr_options() } ggpubr/man/diff_express.Rd0000644000176200001440000000302714333713444015273 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/diff_express.R \docType{data} \name{diff_express} \alias{diff_express} \title{Differential gene expression analysis results} \format{ A data frame with 36028 rows and 5 columns. \describe{ \item{\code{name}}{gene names} \item{\code{baseMean}}{mean expression signal across all samples} \item{\code{log2FoldChange}}{log2 fold change} \item{\code{padj}}{Adjusted p-value}\item{\code{detection_call}}{a numeric vector specifying whether the genes is expressed (value = 1) or not (value = 0).}} } \usage{ data("diff_express") } \description{ Differential gene expression analysis results obtained from comparing the RNAseq data of two different cell populations using DESeq2 } \examples{ data(diff_express) # Default plot ggmaplot(diff_express, main = expression("Group 1" \%->\% "Group 2"), fdr = 0.05, fc = 2, size = 0.4, palette = c("#B31B21", "#1465AC", "darkgray"), genenames = as.vector(diff_express$name), legend = "top", top = 20, font.label = c("bold", 11), font.legend = "bold", font.main = "bold", ggtheme = ggplot2::theme_minimal()) # Add rectangle around labesl ggmaplot(diff_express, main = expression("Group 1" \%->\% "Group 2"), fdr = 0.05, fc = 2, size = 0.4, palette = c("#B31B21", "#1465AC", "darkgray"), genenames = as.vector(diff_express$name), legend = "top", top = 20, font.label = c("bold", 11), label.rectangle = TRUE, font.legend = "bold", font.main = "bold", ggtheme = ggplot2::theme_minimal()) } ggpubr/man/ggballoonplot.Rd0000644000176200001440000001200214333713444015446 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggballoonplot.R \name{ggballoonplot} \alias{ggballoonplot} \title{Ballon plot} \usage{ ggballoonplot( data, x = NULL, y = NULL, size = "value", facet.by = NULL, size.range = c(1, 10), shape = 21, color = "black", fill = "gray", show.label = FALSE, font.label = list(size = 12, color = "black"), rotate.x.text = TRUE, ggtheme = theme_minimal(), ... ) } \arguments{ \item{data}{a data frame. Can be: \itemize{ \item \bold{a standard contingency table} formed by two categorical variables: a data frame with row names and column names. The categories of the first variable are columns and the categories of the second variable are rows. \item \bold{a streched contingency table}: a data frame containing at least three columns corresponding, respectively, to (1) the categories of the first variable, (2) the categories of the second varible, (3) the frequency value. In this case, you should specify the argument x and y in the function \code{ggballoonplot()}}.} \item{x, y}{the column names specifying, respectively, the first and the second variable forming the contingency table. Required only when the data is a stretched contingency table.} \item{size}{point size. By default, the points size reflects the relative magnitude of the value of the corresponding cell (\code{size = "value"}). Can be also numeric (\code{size = 4}).} \item{facet.by}{character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.} \item{size.range}{a numeric vector of length 2 that specifies the minimum and maximum size of the plotting symbol. Default values are \code{size.range = c(1, 10)}.} \item{shape}{points shape. The default value is 21. Alternaive values include 22, 23, 24, 25.} \item{color}{point border line color.} \item{fill}{point fill color. Default is "lightgray". Considered only for points 21 to 25.} \item{show.label}{logical. If TRUE, show the data cell values as point labels.} \item{font.label}{a vector of length 3 indicating respectively the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of point labels. For example font.label = c(14, "bold", "red"). To specify only the size and the style, use font.label = c(14, "plain").} \item{rotate.x.text}{logica. If TRUE (default), rotate the x axis text.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments passed to the function \code{\link{ggpar}}} } \description{ Plot a graphical matrix where each cell contains a dot whose size reflects the relative magnitude of the corresponding component. Useful to visualize contingency table formed by two categorical variables. } \examples{ # Define color palette my_cols <- c("#0D0887FF", "#6A00A8FF", "#B12A90FF", "#E16462FF", "#FCA636FF", "#F0F921FF") # Standard contingency table #::::::::::::::::::::::::::::::::::::::::::::::::::::::::: # Read a contingency table: housetasks # Repartition of 13 housetasks in the couple data <- read.delim( system.file("demo-data/housetasks.txt", package = "ggpubr"), row.names = 1 ) data # Basic ballon plot ggballoonplot(data) # Change color and fill ggballoonplot(data, color = "#0073C2FF", fill = "#0073C2FF") # Change color according to the value of table cells ggballoonplot(data, fill = "value")+ scale_fill_gradientn(colors = my_cols) # Change the plotting symbol shape ggballoonplot(data, fill = "value", shape = 23)+ gradient_fill(c("blue", "white", "red")) # Set points size to 8, but change fill color by values # Sow labels ggballoonplot(data, fill = "value", color = "lightgray", size = 10, show.label = TRUE)+ gradient_fill(c("blue", "white", "red")) # Streched contingency table #::::::::::::::::::::::::::::::::::::::::::::::::::::::::: # Create an Example Data Frame Containing Car x Color data carnames <- c("bmw","renault","mercedes","seat") carcolors <- c("red","white","silver","green") datavals <- round(rnorm(16, mean=100, sd=60),1) car_data <- data.frame(Car = rep(carnames,4), Color = rep(carcolors, c(4,4,4,4) ), Value=datavals ) car_data ggballoonplot(car_data, x = "Car", y = "Color", size = "Value", fill = "Value") + scale_fill_gradientn(colors = my_cols) + guides(size = FALSE) # Grouped frequency table #::::::::::::::::::::::::::::::::::::::::::::::::::::::::: data("Titanic") dframe <- as.data.frame(Titanic) head(dframe) ggballoonplot( dframe, x = "Class", y = "Sex", size = "Freq", fill = "Freq", facet.by = c("Survived", "Age"), ggtheme = theme_bw() )+ scale_fill_gradientn(colors = my_cols) # Hair and Eye Color of Statistics Students data(HairEyeColor) ggballoonplot( as.data.frame(HairEyeColor), x = "Hair", y = "Eye", size = "Freq", ggtheme = theme_gray()) \%>\% facet("Sex") } ggpubr/man/stat_central_tendency.Rd0000644000176200001440000000777214333713444017201 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stat_central_tendency.R \name{stat_central_tendency} \alias{stat_central_tendency} \title{Add Central Tendency Measures to a GGPLot} \usage{ stat_central_tendency( mapping = NULL, data = NULL, geom = c("line", "point"), position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, type = c("mean", "median", "mode"), ... ) } \arguments{ \item{mapping}{Set of aesthetic mappings created by \code{\link[ggplot2:aes]{aes()}}. If specified and \code{inherit.aes = TRUE} (the default), it is combined with the default mapping at the top level of the plot. You must supply \code{mapping} if there is no plot mapping.} \item{data}{The data to be displayed in this layer. There are three options: If \code{NULL}, the default, the data is inherited from the plot data as specified in the call to \code{\link[ggplot2:ggplot]{ggplot()}}. A \code{data.frame}, or other object, will override the plot data. All objects will be fortified to produce a data frame. See \code{\link[ggplot2:fortify]{fortify()}} for which variables will be created. A \code{function} will be called with a single argument, the plot data. The return value must be a \code{data.frame}, and will be used as the layer data. A \code{function} can be created from a \code{formula} (e.g. \code{~ head(.x, 10)}).} \item{geom}{The geometric object to use to display the data, either as a \code{ggproto} \code{Geom} subclass or as a string naming the geom stripped of the \code{geom_} prefix (e.g. \code{"point"} rather than \code{"geom_point"})} \item{position}{Position adjustment, either as a string naming the adjustment (e.g. \code{"jitter"} to use \code{position_jitter}), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.} \item{na.rm}{If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.} \item{show.legend}{logical. Should this layer be included in the legends? \code{NA}, the default, includes if any aesthetics are mapped. \code{FALSE} never includes, and \code{TRUE} always includes. It can also be a named logical vector to finely select the aesthetics to display.} \item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. \code{\link[ggplot2:borders]{borders()}}.} \item{type}{the type of central tendency measure to be used. Possible values include: \code{"mean", "median", "mode"}.} \item{...}{other arguments to pass to \code{\link[ggplot2:geom_path]{geom_line}}.} } \description{ Add central tendency measures (mean, median, mode) to density and histogram plots created using ggplots. Note that, normally, the mode is used for categorical data where we wish to know which is the most common category. Therefore, we can have have two or more values that share the highest frequency. This might be problematic for continuous variable. For continuous variable, we can consider using mean or median as the measures of the central tendency. } \examples{ # Simple density plot data("mtcars") ggdensity(mtcars, x = "mpg", fill = "red") + scale_x_continuous(limits = c(-1, 50)) + stat_central_tendency(type = "mean", linetype = "dashed") # Color by groups data(iris) ggdensity(iris, "Sepal.Length", color = "Species") + stat_central_tendency(aes(color = Species), type = "median", linetype = 2) # Use geom = "point" for central tendency data(iris) ggdensity(iris, "Sepal.Length", color = "Species") + stat_central_tendency( aes(color = Species), type = "median", geom = "point", size = 4 ) # Facet ggdensity(iris, "Sepal.Length", facet.by = "Species") + stat_central_tendency(type = "mean", color = "red", linetype = 2) + stat_central_tendency(type = "median", color = "blue", linetype = 2) } \seealso{ \code{\link{ggdensity}} } ggpubr/man/get_palette.Rd0000644000176200001440000000517514333713444015115 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/get_palette.R \name{get_palette} \alias{get_palette} \title{Generate Color Palettes} \usage{ get_palette(palette = "default", k) } \arguments{ \item{palette}{Color palette. Allowed values include: \itemize{ \item \strong{Grey color palettes}: "grey" or "gray"; \item \strong{RColorBrewer palettes}, see \code{\link[RColorBrewer:ColorBrewer]{brewer.pal}} and details section. Examples of palette names include: "RdBu", "Blues", "Dark2", "Set2", ...; \item \strong{Custom color palettes}. For example, palette = c("#00AFBB", "#E7B800", "#FC4E07"); \item \strong{ggsci scientific journal palettes}, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty". }} \item{k}{the number of colors to generate.} } \value{ Returns a vector of color palettes. } \description{ Generate a palette of k colors from ggsci palettes, RColorbrewer palettes and custom color palettes. Useful to extend RColorBrewer and ggsci to support more colors. } \details{ \strong{RColorBrewer palettes}: To display all available color palettes, type this in R:RColorBrewer::display.brewer.all(). Color palette names include: \itemize{ \item \strong{Sequential palettes}, suited to ordered data that progress from low to high. Palette names include: Blues BuGn BuPu GnBu Greens Greys Oranges OrRd PuBu PuBuGn PuRd Purples RdPu Reds YlGn YlGnBu YlOrBr YlOrRd. \item \strong{Diverging palettes}:Gradient colors. Names include: BrBG PiYG PRGn PuOr RdBu RdGy RdYlBu RdYlGn Spectral. \item \strong{Qualitative palettes}: Best suited to representing nominal or categorical data. Names include: Accent, Dark2, Paired, Pastel1, Pastel2, Set1, Set2, Set3. } } \examples{ data("iris") iris$Species2 <- factor(rep(c(1:10), each = 15)) # Generate a gradient of 10 colors ggscatter(iris, x = "Sepal.Length", y = "Petal.Length", color = "Species2", palette = get_palette(c("#00AFBB", "#E7B800", "#FC4E07"), 10)) # Scatter plot with default color palette ggscatter(iris, x = "Sepal.Length", y = "Petal.Length", color = "Species") # RColorBrewer color palettes ggscatter(iris, x = "Sepal.Length", y = "Petal.Length", color = "Species", palette = get_palette("Dark2", 3)) # ggsci color palettes ggscatter(iris, x = "Sepal.Length", y = "Petal.Length", color = "Species", palette = get_palette("npg", 3)) # Custom color palette ggscatter(iris, x = "Sepal.Length", y = "Petal.Length", color = "Species", palette = c("#00AFBB", "#E7B800", "#FC4E07")) # Or use this ggscatter(iris, x = "Sepal.Length", y = "Petal.Length", color = "Species", palette = get_palette(c("#00AFBB", "#FC4E07"), 3)) } ggpubr/man/border.Rd0000644000176200001440000000123514332553656014074 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/border.R \name{border} \alias{border} \title{Set ggplot Panel Border Line} \usage{ border(color = "black", size = 0.8, linetype = NULL) } \arguments{ \item{color}{border line color.} \item{size}{numeric value specifying border line size.} \item{linetype}{line type. An integer (0:8), a name (blank, solid, dashed, dotted, dotdash, longdash, twodash). Sess \code{\link{show_line_types}}.} } \description{ Change or set ggplot panel border. } \examples{ # Load data data("ToothGrowth") df <- ToothGrowth # Basic plot p <- ggboxplot(df, x = "dose", y = "len") p # Add border p + border() } ggpubr/man/ggtext.Rd0000644000176200001440000000745214333713444014122 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggtext.R \name{ggtext} \alias{ggtext} \title{Text} \usage{ ggtext( data, x = NULL, y = NULL, label = NULL, color = "black", palette = NULL, size = 11, face = "plain", family = "", show.legend = NA, label.select = NULL, repel = FALSE, label.rectangle = FALSE, parse = FALSE, grouping.vars = NULL, position = "identity", ggp = NULL, ggtheme = theme_pubr(), ... ) } \arguments{ \item{data}{a data frame} \item{x, y}{x and y variables for drawing.} \item{label}{the name of the column containing point labels. Can be also a character vector with length = nrow(data).} \item{color}{text font color.} \item{palette}{the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".} \item{size}{text font size.} \item{face}{text font style. Allowed values are one of c("plain", "bold", "italic", "bold.italic").} \item{family}{character vector specifying font family.} \item{show.legend}{logical. Should text be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes.} \item{label.select}{can be of two formats: \itemize{ \item a character vector specifying some labels to show. \item a list containing one or the combination of the following components: \itemize{ \item \code{top.up} and \code{top.down}: to display the labels of the top up/down points. For example, \code{label.select = list(top.up = 10, top.down = 4)}. \item \code{criteria}: to filter, for example, by x and y variabes values, use this: \code{label.select = list(criteria = "`y` > 2 & `y` < 5 & `x` \%in\% c('A', 'B')")}. } }} \item{repel}{a logical value, whether to use ggrepel to avoid overplotting text labels or not.} \item{label.rectangle}{logical value. If TRUE, add rectangle underneath the text, making it easier to read.} \item{parse}{If \code{TRUE}, the labels will be parsed into expressions and displayed as described in \code{?plotmath}.} \item{grouping.vars}{grouping variables to sort the data by, when the user wants to display the top n up/down labels.} \item{position}{Position adjustment, either as a string, or the result of a call to a position adjustment function.} \item{ggp}{a ggplot. If not NULL, points are added to an existing plot.} \item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....} \item{...}{other arguments to be passed to \code{\link{ggpar}}.} } \description{ Add text to a plot. } \details{ The plot can be easily customized using the function ggpar(). Read ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels and position: legend = "right" } } \examples{ # Load data data("mtcars") df <- mtcars df$cyl <- as.factor(df$cyl) df$name <- rownames(df) head(df[, c("wt", "mpg", "cyl")], 3) # Textual annotation # +++++++++++++++++ ggtext(df, x = "wt", y = "mpg", color = "cyl", palette = c("#00AFBB", "#E7B800", "#FC4E07"), label = "name", repel = TRUE) # Add rectangle around label ggtext(df, x = "wt", y = "mpg", color = "cyl", palette = c("#00AFBB", "#E7B800", "#FC4E07"), label = "name", repel = TRUE, label.rectangle = TRUE) } \seealso{ \code{\link{ggpar}} } ggpubr/DESCRIPTION0000644000176200001440000000613714371467262013271 0ustar liggesusersPackage: ggpubr Type: Package Title: 'ggplot2' Based Publication Ready Plots Version: 0.6.0 Date: 2023-02-05 Authors@R: c( person("Alboukadel", "Kassambara", role = c("aut", "cre"), email = "alboukadel.kassambara@gmail.com")) Description: The 'ggplot2' package is excellent and flexible for elegant data visualization in R. However the default generated plots requires some formatting before we can send them for publication. Furthermore, to customize a 'ggplot', the syntax is opaque and this raises the level of difficulty for researchers with no advanced R programming skills. 'ggpubr' provides some easy-to-use functions for creating and customizing 'ggplot2'- based publication ready plots. License: GPL (>= 2) LazyData: TRUE Encoding: UTF-8 Depends: R (>= 3.1.0), ggplot2 (>= 3.4.0) Imports: ggrepel (>= 0.9.2), grid, ggsci, stats, utils, tidyr (>= 1.3.0), purrr, dplyr (>= 0.7.1), cowplot (>= 1.1.1), ggsignif, scales, gridExtra, glue, polynom, rlang (>= 0.4.6), rstatix (>= 0.7.2), tibble, magrittr Suggests: grDevices, knitr, RColorBrewer, gtable, testthat URL: https://rpkgs.datanovia.com/ggpubr/ BugReports: https://github.com/kassambara/ggpubr/issues RoxygenNote: 7.2.3 Collate: 'utilities_color.R' 'utilities_base.R' 'desc_statby.R' 'utilities.R' 'add_summary.R' 'annotate_figure.R' 'as_ggplot.R' 'as_npc.R' 'axis_scale.R' 'background_image.R' 'bgcolor.R' 'border.R' 'compare_means.R' 'create_aes.R' 'diff_express.R' 'facet.R' 'font.R' 'gene_citation.R' 'gene_expression.R' 'geom_bracket.R' 'geom_exec.R' 'utils-aes.R' 'utils_stat_test_label.R' 'geom_pwc.R' 'get_breaks.R' 'get_coord.R' 'get_legend.R' 'get_palette.R' 'ggadd.R' 'ggadjust_pvalue.R' 'ggarrange.R' 'ggballoonplot.R' 'ggpar.R' 'ggbarplot.R' 'ggboxplot.R' 'ggdensity.R' 'ggpie.R' 'ggdonutchart.R' 'stat_conf_ellipse.R' 'stat_chull.R' 'ggdotchart.R' 'ggdotplot.R' 'ggecdf.R' 'ggerrorplot.R' 'ggexport.R' 'gghistogram.R' 'ggline.R' 'ggmaplot.R' 'ggpaired.R' 'ggparagraph.R' 'ggpubr-package.R' 'ggpubr_args.R' 'ggpubr_options.R' 'ggqqplot.R' 'utilities_label.R' 'stat_cor.R' 'stat_stars.R' 'ggscatter.R' 'ggscatterhist.R' 'ggstripchart.R' 'ggsummarystats.R' 'ggtext.R' 'ggtexttable.R' 'ggviolin.R' 'gradient_color.R' 'grids.R' 'npc_to_data_coord.R' 'reexports.R' 'rotate.R' 'rotate_axis_text.R' 'rremove.R' 'set_palette.R' 'show_line_types.R' 'show_point_shapes.R' 'stat_anova_test.R' 'stat_central_tendency.R' 'stat_compare_means.R' 'stat_friedman_test.R' 'stat_kruskal_test.R' 'stat_mean.R' 'stat_overlay_normal_density.R' 'stat_pvalue_manual.R' 'stat_regline_equation.R' 'stat_welch_anova_test.R' 'text_grob.R' 'theme_pubr.R' 'theme_transparent.R' 'utils-geom-signif.R' 'utils-pipe.R' 'utils-tidyr.R' NeedsCompilation: no Packaged: 2023-02-06 02:39:47 UTC; kassambara Author: Alboukadel Kassambara [aut, cre] Maintainer: Alboukadel Kassambara Repository: CRAN Date/Publication: 2023-02-10 16:20:02 UTC ggpubr/tests/0000755000176200001440000000000014344455133012710 5ustar liggesusersggpubr/tests/testthat/0000755000176200001440000000000014371467262014556 5ustar liggesusersggpubr/tests/testthat/test-check_data.R0000644000176200001440000000610414336762430017721 0ustar liggesusers # Gene expression data df_expr <- tibble::tribble( ~sample, ~dataset, ~GATA3, ~PTEN, ~XBP1, "ID.1", "BRCA.mRNA", 2.23, 0.77, 2.91, "ID.2", "BRCA.mRNA", 1.24, 1.32, 1.68, "ID.3", "OV.mRNA", -3.44, 1.65, -1.29, "ID.4", "OV.mRNA", -5.21, 1.11, -0.62, "ID.5", "LUSC.mRNA", -4.01, -0.05, -0.57, "ID.6", "LUSC.mRNA", -3.29, 0.3, -0.02 ) test_that("check_data works when combine=TRUE and length(y) > 1", { observed <- .check_data(df_expr, x = "dataset", y = c("GATA3", "PTEN", "XBP1"), combine = TRUE) expected_y <- c(".value." = ".value.") expected_x <- c("dataset" = "dataset") expected_data <- tibble::tribble( ~sample, ~dataset, ~.y., ~.value., "ID.1", "BRCA.mRNA", "GATA3", 2.23, "ID.2", "BRCA.mRNA", "GATA3", 1.24, "ID.3", "OV.mRNA", "GATA3", -3.44, "ID.4", "OV.mRNA", "GATA3", -5.21, "ID.5", "LUSC.mRNA", "GATA3", -4.01, "ID.6", "LUSC.mRNA", "GATA3", -3.29, "ID.1", "BRCA.mRNA", "PTEN", 0.77, "ID.2", "BRCA.mRNA", "PTEN", 1.32, "ID.3", "OV.mRNA", "PTEN", 1.65, "ID.4", "OV.mRNA", "PTEN", 1.11, "ID.5", "LUSC.mRNA", "PTEN", -0.05, "ID.6", "LUSC.mRNA", "PTEN", 0.3, "ID.1", "BRCA.mRNA", "XBP1", 2.91, "ID.2", "BRCA.mRNA", "XBP1", 1.68, "ID.3", "OV.mRNA", "XBP1", -1.29, "ID.4", "OV.mRNA", "XBP1", -0.62, "ID.5", "LUSC.mRNA", "XBP1", -0.57, "ID.6", "LUSC.mRNA", "XBP1", -0.02 ) %>% dplyr::mutate( .y. = factor(.y., levels = c("GATA3", "PTEN", "XBP1")), dataset = factor(dataset, levels = c("BRCA.mRNA", "OV.mRNA", "LUSC.mRNA")) ) expect_equal(observed$y, expected_y) expect_equal(observed$x, expected_x) expect_equal(observed$data, expected_data) }) test_that("check_data works when combine=TRUE and length(x) > 1", { observed <- .check_data(df_expr, x = c("GATA3", "PTEN", "XBP1"), y = "..density..", combine = TRUE) expected_y <- c("..density.." = "..density..") expected_x <- c(".value." = ".value.") expected_data <- tibble::tribble( ~sample, ~dataset, ~.y., ~.value., "ID.1", "BRCA.mRNA", "GATA3", 2.23, "ID.2", "BRCA.mRNA", "GATA3", 1.24, "ID.3", "OV.mRNA", "GATA3", -3.44, "ID.4", "OV.mRNA", "GATA3", -5.21, "ID.5", "LUSC.mRNA", "GATA3", -4.01, "ID.6", "LUSC.mRNA", "GATA3", -3.29, "ID.1", "BRCA.mRNA", "PTEN", 0.77, "ID.2", "BRCA.mRNA", "PTEN", 1.32, "ID.3", "OV.mRNA", "PTEN", 1.65, "ID.4", "OV.mRNA", "PTEN", 1.11, "ID.5", "LUSC.mRNA", "PTEN", -0.05, "ID.6", "LUSC.mRNA", "PTEN", 0.3, "ID.1", "BRCA.mRNA", "XBP1", 2.91, "ID.2", "BRCA.mRNA", "XBP1", 1.68, "ID.3", "OV.mRNA", "XBP1", -1.29, "ID.4", "OV.mRNA", "XBP1", -0.62, "ID.5", "LUSC.mRNA", "XBP1", -0.57, "ID.6", "LUSC.mRNA", "XBP1", -0.02 ) %>% dplyr::mutate(.y. = factor(.y., levels = c("GATA3", "PTEN", "XBP1"))) expect_equal(observed$y, expected_y) expect_equal(observed$x, expected_x) expect_equal(observed$data, expected_data) }) ggpubr/tests/testthat/test-stat_kruskal_test.R0000644000176200001440000000342014332576562021424 0ustar liggesuserscontext("test-stat_kruskal_test") # Data preparation df <- ToothGrowth df$dose <- as.factor(df$dose) df$id <- rep(1:10, 6) # Add individuals id # Add a random grouping variable set.seed(123) df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20))) df <- df %>% mutate( len = ifelse(group == "grp2", len+2, len), len = ifelse(group == "grp3", len+7, len) ) # Basic plots ----------------------------------- test_that("stat_kruskal_test works for basic ggplots", { bxp <- ggboxplot(df, x = "dose", y = "len") + stat_kruskal_test() bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] expect_equal(as.numeric(stat.test$x), 1) expect_equal(as.numeric(stat.test$y), 36.4) expect_equal(stat.test$label, "Kruskal-Wallis, p < 0.0001") }) # Grouped plots----------------------------------- test_that("stat_kruskal_test works for grouped plots: grouped by x position", { bxp <- ggboxplot(df, x = "group", y = "len", color = "dose") + stat_kruskal_test(aes(group = dose), label = "{p.adj.format}") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] expect_equal(as.numeric(stat.test$x), c(1, 2, 3)) expect_equal(as.numeric(stat.test$y), c(36.4, 36.4, 36.4)) expect_equal(stat.test$label, c("0.0028", "0.0056", "0.0056")) }) test_that("stat_kruskal_test works for grouped plots: grouped by legend variable", { bxp <- ggboxplot(df, x = "group", y = "len", color = "dose") + stat_kruskal_test(aes(group = dose), label = "{p.format}", group.by = "legend.var") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] expect_equal(as.numeric(stat.test$x), c(0.73, 0.73, 0.73)) expect_equal(as.numeric(stat.test$y), c(36.4, 39.62, 42.84)) expect_equal(stat.test$label, c("0.012", "0.018", "0.038")) }) ggpubr/tests/testthat/test-ggadjust_pvalue.R0000644000176200001440000000274314343207174021041 0ustar liggesusers# Data preparation #::::::::::::::::::::::::::::::::::::::: df <- ToothGrowth df$dose <- as.factor(df$dose) # Add a random grouping variable df$group <- factor(rep(c("grp1", "grp2"), 30)) test_that("ggadjust_pvalue works for geom_pwc() pairwise comparison using facet", { p <- ggboxplot(df, x = "supp", y = "len", facet.by = "dose") + geom_pwc(method = "t_test") # Adjust all p-values together after stat_test <- ggadjust_pvalue( p, p.adjust.method = "bonferroni", label = "{p.adj.format}{p.adj.signif}", output = "stat_test" ) stat_test <- stat_test %>% dplyr::select(PANEL, x, y, group, group1, group2, label) %>% mutate(x = as.numeric(x), label = as.character(label)) expected <- tibble::tribble( ~PANEL, ~x, ~y, ~group, ~group1, ~group2, ~label, "1", 1, 34.494, 1, "1", "2", "0.0191*", "1", 1, 35.385, 1, "1", "2", "0.0191*", "1", 2, 35.385, 1, "1", "2", "0.0191*", "2", 1, 34.494, 1, "1", "2", "0.0031**", "2", 1, 35.385, 1, "1", "2", "0.0031**", "2", 2, 35.385, 1, "1", "2", "0.0031**", "3", 1, 34.494, 1, "1", "2", "1ns", "3", 1, 35.385, 1, "1", "2", "1ns", "3", 2, 35.385, 1, "1", "2", "1ns" ) %>% dplyr::mutate(PANEL = as.factor(PANEL)) %>% as.data.frame(stringAsFactor = FALSE) expect_equal(stat_test, expected) }) ggpubr/tests/testthat/test-stat_welch_anova_test.R0000644000176200001440000000344714332576562022247 0ustar liggesuserscontext("test-stat_welch_anova_test") # Data preparation df <- ToothGrowth df$dose <- as.factor(df$dose) df$id <- rep(1:10, 6) # Add individuals id # Add a random grouping variable set.seed(123) df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20))) df <- df %>% mutate( len = ifelse(group == "grp2", len+2, len), len = ifelse(group == "grp3", len+7, len) ) # Basic plots ----------------------------------- test_that("stat_welch_anova_test works for basic ggplots", { bxp <- ggboxplot(df, x = "dose", y = "len") + stat_welch_anova_test() bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] expect_equal(as.numeric(stat.test$x), 1) expect_equal(as.numeric(stat.test$y), 36.4) expect_equal(stat.test$label, "Welch Anova, p < 0.0001") }) # Grouped plots----------------------------------- test_that("stat_welch_anova_test works for grouped plots: grouped by x position", { bxp <- ggboxplot(df, x = "group", y = "len", color = "dose") + stat_welch_anova_test(aes(group = dose), label = "{p.adj.format}") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] expect_equal(as.numeric(stat.test$x), c(1, 2, 3)) expect_equal(as.numeric(stat.test$y), c(36.4, 36.4, 36.4)) expect_equal(stat.test$label, c("0.004", "0.004", "0.0024")) }) test_that("stat_welch_anova_test works for grouped plots: grouped by legend variable", { bxp <- ggboxplot(df, x = "group", y = "len", color = "dose") + stat_welch_anova_test(aes(group = dose), label = "{p.format}", group.by = "legend.var") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] expect_equal(as.numeric(stat.test$x), c(0.73, 0.73, 0.73)) expect_equal(as.numeric(stat.test$y), c(36.4, 39.62, 42.84)) expect_equal(stat.test$label, c("0.039", "0.023", "0.045")) }) ggpubr/tests/testthat/test-create_aes.R0000644000176200001440000000111414333662066017743 0ustar liggesuserscontext("test-create_aes") test_that("parse_expression works for a valid variable name", { observed <- parse_expression("a") expected <- parse(text = "a")[[1]] expect_equal(observed, expected) }) test_that("parse_expression works for a variable name containing space", { observed <- parse_expression("a b") expected <- as.name("a b") expect_equal(observed, expected) }) test_that("parse_expression works for a mathetical expression", { observed <- parse_expression("log2( a + b )") expected <- parse(text = "log2( a + b )")[[1]] expect_equal(observed, expected) }) ggpubr/tests/testthat/test-stat_compare_means.R0000644000176200001440000001326714335521212021515 0ustar liggesusers# Data preparation df <- ToothGrowth df$dose <- as.factor(df$dose) df$group <- factor(rep(c("grp1", "grp2"), 30)) # Comparing two groups .get_stat_test <- function(df, ...){ bxp <- ggboxplot(df, x = "supp", y = "len") + stat_compare_means(...) bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] stat.test } # Pairwise comparison test .get_pwc_test <- function(df, ...){ my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") ) bxp <- ggboxplot(df, x = "dose", y = "len")+ stat_compare_means(comparisons = my_comparisons, method = "t.test", ...) bxp_build <- ggplot2::ggplot_build(bxp) pwc_test <- bxp_build$data[[2]] pwc_test <- pwc_test %>% dplyr::mutate( x = as.numeric(x), xend = as.numeric(xend), annotation = as.character(annotation) ) pwc_test } # two independent tests ----------------------------------- test_that("stat_compare_means works for two independent tests: Wilcoxon test", { stat.test <- .get_stat_test(df) label_coords_expected <- data.frame( stringsAsFactors = FALSE, x = 1, y = c(33.9), label = c("Wilcoxon, p = 0.064") ) label_coords_observed <- stat.test[, c("x", "y", "label")] label_coords_observed$x <- as.numeric(label_coords_observed$x) expect_equal(label_coords_expected, label_coords_observed) }) test_that("stat_compare_means works for two independent tests when method changed to t.test", { stat.test <- .get_stat_test(df, method = "t.test") label_coords_expected <- data.frame( stringsAsFactors = FALSE, x = 1, y = c(33.9), label = c("T-test, p = 0.061") ) label_coords_observed <- stat.test[, c("x", "y", "label")] label_coords_observed$x <- as.numeric(label_coords_observed$x) expect_equal(label_coords_expected, label_coords_observed) }) test_that("stat_compare_means works when label specified as label='p.signif'", { stat.test <- .get_stat_test(df, label = "p.signif") label_coords_expected <- data.frame( stringsAsFactors = FALSE, x = 1, y = c(33.9), label = c("ns") ) label_coords_observed <- stat.test[, c("x", "y", "label")] label_coords_observed$x <- as.numeric(label_coords_observed$x) expect_equal(label_coords_expected, label_coords_observed) }) test_that("stat_compare_means works when label specified as aes(label=..p.signif..)", { stat.test <- .get_stat_test(df, aes(label = ..p.signif..)) label_coords_expected <- data.frame( stringsAsFactors = FALSE, x = 1, y = c(33.9), label = c("ns") ) label_coords_observed <- stat.test[, c("x", "y", "label")] label_coords_observed$x <- as.numeric(label_coords_observed$x) expect_equal(label_coords_expected, label_coords_observed) }) # Paired samples----------------------------------- test_that("stat_compare_means works for paired samples comparison", { stat.test <- .get_stat_test(df, paired = TRUE) label_coords_expected <- data.frame( stringsAsFactors = FALSE, x = 1, y = c(33.9), label = c("Wilcoxon, p = 0.0043") ) label_coords_observed <- stat.test[, c("x", "y", "label")] label_coords_observed$x <- as.numeric(label_coords_observed$x) expect_equal(label_coords_expected, label_coords_observed) }) # More than two groups------------------------------- test_that("stat_compare_means works for pairwise comparisons with multiple groups", { pwc_test <- .get_pwc_test(df) pwc_observed <- pwc_test[, c("x", "xend", "y", "yend", "annotation")] pwc_observed$annotation <- as.character(pwc_observed$annotation) pwc_expected <- data.frame( x = c(1, 1, 2, 1, 1, 3, 2, 2, 3), xend = c(1, 2, 2, 1, 3, 3, 2, 3, 3), y = c(34.494,35.385,35.385,41.622, 42.513,42.513,38.058,38.949,38.949), yend = c(35.385,35.385,34.494,42.513, 42.513,41.622,38.949,38.949,38.058), annotation = c("1.3e-07", "1.3e-07","1.3e-07","4.4e-14","4.4e-14","4.4e-14", "1.9e-05","1.9e-05","1.9e-05"), stringsAsFactors = FALSE ) expect_equal(pwc_expected, pwc_observed) }) # More than two groups------------------------------- test_that("stat_compare_means works for pairwise comparisons with multiple groups when specifying label as label='p.signif'", { pwc_test <- .get_pwc_test(df, label = "p.signif") pwc_observed <- pwc_test[, c("x", "xend", "y", "yend", "annotation")] pwc_observed$annotation <- as.character(pwc_observed$annotation) pwc_expected <- data.frame( x = c(1, 1, 2, 1, 1, 3, 2, 2, 3), xend = c(1, 2, 2, 1, 3, 3, 2, 3, 3), y = c(34.494,35.385,35.385,41.622, 42.513,42.513,38.058,38.949,38.949), yend = c(35.385,35.385,34.494,42.513, 42.513,41.622,38.949,38.949,38.058), annotation = c("****", "****","****","****","****","****", "****","****","****"), stringsAsFactors = FALSE ) expect_equal(pwc_expected, pwc_observed) }) test_that("stat_compare_means works for pairwise comparisons with multiple groups when specifying label as aes(label=after_stat(p.signif))", { pwc_test <- .get_pwc_test(df, aes(label = after_stat(p.signif))) pwc_observed <- pwc_test[, c("x", "xend", "y", "yend", "annotation")] pwc_observed$annotation <- as.character(pwc_observed$annotation) pwc_expected <- data.frame( x = c(1, 1, 2, 1, 1, 3, 2, 2, 3), xend = c(1, 2, 2, 1, 3, 3, 2, 3, 3), y = c(34.494,35.385,35.385,41.622, 42.513,42.513,38.058,38.949,38.949), yend = c(35.385,35.385,34.494,42.513, 42.513,41.622,38.949,38.949,38.058), annotation = c("****", "****","****","****","****","****", "****","****","****"), stringsAsFactors = FALSE ) expect_equal(pwc_expected, pwc_observed) }) ggpubr/tests/testthat/test-stat_anova_test.R0000644000176200001440000002531214332576562021060 0ustar liggesuserscontext("test-stat_anova_test") # Data preparation df <- ToothGrowth df$dose <- as.factor(df$dose) df$id <- rep(1:10, 6) # Add individuals id # Add a random grouping variable set.seed(123) df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20))) df <- df %>% mutate( len = ifelse(group == "grp2", len+2, len), len = ifelse(group == "grp3", len+7, len) ) # Basic plots ----------------------------------- test_that("stat_anova_test works for basic ggplots", { bxp <- ggboxplot(df, x = "dose", y = "len") + stat_anova_test() bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] expect_equal(as.numeric(stat.test$x), 1) expect_equal(as.numeric(stat.test$y), 36.4) expect_equal(stat.test$label, "Anova, p < 0.0001") }) # Grouped plots----------------------------------- test_that("stat_anova_test works for grouped plots: grouped by x position", { bxp <- ggboxplot(df, x = "group", y = "len", color = "dose") + stat_anova_test(aes(group = dose), label = "{p.adj.format}") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] expect_equal(as.numeric(stat.test$x), c(1, 2, 3)) expect_equal(as.numeric(stat.test$y), c(36.4, 36.4, 36.4)) expect_equal(stat.test$label, c("<0.0001", "0.00014", "0.00014")) }) test_that("stat_anova_test works for grouped plots: grouped by legend variable", { bxp <- ggboxplot(df, x = "group", y = "len", color = "dose") + stat_anova_test(aes(group = dose), label = "{p.format}", group.by = "legend.var") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] expect_equal(as.numeric(stat.test$x), c(0.73, 0.73, 0.73)) expect_equal(as.numeric(stat.test$y), c(36.4, 39.62, 42.84)) expect_equal(stat.test$label, c("0.009", "0.012", "0.019")) }) # One-way repeated measure ANOVA ---------------------- test_that("stat_anova_test works for one-way repeated measure anova", { df$id <- as.factor(c(rep(1:10, 3), rep(11:20, 3))) bxp <- ggboxplot(df, x = "dose", y = "len") + stat_anova_test(wid = "id", method = "one_way_repeated") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] expect_equal(as.numeric(stat.test$x), 1) expect_equal(as.numeric(stat.test$y), 36.4) expect_equal(stat.test$label, "Anova, p < 0.0001") }) test_that("stat_anova_test works for grouped one-way repeated measure anova: group by x var", { df$id <- as.factor(rep(1:10, 6)) bxp <- ggboxplot(df, x = "dose", y = "len", color = "supp", palette = "jco") + stat_anova_test(aes(group = supp, color = supp), label = "p", method = "one_way_repeated", wid = "id", group.by = "x.var") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] expect_equal(as.numeric(stat.test$x), c(1, 2, 3)) expect_equal(as.numeric(stat.test$y), c(36.4, 36.4, 36.4)) expect_equal(stat.test$label, c("0.016", "0.05", "0.848")) }) test_that("stat_anova_test works for grouped one-way repeated measure anova: group by legend var", { df$id <- as.factor(rep(1:10, 6)) bxp <- ggboxplot(df, x = "dose", y = "len", color = "supp", palette = "jco") + stat_anova_test(aes(group = supp, color = supp), method = "one_way_repeated", wid = "id", group.by = "legend.var") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] expect_equal(as.numeric(stat.test$x), c(0.8, 0.8)) expect_equal(as.numeric(stat.test$y), c(36.40, 39.62)) expect_equal(stat.test$label, c("Anova, p = 0.004", "Anova, p < 0.0001")) }) test_that("stat_anova_test works for two-way repeated measure anova", { df$id <- as.factor(rep(1:10, 6)) bxp <- ggboxplot(df, x = "dose", y = "len", color = "supp", palette = "jco") + stat_anova_test(aes(group = supp), method = "two_way_repeated", wid = "id") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] expect_equal(as.numeric(stat.test$x), 0.8) expect_equal(as.numeric(stat.test$y), 36.4) expect_equal(stat.test$label, "Anova, p = 0.12") }) # Two-way mixed anova ----------------------------- test_that("stat_anova_test works for two-way mixed anova", { anxiety <- data.frame( score = c(14.1,14.5,15.7,16,16.5,16.9,17,17, 17.3,17.3,17.8,17.9,19.1,19.4,19.8,13.7,14.7,14.9,15.1, 15.8,16.4,16.6,16.9,16.9,17.2,17.8,17.8,18.2,18.4, 19.3,14.6,15,15.5,15.7,16.4,16.9,17.1,17.3,17.5,17.6, 17.8,17.9,18.4,18.5,19,14.4,14.6,15.2,15.5,15.8,16.5, 16.8,17.1,16.9,17.1,17.7,17.7,19.4,19.2,20,13.4,14.8, 14.4,14.8,14.8,15.7,16.9,16.9,16.9,17.3,18.2,17.7, 17.8,18.5,18.9,13,13,12.7,14.1,14.4,14.5,15.6,15.4, 15.6,14.8,16.1,15.7,16.7,16.4,17.2,14.1,14.3,14.9,15.3, 15.7,16.2,16.5,16.6,16.5,16.7,17.3,17.5,19.3,17.3, 19.4,12.7,13.1,13.6,13.6,14.2,14.9,16.1,16.1,16.3,15.9, 17.4,16.9,17.1,17.3,17.7,11.7,11.9,11,12.1,12.3,13.6, 14.3,14.2,14.4,13.8,14.3,13.8,15.4,15.1,15.5), id = as.factor(c("1","2","3","4","5", "6","7","8","9","10","11","12","13","14", "15","16","17","18","19","20","21","22","23", "24","25","26","27","28","29","30","31","32", "33","34","35","36","37","38","39","40","41", "42","43","44","45","1","2","3","4","5", "6","7","8","9","10","11","12","13","14","15", "16","17","18","19","20","21","22","23","24", "25","26","27","28","29","30","31","32", "33","34","35","36","37","38","39","40","41", "42","43","44","45","1","2","3","4","5","6", "7","8","9","10","11","12","13","14","15", "16","17","18","19","20","21","22","23","24", "25","26","27","28","29","30","31","32","33", "34","35","36","37","38","39","40","41","42", "43","44","45")), group = as.factor(c("grp1","grp1","grp1", "grp1","grp1","grp1","grp1","grp1","grp1", "grp1","grp1","grp1","grp1","grp1","grp1","grp2", "grp2","grp2","grp2","grp2","grp2","grp2","grp2", "grp2","grp2","grp2","grp2","grp2","grp2", "grp2","grp3","grp3","grp3","grp3","grp3","grp3", "grp3","grp3","grp3","grp3","grp3","grp3", "grp3","grp3","grp3","grp1","grp1","grp1","grp1", "grp1","grp1","grp1","grp1","grp1","grp1","grp1", "grp1","grp1","grp1","grp1","grp2","grp2", "grp2","grp2","grp2","grp2","grp2","grp2","grp2", "grp2","grp2","grp2","grp2","grp2","grp2","grp3", "grp3","grp3","grp3","grp3","grp3","grp3", "grp3","grp3","grp3","grp3","grp3","grp3","grp3", "grp3","grp1","grp1","grp1","grp1","grp1", "grp1","grp1","grp1","grp1","grp1","grp1","grp1", "grp1","grp1","grp1","grp2","grp2","grp2","grp2", "grp2","grp2","grp2","grp2","grp2","grp2", "grp2","grp2","grp2","grp2","grp2","grp3","grp3", "grp3","grp3","grp3","grp3","grp3","grp3","grp3", "grp3","grp3","grp3","grp3","grp3","grp3")), time = as.factor(c("t1","t1","t1","t1", "t1","t1","t1","t1","t1","t1","t1","t1","t1", "t1","t1","t1","t1","t1","t1","t1","t1", "t1","t1","t1","t1","t1","t1","t1","t1","t1", "t1","t1","t1","t1","t1","t1","t1","t1","t1", "t1","t1","t1","t1","t1","t1","t2","t2","t2", "t2","t2","t2","t2","t2","t2","t2","t2","t2", "t2","t2","t2","t2","t2","t2","t2","t2","t2", "t2","t2","t2","t2","t2","t2","t2","t2", "t2","t2","t2","t2","t2","t2","t2","t2","t2", "t2","t2","t2","t2","t2","t2","t2","t3","t3", "t3","t3","t3","t3","t3","t3","t3","t3","t3", "t3","t3","t3","t3","t3","t3","t3","t3","t3", "t3","t3","t3","t3","t3","t3","t3","t3","t3", "t3","t3","t3","t3","t3","t3","t3","t3", "t3","t3","t3","t3","t3","t3","t3","t3")) ) # two way bxp <- ggboxplot(anxiety, x = "group", y = "score", color = "time") + stat_anova_test(aes(group = time), method = "two_way_mixed", wid = "id") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] res_mixed_anova <- data.frame( x = as.numeric(stat.test$x), y = as.numeric(stat.test$y), label = as.character(stat.test$label), stringsAsFactors = FALSE ) res_mixed_anova_expected <- data.frame(x = 0.73, y = 20, label = "Anova, p < 0.0001", stringsAsFactors = FALSE) # Effect of group: Group by time and compute anova between x groups bxp <- ggboxplot(anxiety, x = "group", y = "score", color = "time") + stat_anova_test(aes(group = time, color = time), method = "one_way", group.by = "legend.var") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] res_group_effect <- data.frame( x = as.numeric(stat.test$x), y = as.numeric(stat.test$y), label = as.character(stat.test$label), stringsAsFactors = FALSE ) res_group_effect_expected <- data.frame(x = c(0.73, 0.73, 0.73), y = c(20.0, 20.9, 21.8), label = c("Anova, p = 0.696", "Anova, p = 0.006", "Anova, p < 0.0001"), stringsAsFactors = FALSE) # Effect of time: group by x groups and perform the test within group (here time) bxp <- ggboxplot(anxiety, x = "group", y = "score", color = "time") + stat_anova_test(aes(group = time), method = "one_way_repeated", wid = "id", group.by = "x.var") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] res_time_effect <- data.frame( x = as.numeric(stat.test$x), y = as.numeric(stat.test$y), label = as.character(stat.test$label), stringsAsFactors = FALSE ) res_time_effect_expected <- data.frame(x = 1:3, y = c(20, 20, 20), label = c("Anova, p < 0.0001", "Anova, p < 0.0001", "Anova, p < 0.0001"), stringsAsFactors = FALSE) expect_equal(res_mixed_anova, res_mixed_anova_expected ) expect_equal(res_group_effect, res_group_effect_expected ) expect_equal(res_time_effect, res_time_effect_expected ) }) ggpubr/tests/testthat/test-add_stat_label.R0000644000176200001440000001562414332576562020611 0ustar liggesuserscontext("test-add_stat_label") stat.test <- data.frame( stringsAsFactors = FALSE, .y. = c("y", "y", "y"), group1 = c("1", "1", "2"), group2 = c("2", "3", "3"), n1 = c(20L, 20L, 20L), n2 = c(20L, 20L, 20L), statistic = c(33.5, 1.5, 61), p = c(7.02e-06, 8.41e-08, 0.000177), p.adj = c(1.4e-05, 2.52e-07, 0.000177), p.adj.signif = c("****", "****", "***"), method = c("Wilcoxon test", "Wilcoxon test", "Wilcoxon test"), xmin = c(1, 1, 2), xmax = c(2, 3, 3), p.signif = c("****", "****", "***"), p.format = c("<0.0001", "<0.0001", "0.00018"), p.adj.format = c("<0.0001", "<0.0001", "0.00018"), n = c(40L, 40L, 40L) ) test_that("add_stat_label works with basic label", { formatted_p <- add_stat_label(stat.test, label = "p.format") adjusted_p <- add_stat_label(stat.test, label = "p.adj") expect_equal(formatted_p$label, c("<0.0001", "<0.0001", "0.00018")) expect_equal(adjusted_p$label, c("1.4e-05", "2.52e-07","0.000177")) }) test_that("add_stat_label works with glue expression", { glueed <- add_stat_label(stat.test, label = "p = {p.format} {p.signif}") expect_equal(glueed$label, c("p < 0.0001 ****", "p < 0.0001 ****", "p = 0.00018 ***")) }) test_that("add_stat_label works with plotmath: Usage of simple equal '='", { simple_equals <- add_stat_label(stat.test, label = "italic(p)={p.format}{p.signif}") simple_equals_space <- add_stat_label(stat.test, label = "italic(p) = {p.format}{p.signif}") simple_equals_tilde <- add_stat_label(stat.test, label = "italic(p)~=~{p.format}{p.signif}") expect_equal( simple_equals$label, c("list(italic(p)<'0.0001'*`****`)", "list(italic(p)<'0.0001'*`****`)", "list(italic(p)=='0.00018'*`***`)") ) expect_equal( simple_equals_space$label, c("list(italic(p)~`<`~'0.0001'*`****`)", "list(italic(p)~`<`~'0.0001'*`****`)", "list(italic(p)~`=`~'0.00018'*`***`)") ) expect_equal( simple_equals_tilde$label, c("list(italic(p)~`<`~'0.0001'*`****`)", "list(italic(p)~`<`~'0.0001'*`****`)", "list(italic(p)~`=`~'0.00018'*`***`)") ) }) test_that("add_stat_label works with plotmath: Escaping stars in p.signif/p.adj.signif", { # datapasta::dpasta(as.character(res$label)) stars <- add_stat_label(stat.test, label = "italic(p)={p.format}{p.signif}") stars_space <- add_stat_label(stat.test, label = "italic(p)={p.format} {p.signif}") stars_tilde <- add_stat_label(stat.test, label = "italic(p)={p.format}~{p.signif}") stars_equals <- add_stat_label(stat.test, label = "italic(p)={p.signif}") stars_equals_space <- add_stat_label(stat.test, label = "italic(p) = {p.signif}") expect_equal( stars$label, c("list(italic(p)<'0.0001'*`****`)", "list(italic(p)<'0.0001'*`****`)", "list(italic(p)=='0.00018'*`***`)") ) expect_equal( stars_space$label, c("list(italic(p)<'0.0001'~`****`)", "list(italic(p)<'0.0001'~`****`)", "list(italic(p)=='0.00018'~`***`)") ) expect_equal( stars_tilde$label, c("list(italic(p)<'0.0001'~`****`)", "list(italic(p)<'0.0001'~`****`)", "list(italic(p)=='0.00018'~`***`)") ) expect_equal( stars_equals$label, c("list(italic(p)==`****`)", "list(italic(p)==`****`)", "list(italic(p)==`***`)") ) expect_equal( stars_equals_space$label, c("list(italic(p)~`=`~`****`)", "list(italic(p)~`=`~`****`)", "list(italic(p)~`=`~`***`)") ) }) test_that("add_stat_label works with ANOVA stats label formats", { res.aov <- data.frame( stringsAsFactors = FALSE, check.names = FALSE, Effect = c("x"), DFn = c(2), DFd = c(57), F = c(29.543), p = c(1.57e-09), `p<.05` = c("*"), ges = c(0.509), p.adj = c(1.57e-09), p.signif = c("****"), p.adj.signif = c("****"), p.format = c("<0.0001"), p.adj.format = c("<0.0001"), n = c(60L), method = c("Anova") ) res.italic <- add_stat_label(res.aov, label = get_anova_test_label_template("as_italic")) res.detailed <- add_stat_label(res.aov, label = get_anova_test_label_template("as_detailed")) res.detailed.italic <- add_stat_label(res.aov, label = get_anova_test_label_template("as_detailed_italic")) res.detailed.expression <- add_stat_label(res.aov, label = get_anova_test_label_template("as_detailed_expression")) res.plotmath <- add_stat_label(res.aov, label = "bold(Anova), italic(F)({DFn}, {DFd}) = {F}, eta2[g] = {ges}, italic(p) = {p.format}{p.signif}, italic(n) = {n}") res.rounding <- add_stat_label(res.aov, label = "Anova, italic(F)({DFn}, {DFd}) = {round(F, 1)}, eta2[g] = {round(ges, 1)}, italic(p) = {p.format}{p.signif}, italic(n) = {n}") # Custom p format res.customp <- add_stat_label(res.aov, label = "Anova, italic(p) = {rstatix::p_format(p, accuracy = 0.0001, digits = 3, leading.zero = FALSE)}{p.signif}") expect_equal(res.italic$label, "list(Anova,~italic(p)~`<`~'0.0001')") expect_equal(res.detailed$label, "Anova, F(2, 57) = 29.543, eta2[g] = 0.509, p < 0.0001, n = 60") expect_equal(res.detailed.italic$label, "list(Anova,~italic(F)('2',~'57')~`=`~'29.543',~eta[g]^'2'~`=`~'0.509',~italic(p)~`<`~'0.0001',~italic(n)~`=`~'60')") expect_equal(res.detailed.expression$label,"list(Anova,~italic(F)('2',~'57')~`=`~'29.543',~eta[g]^'2'~`=`~'0.509',~italic(p)~`<`~'0.0001',~italic(n)~`=`~'60')") expect_equal(res.plotmath$label, "list(bold(Anova),~italic(F)('2',~'57')~`=`~'29.543',~eta[g]^'2'~`=`~'0.509',~italic(p)~`<`~'0.0001'*`****`,~italic(n)~`=`~'60')") expect_equal(res.rounding$label, "list(Anova,~italic(F)('2',~'57')~`=`~'29.5',~eta[g]^'2'~`=`~'0.5',~italic(p)~`<`~'0.0001'*`****`,~italic(n)~`=`~'60')") expect_equal(res.customp$label, "list(Anova,~italic(p)~`<`~'.0001'*`****`)") }) test_that("add_stat_label works with Kruskal-Wallis stats label formats", { res <- data.frame( stringsAsFactors = FALSE, x = c("1"), y = c(36.4), n = c(60L), statistic = c(31.21), df = c(2L), p = c(1.67e-07), method = c("Kruskal-Wallis"), p.adj = c(1.67e-07), p.signif = c("****"), p.adj.signif = c("****"), p.format = c("<0.0001"), p.adj.format = c("<0.0001") ) res.italic <- add_stat_label(res, label = get_kruskal_test_label_template("as_italic")) res.detailed <- add_stat_label(res, label = get_kruskal_test_label_template("as_detailed")) res.detailed.italic <- add_stat_label(res, label = get_kruskal_test_label_template("as_detailed_italic")) res.detailed.expression <- add_stat_label(res, label = get_kruskal_test_label_template("as_detailed_expression")) expect_equal(res.italic$label, "list('Kruskal-Wallis',~italic(p)~`<`~'0.0001')") expect_equal(res.detailed$label, "Kruskal-Wallis, X2(2) = 31.21, p < 0.0001, n = 60") expect_equal(res.detailed.italic$label, "list('Kruskal-Wallis',~italic(chi)^'2'~('2')~`=`~'31.21',~italic(p)~`<`~'0.0001',~italic(n)~`=`~'60')") expect_equal(res.detailed.expression$label,"list('Kruskal-Wallis',~italic(chi)^'2'('2')~`=`~'31.21',~italic(p)~`<`~'0.0001',~italic(n)~`=`~'60')") }) ggpubr/tests/testthat/test-stat_friedman_test.R0000644000176200001440000000422314333051470021522 0ustar liggesuserscontext("test-stat_friedman_test") # Data preparation df <- ToothGrowth df$dose <- as.factor(df$dose) df$id <- rep(1:10, 6) # Add individuals id # Add a random grouping variable set.seed(123) df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20))) df <- df %>% mutate( len = ifelse(group == "grp2", len+2, len), len = ifelse(group == "grp3", len+7, len) ) # Basic plots---------------------- test_that("stat_friedman_test works for basic plots", { df$id <- as.factor(c(rep(1:10, 3), rep(11:20, 3))) bxp <- ggboxplot(df, x = "dose", y = "len") + stat_friedman_test(wid = "id") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] expect_equal(as.numeric(stat.test$x), 1) expect_equal(as.numeric(stat.test$y), 36.4) expect_equal(stat.test$label, "Friedman test, p < 0.0001") }) test_that("stat_friedman_test works for grouped plot: group by x var", { df$id <- as.factor(rep(1:10, 6)) bxp <- ggboxplot(df, x = "dose", y = "len", color = "supp", palette = "jco") + stat_friedman_test(aes(group = supp, color = supp), label = "p.format", wid = "id", group.by = "x.var") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] # Replace possible "1." (with trailing dot) by "1" # this trailing dot appear only on linux stat.test$label <- gsub(pattern = "^1\\.$", replacement = "1", stat.test$label) expect_equal(as.numeric(stat.test$x), c(1, 2, 3)) expect_equal(as.numeric(stat.test$y), c(36.4, 36.4, 36.4)) expect_equal(stat.test$label, c("0.011", "0.058", "1")) }) test_that("stat_friedman_test works for grouped one-way repeated measure anova: group by legend var", { df$id <- as.factor(rep(1:10, 6)) bxp <- ggboxplot(df, x = "dose", y = "len", color = "supp", palette = "jco") + stat_friedman_test(aes(group = supp, color = supp), wid = "id", group.by = "legend.var") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] expect_equal(as.numeric(stat.test$x), c(0.8, 0.8)) expect_equal(as.numeric(stat.test$y), c(36.40, 39.62)) expect_equal(stat.test$label, c("Friedman test, p = 0.02472", "Friedman test, p = 0.00012")) }) ggpubr/tests/testthat/test-compare_means.R0000644000176200001440000001313514337521425020464 0ustar liggesusers data("gene_expression", package = "ggpubr") data("ToothGrowth") test_that("compare_means works for One-sample test", { results <- compare_means(len ~ 1, ToothGrowth, mu = 0) results <- results %>% dplyr::select(.y., group1, group2, p.format, p.signif, method) expected <- tibble::tribble( ~.y., ~group1, ~group2, ~p.format, ~p.signif, ~method, "len", 1, "null model", "1.7e-11", "****", "Wilcoxon" ) expect_equal(results, expected) }) test_that("compare_means works for Two-samples unpaired test", { results <- compare_means(len ~ supp, ToothGrowth) results <- results %>% dplyr::select(.y., group1, group2, p.format, p.signif, method) expected <- tibble::tribble( ~.y., ~group1, ~group2, ~p.format, ~p.signif, ~method, "len", "OJ", "VC", "0.064", "ns", "Wilcoxon" ) expect_equal(results, expected) }) test_that("compare_means works for Two-samples paired test", { results <- compare_means(len ~ supp, ToothGrowth, paired = TRUE) results <- results %>% dplyr::select(.y., group1, group2, p.format, p.signif, method) expected <- tibble::tribble( ~.y., ~group1, ~group2, ~p.format, ~p.signif, ~method, "len", "OJ", "VC", "0.0043", "**", "Wilcoxon" ) expect_equal(results, expected) }) test_that("compare_means works for pairwise comparisons", { results <- compare_means(len ~ dose, ToothGrowth) results <- results %>% dplyr::select(.y., group1, group2, p.format, p.signif, method) expected <- tibble::tribble( ~.y., ~group1, ~group2, ~p.format, ~p.signif, ~method, "len", "0.5", "1", "7.0e-06", "****", "Wilcoxon", "len", "0.5", "2", "8.4e-08", "****", "Wilcoxon", "len", "1", "2", "0.00018", "***", "Wilcoxon" ) expect_equal(results, expected) }) test_that("compare_means works for comparison against reference groups", { results <- compare_means(len ~ dose, ToothGrowth, ref.group = "0.5") results <- results %>% dplyr::select(.y., group1, group2, p.format, p.signif, method) expected <- tibble::tribble( ~.y., ~group1, ~group2, ~p.format, ~p.signif, ~method, "len", "0.5", "1", "7.0e-06", "****", "Wilcoxon", "len", "0.5", "2", "8.4e-08", "****", "Wilcoxon" ) expect_equal(results, expected) }) test_that("compare_means works when using formula with multiple variables", { results <- compare_means(c(GATA3, XBP1, PTEN) ~ dataset, data = gene_expression) results <- results %>% dplyr::select(.y., group1, group2, p.format, p.signif, method) expected <- tibble::tribble( ~.y., ~group1, ~group2, ~p.format, ~p.signif, ~method, "GATA3", "BRCA", "OV", "< 2e-16", "****", "Wilcoxon", "GATA3", "BRCA", "LUSC", "< 2e-16", "****", "Wilcoxon", "GATA3", "OV", "LUSC", "3.0e-08", "****", "Wilcoxon", "XBP1", "BRCA", "OV", "< 2e-16", "****", "Wilcoxon", "XBP1", "BRCA", "LUSC", "< 2e-16", "****", "Wilcoxon", "XBP1", "OV", "LUSC", "4.2e-11", "****", "Wilcoxon", "PTEN", "BRCA", "OV", "6.8e-05", "****", "Wilcoxon", "PTEN", "BRCA", "LUSC", "< 2e-16", "****", "Wilcoxon", "PTEN", "OV", "LUSC", "1.3e-07", "****", "Wilcoxon" ) %>% dplyr::mutate(.y. = factor(.y., levels = c("GATA3", "XBP1", "PTEN"))) expect_equal(results, expected) }) test_that("compare_means works when using ref.group = '.all.'", { results <- compare_means(len ~ dose, ToothGrowth, ref.group = ".all.") results <- results %>% dplyr::select(.y., group1, group2, p.format, p.signif, method) expected <- tibble::tribble( ~.y., ~group1, ~group2, ~p.format, ~p.signif, ~method, "len", ".all.", "0.5", "5.1e-05", "****", "Wilcoxon", "len", ".all.", "1", "0.76404", "ns", "Wilcoxon", "len", ".all.", "2", "0.00018", "***", "Wilcoxon" ) expect_equal(results, expected) }) test_that("compare_means works for Anova", { results <- compare_means(len ~ dose, ToothGrowth, method = "anova") results <- results %>% dplyr::select(.y., p.format, p.signif, method) expected <- tibble::tribble( ~.y., ~p.format, ~p.signif, ~method, "len", "9.5e-16", "****", "Anova" ) expect_equal(results, expected) }) test_that("compare_means works for kruskal.test", { results <- compare_means(len ~ dose, ToothGrowth, method = "kruskal.test") results <- results %>% dplyr::select(.y., p.format, p.signif, method) expected <- tibble::tribble( ~.y., ~p.format, ~p.signif, ~method, "len", "1.5e-09", "****", "Kruskal-Wallis" ) expect_equal(results, expected) }) test_that("compare_means works when grouping variable levels contain group2", { tibble::tribble( ~val, ~gp, ~by, 923 , 'group4','V1', 1252, 'group4','V1', 1442, 'group4','V1', 1398, 'group2','V1', 1858, 'group2','V1', 1330, 'group2','V1', 2593, 'group2','V1', 23 , 'group4','V2', 252, 'group4','V2', 442, 'group4','V2', 398, 'group2','V2', 858, 'group2','V2', 330, 'group2','V2', 593, 'group2','V2' ) %>% dplyr::mutate(gp=factor(gp, levels = c('group2','group4'))) -> dat results <- compare_means(val ~ gp, data = dat, group.by = 'by', paired = F) results <- results %>% dplyr::select(.y., group1, group2, p.format, p.signif, method) expected <- tibble::tribble( ~.y., ~group1, ~group2, ~p.format, ~p.signif, ~method, "val", "group2", "group4", "0.23", "ns", "Wilcoxon", "val", "group2", "group4", "0.23", "ns", "Wilcoxon" ) expect_equal(results, expected) }) ggpubr/tests/testthat/test-get_coord.R0000644000176200001440000000235514332576562017631 0ustar liggesuserscontext("test-get_coord") # Basic plots: one group ----------------------------------- test_that("No transformation is performed when coord specified", { res_10 <- get_coord(coord = 10) res_text <- get_coord(coord = "text") expect_equal(res_10, 10) expect_equal(res_text, "text") }) test_that("get_coord works for one group", { # x- axis res_left <- get_coord( data.ranges = c(1, 3), group = 1, npc = "left", step = 0.1, margin.npc = 0 ) # Y -axis res_top<- get_coord( data.ranges = c(4.2, 36.4), group = 1, npc = "top", step = -0.1, margin.npc = 0 ) expect_equal(res_top, 36.4) }) # Grouped plots -------------------------- test_that("get_coord works for grouped plots", { # x- axis res_left <- get_coord( data.ranges = c(1, 3), group = 1:3, npc = "left", step = 0.1, margin.npc = 0 ) # no transformation when coord specified res_coord <- get_coord( data.ranges = c(1, 3), group = 1:3, coord = 1:3, step = 0.1, margin.npc = 0 ) # Y-axis res_top <- get_coord( data.ranges = c(4.2, 36.4), group = c(1, 2, 3), npc = "top", step = -0.1, margin.npc = 0 ) expect_equal(res_left, c(1.0, 1.2, 1.4)) expect_equal(res_coord, 1:3) expect_equal(res_top, c(36.40, 39.62, 42.84)) }) ggpubr/tests/testthat/test-utils-tidyr.R0000644000176200001440000000114114336762430020140 0ustar liggesusers # Wide data get_wide_data <- function(){ tibble::tibble( before = c(200.1, 190.9, 192.7), after = c(392.9, 393.2, 345.1) ) } test_that("df_gather is sorting rows as done by gather_()", { df <- get_wide_data() expected_output <- tibble::tribble( ~condition, ~val, "before", 200.1, "before", 190.9, "before", 192.7, "after", 392.9, "after", 393.2, "after", 345.1 ) observed_output <- df_gather( df, cols = c("before", "after"), names_to = "condition", values_to = "val" ) expect_equal(observed_output, expected_output) }) ggpubr/tests/testthat/test-as_npc.R0000644000176200001440000000054614332576562017127 0ustar liggesuserscontext("test-as_npc") test_that("as_npc returns the same value as the input when the input is.numeric", { expect_equal(as_npc(0.5), 0.5) }) test_that("as_npc works correctly for character coordinates", { res <- as_npc(c('right', 'left', 'bottom', 'top', 'center', 'centre', 'middle')) expect_equal(res, c(0.95, 0.05, 0.05, 0.95, 0.5, 0.5, 0.5)) }) ggpubr/tests/testthat/test-ggstripchart.R0000644000176200001440000000223614340445172020352 0ustar liggesusers test_that("ggstripchart is reproducible", { p <- ggstripchart(PlantGrowth, x = "group", y = "weight") jitter_data <- layer_data(p, 1) %>% dplyr::select(x, y) %>% dplyr::mutate(x = as.numeric(round(x, 7)), y = as.numeric(round(y, 7))) expected_data <- data.frame( x = c(0.915031,1.1153221,0.9635908, 1.153207,1.1761869,0.8182226,1.0112422,1.1569676,1.020574, 0.9826459,2.1827333,1.9813337,2.0710283,2.0290534, 1.8411699,2.15993,1.8984351,1.8168238,1.9311683,2.1818015, 3.1558157,3.0771214,3.0562027,3.1977079,3.0622823, 3.0834122,3.0176264,3.0376568,2.9156639,2.8588455), y = c(4.1737042,5.5832184,5.1815256, 6.1123637,4.4961969,4.6098224,5.1720677,4.5277313,5.3285454, 5.137853,4.8071424,4.1693164,4.4093098,3.5889508, 5.8672196,3.8271104,6.0278643,4.8897277,4.3181278,4.6928626, 6.3063666,5.1195376,5.5423914,5.4969752,5.3704876, 5.2876523,4.9170203,6.1520265,5.8031604,5.2589957) ) expect_equal(jitter_data, expected_data) }) ggpubr/tests/testthat/test-ggscatter.R0000644000176200001440000000144614334115242017631 0ustar liggesuserscontext("ggscatter") test_that("ggscatter works when there is spaces in variable names", { data("mtcars") df <- mtcars[, c("wt", "mpg")] colnames(df) <- c("Dimension 1", "Dimension 2") p <- ggscatter(df, x = "Dimension 1", y = "Dimension 2" ) p_build <- ggplot2::ggplot_build(p) df_build <- p_build$data[[1]] expect_equal(df$`Dimension 1`, df_build$x) }) test_that("ggscatter can handle non-standard column names when ggpubr.parse_aes global option is set to FALSE (#229)", { data("mtcars") df <- mtcars[, c("wt", "mpg")] colnames(df) <- c("A-A", "B-B") old_options <- options(ggpubr.parse_aes = FALSE) p <- ggscatter(df, x = "A-A", y = "B-B" ) options(old_options) p_build <- ggplot2::ggplot_build(p) df_build <- p_build$data[[1]] expect_equal(df$`A-A`, df_build$x) }) ggpubr/tests/testthat/test-ggboxplot.R0000644000176200001440000000233614340445172017657 0ustar liggesusers test_that("The position of jittered points is reproducible in ggboxplot", { p <- ggboxplot(PlantGrowth, x = "group", y = "weight", add = "jitter") jitter_data <- layer_data(p, 2) %>% dplyr::select(x, y) %>% dplyr::mutate(x = as.numeric(round(x, 7)), y = as.numeric(round(y, 7))) expected_data <-tibble::tribble( ~x, ~y, 0.915031, 4.1737042, 1.1153221, 5.5832184, 0.9635908, 5.1815256, 1.153207, 6.1123637, 1.1761869, 4.4961969, 0.8182226, 4.6098224, 1.0112422, 5.1720677, 1.1569676, 4.5277313, 1.020574, 5.3285454, 0.9826459, 5.137853, 2.1827333, 4.8071424, 1.9813337, 4.1693164, 2.0710283, 4.4093098, 2.0290534, 3.5889508, 1.8411699, 5.8672196, 2.15993, 3.8271104, 1.8984351, 6.0278643, 1.8168238, 4.8897277, 1.9311683, 4.3181278, 2.1818015, 4.6928626, 3.1558157, 6.3063666, 3.0771214, 5.1195376, 3.0562027, 5.5423914, 3.1977079, 5.4969752, 3.0622823, 5.3704876, 3.0834122, 5.2876523, 3.0176264, 4.9170203, 3.0376568, 6.1520265, 2.9156639, 5.8031604, 2.8588455, 5.2589957 ) %>% as.data.frame() expect_equal(jitter_data, expected_data) }) ggpubr/tests/testthat/test-facet.R0000644000176200001440000000110614335517603016731 0ustar liggesusers # Data preparation data("ToothGrowth") df <- ToothGrowth # Adding column name with space df[['spa ced']] <- df[['supp']] test_that("facet works", { p <- ggpubr::ggboxplot(df, "dose", "len", facet.by = "supp") panels <- ggplot2::layer_data(p)[["PANEL"]] %>% unique() %>% as.numeric() expect_equal(panels, c(1, 2)) }) test_that("facet works when column names contain space", { p <- ggpubr::ggboxplot(df, "dose", "len", facet.by = "spa ced") panels <- ggplot2::layer_data(p)[["PANEL"]] %>% unique() %>% as.numeric() expect_equal(panels, c(1, 2)) }) ggpubr/tests/testthat/test-ggtext.R0000644000176200001440000000142414342646623017157 0ustar liggesusersdata("gene_expression", package = "ggpubr") test_that(".get_label_data works when label.select specified as a list", { results <- .get_label_data( data = gene_expression, x = "dataset", y = "XBP1", label ="bcr_patient_barcode", label.select = list(criteria = "`y` > 4.4 & `x` %in% c('BRCA', 'OV')"), ) expected <- tibble::tribble( ~bcr_patient_barcode, ~dataset, ~GATA3, ~PTEN, ~XBP1, "BRCA149", "BRCA", 2.345125, 1.13357142857143, 4.48758333333333, "BRCA536", "BRCA", 3.1135, 0.846142857142857, 4.73675, "BRCA581", "BRCA", 3.38675, 0.152928571428571, 4.5725 ) %>% data.frame(stringsAsFactors = FALSE) expect_equal(results, expected, tolerance = 1e-4) }) ggpubr/tests/testthat/test-gghistogram.R0000644000176200001440000000154714334106676020176 0ustar liggesuserscontext("gghistogram") test_that("gghistogram works when using after_stat()", { p <- gghistogram(iris, x = "Sepal.Length", y = "after_stat(density * width)", bins = 30) p_build <- ggplot2::ggplot_build(p) df <- p_build$data[[1]] observed_count <- df$count expected_count <- c(4, 1, 4, 2, 11, 10, 9, 4, 7, 7, 6, 8, 7, 9, 6, 4, 9, 12, 2, 8, 3, 5, 1, 3, 1, 1, 1, 4, 0, 1) expect_equal(observed_count, expected_count) }) test_that("gghistogram works when using after_stat() with trailing space inside parentheses", { p <- gghistogram(iris, x = "Sepal.Length", y = "after_stat(density )", bins = 30) p_build <- ggplot2::ggplot_build(p) df <- p_build$data[[1]] observed_count <- df$count expected_count <- c(4, 1, 4, 2, 11, 10, 9, 4, 7, 7, 6, 8, 7, 9, 6, 4, 9, 12, 2, 8, 3, 5, 1, 3, 1, 1, 1, 4, 0, 1) expect_equal(observed_count, expected_count) }) ggpubr/tests/testthat/test-geom_pwc.R0000644000176200001440000003531414332576562017465 0ustar liggesuserscontext("test-geom_pwc") # Data preparation df <- ToothGrowth df$dose <- as.factor(df$dose) df$group <- factor(rep(c("grp1", "grp2"), 30)) # Basic plots ----------------------------------- test_that("geom_pwc works for two independent tests", { bxp <- ggboxplot(df, x = "supp", y = "len") + geom_pwc(method = "wilcox_test") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] label_coords <- as.data.frame(get_pwc_label_coords(stat.test)) label_coords_expected <- data.frame( x = 1.5, y = 35.395, angle = 0 ) expect_equal(stat.test$group, c(1, 1, 1)) expect_equal(stat.test$bracket.group, c(1, 1, 1)) expect_equal(as.numeric(stat.test$x), c(1, 1, 2)) expect_equal(as.numeric(stat.test$xend), c(1, 2, 2)) expect_equal(stat.test$y, c(34.494, 35.385, 35.385)) expect_equal(stat.test$yend, c(35.385, 35.385, 34.494)) expect_equal(label_coords, label_coords_expected) }) test_that("geom_pwc works for pairwise comparisons", { bxp <- ggboxplot(df, x = "dose", y = "len") + geom_pwc(method = "wilcox_test") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] label_coords <- as.data.frame(get_pwc_label_coords(stat.test)) label_coords_expected <- data.frame( x = c(1.5, 2.0, 2.5), y = c(35.395, 38.959, 42.523), angle = c(0, 0, 0) ) expect_equal(stat.test$group, c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L)) expect_equal(stat.test$bracket.group, c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L)) expect_equal(as.numeric(stat.test$x), c(1, 1, 2, 1, 1, 2, 2, 3, 3)) expect_equal(as.numeric(stat.test$xend), c(1, 1, 2, 2, 3, 3, 2, 3, 3)) expect_equal(stat.test$y, c(34.494, 38.058, 41.622, 35.385, 38.949, 42.513, 35.385, 38.949, 42.513)) expect_equal(stat.test$yend, c(35.385, 38.949, 42.513, 35.385, 38.949, 42.513, 34.494, 38.058, 41.622)) expect_equal(label_coords, label_coords_expected) }) test_that("geom_pwc works for pairwise comparisons against a reference group", { bxp <- ggboxplot(df, x = "dose", y = "len") + geom_pwc(method = "wilcox_test", ref.group = "0.5") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] label_coords <- as.data.frame(get_pwc_label_coords(stat.test)) label_coords_expected <- data.frame( x = c(1.5, 2.0), y = c(35.395, 38.959), angle = c(0, 0) ) expect_equal(stat.test$group, c(1L, 2L, 1L, 2L, 1L, 2L)) expect_equal(stat.test$bracket.group, c(1L, 2L, 1L, 2L, 1L, 2L)) expect_equal(as.numeric(stat.test$x), c(1, 1, 1, 1, 2, 3)) expect_equal(as.numeric(stat.test$xend), c(1, 1, 2, 3, 2, 3)) expect_equal(stat.test$y, c(34.494, 38.058, 35.385, 38.949, 35.385, 38.949)) expect_equal(stat.test$yend, c(35.385, 38.949, 35.385, 38.949, 34.494, 38.058)) expect_equal(label_coords, label_coords_expected) }) test_that("geom_pwc works for pairwise comparisons against a reference group when remove.bracket=TRUE", { bxp <- ggboxplot(df, x = "dose", y = "len") + geom_pwc(method = "wilcox_test", ref.group = "0.5", remove.bracket = TRUE) bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] label_coords <- as.data.frame(get_pwc_label_coords(stat.test)) label_coords_expected <- data.frame( x = c(2, 3), y = c(35.395, 38.959), angle = c(0, 0) ) expect_equal(label_coords, label_coords_expected) }) test_that("geom_pwc works for pairwise comparisons against all. Same y.position for all labels", { bxp <- ggboxplot(df, x = "dose", y = "len") + geom_pwc(method = "wilcox_test", ref.group = "all") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] label_coords <- as.data.frame(get_pwc_label_coords(stat.test)) label_coords_expected <- data.frame( x = c(1, 2, 3), y = c(35.395, 35.395, 35.395), angle = c(0, 0, 0) ) expect_equal(label_coords, label_coords_expected) }) # Grouped plots ----------------------------------- test_that("Grouped plots: Two groups by x position", { bxp <- ggboxplot(df, x = "dose", y = "len", color = "supp") + geom_pwc(aes(group = supp), method = "wilcox_test") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] label_coords <- as.data.frame(get_pwc_label_coords(stat.test)) label_coords_expected <- data.frame( x = c(1, 2, 3), y = c(35.395, 35.395, 35.395), angle = c(0, 0, 0) ) expect_equal(stat.test$group, c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L)) expect_equal(stat.test$bracket.group, c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L)) expect_equal(as.numeric(stat.test$x), c(0.8, 1.8, 2.8, 0.8, 1.8, 2.8, 1.2, 2.2, 3.2)) expect_equal(as.numeric(stat.test$xend), c(0.8, 1.8, 2.8, 1.2, 2.2, 3.2, 1.2, 2.2, 3.2)) expect_equal(stat.test$y, c(34.494, 34.494, 34.494, 35.385, 35.385, 35.385, 35.385, 35.385, 35.385)) expect_equal(stat.test$yend, c(35.385, 35.385, 35.385, 35.385, 35.385, 35.385, 34.494, 34.494, 34.494)) expect_equal(label_coords, label_coords_expected) }) test_that("Grouped plots: grouping by legend var and comparing x-axis groups", { bxp <- ggboxplot(df, x = "dose", y = "len", color = "supp") + geom_pwc(aes(group = supp, color = supp), group.by = "legend.var") bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] label_coords <- as.data.frame(get_pwc_label_coords(stat.test)) label_coords_expected <- data.frame( x = c(1.3, 1.8, 2.3, 1.7, 2.2, 2.7), y = c(35.395, 38.959, 42.523, 46.087, 49.651, 53.215), angle = c(0, 0, 0, 0, 0, 0) ) expect_equal(stat.test$group, c(1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L)) expect_equal(stat.test$bracket.group, c(1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L)) expect_equal(as.numeric(stat.test$x), c(0.8, 0.8, 1.8, 1.2, 1.2, 2.2, 0.8, 0.8, 1.8, 1.2, 1.2, 2.2, 1.8, 2.8, 2.8, 2.2, 3.2, 3.2)) expect_equal(as.numeric(stat.test$xend), c(0.8, 0.8, 1.8, 1.2, 1.2, 2.2, 1.8, 2.8, 2.8, 2.2, 3.2, 3.2, 1.8, 2.8, 2.8, 2.2, 3.2, 3.2)) expect_equal(stat.test$y, c(34.494, 38.058, 41.622, 45.186, 48.75, 52.314, 35.385, 38.949, 42.513, 46.077, 49.641, 53.205, 35.385, 38.949, 42.513, 46.077, 49.641, 53.205)) expect_equal(stat.test$yend, c(35.385, 38.949, 42.513, 46.077, 49.641, 53.205, 35.385, 38.949, 42.513, 46.077, 49.641, 53.205, 34.494, 38.058, 41.622, 45.186, 48.75, 52.314)) expect_equal(label_coords, label_coords_expected) }) test_that("Grouped plots: grouping by legend var and comparing x-axis groups, using dodge=0 and bracket.group.by = 'legend.var'", { bxp <- ggboxplot(df, x = "dose", y = "len", color = "supp") + geom_pwc( aes(group = supp, color = supp), group.by = "legend.var", dodge = 0, bracket.group.by = "legend.var" ) bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] label_coords <- as.data.frame(get_pwc_label_coords(stat.test)) label_coords_expected <- data.frame( x = c(1.5, 1.5, 2, 2, 2.5, 2.5), y = c(35.395, 38.959, 42.523, 46.087, 49.651, 53.215), angle = c(0, 0, 0, 0, 0, 0) ) expect_equal(stat.test$group, c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L)) expect_equal(stat.test$bracket.group, c(1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L)) expect_equal(as.numeric(stat.test$x), c(1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3)) expect_equal(as.numeric(stat.test$xend), c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 2, 2, 3, 3, 3, 3)) expect_equal(stat.test$y, c(34.494, 38.058, 41.622, 45.186, 48.75, 52.314, 35.385, 38.949, 42.513, 46.077, 49.641, 53.205, 35.385, 38.949, 42.513, 46.077, 49.641, 53.205)) expect_equal(stat.test$yend, c(35.385, 38.949, 42.513, 46.077, 49.641, 53.205, 35.385, 38.949, 42.513, 46.077, 49.641, 53.205, 34.494, 38.058, 41.622, 45.186, 48.75, 52.314)) expect_equal(label_coords, label_coords_expected) }) test_that("Grouped plots: 3 groups at x position. All Pairwise comparisons.", { bxp <- ggboxplot(df, x = "supp", y = "len", color = "dose") + geom_pwc(aes(group = dose)) bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] label_coords <- as.data.frame(get_pwc_label_coords(stat.test)) label_coords$x <- round(label_coords$x, 2) label_coords$y <- round(label_coords$y, 2) label_coords_expected <- data.frame( x = c(0.87, 1, 1.13, 1.87, 2, 2.13), y = c(35.39, 38.96, 42.52, 35.39, 38.96, 42.52), angle = c(0, 0, 0, 0, 0, 0) ) stat.test$x <- round(stat.test$x, 2) stat.test$xend <- round(stat.test$xend, 2) stat.test$y <- round(stat.test$y, 2) stat.test$yend <- round(stat.test$yend, 2) expect_equal(stat.test$group, c(1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L)) expect_equal(stat.test$bracket.group, c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L)) expect_equal(as.numeric(stat.test$x), c(0.73, 0.73, 1, 1.73, 1.73, 2, 0.73, 0.73, 1, 1.73, 1.73, 2, 1, 1.27, 1.27, 2, 2.27, 2.27)) expect_equal(as.numeric(stat.test$xend), c(0.73, 0.73, 1, 1.73, 1.73, 2, 1, 1.27, 1.27, 2, 2.27, 2.27, 1, 1.27, 1.27, 2, 2.27, 2.27)) expect_equal(stat.test$y, c(34.49, 38.06, 41.62, 34.49, 38.06, 41.62, 35.38, 38.95, 42.51, 35.38, 38.95, 42.51, 35.38, 38.95, 42.51, 35.38, 38.95, 42.51)) expect_equal(stat.test$yend, c(35.38, 38.95, 42.51, 35.38, 38.95, 42.51, 35.38, 38.95, 42.51, 35.38, 38.95, 42.51, 34.49, 38.06, 41.62, 34.49, 38.06, 41.62)) expect_equal(label_coords, label_coords_expected) }) test_that("Grouped plots: 3 groups at x position. Pairwise comparisons against a ref group.", { bxp <- ggboxplot(df, x = "supp", y = "len", color = "dose") + geom_pwc(aes(group = dose), ref.group = 1) bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] label_coords <- as.data.frame(get_pwc_label_coords(stat.test)) label_coords$x <- round(label_coords$x, 2) label_coords$y <- round(label_coords$y, 2) label_coords_expected <- data.frame( x = c(0.87, 1, 1.87, 2), y = c(35.39, 38.96, 35.39, 38.96), angle = c(0, 0, 0, 0) ) stat.test$x <- round(stat.test$x, 2) stat.test$xend <- round(stat.test$xend, 2) stat.test$y <- round(stat.test$y, 2) stat.test$yend <- round(stat.test$yend, 2) expect_equal(stat.test$group, c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L)) expect_equal(stat.test$bracket.group, c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L)) expect_equal(as.numeric(stat.test$x), c(0.73, 0.73, 1.73, 1.73, 0.73, 0.73, 1.73, 1.73, 1, 1.27, 2, 2.27)) expect_equal(as.numeric(stat.test$xend), c(0.73, 0.73, 1.73, 1.73, 1, 1.27, 2, 2.27, 1, 1.27, 2, 2.27)) expect_equal(stat.test$y, c(34.49, 38.06, 34.49, 38.06, 35.38, 38.95, 35.38, 38.95, 35.38, 38.95, 35.38, 38.95)) expect_equal(stat.test$yend, c(35.38, 38.95, 35.38, 38.95, 35.38, 38.95, 35.38, 38.95, 34.49, 38.06, 34.49, 38.06)) expect_equal(label_coords, label_coords_expected) }) test_that("Grouped plots: 3 groups at x position. Pairwise comparisons against a ref group. Check label coords when remove.bracket=TRUE.", { bxp <- ggboxplot(df, x = "supp", y = "len", color = "dose") + geom_pwc(aes(group = dose), ref.group = 1, remove.bracket = TRUE) bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] label_coords <- as.data.frame(get_pwc_label_coords(stat.test)) label_coords$x <- round(label_coords$x, 2) label_coords$y <- round(label_coords$y, 2) label_coords_expected <- data.frame( x = c(1, 1.27, 2, 2.27), y = c(35.39, 38.96, 35.39, 38.96), angle = c(0, 0, 0, 0) ) expect_equal(label_coords, label_coords_expected) }) test_that("Grouped plots: test that geom_pwc() works with different number of groups at each x pos.", { # https://github.com/kassambara/ggpubr/issues/326 demo_data <- data.frame( stringsAsFactors = FALSE, Study = c("A","A","A","A","A","A", "A","A","A","A","B","B","B","B","B","B","B","B", "B","B","C","C","C","C","C","C","C","C","C", "C","C","C","C","C","C","D","D","D","D","D", "D","D","D","D","D","D","D","D","D","D"), Studytype = c("X","X","X","X","X","X", "X","X","X","X","X","X","X","X","X","X","X","X", "X","X","Y","Y","Y","Y","Y","Y","Y","Y","Y", "Y","Y","Y","Y","Y","Y","Y","Y","Y","Y","Y", "Y","Y","Y","Y","Y","Y","Y","Y","Y","Y"), Values = c(4469L,4797L,5101L,5397L, 4542L,2780L,4326L,3396L,3657L,3199L,9221L,10176L, 9277L,10500L,9707L,7406L,7756L,7601L,7586L,7353L, 1811L,1485L,3003L,1629L,2495L,4207L,4265L,3629L, 4157L,3495L,2075L,2112L,2973L,3086L,2943L,5664L,6690L, 3538L,5741L,7880L,5848L,6390L,6569L,6114L,6520L, 7389L,6843L,7611L,6621L,7340L), Group = as.factor(c("CTR", "CTR","CTR","CTR","CTR","Dis1","Dis1","Dis1", "Dis1","Dis1","CTR","CTR","CTR","CTR", "CTR","Dis1","Dis1","Dis1","Dis1","Dis1", "CTR","CTR","CTR","CTR","CTR","Dis2","Dis2", "Dis2","Dis2","Dis2","Dis3","Dis3", "Dis3","Dis3","Dis3","CTR","CTR","CTR","CTR", "CTR","Dis2","Dis2","Dis2","Dis2","Dis2", "Dis3","Dis3","Dis3","Dis3","Dis3")) ) bxp <- ggboxplot(demo_data, x = "Study", y = "Values", fill = "Group") + geom_pwc(aes(group = Group), dodge = 0.8, label = "p", ref.group = 1) bxp_build <- ggplot2::ggplot_build(bxp) stat.test <- bxp_build$data[[2]] label_coords <- as.data.frame(get_pwc_label_coords(stat.test)) label_coords$x <- round(label_coords$x, 2) label_coords$y <- round(label_coords$y, 2) label_coords_expected <- data.frame( x = c(1, 2, 2.87, 3, 3.87, 4), y = c(10950.76, 10950.76, 10950.76, 12032.56, 10950.76, 12032.56), angle = c(0, 0, 0, 0, 0, 0) ) stat.test$x <- round(stat.test$x, 2) stat.test$xend <- round(stat.test$xend, 2) stat.test$y <- round(stat.test$y, 2) stat.test$yend <- round(stat.test$yend, 2) expect_equal(stat.test$group, c(1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L)) expect_equal(stat.test$bracket.group, c(1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L)) expect_equal(as.numeric(stat.test$x), c(0.8, 1.8, 2.73, 2.73, 3.73, 3.73, 0.8, 1.8, 2.73, 2.73, 3.73, 3.73, 1.2, 2.2, 3, 3.27, 4, 4.27)) expect_equal(as.numeric(stat.test$xend), c(0.8, 1.8, 2.73, 2.73, 3.73, 3.73, 1.2, 2.2, 3, 3.27, 4, 4.27, 1.2, 2.2, 3, 3.27, 4, 4.27)) expect_equal(stat.test$y, c(10680.3, 10680.3, 10680.3, 11762.1, 10680.3, 11762.1, 10950.75, 10950.75, 10950.75, 12032.55, 10950.75, 12032.55, 10950.75, 10950.75, 10950.75, 12032.55, 10950.75, 12032.55)) expect_equal(stat.test$yend, c(10950.75, 10950.75, 10950.75, 12032.55, 10950.75, 12032.55, 10950.75, 10950.75, 10950.75, 12032.55, 10950.75, 12032.55, 10680.3, 10680.3, 10680.3, 11762.1, 10680.3, 11762.1)) expect_equal(label_coords, label_coords_expected) }) ggpubr/tests/testthat.R0000644000176200001440000000007014332576562014677 0ustar liggesuserslibrary(testthat) library(ggpubr) test_check("ggpubr") ggpubr/R/0000755000176200001440000000000014370060431011737 5ustar liggesusersggpubr/R/ggexport.R0000644000176200001440000000620014337540323013725 0ustar liggesusers#' @include utilities.R NULL #'Export ggplots #'@description Export ggplots #'@inheritParams ggarrange #'@param ... list of plots to be arranged into the grid. The plots can be either #' ggplot2 plot objects, arbitrary gtables or an object of class #' \code{\link{ggarrange}}. #' @param filename File name to create on disk. #'@param width,height plot width and height, respectively (example, width = 800, #' height = 800). Applied only to raster plots: "png", "jpeg", "jpg", "bmp" and #' "tiff". #'@param pointsize the default pointsize of plotted text (example, pointsize = #' 8). Used only for raster plots. #'@param res the resolution in ppi (example, res = 250). Used only for raster #' plots. #'@param verbose logical. If TRUE, show message. #'@author Alboukadel Kassambara #' @examples #' \dontrun{ #' require("magrittr") #' # Load data #' data("ToothGrowth") #' df <- ToothGrowth #' df$dose <- as.factor(df$dose) #' #' # Box plot #' bxp <- ggboxplot(df, x = "dose", y = "len", #' color = "dose", palette = "jco") #' # Dot plot #' dp <- ggdotplot(df, x = "dose", y = "len", #' color = "dose", palette = "jco") #' # Density plot #' dens <- ggdensity(df, x = "len", fill = "dose", palette = "jco") #' #'# Export to pdf #' ggarrange(bxp, dp, dens, ncol = 2) %>% #' ggexport(filename = "test.pdf") #' #' # Export to png #' ggarrange(bxp, dp, dens, ncol = 2) %>% #' ggexport(filename = "test.png") #' } #' #'@export ggexport <- function(..., plotlist = NULL, filename = NULL, ncol = NULL, nrow = NULL, width = 480, height = 480, pointsize = 12, res = NA, verbose = TRUE) { # File name and extension if(is.null(filename)) filename <- .collapse(.random_string(), ".pdf", sep = "") file.ext <- .file_ext(filename) # Device dev <- .device(filename) dev.opts <- list(file = filename) if(file.ext %in% c("ps", "eps")) dev.opts <- dev.opts %>% .add_item(onefile = FALSE, horizontal = FALSE) else if(file.ext %in% c("png", "jpeg", "jpg", "bmp", "tiff")) dev.opts <- dev.opts %>% .add_item(width = width, height = height, pointsize = pointsize, res = res) if(file.ext %in% c("pdf", "svg")){ if(!missing(width)) dev.opts <- dev.opts %>% .add_item(width = width) if(!missing(height)) dev.opts <- dev.opts %>% .add_item(height = height) if(!missing(pointsize)) dev.opts <- dev.opts %>% .add_item(pointsize = pointsize) } #width=800, height=800, pointsize=8, res=250 # Plots plots <- c(list(...), plotlist) nb.plots <- length(plots) if(nb.plots == 1) plots <- plots[[1]] else if(!is.null(ncol) | !is.null(nrow)){ plots <- ggarrange(plotlist = plots, ncol = ncol, nrow = nrow) } if(inherits(plots, "ggarrange") & .is_list(plots)) nb.plots <- length(plots) if(nb.plots > 1 & file.ext %in% c("eps", "ps", "png", "jpeg", "jpg", "tiff", "bmp", "svg")){ filename <- gsub(paste0(".", file.ext), paste0("%03d.",file.ext), filename) dev.opts$file <- filename print(filename) } do.call(dev, dev.opts) utils::capture.output(print(plots)) utils::capture.output(grDevices::dev.off()) if(verbose) message("file saved to ", filename) } ggpubr/R/ggerrorplot.R0000644000176200001440000001453514342705120014440 0ustar liggesusers#'@include utilities.R ggpar.R NULL #'Visualizing Error #'@description Visualizing error. #'@inheritParams ggboxplot #'@inheritParams ggplot2::geom_errorbar #'@param ci the percent range of the confidence interval (default is 0.95). #'@param x,y x and y variables for drawing. #'@param numeric.x.axis logical. If TRUE, x axis will be treated as numeric. Default is FALSE. #'@param order character vector specifying the order of items. Considered only when x axis is a factor variable. #'@param color,fill outline and fill colors. #'@param desc_stat descriptive statistics to be used for visualizing errors. Default value is "mean_se". #'Allowed values are one of , "mean", "mean_se", "mean_sd", "mean_ci", "mean_range", #'"median", "median_iqr", "median_hilow", "median_q1q3", "median_mad", "median_range"; see \code{\link{desc_statby}} for more details. #'@param ... other arguments to be passed to be passed to ggpar(). #'@details The plot can be easily customized using the function ggpar(). Read #' ?ggpar for changing: \itemize{ \item main title and axis labels: main, #' xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis #' scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: #' palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, #' labels and position: legend = "right" \item plot orientation : orientation #' = c("vertical", "horizontal", "reverse") } #' @seealso \code{\link{ggpar}}, \code{\link{ggline}} #' @examples #' #' # Data: ToothGrowth data set we'll be used. #' df<- ToothGrowth #' head(df, 10) #' #' # Plot mean_se #' ggerrorplot(df, x = "dose", y = "len") #' #' #' # Change desc_stat to mean_sd #' # (other values include: mean_sd, mean_ci, median_iqr, ....) #' # Add labels #' ggerrorplot(df, x = "dose", y = "len", #' desc_stat = "mean_sd") #' #' # Change error.plot to "errorbar" and add mean point #' # Visualize the mean of each group #' ggerrorplot(df, x = "dose", y = "len", #' add = "mean", error.plot = "errorbar") #' #' # Horizontal plot #' ggerrorplot(df, x = "dose", y = "len", #' add = "mean", error.plot = "errorbar", #' orientation = "horizontal") #' #' #' # Change error.plot to "crossbar" #' ggerrorplot(df, x = "dose", y = "len", #' error.plot = "crossbar", width = 0.5) #' #' #' # Add jitter points and errors (mean_se) #' ggerrorplot(df, x = "dose", y = "len", #' add = "jitter") #' #' # Add dot and errors (mean_se) #' ggerrorplot(df, x = "dose", y = "len", #' add = "dotplot") #' #' # Multiple groups with error bars and jitter point #' ggerrorplot(df, x = "dose", y = "len", #' color = "supp", palette = "Paired", #' error.plot = "pointrange", #' position = position_dodge(0.5)) # #' #' #' @export ggerrorplot <- function(data, x, y, desc_stat = "mean_se", numeric.x.axis = FALSE, combine = FALSE, merge = FALSE, color = "black", fill = "white", palette = NULL, size = NULL, width = NULL, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, select = NULL, remove = NULL, order = NULL, add = "none", add.params = list(), error.plot = "pointrange", ci = 0.95, position = position_dodge(), ggtheme = theme_pubr(), ...) { # Default options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .opts <- list( desc_stat = desc_stat, numeric.x.axis = numeric.x.axis, combine = combine, merge = merge, color = color, fill = fill, palette = palette, size = size, width = width, title = title, xlab = xlab, ylab = ylab, facet.by = facet.by, panel.labs = panel.labs, short.panel.labs = short.panel.labs, select = select , remove = remove, order = order, add = add, add.params = add.params, error.plot = error.plot, ci = ci, position = position, ggtheme = ggtheme, ...) if(!missing(data)) .opts$data <- data if(!missing(x)) .opts$x <- x if(!missing(y)) .opts$y <- y # User options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .user.opts <- as.list(match.call(expand.dots = TRUE)) .user.opts[[1]] <- NULL # Remove the function name # keep only user arguments for(opt.name in names(.opts)){ if(is.null(.user.opts[[opt.name]])) .opts[[opt.name]] <- NULL } .opts$fun <- ggerrorplot_core if(missing(ggtheme) & (!is.null(facet.by) | combine)) .opts$ggtheme <- theme_pubr(border = TRUE) p <- do.call(.plotter, .opts) if(.is_list(p) & length(p) == 1) p <- p[[1]] return(p) } ggerrorplot_core <- function(data, x, y, desc_stat = "mean_se", numeric.x.axis = FALSE, color = "black", fill = "white", palette = NULL, title = NULL, xlab = NULL, ylab = NULL, size = NULL, width = NULL, select = NULL, order = NULL, add = "none", add.params = list(), error.plot = "pointrange", ci = 0.95, position = position_dodge(0.8), ggtheme = theme_pubr(), ...) { if(numeric.x.axis){} else{ if(!is.null(order)) data[[x]] <- factor(data[[x]], levels = order) else if(!is.factor(data[[x]])) data[[x]] <- as.factor(data[[x]]) } error.plot = error.plot[1] if("none" %in% add) add <- "none" # static summaries for computing mean/median and adding errors if(is.null(add.params$fill)) add.params$fill <- "white" add.params <- .check_add.params(add, add.params, error.plot, data, color, fill, ...) add <- setdiff(add, desc_stat) if(inherits(position, "PositionDodge") & is.null(position$width)) position$width = 0.8 p <- ggplot(data, create_aes(list(x = x, y = y))) add.params <- add.params %>% .add_item(add = add, data = data, error.plot = error.plot, ci = ci, position = position, p = p) p <- do.call(ggadd, add.params) # Main plot add.params <- add.params %>% .add_item(color = color, fill = fill, size = size, width = width, add = desc_stat, p = p) p <- do.call(ggadd, add.params) p <- ggpar(p, palette = palette, ggtheme = ggtheme, title = title, xlab = xlab, ylab = ylab,...) p } ggpubr/R/ggsummarystats.R0000644000176200001440000002133014336762430015165 0ustar liggesusers#' @include utilities.R ggpar.R #' @importFrom rstatix df_split_by get_summary_stats NULL #' GGPLOT with Summary Stats Table Under the Plot #' #' @description Create a ggplot with summary stats (n, median, mean, iqr) table #' under the plot. Read more: \href{https://www.datanovia.com/en/blog/how-to-create-a-beautiful-plots-in-r-with-summary-statistics-labels/}{How to Create a Beautiful Plots in R with Summary Statistics Labels}. #' @inheritParams ggboxplot #' @param digits integer indicating the number of decimal places (round) to be #' used. #' @param table.font.size the summary table font size. #' @param position Position adjustment, either as a string, or the result of a #' call to a position adjustment function. #' @param summaries summary stats to display in the table. Possible values are #' those returned by the function \code{\link[rstatix]{get_summary_stats}()}, #' including: \code{"n", "min", "max", "median", "q1", "q2", "q3", "mad", #' "mean", "sd", "se", "ci"}. #' @param ggfunc a ggpubr function, including: ggboxplot, ggviolin, ggdotplot, #' ggbarplot, ggline, etc. Can be any other ggplot function that accepts the #' following arguments \code{data, x, color, fill, palette, ggtheme, #' facet.by}. #' @param free.panels logical. If TRUE, create free plot panels when the #' argument \code{facet.by} is specified. #'@param labeller Character vector. An alternative to the argument #' \code{short.panel.labs}. Possible values are one of "label_both" (panel #' labelled by both grouping variable names and levels) and "label_value" #' (panel labelled with only grouping levels). #' @param ... other arguments passed to the function \code{\link{ggpar}()}, #' \code{\link{facet}()} or \code{\link{ggarrange}()} when printing the plot. #' @examples #' # Data preparation #' #:::::::::::::::::::::::::::::::::::::::::::::::: #' data("ToothGrowth") #' df <- ToothGrowth #' df$dose <- as.factor(df$dose) #' # Add random QC column #' set.seed(123) #' qc <- rep(c("pass", "fail"), 30) #' df$qc <- as.factor(sample(qc, 60)) #' # Inspect the data #' head(df) #' #' #' # Basic summary stats #' #:::::::::::::::::::::::::::::::::::::::::::::::: #' # Compute summary statistics #' summary.stats <- df %>% #' group_by(dose) %>% #' get_summary_stats(type = "common") #' summary.stats #' #' # Visualize summary table #' ggsummarytable( #' summary.stats, x = "dose", y = c("n", "median", "iqr"), #' ggtheme = theme_bw() #' ) #' #' #' # Create plots with summary table under the plot #' #:::::::::::::::::::::::::::::::::::::::::::::::: #' # Basic plot #' ggsummarystats( #' df, x = "dose", y = "len", #' ggfunc = ggboxplot, add = "jitter" #' ) #' #' # Color by groups #' ggsummarystats( #' df, x = "dose", y = "len", #' ggfunc = ggboxplot, add = "jitter", #' color = "dose", palette = "npg" #' ) #' #' # Create a barplot #' ggsummarystats( #' df, x = "dose", y = "len", #' ggfunc = ggbarplot, add = c("jitter", "median_iqr"), #' color = "dose", palette = "npg" #' ) #' #' # Facet #' #:::::::::::::::::::::::::::::::::::::::::::::::: #' # Specify free.panels = TRUE for free panels #' ggsummarystats( #' df, x = "dose", y = "len", #' ggfunc = ggboxplot, add = "jitter", #' color = "dose", palette = "npg", #' facet.by = c("supp", "qc"), #' labeller = "label_both" #' ) #' #' @describeIn ggsummarystats Create a table of summary stats #' @export ggsummarytable <- function(data, x, y, digits = 0, size = 3, color = "black", palette = NULL, facet.by = NULL, labeller = "label_value", position = "identity", ggtheme = theme_pubr(), ...) { if (missing(ggtheme) & !is.null(facet.by)) { ggtheme <- theme_pubr(border = TRUE) } if (is.null(names(y))) names(y) <- y y_values <- as.vector(y) df <- as.data.frame(data) df$x <- df[[x]] if (color %in% colnames(df)) { if (missing(position)) position <- position_dodge(0.8) group <- color } else { group <- 1 } df <- df %>% mutate_if(is.double, round, digits) %>% unite(col = "label", !!!syms(y_values), sep = "\n") %>% mutate(y = paste(names(y), collapse = "\n")) p <- ggplot(data, aes(x, y)) + geom_exec( geom_text, data = df, label = "label", size = size, color = color, group = group, position = position ) p <- ggpar(p, ggtheme = ggtheme, palette = palette, xlab = x, ...) if (!is.null(facet.by)) p <- facet(p, facet.by = facet.by, labeller = labeller, ...) p + rremove("ylab") } #' @describeIn ggsummarystats Create a ggplot with a summary stat table under the plot. #' @export ggsummarystats <- function(data, x, y, summaries = c("n", "median", "iqr"), ggfunc = ggboxplot, color = "black", fill = "white", palette = NULL, facet.by = NULL, free.panels = FALSE, labeller = "label_value", heights = c(0.80, 0.20), digits = 0, table.font.size = 3, ggtheme = theme_pubr(), ...) { if (missing(ggtheme) & !is.null(facet.by)) { ggtheme <- theme_pubr(border = TRUE) } env <- c(as.list(environment()), list(...)) if (is.null(facet.by)) { results <- do.call(ggsummarystats_core, env) } else { if (free.panels) { results <- do.call(ggsummarystats_free_facet, env) } else { results <- do.call(ggsummarystats_core, env) } } results } #' @method print ggsummarystats #' @param x an object of class \code{ggsummarystats}. #' @param heights a numeric vector of length 2, specifying the heights of the #' main and the summary table, respectively. #' @rdname ggsummarystats #' @export print.ggsummarystats <- function(x, heights = c(0.80, 0.20), ...){ res <- ggarrange(plotlist = x, heights = heights, align = "v", ncol = 1) print(res) invisible(res) } #' @method print ggsummarystats_list #' @param legend character specifying legend position. Allowed values are one of #' c("top", "bottom", "left", "right", "none"). To remove the legend use #' legend = "none". #' @param x a list of \code{ggsummarystats}. #' @rdname ggsummarystats #' @export print.ggsummarystats_list <- function(x, heights = c(0.80, 0.20), legend = NULL, ...){ # Create a common legend, if legend exists legend.grob <- get_legend(x[[1]]$main.plot, position = legend) has.legend <- !is.null(legend.grob) remove_legend <- function(ggsummarystats){ ggsummarystats[[1]] <- ggsummarystats[[1]] + theme(legend.position = "none") ggsummarystats } if(has.legend){ x <- map(x, remove_legend) } # Combining each ggsummarystats x <- x %>% map( function(x, ...) {ggarrange(plotlist = x, ...)}, heights = heights, align = "v", ncol = 1 ) # Combine the list of ggsumarystats res <- ggarrange(plotlist = x, legend = legend, legend.grob = legend.grob) # Add legend if exist print(res) invisible(res) } ggsummarystats_core <- function(data, x, y, summaries = c("n", "median", "iqr"), ggfunc = ggboxplot, color = "black", fill = "white", palette = NULL, ggtheme = theme_pubr(), heights = c(0.80, 0.20), facet.by = NULL, free.panels = FALSE, labeller = "label_value", digits = 0, table.font.size = 3,...) { groups <- c(x, color, fill, facet.by) %>% unique() %>% intersect(colnames(data)) summary.stats <- data %>% group_by(!!!syms(groups)) %>% get_summary_stats(!!y) # No need to repeat the panel label on the table table.facet.by <- facet.by if(free.panels) table.facet.by <- NULL main.plot <- ggfunc( data, x = x, y = y, color = color, fill = fill, palette = palette, ggtheme = ggtheme, facet.by = facet.by, labeller = labeller, ... ) summary.plot <- ggsummarytable( summary.stats, x = x, y = summaries, color = color, palette = palette, legend = "none", ggtheme = ggtheme, facet.by = table.facet.by, labeller = labeller, digits = digits, size = table.font.size ) + clean_table_theme() plots <- list( main.plot = main.plot, summary.plot = summary.plot ) class(plots) <- c("ggsummarystats", "list") plots } ggsummarystats_free_facet <- function(data, x, y, facet.by, labeller = "label_value", ...) { labeller_func <- switch (labeller, label_both = rstatix::df_label_both, label_value = rstatix::df_label_value ) groups <- facet.by data.grouped <- data %>% df_split_by(vars = groups, label_col = "panel", labeller = labeller_func) %>% mutate( plots = map(data, ggsummarystats_core, x = x, y = y, facet.by = "panel", ...) ) plots <- data.grouped$plots names(plots) <- data.grouped$panel class(plots) <- c("ggsummarystats_list", "list") plots } ggpubr/R/ggpie.R0000644000176200001440000001730114345714213013165 0ustar liggesusers#'@include utilities.R ggpar.R NULL #'Pie chart #'@description Create a pie chart. #'@inheritParams ggboxplot #'@inheritParams ggplot2::geom_errorbar #'@param x variable containing values for drawing. #'@param label variable specifying the label of each slice. #'@param lab.pos character specifying the position for labels. Allowed values #' are "out" (for outside) or "in" (for inside). #'@param lab.adjust numeric value, used to adjust label position when lab.pos = #' "in". Increase or decrease this value to see the effect. #'@param lab.font a vector of length 3 indicating respectively the size (e.g.: #' 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the #' color (e.g.: "red") of label font. For example \emph{lab.font= c(4, "bold", #' "red")}. #'@param font.family character vector specifying font family. #'@param color,fill outline and fill colors. #'@param ... other arguments to be passed to be passed to ggpar(). #'@details The plot can be easily customized using the function ggpar(). Read #' ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, #' ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis #' scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette #' = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels #' and position: legend = "right" \item plot orientation : orientation = #' c("vertical", "horizontal", "reverse") } #'@seealso \code{\link{ggpar}}, \code{\link{ggline}} #' @examples #' #' # Data: Create some data #' # +++++++++++++++++++++++++++++++ #' #' df <- data.frame( #' group = c("Male", "Female", "Child"), #' value = c(25, 25, 50)) #' #' head(df) #' #' #' # Basic pie charts #' # ++++++++++++++++++++++++++++++++ #' #' ggpie(df, "value", label = "group") #' #' # Reducing margins around the pie chart #' ggpie(df, "value", label = "group") + #' theme( plot.margin = unit(c(-.75,-.75,-.75,-.75),"cm")) #' #' #' # Change color #' # ++++++++++++++++++++++++++++++++ #' #' # Change fill color by group #' # set line color to white #' # Use custom color palette #' ggpie(df, "value", label = "group", #' fill = "group", color = "white", #' palette = c("#00AFBB", "#E7B800", "#FC4E07") ) #' #' #' # Change label #' # ++++++++++++++++++++++++++++++++ #' #' # Show group names and value as labels #' labs <- paste0(df$group, " (", df$value, "%)") #' ggpie(df, "value", label = labs, #' fill = "group", color = "white", #' palette = c("#00AFBB", "#E7B800", "#FC4E07")) #' #' # Change the position and font color of labels #' ggpie(df, "value", label = labs, #' lab.pos = "in", lab.font = "white", #' fill = "group", color = "white", #' palette = c("#00AFBB", "#E7B800", "#FC4E07")) #' #' #' #'@export ggpie <- function( data, x, label = x, lab.pos = c("out", "in"), lab.adjust = 0, lab.font = c(4, "plain", "black"), font.family = "", color = "black", fill = "white", palette = NULL, size = NULL, ggtheme = theme_pubr(), ... ) { lab.pos <- match.arg(lab.pos) lab.font <- .parse_font(lab.font) %>% .check_pie_labfont() if(length(label) > 1 & length(label) != nrow(data)) stop("label should be of the same length as data") else if(length(label) > 1){ # 1. Add label column data <- data %>% dplyr::mutate(.label. = label) label <- ".label." } # We should order the data in desc order. Because, # in stacked bar plot the order of factor levels are reversed # Very important to have the label in the right place group.vars <- intersect(c(fill, color), names(data)) if(length(group.vars) != 0){ group.val <- dplyr::pull(data, group.vars[1]) data <- data %>% dplyr::arrange(dplyr::desc(group.val)) } # Label y coordinates when placed inside slices .x <- dplyr::pull(data, x) data <- data %>% dplyr::mutate( .lab.ypos. = cumsum(.x) -0.5*.x -lab.adjust ) p <- ggplot(data, create_aes(list(x = "1", y = x))) + geom_exec( geom_bar, data, stat = "identity", fill = fill, color = color, size = size ) p <- ggpar( p, palette = palette, ggtheme = ggtheme, font.family = font.family, ... ) + coord_polar( theta = "y", start = 0, clip = "off" ) + .remove_axis() # Annotate pie slice #::::::::::::::::::::::::::::::::::: if(!is.null(label)){ # Label each slice at the middle of the slice if(lab.pos == "out"){ p <- p + scale_y_continuous( breaks = cumsum(.x) - .x/2, labels = dplyr::pull(data, label) ) + ggplot2::theme(axis.text.x = element_text( face = lab.font$face, color = lab.font$color, size = 2.5*lab.font$size, family = font.family )) } # Compute the cumulative sum as label ypos if(lab.pos == "in"){ p <- p + geom_text( create_aes(list(y = ".lab.ypos.", label = label)), size = lab.font$size, fontface = lab.font$face, colour = lab.font$color, family = font.family )+ clean_theme() } } p } # Old version ggpie_1 <- function(data, x, label = NULL, lab.pos = c("out", "in"), lab.adjust = 0, lab.font = c(4, "bold", "black"), font.family = "", color = "black", fill = "white", palette = NULL, size = NULL, ggtheme = theme_classic(), ...) { lab.pos <- match.arg(lab.pos) lab.font <- .parse_font(lab.font) # data <- data[order(data[, x]), , drop = FALSE] if(fill %in% colnames(data)) { fill_d <- dplyr::pull(data, fill) data[[fill]] <- factor(fill_d, levels = rev(fill_d)) } if(is.null(lab.font)) lab.font <- list(size = 5, face = "bold", color = "black") p <- ggplot(data, create_aes(list(x = 1, y = x)))+ .geom_exec(geom_bar, data, stat = "identity", fill = fill, color = color, size = size) p <- ggpar(p, palette = palette, ggtheme = ggtheme, font.family = font.family, ...) p <- p + coord_polar(theta = "y", start = 0) + theme( axis.title = element_blank(), axis.text.y = element_blank(), axis.line = element_blank(), # panel.border = element_blank(), # panel.grid=element_blank(), axis.ticks = element_blank() ) # label each slice at the midle of the slice if(!is.null(label)){ if(label[1] %in% names(data)) label <- data[[label]] if(lab.pos == "out"){ p <- p + scale_y_continuous( breaks = cumsum(data[[x]]) - data[[x]]/2, labels = label ) } if(lab.pos == "in"){ df <- data y <- df[, x] df[, x] <- (y/(length(y)-lab.adjust)) + c(0, cumsum(y)[-length(y)]) lab.font$size <- ifelse(is.null(lab.font$size), 5, lab.font$size) lab.font$color <- ifelse(is.null(lab.font$color), "black", lab.font$color) lab.font$face <- ifelse(is.null(lab.font$ace), "bold", lab.font$face) p <- p + .geom_exec(geom_text, data = df, x = 1, y = x, label = label, size = lab.font$size, fontface = lab.font$face, colour = lab.font$color, family = font.family )+ theme( axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank()) } } p } # Remove axis elements .remove_axis <- function(){ theme( axis.title = element_blank(), axis.text.y = element_blank(), axis.line = element_blank(), # panel.border = element_blank(), # panel.grid=element_blank(), axis.ticks = element_blank() ) } # Check labels font .check_pie_labfont <- function(lab.font){ lab.font$size <- ifelse(is.null(lab.font$size), 3.9, lab.font$size) lab.font$color <- ifelse(is.null(lab.font$color), "black", lab.font$color) lab.font$face <- ifelse(is.null(lab.font$face), "plain", lab.font$face) lab.font } ggpubr/R/gradient_color.R0000644000176200001440000000371014332553655015073 0ustar liggesusers#' @include utilities_color.R NULL #'Set Gradient Color #' #' @description Change gradient color. #' \itemize{ #' \item \code{gradient_color()}: Change gradient color. #' \item \code{gradient_fill()}: Change gradient fill. #' } #' @param palette the color palette to be used for coloring or filling by #' groups. Allowed values include "grey" for grey color palettes; brewer #' palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", #' "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", #' "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and #' "rickandmorty". Can be also a numeric vector; in this #' case a basic color palette is created using the function #' \link[grDevices]{palette}. #' #' @seealso \link{set_palette}. #' #' #'@examples #' df <- mtcars #' p <- ggscatter(df, x = "wt", y = "mpg", #' color = "mpg") #' #' # Change gradient color #' # Use one custom color #' p + gradient_color("red") #' #' # Two colors #' p + gradient_color(c("blue", "red")) #' #' # Three colors #' p + gradient_color(c("blue", "white", "red")) #' #' # Use RColorBrewer palette #' p + gradient_color("RdYlBu") #' #' # Use ggsci color palette #' p + gradient_color("npg") #' @rdname gradient_color #' @export gradient_color <- function(palette){ if(.is_col_palette(palette)) palette <- .get_pal(palette, k = 3) n_palette <- length(palette) if(n_palette == 1) { palette <- grDevices::colorRampPalette(c("white", palette))(10) palette <- palette[c(1, 10)] n_palette <- 2 } ggplot2::scale_color_gradientn(colours = palette) } #' @rdname gradient_color #' @export gradient_fill <- function(palette){ if(.is_col_palette(palette)) palette <- .get_pal(palette, k = 3) n_palette <- length(palette) if(n_palette == 1) { palette <- grDevices::colorRampPalette(c("white", palette))(10) palette <- palette[c(1, 10)] n_palette <- 2 } ggplot2::scale_fill_gradientn(colours = palette) } ggpubr/R/stat_conf_ellipse.R0000644000176200001440000000675714332576562015615 0ustar liggesusers#' @include utilities.R NULL #' Plot confidence ellipses. #' @description Plot confidence ellipses around barycenters. The method for #' computing confidence ellipses has been modified from \code{FactoMineR::coord.ellipse()}. #' @inheritParams ggplot2::layer #' @inheritParams ggplot2::stat_ellipse #' @param level confidence level used to construct the ellipses. By #' default, 0.95. #' @param npoint number of points used to draw the ellipses. #' @param bary logical value. If TRUE, the coordinates of the ellipse around the #' barycentre of individuals are calculated. #' @seealso \code{\link{stat_conf_ellipse}} #' @examples #' # Load data #' data("mtcars") #' df <- mtcars #' df$cyl <- as.factor(df$cyl) #' #' # scatter plot with confidence ellipses #' ggscatter(df, x = "wt", y = "mpg", color = "cyl")+ #' stat_conf_ellipse(aes(color = cyl)) #' #' ggscatter(df, x = "wt", y = "mpg", color = "cyl")+ #' stat_conf_ellipse(aes(color = cyl, fill = cyl), alpha = 0.1, geom = "polygon") #' #' @export stat_conf_ellipse <- function(mapping = NULL, data = NULL, geom = "path", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, level = 0.95, npoint = 100, bary = TRUE, ...) { layer( stat = StatConfEllipse, data = data, mapping = mapping, geom = geom, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(na.rm = na.rm, level = level, npoint = npoint, bary = bary, ...) ) } StatConfEllipse <- ggproto("StatConfEllipse", Stat, required_aes = c("x", "y"), compute_group = function(data, scales, params, level = 0.95, npoint = 100, bary = TRUE) { .coord_ellipse (data$x, data$y, level = level, npoint = npoint, bary = bary) } ) # Compute confidence ellipses. # x,y x and y variables for drawing. # level confidence level used to construct the ellipses. By # default, 0.95. # npoint number of points used to draw the ellipses. # bary logical value. If TRUE, the coordinates of the ellipse around the # barycentre of individuals are calculated. .coord_ellipse <- function ( x, y, level = 0.95, npoint = 100, bary = FALSE) { .ellipse <- function(x, scale = c(1, 1), centre = c(0, 0), level = 0.95, t = sqrt(stats::qchisq(level, 2)), which = c(1, 2), npoints = 100) { names <- c("x", "y") if (is.matrix(x)) { xind <- which[1] yind <- which[2] r <- x[xind, yind] if (missing(scale)) { scale <- sqrt(c(x[xind, xind], x[yind, yind])) if (scale[1] > 0) r <- r/scale[1] if (scale[2] > 0) r <- r/scale[2] } if (!is.null(dimnames(x)[[1]])) names <- dimnames(x)[[1]][c(xind, yind)] } else r <- x r <- min(max(r, -1), 1) d <- acos(r) a <- seq(0, 2 * pi, len = npoints) matrix(c(t * scale[1] * cos(a + d/2) + centre[1], t * scale[2] * cos(a - d/2) + centre[2]), npoints, 2, dimnames = list(NULL, names)) } center <- c(mean(x, na.rm = TRUE), mean(y, na.rm = TRUE)) tab <- data.frame(x = x, y = y) mat.cov <- stats::cov(tab) if (bary) mat.cov = mat.cov/nrow(tab) res <- .ellipse(mat.cov, centre = center, level = level, npoints = npoint) return(data.frame(res, stringsAsFactors = FALSE)) } ggpubr/R/stat_chull.R0000644000176200001440000000240014332553655014235 0ustar liggesusers#' @include utilities.R ggpar.R NULL #' Plot convex hull of a set of points #' @description Plot convex hull of a set of points. #' @inheritParams ggplot2::layer #' @inheritParams ggplot2::stat_ellipse #' @seealso \code{\link{ggpar}}, \code{\link{ggscatter}} #' @examples #' # Load data #' data("mtcars") #' df <- mtcars #' df$cyl <- as.factor(df$cyl) #' #' # scatter plot with convex hull #' ggscatter(df, x = "wt", y = "mpg", color = "cyl")+ #' stat_chull(aes(color = cyl)) #' #' ggscatter(df, x = "wt", y = "mpg", color = "cyl")+ #' stat_chull(aes(color = cyl, fill = cyl), alpha = 0.1, geom = "polygon") #' #' @export stat_chull <- function(mapping = NULL, data = NULL, geom = "path", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...) { layer( stat = StatChull, data = data, mapping = mapping, geom = geom, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(na.rm = na.rm, ...) ) } # Convex ellipse StatChull <- ggproto("StatChull", Stat, compute_group = function(data, scales) { data[chull(data$x, data$y), , drop = FALSE] }, required_aes = c("x", "y") ) ggpubr/R/ggscatter.R0000644000176200001440000004734014367733317014075 0ustar liggesusers#' @include utilities.R ggpar.R stat_chull.R stat_conf_ellipse.R stat_stars.R stat_cor.R NULL #' Scatter plot #' @description Create a scatter plot. #' @inheritParams ggboxplot #' @inheritParams facet #' @inheritParams ggpar #' @param x x variables for drawing. #' @param y y variables for drawing. #' @param color,fill point colors. #' @param shape point shape. See \code{\link{show_point_shapes}}. #' @param point logical value. If TRUE, show points. #' @param rug logical value. If TRUE, add marginal rug. #' @param add allowed values are one of "none", "reg.line" (for adding linear #' regression line) or "loess" (for adding local regression fitting). #' @param add.params parameters (color, size, linetype) for the argument 'add'; #' e.g.: add.params = list(color = "red"). #' @param conf.int logical value. If TRUE, adds confidence interval. #' @param conf.int.level Level controlling confidence region. Default is 95\%. #' Used only when add != "none" and conf.int = TRUE. #' @param fullrange should the fit span the full range of the plot, or just the #' data. Used only when add != "none". #' @param ellipse logical value. If TRUE, draws ellipses around points. #' @param ellipse.level the size of the concentration ellipse in normal #' probability. #' @param ellipse.type Character specifying frame type. Possible values are #' \code{"convex"}, \code{"confidence"} or types supported by #' \code{\link[ggplot2]{stat_ellipse}()} including one of \code{c("t", "norm", #' "euclid")} for plotting concentration ellipses. #' #' \itemize{ \item \code{"convex"}: plot convex hull of a set o points. \item #' \code{"confidence"}: plot confidence ellipses arround group mean points as #' \code{FactoMineR::coord.ellipse()}. \item \code{"t"}: #' assumes a multivariate t-distribution. \item \code{"norm"}: assumes a #' multivariate normal distribution. \item \code{"euclid"}: draws a circle with #' the radius equal to level, representing the euclidean distance from the #' center. This ellipse probably won't appear circular unless #' \code{\link[ggplot2]{coord_fixed}()} is applied.} #' @param ellipse.alpha Alpha for ellipse specifying the transparency level of #' fill color. Use alpha = 0 for no fill color. #' @param ellipse.border.remove logical value. If TRUE, remove ellipse border lines. #' @param mean.point logical value. If TRUE, group mean points are added to the #' plot. #' @param mean.point.size numeric value specifying the size of mean points. #' @param star.plot logical value. If TRUE, a star plot is generated. #' @param star.plot.lty,star.plot.lwd line type and line width (size) for star #' plot, respectively. #' @param label the name of the column containing point labels. Can be also a #' character vector with length = nrow(data). #' @param font.label a vector of length 3 indicating respectively the size #' (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and #' the color (e.g.: "red") of point labels. For example \emph{font.label = #' c(14, "bold", "red")}. To specify only the size and the style, use #' font.label = c(14, "plain"). #' @param font.family character vector specifying font family. #' @param label.select character vector specifying some labels to show. #' @param repel a logical value, whether to use ggrepel to avoid overplotting #' text labels or not. #' @param label.rectangle logical value. If TRUE, add rectangle underneath the #' text, making it easier to read. #' @param parse If \code{TRUE}, the labels will be parsed into expressions and #' displayed as described in \code{?plotmath}. #' @param cor.coef logical value. If TRUE, correlation coefficient with the #' p-value will be added to the plot. #' @param cor.coeff.args a list of arguments to pass to the function #' \code{\link{stat_cor}} for customizing the displayed correlation #' coefficients. For example: \code{cor.coeff.args = list(method = "pearson", #' label.x.npc = "right", label.y.npc = "top")}. #' @param cor.method method for computing correlation coefficient. Allowed #' values are one of "pearson", "kendall", or "spearman". #' @param cor.coef.coord numeric vector, of length 2, specifying the x and y #' coordinates of the correlation coefficient. Default values are NULL. #' @param cor.coef.size correlation coefficient text font size. #' @param ggp a ggplot. If not NULL, points are added to an existing plot. #' @param show.legend.text logical. Should text be included in the legends? NA, #' the default, includes if any aesthetics are mapped. FALSE never includes, #' and TRUE always includes. #' @param ... other arguments to be passed to \code{\link[ggplot2]{geom_point}} #' and \code{\link{ggpar}}. #' @details The plot can be easily customized using the function ggpar(). Read #' ?ggpar for changing: \itemize{ \item main title and axis labels: main, #' xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis #' scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: #' palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, #' labels and position: legend = "right" \item plot orientation : orientation #' = c("vertical", "horizontal", "reverse") } #' @seealso \code{\link{stat_cor}}, \code{\link{stat_stars}}, \code{\link{stat_conf_ellipse}} and \code{\link{ggpar}}. #' @examples #' # Load data #' data("mtcars") #' df <- mtcars #' df$cyl <- as.factor(df$cyl) #' head(df[, c("wt", "mpg", "cyl")], 3) #' #' # Basic plot #' # +++++++++++++++++++++++++++ #' ggscatter(df, x = "wt", y = "mpg", #' color = "black", shape = 21, size = 3, # Points color, shape and size #' add = "reg.line", # Add regressin line #' add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line #' conf.int = TRUE, # Add confidence interval #' cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor #' cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n") #' ) #' #' # loess method: local regression fitting #' ggscatter(df, x = "wt", y = "mpg", #' add = "loess", conf.int = TRUE) #' #' #' # Control point size by continuous variable values ("qsec") #' ggscatter(df, x = "wt", y = "mpg", #' color = "#00AFBB", size = "qsec") #' #' #' # Change colors #' # +++++++++++++++++++++++++++ #' # Use custom color palette #' # Add marginal rug #' ggscatter(df, x = "wt", y = "mpg", color = "cyl", #' palette = c("#00AFBB", "#E7B800", "#FC4E07") ) #' #' #' #' #' # Add group ellipses and mean points #' # Add stars #' # +++++++++++++++++++ #' ggscatter(df, x = "wt", y = "mpg", #' color = "cyl", shape = "cyl", #' palette = c("#00AFBB", "#E7B800", "#FC4E07"), #' ellipse = TRUE, mean.point = TRUE, #' star.plot = TRUE) #' #' #' # Textual annotation #' # +++++++++++++++++ #' df$name <- rownames(df) #' ggscatter(df, x = "wt", y = "mpg", #' color = "cyl", palette = c("#00AFBB", "#E7B800", "#FC4E07"), #' label = "name", repel = TRUE) #' #' #' @export ggscatter <- function(data, x, y, combine = FALSE, merge = FALSE, color = "black", fill = "lightgray", palette = NULL, shape = 19, size = 2, point = TRUE, rug = FALSE, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, add = c("none", "reg.line", "loess"), add.params = list(), conf.int = FALSE, conf.int.level = 0.95, fullrange = FALSE, ellipse = FALSE, ellipse.level = 0.95, ellipse.type = "norm", ellipse.alpha = 0.1, ellipse.border.remove = FALSE, mean.point = FALSE, mean.point.size = ifelse(is.numeric(size), 2*size, size), star.plot = FALSE, star.plot.lty = 1, star.plot.lwd = NULL, label = NULL, font.label = c(12, "plain"), font.family = "", label.select = NULL, repel = FALSE, label.rectangle = FALSE, parse = FALSE, cor.coef = FALSE, cor.coeff.args = list(), cor.method = "pearson", cor.coef.coord = c(NULL, NULL), cor.coef.size = 4, ggp = NULL, show.legend.text = NA, ggtheme = theme_pubr(), ...){ add <- match.arg(add) # Default options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .opts <- list( combine = combine, merge = merge, color = color, fill = fill, palette = palette, title = title, xlab = xlab, ylab = ylab, facet.by = facet.by, panel.labs = panel.labs, short.panel.labs = short.panel.labs, shape = shape, size = size, point = point, rug = rug, add = add, add.params = add.params, conf.int = conf.int, conf.int.level = conf.int.level, fullrange = fullrange, ellipse = ellipse, ellipse.level = ellipse.level, ellipse.type = ellipse.type, ellipse.alpha = ellipse.alpha, ellipse.border.remove = ellipse.border.remove, mean.point = mean.point, mean.point.size = mean.point.size, star.plot = star.plot, star.plot.lty = star.plot.lty, star.plot.lwd = star.plot.lwd, label = label, font.label = font.label, font.family = font.family, label.select = label.select, repel = repel, label.rectangle = label.rectangle, parse = parse, cor.coef = cor.coef, cor.coeff.args = cor.coeff.args, cor.method = cor.method, cor.coef.coord = cor.coef.coord, cor.coef.size = cor.coef.size, ggp = ggp, show.legend.text = show.legend.text, ggtheme = ggtheme, ...) if(!missing(data)) .opts$data <- data if(!missing(x)) .opts$x <- x if(!missing(y)) .opts$y <- y # User options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .user.opts <- as.list(match.call(expand.dots = TRUE)) .user.opts[[1]] <- NULL # Remove the function name # keep only user arguments for(opt.name in names(.opts)){ if(is.null(.user.opts[[opt.name]])) .opts[[opt.name]] <- NULL } font.label <- .parse_font(font.label) %>% .compact() font.label$color <- ifelse(is.null(font.label$color), color, font.label$color) .opts$font.label <- font.label .opts$fun <- ggscatter_core if(missing(ggtheme) & (!is.null(facet.by) | combine)) .opts$ggtheme <- theme_pubr(border = TRUE) p <- do.call(.plotter, .opts) if(.is_list(p) & length(p) == 1) p <- p[[1]] return(p) } ggscatter_core <- function(data, x, y, color = "black", fill = "lightgray", palette = NULL, shape = 19, size = 2, point = TRUE, rug = FALSE, title = NULL, xlab = NULL, ylab = NULL, add = c("none", "reg.line", "loess"), add.params = list(), conf.int = FALSE, conf.int.level = 0.95, fullrange = FALSE, ellipse = FALSE, ellipse.level = 0.95, ellipse.type = "norm", ellipse.alpha = 0.1, ellipse.border.remove = FALSE, mean.point = FALSE, mean.point.size = ifelse(is.numeric(size), 2*size, size), star.plot = FALSE, star.plot.lty = 1, star.plot.lwd = NULL, label = NULL, font.label = c(12, "plain"), font.family = "", label.select = NULL, repel = FALSE, label.rectangle = FALSE, parse = FALSE, cor.coef = FALSE, cor.coeff.args = list(), cor.method = "pearson", cor.coef.coord = c(NULL, NULL), cor.coef.size = 4, ggp = NULL, show.legend.text = NA, ggtheme = theme_classic(), ...) { add <- match.arg(add) add.params <- .check_add.params(add, add.params, error.plot = "", data, color, fill, ...) if(length(label) >1){ if(length(label) != nrow(data)) stop("The argument label should be a column name or a vector of length = nrow(data). ", "It seems that length(label) != nrow(data)") else data$label.xx <- label label <- "label.xx" } # label font font.label <- .parse_font(font.label) font.label$size <- ifelse(is.null(font.label$size), 12, font.label$size) font.label$color <- ifelse(is.null(font.label$color), color, font.label$color) font.label$face <- ifelse(is.null(font.label$face), "plain", font.label$face) if(is.null(ggp)) p <- ggplot(data, create_aes(list(x = x, y = y))) else p <- ggp if(point) p <- p + .geom_exec(geom_point, data = data, x = x, y = y, color = color, fill = fill, size = size, shape = shape, ...) # Adjust shape when ngroups > 6, to avoid ggplot warnings if(shape %in% colnames(data)){ ngroups <- length(levels(data[[shape]])) if(ngroups > 6) p <- p + scale_shape_manual(values=1:ngroups, labels = levels(data[[shape]])) } # Add marginal rug # +++++++++++ if(rug) p <- p + .geom_exec(geom_rug, data = data, color = color, size = size/2) # Add reg line or loess # ++++++++++++ if(add %in% c("reg.line", "loess")){ add <- ifelse(add == "reg.line", stats::lm, stats::loess) if(is.null(add.params$linetype)) add.params$linetype <- "solid" if(conf.int == FALSE) add.params$fill <- "lightgray" .args <- .geom_exec(NULL, data = data, se = conf.int, level = conf.int.level, color = add.params$color, fill = add.params$fill, linetype = add.params$linetype, size = add.params$size, fullrange = fullrange) mapping <- .args$mapping option <- .args$option option[["method"]] <- add option[["formula"]] <- y ~ x option[["mapping"]] <- create_aes(mapping) p <- p + do.call(geom_smooth, option) } # Add ellipses # +++++++++++ if(ellipse){ grp <- intersect(unique(c(color, fill, shape)), colnames(data))[1] # NO grouping variable if(is.na(grp)) { grp <- factor(rep(1, nrow(data))) grp_name <- "group" data$group <- grp } # Case of grouping variable else { grp_name <- grp data[[grp_name]] <- as.factor(data[[grp_name]]) } if (ellipse.type == 'convex') p <- p + .convex_ellipse(data, x, y, grp_name, color, fill, ellipse.alpha, ellipse.border.remove = ellipse.border.remove) else if(ellipse.type == "confidence") p <- p + .confidence_ellipse(data, x, y, grp_name, color, fill, alpha = ellipse.alpha, level = ellipse.level, ellipse.border.remove = ellipse.border.remove) else if (ellipse.type %in% c('t', 'norm', 'euclid')) p <- p + .stat_ellipse(data, x, y, grp_name, color = color, fill = fill, alpha = ellipse.alpha, type = ellipse.type, level = ellipse.level, ellipse.border.remove = ellipse.border.remove) } # /ellipse # Add mean points # +++++++++ if(mean.point) { p <- p + .geom_exec(stat_mean, data = data, color = color, shape = shape, fill = fill, size = mean.point.size) } # Star plots # ++++++++++++ if(star.plot){ p <- p + .geom_exec(stat_stars, data = data, color = color, linetype = star.plot.lty, size = star.plot.lwd) } #/ star plots # Add textual annotation # ++++++ alpha <- 1 if(!is.null(list(...)$alpha)) alpha <- list(...)$alpha if(!is.null(label)) { lab_data <- data # Select some labels to show if(!is.null(label.select)) lab_data <- subset(lab_data, lab_data[[label]] %in% label.select, drop = FALSE) if(repel){ max.overlaps = getOption("ggrepel.max.overlaps", default = Inf) ggfunc <- ggrepel::geom_text_repel if(label.rectangle) ggfunc <- ggrepel::geom_label_repel p <- p + .geom_exec(ggfunc, data = lab_data, x = x, y = y, label = label, fontface = font.label$face, parse = parse, size = font.label$size/3, color = font.label$color, alpha = alpha, family = font.family, box.padding = unit(0.35, "lines"), point.padding = unit(0.3, "lines"), force = 1, show.legend = show.legend.text, seed=123, max.overlaps = max.overlaps) } else{ ggfunc <- geom_text vjust <- -0.7 if(label.rectangle) { ggfunc <- geom_label vjust <- -0.4 } p <- p + .geom_exec(ggfunc, data = lab_data, x = x, y = y, color = color, label = label, fontface = font.label$face, family = font.family, parse = parse, size = font.label$size/3, color = font.label$color, vjust = vjust, alpha = alpha, show.legend = show.legend.text) } } # Add correlation coefficient if(cor.coef){ if(!missing(cor.method)) cor.coeff.args$method <- cor.method if(!missing(cor.coef.size)) cor.coeff.args$size <- cor.coef.size if(!missing(cor.coef.coord)){ cor.coeff.args$label.x <- cor.coef.coord[1] cor.coeff.args$label.y <- cor.coef.coord[2] } p <- p + do.call(stat_cor, cor.coeff.args) } p <- ggpar(p, palette = palette, ggtheme = ggtheme, title = title, xlab = xlab, ylab = ylab,...) if(font.family != "") p <- p + theme(text = element_text(family = font.family)) p } # Add convex ellipse # data a data frame # x,y: x and y variables # grp_name: grp variable .convex_ellipse <- function(data, x, y, grp_name, color = "black", fill = "lightgray", alpha = 0.1, ellipse.border.remove = FALSE ){ grp_levels <- levels(data[[grp_name]]) if(length(grp_levels) == 1) .geom_exec(geomfunc = stat_chull, data = data, color = color, fill = fill, alpha = alpha, geom = "polygon") else { if( ellipse.border.remove) color <- NULL else color = grp_name .geom_exec(geomfunc = stat_chull, data = data, color = color, fill = grp_name, alpha = alpha, geom = "polygon") } } # Confidence ellipse .confidence_ellipse <- function(data, x, y, grp_name, color = "black", fill = "lightgray", alpha = 0.1, level = 0.95, ellipse.border.remove = FALSE){ grp_levels <- levels(data[[grp_name]]) if(length(grp_levels) == 1) { mapping <- create_aes(list(x = x, y = y)) stat_conf_ellipse(mapping = mapping, data = data, color = color, fill = fill, alpha = alpha, level = level, geom = "polygon") } else { mapping = create_aes(list(x = x, y = y, colour = grp_name, fill = grp_name)) if(ellipse.border.remove ) mapping = create_aes(list(x = x, y = y, colour = NULL, fill = grp_name)) stat_conf_ellipse(mapping = mapping, data = data, level = level, alpha = alpha, geom = 'polygon') } } # Add ggplot2 stat ellipse .stat_ellipse <- function(data, x, y, grp_name, color = "black", fill = "lightgray", alpha = 0.1, type = "norm", level = 0.95, ellipse.border.remove = FALSE) { grp_levels <- levels(data[[grp_name]]) if(length(grp_levels) == 1){ mapping <- create_aes(list(x = x, y = y)) ggplot2::stat_ellipse(mapping = mapping, data = data, level = level, type = type, colour = color, fill = fill, alpha = alpha, geom = 'polygon') } else{ mapping = create_aes(list(x = x, y = y, colour = grp_name, group = grp_name, fill = grp_name)) if(ellipse.border.remove) mapping = create_aes(list(x = x, y = y, group = grp_name, fill = grp_name)) ggplot2::stat_ellipse(mapping = mapping, data = data, level = level, type = type, alpha = alpha, geom = 'polygon') } } ggpubr/R/border.R0000644000176200001440000000131114332553655013350 0ustar liggesusers#'Set ggplot Panel Border Line #' #'@description Change or set ggplot panel border. #'@param color border line color. #'@param size numeric value specifying border line size. #'@param linetype line type. An integer (0:8), a name (blank, solid, dashed, #' dotted, dotdash, longdash, twodash). Sess \code{\link{show_line_types}}. #' #' #'@examples #'# Load data #'data("ToothGrowth") #'df <- ToothGrowth #' #'# Basic plot #'p <- ggboxplot(df, x = "dose", y = "len") #'p #' #'# Add border #' p + border() #'@export border <- function(color = "black", size = 0.8, linetype = NULL){ theme(panel.background = element_rect(color = color, size = size, linetype = linetype), axis.line = element_blank()) } ggpubr/R/desc_statby.R0000644000176200001440000000524514337240660014403 0ustar liggesusers#' Descriptive statistics by groups #' #' @description Computes descriptive statistics by groups for a measure variable. #' #' @param data a data frame. #' @param measure.var the name of a column containing the variable to be summarized. #' @param grps a character vector containing grouping variables; e.g.: grps = c("grp1", "grp2") #' @param ci the percent range of the confidence interval (default is 0.95). #' #' @return #' A data frame containing descriptive statistics, such as: #' \itemize{ #' \item \strong{length}: the number of elements in each group #' \item \strong{min}: minimum #' \item \strong{max}: maximum #' \item \strong{median}: median #' \item \strong{mean}: mean #' \item \strong{iqr}: interquartile range #' \item \strong{mad}: median absolute deviation (see ?MAD) #' \item \strong{sd}: standard deviation of the sample #' \item \strong{se}: standard error of the mean. It's calculated as the sample standard deviation divided by the root of the sample size. #' \item \strong{ci}: confidence interval of the mean #' \item \strong{range}: the range = max - min #' \item \strong{cv}: coefficient of variation, sd/mean #' \item \strong{var}: variance, sd^2 #' } #' #' @examples #' # Load data #'data("ToothGrowth") #' #'# Descriptive statistics #'res <- desc_statby(ToothGrowth, measure.var = "len", #' grps = c("dose", "supp")) #' head(res[, 1:10]) #' #' @export desc_statby <- function(data, measure.var, grps, ci = 0.95){ if(!inherits(data, "data.frame")) stop("data must be a data.frame.") . <- NULL data %>% as.data.frame() %>% df_group_by(vars = grps) %>% do(.summary(.[, measure.var], ci = ci)) %>% as.data.frame() } # Helper function to compute summary statistics .summary <- function(x, ci = 0.95){ if(is.data.frame(x)){ if(ncol(x) == 1) x <- .select_vec(x, 1) } if(!is.numeric(x)) stop("x should be a numeric vector or a data frame with one numeric column") data_sum <- data.frame( length = base::sum(!is.na(x)), min = base::min(x, na.rm=TRUE), max = base::max(x, na.rm=TRUE), median = stats::median(x, na.rm=TRUE), mean = base::mean(x, na.rm=TRUE), iqr = stats::IQR(x, na.rm=TRUE), mad = stats::mad(x, na.rm=TRUE), sd = stats::sd(x, na.rm=TRUE) ) data_sum$se <- data_sum$sd / sqrt(data_sum$length) # standard error # Confidence interval from t-distribution # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1 data_sum$ci <- stats::qt(ci/2 + .5, data_sum$length-1)*data_sum$se data_sum$range <- data_sum$max - data_sum$min data_sum$cv <- data_sum$sd/data_sum$mean data_sum$var <- data_sum$sd^2 data_sum[, 2:ncol(data_sum)] <- data_sum[, 2:ncol(data_sum)] return(data_sum) } ggpubr/R/utils-geom-signif.R0000644000176200001440000000037014332553655015441 0ustar liggesusers#' geom_signif exported from ggsignif package #' #' See \code{ggsignif::\link[ggsignif:stat_signif]{geom_signif}} for details. #' #' @name geom_signif #' @rdname geom_signif #' @keywords internal #' @export #' @importFrom ggsignif geom_signif NULL ggpubr/R/stat_mean.R0000644000176200001440000000267714332553655014066 0ustar liggesusers#' @include utilities.R NULL #' Draw group mean points #' @description Draw the mean point of each group. #' @inheritParams ggplot2::layer #' @param ... other arguments to pass to \code{\link[ggplot2]{geom_point}}. #' @param na.rm If FALSE (the default), removes missing values with a warning. #' If TRUE silently removes missing values. #' @seealso \code{\link{stat_conf_ellipse}}, \code{\link{stat_chull}} and #' \code{\link{ggscatter}} #' @examples #' # Load data #' data("mtcars") #' df <- mtcars #' df$cyl <- as.factor(df$cyl) #' #' # Scatter plot with ellipses and group mean points #' ggscatter(df, x = "wt", y = "mpg", #' color = "cyl", shape = "cyl", ellipse = TRUE)+ #' stat_mean(aes(color = cyl, shape = cyl), size = 4) #' #' @export stat_mean <- function(mapping = NULL, data = NULL, geom = "point", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...) { layer( stat = StatMean, data = data, mapping = mapping, geom = geom, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(na.rm = na.rm, ...) ) } StatMean<- ggproto("StatMean", Stat, required_aes = c("x", "y"), compute_group = function(data, scales) { res <- data.frame(x = mean(data$x, na.rm = TRUE), y = mean(data$y, na.rm = TRUE)) res } ) ggpubr/R/ggecdf.R0000644000176200001440000000667014332553655013327 0ustar liggesusers#' @include utilities.R ggpar.R NULL #' Empirical cumulative density function #' @description Empirical Cumulative Density Function (ECDF). #' @inheritParams ggboxplot #' @param x variable to be drawn. #' @param color line and point color. #' @param linetype line type. See \code{\link{show_line_types}}. #' @param size line and point size. #' @param ... other arguments to be passed to #' \code{\link[ggplot2]{stat_ecdf}} and \code{\link{ggpar}}. #' @details The plot can be easily customized using the function ggpar(). Read #' ?ggpar for changing: \itemize{ \item main title and axis labels: main, #' xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis #' scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: #' palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, #' labels and position: legend = "right" \item plot orientation : orientation #' = c("vertical", "horizontal", "reverse") } #' @seealso \code{\link{ggpar}} #' #' @examples #' # Create some data format #' set.seed(1234) #' wdata = data.frame( #' sex = factor(rep(c("F", "M"), each=200)), #' weight = c(rnorm(200, 55), rnorm(200, 58))) #' #' head(wdata, 4) #' #' # Basic ECDF plot #' ggecdf(wdata, x = "weight") #' #' # Change colors and linetype by groups ("sex") #' # Use custom palette #' ggecdf(wdata, x = "weight", #' color = "sex", linetype = "sex", #' palette = c("#00AFBB", "#E7B800")) #' #' @export ggecdf <- function(data, x, combine = FALSE, merge = FALSE, color = "black", palette = NULL, size = NULL, linetype = "solid", title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, ggtheme = theme_pubr(), ...){ # Default options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .opts <- list( combine = combine, merge = merge, color = color, palette = palette, linetype = linetype, size = size, title = title, xlab = xlab, ylab = ylab, facet.by = facet.by, panel.labs = panel.labs, short.panel.labs = short.panel.labs, ggtheme = ggtheme, ...) if(!missing(data)) .opts$data <- data if(!missing(x)) .opts$x <- x # User options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .user.opts <- as.list(match.call(expand.dots = TRUE)) .user.opts[[1]] <- NULL # Remove the function name # keep only user arguments for(opt.name in names(.opts)){ if(is.null(.user.opts[[opt.name]])) .opts[[opt.name]] <- NULL } .opts$fun <- ggecdf_core .opts$y <- "..ecdf.." if(missing(ggtheme) & (!is.null(facet.by) | combine)) .opts$ggtheme <- theme_pubr(border = TRUE) p <- do.call(.plotter, .opts) if(.is_list(p) & length(p) == 1) p <- p[[1]] return(p) } ggecdf_core <- function(data, x, y = "..ecdf..", color = "black", palette = NULL, size = NULL, linetype = "solid", title = NULL, xlab = NULL, ylab = NULL, ggtheme = theme_classic(), ...) { p <- ggplot(data, create_aes(list(x = x))) p <- p + geom_exec(stat_ecdf, data = data, color = color, size = size, linetype = linetype)+ labs(y = paste0("F(", x, ")")) p <- ggpar(p, palette = palette, ggtheme = ggtheme, title = title, xlab = xlab, ylab = ylab,...) p } ggpubr/R/stat_cor.R0000644000176200001440000002601714332553655013723 0ustar liggesusers#' @include utilities.R utilities_label.R NULL #'Add Correlation Coefficients with P-values to a Scatter Plot #'@description Add correlation coefficients with p-values to a scatter plot. Can #' be also used to add `R2`. #'@inheritParams ggplot2::layer #'@param method a character string indicating which correlation coefficient (or #' covariance) is to be computed. One of "pearson" (default), "kendall", or #' "spearman". #'@param alternative a character string specifying the alternative hypothesis, #' must be one of "two.sided" (default), "greater" or "less". You can specify #' just the initial letter. #'@param cor.coef.name character. Can be one of \code{"R"} (pearson coef), #' \code{"rho"} (spearman coef) and \code{"tau"} (kendall coef). Uppercase and #' lowercase are allowed. #'@param label.sep a character string to separate the terms. Default is ", ", to #' separate the correlation coefficient and the p.value. #'@param label.x.npc,label.y.npc can be \code{numeric} or \code{character} #' vector of the same length as the number of groups and/or panels. If too #' short they will be recycled. \itemize{ \item If \code{numeric}, value should #' be between 0 and 1. Coordinates to be used for positioning the label, #' expressed in "normalized parent coordinates". \item If \code{character}, #' allowed values include: i) one of c('right', 'left', 'center', 'centre', #' 'middle') for x-axis; ii) and one of c( 'bottom', 'top', 'center', 'centre', #' 'middle') for y-axis.} #' #' If too short they will be recycled. #'@param label.x,label.y \code{numeric} Coordinates (in data units) to be used #' for absolute positioning of the label. If too short they will be recycled. #'@param output.type character One of "expression", "latex", "tex" or "text". #'@param digits,r.digits,p.digits integer indicating the number of decimal #' places (round) or significant digits (signif) to be used for the correlation #' coefficient and the p-value, respectively.. #'@param r.accuracy a real value specifying the number of decimal places of #' precision for the correlation coefficient. Default is NULL. Use (e.g.) 0.01 #' to show 2 decimal places of precision. If specified, then \code{r.digits} is #' ignored. #'@param p.accuracy a real value specifying the number of decimal places of #' precision for the p-value. Default is NULL. Use (e.g.) 0.0001 to show 4 #' decimal places of precision. If specified, then \code{p.digits} is ignored. #'@param ... other arguments to pass to \code{\link[ggplot2]{geom_text}} or #' \code{\link[ggplot2:geom_text]{geom_label}}. #'@param na.rm If FALSE (the default), removes missing values with a warning. If #' TRUE silently removes missing values. #'@seealso \code{\link{ggscatter}} #'@section Computed variables: \describe{ \item{r}{correlation coefficient} #' \item{rr}{correlation coefficient squared} \item{r.label}{formatted label #' for the correlation coefficient} \item{rr.label}{formatted label for the #' squared correlation coefficient} \item{p.label}{label for the p-value} #' \item{label}{default labeldisplayed by \code{stat_cor()}} } #' #' @examples #' # Load data #' data("mtcars") #' df <- mtcars #' df$cyl <- as.factor(df$cyl) #' #' # Scatter plot with correlation coefficient #' #::::::::::::::::::::::::::::::::::::::::::::::::: #' sp <- ggscatter(df, x = "wt", y = "mpg", #' add = "reg.line", # Add regressin line #' add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line #' conf.int = TRUE # Add confidence interval #' ) #' # Add correlation coefficient #' sp + stat_cor(method = "pearson", label.x = 3, label.y = 30) #' #' # Specify the number of decimal places of precision for p and r #' # Using 3 decimal places for the p-value and #' # 2 decimal places for the correlation coefficient (r) #' sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01) #' #' # Show only the r.label but not the p.label #' sp + stat_cor(aes(label = ..r.label..), label.x = 3) #' #'# Use R2 instead of R #'ggscatter(df, x = "wt", y = "mpg", add = "reg.line") + #' stat_cor( #' aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~")), #' label.x = 3 #' ) #' #' # Color by groups and facet #' #:::::::::::::::::::::::::::::::::::::::::::::::::::: #' sp <- ggscatter(df, x = "wt", y = "mpg", #' color = "cyl", palette = "jco", #' add = "reg.line", conf.int = TRUE) #' sp + stat_cor(aes(color = cyl), label.x = 3) #' #'@export stat_cor <- function(mapping = NULL, data = NULL, method = "pearson", alternative = "two.sided", cor.coef.name = c("R", "rho", "tau"), label.sep = ", ", label.x.npc = "left", label.y.npc = "top", label.x = NULL, label.y = NULL, output.type = "expression", digits = 2, r.digits = digits, p.digits = digits, r.accuracy = NULL, p.accuracy = NULL, geom = "text", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...) { parse <- ifelse(output.type == "expression", TRUE, FALSE) cor.coef.name = cor.coef.name[1] layer( stat = StatCor, data = data, mapping = mapping, geom = geom, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(label.x.npc = label.x.npc , label.y.npc = label.y.npc, label.x = label.x, label.y = label.y, label.sep = label.sep, method = method, alternative = alternative, output.type = output.type, digits = digits, r.digits = r.digits, p.digits = p.digits, r.accuracy = r.accuracy, p.accuracy = p.accuracy, cor.coef.name = cor.coef.name, parse = parse, na.rm = na.rm, ...) ) } StatCor<- ggproto("StatCor", Stat, required_aes = c("x", "y"), default_aes = aes(hjust = ..hjust.., vjust = ..vjust..), compute_group = function(data, scales, method, alternative, label.x.npc, label.y.npc, label.x, label.y, label.sep, output.type, digits, r.digits, p.digits, r.accuracy, p.accuracy, cor.coef.name) { if (length(unique(data$x)) < 2) { # Not enough data to perform test return(data.frame()) } # Returns a data frame with estimate, p.value, label, method .test <- .cor_test( data$x, data$y, method = method, alternative = alternative, label.sep = label.sep, output.type = output.type, digits = digits, r.digits = r.digits, p.digits = p.digits, r.accuracy = r.accuracy, p.accuracy = p.accuracy, cor.coef.name = cor.coef.name ) # Returns a data frame with label: x, y, hjust, vjust .label.pms <- .label_params(data = data, scales = scales, label.x.npc = label.x.npc, label.y.npc = label.y.npc, label.x = label.x, label.y = label.y ) %>% mutate(hjust = 0) cbind(.test, .label.pms) } ) # Correlation test #:::::::::::::::::::::::::::::::::::::::: # Returns a data frame: estimatel|p.value|method|label .cor_test <- function(x, y, method = "pearson", alternative = "two.sided", label.sep = ", ", output.type = "expression", digits = 2, r.digits = digits, p.digits = digits, r.accuracy = NULL, p.accuracy = NULL, cor.coef.name = "R"){ # Overwritting digits by accuracy, if specified if(!is.null(p.accuracy)){ nb_decimal_places <- round(abs(log10(p.accuracy))) p.digits <- nb_decimal_places } if(!is.null(r.accuracy)){ nb_decimal_places <- round(abs(log10(r.accuracy))) r.digits <- nb_decimal_places } # Correlation analyses .cor <- suppressWarnings(stats::cor.test( x, y, method = method, alternative = alternative, use = "complete.obs" )) estimate <- p.value <- p <- r <- rr <- NULL z <- data.frame(estimate = .cor$estimate, p.value = .cor$p.value, method = method) %>% mutate( r = signif(estimate, r.digits), rr = signif(estimate^2, r.digits), p = signif(p.value, p.digits) ) # Defining p and r labels pval <- .cor$p.value z <- z %>% dplyr::mutate( r.label = get_corcoef_label( r, accuracy = r.accuracy, prefix = "R", cor.coef.name = cor.coef.name, type = output.type ), rr.label = get_corcoef_label( rr, accuracy = r.accuracy, prefix = "R2", cor.coef.name = cor.coef.name, type = output.type ), p.label = get_p_label( p, accuracy = p.accuracy, type = output.type ) ) # Defining correlation labels if(output.type == "expression"){ if(label.sep == "\n"){ # Line break at each comma cortxt <- paste0("atop(", z$r.label, ",", z$p.label, ")") } else{ label.sep <- trimws(label.sep) if(label.sep == "") label.sep <- "~" # Using "*" to avoid the space between the R2 value and comma else label.sep <- paste0("*`", label.sep, "`~") cortxt <- paste0(z$r.label, label.sep, z$p.label) } } else if (output.type %in% c("latex", "tex", "text")){ cortxt <- paste0(z$r.label, label.sep, z$p.label) } z$label <- cortxt z } # Formatting R and P ---------------------- get_p_label <- function(x, accuracy = 0.0001, type = "expression"){ if(is.null(accuracy)){ label <- ifelse(x < 2.2e-16, "p < 2.2e-16", paste0("p = ", x)) } else if (!(accuracy < 1)){ stop( "Accuracy should be < 1; For example use 0.01, 0.001, 0.0001, etc.", call. = FALSE ) } else{ label <- scales::pvalue(x, accuracy = accuracy, add_p = TRUE) # Add space before and after: = or < label <- gsub(pattern = "(=|<)", replacement = " \\1 ", x = label) } if(type == "expression"){ label <- gsub(pattern = "p = ", replacement = "italic(p)~`=`~", x = label, fixed = TRUE) label <- gsub(pattern = "p < ", replacement = "italic(p)~`<`~", x = label, fixed = TRUE) } label } # Prefix can be R or R^2. # cor.coef.name: R, rho, tau get_corcoef_label <- function(x, accuracy = 0.01, prefix = "R", cor.coef.name = "R", type = "expression"){ if(is.null(accuracy)){ label <- paste0(prefix, " = ", x) } else if(!(accuracy < 1)){ stop( "Accuracy should be < 1; For example use 0.01, 0.001, 0.0001, etc.", call. = FALSE ) } else{ nb_decimal_places <- round(abs(log10(accuracy))) label <- formatC(x, digits = nb_decimal_places, format = "f") label <- paste0(prefix, " = ", label) } if(type == "expression"){ label <- gsub(pattern = "R2 = ", replacement = "italic(R)^2~`=`~", x = label, fixed = TRUE) label <- gsub(pattern = "R = ", replacement = "italic(R)~`=`~", x = label, fixed = TRUE) } label <- gsub(pattern = "R", cor.coef.name, x = label, fixed = TRUE) label } ggpubr/R/theme_transparent.R0000644000176200001440000000221514332553655015622 0ustar liggesusers#' @include utilities.R #' @import ggplot2 NULL #'Create a ggplot with Transparent Background #' #'@description Create a ggplot with transparent background. #'@param base_size base font size #'@param base_family base font family #'@seealso \code{\link{theme_pubr}} #' @examples #'# Create a scatter plot #'sp <- ggscatter(iris, x = "Sepal.Length", y = "Sepal.Width", #' color = "Species", palette = "jco", #' size = 3, alpha = 0.6) #'sp #' #'# Transparent theme #'sp + theme_transparent() #' #'@export theme_transparent <- function (base_size = 12, base_family = "") { theme_classic(base_size = base_size, base_family = base_family) + theme( panel.background = element_rect(fill = "transparent", colour = NA), plot.background = element_rect(fill = "transparent", colour = NA), legend.background = element_rect(fill = "transparent", colour = NA), legend.box.background = element_rect(fill = "transparent", colour = NA), axis.line = element_blank(), axis.title = element_blank(), plot.title = element_blank(), axis.text = element_blank(), axis.ticks = element_blank() ) } ggpubr/R/utils-tidyr.R0000644000176200001440000000076414336762430014374 0ustar liggesusers# Transform data into long format-------------------- # Replacement of deprecated tidyr::gather_() # wrapper around tidyr::pivot_longer() but using `cols_vary = "slowest"` as default # instead of `cols_vary = "fastest"` df_gather <- function(data, cols, cols_vary = "slowest", names_to = "name", values_to = "value", ...){ data %>% tidyr::pivot_longer( cols = dplyr::all_of(cols), names_to = names_to, values_to = values_to, cols_vary = cols_vary, ... ) } ggpubr/R/text_grob.R0000644000176200001440000000306014332553655014073 0ustar liggesusers#' @include utilities.R NULL #' Create a Text Graphical object #' @description Create easily a customized text grob (graphical object). Wrapper around #' \code{\link[grid:grid.text]{textGrob}}. #' @inheritParams grid::grid.text #' @param color text font color. #' @param face font face. Allowed values include one of \code{"plain", "bold", #' "italic", "bold.italic"}. #' @param size font size (e.g.: size = 12) #' @param lineheight line height (e.g.: lineheight = 2). #' @param family font family. #' @param ... other arguments passed to \link[grid:grid.text]{textGrob}. #' @return a text grob. #' @examples #'text <- paste("iris data set gives the measurements in cm", #' "of the variables sepal length and width", #' "and petal length and width, respectively,", #' "for 50 flowers from each of 3 species of iris.", #' "The species are Iris setosa, versicolor, and virginica.", sep = "\n") #' #'# Create a text grob #'tgrob <- text_grob(text, face = "italic", color = "steelblue") #'# Draw the text #'as_ggplot(tgrob) #' #' @export text_grob <- function(label, just = "centre", hjust = NULL, vjust = NULL, rot = 0, color = "black", face = "plain", size = NULL, lineheight = NULL, family = NULL, ...) { gp <- grid::gpar(col = color, fontface = face, fontsize = size, lineheight = lineheight, fontfamily = family) tgrob <- grid::textGrob(label = label, just = just, hjust = hjust, vjust = vjust, rot = rot, gp = gp, ...) tgrob } ggpubr/R/utilities_color.R0000644000176200001440000001475714332553655015326 0ustar liggesusers .brewerpal <- function(){ c( # sequential 'Blues', 'BuGn', 'BuPu', 'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu', 'Reds', 'YlGn', 'YlGnBu YlOrBr', 'YlOrRd', #Divergent 'BrBG', 'PiYG', 'PRGn', 'PuOr', 'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral', # Qualitative 'Accent', 'Dark2', 'Paired', 'Pastel1', 'Pastel2', 'Set1', 'Set2', 'Set3' ) } .ggscipal <- function(){ # Scientific Journal and Sci-Fi Themed Color Palettes for ggplot2 # ggsci package: https://cran.r-project.org/web/packages/ggsci/vignettes/ggsci.html c("npg", "aaas", "nejm", "lancet", "jama", "jco", "ucscgb", "d3", "locuszoom", "igv", "uchicago", "startrek", "tron", "futurama", "rickandmorty", "simpsons") } # Check if color palette or default hue .is_col_palette <- function(pal){ if(is.null(pal)) return(FALSE) else return(length(pal)==1 & pal[1] %in% c(.brewerpal(), .ggscipal(), "default", "hue", "grey_pal", "gray_pal")) } .is_color_palette <- .is_col_palette # alias # Change color manually # possible value for palette: brewer palette, "grey" or a vector of colors .ggcolor <- function(palette = NULL, ...) { color_palette(palette = palette, ...) } # Change fill color manually # possible value for palette: brewer palette, "grey" or a vector of colors .ggfill <- function(palette = NULL, ...) { fill_palette(palette = palette, ...) } # Helper function to use palette from ggsci package # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% .scale_color_ggsci <- function(palette = c("npg", "aaas", "nejm", "lancet", "jama", "jco", "ucscgb", "d3", "locuszoom", "igv", "uchicago", "startrek", "tron", "futurama", "rickandmorty", "simpsons")) { pal <- match.arg(palette) functs <- list( npg = ggsci::scale_color_npg(), aaas = ggsci::scale_color_aaas(), nejm = ggsci::scale_color_nejm(), lancet = ggsci::scale_color_lancet(), jama = ggsci::scale_color_jama(), jco = ggsci::scale_color_jco(), ucscgb = ggsci::scale_color_ucscgb(), d3 = ggsci::scale_color_d3(), locuszoom = ggsci::scale_color_locuszoom(), igv = ggsci::scale_color_igv(), uchicago = ggsci::scale_color_uchicago(), startrek = ggsci::scale_color_startrek(), tron = ggsci::scale_color_tron(), futurama = ggsci::scale_color_futurama(), rickandmorty = ggsci::scale_color_rickandmorty(), simpsons = ggsci::scale_color_simpsons() ) functs[[pal]] } .scale_fill_ggsci <- function(palette = c("npg", "aaas", "nejm", "lancet", "jama", "jco", "ucscgb", "d3", "locuszoom", "igv", "uchicago", "startrek", "tron", "futurama", "rickandmorty", "simpsons")) { pal <- match.arg(palette) functs <- list( npg = ggsci::scale_fill_npg(), aaas = ggsci::scale_fill_aaas(), nejm = ggsci::scale_fill_nejm(), lancet = ggsci::scale_fill_lancet(), jama = ggsci::scale_fill_jama(), jco = ggsci::scale_fill_jco(), ucscgb = ggsci::scale_fill_ucscgb(), d3 = ggsci::scale_fill_d3(), locuszoom = ggsci::scale_fill_locuszoom(), igv = ggsci::scale_fill_igv(), uchicago = ggsci::scale_fill_uchicago(), startrek = ggsci::scale_fill_startrek(), tron = ggsci::scale_fill_tron(), futurama = ggsci::scale_fill_futurama(), rickandmorty = ggsci::scale_fill_rickandmorty(), simpsons = ggsci::scale_fill_simpsons() ) functs[[pal]] } # Generate color palette from ggsci or Rcolorbrewer #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # pal could be a brewer or ggsci palette .get_pal <- function(pal = "default", k){ if(pal %in% .brewerpal()) .get_brewer_pal(pal, k) else if(pal %in% .ggscipal()) .get_ggsci_pal(pal, k) else if(pal %in% c("default", "hue")){ hues <- seq(15, 375, length = k + 1) grDevices::hcl(h = hues, l = 65, c = 100, alpha = 1)[1:k] } } .get_palette <- .get_pal # alias # Generate color palette from ggsci # k the number of color .get_ggsci_pal <- function(palette = c("npg", "aaas", "nejm", "lancet", "jama", "jco", "ucscgb", "d3", "locuszoom", "igv", "uchicago", "startrek", "tron", "futurama", "rickandmorty", "simpsons"), k) { pal <- match.arg(palette) if(pal %in% c("npg", "aaas", "jco", "d3")) max_k <- 10 else if (pal %in% c("nejm")) max_k <- 8 else if (pal %in% c("jama", "locuszoom", "startrek", "tron")) max_k <- 7 else if (pal %in% c("igv")) max_k <- 51 else if (pal %in% c("lancet", "uchicago")) max_k <- 9 else if (pal %in% c("ucscgb")) max_k <- 26 else if (pal %in% c("futurama", "rickandmorty")) max_k <- 12 else if (pal %in% c("simpsons")) max_k <- 16 else stop("Don't support palette name: ", pal) functs <- list( npg = ggsci::pal_npg(), aaas = ggsci::pal_aaas(), nejm = ggsci::pal_nejm(), lancet = ggsci::pal_lancet(), jama = ggsci::pal_jama(), jco = ggsci::pal_jco(), ucscgb = ggsci::pal_ucscgb(), d3 = ggsci::pal_d3(), locuszoom = ggsci::pal_locuszoom(), igv = ggsci::pal_igv(), uchicago = ggsci::pal_uchicago(), startrek = ggsci::pal_startrek(), tron = ggsci::pal_tron(), futurama = ggsci::pal_futurama(), rickandmorty = ggsci::pal_rickandmorty(), simpsons = ggsci::pal_simpsons() ) if(k <= max_k) functs[[pal]](k) else grDevices::colorRampPalette(functs[[pal]](max_k))(k) } # Generate a color palette from brewer .get_brewer_pal <- function(palette, k){ if (!requireNamespace("RColorBrewer", quietly = TRUE)) { stop("RColorBrewer package needed. Please install it using install.packages('RColorBrewer').") } initial.k <- k k <- max(c(k, 3)) # Kshoud be at least 3 pal <- palette[1] sequential <- c('Blues', 'BuGn', 'BuPu', 'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu', 'Reds','YlGn', 'YlGnBu YlOrBr', 'YlOrRd') divergent <- c('BrBG', 'PiYG', 'PRGn', 'PuOr', 'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral') if(pal %in% sequential) max_k <- 9 else if(pal %in% divergent) max_k <- 11 else if(pal %in% c('Accent','Dark2','Pastel2', 'Set2')) max_k <- 8 else if(pal %in% c('Pastel1', 'Set1')) max_k <- 9 else if(pal %in% c('Paired', 'Set3')) max_k <- 12 else stop("Don't support palette name: ", pal) if(k <= max_k) { cols <- RColorBrewer::brewer.pal(k, palette) if(initial.k == 2) cols <- cols[c(1,3)] else if(initial.k == 1) cols <- cols[1] cols } else grDevices::colorRampPalette(RColorBrewer::brewer.pal(max_k, palette))(k) } # Set gradient colors # cols a vector of colors .gradient_col <- function (cols){ gradient_color(cols) } ggpubr/R/rotate_axis_text.R0000644000176200001440000000265314332553655015473 0ustar liggesusers#'Rotate Axes Text #' #'@description Rotate the x-axis text (tick mark labels). #'\itemize{ #'\item \code{rotate_x_text}(): Rotate x axis text. #'\item \code{rotate_y_text}(): Rotate y axis text. #'} #' #'@param angle numeric value specifying the rotation angle. Default is 90 for vertical x-axis text. #'@param hjust horizontal justification (in [0, 1]). #'@param vjust vertical justification (in [0, 1]). #'@param ... other arguments to pass to the function #' \code{\link[ggplot2:element]{element_text}()}. #' #'@examples #'# Load data #'data("ToothGrowth") #'df <- ToothGrowth #' #'# Basic plot #'p <- ggboxplot(df, x = "dose", y = "len") #'p #'# Vertical x axis text #'p + rotate_x_text() #'# Set rotation angle to 45 #'p + rotate_x_text(45) #'p + rotate_y_text(45) #' #'@name rotate_axis_text #'@rdname rotate_axis_text #'@export rotate_x_text <- function(angle = 90, hjust = NULL, vjust = NULL, ...){ if(missing(hjust) & angle > 5) hjust <- 1 if(missing(vjust) & angle == 90) vjust <- 0.5 theme( axis.text.x = element_text(angle = angle, hjust = hjust, vjust = vjust, ...) ) } #'@name rotate_axis_text #'@rdname rotate_axis_text #'@export rotate_y_text <- function(angle = 90, hjust = NULL, vjust = NULL, ...){ if(missing(hjust) & angle == 90) hjust <- 0.5 else if(missing(hjust) & angle > 5) hjust <- 1 theme( axis.text.y = element_text(angle = angle, hjust = hjust, vjust = vjust, ...) ) } ggpubr/R/as_ggplot.R0000644000176200001440000000267614343207173014062 0ustar liggesusers#' @include utilities.R NULL #'Storing grid.arrange() arrangeGrob() and plots #' #'@description Transform the output of #' \code{\link[gridExtra:arrangeGrob]{arrangeGrob}()} and #' \code{\link[gridExtra:arrangeGrob]{grid.arrange}()} to a an object of class #' ggplot. #'@param x an object of class gtable or grob as returned by the functions #' \code{\link[gridExtra:arrangeGrob]{arrangeGrob}()} and #' \code{\link[gridExtra:arrangeGrob]{grid.arrange}()}. #'@return an object of class ggplot. #' #' @examples #' # Creat some plots #'bxp <- ggboxplot(iris, x = "Species", y = "Sepal.Length") #'vp <- ggviolin(iris, x = "Species", y = "Sepal.Length", #' add = "mean_sd") #' #'# Arrange the plots in one page #'# Returns a gtable (grob) object #'library(gridExtra) #'gt <- arrangeGrob(bxp, vp, ncol = 2) #' #'# Transform to a ggplot and print #'as_ggplot(gt) #' #'@export as_ggplot <- function(x){ # Open null device to avoid blank page before plot------ # see cowplot:::as_grob.ggplot null_device <- base::getOption( "ggpubr.null_device", default = cowplot::pdf_null_device ) cur_dev <- grDevices::dev.cur() # Open null device to avoid blank page before plot null_device(width = 6, height = 6) null_dev <- grDevices::dev.cur() on.exit({ grDevices::dev.off(null_dev) if (cur_dev > 1) grDevices::dev.set(cur_dev) }) # Convert to ggplot------------- cowplot::ggdraw() + cowplot::draw_grob(grid::grobTree(x)) } ggpubr/R/get_breaks.R0000644000176200001440000000466414332553655014217 0ustar liggesusers#' Easy Break Creation for Numeric Axes #' #' @description Creates breaks for numeric axes to be used in the functions #' \code{\link[ggplot2:scale_continuous]{scale_x_continuous}()} and #' \code{\link[ggplot2:scale_continuous]{scale_y_continuous}()}. Can be used to increase the #' number of x and y ticks by specifying the option \code{n}. It's also #' possible to control axis breaks by specifying a step between ticks. For #' example, if \code{by = 5}, a tick mark is shown on every 5. #' @param n number of breaks. #' @param by number: the step between breaks. #' @param from the starting value of breaks. By default, 0 is used for positive #' variables #' @param to the end values of breaks. This corresponds generally to the maximum #' limit of the axis. #' @return a break function #' #' @examples #' #' # Generate 5 breaks for a variable x #' get_breaks(n = 5)(x = 1:100) #' #' # Generate breaks using an increasing step #' get_breaks(by = 10)(x = 1:100) #' #' # Combine with ggplot scale_xx functions #' library(ggplot2) #' #' # Create a basic plot #' p <- ggscatter(mtcars, x = "wt", y = "mpg") #' p #' #' # Increase the number of ticks #' p + #' scale_x_continuous(breaks = get_breaks(n = 10)) + #' scale_y_continuous(breaks = get_breaks(n = 10)) #' #' # Set ticks according to a specific step, starting from 0 #' p + scale_x_continuous( #' breaks = get_breaks(by = 1.5, from = 0), #' limits = c(0, 6) #' ) + #' scale_y_continuous( #' breaks = get_breaks(by = 10, from = 0), #' limits = c(0, 40) #' ) #' #' @export get_breaks <- function(n = NULL, by = NULL, from = NULL, to = NULL){ breaks <- ggplot2::waiver() if(!is.null(n)){ breaks <- get_breaks_number(n = n) } else if(is.numeric(by)){ breaks <- get_breaks_position(by = by, from = from, to = to) } breaks } # Set the number of breaks get_breaks_number <- function(n){ scales::breaks_extended(n = n) } # Set breaks using increasing step # Adapted from scales::breaks_extended get_breaks_position <- function(by, from = NULL, to = NULL){ by_default <- by from_default <- from to_default <- to function(x, by = by_default, from = from_default, to = to_default) { x <- x[is.finite(x)] if (length(x) == 0) { return(numeric()) } rng <- range(x) if( rng[1] > 0 & is.null(from)) from <- 0 xmin <- ifelse(is.null(from), floor(rng[1]), from) xmax <- ifelse(is.null(to), rng[2], to) seq(from = xmin, to = xmax, by = by) } } ggpubr/R/compare_means.R0000644000176200001440000003447214342652146014716 0ustar liggesusers#' @include utilities.R NULL #'Comparison of Means #'@description Performs one or multiple mean comparisons. #'@param formula a formula of the form \code{x ~ group} where \code{x} is a #' numeric variable giving the data values and \code{group} is a factor with #' one or multiple levels giving the corresponding groups. For example, #' \code{formula = TP53 ~ cancer_group}. #' #' It's also possible to perform the test for multiple response variables at #' the same time. For example, \code{formula = c(TP53, PTEN) ~ cancer_group}. #'@param data a data.frame containing the variables in the formula. #'@param method the type of test. Default is \link[stats]{wilcox.test}. Allowed #' values include: \itemize{ \item \code{\link[stats]{t.test}} (parametric) and #' \code{\link[stats]{wilcox.test}} (non-parametric). Perform comparison #' between two groups of samples. If the grouping variable contains more than #' two levels, then a pairwise comparison is performed. \item #' \code{\link[stats]{anova}} (parametric) and #' \code{\link[stats]{kruskal.test}} (non-parametric). Perform one-way ANOVA #' test comparing multiple groups. } #'@param paired a logical indicating whether you want a paired test. Used only #' in \code{\link[stats]{t.test}} and in \link[stats]{wilcox.test}. #'@param group.by a character vector containing the name of grouping variables. #'@param ref.group a character string specifying the reference group. If #' specified, for a given grouping variable, each of the group levels will be #' compared to the reference group (i.e. control group). #' #' \code{ref.group} can be also \code{".all."}. In this case, each of the #' grouping variable levels is compared to all (i.e. basemean). #'@param symnum.args a list of arguments to pass to the function #' \code{\link[stats]{symnum}} for symbolic number coding of p-values. For #' example, \code{symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, #' 0.01, 0.05, Inf), symbols = c("****", "***", "**", "*", "ns"))}. #' #' In other words, we use the following convention for symbols indicating #' statistical significance: \itemize{ \item \code{ns}: p > 0.05 \item #' \code{*}: p <= 0.05 \item \code{**}: p <= 0.01 \item \code{***}: p <= 0.001 \item \code{****}: p <= 0.0001 } #'@param p.adjust.method method for adjusting p values (see #' \code{\link[stats]{p.adjust}}). Has impact only in a situation, where #' multiple pairwise tests are performed; or when there are multiple grouping #' variables. Allowed values include "holm", "hochberg", "hommel", #' "bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p #' value (not recommended), use p.adjust.method = "none". #' #' Note that, when the \code{formula} contains multiple variables, the p-value #' adjustment is done independently for each variable. #'@return return a data frame with the following columns: #'\itemize{ #'\item \code{.y.}: the y variable used in the test. #'\item \code{group1,group2}: the compared groups in the pairwise tests. #'Available only when \code{method = "t.test"} or \code{method = "wilcox.test"}. #'\item \code{p}: the p-value. #'\item \code{p.adj}: the adjusted p-value. Default for \code{p.adjust.method = "holm"}. #'\item \code{p.format}: the formatted p-value. #'\item \code{p.signif}: the significance level. #'\item \code{method}: the statistical test used to compare groups. #' #' #'} #'@param ... Other arguments to be passed to the test function. #' @examples #' # Load data #' #::::::::::::::::::::::::::::::::::::::: #' data("ToothGrowth") #' df <- ToothGrowth #' #' # One-sample test #' #::::::::::::::::::::::::::::::::::::::::: #' compare_means(len ~ 1, df, mu = 0) #' #' # Two-samples unpaired test #' #::::::::::::::::::::::::::::::::::::::::: #' compare_means(len ~ supp, df) #' #' # Two-samples paired test #' #::::::::::::::::::::::::::::::::::::::::: #' compare_means(len ~ supp, df, paired = TRUE) #' #' # Compare supp levels after grouping the data by "dose" #' #:::::::::::::::::::::::::::::::::::::::: #' compare_means(len ~ supp, df, group.by = "dose") #' #' # pairwise comparisons #' #:::::::::::::::::::::::::::::::::::::::: #' # As dose contains more thant two levels ==> #' # pairwise test is automatically performed. #' compare_means(len ~ dose, df) #' #' # Comparison against reference group #' #:::::::::::::::::::::::::::::::::::::::: #' compare_means(len ~ dose, df, ref.group = "0.5") #' #' # Comparison against all #' #:::::::::::::::::::::::::::::::::::::::: #' compare_means(len ~ dose, df, ref.group = ".all.") #' #' # Anova and kruskal.test #' #:::::::::::::::::::::::::::::::::::::::: #' compare_means(len ~ dose, df, method = "anova") #' compare_means(len ~ dose, df, method = "kruskal.test") #' @rdname compare_means #' @export compare_means <- function(formula, data, method = "wilcox.test", paired = FALSE, group.by = NULL, ref.group = NULL, symnum.args = list(), p.adjust.method = "holm", ...) { . <- NULL method.info <- .method_info(method) method <- method.info$method method.name <- method.info$name if(.is_empty(symnum.args)) symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1), symbols = c("****", "***", "**", "*", "ns")) if(!inherits(data, "data.frame")) stop("data must be a data.frame.") variables <- response.var <- .formula_left_variables(formula) group <- .formula_right_variables(formula) if(group == "1") group <- NULL # NULL model if(!.is_empty(group)){ group.vals <- .select_vec(data, group) if(!is.factor(group.vals)) data[[group]] <- factor(group.vals, levels = unique(group.vals)) } # Keep only variables of interest data <- data %>% df_select(vars = c(group.by, group, variables)) # Case of formula with multiple variables # 1. Gather the data # 2. group by variable # 3. Perform pairwise test between levels of each grouing variable #::::::::::::::::::::::::::::::::::::::::::::::::::::: # ex: formula = c(GATA3, XBP1, DEPDC1) ~ group if(.is_multi_formula(formula)){ data <- df_gather(data, cols = variables, names_to = ".y.", values_to = ".value.") %>% dplyr::mutate(.y. = factor(.data$.y., levels = unique(.data$.y.))) response.var <- ".value." group.by = c(group.by, ".y.") formula <- .collapse(response.var, group, sep = " ~ ") %>% stats::as.formula() } # Check if comparisons should be done against a reference group #::::::::::::::::::::::::::::::::::::::::::::::::::::: if(!is.null(ref.group)){ group.vals <- .select_vec(data, group) if(is.factor(group.vals)) group.levs <- levels(group.vals) else group.levs <- unique(group.vals) if(ref.group %in% group.levs){ data[[group]] <- stats::relevel(group.vals, ref.group) } if(ref.group == ".all."){ data <- data %>% mutate(.group. = as.character(group.vals), .all. = ".all.") # Add 'all' column # Create a new grouping column gathering group and the .all. columns .group.name. <- NULL data <- data %>% df_gather(cols = c(".group.", ".all."), names_to = ".group.name.", values_to = ".group.") %>% dplyr::select(-.group.name.) data[[".group."]] <- factor(data[[".group."]], levels = c(".all.", group.levs)) group <- ".group." formula <- .collapse(response.var, group, sep = " ~ ") %>% stats::as.formula() } else if(!(ref.group %in% group.levs)){ stop("Can't find specified reference group: ", ref.group, ". ", "Allowed values include one of: ", .collapse(group.levs, sep = ", "), call. = FALSE) } } # Peform the test #::::::::::::::::::::::::::::::::::::::::::::::::::::: test.func <- .test_pairwise if(method %in% c("anova", "kruskal.test")) test.func <- .test_multigroups if(is.null(group.by)){ res <- test.func(formula = formula, data = data, method = method, paired = paired, p.adjust.method = "none", ...) } else{ grouped.d <- .group_by(data, group.by) res <- grouped.d %>% mutate(p = purrr::map( data, test.func, formula = formula, method = method, paired = paired, p.adjust.method = "none",...) ) %>% df_select(vars = c(group.by, "p")) %>% unnest(cols = "p") } # Add response variables to the result #::::::::::::::::::::::::::::::::::::::::::::::::::::: if(!c(".y." %in% colnames(res))) res <- res %>% dplyr::mutate(.y. = variables) %>% dplyr::select(!!!syms(c(group.by, ".y.")), dplyr::everything()) # Select only reference groups if any #:::::::::::::::::::::::::::::::::::::::::::::::::::::::: if(!is.null(ref.group)){ group.levs <- .select_vec(data, group) %>% .levels() group1 <- NULL res <- res %>% dplyr::filter(group1 == ref.group | group2 == ref.group) # ref.group should be always in group1 column # swap group1 and group2 if group2 contains ref.group group2 <- res$group2 res <- transform(res, group1 = ifelse(group2 == ref.group, group2, group1), group2 = ifelse(group2 == ref.group, group1, group2)) } # Formatting and adjusting pvalues, and adding significance symbols #::::::::::::::::::::::::::::::::::::::::::::::::::::: symnum.args$x <- res$p pvalue.signif <- do.call(stats::symnum, symnum.args) %>% as.character() pvalue.format <- format.pval(res$p, digits = 2) .y. <- p.adj <- NULL .p.adjust <- function(d, ...) {data.frame(p.adj = stats::p.adjust(d$p, ...))} by_y <- res %>% group_by(.y.) pvalue.adj <- do(by_y, .p.adjust(., method = p.adjust.method)) res <- res %>% dplyr::ungroup() %>% mutate(p.adj = pvalue.adj$p.adj, p.format = pvalue.format, p.signif = pvalue.signif, method = method.name) res %>% mutate(p.adj = signif(p.adj, digits = 2)) %>% tibble::as_tibble() } # Check and get test method info #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: # return a list(method, name) .method_info <- function(method){ if(is.null(method)) method = "wilcox.test" allowed.methods <- list( t = "t.test", t.test = "t.test", student = "t.test", wiloxon = "wilcox.test", wilcox = "wilcox.test", wilcox.test = "wilcox.test", anova = "anova", aov = "anova", kruskal = "kruskal.test", kruskal.test = "kruskal.test") method.names <- list( t.test = "T-test", wilcox.test = "Wilcoxon", anova = "Anova", kruskal.test = "Kruskal-Wallis") if(!(method %in% names(allowed.methods))) stop("Non-supported method specified. Allowed methods are one of: ", .collapse(allowed.methods, sep =", ")) method <- allowed.methods[[method]] method.name <- method.names[[method]] list(method = method, name = method.name) } # Comparing two groups #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .test <- function(data, formula, method = "t.test", ...) { test <- match.fun(method) x <- deparse(formula[[2]]) group <- attr(stats::terms(formula), "term.labels") if(.is_empty(group)) # Case of null model test.opts <- list(x = .select_vec(data, x), ...) else test.opts <- list(formula = formula, data = data, ...) res <- data.frame(p = suppressWarnings(do.call(test, test.opts)$p.value)) group1 <- group2 <- NULL if(!.is_empty(group)){ group.lev <- .select_vec(data, group) %>% levels() res <- res %>% dplyr::mutate( group1 = group.lev[1], group2 = group.lev[2] ) %>% dplyr::select(group1,group2, dplyr::everything()) } else res <- res %>% dplyr::mutate(group1 = 1, group2 = "null model") %>% dplyr::select(group1, group2, everything()) res } # pairwise test #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .test_pairwise <- function(data, formula, method = "wilcox.test", paired = FALSE, pool.sd = !paired, ...) { x <- deparse(formula[[2]]) group <- attr(stats::terms(formula), "term.labels") # One sample test if(.is_empty(group)){ res <- .test(data, formula, method = method, ...) return(res) } # Pairwise test method <- switch(method, t.test = "pairwise.t.test", wilcox.test = "pairwise.wilcox.test") test <- match.fun(method) test.opts <- list(x = .select_vec(data, x), g = .select_vec(data, group), paired = paired, ...) # if(method == "pairwise.wilcox.test") test.opts$exact <- FALSE if(method == "pairwise.t.test"){ if(missing(pool.sd)){ if(!paired) pool.sd <- FALSE } test.opts$pool.sd <- pool.sd } pvalues <- suppressWarnings(do.call(test, test.opts)$p.value) %>% as.data.frame() ..group1.. <- ..group2.. <- p <- NULL pvalues$..group2.. <- rownames(pvalues) pvalues <- pvalues %>% tidyr::gather(key = "..group1..", value = "p", -..group2..) %>% dplyr::select(group1 = ..group1.., group2 = ..group2.., p) %>% dplyr::filter(!is.na(p)) pvalues } # Compare multiple groups #:::::::::::::::::::::::::::::::::::::::::::::::::: .test_multigroups <- function(data, formula, method = c("anova", "kruskal.test"), ...){ method <- match.arg(method) . <- NULL if(method == "anova") pvalue <- stats::lm(formula, data = data) %>% stats::anova(.) %>% .$`Pr(>F)` %>% .[1] else if(method == "kruskal.test"){ pvalue <- stats::kruskal.test(formula, data = data)$p.value } data.frame(p = pvalue) } # Formula with multiple response variables #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: # ex formula = c(GATA3, XBP1, DEPDC1) ~ group .is_multi_formula <- function(formula){ x <- grep(",", formula) !.is_empty(x) } # Get formula variables .formula_left_variables <- function(formula){ . <- NULL x <- deparse(formula[[2]]) %>% gsub("c\\(|\\)|\\s", "", .) %>% strsplit(",") %>% unlist() x } .formula_right_variables <- function(formula){ group <- attr(stats::terms(formula), "term.labels") if(.is_empty(group)) group <- "1" group } .update_test_arguments <- function(formula, data, group.by){ variables <- .formula_left_variables(formula) group <- .formula_right_variables(formula) data <- data %>% df_gather(cols = variables, names_to = ".y.", values_to = ".value.") %>% dplyr::mutate(.y. = factor(.data$.y., levels = unique(.data$.y.))) formula <- .collapse(".value.", group, sep = " ~ ") %>% stats::as.formula() group.by = c(group.by, ".y.") } ggpubr/R/ggdotplot.R0000644000176200001440000001371514342705267014110 0ustar liggesusers#' @include utilities.R ggpar.R NULL #' Dot plot #' @description Create a dot plot. #' @inheritParams ggboxplot #' @param binwidth numeric value specifying bin width. use value between 0 and 1 #' when you have a strong dense dotplot. For example binwidth = 0.2. #' @param ... other arguments to be passed to #' \code{\link[ggplot2]{geom_dotplot}}, \code{\link{ggpar}} and #' \code{\link{facet}}. #' @details The plot can be easily customized using the function ggpar(). Read #' ?ggpar for changing: \itemize{ \item main title and axis labels: main, #' xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis #' scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: #' palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, #' labels and position: legend = "right" \item plot orientation : orientation #' = c("vertical", "horizontal", "reverse") } #'@seealso \code{\link{ggpar}}, \code{\link{ggviolin}}, \code{\link{ggboxplot}} #' and \code{\link{ggstripchart}}. #' @examples #' # Load data #' data("ToothGrowth") #' df <- ToothGrowth #' #' # Basic plot with summary statistics : mean_sd #' # +++++++++++++++++++++++++++ #' ggdotplot(df, x = "dose", y = "len", #' add = "mean_sd") #' #' # Change error.plot to "crossbar" #' ggdotplot(df, x = "dose", y = "len", #' add = "mean_sd", add.params = list(width = 0.5), #' error.plot = "crossbar") #' #' #' # Add box plot #' ggdotplot(df, x = "dose", y = "len", #' add = "boxplot") #' #' # Add violin + mean_sd #' ggdotplot(df, x = "dose", y = "len", #' add = c("violin", "mean_sd")) #' #' #' # Change colors #' # +++++++++++++++++++++++++++ #' # Change fill and outline colors by groups: dose #' # Use custom color palette #' ggdotplot(df, "dose", "len", #' add = "boxplot", #' color = "dose", fill = "dose", #' palette = c("#00AFBB", "#E7B800", "#FC4E07")) #' #' #' # Plot with multiple groups #' # +++++++++++++++++++++ #' # Change color by a second group : "supp" #' ggdotplot(df, "dose", "len", fill = "supp", color = "supp", #' palette = c("#00AFBB", "#E7B800")) #' #' #' @export ggdotplot <- function(data, x, y, combine = FALSE, merge = FALSE, color = "black", fill = "lightgray", palette = NULL, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, size = NULL, binwidth = NULL, select = NULL, remove = NULL, order = NULL, add = "mean_se", add.params = list(), error.plot = "pointrange", label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, ggtheme = theme_pubr(), ...) { # Default options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .opts <- list( combine = combine, merge = merge, color = color, fill = fill, palette = palette, title = title, xlab = xlab, ylab = ylab, facet.by = facet.by, panel.labs = panel.labs, short.panel.labs = short.panel.labs, size = size, binwidth = binwidth, select = select , remove = remove, order = order, add = add, add.params = add.params, error.plot = error.plot, label = label, font.label = font.label, label.select = label.select, repel = repel, label.rectangle = label.rectangle, ggtheme = ggtheme, ...) if(!missing(data)) .opts$data <- data if(!missing(x)) .opts$x <- x if(!missing(y)) .opts$y <- y # User options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .user.opts <- as.list(match.call(expand.dots = TRUE)) .user.opts[[1]] <- NULL # Remove the function name # keep only user arguments for(opt.name in names(.opts)){ if(is.null(.user.opts[[opt.name]])) .opts[[opt.name]] <- NULL } .opts$fun <- ggdotplot_core if(missing(ggtheme) & (!is.null(facet.by) | combine)) .opts$ggtheme <- theme_pubr(border = TRUE) p <- do.call(.plotter, .opts) if(.is_list(p) & length(p) == 1) p <- p[[1]] return(p) } ggdotplot_core <- function(data, x, y, color = "black", fill = "lightgray", palette = NULL, title = NULL, xlab = NULL, ylab = NULL, size = NULL, dotsize = size, binwidth = NULL, add = "mean_se", add.params = list(), error.plot = "pointrange", position = position_dodge(0.8), ggtheme = theme_pubr(), ...) { if(!is.factor(data[[x]])) data[[x]] <- as.factor(data[[x]]) . <- NULL p <- ggplot(data, create_aes(list(x = x, y = y))) if("none" %in% add) add <- "none" if(is.null(add.params$fill)) add.params$fill <- "white" add.params <- .check_add.params(add, add.params, error.plot, data, color, fill, ...) # plot boxplot | violin | crossbar before jitter if( any( c("boxplot", "violin") %in% add)){ p <- add.params %>% .add_item(p = p, add = intersect(add, c("boxplot", "violin") )) %>% do.call(ggadd, .) } if(error.plot == "crossbar"){ p <- add.params %>% .add_item(p = p, error.plot = error.plot, add = setdiff(add, c("boxplot", "violin", "jitter"))) %>% do.call(ggadd, .) } # Plot jitter p <- p + geom_exec(geom_dotplot, data = data, binaxis = "y", stackdir = "center", color = color, fill = fill, position = position, stackratio = 1, dotsize = dotsize, binwidth = binwidth, ...) # Add errors if(error.plot == "crossbar"){} else p <- add.params %>% .add_item(p = p, error.plot = error.plot, add = setdiff(add, c("boxplot", "violin", "jitter"))) %>% do.call(ggadd, .) p <- ggpar(p, palette = palette, ggtheme = ggtheme, title = title, xlab = xlab, ylab = ylab,...) p } ggpubr/R/geom_bracket.R0000644000176200001440000004030314332553655014521 0ustar liggesusers#' @include utilities.R NULL StatBracket <- ggplot2::ggproto("StatBracket", ggplot2::Stat, required_aes = c("x", "y", "group"), setup_params = function(data, params) { if(length(params$tip.length) == 1) params$tip.length <- rep(params$tip.length, max(length(params$xmin), 1) * 2) if(length(params$tip.length) == length(params$xmin)) params$tip.length <- rep(params$tip.length, each=2) return(params) }, compute_group = function(data, scales, tip.length) { yrange <- scales$y$range$range y.scale.range <- yrange[2] - yrange[1] bracket.shorten <- data$bracket.shorten/2 xmin <- data$xmin + bracket.shorten xmax <- data$xmax - bracket.shorten y.position <- data$y.position + (y.scale.range*data$step.increase) + data$bracket.nudge.y label <- data$label if(is.character(xmin)){ xmin <- scales$x$map(xmin) } if(is.character(xmax)){ xmax <- scales$x$map(xmax) } if("tip.length" %in% colnames(data)){ tip.length <- rep(data$tip.length, each=2) } # Preparing bracket data data <- dplyr::bind_rows(data, data, data) data$x <- c(xmin, xmin, xmax) data$xend = c(xmin, xmax, xmax) data$y <- c(y.position - y.scale.range*tip.length[seq_len(length(tip.length))%% 2 == 1], y.position, y.position) data$yend <- c(y.position, y.position, y.position-y.scale.range*tip.length[seq_len(length(tip.length))%% 2 == 0]) data$annotation <- rep(label, 3) data } ) #' Add Brackets with Labels to a GGPlot #' @description add brackets with label annotation to a ggplot. Helpers for #' adding p-value or significance levels to a plot. #' @param label character vector with alternative label, if not null test is #' ignored #' @param type the label type. Can be one of "text" and "expression" (for #' parsing plotmath expression). #' @param xmin numeric vector with the positions of the left sides of the #' brackets #' @param xmax numeric vector with the positions of the right sides of the #' brackets #' @param y.position numeric vector with the y positions of the brackets #' @param size change the width of the lines of the bracket #' @param label.size change the size of the label text #' @param family change the font used for the text #' @param vjust move the text up or down relative to the bracket #' @param step.increase numeric vector with the increase in fraction of total #' height for every additional comparison to minimize overlap. #' @param bracket.nudge.y Vertical adjustment to nudge brackets by. Useful to #' move up or move down the bracket. If positive value, brackets will be moved #' up; if negative value, brackets are moved down. #' @param bracket.shorten a small numeric value in [0-1] for shortening the with #' of bracket. #' @param step.group.by a variable name for grouping brackets before adding #' step.increase. Useful to group bracket by facet panel. #' @param tip.length numeric vector with the fraction of total height that the #' bar goes down to indicate the precise column #' @param na.rm If \code{FALSE} (the default), removes missing values with a #' warning. If \code{TRUE} silently removes missing values. #' @param coord.flip logical. If \code{TRUE}, flip x and y coordinates so that #' horizontal becomes vertical, and vertical, horizontal. When adding the #' p-values to a horizontal ggplot (generated using #' \code{\link[ggplot2]{coord_flip}()}), you need to specify the option #' \code{coord.flip = TRUE}. #' @param ... other arguments passed on to \code{\link{layer}}. These are often #' aesthetics, used to set an aesthetic to a fixed value, like \code{color = #' "red"} or \code{size = 3}. They may also be parameters to the paired #' geom/stat. #' @inheritParams ggplot2::layer #' @examples #' df <- ToothGrowth #' df$dose <- factor(df$dose) #' #' # Add bracket with labels #' ggboxplot(df, x = "dose", y = "len") + #' geom_bracket( #' xmin = "0.5", xmax = "1", y.position = 30, #' label = "t-test, p < 0.05" #' ) #' #' # Customize bracket tip.length tip.length #' ggboxplot(df, x = "dose", y = "len") + #' geom_bracket( #' xmin = "0.5", xmax = "1", y.position = 30, #' label = "t-test, p < 0.05", tip.length = c(0.2, 0.02) #' ) #' #' #Using plotmath expression #' ggboxplot(df, x = "dose", y = "len") + #' geom_bracket( #' xmin = "0.5", xmax = "1", y.position = 30, #' label = "list(~italic(p)<=0.001)", type = "expression", #' tip.length = c(0.2, 0.02) #' ) #' #' # Specify multiple brackets manually #' ggboxplot(df, x = "dose", y = "len") + #' geom_bracket( #' xmin = c("0.5", "1"), xmax = c("1", "2"), #' y.position = c(30, 35), label = c("***", "**"), #' tip.length = 0.01 #' ) #' #' # Compute statistical tests and add p-values #' stat.test <- compare_means(len ~ dose, ToothGrowth, method = "t.test") #' ggboxplot(df, x = "dose", y = "len") + #' geom_bracket( #' aes(xmin = group1, xmax = group2, label = signif(p, 2)), #' data = stat.test, y.position = 35 #' ) #' #' # Increase step length between brackets #' ggboxplot(df, x = "dose", y = "len") + #' geom_bracket( #' aes(xmin = group1, xmax = group2, label = signif(p, 2)), #' data = stat.test, y.position = 35, step.increase = 0.1 #' ) #' #' # Or specify the positions of each comparison #' ggboxplot(df, x = "dose", y = "len") + #' geom_bracket( #' aes(xmin = group1, xmax = group2, label = signif(p, 2)), #' data = stat.test, y.position = c(32, 35, 38) #' ) #' @rdname geom_bracket #' @export stat_bracket <- function(mapping = NULL, data = NULL, position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, label = NULL, type = c("text", "expression"), y.position=NULL, xmin = NULL, xmax = NULL, step.increase = 0, step.group.by = NULL, tip.length = 0.03, bracket.nudge.y = 0, bracket.shorten = 0, size = 0.3, label.size = 3.88, family="", vjust = 0, ...) { if(! is.null(data) & ! is.null(mapping)){ if(! "x" %in% names(data)) mapping$x <- 1 if(! "y" %in% names(data)) mapping$y <- 1 } type <- match.arg(type) ggplot2::layer( stat = StatBracket, data = data, mapping = mapping, geom = "bracket", position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list( label=label, type = type, y.position=y.position,xmin=xmin, xmax=xmax, step.increase=step.increase, bracket.nudge.y = bracket.nudge.y, bracket.shorten = bracket.shorten, step.group.by = step.group.by, tip.length=tip.length, size=size, label.size=label.size, family=family, vjust=vjust, na.rm = na.rm, ...) ) } GeomBracket <- ggplot2::ggproto("GeomBracket", ggplot2::Geom, required_aes = c("x", "xend", "y", "yend", "annotation"), default_aes = ggplot2::aes( shape = 19, colour = "black", label.size = 3.88, angle = NULL, hjust = 0.5, vjust = 0, alpha = NA, family = "", fontface = 1, lineheight = 1.2, linetype=1, size = 0.3, xmin = NULL, xmax = NULL, label = NULL, y.position = NULL, step.increase = 0, bracket.nudge.y = 0, bracket.shorten = 0 # Added to avoid aesthetics warning ), # draw_key = function(...){grid::nullGrob()}, # for legend: draw_key = draw_key_path, draw_group = function(data, panel_params, coord, type = "text", coord.flip = FALSE) { lab <- as.character(data$annotation) if(type == "expression"){ lab <- parse_as_expression(lab) } coords <- coord$transform(data, panel_params) label.x <- mean(c(coords$x[1], tail(coords$xend, n=1))) label.y <- max(c(coords$y, coords$yend))+0.01 label.angle <- coords$angle if(coord.flip){ label.y <- mean(c(coords$y[1], tail(coords$yend, n=1))) label.x <- max(c(coords$x, coords$xend))+0.01 if(is.null(label.angle)) label.angle <- -90 } if(is.null(label.angle)) label.angle <- 0 grid::gList( grid::textGrob( label = lab, x = label.x, y = label.y, default.units = "native", hjust = coords$hjust, vjust = coords$vjust, rot = label.angle, gp = grid::gpar( col = scales::alpha(coords$colour, coords$alpha), fontsize = coords$label.size * ggplot2::.pt, fontfamily = coords$family, fontface = coords$fontface, lineheight = coords$lineheight ) ), grid::segmentsGrob( coords$x, coords$y, default.units = "native", coords$xend, coords$yend, gp = grid::gpar( col = scales::alpha(coords$colour, coords$alpha), lty = coords$linetype, lwd = coords$size * ggplot2::.pt ) ) ) } ) #' @rdname geom_bracket #' @export geom_bracket <- function(mapping = NULL, data = NULL, stat = "bracket", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, label = NULL, type = c("text", "expression"), y.position = NULL, xmin = NULL, xmax = NULL, step.increase = 0, step.group.by = NULL, tip.length = 0.03, bracket.nudge.y = 0, bracket.shorten = 0, size = 0.3, label.size = 3.88, family="", vjust = 0, coord.flip = FALSE, ...) { type <- match.arg(type) data <- build_signif_data( data = data, label = label, y.position = y.position, xmin = xmin, xmax = xmax, step.increase = step.increase, bracket.nudge.y = bracket.nudge.y, bracket.shorten = bracket.shorten, step.group.by = step.group.by, vjust = vjust ) mapping <- build_signif_mapping(mapping, data) ggplot2::layer( stat = stat, geom = GeomBracket, mapping = mapping, data = data, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list( type = type, tip.length = tip.length, size = size, label.size = label.size, family = family, na.rm = na.rm, coord.flip = coord.flip, ... ) ) } # Guess column to be used as significance labem guess_signif_label_column <- function(data){ potential.label <- c( "label", "labels", "p.adj.signif", "p.adj", "padj", "p.signif", "p.value", "pval", "p.val", "p" ) res <- intersect(potential.label, colnames(data)) if(length(res) > 0){ res <- res[1] } else{ stop("label is missing") } res } build_signif_data <- function(data = NULL, label = NULL, y.position = NULL, xmin = NULL, xmax = NULL, step.increase = 0, bracket.nudge.y = 0, bracket.shorten = 0, step.group.by = NULL, vjust = 0){ add_step_increase <- function(data, step.increase){ comparisons.number <- 0:(nrow(data)-1) step.increase <- step.increase*comparisons.number data <- data %>% mutate(step.increase = !!step.increase) data } if(is.null(data)){ data <- data.frame( label = label, y.position = y.position, xmin = xmin, xmax = xmax ) %>% mutate(vjust = !!vjust) } else{ if(!is.null(label)) data <- data %>% mutate(label = !!label) if(!is.null(y.position)) data <- data %>% mutate(y.position = !!y.position) if(!is.null(xmin)) data <- data %>% mutate(xmin = !!xmin) if(!is.null(xmax)) data <- data %>% mutate(xmax = !!xmax) if(!identical(vjust, 0)) data <- data %>% mutate(vjust = !!vjust) } # add vjust column if doesn't exist if(!("vjust" %in% colnames(data))) data <- data %>% mutate(vjust = !!vjust) if(!("bracket.nudge.y" %in% colnames(data))) data <- data %>% mutate(bracket.nudge.y = !!bracket.nudge.y) if(!("bracket.shorten" %in% colnames(data))) data <- data %>% mutate(bracket.shorten= !!bracket.shorten) if(is.null(step.group.by)){ data <- data %>% add_step_increase(step.increase) } else{ data <- data %>% dplyr::arrange(!!!syms(c(step.group.by, "y.position"))) %>% group_by(!!!syms(step.group.by)) %>% tidyr::nest() %>% dplyr::mutate(step.increase = purrr::map(data, add_step_increase, !!step.increase)) %>% dplyr::select(-data) %>% unnest(cols = "step.increase") } data } build_signif_mapping <- function(mapping, data){ if(is.null(mapping)){ # Check if required variables are present in data required.vars <- c("xmin", "xmax", "y.position") missing.required.vars <- setdiff(required.vars, colnames(data)) if(length(missing.required.vars) > 0){ stop( "Required variables are missing in the data: ", paste(missing.required.vars, collapse = ", ") ) } mapping <- ggplot2::aes() } if(is.null(mapping$label)){ label.col <- guess_signif_label_column(data) data$label <- data %>% dplyr::pull(!!label.col) mapping$label <- data$label } if(is.null(mapping$xmin)) mapping$xmin <- data$xmin if(is.null(mapping$xmax)) mapping$xmax <- data$xmax if(is.null(mapping$y.position)) mapping$y.position <- data$y.position if(is.null(mapping$group)) mapping$group <- 1:nrow(data) if(is.null(mapping$step.increase)) mapping$step.increase <- data$step.increase if(is.null(mapping$vjust)) mapping$vjust <- data$vjust if(is.null(mapping$bracket.nudge.y)) mapping$bracket.nudge.y <- data$bracket.nudge.y if(is.null(mapping$bracket.shorten)) mapping$bracket.shorten <- data$bracket.shorten if(! "x" %in% names(mapping)){ mapping$x <- mapping$xmin } if(! "y" %in% names(mapping)){ mapping$y <- mapping$y.position } mapping } # Source: https://github.com/tidyverse/ggplot2/issues/2864 parse_as_expression <- function(text) { stopifnot(is.character(text)) out <- vector("expression", length(text)) for (i in seq_along(text)) { expr <- parse(text = text[[i]]) out[[i]] <- if (length(expr) == 0) NA else expr[[1]] } out } ggpubr/R/utils-aes.R0000644000176200001440000000031214332576562014003 0ustar liggesusers# Extract group name from mapping, aes aes_get_group <- function(mapping){ group <- NULL if(!is.null(mapping)){ if(!is.null(mapping$group)) group <- rlang::as_label(mapping$group) } group } ggpubr/R/ggdotchart.R0000644000176200001440000002311314342704672014223 0ustar liggesusers#' @include utilities.R ggpar.R stat_chull.R stat_conf_ellipse.R NULL #' Cleveland's Dot Plots #' @description Draw a Cleveland dot plot. #' @inheritParams ggboxplot #' @inheritParams ggpar #' @param data a data frame #' @param x,y x and y variables for drawing. #' @param color,size points color and size. #' @param dot.size numeric value specifying the dot size. #' @param shape point shape. See \code{\link{show_point_shapes}}. #' @param label the name of the column containing point labels. #' @param group an optional column name indicating how the elements of x are #' grouped. #' @param sorting a character vector for sorting into ascending or descending #' order. Allowed values are one of "descending", "ascending" and "none". Partial #' match are allowed (e.g. sorting = "desc" or "asc"). Default is #' "descending". #' @param x.text.col logical. If TRUE (default), x axis texts are colored by #' groups. #' @param position Position adjustment, either as a string, or the result of a #' call to a position adjustment function. #' @param ... other arguments to be passed to \code{\link[ggplot2]{geom_point}} #' and \code{\link{ggpar}}. #' @details The plot can be easily customized using the function ggpar(). Read #' ?ggpar for changing: \itemize{ \item main title and axis labels: main, #' xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis #' scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: #' palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, #' labels and position: legend = "right" \item plot orientation : orientation #' = c("vertical", "horizontal", "reverse") } #' @seealso \code{\link{ggpar}} #' @examples #' # Load data #' data("mtcars") #' df <- mtcars #' df$cyl <- as.factor(df$cyl) #' df$name <- rownames(df) #' head(df[, c("wt", "mpg", "cyl")], 3) #' #' # Basic plot #' ggdotchart(df, x = "name", y ="mpg", #' ggtheme = theme_bw()) #' #' # Change colors by group cyl #' ggdotchart(df, x = "name", y = "mpg", #' group = "cyl", color = "cyl", #' palette = c('#999999','#E69F00','#56B4E9'), #' rotate = TRUE, #' sorting = "descending", #' ggtheme = theme_bw(), #' y.text.col = TRUE ) #' #' #'# Plot with multiple groups #'# +++++++++++++++++++++ #'# Create some data #'df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3), #' dose=rep(c("D0.5", "D1", "D2"),2), #' len=c(6.8, 15, 33, 4.2, 10, 29.5)) #'print(df2) #' #'ggdotchart(df2, x = "dose", y = "len", #' color = "supp", size = 3, #' add = "segment", #' add.params = list(color = "lightgray", size = 1.5), #' position = position_dodge(0.3), #' palette = "jco", #' ggtheme = theme_pubclean() #') #' #' #' @export ggdotchart <- function(data, x, y, group = NULL, combine = FALSE, color = "black", palette = NULL, shape = 19, size = NULL, dot.size = size, sorting = c("ascending", "descending", "none"), add = c("none", "segment"), add.params = list(), x.text.col = TRUE, rotate = FALSE, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, select = NULL, remove = NULL, order = NULL, label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, position = "identity", ggtheme = theme_pubr(), ...){ # Default options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .opts <- list( data = data, x = x, y = y, group = group, combine = combine, color = color, palette = palette, shape = shape, size = size, dot.size = dot.size, sorting = sorting, x.text.col = x.text.col, rotate = rotate, title = title, xlab = xlab, ylab = ylab, facet.by = facet.by, panel.labs = panel.labs, short.panel.labs = short.panel.labs, select = select , remove = remove, order = order, add = add, add.params = add.params, label = label, font.label = font.label, label.select = label.select, repel = repel, label.rectangle = label.rectangle, position = position, ggtheme = ggtheme, ...) # User options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .user.opts <- as.list(match.call(expand.dots = TRUE)) .user.opts[[1]] <- NULL # Remove the function name # keep only user arguments for(opt.name in names(.opts)){ if(is.null(.user.opts[[opt.name]])) .opts[[opt.name]] <- NULL } .opts$fun <- ggdotchart_core .opts$fun_name <- "ggdotchart" if(missing(ggtheme) & (!is.null(facet.by) | combine)) .opts$ggtheme <- theme_pubr(border = TRUE) p <- do.call(.plotter, .opts) if(.is_list(p) & length(p) == 1) p <- p[[1]] return(p) } ggdotchart_core <- function(data, x, y, group = NULL, color = "black", palette = NULL, shape = 19, size = NULL, dot.size = size, sorting = c("ascending", "descending", "none"), add = c("none", "segments"), add.params = list(), x.text.col = FALSE, rotate = FALSE, title = NULL, xlab = NULL, ylab = NULL, ggtheme = theme_bw(), position = "identity", ...) { add <- match.arg(add) if(!is.null(group)){ if(group == 1) group <- NULL } # if(is.null(group) & color[1] %in% names(data)){ # group <- color[1] # } .dots <- list(...) sorting <- match.arg(sorting) decreasing <- ifelse(sorting == "descending", FALSE, TRUE) x.text.angle <- ifelse(rotate, 0, 90) if(!is.null(.dots$y.text.col)) x.text.col <- .dots$y.text.col data <- as.data.frame(data) label <- .select_vec(data, x) . <- NULL if(rotate & sorting == "descending") sorting <- "ascending" else if(rotate & sorting == "ascending") sorting <- "descending" if(sorting != "none"){ if(is.null(group)){ if(sorting == "descending") data <- arrange(data, desc(!!sym(y))) else if(sorting == "ascending") data <- arrange(data, !!sym(y)) } else if(group != 1){ if(sorting == "descending") data <- arrange(data, !!sym(group), desc(!!sym(y))) else if(sorting == "ascending") data <- arrange(data, !!!syms(c(group, y))) } data[[x]] <- factor(data[[x]], levels = unique(as.vector(data[[x]]))) } if(!is.factor(data[[x]])) data[[x]] <- as.factor(data[[x]]) p <- ggplot(data, create_aes(list(x = x, y = y))) if(add == "segments"){ seg.opts <- geom_exec(data = data, color = color, size = size, position = position) mapping <- seg.opts$mapping %>% .add_item(x = x, ymin = 0, ymax = y, group = group) option <- seg.opts$option # mapping <- seg.opts$mapping %>% # .add_item(y = 0, x = x, yend = y, xend = x) # option <- seg.opts$option seg.col <- "lightgray" if(!is.null(add.params$color)) seg.col <- add.params$color else if(!is.null(add.params$colour)) seg.col <- add.params$colour if(seg.col %in% names(data)) mapping$color <- seg.col else option$color <- seg.col if(!is.null(add.params$size)) option$size <- add.params$size # if(!is.null(add.params$color)) # option$color <- add.params$color # else if(!is.null(add.params$colour)) # option$color <- add.params$colour # if(!is.null(add.params$size)) # option$size <- add.params$size option[["mapping"]] <- create_aes(mapping) p <- p + do.call(geom_linerange, option) } p <- p + geom_exec(geom_point, data = data, shape = shape, color = color, size = dot.size, position = position) p <- ggpar(p, palette = palette, ggtheme = ggtheme, x.text.angle = x.text.angle, title = title, xlab = xlab, ylab = ylab, ...) # Change x axis text colors if(x.text.col){ if(!rotate) p <- .set_x_text_col(p, label, x.text.angle) else p <- .set_y_text_col(p, label, x.text.angle) } if(x.text.angle == 90 & !rotate){ p <- p + theme(axis.text.x = element_text(vjust = 0.5)) } if(rotate) p <- p + coord_flip() p } # Set x text color .set_x_text_col <- function(p, label, angle){ g <- ggplot2::ggplot_build(p) cols <- unlist(g$data[[1]]["colour"]) names(cols) <- as.vector(label) # Give every color an appropriate name p + theme(axis.text.x = element_text(colour = cols, angle = angle, hjust = 1)) } # Set y text color .set_y_text_col <- function(p, label, angle){ g <- ggplot2::ggplot_build(p) cols <- unlist(g$data[[1]]["colour"]) names(cols) <- as.vector(label) # Give every color an appropriate name p + theme(axis.text.y = element_text(colour = cols)) } # Helper functions # +++++++++++++++++++++++++ #' @export #' @rdname ggdotchart theme_cleveland <- function(rotate = TRUE){ if(rotate){ theme(panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank(), panel.grid.major.y = element_line(colour = "grey70", linetype = "dashed"), axis.title.y = element_blank(), axis.ticks.y = element_blank()) } else{ theme(panel.grid.major.x = element_line(colour = "grey70", linetype = "dashed"), panel.grid.major.y = element_blank(), panel.grid.minor.y = element_blank(), axis.title.x = element_blank(), axis.ticks.x = element_blank()) } } ggpubr/R/utilities.R0000644000176200001440000007162314370060226014110 0ustar liggesusers#' @include desc_statby.R utilities_base.R utilities_color.R NULL #' @import ggplot2 #' @importFrom magrittr %>% #' @importFrom rstatix df_group_by df_nest_by df_select df_arrange #' @importFrom tibble as_tibble #' @importFrom dplyr group_by mutate mutate_if group_nest arrange desc #' @importFrom purrr map2 map #' @importFrom tidyr unite #' @importFrom dplyr do select distinct #' @importFrom dplyr summarise #' @importFrom dplyr everything #' @importFrom grid drawDetails #' @importFrom rlang !! #' @importFrom rlang !!! #' @importFrom rlang syms .data required_package <- function(pkg){ if (!requireNamespace(pkg, quietly = TRUE)) { stop( pkg, " package needed to be installed before using this function. ", "Type this in R: install.packages('", pkg, "')" ) } } # Unnesting, adapt to tidyr 1.0.0 unnest <- function(data, cols = "data", ...){ if(is_pkg_version_sup("tidyr", "0.8.3")){ results <- tidyr::unnest(data, cols = cols, ...) } else {results <- tidyr::unnest(data, ...)} results } # Check if an installed package version is superior to a specified version # Version, pkg: character vector is_pkg_version_sup<- function(pkg, version){ vv <- as.character(utils::packageVersion(pkg)) cc <- utils::compareVersion(vv, version) > 0 cc } keep_only_tbl_df_classes <- function(x){ toremove <- setdiff(class(x), c("tbl_df", "tbl", "data.frame")) if(length(toremove) > 0){ class(x) <- setdiff(class(x), toremove) } x } #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # Execute a geom_* function from ggplot2 #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # geomfunc : gem_*() functions # data data for mapping # ... argument accepeted by the function # return a plot if geomfunc!=Null or a list(option, mapping) if geomfunc = NULL .geom_exec <- function (geomfunc = NULL, data = NULL, position = NULL, ...) { geom_exec(geomfunc = geomfunc, data = data, position = position, ...) } # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # Official argument from ggplot2 # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # bar plot arguments .barplot_params <- function(...){ x <- list(...) res <- list() res$width <- x$width res$binwidth <- x$binwidth res$na.rm <- ifelse(!is.null(x$na.rm), x$na.rm, FALSE) res$show.legend <- ifelse(!is.null(x$show.legend), x$show.legend, NA) res$inherit.aes <- ifelse(!is.null(x$inherit.aes), x$inherit.aes, TRUE) return(res) } # box plot arguments .boxplot_params <- function(...){ x <- list(...) res <- list() res$outlier.colour <- x$outlier.colour res$outlier.shape <- ifelse(!is.null(x$outlier.shape), x$outlier.shape, 19) res$outlier.size <- ifelse(!is.null(x$outlier.size), x$outlier.size, 1.5) res$outlier.stroke <- ifelse(!is.null(x$outlier.stroke), x$outlier.stroke, 0.5) res$notch <- ifelse(!is.null(x$notch), x$notch, FALSE) res$notchwidth <- ifelse(!is.null(x$notchwidth), x$notchwidth, 0.5) res$varwidth <- ifelse(!is.null(x$varwidth), x$varwidth, FALSE) res$na.rm <- ifelse(!is.null(x$na.rm), x$na.rm, FALSE) res$show.legend <- ifelse(!is.null(x$show.legend), x$show.legend, NA) res$inherit.aes <- ifelse(!is.null(x$inherit.aes), x$inherit.aes, TRUE) return(res) } .dotplot_params <- function(...){ x <- list(...) res <- list() res$stackratio <- ifelse(!is.null(x$stackratio ), x$stackratio, 1) res$width <- ifelse(!is.null(x$width), x$width, 0.9) return(res) } .violin_params <- function(...){ x <- list(...) res <- list() res$stat <- ifelse(!is.null(x$stat ), x$stat, "ydensity") res$draw_quantiles <- x$draw_quantiles res$scale <- ifelse(!is.null(x$scale), x$scale, "area") res$trim <- ifelse(!is.null(x$trim), x$trim, TRUE) return(res) } .hist_params <- function(...){ x <- list(...) res <- list() res$binwidth <- x$binwidth res$bins <- x$bins return(res) } .standard_params <- function(...){ x <- list(...) res <- list() res$color <- ifelse(!is.null(x$color), x$color, "black") res$color <- ifelse(!is.null(x$colour), x$colour, res$color) res$linetype <- ifelse(!is.null(x$linetype), x$linetype, "solid") res$size <- ifelse(!is.null(x$size), x$size, 1) res$fill <- ifelse(!is.null(x$fill), x$fill, "black") res$shape <- ifelse(!is.null(x$shape), x$shape, 19) res } #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # Graphical parameters #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # Set plot orientation # %%%%%%%%%%%%%%%%%%%%%%%%%%%%% .set_orientation <- function(p, orientation = c("vertical", "horizontal", "reverse")) { ori <- match.arg(orientation) if (ori == "horizontal") p + coord_flip() else if (ori == "reverse") p + scale_y_reverse() else p } # Change title and labels # %%%%%%%%%%%%%%%%%%%%%%%%%%%%% .labs <- function(p, main = NULL, xlab = NULL, ylab = NULL, font.main = NULL, font.x = NULL, font.y = NULL, submain = NULL, caption = NULL, font.submain = NULL, font.caption = NULL) { font.main <- .parse_font(font.main) font.x <- .parse_font(font.x) font.y <- .parse_font(font.y) font.submain <- .parse_font(font.submain) font.caption <- .parse_font(font.caption) if(is.logical(main)){ if(!main) main <- NULL } if(is.logical(submain)){ if(!submain) submain <- NULL } if(is.logical(caption)){ if(!caption) caption <- NULL } if (!is.null(main)) { p <- p + labs(title = main) } if (!is.null(submain)) { p <- p + labs(subtitle = submain) } if (!is.null(caption)) { p <- p + labs(caption = caption) } if (!is.null(xlab)) { if (xlab == FALSE) p <- p + theme(axis.title.x = element_blank()) else p <- p + labs(x = xlab) } if (!is.null(ylab)) { if (ylab == FALSE) p <- p + theme(axis.title.y = element_blank()) else p <- p + labs(y = ylab) } if (!is.null(font.main)) p <- p + theme( plot.title = element_text( size = font.main$size, lineheight = 1.0, face = font.main$face, colour = font.main$color ) ) if (!is.null(font.submain)) p <- p + theme( plot.subtitle = element_text( size = font.submain$size, lineheight = 1.0, face = font.submain$face, colour = font.submain$color ) ) if (!is.null(font.caption)) p <- p + theme( plot.caption = element_text( size = font.caption$size, lineheight = 1.0, face = font.caption$face, colour = font.caption$color ) ) if (!is.null(font.x)) p <- p + theme(axis.title.x = element_text( size = font.x$size, face = font.x$face, colour = font.x$color )) if (!is.null(font.y)) p <- p + theme(axis.title.y = element_text( size = font.y$size, face = font.y$face, colour = font.y$color )) p } # ticks # %%%%%%%%%%%%%%%%%%%%%%%%%%%%% .set_ticks <- function(ticks = TRUE, tickslab = TRUE, font.tickslab = NULL, xtickslab.rt = NULL, ytickslab.rt = NULL, font.xtickslab = font.tickslab, font.ytickslab = font.tickslab) { . <- xhjust <- xvjust <- NULL if(!is.null(xtickslab.rt)) { if(xtickslab.rt > 5) xhjust <- 1 if(xtickslab.rt == 90) xvjust <- 0.5 } else xhjust <- NULL if (ticks) ticks <- element_line(colour = "black") else ticks <- element_blank() if (is.null(font.xtickslab)) font.x <- list() else font.x <- .parse_font(font.xtickslab) if (is.null(font.ytickslab)) font.y <- list() else font.y <- .parse_font(font.ytickslab) if (tickslab) { xtickslab <- font.x %>% .add_item(hjust = xhjust, vjust = xvjust, angle = xtickslab.rt) %>% do.call(element_text, .) ytickslab <- font.y %>% .add_item(angle = ytickslab.rt) %>% do.call(element_text, .) } else { xtickslab <- element_blank() ytickslab <- element_blank() } theme( axis.ticks = ticks, axis.text.x = xtickslab, axis.text.y = ytickslab ) } # Change Axis limits # %%%%%%%%%%%%%%%%%%%%%%%%%%%%% .set_axis_limits <- function(xlim = NULL, ylim = NULL){ if(!is.null(xlim) | !is.null(ylim)) coord_cartesian(xlim, ylim) } # Axis scales # %%%%%%%%%%%%%%%%%%%%%%%%%%%%% .set_scale <- function (p, xscale = c("none", "log2", "log10", "sqrt"), yscale = c("none", "log2", "log10", "sqrt"), format.scale = FALSE) { xscale <- match.arg(xscale) yscale <- match.arg(yscale) .x <- ".x" if(format.scale){ if(!requireNamespace("scales")) stop("The R package 'scales' is required.") if(yscale == "log2"){ p <- p + scale_y_continuous(trans = scales::log2_trans(), breaks = scales::trans_breaks("log2", function(x) 2^x), labels = scales::trans_format("log2", scales::math_format(2^.x))) } else if(yscale == "log10"){ p <- p + scale_y_continuous(trans = scales::log10_trans(), breaks = scales::trans_breaks("log10", function(x) 10^x), labels = scales::trans_format("log10", scales::math_format(10^.x))) } if(xscale == "log2"){ p <- p + scale_x_continuous(trans = scales::log2_trans(), breaks = scales::trans_breaks("log2", function(x) 2^x), labels = scales::trans_format("log2", scales::math_format(2^.x))) } else if(xscale == "log10"){ p <- p + scale_x_continuous(trans = scales::log10_trans(), breaks = scales::trans_breaks("log10", function(x) 10^x), labels = scales::trans_format("log10", scales::math_format(10^.x))) } } else{ if(xscale != "none") p <- p + scale_x_continuous(trans = xscale) if(yscale != "none") p <- p + scale_y_continuous(trans = yscale) } p } # Legends # %%%%%%%%%%%%%%%%%%%%%%%%%%%%% .set_legend <- function(p, legend = NULL, legend.title = NULL, font.legend = NULL) { if(is.null(legend.title)) legend.title = waiver() font <- .parse_font(font.legend) if(!is.null(legend)) p <- p + theme(legend.position = legend) if(!.is_empty(legend.title)){ if(.is_list(legend.title)) p <- p + do.call(ggplot2::labs, legend.title) else p <- p + labs(color = legend.title, fill = legend.title, linetype = legend.title, shape = legend.title) } if(!is.null(font)){ p <- p + theme( legend.text = element_text(size = font$size, face = font$face, colour = font$color), legend.title = element_text(size = font$size, face = font$face, colour = font$color) ) } p } # Set ticks by # %%%%%%%%%%%%%%%%%%%%%%%%%%%%% .set_ticksby <- function(p, xticks.by = NULL, yticks.by = NULL) { if(!is.null(yticks.by)) { # Forcing ymin to start at 0 when distribution plot gg_mapping <- .get_gg_xy_variables(p) is_density_plot <- gg_mapping["y"] %in% c("..count..", "..density..", "..ecdf..") ymin <- NULL if(is_density_plot) ymin <- 0 p <- p + scale_y_continuous(breaks = get_breaks(by = yticks.by, from = ymin)) } else if(!is.null(xticks.by)) { p <- p + scale_x_continuous(breaks = get_breaks(by = xticks.by)) } p } # %%%%%%%%%%%%%%%%%%%%%%%%%%%%% # Add stat # %%%%%%%%%%%%%%%%%%%%%%%%%%%%% .check_add.params <- function(add, add.params, error.plot, data, color, fill, ...){ if(color %in% names(data) & is.null(add.params$color)) add.params$color <- color if(fill %in% names(data) & is.null(add.params$fill)) add.params$fill <- fill if(is.null(add.params$color)) add.params$color <- color if(is.null(add.params$fill) & ("crossbar" %in% error.plot | "boxplot" %in% add | "violin" %in% add)) add.params$fill <- fill if(is.null(add.params$fill)) add.params$fill <- add.params$color #else add.params$fill <- add.params$color if(!is.null(list(...)$shape) & is.null(add.params$shape)) add.params$shape <- list(...)$shape add.params } # Calculate the mean and the SD in each group #+++++++++++++++++++++++++ # data : a data frame # varname : the name of the variable to be summariezed # grps : column names to be used as grouping variables # .mean_sd <- function(data, varname, grps){ # summary_func <- function(x, col){ # c(mean = base::mean(x[[col]], na.rm=TRUE), # sd = stats::sd(x[[col]], na.rm=TRUE)) # } # data_sum <- plyr::ddply(data, grps, .fun=summary_func, varname) # data_sum$ymin <- data_sum$mean-data_sum$sd # data_sum$ymax <- data_sum$mean+data_sum$sd # names(data_sum)[ncol(data_sum)-3] <- varname # # data_sum <- plyr::rename(data_sum, c("mean" = varname)) # return(data_sum) # } # Summary functions .summary_functions <- function(){ c("mean", "mean_se", "mean_se_", "mean_sd", "mean_ci", "mean_range", "median", "median_iqr", "median_hilow", "median_hilow_", "median_q1q3", "median_mad", "median_range") } .errorbar_functions <- function(){ setdiff(.summary_functions(), c("mean", "median")) } # parse font # %%%%%%%%%%%%%%%%%%%%%%%%%%%%% .parse_font <- function(font){ if(is.null(font)) res <- NULL else if(inherits(font, "list")) res <- font else{ # matching size and face size <- grep("^[0-9]+$", font, perl = TRUE) face <- grep("plain|bold|italic|bold.italic", font, perl = TRUE) if(length(size) == 0) size <- NULL else size <- as.numeric(font[size]) if(length(face) == 0) face <- NULL else face <- font[face] color <- setdiff(font, c(size, face)) if(length(color) == 0) color <- NULL res <- list(size=size, face = face, color = color) } res } # Add annotation to a plot # label: text to be added to a plot # size: text size # coord: x and coordinates .ggannotate <- function (label, size = 12, coord = c(NULL, NULL)){ if(is.null(unique(coord))){ grob <- grid::grobTree(grid::textGrob(label, x = 0.3, y = 0.80, hjust=0, gp = grid::gpar(col = "black", fontsize = size, fontface = "plain"))) ggplot2::annotation_custom(grob) } else{ ggplot2::annotate("text", x = coord[1], y = coord[2], label = label, size = size/3) } } #::::::::::::::::::::::::::::::::::::::::: # Check the data provided by user #::::::::::::::::::::::::::::::::::::::::: # combine: if TRUE, gather y variables # return a list(data, x, y) .check_data <- function(data, x, y, combine = FALSE) { if(missing(x) & missing(y)){ if(!is.numeric(data)) stop("x and y are missing. In this case data should be a numeric vector.") else{ data <- data.frame(y = data, x = rep(1, length(data))) x <- "x" y <- "y" } } else if(missing(x)) { x <- "x" if(is.numeric(data)) data <- data.frame(x = data) else data$x <- rep("1", nrow(data)) } # A list of y elements to plot else if(length(y) > 1){ if(!all(y %in% colnames(data))){ not_found <- setdiff(y , colnames(data)) y <- intersect(y, colnames(data)) if(.is_empty(y)) stop("Can't find the y elements in the data.") else if(!.is_empty(not_found)) warning("Can't find the following element in the data: ", .collapse(not_found)) } } if(inherits(data, c("tbl_df", "tbl"))) data <- as.data.frame(data) # Combining y variables #...................................................... if(is.null(y)) y <- "" if(combine & length(y) > 1){ data <- data %>% df_gather(cols = y, names_to = ".y.", values_to = ".value.") %>% dplyr::mutate(.y. = factor(.data$.y., levels = unique(.data$.y.))) y <- ".value." } # Combining x variables: Case of density plot or histograms #...................................................... else if(combine & length(x) > 1 & y[1] %in% c("..density..", "..count..", "..ecdf..", "..qq..")){ data <- data %>% df_gather(cols = x, names_to = ".y.", values_to = ".value.") %>% dplyr::mutate(.y. = factor(.data$.y., levels = unique(.data$.y.))) x <- ".value." } # If not factor, x elements on the plot should # appear in the same order as in the data if(is.character(data[[x]])) data[[x]] <- factor(data[[x]], levels = unique(data[[x]])) y <- unique(y) names(y) <- y x <- unique(x) names(x) <- x if(y[1] %in% c("..density..", "..count..", "..ecdf..", "..qq..")) list(x = x, data = data, y = y) # The name of plots are x variables else list(y = y, data = data, x = x) # The name of plots will be y variables } # Adjust shape when ngroups > 6, to avoid ggplot warnings .scale_point_shape <- function(p, data, shape){ if(shape %in% colnames(data)){ grp <- data[[shape]] if(!inherits(grp, "factor")) grp <- as.factor(grp) ngroups <- length(levels(data[[shape]])) if(ngroups > 6) p <- p + scale_shape_manual(values=1:ngroups, labels = levels(data[[shape]])) } p } # Get not numeric columns in a data.frame .get_not_numeric_vars <- function(data_frame){ is_numeric <- sapply(data_frame, is.numeric) if(sum(!is_numeric) == 0) res = NULL else res <- colnames(data_frame[, !is_numeric, drop = FALSE]) res } # Get the current color used in ggplot .get_ggplot_ncolors <- function(p){ g <- ggplot_build(p) gdata <- g$data[[1]] cols <- fills <- 1 if("colour" %in% names(gdata)) cols <- unique(unlist(gdata["colour"])) if("fills" %in% names(gdata)) fills <- unique(unlist(gdata["fill"])) max(length(cols), length(fills)) } # Check if character string is a valid color representation .is_color <- function(x) { sapply(x, function(X) { tryCatch(is.matrix(grDevices::col2rgb(X)), error = function(e) FALSE) }) } # Collapse one or two vectors #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .collapse <- function(x, y = NULL, sep = "."){ if(missing(y)) paste(x, collapse = sep) else if(is.null(x) & is.null(y)) return(NULL) else if(is.null(x)) return (as.character(y)) else if(is.null(y)) return(as.character(x)) else paste0(x, sep, y) } # Check if en object is empty #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .is_empty <- function(x){ length(x) == 0 } # Remove NULL items in a vector or list # # x a vector or list .compact <- function(x){Filter(Negate(is.null), x)} # Check if is a list #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .is_list <- function(x){ inherits(x, "list") } # Returns the levels of a factor variable #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .levels <- function(x){ if(!is.factor(x)) x <- as.factor(x) levels(x) } # Remove items from a list #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .remove_item <- function(.list, items){ for(item in items) .list[[item]] <- NULL .list } # Additems in a list #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .add_item <- function(.list, ...){ pms <- list(...) for(pms.names in names(pms)){ .list[[pms.names]] <- pms[[pms.names]] } .list } # Select a colun as vector from tiblle data frame .select_vec <- function(df, column){ dplyr::pull(df, column) } # Select the top up or down rows of a data frame sorted by variables #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: # - df: data frame # - x: x axis variables (grouping variables) # - y: y axis variables (sorting variables) # - n the number of rows # - grps: other grouping variables .top_up <- function(df, x, y, n, grouping.vars = NULL){ . <- NULL grouping.vars <- c(x, grouping.vars) %>% unique() df %>% df_arrange(vars = c(grouping.vars, y)) %>% df_group_by(vars = grouping.vars) %>% do(utils::tail(., n)) } .top_down <- function(df, x, y, n, grouping.vars = NULL){ . <- NULL grouping.vars <- c(x, grouping.vars) %>% unique() df %>% df_arrange(vars = c(grouping.vars, y)) %>% df_group_by(vars = grouping.vars) %>% do(utils::head(., n)) } #:::::::::::::::::::::::::::::::::::::::::::::::::::::::::: # Apply ggpubr functions on a data #:::::::::::::::::::::::::::::::::::::::::::::::::::::::::: # fun: function, can be ggboxplot, ggdotplot, ggstripchart, ... .plotter <- function(fun, data, x, y, combine = FALSE, merge = FALSE, color = "black", fill = "white", title = NULL, xlab = NULL, ylab = NULL, legend = NULL, legend.title = NULL, facet.by = NULL, select = NULL, remove = NULL, order = NULL, add = "none", add.params = list(), label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, font.family = "", parse = FALSE, ggtheme = theme_pubr(), fun_name = "", group = 1, # used only by ggline show.legend.text = NA, ...) { if(is.logical(merge)){ if(merge) merge = "asis" else merge = "none" } if(combine & merge != "none") stop("You should use either combine = TRUE or merge = TRUE, but not both together.") font.label <- .parse_font(font.label) if(is.null(label) & fun_name == "barplot") label <- FALSE .lab <- label if(fun_name != "barplot") .lab <- NULL if(!missing(x) & !missing(y)){ if(length(y) == 1 & length(x) == 1){ combine <- FALSE merge <- "none" } } # Check data #::::::::::::::::::::::::::::::::::::::::::::::::::::::::: # - returns a list of updated main options: # list(y, data, x) opts <- .check_data(data, x, y, combine = combine | merge != "none") data <- opts$data x <- opts$x y <- opts$y is_density_plot <- y[1] %in% c("..count..", "..density..", "..ecdf..", "..qq..") if(combine) facet.by <- ".y." # Faceting by y variables if(merge != "none"){ if(!is_density_plot) facet.by <- NULL if(is.null(legend.title)) legend.title <- "" # remove .y. in the legend } # Updating parameters after merging #::::::::::::::::::::::::::::::::::::::::::::::::::::::::: # Special case for density and histograms: # x are variables and y is ..count.. or ..density.. # after merging ggpubr add a new column .y. which hold x variables # User might want to color by x variables as follow color = ".x." and # he aren't aware that the column is ".y." --> so we should translate this (see from line 1055) user.add.color <- add.params$color geom.text.position <- "identity" if(merge == "asis" ){ .grouping.var <- ".y." # y variables become grouping variable } else if(merge == "flip"){ .grouping.var <- opts$x # x variable becomes grouping variable opts$x <- ".y." # y variables become x tick labels if(is.null(xlab)) xlab <- FALSE } if(merge == "asis" | merge == "flip"){ if(is_density_plot){ color <- ifelse(color == ".x.", ".y.", color) fill <- ifelse(fill == ".x.", ".y.", fill) } if(any(c(color, fill) %in% names(data))){ add.params$color <- font.label$color <- ifelse(color %in% names(data), color, fill) } else if(!all(c(color, fill) %in% names(data))){ color <- add.params$color <- font.label$color <- .grouping.var #fill <- "white" } group <- .grouping.var geom.text.position <- position_dodge(0.8) } if(!combine & merge == "none" & length(opts$y) > 1 & is.null(title)) title <- opts$y if(!combine & merge == "none" & is.null(title)){ if(length(opts$y) > 1) title <- opts$y else if (length(opts$x) > 1 & is_density_plot) # case of density plot title <- opts$x } # Item to display x <- opts$data[[opts$x]] %>% as.vector() if(!is.null(select)) opts$data <- subset(opts$data, x %in% select) if(!is.null(remove)) opts$data <- subset(opts$data, !(x %in% remove)) if(!is.null(order)) opts$data[[opts$x]] <- factor(opts$data[[opts$x]], levels = order) # Add additional options, which can be potentially vectorized # when multiple plots opts <- opts %>% c(list(title = title, xlab = xlab, ylab = ylab)) %>% .compact() data <- opts$data opts$data <- list(opts$data) if(fun_name %in% c("ggline", "ggdotchart")) opts$group <- group # Plotting #:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: # Apply function to each y variables p <- purrr::pmap(opts, fun, color = color, fill = fill, legend = legend, legend.title = legend.title, ggtheme = ggtheme, facet.by = facet.by, add = add, add.params = add.params , # group = group, # for line plot user.add.color = user.add.color, label = .lab, # used only in ggbarplot font.label = font.label, repel = repel, label.rectangle = label.rectangle, ...) # Faceting if(!is.null(facet.by)) p <-purrr::map(p, facet, facet.by = facet.by, ...) # Add labels if(!is.null(label) & fun_name != "barplot"){ if(is.logical(label)){ if(label) label <- opts$y } grouping.vars <- intersect(c(facet.by, color, fill), colnames(data)) label.opts <- font.label %>% .add_item(data = data, x = opts$x, y = opts$y, label = label, label.select = label.select, repel = repel, label.rectangle = label.rectangle, family = font.family, parse = parse, ggtheme = NULL, grouping.vars = grouping.vars, facet.by = facet.by, position = geom.text.position, show.legend = show.legend.text) p <- purrr::map(p, function(p, label.opts){ . <- NULL label.opts %>% .add_item(ggp = p) %>% do.call(ggtext, .) }, label.opts ) } # Take into account the legend argument, when the main plot has no legend and ggtext has legend p <-purrr::map(p, ggpar, legend = legend, legend.title = legend.title) if(.is_list(p) & length(p) == 1) p <- p[[1]] p } # get the geometry of the first layer #::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .geom <- function(p, .layer = 1){ . <- NULL if(is.null(p) | .is_empty(p$layers)) return("") class(p$layers[[.layer]]$geom)[1] %>% tolower() %>% gsub("geom", "", .) } # Get the mapping variables of the first layer #::::::::::::::::::::::::::::::::::::::::::::::::: .mapping <- function(p){ if(is.null(p)) return(list()) . <- NULL layer0.mapping <- as.character(p$mapping) %>% gsub("~", "", .) layer0.mapping.labels <- p$mapping %>% names() names(layer0.mapping) <- layer0.mapping.labels layer1.mapping <- NULL if(!.is_empty(p$layers)){ layer1.mapping <- p$layers[[1]]$mapping %>% as.character() %>% gsub("~", "", .) layer1.mapping.labels <- p$layers[[1]]$mapping %>% names() names(layer1.mapping) <- layer1.mapping.labels } c(layer0.mapping, layer1.mapping) %>% as.list() } # Call geom_exec function to update a plot #::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .update_plot <- function(opts, p){ p + do.call(geom_exec, opts) } # Get ggplot2 x and y variable # :::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .get_gg_xy_variables <- function(p){ . <- NULL x <- p$mapping['x'] %>% as.character() %>% gsub("~", "", .) y <- p$mapping['y'] %>% as.character() %>% gsub("~", "", .) xy <- c(x, y) names(xy) <- c("x", "y") return(xy) } # Add mean or median line # used by ggdensity and gghistogram #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: # p: main plot # data: data frame # x: measure variables # add: center to add # grouping.vars: grouping variables .add_center_line <- function(p, add = c("none", "mean", "median"), grouping.vars = NULL, color = "black", linetype = "dashed", size = NULL) { add <- match.arg(add) data <- p$data # x <- .mapping(p)$x .mapping <- .get_gg_xy_variables(p) x <- .mapping["x"] if(!(add %in% c("mean", "median"))) return(p) compute_center <- switch(add, mean = mean, median = stats::median) # NO grouping variable if(.is_empty(grouping.vars)) { m <- ifelse(add == "mean", mean(data[[x]], na.rm = TRUE), stats::median(data[[x]], na.rm = TRUE)) p <- p + geom_exec(geom_vline, data = data, xintercept = m, color = color, linetype = linetype, size = size) } # Case of grouping variable else { data_sum <- data %>% group_by(!!!syms(grouping.vars)) %>% summarise(.center = compute_center(!!sym(x), na.rm = TRUE)) p <- p + geom_exec(geom_vline, data = data_sum, xintercept = ".center", color = color, linetype = linetype, size = size) } p } # Check legend argument .check_legend <- function(legend){ allowed.values <- c("top", "bottom", "left", "right", "none") if(is.null(legend) | is.numeric(legend)) return(legend) else if(is.logical(legend)){ if(legend) legend <- "top" else legend <- "none" } else if(is.character(legend)){ legend <- legend[1] if(!legend %in% allowed.values) stop("Argument legend should be one of ", .collapse(allowed.values, sep = ", ")) } return (legend) } ggpubr/R/annotate_figure.R0000644000176200001440000000577014344267673015267 0ustar liggesusers#' @include utilities.R NULL #'Annotate Arranged Figure #' #'@description Annotate figures including: i) ggplots, ii) arranged ggplots from #' \code{\link{ggarrange}()}, \code{\link[gridExtra:arrangeGrob]{grid.arrange}()} and #' \code{\link[cowplot]{plot_grid}()}. #'@param p (arranged) ggplots. #'@param top,bottom,left,right optional string, or grob. #'@param fig.lab figure label (e.g.: "Figure 1"). #'@param fig.lab.pos position of the figure label, can be one of "top.left", #' "top", "top.right", "bottom.left", "bottom", "bottom.right". Default is #' "top.left". #'@param fig.lab.size optional size of the figure label. #'@param fig.lab.face optional font face of the figure label. Allowed values #' include: "plain", "bold", "italic", "bold.italic". #'@author Alboukadel Kassambara \email{alboukadel.kassambara@@gmail.com} #'@seealso \code{\link{ggarrange}()} #' @examples #' data("ToothGrowth") #' df <- ToothGrowth #' df$dose <- as.factor(df$dose) #' #' # Create some plots #' # :::::::::::::::::::::::::::::::::::::::::::::::::: #' # Box plot #' bxp <- ggboxplot(df, x = "dose", y = "len", #' color = "dose", palette = "jco") #' # Dot plot #' dp <- ggdotplot(df, x = "dose", y = "len", #' color = "dose", palette = "jco") #' # Density plot #' dens <- ggdensity(df, x = "len", fill = "dose", palette = "jco") #' #' # Arrange and annotate #' # :::::::::::::::::::::::::::::::::::::::::::::::::: #' figure <- ggarrange(bxp, dp, dens, ncol = 2, nrow = 2) #'annotate_figure(figure, #' top = text_grob("Visualizing Tooth Growth", color = "red", face = "bold", size = 14), #' bottom = text_grob("Data source: \n ToothGrowth data set", color = "blue", #' hjust = 1, x = 1, face = "italic", size = 10), #' left = text_grob("Figure arranged using ggpubr", color = "green", rot = 90), #' right = text_grob(bquote("Superscript: ("*kg~NH[3]~ha^-1~yr^-1*")"), rot = 90), #' fig.lab = "Figure 1", fig.lab.face = "bold" #') #' #' #'@export annotate_figure <- function(p, top = NULL, bottom = NULL, left = NULL, right = NULL, fig.lab = NULL, fig.lab.pos = c("top.left", "top", "top.right", "bottom.left", "bottom", "bottom.right"), fig.lab.size, fig.lab.face ) { fig.lab.pos <- match.arg(fig.lab.pos) annot.args <- list(top = top, bottom = bottom, left = left,right = right) %>% .compact() lab.args <- list(label = fig.lab, position = fig.lab.pos) %>% .compact() if(!missing(fig.lab.size)) lab.args$size <- fig.lab.size if(!missing(fig.lab.face)) lab.args$fontface <- fig.lab.face if(!.is_empty(annot.args)){ p <- gridExtra::arrangeGrob(p, top = top, bottom = bottom, left = left,right = right) %>% as_ggplot() } if(!is.null(fig.lab)){ p <- cowplot::ggdraw(p) + do.call(cowplot::draw_figure_label, lab.args) } p } ggpubr/R/bgcolor.R0000644000176200001440000000074714332553655013536 0ustar liggesusers#'Change ggplot Panel Background Color #' #' @description Change ggplot panel background color. #' @param color background color. #' #' @seealso \link{border}(). #' #'@examples #'# Load data #'data("ToothGrowth") #'df <- ToothGrowth #' #'# Basic plot #'p <- ggboxplot(df, x = "dose", y = "len") #'p #' #'# Change panel background color #' p + #' bgcolor("#BFD5E3")+ #' border("#BFD5E3") #'@export bgcolor <- function(color){ theme(panel.background = element_rect(fill = color)) } ggpubr/R/utilities_label.R0000644000176200001440000001064314332553655015255 0ustar liggesusers#:::::::::::::::::::::::::::::::::::::::::::::::::::::::: # Helper function for adding annotation to a ggplot #:::::::::::::::::::::::::::::::::::::::::::::::::::::::: # Get label parameters for each group #::::::::::::::::::::::::::::::::::::::::::::::::::::::::: # Returns a data frame with x, y, hjust, vjust # group.id is the index position of the group in a boxplot for example .label_params <- function(data, scales, label.x.npc = "left", label.y.npc = "right", label.x = NULL, label.y = NULL, .by = c("group", "panel"), group.id = NULL, ...) { .by <- match.arg(.by) # Check label coordinates for each group #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: if(is.null(group.id)) group.id <- group.id <- abs(data$group[1]) label.x.npc <- .group_coord(label.x.npc, group.id) label.y.npc <- .group_coord(label.y.npc, group.id) label.x <- .group_coord(label.x, group.id) label.y <- .group_coord(label.y, group.id) .check_npc_coord(label.x.npc, axis = "x") .check_npc_coord(label.y.npc, axis = "y") if (length(label.x) > 0) { x <- label.x hjust <- 0.5 } else if (length(label.x.npc) > 0) { if (is.numeric(label.x.npc)) { x <- scales$x$dimension()[1] + label.x.npc * diff(scales$x$dimension()) hjust <- 0.5 } else if (is.character(label.x.npc)) { if (label.x.npc == "right") { x <- scales$x$dimension()[2] hjust <- 1 } else if (label.x.npc %in% c("center", "centre", "middle")) { x <- mean(scales$x$dimension()) hjust <- 0.5 } else if (label.x.npc == "left") { x <- scales$x$dimension()[1] hjust <- 0 } } } if (length(label.y) > 0) { y <- label.y vjust <- 0.5 } else if (length(label.y.npc) > 0) { if (is.numeric(label.y.npc)) { y <- scales$y$dimension()[1] + label.y.npc * diff(scales$y$dimension()) vjust <- 1.4 * group.id - (0.7 * length(group.id)) } else if (is.character(label.y.npc)) { if (label.y.npc == "bottom") { y <- scales$y$dimension()[1] vjust <- -1.4 * group.id } else if (label.y.npc %in% c("center", "centre", "middle")) { y <- mean(scales$y$dimension()) vjust <- 1.4 * group.id - (0.7 * length(group.id)) } else if (label.y.npc == "top") { y <- scales$y$dimension()[2] vjust <- 1.4 * group.id } } } if(.by == "panel"){ hjust <- 0.5 vjust = 0.5 } data.frame(x = x, y = y, hjust = hjust, vjust = vjust) } # Get label parameters by group # Useful in boxplot, where group.ids is the index of the group: 1, 2, 3, etc # Useful only when computation is done by panel .label_params_by_group <- function(..., group.ids){ purrr::map(group.ids, function(group.id, ...){.label_params(..., group.id = group.id)}, ...) %>% dplyr::bind_rows() #%>% #dplyr::mutate(x = group.ids) } # Check label coordinates for each group #::::::::::::::::::::::::::::::::::::::::: # coord.values: label coordinate for each group. If too short, they are recycled. # group.id the id of groups as returned by ggplot_build() .group_coord <- function(coord.values, group.id){ if(!.is_empty(coord.values)){ coord.values <- ifelse(length(coord.values) >= group.id, coord.values[group.id], coord.values[1]) } coord.values } # Check NPC coord #::::::::::::::::::::::::::::::::::::::::: # npc: Normalised Parent Coordinates. # The origin of the viewport is (0, 0) and the viewport has a width and height of 1 unit. # For example, (0.5, 0.5) is the centre of the viewport. # coord: should be between 0 and 1 # axis: should be "x" or "y" .check_npc_coord <- function(.coord, axis = c("x", "y")){ axis <- match.arg(axis) if(axis == "x") allowed.values <- c('right', 'left', 'center', 'centre', 'middle') else if(axis == "y") allowed.values <- c( 'bottom', 'top', 'center', 'centre', 'middle') .message <- paste0("'*.npc coord for ", axis, " axis should be either a numeric value in [0-1] ", "or a character strings including one of ", .collapse(allowed.values, sep = ", ")) if(!is.null(.coord)){ if(is.numeric(.coord)){ if (any(.coord < 0 | .coord > 1)) { stop(.message) } } else if(is.character(.coord)){ if(!(.coord %in% allowed.values)) stop(.message) } else stop(.message) } } ggpubr/R/ggscatterhist.R0000644000176200001440000002350214344271270014745 0ustar liggesusers#' @include utilities.R NULL #'Scatter Plot with Marginal Histograms #'@description Create a scatter plot with marginal histograms, density plots or #' box plots. #'@inheritParams ggscatter #'@param main.plot.size the width of the main plot. Default is 2. #'@param margin.plot.size the width of the marginal plot. Default is 1. #'@param group a grouping variable. Change points color and shape by groups if #' the options \code{color} and \code{shape} are missing. Should be also #' specified when you want to create a marginal box plot that is grouped. #'@param margin.plot the type of the marginal plot. Default is "hist". #'@param margin.params parameters to be applied to the marginal plots. #'@param margin.ggtheme the theme of the marginal plot. Default is #' \code{\link[ggplot2:ggtheme]{theme_void}()}. #'@param margin.space logical value. If TRUE, adds space between the main plot #' and the marginal plot. #'@param bins Number of histogram bins. Defaults to 30. Pick a better value that #' fit to your data. #'@param linetype line type ("solid", "dashed", ...) #'@param legend specify the legend position. Allowed values include: "top", #' "bottom", "left", "right". #'@param ggtheme the theme to be used for the scatter plot. Default is #' \code{\link{theme_pubr}()}. #'@param print logical value. If \code{TRUE} (default), print the plot. #'@param ... other arguments passed to the function \code{\link{ggscatter}()}. #'@return an object of class \code{ggscatterhist}, which is list of ggplots, #' including the following elements: \itemize{\item sp: main scatter plot; #' \item xplot: marginal x-axis plot; \item yplot: marginal y-axis plot. }. #' #' User can modify each of plot before printing. #' #' @examples #' # Basic scatter plot with marginal density plot #' ggscatterhist(iris, x = "Sepal.Length", y = "Sepal.Width", #' color = "#00AFBB", #' margin.params = list(fill = "lightgray")) #' #' #' # Grouped data #'ggscatterhist( #' iris, x = "Sepal.Length", y = "Sepal.Width", #' color = "Species", size = 3, alpha = 0.6, #' palette = c("#00AFBB", "#E7B800", "#FC4E07"), #' margin.params = list(fill = "Species", color = "black", size = 0.2) #') #' #'# Use boxplot as marginal #'ggscatterhist( #' iris, x = "Sepal.Length", y = "Sepal.Width", #' color = "Species", size = 3, alpha = 0.6, #' palette = c("#00AFBB", "#E7B800", "#FC4E07"), #' margin.plot = "boxplot", #' ggtheme = theme_bw() #') #' #'# Add vertical and horizontal line to a ggscatterhist #'plots <- ggscatterhist(iris, x = "Sepal.Length", y = "Sepal.Width", print = FALSE) #'plots$sp <- plots$sp + #' geom_hline(yintercept = 3, linetype = "dashed", color = "blue") + #' geom_vline(xintercept = 6, linetype = "dashed", color = "red") #'plots #' #'@export ggscatterhist <- function( data, x, y, group = NULL, color = "black", fill = NA, palette = NULL, shape = 19, size = 2, linetype = "solid", bins = 30, margin.plot = c("density", "histogram", "boxplot"), margin.params = list(), margin.ggtheme = theme_void(), margin.space = FALSE, main.plot.size = 2, margin.plot.size = 1, title = NULL, xlab = NULL, ylab = NULL, legend = "top", ggtheme = theme_pubr(), print = TRUE, ... ) { if(!has_cowplot_v0.9()){ warning("Install the latest developmental version of cowplot on github ", "to fully use all the feature of ggscatterhist", .call = FALSE) # margin.space = TRUE } margin.plot <- match.arg(margin.plot) margin.params <- .check_margin_params( margin.params, data, color, fill, linetype ) if(margin.plot == "histogram") { if(is.null(margin.params$position)){ margin.params$position <- "identity" } margin.params <- margin.params %>% .add_item(bins = bins) } if(!is.null(group)){ if(missing(color)) color = group if(missing(shape)) shape = group } . <- NULL sp <- ggscatter( data, x, y, color = color, fill = fill, palette = palette, shape = shape, size = size, xlab = xlab, ylab = ylab, ggtheme = ggtheme, title = title, legend = legend, ... ) # Type of graphics to be added in the margins geomfunc <- switch (margin.plot, histogram = geom_histogram, density = geom_density, boxplot = geom_boxplot, geom_histogram ) # Define the x and the y variables depending on # the geometry used in margins if(margin.plot %in% c("density", "histogram")){ xplot.x <- x xplot.y <- NULL yplot.x <- y yplot.y <- NULL } else if(margin.plot %in% c("boxplot")){ if(is.null(group)) { data <- data %>% mutate(.xgroupx. = factor(1)) group = ".xgroupx." } xplot.x <- group xplot.y <- x yplot.x <- group yplot.y <- y } # Create the different marginal plot xplot <- ggplot() + margin.params %>% .add_item(geomfunc = geomfunc, data = data, x = xplot.x, y = xplot.y, alpha = 0.7) %>% do.call(geom_exec, .) xplot <- set_palette(xplot, palette) yplot <- ggplot() + margin.params %>% .add_item(geomfunc = geomfunc, data = data, x = yplot.x, y = yplot.y, alpha = 0.7) %>% do.call(geom_exec, .) yplot <- set_palette(yplot, palette) # Flip the marginal plots if(margin.plot %in% c("density", "histogram")) yplot <- yplot + coord_flip() else if(margin.plot %in% c("boxplot")) xplot <- xplot + coord_flip() # Cleaning the plot .legend <- get_legend(sp) sp <- sp + theme(plot.margin = grid::unit(c(0,0,0.25,0.25), "cm")) xplot <- xplot + margin.ggtheme + clean_theme() + rremove("legend") + theme( plot.margin = grid::unit(c(0,0,0,0), "cm")) yplot <- yplot + margin.ggtheme + clean_theme() + rremove("legend") + theme(plot.margin = grid::unit(c(0,0,0,0), "cm")) plots <- list(sp = sp, xplot = xplot, yplot = yplot) class(plots) <- c("ggscatterhist", "list") if(print){ res <- print( plots, margin.space = margin.space, main.plot.size = main.plot.size, margin.plot.size = margin.plot.size, title = title, legend = legend ) } invisible(plots) } #' @method print ggscatterhist #' @param x an object of class \code{ggscatterhist}. #' @rdname ggscatterhist #' @export print.ggscatterhist <- function(x, margin.space = FALSE, main.plot.size = 2, margin.plot.size = 1, title = NULL, legend = "top", ...){ sp <- x$sp xplot <- x$xplot yplot <- x$yplot .legend <- get_legend(sp) if(margin.space){ common.legend <- FALSE if(!is.null(.legend)) common.legend = TRUE sp <- sp + theme( plot.title = element_blank(), plot.subtitle = element_blank() ) fig <- ggarrange( xplot, NULL, sp, yplot, ncol = 2, nrow = 2, align = "hv", widths = c(main.plot.size, margin.plot.size), heights = c(margin.plot.size, main.plot.size), common.legend = common.legend, legend = legend ) if(!is.null(title)){ fig <- annotate_figure( fig, top = text_grob(title, color = "black", size = 13, face = "bold") ) } } else{ if(!is.null(title)) sp <- sp + ggtitle(title) fig <- .insert_xaxis_grob(sp, xplot, grid::unit(margin.plot.size/5, "null"), position = "top") fig <- .insert_yaxis_grob(fig, yplot, grid::unit(margin.plot.size/5, "null"), position = "right") fig <- cowplot::ggdraw(fig) } print(fig) invisible(fig) } has_cowplot_v0.9 <- function(){ vv <- as.character(utils::packageVersion("cowplot")) cc <- utils::compareVersion(vv, "0.8.0.8") > 0 cc } .check_margin_params <- function(params, data, color = "black", fill = NA, linetype = "slid"){ if(is.null(params$color)) { if(color %in% colnames(data)){ col.val <- dplyr::pull(data, color) if(!is.factor(col.val)) params$color <- "black" else params$color <- color } } if(is.null(params$fill)) { if(fill %in% colnames(data)){ fill.val <- .select_vec(data, fill) if(!is.factor(fill.val)) params$fill <- "black" else params$fill <- fill } } if(is.null(params$linetype)) params$linetype <- linetype params } # Helper functions to insert marginal plots #:::::::::::::::::::::::::::::::::::::::::::::::::::: # Use cowplot::insert_xaxis_grob and cowplot::insert_yaxis_grob, # when 0.9 stable version released .insert_xaxis_grob <- function ( plot, grob, height = grid::unit(0.2, "null"), position = c("top", "bottom") ) { if(inherits(grob, "ggplot")) grob <- .get_panel(grob) gt <- .plot_to_gtable(plot) pp <- gt$layout[gt$layout$name == "panel", ] if (position[1] == "top") { g <- gtable::gtable_add_rows(gt, height, pp$t - 1) g <- gtable::gtable_add_grob( g, grob, pp$t, pp$l, pp$t, pp$r, clip = "inherit", name = "xaxis-grob-t" ) } else { g <- gtable::gtable_add_rows(gt, height, pp$b) g <- gtable::gtable_add_grob( g, grob, pp$b + 1, pp$l, pp$b + 1, pp$r, clip = "inherit", name = "xaxis-grob-b" ) } } .insert_yaxis_grob <- function ( plot, grob, width = grid::unit(0.2, "null"), position = c("right", "left")) { if(inherits(grob, "ggplot")) grob <- .get_panel(grob) gt <- .plot_to_gtable(plot) pp <- gt$layout[gt$layout$name == "panel", ] if (position[1] == "right") { g <- gtable::gtable_add_cols(gt, width, pp$r) g <- gtable::gtable_add_grob( g, grob, pp$t, pp$r + 1, pp$b, pp$r + 1, clip = "inherit", name = "yaxis-grob-r" ) } else { g <- gtable::gtable_add_cols(gt, width, pp$l - 1) g <- gtable::gtable_add_grob( g, grob, pp$t, pp$l, pp$b, pp$l, clip = "inherit", name = "yaxis-grob-l" ) } } .plot_to_gtable <- function (plot) { if(inherits(plot, "gtable")) return(plot) else ggplot2::ggplotGrob(plot) } .get_panel <- function (plot) { gt <- .plot_to_gtable(plot) panelIndex <- which(gt$layout$name == "panel") panel <- gt$grobs[[panelIndex]] } ggpubr/R/ggbarplot.R0000644000176200001440000004023414342651420014051 0ustar liggesusers#' @include utilities.R ggpar.R NULL #' Bar plot #' @description Create a bar plot. #' @inheritParams ggboxplot #' @inheritParams ggplot2::geom_bar #' @param x,y x and y variables for drawing. #' @param color,fill outline and fill colors. #' @param sort.val a string specifying whether the value should be sorted. #' Allowed values are "none" (no sorting), "asc" (for ascending) or "desc" (for descending). #' @param sort.by.groups logical value. If TRUE the data are sorted by groups. #' Used only when sort.val != "none". #' @param top a numeric value specifying the number of top elements to be shown. #' @param label specify whether to add labels on the bar plot. Allowed values #' are: \itemize{ \item \strong{logical value}: If TRUE, y values is added as #' labels on the bar plot \item \strong{character vector}: Used as text #' labels; must be the same length as y. } #' @param lab.col,lab.size text color and size for labels. #' @param lab.pos character specifying the position for labels. Allowed values #' are "out" (for outside) or "in" (for inside). Ignored when lab.vjust != #' NULL. #' @param lab.vjust numeric, vertical justification of labels. Provide negative #' value (e.g.: -0.4) to put labels outside the bars or positive value to put #' labels inside (e.g.: 2). #' @param lab.hjust numeric, horizontal justification of labels. #' @param lab.nb.digits integer indicating the number of decimal places (round) to be used. #' @param ... other arguments to be passed to be passed to ggpar(). #' @details The plot can be easily customized using the function ggpar(). Read #' ?ggpar for changing: \itemize{ \item main title and axis labels: main, #' xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis #' scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: #' palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, #' labels and position: legend = "right" \item plot orientation : orientation #' = c("vertical", "horizontal", "reverse") } #' @seealso \code{\link{ggpar}}, \code{\link{ggline}} #' @examples #' # Data #' df <- data.frame(dose=c("D0.5", "D1", "D2"), #' len=c(4.2, 10, 29.5)) #' print(df) #' #' # Basic plot with label outsite #' # +++++++++++++++++++++++++++ #' ggbarplot(df, x = "dose", y = "len", #' label = TRUE, label.pos = "out") #' #' # Change width #' ggbarplot(df, x = "dose", y = "len", width = 0.5) #' #' # Change the plot orientation: horizontal #' ggbarplot(df, "dose", "len", orientation = "horiz") #' #' # Change the default order of items #' ggbarplot(df, "dose", "len", #' order = c("D2", "D1", "D0.5")) #' #' #' # Change colors #' # +++++++++++++++++++++++++++ #' #' # Change fill and outline color #' # add labels inside bars #' ggbarplot(df, "dose", "len", #' fill = "steelblue", color = "steelblue", #' label = TRUE, lab.pos = "in", lab.col = "white") #' #' # Change colors by groups: dose #' # Use custom color palette #' ggbarplot(df, "dose", "len", color = "dose", #' palette = c("#00AFBB", "#E7B800", "#FC4E07")) #' #' # Change fill and outline colors by groups #' ggbarplot(df, "dose", "len", #' fill = "dose", color = "dose", #' palette = c("#00AFBB", "#E7B800", "#FC4E07")) #' #' #' # Plot with multiple groups #' # +++++++++++++++++++++ #' #' # Create some data #' df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3), #' dose=rep(c("D0.5", "D1", "D2"),2), #' len=c(6.8, 15, 33, 4.2, 10, 29.5)) #' print(df2) #' #' # Plot "len" by "dose" and change color by a second group: "supp" #' # Add labels inside bars #' ggbarplot(df2, "dose", "len", #' fill = "supp", color = "supp", palette = "Paired", #' label = TRUE, lab.col = "white", lab.pos = "in") #' #' # Change position: Interleaved (dodged) bar plot #' ggbarplot(df2, "dose", "len", #' fill = "supp", color = "supp", palette = "Paired", #' label = TRUE, #' position = position_dodge(0.9)) #' #' # Add points and errors #' # ++++++++++++++++++++++++++ #' #' # Data: ToothGrowth data set we'll be used. #' df3 <- ToothGrowth #' head(df3, 10) #' #' # It can be seen that for each group we have #' # different values #' ggbarplot(df3, x = "dose", y = "len") #' #' # Visualize the mean of each group #' ggbarplot(df3, x = "dose", y = "len", #' add = "mean") #' #' # Add error bars: mean_se #' # (other values include: mean_sd, mean_ci, median_iqr, ....) #' # Add labels #' ggbarplot(df3, x = "dose", y = "len", #' add = "mean_se", label = TRUE, lab.vjust = -1.6) #' #' # Use only "upper_errorbar" #' ggbarplot(df3, x = "dose", y = "len", #' add = "mean_se", error.plot = "upper_errorbar") #' #' # Change error.plot to "pointrange" #' ggbarplot(df3, x = "dose", y = "len", #' add = "mean_se", error.plot = "pointrange") #' #' # Add jitter points and errors (mean_se) #' ggbarplot(df3, x = "dose", y = "len", #' add = c("mean_se", "jitter")) #' #' # Add dot and errors (mean_se) #' ggbarplot(df3, x = "dose", y = "len", #' add = c("mean_se", "dotplot")) #' #' # Multiple groups with error bars and jitter point #' ggbarplot(df3, x = "dose", y = "len", color = "supp", #' add = "mean_se", palette = c("#00AFBB", "#E7B800"), #' position = position_dodge()) # #' #' #' @export ggbarplot <- function(data, x, y, combine = FALSE, merge = FALSE, color = "black", fill = "white", palette = NULL, size = NULL, width = NULL, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, select = NULL, remove = NULL, order = NULL, add = "none", add.params = list(), error.plot = "errorbar", label = FALSE, lab.col = "black", lab.size = 4, lab.pos = c("out", "in"), lab.vjust = NULL, lab.hjust = NULL, lab.nb.digits = NULL, sort.val = c("none", "desc", "asc"), sort.by.groups = TRUE, top = Inf, position = position_stack(), ggtheme = theme_pubr(), ...) { # Default options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .opts <- list( combine = combine, merge = merge, color = color, fill = fill, palette = palette, size = size, width = width, title = title, xlab = xlab, ylab = ylab, facet.by = facet.by, panel.labs = panel.labs, short.panel.labs = short.panel.labs, select = select , remove = remove, order = order, add = add, add.params = add.params, error.plot = error.plot, label = label, lab.col = lab.col, lab.size = lab.size, lab.pos = lab.pos, lab.vjust = lab.vjust, lab.hjust = lab.hjust, lab.nb.digits = lab.nb.digits, sort.val = sort.val, sort.by.groups = sort.by.groups, top = top, position = position, ggtheme = ggtheme, ...) if(!missing(data)) .opts$data <- data if(!missing(x)) .opts$x <- x if(!missing(y)) .opts$y <- y # User options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .user.opts <- as.list(match.call(expand.dots = TRUE)) .user.opts[[1]] <- NULL # Remove the function name # keep only user arguments for(opt.name in names(.opts)){ if(is.null(.user.opts[[opt.name]])) .opts[[opt.name]] <- NULL } if(is.logical(merge)){ if(merge & missing(position)) .opts$position <- position_dodge(0.8) if(merge & missing(lab.col)) .opts$lab.col <- ".y." } else if(is.character(merge)){ .opts$position <- position_dodge(0.8) } .opts$fun <- ggbarplot_core .opts$fun_name <- "barplot" if(missing(ggtheme) & (!is.null(facet.by) | combine)) .opts$ggtheme <- theme_pubr(border = TRUE) p <- do.call(.plotter, .opts) if(.is_list(p) & length(p) == 1) p <- p[[1]] return(p) } ggbarplot_core <- function(data, x, y, color = "black", fill = "white", palette = NULL, size = NULL, width = 0.7, title = NULL, xlab = NULL, ylab = NULL, label = FALSE, lab.col = "black", lab.size = 4, lab.pos = c("out", "in"), lab.vjust = NULL, lab.hjust = NULL, lab.nb.digits = NULL, select = NULL, order = NULL, facet.by = NULL, sort.val = c("none", "desc", "asc"), sort.by.groups = TRUE, merge = FALSE, top = Inf, add = "none", add.params = list(), error.plot = "errorbar", position = position_stack(), ggtheme = theme_pubr(), ...) { sort.val <- match.arg(sort.val) if(!is.null(order)) data[[x]] <- factor(data[[x]], levels = order) else { xx <- .select_vec(data, x) if(inherits(xx, c("character", "numeric"))) data[[x]] <- as.factor(data[[x]]) } error.plot = error.plot[1] lab.pos <- match.arg(lab.pos) label <- as.vector(label) if("none" %in% add) add <- "none" grouping.vars <- intersect(c(x, color, fill, facet.by), names(data)) . <- NULL # static summaries for computing mean/median and adding errors if(is.null(add.params$fill)) add.params$fill <- "white" if(is.null(add.params$group)){ if(fill %in% names(data)) add.params$group <- fill else if(color %in% names(data)) add.params$group <- color } add.params <- .check_add.params(add, add.params, error.plot, data, color, fill, ...) if(any(.summary_functions() %in% add)) { data_sum <- desc_statby(data, measure.var = y, grps = grouping.vars) summary.funcs <- intersect(.summary_functions(), add) if(length(summary.funcs) > 1) stop("Only one summary function is allowed. ", "Choose one of ", .collapse(.summary_functions(), sep = ", ")) .center <- .get_errorbar_center_func(summary.funcs) add <- setdiff(add, .center) names(data_sum)[which(names(data_sum) == .center)] <- y if(inherits(xx, c("character", "numeric"))) data_sum[, x] <- .select_vec(data_sum, x) %>% as.factor() } else data_sum <- data # Sorting #%%%%%%%%%%%%%%%%%%%%%%%%%%%%% if(top !=Inf & sort.val == "none") sort.val = "desc" if(top !=Inf) { data_sum <- data_sum[order(-data_sum[, y]), ] data_sum <- utils::head(data_sum, n = top) } grps <- unique(intersect(c(color, fill), names(data))) if(length(grps) > 0) grps <- .get_not_numeric_vars(data[, grps, drop = FALSE]) ngrps <- length(grps) if(!sort.by.groups) ngrps <- 0 # Variables for ordering if(ngrps > 0) dd <- data_sum[, c(grps, y)] else dd <- data_sum[, y, drop = FALSE] if(sort.val == "desc") dd[, y] <- -dd[, y] # Sorting if(sort.val != "none") { if(ngrps == 0) data_sum <- data_sum[order(dd[, y]),] else if(ngrps == 1) data_sum <- data_sum[order(dd[, 1], dd[, y]),] else if(ngrps == 2) data_sum <- data_sum[order(dd[, 1], dd[, 2], dd[, y]),] data_sum[, x] <- factor(data_sum[, x], levels = data_sum[, x]) } # Main plot #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% if(inherits(position, "PositionDodge") & is.null(position$width)) position$width = 0.95 p <- ggplot(data, create_aes(list(x = x, y = y))) p <- p + geom_exec(geom_bar, data = data_sum, stat = "identity", color = color, fill = fill, position = position, size = size, width = width, ...) # Add errors add.params <- add.params %>% .add_item(p = p, error.plot = error.plot) is.stacked.position <- inherits(position, "PositionStack") stack.groups <- unique(c(x, facet.by)) nb.bars.by.xposition <- data_sum %>% group_by(!!!syms(stack.groups)) %>% dplyr::count() %>% dplyr::pull(.data$n) %>% max() if(is.stacked.position) add.position <- "identity" else add.position <- position if(is.stacked.position & nb.bars.by.xposition >=2) { p <- add.params %>% .add_item(add = .remove_errorbar_func(add), position = add.position) %>% do.call(ggadd, .) if(any(.errorbar_functions() %in% add)){ p <- p + .geom_stacked_errorbar( data_sum, x, y, color = add.params$color, fill = add.params$fill, group = add.params$group, facet.by = facet.by, func = .get_summary_func(add), error.plot = error.plot ) } } else { p <- add.params %>% .add_item(add = add, position = add.position) %>% do.call(ggadd, .) } # Add labels add.label <- FALSE if(is.logical(label)){ .lab <- y add.label <- label } else { # Add user specified labels as data column data_sum$.ulabel. <- label .lab <- ".ulabel." add.label <- TRUE } if(add.label) { if(is.null(lab.vjust)) lab.vjust <- ifelse(lab.pos == "out", -0.4, 2 ) if(is.null(lab.hjust)) lab.hjust <- 0.5 if(!is.null(lab.nb.digits)){ if(is.numeric(.lab)) .lab <- round(.lab, digits = lab.nb.digits) else if(.lab[1] %in% colnames(data_sum)) data_sum[, .lab] <- dplyr::pull(data_sum, .lab) %>% round(digits = lab.nb.digits) } # pos <- "identity" # if color or fill by groups .cols <- unique(c(color, fill)) if(any(.cols %in% names(data))){ .in <- which(.cols %in% names(data)) lab.fill <- color.var <- .cols[.in] data_sum <- data_sum %>% dplyr::arrange(!!!syms(x), desc(!!!syms(color.var))) group <- intersect(.cols, names(data))[1]# You should specify group for dodging text p <- p + geom_exec(geom_text, data = data_sum, label = .lab, #fill = lab.fill vjust = lab.vjust, hjust = lab.hjust, size = lab.size, color = lab.col, fontface = "plain", position = position, group = group) } else{ p <- p + geom_exec(geom_text, data = data_sum, label = .lab, vjust = lab.vjust, hjust = lab.hjust, size = lab.size, color = lab.col, fontface = "plain", position = position) } } # To do # top10, visualizing error p <- ggpar(p, palette = palette, ggtheme = ggtheme, title = title, xlab = xlab, ylab = ylab,...) p } # Stacked error bar ---------------------------- .geom_stacked_errorbar <- function(data_sum, x, y, color = NULL, fill = NULL, facet.by = NULL, group = NULL, func = "mean_se", error.plot = "errorbar"){ stack.groups <- unique(c(x, facet.by)) legend.var <- intersect(unique(c(color, fill, group)), colnames(data_sum)) error <- .get_errorbar_error_func(func) error.value <- data_sum %>% dplyr::pull(!!error) desc <- dplyr::desc errorbar.position <- data_sum %>% group_by(!!!syms(stack.groups)) %>% dplyr::arrange(!!sym(x), desc(!!sym(legend.var))) %>% dplyr::mutate( y = cumsum(!!sym(y)), ymin = .data$y - !!sym(error), ymax = .data$y + !!sym(error) ) %>% dplyr::ungroup() geom_error <- .get_geom_error_function(error.plot) args <- geom_exec( data = errorbar.position, color = color, group = group, x = x, ymin = "ymin", ymax = "ymax" ) mapping <- args$mapping option <- args$option if(error.plot == "errorbar") option$width <- 0.15 option[["mapping"]] <- create_aes(mapping) do.call(geom_error, option) } .get_geom_error_function <- function(error.plot = "errorbar"){ error.plot <- error.plot[1] geom_func <- ggplot2::geom_errorbar if(error.plot %in% c("pointrange", "lower_pointrange", "upper_pointrange")) geom_func <- ggplot2::geom_pointrange else if(error.plot %in% c("linerange", "lower_linerange", "upper_linerange")) geom_func <- ggplot2::geom_linerange else if(error.plot %in% c("errorbar", "lower_errorbar", "upper_errorbar")) geom_func <- ggplot2::geom_errorbar geom_func } .is_stacked <- function(p){ inherits(p$layers[[1]]$position, "PositionStack") } # remove "mean_se", "mean_sd", etc .remove_errorbar_func <- function(add){ setdiff(add, .errorbar_functions()) } # return "mean_se" .get_summary_func <- function(add){ intersect(.errorbar_functions(), add) } # Returns: mean or median .get_errorbar_center_func <- function(func = "mean_se"){ . <- NULL func %>% strsplit("_", fixed = TRUE) %>% unlist() %>% .[1] } # Returns se, sd, iqr .get_errorbar_error_func <- function(func = "mean_se"){ res <- func %>% strsplit("_", fixed = TRUE) %>% unlist() if(length(res) >= 2){ res <- res[2] } else res <- NULL res } ggpubr/R/diff_express.R0000644000176200001440000000277514332553655014573 0ustar liggesusers#' Differential gene expression analysis results #' #' @description Differential gene expression analysis results obtained from #' comparing the RNAseq data of two different cell populations using DESeq2 #' @name diff_express #' @docType data #' @usage data("diff_express") #' @format A data frame with 36028 rows and 5 columns. \describe{ #' \item{\code{name}}{gene names} \item{\code{baseMean}}{mean expression #' signal across all samples} \item{\code{log2FoldChange}}{log2 fold change} #' \item{\code{padj}}{Adjusted p-value}\item{\code{detection_call}}{a numeric #' vector specifying whether the genes is expressed (value = 1) or not (value = 0).}} #' #' @examples #' data(diff_express) #' #' # Default plot #'ggmaplot(diff_express, main = expression("Group 1" %->% "Group 2"), #' fdr = 0.05, fc = 2, size = 0.4, #' palette = c("#B31B21", "#1465AC", "darkgray"), #' genenames = as.vector(diff_express$name), #' legend = "top", top = 20, #' font.label = c("bold", 11), #' font.legend = "bold", #' font.main = "bold", #' ggtheme = ggplot2::theme_minimal()) #' #' # Add rectangle around labesl #'ggmaplot(diff_express, main = expression("Group 1" %->% "Group 2"), #' fdr = 0.05, fc = 2, size = 0.4, #' palette = c("#B31B21", "#1465AC", "darkgray"), #' genenames = as.vector(diff_express$name), #' legend = "top", top = 20, #' font.label = c("bold", 11), label.rectangle = TRUE, #' font.legend = "bold", #' font.main = "bold", #' ggtheme = ggplot2::theme_minimal()) #' NULL ggpubr/R/ggstripchart.R0000644000176200001440000001577614342652232014610 0ustar liggesusers#' @include utilities.R ggpar.R NULL #' Stripcharts #' @description Create a stripchart, also known as one dimensional scatter #' plots. These plots are suitable compared to box plots when sample sizes are #' small. #' @inheritParams ggboxplot #' @param shape point shape #' @param jitter the amount of jitter. #' @param position position adjustment, either as a string, or the result of a #' call to a position adjustment function. Used to adjust position for #' multiple groups. #' @param ... other arguments to be passed to #' \code{\link[ggplot2]{geom_jitter}}, \code{\link{ggpar}} and #' \code{\link{facet}}. #' @details The plot can be easily customized using the function ggpar(). Read #' ?ggpar for changing: \itemize{ \item main title and axis labels: main, #' xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis #' scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: #' palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, #' labels and position: legend = "right" \item plot orientation : orientation #' = c("vertical", "horizontal", "reverse") } #'@seealso \code{\link{ggpar}}, \code{\link{ggviolin}}, \code{\link{ggdotplot}} #' and \code{\link{ggboxplot}}. #' @examples #' # Load data #' data("ToothGrowth") #' df <- ToothGrowth #' #' # Basic plot with summary statistics: mean_se #' # +++++++++++++++++++++++++++ #' # Change point shapes by groups: "dose" #' ggstripchart(df, x = "dose", y = "len", #' shape = "dose", size = 3, #' add = "mean_se") #' #' # Use mean_sd #' # Change error.plot to "crossbar" #' ggstripchart(df, x = "dose", y = "len", #' shape = "dose", size = 3, #' add = "mean_sd", add.params = list(width = 0.5), #' error.plot = "crossbar") #' #' #' #' # Add summary statistics #' # ++++++++++++++++++++++++++ #' #' # Add box plot #' ggstripchart(df, x = "dose", y = "len", #' shape = "dose", add = "boxplot") #' #' # Add violin + mean_sd #' ggstripchart(df, x = "dose", y = "len", #' shape = "dose", add = c("violin", "mean_sd")) #' #' #' # Change colors #' # +++++++++++++++++++++++++++ #' # Change colors by groups: dose #' # Use custom color palette #' ggstripchart(df, "dose", "len", shape = "dose", #' color = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07"), #' add = "mean_sd") #' #' #' #' # Plot with multiple groups #' # +++++++++++++++++++++ #' # Change shape and color by a second group : "supp" #' ggstripchart(df, "dose", "len", shape = "supp", #' color = "supp", palette = c("#00AFBB", "#E7B800")) #' #' # Adjust point position #' ggstripchart(df, "dose", "len", shape = "supp", #' color = "supp", palette = c("#00AFBB", "#E7B800"), #' position = position_dodge(0.8) ) #' #' # You can also use position_jitterdodge() #' # but fill aesthetic is required #' ggstripchart(df, "dose", "len", shape = "supp", #' color = "supp", palette = c("#00AFBB", "#E7B800"), #' position = position_jitterdodge() ) #' #' # Add boxplot #' ggstripchart(df, "dose", "len", shape = "supp", #' color = "supp", palette = c("#00AFBB", "#E7B800"), #' add = "boxplot", add.params = list(color = "black") ) #' #' @export ggstripchart <- function(data, x, y, combine = FALSE, merge = FALSE, color = "black", fill = "white", palette = NULL, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, shape = 19, size = NULL, select = NULL, remove = NULL, order = NULL, add = "mean_se", add.params = list(), error.plot = "pointrange", label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, jitter = 0.2, position = position_jitter(jitter, seed = 123), ggtheme = theme_pubr(), ...) { # Default options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .opts <- list( combine = combine, merge = merge, color = color, fill = fill, palette = palette, title = title, xlab = xlab, ylab = ylab, facet.by = facet.by, panel.labs = panel.labs, short.panel.labs = short.panel.labs, shape = shape, size = size, select = select , remove = remove, order = order, add = add, add.params = add.params, error.plot = error.plot, label = label, font.label = font.label, label.select = label.select, repel = repel, label.rectangle = label.rectangle, jitter = jitter, position = position, ggtheme = ggtheme, ...) if(!missing(data)) .opts$data <- data if(!missing(x)) .opts$x <- x if(!missing(y)) .opts$y <- y # User options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .user.opts <- as.list(match.call(expand.dots = TRUE)) .user.opts[[1]] <- NULL # Remove the function name # keep only user arguments for(opt.name in names(.opts)){ if(is.null(.user.opts[[opt.name]])) .opts[[opt.name]] <- NULL } .opts$fun <- ggstripchart_core if(missing(ggtheme) & (!is.null(facet.by) | combine)) .opts$ggtheme <- theme_pubr(border = TRUE) p <- do.call(.plotter, .opts) if(.is_list(p) & length(p) == 1) p <- p[[1]] return(p) } ggstripchart_core <- function(data, x, y, color = "black", fill = "white", palette = NULL, title = NULL, xlab = NULL, ylab = NULL, shape = 19, size = NULL, add = "mean_se", add.params = list(), error.plot = "pointrange", jitter = 0.2, position = position_jitter(jitter, seed = 123), ggtheme = theme_pubr(), ...) { if(!is.factor(data[[x]])) data[[x]] <- as.factor(data[[x]]) . <- NULL p <- ggplot(data, create_aes(list(x = x, y = y))) if("none" %in% add) add <- "none" add.params <- .check_add.params(add, add.params, error.plot, data, color, fill, ...) # plot boxplot | violin | crossbar before jitter if( any( c("boxplot", "violin") %in% add)){ p <- add.params %>% .add_item(p = p, add = intersect(add, c("boxplot", "violin") )) %>% do.call(ggadd, .) } if(error.plot == "crossbar"){ p <- add.params %>% .add_item(p = p, error.plot = error.plot, add = setdiff(add, c("boxplot", "violin", "jitter"))) %>% do.call(ggadd, .) } # Plot jitter p <- p + geom_exec(geom_jitter, data = data, color = color, fill = fill, shape = shape, position = position, size = size, ...) # Add errors if(error.plot == "crossbar"){} else p <- add.params %>% .add_item(p = p, error.plot = error.plot, add = setdiff(add, c("boxplot", "violin", "jitter"))) %>% do.call(ggadd, .) p <- ggpar(p, palette = palette, ggtheme = ggtheme, title = title, xlab = xlab, ylab = ylab,...) p } ggpubr/R/get_legend.R0000644000176200001440000000271414332553655014200 0ustar liggesusers#' @include utilities.R NULL #' Extract Legends from a ggplot object #' @description Extract the legend labels from a ggplot object. #' @param p an object of class ggplot or a list of ggplots. If p is a list, only the first legend is returned. #' @param position character specifying legend position. Allowed values are one of #' c("top", "bottom", "left", "right", "none"). To remove the legend use #' legend = "none". #' @return an object of class gtable. #' @examples #' # Create a scatter plot #' p <- ggscatter(iris, x = "Sepal.Length", y = "Sepal.Width", #' color = "Species", palette = "jco", #' ggtheme = theme_minimal()) #' p #' #' # Extract the legend. Returns a gtable #' leg <- get_legend(p) #' #' # Convert to a ggplot and print #' as_ggplot(leg) #' #' @export get_legend <- function(p, position = NULL){ if(.is_list(p)){ continue <- TRUE i <- 1 while(i <= length(p) & continue){ leg <- .get_legend(p[[i]], position = position) if(!is.null(leg)) continue <- FALSE i <- i+1 } } else{ leg <- .get_legend(p, position = position) } leg } # Return legend for one plot .get_legend <- function(p, position = NULL){ if(is.null(p)) return(NULL) if(!is.null(position)){ p <- p + theme(legend.position = position) } tmp <- ggplot_gtable(ggplot_build(p)) leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box") if(length(leg) > 0) leg <- tmp$grobs[[leg]] else leg <- NULL leg } ggpubr/R/gghistogram.R0000644000176200001440000002044514334523053014406 0ustar liggesusers#' @include utilities.R ggpar.R NULL #' Histogram plot #' @description Create a histogram plot. #' @inheritParams ggboxplot #' @param x variable to be drawn. #' @param y one of "density" or "count". #' @param weight a variable name available in the input data for creating a weighted histogram. #' @param color,fill histogram line color and fill color. #' @param linetype line type. See \code{\link{show_line_types}}. #' @param alpha numeric value specifying fill color transparency. Value should #' be in [0, 1], where 0 is full transparency and 1 is no transparency. #' @param bins Number of bins. Defaults to 30. #' @param binwidth numeric value specifying bin width. use value between 0 and 1 #' when you have a strong dense dotplot. For example binwidth = 0.2. #' @param add allowed values are one of "mean" or "median" (for adding mean or #' median line, respectively). #' @param add.params parameters (color, size, linetype) for the argument 'add'; #' e.g.: add.params = list(color = "red"). #' @param rug logical value. If TRUE, add marginal rug. #' @param add_density logical value. If TRUE, add density curves. #' @param position Position adjustment, either as a string, or the result of a #' call to a position adjustment function. Allowed values include "identity", #' "stack", "dodge". #' @param ... other arguments to be passed to #' \code{\link[ggplot2]{geom_histogram}} and \code{\link{ggpar}}. #' @details The plot can be easily customized using the function ggpar(). Read #' ?ggpar for changing: \itemize{ \item main title and axis labels: main, #' xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis #' scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: #' palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, #' labels and position: legend = "right" \item plot orientation : orientation #' = c("vertical", "horizontal", "reverse") } #' @seealso \code{\link{ggdensity}} and \code{\link{ggpar}} #' #' @examples #' # Create some data format #' set.seed(1234) #' wdata = data.frame( #' sex = factor(rep(c("F", "M"), each=200)), #' weight = c(rnorm(200, 55), rnorm(200, 58))) #' #' head(wdata, 4) #' #' # Basic density plot #' # Add mean line and marginal rug #' gghistogram(wdata, x = "weight", fill = "lightgray", #' add = "mean", rug = TRUE) #' #' # Change outline colors by groups ("sex") #' # Use custom color palette #' gghistogram(wdata, x = "weight", #' add = "mean", rug = TRUE, #' color = "sex", palette = c("#00AFBB", "#E7B800")) #' #' # Change outline and fill colors by groups ("sex") #' # Use custom color palette #' gghistogram(wdata, x = "weight", #' add = "mean", rug = TRUE, #' color = "sex", fill = "sex", #' palette = c("#00AFBB", "#E7B800")) #' #' #' #' # Combine histogram and density plots #' gghistogram(wdata, x = "weight", #' add = "mean", rug = TRUE, #' fill = "sex", palette = c("#00AFBB", "#E7B800"), #' add_density = TRUE) #' #' # Weighted histogram #' gghistogram(iris, x = "Sepal.Length", weight = "Petal.Length") #' @export gghistogram <- function(data, x, y = "count", combine = FALSE, merge = FALSE, weight = NULL, color = "black", fill = NA, palette = NULL, size = NULL, linetype = "solid", alpha = 0.5, bins = NULL, binwidth = NULL, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, add = c("none", "mean", "median"), add.params = list(linetype = "dashed"), rug = FALSE, add_density = FALSE, label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, position = position_identity(), ggtheme = theme_pubr(), ...) { # Default options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .opts <- list( combine = combine, merge = merge, color = color, fill = fill, palette = palette, linetype = linetype, size = size, alpha = alpha, bins = bins, binwidth = binwidth, weight = weight, title = title, xlab = xlab, ylab = ylab, facet.by = facet.by, panel.labs = panel.labs, short.panel.labs = short.panel.labs, add = add, add.params = add.params, rug = rug, add_density = add_density, label = label, font.label = font.label, label.select = label.select, repel = repel, label.rectangle = label.rectangle, position = position, ggtheme = ggtheme, ...) if(!missing(data)) .opts$data <- data if(!missing(x)) .opts$x <- x if(!missing(y)) .opts$y <- y # User options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .user.opts <- as.list(match.call(expand.dots = TRUE)) .user.opts[[1]] <- NULL # Remove the function name # keep only user arguments for(opt.name in names(.opts)){ if(is.null(.user.opts[[opt.name]])) .opts[[opt.name]] <- NULL } .opts$fun <- gghistogram_core if(missing(ggtheme) & (!is.null(facet.by) | combine)) .opts$ggtheme <- theme_pubr(border = TRUE) if(missing(y)) .opts$y <- y if(missing(add.params)) .opts$add.params <- add.params p <- do.call(.plotter, .opts) if(.is_list(p) & length(p) == 1) p <- p[[1]] return(p) } gghistogram_core <- function(data, x, y = "count", weight = NULL, color = "black", fill = NA, palette = NULL, size = NULL, linetype = "solid", alpha = 0.5, bins = NULL, binwidth = NULL, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, add = c("none", "mean", "median"), add.params = list(linetype = "dashed"), rug = FALSE, add_density = FALSE, position = position_identity(), ggtheme = theme_classic(), ...) { grouping.vars <- grp <- c(color, fill, linetype, size, alpha, facet.by) %>% unique() %>% intersect(colnames(data)) color2 <- c(color, fill) %>% unique() %>% intersect(colnames(data)) if(.is_empty(color2)) color2 <- color # Check bins if(is.null(bins) & is.null(binwidth)){ bins <- 30 warning("Using `bins = 30` by default. Pick better value with the argument `bins`.", call.= FALSE) } add <- match.arg(add) if(is.null(add.params$color)){ if(!.is_empty(color2)) add.params$color <- color2 } add.params <- .check_add.params(add, add.params, error.plot = "", data, color, fill, ...) if(is.null(add.params$size)) add.params$size <- size if(is.null(add.params$linetype)) add.params$linetype <- linetype # if(add_density) y <- "..density.." if (y %in% c("..density..", "density")) y <- "after_stat(density)" else if (y %in% c("..count..", "count")) y <- "after_stat(count)" p <- ggplot(data, create_aes(list(x = x, y = y))) p <- p + geom_exec(geom_histogram, data = data, color = color, fill = fill, size = size, linetype = linetype, alpha = alpha, bins = bins, binwidth = binwidth, weight = weight, position = position, ...) # Add mean/median if(add %in% c("mean", "median")){ p <- p %>% .add_center_line(add = add, grouping.vars = grouping.vars, color = add.params$color, linetype = add.params$linetype, size = add.params$size) } # Add marginal rug if(rug) { grps <- c(color, fill, linetype, size, alpha) %>% unique() %>% intersect(colnames(data)) alpha <- ifelse(.is_empty(grps), 1, alpha) .args <- geom_exec(NULL, data = data, color = color2, sides = "b", alpha = alpha) mapping <- .args$mapping mapping[["y"]] <- 0 option <- .args$option option[["mapping"]] <- create_aes(mapping) p <- p + do.call(geom_rug, option) } # Add density curve if(add_density) p <- p + geom_exec(geom_density, data = data, color = add.params$color, linetype = linetype, alpha = alpha, size = add.params$size) p <- ggpar(p, palette = palette, ggtheme = ggtheme, title = title, xlab = xlab, ylab = ylab,...) p } ggpubr/R/ggqqplot.R0000644000176200001440000002042514341737601013733 0ustar liggesusers#' @include utilities.R ggpar.R NULL #' QQ Plots #' @description Quantile-Quantile plot. #' @inheritParams ggboxplot #' @param x variable to be drawn. #' @param color point color. #' @param size point size. #' @param shape point shape. #' @param add character vector. Allowed values are one of "none" and "qqline" #' (for adding qqline). #' @param add.params parameters (color, size, linetype) for the #' argument 'add'; e.g.: add.params = list(color = "red"). #' @param conf.int logical value. If TRUE, confidence interval is added. #' @param conf.int.level the confidence level. Default value is 0.95. #' @param ... other arguments to be passed to \code{\link{ggpar}}. #' @details The plot can be easily customized using the function ggpar(). Read #' ?ggpar for changing: \itemize{ \item main title and axis labels: main, #' xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis #' scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: #' palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, #' labels and position: legend = "right" \item plot orientation : orientation #' = c("vertical", "horizontal", "reverse") } #' @seealso \code{\link{ggpar}} #' #' @examples #' # Create some data format #' set.seed(1234) #' wdata = data.frame( #' sex = factor(rep(c("F", "M"), each=200)), #' weight = c(rnorm(200, 55), rnorm(200, 58))) #' #' head(wdata, 4) #' #' # Basic QQ plot #' ggqqplot(wdata, x = "weight") #' #' # Change colors and shape by groups ("sex") #' # Use custom palette #' ggqqplot(wdata, x = "weight", #' color = "sex", palette = c("#00AFBB", "#E7B800")) #' #' @export ggqqplot <- function(data, x, combine = FALSE, merge = FALSE, color = "black", palette = NULL, size = NULL, shape = NULL, add = c( "qqline", "none"), add.params = list(linetype = "solid"), conf.int = TRUE, conf.int.level = 0.95, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, ggtheme = theme_pubr(), ...) { # Default options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .opts <- list( combine = combine, merge = merge, color = color, palette = palette, size = size, shape = shape, title = title, xlab = xlab, ylab = ylab, facet.by = facet.by, panel.labs = panel.labs, short.panel.labs = short.panel.labs, conf.int = conf.int, conf.int.level = conf.int.level, ggtheme = ggtheme, ...) if(!missing(data)) .opts$data <- data if(!missing(x)) .opts$x <- x # User options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .user.opts <- as.list(match.call(expand.dots = TRUE)) .user.opts[[1]] <- NULL # Remove the function name # keep only user arguments for(opt.name in names(.opts)){ if(is.null(.user.opts[[opt.name]])) .opts[[opt.name]] <- NULL } .opts$fun <- ggqqplot_core .opts$y <- "..qq.." if(is.null(.opts$xlab)) .opts$xlab <- "Theoretical" if(is.null(.opts$ylab)) .opts$ylab <- "Sample" if(is.null(.opts$add)) .opts$add <- "qqline" if(missing(ggtheme) & (!is.null(facet.by) | combine)) .opts$ggtheme <- theme_pubr(border = TRUE) p <- do.call(.plotter, .opts) if(.is_list(p) & length(p) == 1) p <- p[[1]] return(p) } ggqqplot_core <- function(data, x, y = "..qq..", color = "black", palette = NULL, size = NULL, shape = NULL, fill = "white", add = c( "qqline", "none"), add.params = list(linetype = "solid"), conf.int = TRUE, conf.int.level = 0.95, title = NULL, xlab = NULL, ylab = NULL, ggtheme = theme_pubr(), ...) { # Check data .dd <- .check_data(data, x, y=NULL) data <- .dd$data x <- .dd$x y <- .dd$y group <- c(shape, color) %>% unique() %>% intersect(colnames(data)) group <- ifelse(.is_empty(group), 1, group[1]) add <- match.arg(add) add.params <- .check_add.params(add, add.params, error.plot = "", data, color, fill = fill, ...) if(is.null(add.params$size)) add.params$size <- size if(is.null(add.params$linetype)) add.params$linetype <- "solid" p <- ggplot(data, create_aes(list(sample = x))) p <- p + geom_exec(stat_qq, data = data, color = color, size = size, shape = shape) if ("qqline" %in% add) p <- p + geom_exec(.stat_qqline, data = data, color = add.params$color, size = add.params$size, linetype = add.params$linetype, group = group) # Confidence interval if(conf.int){ p <- p + geom_exec(.stat_qq_confint, data = data, fill = color, alpha = 0.2, conf.int.level = conf.int.level, group = group) } p <- ggpar(p, palette = palette, ggtheme = ggtheme, title = title, xlab = xlab, ylab = ylab,...) p } # Helper functions ######################## # from: http://stackoverflow.com/questions/4357031/qqnorm-and-qqline-in-ggplot2 # qf : qfunction (e.g.: qnorm) .qq_line <- function(data, qf, na.rm) { q.sample <- stats::quantile(data, c(0.25, 0.75), na.rm = na.rm) q.theory <- qf(c(0.25, 0.75)) slope <- diff(q.sample) / diff(q.theory) intercept <- q.sample[1] - slope * q.theory[1] list(slope = slope, intercept = intercept) } StatQQLine <- ggproto("StatQQLine", Stat, # http://docs.ggplot2.org/current/vignettes/extending-ggplot2.html # https://github.com/hadley/ggplot2/blob/master/R/stat-qq.r required_aes = c('sample'), compute_group = function(data, scales, distribution = stats::qnorm, dparams = list(), conf.int.level = 0.95, na.rm = FALSE) { qf <- function(p) do.call(distribution, c(list(p = p), dparams)) n <- length(data$sample) P <- stats::ppoints(n) theoretical <- qf(P) qq <- .qq_line(data$sample, qf = qf, na.rm = na.rm) line <- qq$intercept + theoretical * qq$slope # Confidence interval zz <- stats::qnorm(1 - (1 - conf.int.level)/2) SE <- (qq$slope/stats::dnorm(theoretical)) * sqrt(P * (1 - P)/n) fit.value <- qq$intercept + qq$slope * theoretical ymax <- fit.value + zz * SE ymin <- fit.value - zz * SE data.frame(sample = line, x = theoretical, y = line, ymin = ymin, ymax = ymax) } ) .stat_qqline <- function(mapping = NULL, data = NULL, geom = "line", position = "identity", ..., distribution = stats::qnorm, dparams = list(), na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, conf.int.level = 0.95) { layer(stat = StatQQLine, data = data, mapping = mapping, geom = geom, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(distribution = distribution, dparams = dparams, na.rm = na.rm, conf.int.level = conf.int.level, ...)) } .stat_qq_confint <- function(mapping = NULL, data = NULL, geom = "ribbon", position = "identity", ..., distribution = stats::qnorm, dparams = list(), na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, conf.int.level = 0.95) { layer(stat = StatQQLine, data = data, mapping = mapping, geom = geom, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(distribution = distribution, dparams = dparams, na.rm = na.rm, conf.int.level = conf.int.level, ...)) } ggpubr/R/utilities_base.R0000644000176200001440000000624214344547604015110 0ustar liggesusers#:::::::::::::::::::::::::::::::::::::::::::::::::::: # General helper functions #:::::::::::::::::::::::::::::::::::::::::::::::::::: # Get random string .random_string <-function(.length = 7){ index <- sample(1:26, .length) paste(letters[index], collapse = "") } # get file extension .file_ext <- function(x){ pos <- regexpr("\\.([[:alnum:]]+)$", x) ifelse(pos > -1L, substring(x, pos + 1L), "") } # extract device name from file name .device <- function (filename) { device <- .file_ext(filename) devices <- list(eps = grDevices::postscript, ps = grDevices::postscript, pdf = grDevices::pdf, svg = grDevices::svg, png = grDevices::png, jpg = grDevices::jpeg, jpeg = grDevices::jpeg, bmp = grDevices::bmp, tiff = grDevices::tiff) dev <- devices[[device]] if (is.null(dev)) { stop("Unknown graphics device '", device, "'", call. = FALSE) } dev } # Grouping data by variables #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .group_by <- function(data, grouping.vars){ . <- NULL # used in pipes # Grouping the data ==> list of data sets grouped.d <- data %>% df_nest_by(vars = grouping.vars) # Defining names for the list of data sets. # names = combination of the levels of the grouping variables .names.df <- grouped.d[, grouping.vars, drop = FALSE] .names <- .paste_colnames(.names.df, sep = ":") %>% apply(1, paste, collapse = ", ") names(grouped.d$data) <- .names return(grouped.d) } # Pasting the column name to each value of a dataframe #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .paste_colnames <- function(data, sep = "."){ data <- as.data.frame(data) if(ncol(data) == 1){ res <- paste0(colnames(data), ".", data[[1]]) res <- data.frame(x = res, stringsAsFactors = FALSE) colnames(res) <- colnames(data) return(res) } res <- apply(data, 1, function(row, cname){paste(cname, row, sep = sep)}, colnames(data) ) %>% t() %>% as.data.frame(stringsAsFactors = FALSE) colnames(res) <- colnames(data) res } # Setting seed with possibility to restore initial random state #::::::::::::::::::::::::::::::::::::::::::::::: # Ref: https://github.com/florianhartig/DHARMa/blob/master/DHARMa/R/random.R # seed numeric value to be used for setting seed # return a function named restore_random_state() # for restoring intitial random state # # Examples: # seed <- set_seed(123) # Restore back initial state # seed$restore_random_state() set_seed <- function(seed){ # Record current random state current <- mget(".Random.seed", envir = .GlobalEnv, ifnotfound = list(NULL))[[1]] restore_random_state <- function(){ if(is.null(current)) rm(".Random.seed", envir = .GlobalEnv) else assign(".Random.seed", current , envir = .GlobalEnv) } # Setting seed if(is.numeric(seed)) set.seed(seed) # ensuring that RNG has been initialized if (is.null(current)) stats::runif(1) # Returning function for restoring state invisible(list(restore_random_state = restore_random_state)) } ggpubr/R/ggdensity.R0000644000176200001440000001442114334523134014065 0ustar liggesusers#' @include utilities.R ggpar.R NULL #' Density plot #' @description Create a density plot. #' @inheritParams ggboxplot #' @param x variable to be drawn. #' @param y one of "density" or "count". #' @param color,fill density line color and fill color. #' @param linetype line type. See \code{\link{show_line_types}}. #' @param alpha numeric value specifying fill color transparency. Value should #' be in [0, 1], where 0 is full transparency and 1 is no transparency. #' @param add allowed values are one of "mean" or "median" (for adding mean or #' median line, respectively). #' @param add.params parameters (color, size, linetype) for the argument 'add'; #' e.g.: add.params = list(color = "red"). #' @param rug logical value. If TRUE, add marginal rug. #' @param ... other arguments to be passed to #' \code{\link[ggplot2]{geom_density}} and \code{\link{ggpar}}. #' @details The plot can be easily customized using the function ggpar(). Read #' ?ggpar for changing: \itemize{ \item main title and axis labels: main, #' xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis #' scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: #' palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, #' labels and position: legend = "right" \item plot orientation : orientation #' = c("vertical", "horizontal", "reverse") } #' @seealso \code{\link{gghistogram}} and \code{\link{ggpar}}. #' #' @examples #' # Create some data format #' set.seed(1234) #' wdata = data.frame( #' sex = factor(rep(c("F", "M"), each=200)), #' weight = c(rnorm(200, 55), rnorm(200, 58))) #' #' head(wdata, 4) #' #' # Basic density plot #' # Add mean line and marginal rug #' ggdensity(wdata, x = "weight", fill = "lightgray", #' add = "mean", rug = TRUE) #' #' # Change outline colors by groups ("sex") #' # Use custom palette #' ggdensity(wdata, x = "weight", #' add = "mean", rug = TRUE, #' color = "sex", palette = c("#00AFBB", "#E7B800")) #' #' #' # Change outline and fill colors by groups ("sex") #' # Use custom palette #' ggdensity(wdata, x = "weight", #' add = "mean", rug = TRUE, #' color = "sex", fill = "sex", #' palette = c("#00AFBB", "#E7B800")) #' #' #' @export ggdensity <- function(data, x, y = "density", combine = FALSE, merge = FALSE, color = "black", fill = NA, palette = NULL, size = NULL, linetype = "solid", alpha = 0.5, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, add = c("none", "mean", "median"), add.params = list(linetype = "dashed"), rug = FALSE, label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, ggtheme = theme_pubr(), ...){ # Default options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .opts <- list( combine = combine, merge = merge, color = color, fill = fill, palette = palette, linetype = linetype, size = size, alpha = alpha, title = title, xlab = xlab, ylab = ylab, facet.by = facet.by, panel.labs = panel.labs, short.panel.labs = short.panel.labs, add = add, add.params = add.params, rug = rug, label = label, font.label = font.label, label.select = label.select, repel = repel, label.rectangle = label.rectangle, ggtheme = ggtheme, ...) if(!missing(data)) .opts$data <- data if(!missing(x)) .opts$x <- x if(!missing(y)) .opts$y <- y # User options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .user.opts <- as.list(match.call(expand.dots = TRUE)) .user.opts[[1]] <- NULL # Remove the function name # keep only user arguments for(opt.name in names(.opts)){ if(is.null(.user.opts[[opt.name]])) .opts[[opt.name]] <- NULL } .opts$fun <- ggdensity_core if(missing(ggtheme) & (!is.null(facet.by) | combine)) .opts$ggtheme <- theme_pubr(border = TRUE) if(missing(y)) .opts$y <- y if(missing(add.params)) .opts$add.params <- add.params p <- do.call(.plotter, .opts) if(.is_list(p) & length(p) == 1) p <- p[[1]] return(p) } ggdensity_core <- function(data, x, y = "density", color = "black", fill = NA, palette = NULL, size = NULL, linetype = "solid", alpha = 0.5, title = NULL, xlab = NULL, ylab = NULL, add = c("none", "mean", "median"), add.params = list(linetype = "dashed"), rug = FALSE, facet.by = NULL, ggtheme = theme_classic(), ...) { grouping.vars <- grp <- c(color, fill, linetype, size, alpha, facet.by) %>% unique() %>% intersect(colnames(data)) add <- match.arg(add) add.params <- .check_add.params(add, add.params, error.plot = "", data, color, fill, ...) if(is.null(add.params$size)) add.params$size <- size if(is.null(add.params$linetype)) add.params$linetype <- linetype if (y %in% c("..density..", "density")) y <- "after_stat(density)" else if (y %in% c("..count..", "count")) y <- "after_stat(count)" p <- ggplot(data, create_aes(list(x = x, y = y))) p <- p + geom_exec(geom_density, data = data, color = color, fill = fill, size = size, linetype = linetype, alpha = alpha, ...) # Add mean/median if(add %in% c("mean", "median")){ p <- p %>% .add_center_line(add = add, grouping.vars = grouping.vars, color = add.params$color, linetype = add.params$linetype, size = add.params$size) } # Add marginal rug if(rug) { grps <- c(color, fill, linetype, size, alpha) %>% unique() %>% intersect(colnames(data)) alpha <- ifelse(.is_empty(grps), 1, alpha) .args <- geom_exec(NULL, data = data, color = color, sides = "b", alpha = alpha) mapping <- .args$mapping mapping[["y"]] <- 0 option <- .args$option option[["mapping"]] <- create_aes(mapping) p <- p + do.call(geom_rug, option) } p <- ggpar(p, palette = palette, ggtheme = ggtheme, title = title, xlab = xlab, ylab = ylab,...) p } ggpubr/R/ggmaplot.R0000644000176200001440000002256014340446656013717 0ustar liggesusers#' @include utilities.R ggpar.R NULL #'MA-plot from means and log fold changes #'@description Make MA-plot which is a scatter plot of log2 fold changes (M, on #' the y-axis) versus the average expression signal (A, on the x-axis). \code{M #' = log2(x/y)} and \code{A = (log2(x) + log2(y))/2 = log2(xy)*1/2}, where x #' and y are respectively the mean of the two groups being compared. #'@inheritParams ggboxplot #'@inheritParams ggpar #'@param data an object of class DESeqResults, get_diff, DE_Results, matrix or #' data frame containing the columns baseMean (or baseMeanLog2), #' log2FoldChange, and padj. Rows are genes. #' #' Two possible formats are accepted for the input data: \itemize{ \item 1/ #' \code{baseMean | log2FoldChange | padj}. This is a typical output from #' DESeq2 pipeline. Here, we'll use log2(baseMean) as the x-axis variable. #' \item 2/ \code{baseMeanLog2 | log2FoldChange | padj}. Here, baseMeanLog2 is #' assumed to be the mean of logged values; so we'll use it as the x-axis #' variable without any transformation. This is the real A in MA plot. In other #' words, it is the average of two log-scales values: \code{A = (log2(x) + #' log2(y))/2 = log2(xy)*1/2} } #' #' Terminology: #' #' \itemize{ \item baseMean: the mean expression of genes in the two groups. #' \item log2FoldChange: the log2 fold changes of group 2 compared to group 1 #' \item padj: the adjusted p-value of the used statiscal test. } #'@param fdr Accepted false discovery rate for considering genes as #' differentially expressed. #'@param fc the fold change threshold. Only genes with a fold change >= fc and #' padj <= fdr are considered as significantly differentially expressed. #'@param genenames a character vector of length nrow(data) specifying gene names #' corresponding to each row. Used for point labels. #'@param detection_call a numeric vector with length = nrow(data), specifying if #' the genes is expressed (value = 1) or not (value = 0). For example #' detection_call = c(1, 1, 0, 1, 0, 1). Default is NULL. If detection_call #' column is available in data, it will be used. #'@param size points size. #'@param alpha numeric value betwenn 0 an 1 specifying point alpha for #' controlling transparency. For example, use alpha = 0.5. #'@param seed Random seed passed to \code{set.seed}. if #' \code{NA}, set.seed will not be called. Default is 42 for reproducibility. #'@param font.label a vector of length 3 indicating respectively the size (e.g.: #' 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the #' color (e.g.: "red") of point labels. For example \emph{font.label = c(14, #' "bold", "red")}. #'@param label.rectangle logical value. If TRUE, add rectangle underneath the #' text, making it easier to read. #'@param top the number of top genes to be shown on the plot. Use top = 0 to #' hide to gene labels. #'@param select.top.method methods to be used for selecting top genes. Allowed #' values include "padj" and "fc" for selecting by adjusted p values or fold #' changes, respectively. #'@param label.select character vector specifying some labels to show. #'@param ... other arguments to be passed to \code{\link{ggpar}}. #'@return returns a ggplot. #' @examples #' data(diff_express) #' #' # Default plot #'ggmaplot(diff_express, main = expression("Group 1" %->% "Group 2"), #' fdr = 0.05, fc = 2, size = 0.4, #' palette = c("#B31B21", "#1465AC", "darkgray"), #' genenames = as.vector(diff_express$name), #' legend = "top", top = 20, #' font.label = c("bold", 11), #' font.legend = "bold", #' font.main = "bold", #' ggtheme = ggplot2::theme_minimal()) #' #' # Add rectangle around labels #'ggmaplot(diff_express, main = expression("Group 1" %->% "Group 2"), #' fdr = 0.05, fc = 2, size = 0.4, #' palette = c("#B31B21", "#1465AC", "darkgray"), #' genenames = as.vector(diff_express$name), #' legend = "top", top = 20, #' font.label = c("bold", 11), label.rectangle = TRUE, #' font.legend = "bold", #' font.main = "bold", #' ggtheme = ggplot2::theme_minimal()) #' #' # Select specific genes to show #' # set top = 0, then specify genes using label.select argument #' ggmaplot(diff_express, main = expression("Group 1" %->% "Group 2"), #' fdr = 0.05, fc = 2, size = 0.4, #' genenames = as.vector(diff_express$name), #' ggtheme = ggplot2::theme_minimal(), #' top = 0, label.select = c("BUB1", "CD83") #' ) #' #'@export ggmaplot <- function (data, fdr = 0.05, fc = 1.5, genenames = NULL, detection_call = NULL, size = NULL, alpha = 1, seed = 42, font.label = c(12, "plain", "black"), label.rectangle = FALSE, palette = c("#B31B21", "#1465AC", "darkgray"), top = 15, select.top.method = c("padj", "fc"), label.select = NULL, main = NULL, xlab = "Log2 mean expression", ylab = "Log2 fold change", ggtheme = theme_classic(),...) { if(!base::inherits(data, c("matrix", "data.frame", "DataFrame", "DE_Results", "DESeqResults"))) stop("data must be an object of class matrix, data.frame, DataFrame, DE_Results or DESeqResults") if(!is.null(detection_call)){ if(nrow(data)!=length(detection_call)) stop("detection_call must be a numeric vector of length = nrow(data)") } else if("detection_call" %in% colnames(data)){ detection_call <- as.vector(data$detection_call) } else detection_call = rep(1, nrow(data)) # Legend position if(is.null(list(...)$legend)) legend <- c(0.12, 0.9) # If basemean logged, we'll leave it as is, otherwise log2 transform is.basemean.logged <- "baseMeanLog2" %in% colnames(data) if(is.basemean.logged){ data$baseMean <- data$baseMeanLog2 } else if("baseMean" %in% colnames(data)){ data$baseMean <- log2(data$baseMean +1) } # Check data format ss <- base::setdiff(c("baseMean", "log2FoldChange", "padj"), colnames(data)) if(length(ss)>0) stop("The colnames of data must contain: ", paste(ss, collapse = ", ")) if(is.null(genenames)) genenames <- rownames(data) else if(length(genenames)!=nrow(data)) stop("genenames should be of length nrow(data).") sig <- rep(3, nrow(data)) sig[which(data$padj <= fdr & data$log2FoldChange < 0 & abs(data$log2FoldChange) >= log2(fc) & detection_call ==1)] = 2 sig[which(data$padj <= fdr & data$log2FoldChange > 0 & abs(data$log2FoldChange) >= log2(fc) & detection_call ==1)] = 1 data <- data.frame(name = genenames, mean = data$baseMean, lfc = data$log2FoldChange, padj = data$padj, sig = sig) # Change level labels . <- NULL data$sig <- as.factor(data$sig) .lev <- .levels(data$sig) %>% as.numeric() palette <- palette[.lev] new.levels <- c( paste0("Up: ", sum(sig == 1)), paste0("Down: ", sum(sig == 2)), "NS" ) %>% .[.lev] data$sig <- factor(data$sig, labels = new.levels) # Ordering for selecting top gene select.top.method <- match.arg(select.top.method) if(select.top.method == "padj") data <- data[order(data$padj), ] else if(select.top.method == "fc") data <- data[order(abs(data$lfc), decreasing = TRUE), ] # select data for top genes complete_data <- stats::na.omit(data) labs_data <- subset(complete_data, padj <= fdr & name!="" & abs(lfc) >= log2(fc)) labs_data <- utils::head(labs_data, top) # Select some specific labels to show if(!is.null(label.select)){ selected_labels <- complete_data %>% subset(complete_data$name %in% label.select, drop = FALSE) labs_data <- dplyr::bind_rows(labs_data, selected_labels) %>% dplyr::distinct(.data$name, .keep_all = TRUE) } font.label <- .parse_font(font.label) font.label$size <- ifelse(is.null(font.label$size), 12, font.label$size) font.label$color <- ifelse(is.null(font.label$color), "black", font.label$color) font.label$face <- ifelse(is.null(font.label$face), "plain", font.label$face) # Plot mean <- lfc <- sig <- name <- padj <- NULL p <- ggplot(data, aes(x = mean, y = lfc)) + geom_point(aes(color = sig), size = size, alpha = alpha) max.overlaps = getOption("ggrepel.max.overlaps", default = Inf) if(label.rectangle){ p <- p + ggrepel::geom_label_repel(data = labs_data, mapping = aes(label = name), box.padding = unit(0.35, "lines"), point.padding = unit(0.3, "lines"), force = 1, seed = seed, fontface = font.label$face, size = font.label$size/3, color = font.label$color, max.overlaps = max.overlaps) } else{ p <- p + ggrepel::geom_text_repel(data = labs_data, mapping = aes(label = name), box.padding = unit(0.35, "lines"), point.padding = unit(0.3, "lines"), force = 1, seed = seed, fontface = font.label$face, size = font.label$size/3, color = font.label$color, max.overlaps = max.overlaps) } p <- p + scale_x_continuous(breaks=seq(0, max(data$mean), 2))+ labs(x = xlab, y = ylab, title = main, color = "")+ # to remove legend title use color = "" geom_hline(yintercept = c(0, -log2(fc), log2(fc)), linetype = c(1, 2, 2), color = c("black", "black", "black")) p <- ggpar(p, palette = palette, ggtheme = ggtheme, ...) p } ggpubr/R/stat_overlay_normal_density.R0000644000176200001440000000427314332553655017730 0ustar liggesusers#' @include utilities.R NULL #' Overlay Normal Density Plot #' #' @description Overlay normal density plot (with the same mean and SD) to the #' density distribution of 'x'. This is useful for visually inspecting the #' degree of deviance from normality. #' @inheritParams ggplot2::layer #' @param ... other arguments to pass to \code{\link[ggplot2:geom_path]{geom_line}}. #' @param na.rm If FALSE (the default), removes missing values with a warning. #' If TRUE silently removes missing values. #' @seealso \code{\link{ggdensity}} #' @examples #' # Simpledensity plot #' data("mtcars") #' ggdensity(mtcars, x = "mpg", fill = "red") + #' scale_x_continuous(limits = c(-1, 50)) + #' stat_overlay_normal_density(color = "red", linetype = "dashed") #' #' # Color by groups #' data(iris) #' ggdensity(iris, "Sepal.Length", color = "Species") + #' stat_overlay_normal_density(aes(color = Species), linetype = "dashed") #' #' #' # Facet #' ggdensity(iris, "Sepal.Length", facet.by = "Species") + #' stat_overlay_normal_density(color = "red", linetype = "dashed") #' #' @export stat_overlay_normal_density <- function(mapping = NULL, data = NULL, geom = "line", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...) { if(is.null(mapping)){ mapping <- ggplot2::aes(y = NULL) }else{ mapping["y"] <- list(NULL) } layer( stat = StatOverlayNormalDensity, data = data, mapping = mapping, geom = geom, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(na.rm = na.rm, ...) ) } StatOverlayNormalDensity<- ggproto("StatOverlayNormalDensity", Stat, required_aes = c("x"), compute_group = function(data, scales) { x <- data$x .mean <- mean(x, na.rm = TRUE) .sd <- stats::sd(x, na.rm = TRUE) probability.points <- stats::ppoints(length(x[!(is.na(x))])) res.density <- stats::density(stats::qnorm(probability.points, .mean, .sd)) res.density <- data.frame(x = res.density$x, y = res.density$y) res.density } ) ggpubr/R/add_summary.R0000644000176200001440000002353114334273471014405 0ustar liggesusers#' @include utilities.R NULL #'Add Summary Statistics onto a ggplot. #'@description add summary statistics onto a ggplot. #'@param p a ggplot on which you want to add summary statistics. #'@param fun a function that is given the complete data and should return a data #' frame with variables ymin, y, and ymax. Allowed values are one of: "mean", #' "mean_se", "mean_sd", "mean_ci", "mean_range", "median", "median_iqr", #' "median_hilow", "median_q1q3", "median_mad", "median_range". #'@param error.plot plot type used to visualize error. Allowed values are one of #' \code{c("pointrange", "linerange", "crossbar", "errorbar", "upper_errorbar", #' "lower_errorbar", "upper_pointrange", "lower_pointrange", "upper_linerange", #' "lower_linerange")}. Default value is "pointrange". #'@param color point or outline color. #'@param fill fill color. Used only whne \code{error.plot = "crossbar"}. #'@param group grouping variable. Allowed values are 1 (for one group) or a #' character vector specifying the name of the grouping variable. Used only for #' adding statistical summary per group. #'@param width numeric value between 0 and 1 specifying bar or box width. #' Example width = 0.8. Used only when \code{error.plot} is one of #' c("crossbar", "errorbar"). #'@param shape point shape. Allowed values can be displayed using the function #' \code{\link{show_point_shapes}()}. #'@param size numeric value in [0-1] specifying point and line size. #'@param linetype line type. #'@param show.legend logical. Should this layer be included in the legends? NA, #' the default, includes if any aesthetics are mapped. \code{FALSE} never includes, #' and TRUE always includes. It can also be a named logical vector to finely #' select the aesthetics to display. #'@param data a \code{data.frame} to be displayed. If \code{NULL}, the default, #' the data is inherited from the plot data as specified in the call to #' \link[ggplot2]{ggplot}. #'@param position position adjustment, either as a string, or the result of a #' call to a position adjustment function. Used to adjust position for multiple #' groups. #'@param x a numeric vector. #'@param ci the percent range of the confidence interval (default is 0.95). #'@param error.limit allowed values are one of ("both", "lower", "upper", #' "none") specifying whether to plot the lower and/or the upper limits of #' error interval. #'@examples #' #'# Basic violin plot #'p <- ggviolin(ToothGrowth, x = "dose", y = "len", add = "none") #'p #' #'# Add mean_sd #'add_summary(p, "mean_sd") #' #' #'@describeIn add_summary add summary statistics onto a ggplot. #'@export add_summary <- function(p, fun = "mean_se", error.plot = "pointrange", color = "black", fill = "white", group = 1, width = NULL, shape = 19, size = 1, linetype = 1, show.legend = NA, ci = 0.95, data = NULL, position = position_dodge(0.8)) { if(is.null(data)) data <- p$data if(fun == "mean_se") fun <- "mean_se_" else if(fun == "median_hilow") fun <- "median_hilow_" allowed.fun <- .summary_functions() if(!(fun %in% allowed.fun)) stop("Don't support ", fun, ". Possibilities for the argument fun are: ", .collapse(allowed.fun, sep = ", ")) allowed.error.plot = c("pointrange", "linerange", "crossbar", "errorbar", "upper_errorbar", "lower_errorbar", "upper_pointrange", "lower_pointrange", "upper_linerange", "lower_linerange") if(!(error.plot %in% allowed.error.plot)) stop("Don't support ", error.plot, ". Possibilities for the argument error.plot are: ", .collapse(allowed.error.plot, sep = ", ")) if(missing(width)) width <- 0.8 .map <- .mapping(p) if(missing(color) & !is.null(.map$colour)) color <- .map$colour if(missing(fill) & !is.null(.map$fill)) fill <- .map$fill # Error limits #:::::::::::::::::::::::::::::::::::::::::::::::::: . <- NULL error.limit <- strsplit(error.plot, "_") %>% unlist() %>% .[1] if(!(error.limit %in% c("upper", "lower"))) error.limit <- "both" if(fun %in% c("mean", "median")) error.limit <- "none" # Defining plot geometry #:::::::::::::::::::::::::::::::::::::::::::::::::: geom <- error.plot if(error.plot %in% c("pointrange", "lower_pointrange", "upper_pointrange")) geom <- "pointrange" else if(error.plot %in% c("linerange", "lower_linerange", "upper_linerange")) geom <- "linerange" else if(error.plot %in% c("errorbar", "lower_errorbar", "upper_errorbar")) geom <- "errorbar" fun.data <- fun.y <- fun.ymin <- fun.ymax <- NULL if(fun %in% c("mean", "median")){ fun.y <- fun.ymin <- fun.ymax <- fun } else fun.data <- fun # General option #:::::::::::::::::::::::::::::::::::::::::::::::::: opts <- list(geomfunc = "stat_summary", fun.data = fun.data, fun.y = fun.y, fun.ymin = fun.ymin, fun.ymax = fun.ymax, color = color, geom = geom, size = size, linetype = linetype, show.legend = show.legend, data = data, position = position, fun.args = list(error.limit = error.limit), group = group) if(fun %in% c("mean_ci", "median_hilow_")){ opts$fun.args$ci <- ci } # Specific option #:::::::::::::::::::::::::::::::::::::::::::::::::: if(geom == "crossbar") opts <- opts %>% .add_item(fill = fill, width = width) else if(geom == "errorbar"){ if(missing(width)) opts$width = 0.1 else opts$width = width } opts %>% .update_plot(p) } #' @describeIn add_summary returns the \code{mean} and the error limits defined by the #' \code{standard error}. We used the name \code{mean_se_}() to avoid masking \code{\link[ggplot2]{mean_se}}(). #' @export mean_se_ <- function(x, error.limit = "both") { length <- base::sum(!is.na(x)) sd = stats::sd(x, na.rm=TRUE) se <- sd / sqrt(length) .mean <- base::mean(x, na.rm = TRUE) data.frame( y = .mean, ymin = .mean - se, ymax = .mean + se ) %>% .format_error(error.limit) } #' @describeIn add_summary returns the \code{mean} and the error limits defined by the #' \code{standard deviation}. #' @export mean_sd <- function(x, error.limit = "both"){ sd = stats::sd(x, na.rm=TRUE) .mean <- base::mean(x, na.rm = TRUE) data.frame( y = .mean, ymin = .mean - sd, ymax = .mean + sd ) %>% .format_error(error.limit) } #' @describeIn add_summary returns the \code{mean} and the error limits defined by the #' \code{confidence interval}. #' @export mean_ci <- function(x, ci = 0.95, error.limit = "both"){ length <- base::sum(!is.na(x)) sd = stats::sd(x, na.rm=TRUE) se <- sd / sqrt(length) .mean <- base::mean(x, na.rm = TRUE) ci <- stats::qt(ci/2 + .5, length-1)*se data.frame( y = .mean, ymin = .mean - ci, ymax = .mean + ci ) %>% .format_error(error.limit) } #' @describeIn add_summary returns the \code{mean} and the error limits defined by the #' \code{range = max - min}. #' @export mean_range <- function(x, error.limit = "both"){ .mean <- base::mean(x, na.rm = TRUE) .min <- base::min(x, na.rm=TRUE) .max <- base::max(x, na.rm=TRUE) .range <- .max - .min data.frame( y = .mean, ymin = .mean - .range, ymax = .mean + .range ) %>% .format_error(error.limit) } #' @describeIn add_summary returns the \code{median} and the error limits #' defined by the \code{interquartile range}. #' @export median_iqr <- function(x, error.limit = "both"){ .median = stats::median(x, na.rm=TRUE) .iqr <- stats::IQR(x, na.rm=TRUE) data.frame( y = .median, ymin = .median - .iqr, ymax = .median + .iqr ) %>% .format_error(error.limit) } #' @describeIn add_summary computes the sample median and a selected pair of #' outer quantiles having equal tail areas. This function is a reformatted #' version of \code{Hmisc::smedian.hilow()}. The confidence limits are computed #' as follow: \code{lower.limits = (1-ci)/2} percentiles; \code{upper.limits = #' (1+ci)/2} percentiles. By default (\code{ci = 0.95}), the 2.5th and the #' 97.5th percentiles are used as the lower and the upper confidence limits, #' respectively. If you want to use the 25th and the 75th percentiles as the #' confidence limits, then specify \code{ci = 0.5} or use the function #' \code{median_q1q3()}. #' @export median_hilow_ <- function(x, ci = 0.95, error.limit = "both"){ quant <- stats::quantile( x, probs = c(0.5, (1 - ci)/2, (1 + ci)/2), na.rm = TRUE ) names(quant) <- c("median", "lower", "upper") data.frame( y = quant["median"], ymin = quant["lower"], ymax = quant["upper"] ) %>% .format_error(error.limit) } #' @describeIn add_summary computes the sample median and, the 25th and 75th #' percentiles. Wrapper around the function \code{median_hilow_()} using #' \code{ci = 0.5}. #' @export median_q1q3 <- function(x, error.limit = "both"){ median_hilow_(x, ci = 0.5, error.limit = error.limit) } #' @describeIn add_summary returns the \code{median} and the error limits #' defined by the \code{median absolute deviation}. #' @export median_mad <- function(x, error.limit = "both"){ .median = stats::median(x, na.rm=TRUE) .mad = stats::mad(x, na.rm=TRUE) data.frame( y = .median, ymin = .median - .mad, ymax = .median + .mad ) %>% .format_error(error.limit) } #' @describeIn add_summary returns the \code{median} and the error limits #' defined by the \code{range = max - min}. #' @export median_range <- function(x, error.limit = "both"){ .median = stats::median(x, na.rm=TRUE) .min <- base::min(x, na.rm=TRUE) .max <- base::max(x, na.rm=TRUE) .range <- .max - .min data.frame( y = .median, ymin = .median - .range, ymax = .median + .range ) %>% .format_error(error.limit) } # Format error .format_error <- function(d, error.limit = "both"){ if(error.limit == "upper") d$ymin <- d$y else if(error.limit == "lower") d$ymax <- d$y else if(error.limit == "none") d$ymin <- d$ymax <- d$y d } ggpubr/R/ggadd.R0000644000176200001440000001533114340445172013141 0ustar liggesusers#' @include utilities.R NULL #'Add Summary Statistics or a Geom onto a ggplot #'@description Add summary statistics or a geometry onto a ggplot. #'@inheritParams add_summary #'@param p a ggplot #'@param add character vector specifying other plot elements to be added. #' Allowed values are one or the combination of: "none", "dotplot", "jitter", #' "boxplot", "point", "mean", "mean_se", "mean_sd", "mean_ci", "mean_range", #' "median", "median_iqr", "median_hilow", "median_q1q3", "median_mad", #' "median_range". #'@param color point or outline color. #'@param fill fill color. Used only when \code{error.plot = "crossbar"}. #'@param group grouping variable. Allowed values are 1 (for one group) or a #' character vector specifying the name of the grouping variable. Used only for #' adding statistical summary per group. #'@param width numeric value between 0 and 1 specifying bar or box width. #' Example width = 0.8. Used only when \code{error.plot} is one of #' c("crossbar", "errorbar"). #'@param shape point shape. Allowed values can be displayed using the function #' \code{\link{show_point_shapes}()}. #'@param size numeric value in [0-1] specifying point and line size. #'@param linetype line type. #'@param show.legend logical. Should this layer be included in the legends? NA, #' the default, includes if any aesthetics are mapped. \code{FALSE} never #' includes, and TRUE always includes. It can also be a named logical vector to #' finely select the aesthetics to display. #'@param alpha numeric value specifying fill color transparency. Value should be #' in [0, 1], where 0 is full transparency and 1 is no transparency. #'@param jitter a numeric value specifying the amount of jittering. Used only #' when \code{add} contains "jitter". #'@param seed A random seed to make the jitter reproducible. Default is `123`. Useful if you need #' to apply the same jitter twice, e.g., for a point and a corresponding label. #' The random seed is reset after jittering. If `NA`, the #' seed is initialized with a random value; this makes sure that two subsequent #' calls start with a different seed. Use NULL to use the current random seed #' and also avoid resetting (the behaviour of ggplot 2.2.1 and earlier). #'@param binwidth numeric value specifying bin width. use value between 0 and 1 #' when you have a strong dense dotplot. For example binwidth = 0.2. Used only #' when \code{add} contains "dotplot". #'@param dotsize as \code{size} but applied only to dotplot. #'@param p_geom the geometry of the main plot. Ex: p_geom = "geom_line". If #' NULL, the geometry is extracted from p. Used only by \link{ggline}(). #' @examples #'# Basic violin plot #'data("ToothGrowth") #'p <- ggviolin(ToothGrowth, x = "dose", y = "len", add = "none") #' #'# Add mean +/- SD and jitter points #'p %>% ggadd(c("mean_sd", "jitter"), color = "dose") #' #'# Add box plot #'p %>% ggadd(c("boxplot", "jitter"), color = "dose") #' #'@export ggadd <- function(p, add = NULL, color = "black", fill = "white", group = 1, width = 1, shape = 19, size = NULL, alpha = 1, jitter = 0.2, seed = 123, binwidth = NULL, dotsize = size, linetype = 1, show.legend = NA, error.plot = "pointrange", ci = 0.95, data = NULL, position = position_dodge(0.8), p_geom = "" ) { . <- NULL if(missing(group)) group <- NULL # Checkpoints #::::::::::::::::::::::::::::::::::::::::::: if("none" %in% add) add <- "none" # Adding mean or median point center <- intersect(c("mean", "median"), add) if(length(center) == 2) stop("Use mean or mdedian, but not both at the same time.") # Adding error bars errors <- intersect(.errorbar_functions(), add) if(length(errors) > 1) stop("Choose only one of these: ", .collapse(errors, sep = ", ")) if(is.null(dotsize)) dotsize =1 # Data and mapping #::::::::::::::::::::::::::::::::::::::::::: if(is.null(data)) data <- p$data .map <- .mapping(p) x <- .map$x y <- .map$y if(missing(color) & !is.null(.map$colour)) color <- .map$colour if(missing(fill) & !is.null(.map$fill)) fill <- .map$fill ngrps <- intersect(names(data), c(.map$x, fill, color, group)) %>% length() # number of grouping variables # Amount of jittering when add = "jitter" #::::::::::::::::::::::::::::::::::::::::::: .jitter <- jitter if(is.numeric(jitter)) jitter <- position_jitter(jitter, seed = seed) if(p_geom == "geom_line" & ngrps > 1){} else if(ngrps > 1) jitter <- position_jitterdodge(jitter.width = .jitter, dodge.width = 0.8, seed = seed) common.opts <- opts <- list(data = data, color = color, fill = fill, size = size, position = position, alpha = alpha, show.legend = show.legend) if ("boxplot" %in% add) { if(.geom(p) == "violin" & missing(width)) width = 0.2 else if(missing(width)) width = 0.7 p <- common.opts %>% .add_item(geomfunc = geom_boxplot, width = width) %>% .update_plot(p) } if ("violin" %in% add) { if(missing(width)) width = 1 p <- common.opts %>% .add_item(geomfunc = geom_violin, width = width, trim = FALSE) %>% .update_plot(p) } if ( "dotplot" %in% add ) { p <- common.opts %>% .add_item(geomfunc = geom_dotplot, binaxis = 'y', stackdir = 'center', dotsize = dotsize, stackratio = 1, binwidth = binwidth) %>% .update_plot(p) } if ( "jitter" %in% add ){ jitter.opts <- common.opts if(!(shape %in% 21:25)) jitter.opts$fill <- NULL p <- jitter.opts %>% .add_item(geomfunc = geom_jitter, position = jitter, shape = shape) %>% .update_plot(p) } if ( "point" %in% add ) { p <- common.opts %>% .remove_item("fill") %>% .add_item(geomfunc = geom_point, group = group) %>% .update_plot(p) } if ( "line" %in% add ) { p <- common.opts %>% .remove_item("fill") %>% .add_item(geomfunc = geom_line, group = 1) %>% .update_plot(p) } # Add mean or median #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: if(!.is_empty(center)) p <- p %>% add_summary(fun = center, color = color, shape = shape, position = position, size = size, group = group) # Add erors #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: if(!.is_empty(errors)){ if(missing(width)){ if(error.plot %in% c("errorbar", "lower_errorbar", "upper_errorbar")) width <- 0.1 else if(error.plot == "crossbar" & .geom(p) == "violin") width = 0.2 } p <- p %>% add_summary(errors, error.plot = error.plot, color = color, shape = shape, position = position, size = size, width = width, ci = ci, group = group, linetype = linetype, show.legend = show.legend) } p } ggpubr/R/show_line_types.R0000644000176200001440000000142214332553655015311 0ustar liggesusers#' @include utilities.R ggpar.R NULL #' Line types available in R #' @description Show line types available in R. #' @return a ggplot. #' #' @seealso \code{\link{ggpar}} and \code{\link{ggline}}. #' @examples #' show_line_types()+ #' theme_minimal() #' @export show_line_types <- function() { lt <- c("blank", "solid", "dashed", "dotted", "dotdash", "longdash", "twodash") d <- data.frame(lt = factor(lt, levels = lt)) ggplot() + scale_x_continuous(name="", limits=c(0,1), breaks=NULL) + scale_linetype_identity() + geom_segment(data=d, mapping=aes(x=0, xend=1, y=lt, yend=lt, linetype=lt))+ labs(title = "Line types available in R", y = "")+ theme(axis.text.y = element_text(face="bold", color="black")) } ggpubr/R/stat_kruskal_test.R0000644000176200001440000001077614332576562015662 0ustar liggesusers#' @include utilities.R utils_stat_test_label.R stat_anova_test.R NULL #'Add Kruskal-Wallis Test P-values to a GGPlot #'@description Add Kruskal-Wallis test p-values to a ggplot, such as #' box blots, dot plots and stripcharts. #'@inheritParams ggplot2::layer #'@inheritParams stat_pvalue_manual #'@inheritParams stat_anova_test #' #'@section Computed variables: \itemize{ #' \item{statistic}: the Kruskal-Wallis rank sum chi-squared statistic used to compute the p-value. #' \item{p}: p-value. #' \item{p.adj}: Adjusted p-values. #' \item{p.signif}: P-value significance. #' \item{p.adj.signif}: Adjusted p-value significance. #' \item{p.format}: Formated p-value. #' \item{p.adj.format}: Formated adjusted p-value. #' \item{n}: number of samples. #' } #' #' @examples #' # Data preparation #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' # Transform `dose` into factor variable #' df <- ToothGrowth #' df$dose <- as.factor(df$dose) #' # Add a random grouping variable #' set.seed(123) #' df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20))) #' df$len <- ifelse(df$group == "grp2", df$len+2, df$len) #' df$len <- ifelse(df$group == "grp3", df$len+7, df$len) #' head(df, 3) #' #' #' # Basic boxplot #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' # Create a basic boxplot #' # Add 5% and 10% space to the plot bottom and the top, respectively #' bxp <- ggboxplot(df, x = "dose", y = "len") + #' scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) #' #' # Add the p-value to the boxplot #' bxp + stat_kruskal_test() #' #' # Change the label position #' # Using coordinates in data units #' bxp + stat_kruskal_test(label.x = "1", label.y = 10, hjust = 0) #' #' # Format the p-value differently #' custom_p_format <- function(p) { #' rstatix::p_format(p, accuracy = 0.0001, digits = 3, leading.zero = FALSE) #' } #' bxp + stat_kruskal_test( #' label = "Kruskal-Wallis, italic(p) = {custom_p_format(p)}{p.signif}" #' ) #' #' # Show a detailed label in italic #' bxp + stat_kruskal_test(label = "as_detailed_italic") #' #' #' # Faceted plots #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' # Create a ggplot facet #' bxp <- ggboxplot(df, x = "dose", y = "len", facet.by = "supp") + #' scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) #' # Add p-values #' bxp + stat_kruskal_test() #' #' #' # Grouped plots #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' bxp2 <- ggboxplot(df, x = "group", y = "len", color = "dose", palette = "npg") #' #' # For each x-position, computes tests between legend groups #' bxp2 + stat_kruskal_test(aes(group = dose), label = "p = {p.format}{p.signif}") #' #' # For each legend group, computes tests between x variable groups #' bxp2 + stat_kruskal_test(aes(group = dose, color = dose), group.by = "legend.var") #' #' @export stat_kruskal_test <- function(mapping = NULL, data = NULL, group.by = NULL, label = "{method}, p = {p.format}", label.x.npc = "left", label.y.npc = "top", label.x = NULL, label.y = NULL, step.increase = 0.1, p.adjust.method = "holm", significance = list(), geom = "text", position = "identity", na.rm = FALSE, show.legend = FALSE, inherit.aes = TRUE, parse = FALSE, ...) { label <- get_kruskal_test_label_template(label) if(missing(parse) & is_plotmath_expression(label)){ parse <- TRUE } layer( stat = StatCompareMultipleMeans, data = data, mapping = mapping, geom = geom, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list( method = "kruskal_test", group.by = group.by, method.args = list(), correction = "none", na.rm = na.rm, stat.label = label, label.x.npc = label.x.npc , label.y.npc = label.y.npc, label.x = label.x, label.y = label.y, parse = parse, is.group.specified = is_group_aes_specified(mapping), step.increase = step.increase, p.adjust.method = p.adjust.method, significance = fortify_signif_symbols_encoding(significance), ... ) ) } get_kruskal_test_label_template <- function(label){ switch( label, as_italic = "{method}, italic(p) = {p.format}", as_detailed = "{method}, X2({df}) = {statistic}, p = {p.format}, n = {n}", as_detailed_italic = "{method}, italic(chi)^2 ({df}) = {statistic}, italic(p) = {p.format}, italic(n) = {n}", as_detailed_expression = "{method}, italic(chi)^2({df}) = {statistic}, italic(p) = {p.format}, italic(n) = {n}", label ) } ggpubr/R/utils-pipe.R0000644000176200001440000000031714332553655014173 0ustar liggesusers#' Pipe operator #' #' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. #' #' @name %>% #' @rdname pipe #' @keywords internal #' @export #' @importFrom magrittr %>% #' @usage lhs \%>\% rhs NULL ggpubr/R/show_point_shapes.R0000644000176200001440000000137514332553655015641 0ustar liggesusers#' @include utilities.R NULL #' Point shapes available in R #' @description Show point shapes available in R. #' @return a ggplot. #' #' @seealso \code{\link{ggpar}} and \code{\link{ggline}}. #' @examples #' show_point_shapes()+ #' theme_minimal() #' @export show_point_shapes <- function() { d=data.frame(p=c(0:25)) p <- ggplot() + scale_y_continuous(name="") + scale_x_continuous(name="") + scale_shape_identity() + geom_point(data=d, mapping=aes(x=p%%6, y=p%/%6, shape=p), size=5, fill="blue") + geom_text(data=d, mapping=aes(x=p%%6, y=p%/%6+0.25, label=p), size=3)+ scale_y_reverse() + theme( axis.title = element_blank() ) ggpar(p, ticks = FALSE, tickslab = FALSE, main = "Point shapes available in R") } ggpubr/R/stat_pvalue_manual.R0000644000176200001440000003204214332576562015766 0ustar liggesusers#' @include utilities.R geom_bracket.R #' @importFrom dplyr pull #' @importFrom glue glue NULL #'Add Manually P-values to a ggplot #' #'@description Add manually p-values to a ggplot, such as box blots, dot plots #' and stripcharts. Frequently asked questions are available on \href{https://www.datanovia.com/en/blog/tag/ggpubr/}{Datanovia ggpubr FAQ page}, for example: #' \itemize{ #' \item \href{https://www.datanovia.com/en/blog/how-to-add-p-values-onto-basic-ggplots/}{How to Add P-Values onto Basic GGPLOTS} #' \item \href{https://www.datanovia.com/en/blog/ggpubr-how-to-add-adjusted-p-values-to-a-multi-panel-ggplot/}{How to Add Adjusted P-values to a Multi-Panel GGPlot} #' \item \href{https://www.datanovia.com/en/blog/how-to-add-p-values-to-ggplot-facets/}{How to Add P-values to GGPLOT Facets} #' \item \href{https://www.datanovia.com/en/blog/ggpubr-how-to-add-p-values-generated-elsewhere-to-a-ggplot/}{How to Add P-Values Generated Elsewhere to a GGPLOT} #' \item \href{https://www.datanovia.com/en/blog/how-to-add-p-values-onto-a-grouped-ggplot-using-the-ggpubr-r-package/}{How to Add P-Values onto a Grouped GGPLOT using the GGPUBR R Package} #' \item \href{https://www.datanovia.com/en/blog/how-to-create-stacked-bar-plots-with-error-bars-and-p-values/}{How to Create Stacked Bar Plots with Error Bars and P-values} #' \item \href{https://www.datanovia.com/en/blog/how-to-add-p-values-onto-horizontal-ggplots/}{How to Add P-Values onto Horizontal GGPLOTS} #' } #'@inheritParams geom_bracket #'@param data a data frame containing statitistical test results. The expected #' default format should contain the following columns: \code{group1 | group2 | #' p | y.position | etc}. \code{group1} and \code{group2} are the groups that #' have been compared. \code{p} is the resulting p-value. \code{y.position} is #' the y coordinates of the p-values in the plot. #'@param label the column containing the label (e.g.: label = "p" or label = #' "p.adj"), where \code{p} is the p-value. Can be also an expression that can #' be formatted by the \code{\link[glue]{glue}()} package. For example, when #' specifying label = "t-test, p = \{p\}", the expression \{p\} will be #' replaced by its value. #'@param y.position column containing the coordinates (in data units) to be used #' for absolute positioning of the label. Default value is "y.position". Can be #' also a numeric vector. #'@param xmin column containing the position of the left sides of the brackets. #' Default value is "group1". #'@param xmax (optional) column containing the position of the right sides of #' the brackets. Default value is "group2". If NULL, the p-values are plotted #' as a simple text. #'@param x x position of the p-value. Should be used only when you want plot the #' p-value as text (without brackets). #'@param size,label.size size of label text. #'@param bracket.size Width of the lines of the bracket. #'@param color text and line color. Can be variable name in the data for coloring by groups. #'@param linetype linetype. Can be variable name in the data for changing linetype by groups. #'@param tip.length numeric vector with the fraction of total height that the #' bar goes down to indicate the precise column. Default is 0.03. #'@param remove.bracket logical, if \code{TRUE}, brackets are removed from the #' plot. Considered only in the situation, where comparisons are performed #' against reference group or against "all". #'@param hide.ns can be logical value or a character vector. #'\itemize{ #' \item Case when logical value. If TRUE, hide ns symbol when displaying #' significance levels. Filter is done by checking the column #' \code{p.adj.signif}, \code{p.signif}, \code{p.adj} and \code{p}. #' \item Case when character value. Possible values are "p" or "p.adj", for filtering out non significant. #' } #'@param vjust move the text up or down relative to the bracket. Can be also a #' column name available in the data. #'@param position position adjustment, either as a string, or the result of a #' call to a position adjustment function. #'@param ... other arguments passed to the function \code{geom_bracket()} or #' \code{geom_text()} #'@seealso \code{\link{stat_compare_means}} #'@examples #' #'# T-test #'stat.test <- compare_means( #' len ~ dose, data = ToothGrowth, #' method = "t.test" #') #'stat.test #' #'# Create a simple box plot #'p <- ggboxplot(ToothGrowth, x = "dose", y = "len") #'p #' #'# Perform a t-test between groups #'stat.test <- compare_means( #' len ~ dose, data = ToothGrowth, #' method = "t.test" #') #'stat.test #' #'# Add manually p-values from stat.test data #'# First specify the y.position of each comparison #'stat.test <- stat.test %>% #' mutate(y.position = c(29, 35, 39)) #'p + stat_pvalue_manual(stat.test, label = "p.adj") #' #'# Customize the label with glue expression #'# (https://github.com/tidyverse/glue) #'p + stat_pvalue_manual(stat.test, label = "p = {p.adj}") #' #' #' # Grouped bar plots #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' ToothGrowth$dose <- as.factor(ToothGrowth$dose) #' # Comparisons against reference #' stat.test <- compare_means( #' len ~ dose, data = ToothGrowth, group.by = "supp", #' method = "t.test", ref.group = "0.5" #' ) #' stat.test #' # Plot #' bp <- ggbarplot(ToothGrowth, x = "supp", y = "len", #' fill = "dose", palette = "jco", #' add = "mean_sd", add.params = list(group = "dose"), #' position = position_dodge(0.8)) #' bp + stat_pvalue_manual( #' stat.test, x = "supp", y.position = 33, #' label = "p.signif", #' position = position_dodge(0.8) #' ) #' #'@export stat_pvalue_manual <- function( data, label = NULL, y.position = "y.position", xmin = "group1", xmax = "group2", x = NULL, size = 3.88, label.size = size, bracket.size = 0.3, bracket.nudge.y = 0, bracket.shorten = 0, color = "black", linetype = 1, tip.length = 0.03, remove.bracket = FALSE, step.increase = 0, step.group.by = NULL, hide.ns = FALSE, vjust = 0, coord.flip = FALSE, position = "identity", ... ) { if(is.null(label)){ label <- guess_signif_label_column(data) } # Hide NS if(is.logical(hide.ns)){ if(hide.ns) data <- remove_ns(data) } else if (is.character(hide.ns)){ filter <- dplyr::filter if(hide.ns == "p") data <- data %>% filter(.data$p <= 0.05) else if(hide.ns == "p.adj") data <- data %>% filter(.data$p.adj <= 0.05) } data <- asserttat_group_columns_exists(data) comparison <- detect_comparison_type(data) all.x.is.missing <- is.null(x) & missing(xmin) & missing(xmax) if(all(data$group1 == "all") & all.x.is.missing){ is.grouped <- length(data$group2) > length(unique(data$group2)) if(!is.grouped) x <- "group2" # labels will be plotted at x = "group2" } # Detect automatically if xmin and xmax exists in the data. if(all.x.is.missing){ if(all(c("xmin", "xmax") %in% colnames(data))){ xmin <- "xmin" xmax <- "xmax" } } # should stay before (!is.null(x)) if(remove.bracket){ group1.length <- unique(data$group1) %>% length() if(group1.length == 1) { xmin <- xmax xmax <- NULL } } # P-value displayed as text (without brackets) if(!is.null(x)){ xmin <- x xmax <- NULL } # If label is a glue package expression if(.contains_curlybracket(label)){ data <- data %>% mutate(label = glue(label)) label <- "label" } available.variables <- colnames(data) if(!(label %in% available.variables)) stop("can't find the label variable '", label, "' in the data") if(!(xmin %in% available.variables)) stop("can't find the xmin variable '", xmin, "' in the data") y.position <- .valide_y_position(y.position, data) if(is.numeric(y.position)){ data$y.position <- y.position y.position <- "y.position" } # If xmax is null, pvalue is drawn as text if(!is.null(xmax)) { xmax <- data %>% pull(!!xmax) pvalue.geom <- "bracket" } else { xmax <- NA pvalue.geom <- "text" } if(!is.null(xmin)){ xmin <- data %>% pull(!!xmin) } else{ xmin <- NA } # Build the statistical table for plotting xxmax <-xmax # so that mutate will avoid re-using an existing xmax in the data xxmin <- xmin data <- data %>% dplyr::mutate( label = as.character(data %>% pull(!!label)), y.position = data %>% pull(!!y.position), xmin = xxmin, xmax = xxmax ) # vjust if(is.character(vjust)){ vjust <- data %>% pull(!!vjust) } else if(missing(vjust)){ vjust <- guess_labels_default_vjust(data$label) } data <- data %>% mutate(vjust = !!vjust) if(pvalue.geom == "bracket"){ if(identical(data$xmin, data$xmax) | remove.bracket){ # case when ref.group = "all" bracket.size = 0 } geom_exec( geom_bracket, data = data, xmin = "xmin", xmax = "xmax", label = "label", y.position = "y.position", vjust = "vjust", group = 1:nrow(data), tip.length = tip.length, label.size = label.size, size = bracket.size, bracket.nudge.y = bracket.nudge.y, bracket.shorten = bracket.shorten, color = color, linetype = linetype, step.increase = step.increase, step.group.by = step.group.by, coord.flip = coord.flip, position = position, ... ) } else{ if(comparison == "each_vs_ref"){ ref.group <- unique(data$group1) group2 <- NULL data <- add_ctr_rows(data, ref.group = ref.group) mapping <- aes(x = xmin, y = y.position, vjust = vjust, label = label, group = group2) if(missing(position) & !missing(x)){ if (is_grouping_variable(x)) position <- position_dodge(0.8) } } else{ mapping <- aes(x = xmin, y = y.position, vjust = vjust, label = label) } option <- list(data = data, size = label.size, position = position, ...) if(color %in% colnames(data)) mapping$colour <- rlang::ensym(color) else option$color <- color option[["mapping"]] <- mapping do.call(geom_text, option) } } asserttat_group_columns_exists <- function(data){ groups.exist <- all(c("group1", "group2") %in% colnames(data)) if(!groups.exist){ if(inherits(data, "rstatix_test") & "group" %in% colnames(data)){ data$group1 <- "all" data$group2 <- data$group } else{ stop("data should contain group1 and group2 columns") } } invisible(data) } # get validate p-value y-position .valide_y_position <- function(y.position, data){ if(is.numeric(y.position)){ number.of.test <- nrow(data) number.of.ycoord <- length(y.position) xtimes <- number.of.test/number.of.ycoord if(number.of.ycoord < number.of.test) y.position <- rep(y.position, xtimes) } else if(is.character(y.position)){ if(!(y.position %in% colnames(data))) stop("can't find the y.position variable '", y.position, "' in the data") } return(y.position) } # Check if a string contains curly bracket .contains_curlybracket <- function(x){ grepl("\\{|\\}", x, perl = TRUE) } # For ctr rows: the comparaison of ctr against itself # useful only when positionning the label of grouped bars add_ctr_rows <- function(data, ref.group){ xmin <- NULL data <- keep_only_tbl_df_classes(data) ctr <- data %>% dplyr::distinct(xmin, .keep_all = TRUE) %>% mutate(group2 = ref.group) %>% mutate(label = " ") dplyr::bind_rows(ctr, data) } # Returns the type of comparisons: one_group, two_groups, each_vs_ref, pairwise detect_comparison_type <- function(data){ ngroup1 <- unique(data$group1) %>% length() ngroup2 <- unique(data$group2) %>% length() if(is_null_model(data)){ type = "one_group" } else if(ngroup1 == 1 & ngroup2 >= 2){ type = "each_vs_ref" } else if(ngroup1 == 1 & ngroup2 == 1){ type = "two_groups" } else if (ngroup1 >= 2 & ngroup2 >= 2){ type = "pairwise" } else if(all(c("group1", "group2") %in% colnames(data))){ # filtered data type = "pairwise" } else{ stop("Make sure that group1 and group2 columns exist in the data.") } type } is_null_model <- function(data){ group2 <- unique(data$group2) .diff <- setdiff(group2, "null model") length(.diff) == 0 } is_grouping_variable <- function(x){ !(x %in% c("group1", "group2")) } # Check if label column contains stars contains_signif_stars <- function(data, label.col){ result <- FALSE if(label.col[1] %in% colnames(data)){ labels <- data %>% pull(!!label.col) stars <- c("****", "***", "**", "*") result <- any(labels %in% stars) } result } # guess label default vjust guess_label_default_vjust <- function(label){ if(label %in% c("****", "***", "**", "*")) vjust <- 0.5 else vjust <- 0 vjust } guess_labels_default_vjust <- function(labels){ labels %>% purrr::map(guess_label_default_vjust) %>% unlist() } # remove non significant remove_ns <- function(data){ filter <- dplyr::filter columns <- colnames(data) if("p.adj.signif" %in% columns){ data <- data %>% filter(.data$p.adj.signif != "ns") } else if("p.adj" %in% columns){ data <- data %>% filter(.data$p.adj <= 0.05) } else if("p.signif" %in% columns){ data <- data %>% filter(.data$p.signif != "ns") } else if("p" %in% columns){ data <- data %>% filter(.data$p <= 0.05) } data } ggpubr/R/rotate.R0000644000176200001440000000076014332553655013400 0ustar liggesusers#'Rotate a ggplot Horizontally #' #'@description Rotate a ggplot to create horizontal plots. Wrapper around #' \code{\link[ggplot2]{coord_flip}}. #'@param ... other arguments to pass to \code{\link[ggplot2]{coord_flip}}. #' #'@examples #'# Load data #'data("ToothGrowth") #'df <- ToothGrowth #' #'# Basic plot #'p <- ggboxplot(df, x = "dose", y = "len", #' color = "dose", palette = "jco") #'p #'# Create horizontal plots #'p + rotate() #'@export rotate <- function(...){ coord_flip(...) } ggpubr/R/ggpar.R0000644000176200001440000002113714367732414013203 0ustar liggesusers#' @include utilities.R NULL #' Graphical parameters #' #' @param p an object of class ggplot or a list of ggplots #' #' @param palette the color palette to be used for coloring or filling by #' groups. Allowed values include "grey" for grey color palettes; brewer #' palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", #' "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", #' "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and #' "rickandmorty". Can be also a numeric vector of length(groups); in this #' case a basic color palette is created using the function #' \link[grDevices]{palette}. #' @param gradient.cols vector of colors to use for n-colour gradient. Allowed #' values include brewer and ggsci color palettes. #' @param main plot main title. #' @param title plot main title. #' @param submain,subtitle plot subtitle. #' @param caption plot caption. #' @param xlab character vector specifying x axis labels. Use xlab = FALSE to #' hide xlab. #' @param ylab character vector specifying y axis labels. Use ylab = FALSE to #' hide ylab. #' @param font.main,font.submain,font.caption,font.x,font.y a vector of length 3 #' indicating respectively the size (e.g.: 14), the style (e.g.: "plain", #' "bold", "italic", "bold.italic") and the color (e.g.: "red") of main title, #' subtitle, caption, xlab and ylab, respectively. For example \emph{font.x = #' c(14, "bold", "red")}. Use font.x = 14, to change only font size; or use #' font.x = "bold", to change only font face. #' @param font.title,font.subtitle alias of font.submain and font.submain, #' respectively. #' @param font.family character vector specifying font family. #' @param xlim,ylim a numeric vector of length 2, specifying x and y axis #' limits (minimum and maximum), respectively. e.g.: ylim = c(0, 50). #' @param xscale,yscale x and y axis scale, respectively. Allowed values are one #' of c("none", "log2", "log10", "sqrt"); e.g.: yscale="log2". #' @param format.scale logical value. If TRUE, axis tick mark labels will be #' formatted when xscale or yscale = "log2" or "log10". #' @param legend character specifying legend position. Allowed values are one of #' c("top", "bottom", "left", "right", "none"). To remove the legend use #' legend = "none". Legend position can be also specified using a numeric #' vector c(x, y); see details section. #' @param legend.title legend title, e.g.: \code{legend.title = "Species"}. Can #' be also a list, \code{legend.title = list(color = "Species", linetype = "Species", #' shape = "Species")}. #' @param font.legend legend text font style; e.g.: font.legend = c(10, "plain", #' "black"). #' @param ticks logical value. Default is TRUE. If FALSE, hide axis tick marks. #' @param tickslab logical value. Default is TRUE. If FALSE, hide axis tick #' labels. #' @param font.tickslab,font.xtickslab,font.ytickslab Font style (size, face, #' color) for tick labels, e.g.: c(14, "bold", "red"). #' @param x.text.angle,y.text.angle Numeric value specifying the rotation angle #' of x and y axis tick labels, respectively. Default value is NULL. For #' vertical x axis texts use x.text.angle = 90. #' @param xtickslab.rt,ytickslab.rt Same as x.text.angle and y.text.angle, #' respectively. Will be deprecated in the near future. #' @param xticks.by,yticks.by numeric value controlling x and y axis breaks, #' respectively. For example, if yticks.by = 5, a tick mark is shown on every #' 5. Default value is NULL. #' @param rotate logical value. If TRUE, rotate the graph by setting the plot #' orientation to horizontal. #' @param orientation change the orientation of the plot. Allowed values are one #' of c( "vertical", "horizontal", "reverse"). Partial match is allowed. #' @param ggtheme function, ggplot2 theme name. Default value is theme_pubr(). #' Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), #' theme_minimal(), theme_classic(), theme_void(), .... #' @param ... not used #' @examples #' # Load data #' data("ToothGrowth") #' df <- ToothGrowth #' #' # Basic box plot #' # +++++++++++++++++++++++++++ #' #' p <- ggboxplot(df, x = "dose", y = "len") #' #' # Change the plot orientation: horizontal #' ggpar(p, orientation = "horiz") #' #' #' # Change main title and axis labels #' # ++++++++++++++++++++++++++++ #' #' ggpar(p, #' main = "Plot of length \n by dose", #' xlab = "Dose (mg)", ylab = "Length") #' #' # Title font styles: 'plain', 'italic', 'bold', 'bold.italic' #' ggpar(p, #' main = "Length by dose", #' font.main = c(14,"bold.italic", "red"), #' font.x = c(14, "bold", "#2E9FDF"), #' font.y = c(14, "bold", "#E7B800")) #' #' # Hide axis labels #' ggpar(p, xlab = FALSE, ylab = FALSE) #' #' #' # Change colors #' # ++++++++++++++++++++++ #' #' # Change outline colors by groups: dose #' p2 <- ggboxplot(df, "dose", "len", color = "dose") #' p2 #' #' # Use custom color palette #' ggpar(p2, palette = c("#00AFBB", "#E7B800", "#FC4E07")) #' #' # Use brewer palette #' ggpar(p2, palette = "Dark2" ) #' #' # Use grey palette #' ggpar(p2, palette = "grey") #' #' # Use scientific journal palette from ggsci package #' ggpar(p2, palette = "npg") # nature #' #' # Axis ticks, limits, scales #' # +++++++++++++++++++++++++ #' #' # Axis ticks labels and rotation #' ggpar(p, #' font.tickslab = c(14,"bold", "#993333"), #' xtickslab.rt = 45, ytickslab.rt = 45) #' # Hide axis ticks and tick labels #' ggpar(p, ticks = FALSE, tickslab = FALSE) #' #' # Axis limits #' ggpar(p, ylim = c(0, 50)) #' #' # Axis scale #' ggpar(p, yscale = "log2") #' #' # Format axis scale #' ggpar(p, yscale = "log2", format.scale = TRUE) #' #' # Legends #' # ++++++++++++++++++ #' # Change legend position and title #' ggpar(p2, #' legend = "right", legend.title = "Dose (mg)", #' font.legend = c(10, "bold", "red")) #' #' @export ggpar <- function(p, palette = NULL, gradient.cols = NULL, main = NULL, submain = NULL, caption = NULL, xlab = NULL, ylab = NULL, title = NULL, subtitle = NULL, font.main = NULL, font.submain = NULL, font.x = NULL, font.y = NULL, font.caption = NULL, font.title = NULL, font.subtitle = NULL, font.family = "", xlim = NULL, ylim = NULL, xscale = c("none", "log2", "log10", "sqrt"), yscale = c("none", "log2", "log10", "sqrt"), format.scale = FALSE, legend = NULL, legend.title = NULL, font.legend = NULL, ticks = TRUE, tickslab = TRUE, font.tickslab = NULL, font.xtickslab = font.tickslab, font.ytickslab = font.tickslab, x.text.angle = NULL, y.text.angle = NULL, xtickslab.rt = x.text.angle, ytickslab.rt = y.text.angle, xticks.by = NULL, yticks.by = NULL, rotate = FALSE, orientation = c("vertical", "horizontal", "reverse"), ggtheme = NULL, ...) { original.p <- p if(rotate) orientation <- "horizontal" if(is.ggplot(original.p)) list.plots <- list(original.p) else if(is.list(original.p)) list.plots <- original.p else stop("Can't handle an object of class ", class (original.p)) if(!is.null(title)) main <- title if(!is.null(subtitle)) submain <- subtitle if(!is.null(font.title)) font.main <- font.title if(!is.null(font.subtitle)) font.submain <- font.subtitle if(is.numeric(palette)) palette <- grDevices::palette()[palette] for(i in 1:length(list.plots)){ p <- list.plots[[i]] if(is.ggplot(p)){ p <- p + .ggcolor(palette)+ .ggfill(palette) if(!is.null(ggtheme)) p <- p + ggtheme # labs_pubr() + if(!is.null(gradient.cols)) p <- p + .gradient_col(gradient.cols) p <- p +.set_ticks(ticks, tickslab, font.tickslab, xtickslab.rt, ytickslab.rt, font.xtickslab = font.xtickslab, font.ytickslab = font.ytickslab) p <- .set_ticksby(p, xticks.by, yticks.by) p <- p + .set_axis_limits(xlim, ylim) p <-.set_legend(p, legend, legend.title, font.legend) p <- .set_scale(p, xscale = xscale, yscale = yscale, format.scale = format.scale) p <- .labs(p, main, xlab, ylab, font.main, font.x, font.y, submain = submain, caption = caption, font.submain = font.submain, font.caption = font.caption) p <- .set_orientation(p, orientation) if(font.family != "") p <- p + theme(text = element_text(family = font.family)) list.plots[[i]] <- p } } if(is.ggplot(original.p)) list.plots[[1]] else list.plots } ggpubr/R/ggpaired.R0000644000176200001440000001214614342650175013661 0ustar liggesusers#'@include utilities.R ggpar.R NULL #'Plot Paired Data #'@description Plot paired data. #'@inheritParams ggboxplot #'@param cond1 variable name corresponding to the first condition. #'@param cond2 variable name corresponding to the second condition. #'@param x,y x and y variables, where x is a grouping variable and y contains #' values for each group. Considered only when \code{cond1} and \code{cond2} #' are missing. #'@param id variable name corresponding to paired samples' id. Used to connect #' paired points with lines. #'@param color points and box plot colors. To color by conditions, use color = #' "condition". #'@param fill box plot fill color. To change fill color by conditions, use fill #' = "condition". #'@param line.color line color. #'@param linetype line type. #'@param point.size,line.size point and line size, respectively. #'@param width box plot width. #'@param ... other arguments to be passed to be passed to \link{ggpar}(). #' @examples #' #'# Example 1 #'#:::::::::::::::::::::::::::::::::::::::::: #' before <-c(200.1, 190.9, 192.7, 213, 241.4, 196.9, 172.2, 185.5, 205.2, 193.7) #' after <-c(392.9, 393.2, 345.1, 393, 434, 427.9, 422, 383.9, 392.3, 352.2) #' #' d <- data.frame(before = before, after = after) #' ggpaired(d, cond1 = "before", cond2 = "after", #' fill = "condition", palette = "jco") #' #'# Example 2 #'#:::::::::::::::::::::::::::::::::::::::::: #'ggpaired(ToothGrowth, x = "supp", y = "len", #' color = "supp", line.color = "gray", line.size = 0.4, #' palette = "npg") #' #'@export ggpaired <- function(data, cond1, cond2, x = NULL, y = NULL, id = NULL, color = "black", fill = "white", palette = NULL, width = 0.5, point.size = 1.2, line.size = 0.5, line.color = "black", linetype = "solid", title = NULL, xlab = "Condition", ylab = "Value", facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, ggtheme = theme_pubr(), ...){ grouping.vars <- c(x, color, fill) %>% unique() %>% intersect(colnames(data)) if(!missing(cond1) & !missing(cond2)){ data <- data %>% df_gather( cols = c(cond1, cond2), names_to = "condition", values_to = "val" ) data$condition<- factor(data$condition, levels = c(cond1, cond2)) x <- "condition" y <- "val" } else if(!is.null(x) & !is.null(y)){ if(missing(xlab)) xlab <- x if(missing(ylab)) ylab <- y } # Default options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .opts <- list( id = id, color = color, fill = fill, palette = palette, width = width, point.size = point.size, line.size = line.size, line.color = line.color, linetype = linetype, title = title, xlab = xlab, ylab = ylab, facet.by = facet.by, panel.labs = panel.labs, short.panel.labs = short.panel.labs, label = label, font.label = font.label, label.select = label.select, repel = repel, label.rectangle = label.rectangle, ggtheme = ggtheme, ...) # User options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .user.opts <- as.list(match.call(expand.dots = TRUE)) .user.opts[[1]] <- NULL # Remove the function name # keep only user arguments for(opt.name in names(.opts)){ if(is.null(.user.opts[[opt.name]])) .opts[[opt.name]] <- NULL } .opts$data <- data .opts$x <- x .opts$y <- y .opts$fun <- ggpaired_core if(missing(ggtheme) & (!is.null(facet.by))) .opts$ggtheme <- theme_pubr(border = TRUE) p <- do.call(.plotter, .opts) if(.is_list(p) & length(p) == 1) p <- p[[1]] return(p) } ggpaired_core <- function(data, x = NULL, y = NULL, id = NULL, color = "black", fill = "white", palette = NULL, width = 0.5, point.size = 1.2, line.size = 0.5, line.color = "black", linetype = "solid", title = NULL, xlab = "Condition", ylab = "Value", ggtheme = theme_pubr(), ...) { if(!is.factor(data[[x]])) data[[x]] <- as.factor(data[[x]]) grouping.vars <- c(x, color, fill) %>% unique() %>% intersect(colnames(data)) # Add paired sample ids if(!is.null(id)) id <- .select_vec(data, id) else id <- rep(1:(nrow(data)/2), 2) data$id <- id position <- "identity" # if(length(grouping.vars) > 1) # position <- position_dodge(0.8) condition <- val <- id <- NULL p <- ggplot(data, create_aes(list(x = x, y = y))) + geom_exec(geom_boxplot, data = data, color = color, fill = fill, width = width, position = position)+ geom_exec(geom_line, data = data, group = "id", color = line.color, size = line.size, linetype = linetype, position = position) + geom_exec(geom_point, data = data, color = color, size = point.size, position = position) p <- ggpar(p, palette = palette, ggtheme = ggtheme, xlab = xlab, ylab = ylab, title = title, ...) p } ggpubr/R/ggballoonplot.R0000644000176200001440000002157614342705520014744 0ustar liggesusers#' Ballon plot #' #' @description Plot a graphical matrix where each cell contains a dot whose #' size reflects the relative magnitude of the corresponding component. Useful #' to visualize contingency table formed by two categorical variables. #' #' @inheritParams ggpar #' @param data a data frame. Can be: \itemize{ \item \bold{a standard #' contingency table} formed by two categorical variables: a data frame with #' row names and column names. The categories of the first variable are #' columns and the categories of the second variable are rows. \item \bold{a #' streched contingency table}: a data frame containing at least three columns #' corresponding, respectively, to (1) the categories of the first variable, #' (2) the categories of the second varible, (3) the frequency value. In this #' case, you should specify the argument x and y in the function #' \code{ggballoonplot()}}. #' @param x,y the column names specifying, respectively, the first and the #' second variable forming the contingency table. Required only when the data #' is a stretched contingency table. #' @param color point border line color. #' @param fill point fill color. Default is "lightgray". Considered only for #' points 21 to 25. #' @param shape points shape. The default value is 21. Alternaive values include #' 22, 23, 24, 25. #' @param size point size. By default, the points size reflects the relative #' magnitude of the value of the corresponding cell (\code{size = "value"}). #' Can be also numeric (\code{size = 4}). #' @param size.range a numeric vector of length 2 that specifies the minimum and #' maximum size of the plotting symbol. Default values are \code{size.range = #' c(1, 10)}. #' @param facet.by character vector, of length 1 or 2, specifying grouping variables for #' faceting the plot into multiple panels. Should be in the data. #' @param show.label logical. If TRUE, show the data cell values as point #' labels. #' @param font.label a vector of length 3 indicating respectively the size #' (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and #' the color (e.g.: "red") of point labels. For example font.label = c(14, #' "bold", "red"). To specify only the size and the style, use font.label = #' c(14, "plain"). #' @param rotate.x.text logica. If TRUE (default), rotate the x axis text. #' @param ... other arguments passed to the function \code{\link{ggpar}} #' #' @examples #' # Define color palette #' my_cols <- c("#0D0887FF", "#6A00A8FF", "#B12A90FF", #' "#E16462FF", "#FCA636FF", "#F0F921FF") #' #' # Standard contingency table #' #::::::::::::::::::::::::::::::::::::::::::::::::::::::::: #' # Read a contingency table: housetasks #' # Repartition of 13 housetasks in the couple #' data <- read.delim( #' system.file("demo-data/housetasks.txt", package = "ggpubr"), #' row.names = 1 #' ) #' data #' #' # Basic ballon plot #' ggballoonplot(data) #' #' # Change color and fill #' ggballoonplot(data, color = "#0073C2FF", fill = "#0073C2FF") #' #' #' # Change color according to the value of table cells #' ggballoonplot(data, fill = "value")+ #' scale_fill_gradientn(colors = my_cols) #' #' # Change the plotting symbol shape #' ggballoonplot(data, fill = "value", shape = 23)+ #' gradient_fill(c("blue", "white", "red")) #' #' #' # Set points size to 8, but change fill color by values #' # Sow labels #' ggballoonplot(data, fill = "value", color = "lightgray", #' size = 10, show.label = TRUE)+ #' gradient_fill(c("blue", "white", "red")) #' #' # Streched contingency table #' #::::::::::::::::::::::::::::::::::::::::::::::::::::::::: #' #' # Create an Example Data Frame Containing Car x Color data #' carnames <- c("bmw","renault","mercedes","seat") #' carcolors <- c("red","white","silver","green") #' datavals <- round(rnorm(16, mean=100, sd=60),1) #' car_data <- data.frame(Car = rep(carnames,4), #' Color = rep(carcolors, c(4,4,4,4) ), #' Value=datavals ) #' #' car_data #' #' ggballoonplot(car_data, x = "Car", y = "Color", #' size = "Value", fill = "Value") + #' scale_fill_gradientn(colors = my_cols) + #' guides(size = FALSE) #' #' #' # Grouped frequency table #' #::::::::::::::::::::::::::::::::::::::::::::::::::::::::: #'data("Titanic") #'dframe <- as.data.frame(Titanic) #'head(dframe) #'ggballoonplot( #' dframe, x = "Class", y = "Sex", #' size = "Freq", fill = "Freq", #' facet.by = c("Survived", "Age"), #' ggtheme = theme_bw() #')+ #' scale_fill_gradientn(colors = my_cols) #' #'# Hair and Eye Color of Statistics Students #'data(HairEyeColor) #'ggballoonplot( as.data.frame(HairEyeColor), #' x = "Hair", y = "Eye", size = "Freq", #' ggtheme = theme_gray()) %>% #' facet("Sex") #' #' #' @export ggballoonplot <- function( data, x = NULL, y = NULL, size = "value", facet.by = NULL, size.range = c(1, 10), shape = 21, color = "black", fill = "gray", show.label = FALSE, font.label = list(size = 12, color = "black"), rotate.x.text = TRUE, ggtheme = theme_minimal(), ... ) { if(inherits(data, "matrix")) data <- as.data.frame(data) # Check the data #:::::::::::::::::::::::::::::::::::::::::::::::::::::: # case 1: x and y specified. we assume that the data is streched # if size not specified, then take the third column as size values # detect fill variable if misspecified if(!is.null(x) & !is.null(y)){ if(missing(size)) size <- colnames(data)[3] if(fill == "value" & !("value" %in% colnames(data))) fill <- colnames(data)[3] label <- colnames(data)[3] } # case 2: x and y are not specified # - check if the data is streched: # If yes, then consider the first 3 columns as x, y and size values # If no, then strech the data and continu else if(is.null(x) | is.null(y)){ if(.is_streched(data)){ .cnames <- colnames(data) x <- .cnames[1] y <- .cnames[2] if(missing(size)) size <- .cnames[3] if(fill == "value" & !("value" %in% .cnames)) fill <- .cnames[3] label <- .cnames[3] # Reverse y levels so that it appears in the right order on the plot y.val <- dplyr::pull(data, 2) data[[2]] <- y.val %>% factor(levels = rev(.levels(y.val))) } else { data <- .df_strech(data) # Strech the data into 3 columns: .row|.col|.value x <- ".col" y <- ".row" if(missing(size)) size <- "value" label <- "value" } } p <- ggplot(data, create_aes(list(x = x, y = y))) + geom_exec( geom_point, data = data, size = size, fill = fill, shape = shape, color = color ) p <- p + scale_size(range = size.range)+ guides( size = guide_legend(reverse=TRUE))+ ggtheme + theme( axis.title.x = ggplot2::element_blank(), axis.title.y = ggplot2::element_blank() ) if (show.label){ font.label <- .check_lab_font(font.label) text.args <- font.label %>% .add_item( geomfunc = geom_text, data = data, x = x, y = y, label = label ) p <- p + do.call(geom_exec, text.args) } if(rotate.x.text) p <- p + rotate_x_text(45, hjust = 1, vjust = 1) if(!is.null(facet.by)) p <- facet(p, facet.by) ggpar(p, ...) } # Font label .check_lab_font <- function(font.label){ font.label <- .parse_font(font.label) font.label$size <- ifelse(is.null(font.label$size), 4, font.label$size/3) font.label$color <- ifelse(is.null(font.label$color), "black", font.label$color) font.label$face <- ifelse(is.null(font.label$face), "plain", font.label$face) font.label } # strech a data frame with row names # returns a data frame with 3 columns .row, .col, value .df_strech <- function(data){ .col.names <- colnames(data) .row.names <- rownames(data) data <- data %>% dplyr::mutate(.row = .row.names) %>% dplyr::select(.row, dplyr::everything()) # Sretch the data into three columns .col <- .row <- NULL data <- data %>% tidyr::gather( key= ".col", value = "value", -.row ) %>% dplyr::mutate( .col = factor(.col, levels = .col.names), .row = factor(.row, levels = rev(.row.names)) ) data } # Check if the contingency table is in the streched format .is_streched <- function(data, x = NULL, y = NULL){ streched <- TRUE if(is.null(x) | is.null(y)){ if(.is_numeric_data(data)) streched <- FALSE else{ x <- dplyr::pull(data, 1) y <- dplyr::pull(data, 2) z <- dplyr::pull(data, 3) streched <- ( (is.character(x) | is.factor(x)) & (is.character(y) | is.factor(y)) & is.numeric(z) ) } } streched } # Check if a data matrix is numeric .is_numeric_data <- function(x){ all(apply(x, 2, is.numeric)) } # For tible data. The first column should be row names .is_correct_tbl <- function(x){ ok <- FALSE if(inherits(x,"tbl_df" )) ok <- TRUE if(ok){ .rows <- dplyr::pull(x, 1) ok <- is.character(.rows) & .is_numeric_data(x[,-1]) } ok } ggpubr/R/as_npc.R0000644000176200001440000000765714332576562013363 0ustar liggesusers#' Convert Character Coordinates into Normalized Parent Coordinates (NPC) #' #' Convert character coordinates to npc units and shift postions to avoid #' overlaps when grouping is active. If numeric validate npc values. #' #' @param value numeric (in [0-1]) or character vector of coordinates. If #' character, should be one of \code{c('right', 'left', 'bottom', 'top', #' 'center', 'centre', 'middle')}. #' @param group integer ggplot's group id. Used to shift coordinates to avoid #' overlaps. #' @param step numeric value in [0-1]. The step size for shifting coordinates #' in npc units. Considered as horizontal step for x-axis and vertical step #' for y-axis. For y-axis, the step value can be negative to reverse the order of groups. #' @param margin.npc numeric [0-1] The margin added towards the nearest #' plotting area edge when converting character coordinates into npc. #' @param axis the concerned axis . Should be one of \code{c("xy", "x", "y")}. #' #' @return A numeric vector with values in the range [0-1] representing npc #' coordinates. #' @seealso \code{\link{npc_to_data_coord}}, \code{\link{get_coord}}. #' @details the \code{as_npc()} function is an adaptation from #' \code{ggpmisc::compute_npc()}. #' #' @examples #' as_npc(c("left", "right")) #' as_npc(c("top", "right")) #' #' @describeIn as_npc converts x or y coordinate values into npc. Input values #' should be numeric or one of the following values \code{c('right', 'left', #' 'bottom', 'top', 'center', 'centre', 'middle')}. #' @export as_npc <- function(value, group = 1L, step = 0.1, margin.npc = 0.05, axis = c("xy", "x", "y")) { group <- abs(group) axis <- match.arg(axis) if (is.factor(value)) { value <- as.character(value) } assertthat_coord_is_valid(value, axis = axis) if (is.character(value)) { map <- c(right = 1 - margin.npc, left = 0 + margin.npc, top = 1 - margin.npc, bottom = 0 + margin.npc, centre = 0.5, center = 0.5, middle = 0.5, NA_real_) value <- unname(map[value]) } if (any(group > 1L) & step != 0) { value <- value + (group - 1L) * step * ifelse(value < 0.5, 1, -1) } #value <- ifelse(value > 1, 1, value) #value <- ifelse(value < 0, 0, value) value } #' @describeIn as_npc converts x coordinate values into npc. Input values should #' be numeric or one of the following values \code{c('right', 'left', #' 'center', 'centre', 'middle')}. Wrapper around \code{as_npc(axis = "x")}. #' @export as_npcx <- function(value, group = 1L, step = 0.1, margin.npc = 0.05) { as_npc(value, group = group, step = step, margin.npc = margin.npc, axis = "x" ) } #' @describeIn as_npc converts y coordinate values into npc. Input values should #' be numeric or one of the following values \code{c( 'bottom', 'top', #' 'center', 'centre', 'middle')}. Wrapper around \code{as_npc(axis = "y")}. #' @export as_npcy <- function(value, group = 1L, step = 0.1, margin.npc = 0.05) { as_npc(value, group = group, step = step, margin.npc = margin.npc, axis = "y" ) } # Check NPC coord----------------------------------------- # coord: should be between 0 and 1 or a character # axis: should be "x" or "y" assertthat_coord_is_valid <- function(.coord, axis = c("x", "y", "xy")){ axis <- match.arg(axis) allowed.values <- switch (axis, x = c('right', 'left', 'center', 'centre', 'middle'), y = c( 'bottom', 'top', 'center', 'centre', 'middle'), xy = c('right', 'left', 'bottom', 'top', 'center', 'centre', 'middle') ) .message <- paste0( "'*.npc coord for ", axis, " axis should be either a numeric value in [0-1] ", "or a character strings including one of: ", paste(allowed.values, collapse = ", ") ) if(is.numeric(.coord)){ if (any(.coord < 0 | .coord > 1)) { stop(.message, call. = FALSE) } } else if(is.character(.coord)){ if(!all(.coord %in% allowed.values)) stop(.message, call. = FALSE) } } ggpubr/R/gene_expression.R0000644000176200001440000000162514336762430015275 0ustar liggesusers#'Gene Expression Data #' #'@description Gene expression data extracted from TCGA using the `RTCGA` and #' `RTCGA.mRNA` R packages. It contains the mRNA expression for 3 genes - #' GATA3, PTEN and XBP1- from 3 different datasets: Breast invasive carcinoma #' (BRCA), Ovarian serous cystadenocarcinoma (OV) and Lung squamous cell #' carcinoma (LUSC) #'@name gene_expression #'@docType data #'@usage data("gene_expression") #'@format A data frame with 1305 rows and 5 columns. \describe{ #' \item{\code{bcr_patient_barcode}}{sample ID} \item{\code{dataset}}{cance type} \item{\code{GATA3}}{GATA3 gene expression} #' \item{\code{PTEN}}{PTEN gene expression}\item{\code{XBP1}}{XBP1 gene expression.}} #' #' @examples #' data(gene_expression) #' #' ggboxplot(gene_expression, x = "dataset", #' y = c("GATA3", "PTEN", "XBP1"), #' combine = TRUE, #' ylab = "Expression", #' color = "dataset", palette = "jco") #' NULL ggpubr/R/theme_pubr.R0000644000176200001440000001343314332553655014235 0ustar liggesusers#' @include utilities.R #' @import ggplot2 NULL #'Publication ready theme #' #'@description \itemize{ \item \strong{theme_pubr()}: Create a publication ready #' theme \item \strong{theme_pubclean()}: a clean theme without axis lines, to #' direct more attention to the data. \item \strong{labs_pubr()}: Format only #' plot labels to a publication ready style \item \strong{theme_classic2()}: #' Create a classic theme with axis lines. \item \strong{clean_theme()}: Remove #' axis lines, ticks, texts and titles. \item \strong{clean_table_theme()}: #' Clean the the theme of a table, such as those created by #' \code{\link{ggsummarytable}()}}. #'@param base_size base font size #'@param base_family base font family #'@param border logical value. Default is FALSE. If TRUE, add panel border. #'@param margin logical value. Default is TRUE. If FALSE, reduce plot margin. #'@param legend character specifying legend position. Allowed values are one of #' c("top", "bottom", "left", "right", "none"). Default is "top" side position. #' to remove the legend use legend = "none". Legend position can be also #' specified using a numeric vector c(x, y). In this case it is possible to #' position the legend inside the plotting area. x and y are the coordinates of #' the legend box. Their values should be between 0 and 1. c(0,0) corresponds #' to the "bottom left" and c(1,1) corresponds to the "top right" position. For #' instance use legend = c(0.8, 0.2). #'@param x.text.angle Rotation angle of x axis tick labels. Default value is 0. #' Use 90 for vertical text. #'@param flip logical. If TRUE, grid lines are added to y axis instead of x #' axis. #' @examples #' p <- ggplot(mtcars, aes(x = wt, y = mpg)) + #' geom_point(aes(color = gear)) #' #' # Default plot #' p #' #' # Use theme_pubr() #' p + theme_pubr() #' #' # Format labels #' p + labs_pubr() #' #'@name theme_pubr #'@rdname theme_pubr #'@export theme_pubr <- function (base_size = 12, base_family = "", border = FALSE, margin = TRUE, legend = c("top", "bottom", "left", "right", "none"), x.text.angle = 0) { half_line <- base_size/2 if(!is.numeric(legend)) legend <- match.arg(legend) if(x.text.angle > 5) xhjust <- 1 else xhjust <- NULL if(border){ panel.border <- element_rect(fill = NA, colour = "black", size = 0.7) axis.line <- element_blank() } else{ panel.border <- element_blank() axis.line = element_line(colour = "black", size = 0.5) } if(margin) plot.margin <- margin(half_line, half_line, half_line, half_line) else plot.margin <- unit(c(0.5,0.3,0.3,0.3),"mm") .theme <- theme_bw(base_size = base_size, base_family = base_family) %+replace% theme(panel.border = panel.border, panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = axis.line, axis.text = element_text(color = "black"), legend.key = element_blank(), strip.background = element_rect(fill = "#F2F2F2", colour = "black", size = 0.7), plot.margin = plot.margin, legend.position = legend, complete = TRUE) if(x.text.angle!=0) .theme <- .theme + theme(axis.text.x = element_text(angle = x.text.angle, hjust = xhjust)) .theme } #' @rdname theme_pubr #' @export theme_pubclean <- function (base_size = 12, base_family = "", flip = FALSE) { res <- theme_grey(base_size = base_size, base_family = base_family) + theme( panel.background = element_rect(fill = "white"), legend.background = element_rect(fill = "white"), legend.position = "top" ) if(flip){ res <- res + theme( panel.grid.major.x = element_line(linetype = "dotted", color = "grey"), axis.line.y = element_line(color = "black") ) } else{ res <- res + theme( panel.grid.major.y = element_line(linetype = "dotted", color = "grey") ) } res } #' @rdname theme_pubr #' @export labs_pubr <- function(base_size = 14, base_family = ""){ theme( text = element_text(family = base_family, face = "plain", colour = "black", size = base_size, lineheight = 0.9, hjust = 0.5, vjust = 0.5, angle = 0, margin = margin(), debug = FALSE), # Tick labels axis.text.x = element_text(size = rel(0.86), colour = "black", face = "bold"), axis.text.y = element_text(size = rel(0.86), colour = "black", face = "bold"), # Axis labels axis.title = element_text(size = rel(1), colour = "black", face = "bold"), # Main title plot.title = element_text(size = rel(1), colour = "black" , lineheight=1.0, face = "bold"), legend.title = element_text(size = rel(0.7), face = "bold", colour = "black"), legend.text = element_text(size = rel(0.7), face = "plain", colour = "black") ) } #' @rdname theme_pubr #' @export theme_classic2 <- function (base_size = 12, base_family = "") { theme_classic(base_size = base_size, base_family = base_family) %+replace% theme( axis.line.x = element_line(), axis.line.y = element_line() ) } #' @export #' @rdname theme_pubr clean_theme <- function() { theme( axis.line.x = element_blank(), axis.line.y = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank() ) } #' @export #' @rdname theme_pubr clean_table_theme <- function () { theme(axis.line.x = element_blank(), axis.line.y = element_blank(), axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) } ggpubr/R/ggboxplot.R0000644000176200001440000002445114340223022014067 0ustar liggesusers#' @include utilities.R ggpar.R NULL #'Box plot #'@description Create a box plot with points. Box plots display a group of #' numerical data through their quartiles. #'@param data a data frame #'@param x character string containing the name of x variable. #'@param y character vector containing one or more variables to plot #'@param combine logical value. Default is FALSE. Used only when y is a vector #' containing multiple variables to plot. If TRUE, create a multi-panel plot by #' combining the plot of y variables. #'@param merge logical or character value. Default is FALSE. Used only when y is #' a vector containing multiple variables to plot. If TRUE, merge multiple y #' variables in the same plotting area. Allowed values include also "asis" #' (TRUE) and "flip". If merge = "flip", then y variables are used as x tick #' labels and the x variable is used as grouping variable. #'@param color outline color. #'@param fill fill color. #'@param palette the color palette to be used for coloring or filling by groups. #' Allowed values include "grey" for grey color palettes; brewer palettes e.g. #' "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and #' scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", #' "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty". #'@param bxp.errorbar logical value. If TRUE, shows error bars of box plots. #'@param bxp.errorbar.width numeric value specifying the width of box plot error #' bars. Default is 0.4. #'@param linetype line types. #'@param size Numeric value (e.g.: size = 1). change the size of points and #' outlines. #'@param width numeric value between 0 and 1 specifying box width. #'@inheritParams ggplot2::geom_boxplot #'@param outlier.shape point shape of outlier. Default is 19. To hide outlier, #' specify \code{outlier.shape = NA}. When jitter is added, then outliers will #' be automatically hidden. #'@inheritParams facet #'@inheritParams ggpar #'@inheritParams ggtext #'@param select character vector specifying which items to display. #'@param remove character vector specifying which items to remove from the plot. #'@param order character vector specifying the order of items. #'@param add character vector for adding another plot element (e.g.: dot plot or #' error bars). Allowed values are one or the combination of: "none", #' "dotplot", "jitter", "boxplot", "point", "mean", "mean_se", "mean_sd", #' "mean_ci", "mean_range", "median", "median_iqr", "median_hilow", #' "median_q1q3", "median_mad", "median_range"; see ?desc_statby for more #' details. #'@param add.params parameters (color, shape, size, fill, linetype) for the #' argument 'add'; e.g.: add.params = list(color = "red"). #'@param error.plot plot type used to visualize error. Allowed values are one of #' c("pointrange", "linerange", "crossbar", "errorbar", "upper_errorbar", #' "lower_errorbar", "upper_pointrange", "lower_pointrange", "upper_linerange", #' "lower_linerange"). Default value is "pointrange" or "errorbar". Used only #' when add != "none" and add contains one "mean_*" or "med_*" where "*" = sd, #' se, .... #'@param font.label a list which can contain the combination of the following #' elements: the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", #' "bold.italic") and the color (e.g.: "red") of labels. For example font.label #' = list(size = 14, face = "bold", color ="red"). To specify only the size and #' the style, use font.label = list(size = 14, face = "plain"). #'@param ggtheme function, ggplot2 theme name. Default value is theme_pubr(). #' Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), #' theme_minimal(), theme_classic(), theme_void(), .... #'@param ... other arguments to be passed to #' \code{\link[ggplot2]{geom_boxplot}}, \code{\link{ggpar}} and #' \code{\link{facet}}. #'@details The plot can be easily customized using the function ggpar(). Read #' ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, #' ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis #' scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette #' = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels #' and position: legend = "right" \item plot orientation : orientation = #' c("vertical", "horizontal", "reverse") } #' #'@section Suggestions for the argument "add": Suggested values are one of #' c("dotplot", "jitter"). #' #'@seealso \code{\link{ggpar}}, \code{\link{ggviolin}}, \code{\link{ggdotplot}} #' and \code{\link{ggstripchart}}. #' @examples #' # Load data #' data("ToothGrowth") #' df <- ToothGrowth #' #' # Basic plot #' # +++++++++++++++++++++++++++ #' # width: change box plots width #' ggboxplot(df, x = "dose", y = "len", width = 0.8) #' #' # Change orientation: horizontal #' ggboxplot(df, "dose", "len", orientation = "horizontal") #' #' # Notched box plot #' ggboxplot(df, x = "dose", y = "len", #' notch = TRUE) #' #' # Add dots #' # ++++++++++++++++++++++++++ #' ggboxplot(df, x = "dose", y = "len", #' add = "dotplot") #' #' # Add jitter points and change the shape by groups #' ggboxplot(df, x = "dose", y = "len", #' add = "jitter", shape = "dose") #' #' #' # Select and order items #' # ++++++++++++++++++++++++++++++ #' #' # Select which items to display: "0.5" and "2" #' ggboxplot(df, "dose", "len", #' select = c("0.5", "2")) #' #' # Change the default order of items #' ggboxplot(df, "dose", "len", #' order = c("2", "1", "0.5")) #' #' #' # Change colors #' # +++++++++++++++++++++++++++ #' # Change outline and fill colors #' ggboxplot(df, "dose", "len", #' color = "black", fill = "gray") #' #' # Change outline colors by groups: dose #' # Use custom color palette #' # Add jitter points and change the shape by groups #' ggboxplot(df, "dose", "len", #' color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"), #' add = "jitter", shape = "dose") #' #' # Change fill color by groups: dose #' ggboxplot(df, "dose", "len", #' fill = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07")) #' #' #' # Box plot with multiple groups #' # +++++++++++++++++++++ #' # fill or color box plot by a second group : "supp" #' ggboxplot(df, "dose", "len", color = "supp", #' palette = c("#00AFBB", "#E7B800")) #' #'@export ggboxplot <- function(data, x, y, combine = FALSE, merge = FALSE, color = "black", fill = "white", palette = NULL, title = NULL, xlab = NULL, ylab = NULL, bxp.errorbar = FALSE, bxp.errorbar.width = 0.4, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, linetype = "solid", size = NULL, width = 0.7, notch = FALSE, outlier.shape = 19, select = NULL, remove = NULL, order = NULL, add = "none", add.params = list(), error.plot = "pointrange", label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, ggtheme = theme_pubr(),...) { # Default options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .opts <- list( combine = combine, merge = merge, color = color, fill = fill, palette = palette, title = title, xlab = xlab, ylab = ylab, bxp.errorbar = bxp.errorbar, bxp.errorbar.width = bxp.errorbar.width, facet.by = facet.by, panel.labs = panel.labs, short.panel.labs = short.panel.labs, linetype = linetype, size = size, width = width, notch = notch, outlier.shape = outlier.shape, select = select , remove = remove, order = order, add = add, add.params = add.params, error.plot = error.plot, label = label, font.label = font.label, label.select = label.select, repel = repel, label.rectangle = label.rectangle, ggtheme = ggtheme, ...) if(!missing(data)) .opts$data <- data if(!missing(x)) .opts$x <- x if(!missing(y)) .opts$y <- y # User options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .user.opts <- as.list(match.call(expand.dots = TRUE)) .user.opts[[1]] <- NULL # Remove the function name # keep only user arguments for(opt.name in names(.opts)){ if(is.null(.user.opts[[opt.name]])) .opts[[opt.name]] <- NULL } .opts$fun <- ggboxplot_core if(missing(ggtheme) & (!is.null(facet.by) | combine)) .opts$ggtheme <- theme_pubr(border = TRUE) p <- do.call(.plotter, .opts) if(.is_list(p) & length(p) == 1) p <- p[[1]] return(p) } ggboxplot_core <- function(data, x, y, color = "black", fill = "white", palette = NULL, linetype = "solid", size = NULL, width = 0.7, notch = FALSE, outlier.shape = 19, title = NULL, xlab = NULL, ylab = NULL, bxp.errorbar = FALSE, bxp.errorbar.width = 0.4, add = "none", add.params = list(), error.plot = "pointrange", ggtheme = theme_pubr(), ...) { if(!is.factor(data[[x]])) data[[x]] <- as.factor(data[[x]]) if("jitter" %in% add) outlier.shape <- NA p <- ggplot(data, create_aes(list(x = x, y = y))) if(bxp.errorbar){ if(fill %in% colnames(data)){ # Important, so that the fill grouping is taken into account in the errorbar p <- p + geom_exec( geomfunc = stat_boxplot, data = data, geom = "errorbar", width = bxp.errorbar.width, color = color, fill = fill, linetype = linetype, position = position_dodge(0.8) ) } else{ p <- p + geom_exec( geomfunc = stat_boxplot, data = data, geom = "errorbar", width = bxp.errorbar.width, color = color, linetype = linetype, position = position_dodge(0.8) ) } } p <- p + geom_exec(geom_boxplot, data = data, color = color, fill = fill, linetype = linetype, size = size, width = width, notch = notch, outlier.shape = outlier.shape, position = position_dodge(0.8), size = size,...) # Add add.params <- .check_add.params(add, add.params, error.plot, data, color, fill, ...) %>% .add_item(p = p, add = add, error.plot = error.plot) p <- do.call(ggadd, add.params) %>% ggpar(palette = palette, ggtheme = ggtheme, title = title, xlab = xlab, ylab = ylab, ...) p } ggpubr/R/rremove.R0000644000176200001440000000530514332553655013561 0ustar liggesusers#'Remove a ggplot Component #' #' @description Remove a specific component from a ggplot. #' #'@param object character string specifying the plot components. Allowed values #' include: \itemize{ #' \item \code{"grid"} for both x and y grids #' \item \code{"x.grid"} for x axis grids #' \item \code{"y.grid"} for y axis grids #' \item \code{"axis"} for both x and y axes #' \item \code{"x.axis"} for x axis #' \item \code{"y.axis"} for y axis #' #' \item \code{"xlab", or "x.title"} #' for x axis label #' \item \code{"ylab", or "y.title"} for y axis label #' \item \code{"xylab", "xy.title" or "axis.title"} for both x and y axis #' labels #' \item \code{"x.text"} for x axis texts (x axis tick labels) #' \item \code{"y.text"} for y axis texts (y axis tick labels) #' \item \code{"xy.text"} or \code{"axis.text"} for both x and y axis texts #' #' \item \code{"ticks"} for both x and y ticks #' \item \code{"x.ticks"} for x ticks #' \item \code{"y.ticks"} for y ticks #' #' \item \code{"legend.title"} for the legend title #' \item \code{"legend"} for the legend #' } #' #'@examples #'# Load data #'data("ToothGrowth") #' #'# Basic plot #'p <- ggboxplot(ToothGrowth, x = "dose", y = "len", #' ggtheme = theme_gray()) #'p #' #'# Remove all grids #' p + rremove("grid") #' #' # Remove only x grids #' p + rremove("x.grid") #'@export rremove <- function(object){ blank <- element_blank() switch(object, grid = theme(panel.grid.minor = blank, panel.grid.major = blank), x.grid = theme(panel.grid.minor.x = blank, panel.grid.major.x = blank), y.grid = theme(panel.grid.minor.y = blank, panel.grid.major.y = blank), axis = theme(axis.line = blank), x.axis = theme(axis.line.x = blank), y.axis = theme(axis.line.y = blank), xlab = theme(axis.title.x = blank), x.title = theme(axis.title.x = blank), ylab = theme(axis.title.y = blank), y.title = theme(axis.title.y = blank), xy.title = theme(axis.title.x = blank, axis.title.y = blank), xylab = theme(axis.title.x = blank, axis.title.y = blank), axis.title = theme(axis.title.x = blank, axis.title.y = blank), x.ticks = theme(axis.ticks.x = blank), y.ticks = theme(axis.ticks.y = blank), ticks = theme(axis.ticks.x = blank, axis.ticks.y = blank), x.text = theme(axis.text.x = blank), y.text = theme(axis.text.y = blank), xy.text = theme(axis.text.x = blank, axis.text.y = blank), axis.text = theme(axis.text.x = blank, axis.text.y = blank), legend.title = theme(legend.title = blank), legend = theme(legend.position = "none"), stop("Don't support ", object) ) } ggpubr/R/ggarrange.R0000644000176200001440000001742214343207173014033 0ustar liggesusers#' @include utilities.R NULL #' Arrange Multiple ggplots #' #' @description Arrange multiple ggplots on the same page. Wrapper around #' \code{\link[cowplot]{plot_grid}()}. Can arrange multiple ggplots over #' multiple pages, compared to the standard #' \code{\link[cowplot]{plot_grid}()}. Can also create a common unique legend #' for multiple plots. #' @inheritParams cowplot::plot_grid #' @param ... list of plots to be arranged into the grid. The plots can be #' either ggplot2 plot objects or arbitrary gtables. #' @param plotlist (optional) list of plots to display. #' @param ncol (optional) number of columns in the plot grid. #' @param nrow (optional) number of rows in the plot grid. #' @param labels (optional) list of labels to be added to the plots. You can #' also set labels="AUTO" to auto-generate upper-case labels or labels="auto" #' to auto-generate lower-case labels. #' @param font.label a list of arguments for customizing labels. Allowed values #' are the combination of the following elements: size (e.g.: 14), face (e.g.: #' "plain", "bold", "italic", "bold.italic"), color (e.g.: "red") and family. #' For example font.label = list(size = 14, face = "bold", color ="red"). #' @param label.x (optional) Single value or vector of x positions for plot #' labels, relative to each subplot. Defaults to 0 for all labels. (Each label #' is placed all the way to the left of each plot.) #' @param label.y (optional) Single value or vector of y positions for plot #' labels, relative to each subplot. Defaults to 1 for all labels. (Each label #' is placed all the way to the top of each plot.) #' @param widths (optional) numerical vector of relative columns widths. For #' example, in a two-column grid, widths = c(2, 1) would make the first column #' twice as wide as the second column. #' @param heights same as \code{widths} but for column heights. #' @param legend character specifying legend position. Allowed values are one of #' c("top", "bottom", "left", "right", "none"). To remove the legend use #' legend = "none". #' @param common.legend logical value. Default is FALSE. If TRUE, a common #' unique legend will be created for arranged plots. #' @param legend.grob a legend grob as returned by the function #' \code{\link{get_legend}()}. If provided, it will be used as the common #' legend. #' @return return an object of class \code{ggarrange}, which is a ggplot or a #' list of ggplot. #' @author Alboukadel Kassambara \email{alboukadel.kassambara@@gmail.com} #' @seealso \code{\link{annotate_figure}()} #' @examples #' data("ToothGrowth") #' df <- ToothGrowth #' df$dose <- as.factor(df$dose) #' #' # Create some plots #' # :::::::::::::::::::::::::::::::::::::::::::::::::: #' # Box plot #' bxp <- ggboxplot(df, x = "dose", y = "len", #' color = "dose", palette = "jco") #' # Dot plot #' dp <- ggdotplot(df, x = "dose", y = "len", #' color = "dose", palette = "jco") #' # Density plot #' dens <- ggdensity(df, x = "len", fill = "dose", palette = "jco") #' #' # Arrange #' # :::::::::::::::::::::::::::::::::::::::::::::::::: #' ggarrange(bxp, dp, dens, ncol = 2, nrow = 2) #' # Use a common legend for multiple plots #' ggarrange(bxp, dp, common.legend = TRUE) #' #' #' @export ggarrange <- function(..., plotlist = NULL, ncol = NULL, nrow = NULL, labels = NULL, label.x = 0, label.y = 1, hjust = -0.5, vjust = 1.5, font.label = list(size = 14, color = "black", face = "bold", family = NULL), align = c("none", "h", "v", "hv"), widths = 1, heights = 1, legend = NULL, common.legend = FALSE, legend.grob = NULL ) { # Open null device to avoid blank page before plot------ # see cowplot:::as_grob.ggplot null_device <- base::getOption("ggpubr.null_device", default = cowplot::pdf_null_device) cur_dev <- grDevices::dev.cur() # Open null device to avoid blank page before plot null_device(width = 6, height = 6) null_dev <- grDevices::dev.cur() on.exit({ grDevices::dev.off(null_dev) if (cur_dev > 1) grDevices::dev.set(cur_dev) }) plots <- c(list(...), plotlist) align <- match.arg(align) nb.plots <- length(plots) page.layout <- .get_layout(ncol, nrow, nb.plots) ncol <- page.layout$ncol nrow <- page.layout$nrow nb.plots.per.page <- .nbplots_per_page(ncol, nrow) if(!is.null(legend.grob)) common.legend <- TRUE if(is.null(legend) & common.legend) legend <- "top" legend <- .check_legend(legend) if(!is.null(legend)) plots <- purrr::map( plots, function(x) {if(!is.null(x)) x + theme(legend.position = legend) else x} ) if(common.legend){ if(is.null(legend.grob)) legend.grob <- get_legend(plots) plots <- purrr::map( plots, function(x) {if(!is.null(x)) x + theme(legend.position = "none") else x} ) } # Split plots over multiple pages if(nb.plots > nb.plots.per.page){ plots <- split(plots, ceiling(seq_along(plots)/nb.plots.per.page)) } # One unique page else plots <- list(plots) # label arguments .lab <- .update_label_pms(font.label, label.x = label.x, label.y = label.y, hjust = hjust, vjust = vjust) res <- purrr::map(plots, .plot_grid, ncol = ncol, nrow = nrow, labels = labels, label_size = .lab$size, label_fontfamily = .lab$family, label_fontface = .lab$face, label_colour = .lab$color, label_x = .lab$label.x, label_y = .lab$label.y, hjust = .lab$hjust, vjust = .lab$vjust, align = align, rel_widths = widths, rel_heights = heights, legend = legend, common.legend.grob = legend.grob ) if(length(res) == 1) res <- res[[1]] class(res) <- c(class(res), "ggarrange") res } .get_layout <- function(ncol, nrow, nb.plots){ if(!is.null(ncol) & !is.null(nrow)){} else if(!is.null(ncol)){ if(ncol == 1) nrow = nb.plots } else if(!is.null(nrow)){ if(nrow == 1) ncol = nb.plots } list(ncol = ncol, nrow = nrow) } # Compute number of plots per page .nbplots_per_page <- function(ncol = NULL, nrow = NULL){ if(!is.null(ncol) & !is.null(nrow)) ncol * nrow else if(!is.null(ncol)) ncol else if(!is.null(nrow)) nrow else Inf } .plot_grid <- function(plotlist, legend = "top", common.legend.grob = NULL, ... ){ res <- cowplot::plot_grid(plotlist = plotlist, ...) if(is.null(common.legend.grob)) return(res) else { leg <- common.legend.grob lheight <- sum(leg$height) lwidth <- sum(leg$width) } arrangeGrob <- gridExtra::arrangeGrob unit.c <- grid::unit.c .unit <- grid::unit(1, "npc") res <- switch(legend, top = arrangeGrob(leg, res, ncol = 1, heights = unit.c(lheight, .unit - lheight)), bottom = arrangeGrob(res, leg, ncol = 1, heights = unit.c(unit(1, "npc") - lheight, lheight)), left = arrangeGrob(leg, res, ncol = 2, widths = unit.c(lwidth, .unit - lwidth)), right = arrangeGrob(res, leg, ncol = 2, widths = unit.c(.unit - lwidth, lwidth)) ) p <- cowplot::ggdraw() + cowplot::draw_grob(grid::grobTree(res)) p } # update label parameters for cowplot::plot_grid() .update_label_pms <- function(font.label, label.x = 0, label.y = 1, hjust = -0.5, vjust = 1.5) { .font <- list(size = 14, color = "black", face = "bold", family = NULL) new.font.names <- names(font.label) for(i in new.font.names) .font[[i]] <- font.label[[i]] pms <- .font list( size = pms$size, family = pms$family, face = pms$face, color = pms$color, label.x = label.x, label.y = label.y, hjust = hjust, vjust = vjust ) } ggpubr/R/npc_to_data_coord.R0000644000176200001440000000131214332576562015537 0ustar liggesusers#' Convert NPC to Data Coordinates #' @description Convert NPC (Normalized Parent Coordinates) into data coordinates. #' @param npc a numeric vector. Each value should be in [0-1] #' @param data.ranges a numeric vector of length 2 containing the data ranges (minimum and the maximum) #' @return a numeric vector representing data coordinates. #' @seealso \code{\link{as_npc}}, \code{\link{get_coord}}. #' @examples #' npc_to_data_coord(npc = c(0.2, 0.95), data.ranges = c(1, 20)) #' as_npc(c("top", "right")) %>% #' npc_to_data_coord(data.ranges = c(1, 20)) #' #' @rdname npc_to_data_coord #' @export npc_to_data_coord <- function(npc, data.ranges){ data.ranges[1] + npc * (data.ranges[2] - data.ranges[1]) } ggpubr/R/ggadjust_pvalue.R0000644000176200001440000000767114343207173015267 0ustar liggesusers#' @include utilities.R geom_pwc.R NULL #'Adjust p-values Displayed on a GGPlot #'@description Adjust p-values produced by \code{\link{geom_pwc}()} on a ggplot. #' This is mainly useful when using facet, where p-values are generally #' computed and adjusted by panel without taking into account the other panels. #' In this case, one might want to adjust after the p-values of all panels together. #'@inheritParams geom_pwc #'@param p a ggplot #'@param layer An integer indicating the statistical layer rank in the ggplot #' (in the order added to the plot). #'@param output character. Possible values are one of \code{c("plot", #' "stat_test")}. Default is "plot". #'@examples #' # Data preparation #' #::::::::::::::::::::::::::::::::::::::: #' df <- ToothGrowth #' df$dose <- as.factor(df$dose) #' # Add a random grouping variable #' df$group <- factor(rep(c("grp1", "grp2"), 30)) #' head(df, 3) #' #' # Boxplot: Two groups by panel #' #::::::::::::::::::::::::::::::::::::::: #' # Create a box plot #' bxp <- ggboxplot( #' df, x = "supp", y = "len", fill = "#00AFBB", #' facet.by = "dose" #' ) #' # Make facet and add p-values #' bxp <- bxp + geom_pwc(method = "t_test") #' bxp #' # Adjust all p-values together after #' ggadjust_pvalue( #' bxp, p.adjust.method = "bonferroni", #' label = "{p.adj.format}{p.adj.signif}", hide.ns = TRUE #' ) #' #' #' # Boxplot: Three groups by panel #' #::::::::::::::::::::::::::::::::::::::: #' # Create a box plot #' bxp <- ggboxplot( #' df, x = "dose", y = "len", fill = "#00AFBB", #' facet.by = "supp" #' ) #' # Make facet and add p-values #' bxp <- bxp + geom_pwc(method = "t_test") #' bxp #' # Adjust all p-values together after #' ggadjust_pvalue( #' bxp, p.adjust.method = "bonferroni", #' label = "{p.adj.format}{p.adj.signif}" #' ) #'@export ggadjust_pvalue <- function(p, layer = NULL, p.adjust.method = "holm", label = "p.adj", hide.ns = NULL, symnum.args = list(), output = c("plot", "stat_test")){ output <- match.arg(output) .build <- ggplot_build(p) .build_data <- .build$data # Pairwise comparison-------------------------- # Find layer containing statistical test data key_columns <- c("group1", "group2", "p") if(is.null(layer)){ for(i in 1:length(.build_data)){ if(all( key_columns %in% colnames(.build_data[[i]]))){ layer <- i break } } } if(is.null(layer)){ stop("Can't find any layer containing statiscal tests") } stat_test <- .build$data[[layer]] sy <- fortify_signif_symbols_encoding(symnum.args) if(all(is.na(stat_test$p))){ warning( "p-values can't be adjusted for the specified stat method.\n", "The result of the method doesn't contain the p column.\n", "Note that, tests such as tukey_hsd or games_howell_test handle p-value adjustement ", "internally; they only return the p.adj.", call. = FALSE ) label <- gsub(pattern = "p.format", replacement = "p.adj.format", label) } else{ padjusted <- stat_test %>% dplyr::select(dplyr::all_of(c("PANEL", "group", "group1", "group2", "p"))) %>% dplyr::distinct(.keep_all = TRUE) %>% rstatix::adjust_pvalue(method = p.adjust.method) # Hide NS if hide.ns not null if(!is.null(hide.ns)) padjusted <- rstatix::remove_ns(padjusted, col = hide.ns) p <- p.adj <- NULL stat_test <- stat_test %>% dplyr::select(-dplyr::one_of(c("p", "p.adj", "p.format", "p.adj.format", "label"))) %>% dplyr::inner_join(padjusted, by = c("PANEL", "group", "group1", "group2")) %>% rstatix::p_format(p, p.adj, new.col = TRUE, accuracy = 1e-4) %>% rstatix::add_significance(p.col = "p", cutpoints = sy$cutpoints, symbols = sy$symbols) %>% rstatix::add_significance(p.col = "p.adj", cutpoints = sy$cutpoints, symbols = sy$symbols) %>% add_stat_label(label = label) .build$data[[layer]] <- stat_test } if(output == "stat_test"){ return(stat_test) } as_ggplot(ggplot_gtable(.build)) } ggpubr/R/stat_regline_equation.R0000644000176200001440000001674014332553655016474 0ustar liggesusers#' @include utilities.R utilities_label.R #' @importFrom dplyr everything #' @importFrom dplyr select NULL #'Add Regression Line Equation and R-Square to a GGPLOT. #'@description Add regression line equation and R^2 to a ggplot. Regression #' model is fitted using the function \code{\link[stats]{lm}}. #'@inheritParams ggplot2::layer #'@param formula a formula object #'@param label.x.npc,label.y.npc can be \code{numeric} or \code{character} #' vector of the same length as the number of groups and/or panels. If too #' short they will be recycled. \itemize{ \item If \code{numeric}, value should #' be between 0 and 1. Coordinates to be used for positioning the label, #' expressed in "normalized parent coordinates". \item If \code{character}, #' allowed values include: i) one of c('right', 'left', 'center', 'centre', #' 'middle') for x-axis; ii) and one of c( 'bottom', 'top', 'center', 'centre', #' 'middle') for y-axis.} #' #' If too short they will be recycled. #'@param label.x,label.y \code{numeric} Coordinates (in data units) to be used #' for absolute positioning of the label. If too short they will be recycled. #'@param output.type character One of "expression", "latex" or "text". #'@param ... other arguments to pass to \code{\link[ggplot2]{geom_text}} or #' \code{\link[ggplot2:geom_text]{geom_label}}. #'@param na.rm If FALSE (the default), removes missing values with a warning. If #' TRUE silently removes missing values. #'@seealso \code{\link{ggscatter}} #'@references the source code of the function \code{stat_regline_equation()} is #' inspired from the code of the function \code{stat_poly_eq()} (in ggpmisc #' package). #' #' @section Computed variables: #' \describe{ \item{x}{x position for left edge} #' \item{y}{y position near upper edge} #' \item{eq.label}{equation for the #' fitted polynomial as a character string to be parsed} #' \item{rr.label}{\eqn{R^2} of the fitted model as a character string to be parsed} #' \item{adj.rr.label}{Adjusted \eqn{R^2} of the fitted model as a character string #' to be parsed} #' \item{AIC.label}{AIC for the fitted model.} #' \item{BIC.label}{BIC for the fitted model.} #' \item{hjust}{Set to zero to override the default of the "text" geom.}} #' @examples #' #' # Simple scatter plot with correlation coefficient and #' # regression line #' #:::::::::::::::::::::::::::::::::::::::::::::::::::: #' ggscatter(mtcars, x = "wt", y = "mpg", add = "reg.line") + #' stat_cor(label.x = 3, label.y = 34) + #' stat_regline_equation(label.x = 3, label.y = 32) #' #' #' # Groupped scatter plot #' #:::::::::::::::::::::::::::::::::::::::::::::::::::: #' ggscatter( #' iris, x = "Sepal.Length", y = "Sepal.Width", #' color = "Species", palette = "jco", #' add = "reg.line" #' ) + #' facet_wrap(~Species) + #' stat_cor(label.y = 4.4) + #' stat_regline_equation(label.y = 4.2) #' #' # Polynomial equation #' #:::::::::::::::::::::::::::::::::::::::::::::::::::: #' #' # Demo data #' set.seed(4321) #' x <- 1:100 #' y <- (x + x^2 + x^3) + rnorm(length(x), mean = 0, sd = mean(x^3) / 4) #' my.data <- data.frame(x, y, group = c("A", "B"), #' y2 = y * c(0.5,2), block = c("a", "a", "b", "b")) #' #' # Fit polynomial regression line and add labels #' formula <- y ~ poly(x, 3, raw = TRUE) #' p <- ggplot(my.data, aes(x, y2, color = group)) + #' geom_point() + #' stat_smooth(aes(fill = group, color = group), method = "lm", formula = formula) + #' stat_regline_equation( #' aes(label = paste(..eq.label.., ..adj.rr.label.., sep = "~~~~")), #' formula = formula #' ) + #' theme_bw() #' ggpar(p, palette = "jco") #' #'@export stat_regline_equation <- function( mapping = NULL, data = NULL, formula = y~x, label.x.npc = "left", label.y.npc = "top", label.x = NULL, label.y = NULL, output.type = "expression", geom = "text", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ... ) { parse <- ifelse(output.type == "expression", TRUE, FALSE) layer( stat = StatReglineEquation, data = data, mapping = mapping, geom = geom, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(formula = formula, label.x.npc = label.x.npc , label.y.npc = label.y.npc, label.x = label.x, label.y = label.y, output.type = output.type, parse = parse, na.rm = na.rm, ...) ) } StatReglineEquation<- ggproto("StatReglineEquation", Stat, required_aes = c("x", "y"), default_aes = aes(label = ..eq.label.., hjust = ..hjust.., vjust = ..vjust..), compute_group = function(data, scales, formula, label.x.npc, label.y.npc, label.x, label.y, output.type) { force(data) if (length(unique(data$x)) < 2) { return(data.frame()) # Not enough data to perform test } .test <- .stat_lm(formula, data, output.type = output.type) # Returns a data frame with label: x, y, hjust, vjust .label.pms <- .label_params(data = data, scales = scales, label.x.npc = label.x.npc, label.y.npc = label.y.npc, label.x = label.x, label.y = label.y ) %>% mutate(hjust = 0) cbind(.test, .label.pms) } ) # Compute regression line equation .stat_lm <- function(formula, data, output.type = "expression"){ res.lm <- stats::lm(formula, data) coefs <- stats::coef(res.lm) formula.rhs.chr <- as.character(formula)[3] if (grepl("-1", formula.rhs.chr) || grepl("- 1", formula.rhs.chr)) { coefs <- c(0, coefs) } rr <- summary(res.lm)$r.squared %>% signif(2) adj.rr <- summary(res.lm)$adj.r.squared %>% signif(2) AIC <- stats::AIC(res.lm) %>% signif(2) BIC <- stats::BIC(res.lm) %>% signif(2) # Build model equation eq.char <- as.character(signif(polynom::as.polynomial(coefs), 2)) eq.char <- gsub("e([+-]?[0-9]*)", "%*%10^\\1", eq.char) if (output.type %in% c("latex", "tex", "tikz")) { eq.char <- gsub("*", " ", eq.char, fixed = TRUE) } # Add y if (output.type == "expression") { lhs <- "italic(y)~`=`~" } else if (output.type %in% c("latex", "tex", "tikz", "text")) { lhs <- "y = " } eq.char <- paste(lhs, eq.char, sep = "") # Build data frame with the output if (output.type == "expression") { eq.x.rhs = "~italic(x)" } else { eq.x.rhs = " x" } if (output.type == "expression") { z <- data.frame(eq.label = gsub("x", eq.x.rhs, eq.char, fixed = TRUE), rr.label = paste("italic(R)^2", rr, sep = "~`=`~"), adj.rr.label = paste("italic(R)[adj]^2", adj.rr, sep = "~`=`~"), AIC.label = paste("AIC", AIC, sep = "~`=`~"), BIC.label = paste("BIC", BIC, sep = "~`=`~")) } else if (output.type %in% c("latex", "tex", "text")) { z <- data.frame(eq.label = gsub("x", eq.x.rhs, eq.char, fixed = TRUE), rr.label = paste("R^2", rr, sep = " = "), adj.rr.label = paste("R_{adj}^2",adj.rr, sep = " = "), AIC.label = paste("AIC", AIC, sep = " = "), BIC.label = paste("BIC", BIC, sep = " = ")) } z <- z %>% mutate(rr = rr, adj.rr = adj.rr, AIC = AIC, BIC = BIC) %>% dplyr::select(rr, adj.rr, AIC, BIC, everything()) z } ggpubr/R/geom_pwc.R0000644000176200001440000010374514343207173013702 0ustar liggesusers#' @include utilities.R utils_stat_test_label.R utils-aes.R NULL #'Add Pairwise Comparisons P-values to a GGPlot #'@description add pairwise comparison p-values to a ggplot such as box plots, #' dot plots and stripcharts. #'@param method a character string indicating which method to be used for #' pairwise comparisons. Default is \code{"wilcox_test"}. Allowed methods #' include pairwise comparisons methods implemented in the \code{rstatix} R #' package. These methods are: \code{"wilcox_test", "t_test", "sign_test", #' "dunn_test", "emmeans_test", "tukey_hsd", "games_howell_test"}. #'@param method.args a list of additional arguments used for the test method. #' For example one might use \code{method.args = list(alternative = "greater")} #' for wilcoxon test. #'@param ref.group a character string or a numeric value specifying the #' reference group. If specified, for a given grouping variable, each of the #' group levels will be compared to the reference group (i.e. control group). #' #' \code{ref.group} can be also \code{"all"}. In this case, each of the #' grouping variable levels is compared to all (i.e. basemean). #' #' Allowed values can be: \itemize{ \item \strong{numeric value}: specifying #' the rank of the reference group. For example, use \code{ref.group = 1} when #' the first group is the reference; use \code{ref.group = 2} when the second #' group is the reference, and so on. This works for all situations, including #' i) when comparisons are performed between x-axis groups and ii) when #' comparisons are performed between legend groups. \item \strong{character #' value}: For example, you can use \code{ref.group = "ctrl"} instead of using #' the numeric rank value of the "ctrl" group. \item \strong{"all"}: In this #' case, each of the grouping variable levels is compared to all (i.e. #' basemean). } #'@param label character string specifying label. Can be: \itemize{ \item the #' column containing the label (e.g.: \code{label = "p"} or \code{label = #' "p.adj"}), where \code{p} is the p-value. Other possible values are #' \code{"p.signif", "p.adj.signif", "p.format", "p.adj.format"}. \item an #' expression that can be formatted by the \code{\link[glue]{glue}()} package. #' For example, when specifying \code{label = "Wilcoxon, p = \{p\}"}, the #' expression \{p\} will be replaced by its value. \item a combination of #' plotmath expressions and glue expressions. You may want some of the #' statistical parameter in italic; for example:\code{label = "Wilcoxon, #' italic(p)= {p}"}}. #'@param y.position numeric vector with the y positions of the brackets #'@param group.by (optional) character vector specifying the grouping variable; #' it should be used only for grouped plots. Possible values are : \itemize{ #' \item \code{"x.var"}: Group by the x-axis variable and perform the test #' between legend groups. In other words, the p-value is compute between legend #' groups at each x position \item \code{"legend.var"}: Group by the legend #' variable and perform the test between x-axis groups. In other words, the #' test is performed between the x-groups for each legend level. } #'@param dodge dodge width for grouped ggplot/test. Default is 0.8. It's used to #' dodge the brackets position when \code{group.by = "legend.var"}. #'@param stack logical value. Default is FALSE; should be set to TRUE for #' stacked bar plots or line plots. If TRUE, then the brackets are #' automatically removed and the \code{dodge} value is set to zero. #'@param bracket.nudge.y Vertical adjustment to nudge brackets by (in fraction #' of the total height). Useful to move up or move down the bracket. If #' positive value, brackets will be moved up; if negative value, brackets are #' moved down. #'@param bracket.shorten a small numeric value in [0-1] for shortening the width #' of bracket. #'@param bracket.group.by (optional); a variable name for grouping brackets #' before adding step.increase. Useful for grouped plots. Possible values #' include \code{"x.var"} and \code{"legend.var"}. #'@param step.increase numeric vector with the increase in fraction of total #' height for every additional comparison to minimize overlap. #'@param tip.length numeric vector with the fraction of total height that the #' bar goes down to indicate the precise column/ #'@param size change the width of the lines of the bracket #'@param label.size change the size of the label text #'@param family change the font used for the text #'@param vjust move the text up or down relative to the bracket. #'@param hjust move the text left or right relative to the bracket. #'@param p.adjust.method method for adjusting p values (see #' \code{\link[stats]{p.adjust}}). Has impact only in a situation, where #' multiple pairwise tests are performed; or when there are multiple grouping #' variables. Ignored when the specified method is \code{"tukey_hsd"} or #' \code{"games_howell_test"} because they come with internal p adjustment #' method. Allowed values include "holm", "hochberg", "hommel", "bonferroni", #' "BH", "BY", "fdr", "none". If you don't want to adjust the p value (not #' recommended), use p.adjust.method = "none". #'@param p.adjust.by possible value is one of \code{c("group", "panel")}. #' Default is \code{"group"}: for a grouped data, if pairwise test is #' performed, then the p-values are adjusted for each group level #' independently. P-values are adjusted by panel when \code{p.adjust.by = #' "panel"}. #'@param symnum.args a list of arguments to pass to the function #' \code{\link[stats]{symnum}} for symbolic number coding of p-values. For #' example, \code{symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, #' 0.05, Inf), symbols = c("****", "***", "**", "*", "ns"))}. #' #' In other words, we use the following convention for symbols indicating #' statistical significance: \itemize{ \item \code{ns}: p > 0.05 \item #' \code{*}: p <= 0.05 \item \code{**}: p <= 0.01 \item \code{***}: p <= 0.001 #' \item \code{****}: p <= 0.0001 } #'@param hide.ns can be logical value (\code{TRUE} or \code{FALSE}) or a character vector (\code{"p.adj"} or \code{"p"}). #'@param remove.bracket logical, if \code{TRUE}, brackets are removed from the #' plot. \itemize{ \item Case when logical value. If TRUE, hide ns symbol when #' displaying significance levels. Filter is done by checking the column #' \code{p.adj.signif}, \code{p.signif}, \code{p.adj} and \code{p}. \item Case #' when character value. Possible values are "p" or "p.adj", for filtering out #' non significant. } #'@param na.rm If \code{FALSE} (the default), removes missing values with a #' warning. If \code{TRUE} silently removes missing values. #'@param parse logical for parsing plotmath expression. #'@param ... other arguments passed on to \code{\link{layer}}. These are often #' aesthetics, used to set an aesthetic to a fixed value, like \code{color = #' "red"} or \code{size = 3}. They may also be parameters to the paired #' geom/stat. #' @details #' \bold{Notes on adjusted p-values and facet}. When using the ggplot facet functions, the p-values are computed and adjusted by panel, without taking into account the other panels. This is by design in ggplot2. #' #' In this case, when there is only one computed p-value by panel, then using `label = "p"` or `label = "p.adj"` will give the same results using `geom_pwc()`. Again, p-value computation and adjustment in a given facet panel is done independently to the other panels. #' #' One might want to adjust the p-values of all the facet panels together. There are two solutions for that: #' #' \itemize{ #' \item Using \code{\link{ggadjust_pvalue}(p)} after creating the plot \code{p} #' \item or adding the adjusted p-value manually using \code{\link{stat_pvalue_manual}()}. Read more at: #' \itemize{ #' \item \href{https://www.datanovia.com/en/blog/how-to-add-p-values-to-ggplot-facets/}{How to Add P-values to GGPLOT Facets} #' \item \href{https://www.datanovia.com/en/blog/add-p-values-to-ggplot-facets-with-different-scales/}{Add P-values to GGPLOT Facets with Different Scales} #' } #' } #'@inheritParams ggplot2::layer #' @examples #' df <- ToothGrowth #' df$dose <- factor(df$dose) #' #'@rdname geom_pwc #' @seealso \code{\link{ggadjust_pvalue}} #'@examples #' # Data preparation #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' # Transform `dose` into factor variable #' df <- ToothGrowth #' df$dose <- as.factor(df$dose) #' # Add a random grouping variable #' df$group <- factor(rep(c("grp1", "grp2"), 30)) #' head(df, 3) #' #' #' # Two groups by x position #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' #' # Create a box plot #' # Add 10% spaces between the p-value labels and the plot border #' bxp <- ggboxplot( #' df, x = "dose", y = "len", #' color = "supp", palette = c("#00AFBB", "#E7B800") #' ) + #' scale_y_continuous(expand = expansion(mult = c(0.05, 0.10))) #' #' #' # Add p-values onto the box plots #' # label can be "p.format" or "p.adj.format" #' bxp + geom_pwc( #' aes(group = supp), tip.length = 0, #' method = "t_test", label = "p.format" #' ) #' #' # Show adjusted p-values and significance levels #' # Hide ns (non-significant) #' bxp + geom_pwc( #' aes(group = supp), tip.length = 0, #' method = "t_test", label = "{p.adj.format}{p.adj.signif}", #' p.adjust.method = "bonferroni", p.adjust.by = "panel", #' hide.ns = TRUE #' ) #' #' # Complex cases #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' # 1. Add p-values of OJ vs VC at each dose group #' bxp.complex <- bxp + #' geom_pwc( #' aes(group = supp), tip.length = 0, #' method = "t_test", label = "p.adj.format", #' p.adjust.method = "bonferroni", p.adjust.by = "panel" #' ) #' # 2. Add pairwise comparisons between dose levels #' # Nudge up the brackets by 20% of the total height #' bxp.complex <- bxp.complex + #' geom_pwc( #' method = "t_test", label = "p.adj.format", #' p.adjust.method = "bonferroni", #' bracket.nudge.y = 0.2 #' ) #' # 3. Display the plot #' bxp.complex #' #' #' # Three groups by x position #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' #' # Simple plots #' #_____________________________________ #' #' # Box plots with p-values #' bxp <- ggboxplot( #' df, x = "supp", y = "len", fill = "dose", #' palette = "npg" #' ) #' bxp + #' geom_pwc( #' aes(group = dose), tip.length = 0, #' method = "t_test", label = "p.adj.format", #' bracket.nudge.y = -0.08 #' ) + #' scale_y_continuous(expand = expansion(mult = c(0, 0.1))) #' #' # Bar plots with p-values #' bp <- ggbarplot( #' df, x = "supp", y = "len", fill = "dose", #' palette = "npg", add = "mean_sd", #' position = position_dodge(0.8) #' ) #' bp + #' geom_pwc( #' aes(group = dose), tip.length = 0, #' method = "t_test", label = "p.adj.format", #' bracket.nudge.y = -0.08 #' ) + #' scale_y_continuous(expand = expansion(mult = c(0, 0.1))) #' #'@export stat_pwc <- function(mapping = NULL, data = NULL, method = "wilcox_test", method.args = list(), ref.group = NULL, label = "p.format", y.position = NULL, group.by = NULL, dodge = 0.8, bracket.nudge.y = 0.05, bracket.shorten = 0, bracket.group.by = c("x.var", "legend.var"), step.increase = 0.12, tip.length = 0.03, size = 0.3, label.size = 3.88, family="", vjust = 0, hjust = 0.5, p.adjust.method = "holm", p.adjust.by = c("group", "panel"), symnum.args = list(), hide.ns = FALSE, remove.bracket = FALSE, position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, parse = FALSE, ...) { p.adjust.by <- match.arg(p.adjust.by) if(missing(parse) & is_plotmath_expression(label)){ parse <- TRUE } if(is.null(group.by)) group.by <- "x.var" bracket.group.by <- match.arg(bracket.group.by) if(!is.null(ref.group)){ if(ref.group %in% c("all", ".all.")){ if(missing(step.increase)) step.increase <- 0 remove.bracket <- TRUE } } # Keep legend variable in memory, we'll be useful to guess ref.group id # related question: https://stackoverflow.com/questions/63640543/how-to-access-to-a-legend-group-id-inside-a-ggplot2-extension legend.var <- aes_get_group(mapping) if(!is.null(legend.var)) mapping$legend.var <- as.name(legend.var) ggplot2::layer( stat = StatPwc, data = data, mapping = mapping, geom = "pwc", position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list( method = method, method.args = method.args, ref.group = ref.group, stat.label = label, y.position = y.position, group.by = group.by, dodge = dodge, bracket.nudge.y = bracket.nudge.y, bracket.shorten = bracket.shorten, bracket.group.by = bracket.group.by, step.increase = step.increase, tip.length = tip.length, size=size, label.size=label.size, remove.bracket = remove.bracket, family=family, vjust=vjust, hjust = hjust, na.rm = na.rm, p.adjust.method = p.adjust.method, p.adjust.by = p.adjust.by, symnum.args = fortify_signif_symbols_encoding(symnum.args), hide.ns = hide.ns, parse = parse, ...) ) } StatPwc <- ggplot2::ggproto("StatPwc", ggplot2::Stat, required_aes = c("x", "y", "group"), setup_params = function(data, params) { if(length(params$tip.length) == 1) params$tip.length <- rep(params$tip.length, max(length(params$xmin), 1) * 2) if(length(params$tip.length) == length(params$xmin)) params$tip.length <- rep(params$tip.length, each=2) # Statistical test methods functions and arguments method <- params$method method.args <- params$method.args if(is.null(method.args$p.adjust.method)){ method.args$p.adjust.method <- params$p.adjust.method } pwc_func <- get_pwc_stat_function(method, method.args) params$method <- pwc_func$method params$method.args <- pwc_func$method.args return(params) }, compute_panel = function(self, data, scales, method, method.args, ref.group, tip.length, stat.label, y.position, step.increase, bracket.nudge.y, bracket.shorten, bracket.group.by, p.adjust.method, p.adjust.by, symnum.args, hide.ns, group.by, dodge, remove.bracket) { # Compute the statistical tests df <- data %>% mutate(x = as.factor(.data$x)) is.grouped.plots <- contains_multiple_grouping_vars(df) formula <- y ~ x grouping.var <- NULL if(is.grouped.plots){ if(group.by == "legend.var"){ grouping.var <- "group" formula <- y ~ x } else { grouping.var <- "x" formula <- y ~ group } df <- df %>% rstatix::df_group_by(vars = grouping.var) } is.comparisons.between.legend.grps <- is.grouped.plots & group.by == "x.var" # Comparison against reference group ref.group.id <- get_ref_group_id( scales = scales, data = df, ref.group = ref.group, is.comparisons.between.legend.grps = is.comparisons.between.legend.grps ) if(!is.null(ref.group.id)) { method.args <- method.args %>% .add_item(ref.group = ref.group.id) } method.args <- method.args %>% .add_item(data = df, formula = formula) stat.test <- do.call(method, method.args) # Add method name method.name <- rstatix::get_description(stat.test) if(method.name == "") { stat.label <- gsub(pattern = "\\{method\\},\\s?", replacement = "", stat.label) } stat.test$method <- method.name # P-value adjustment, formatting and significance if(!("p.adj" %in% colnames(stat.test))){ # Case of one comparison of two groups stat.test <- stat.test %>% rstatix::adjust_pvalue(method = p.adjust.method) } # Adjust all the p-values in a given panel # no matter the grouping if(p.adjust.by == "panel"){ if("p" %in% colnames(stat.test)){ stat.test <- stat.test %>% rstatix::adjust_pvalue(method = p.adjust.method) } else{ warning( "p-values can't be adjusted by panel for the specified stat method.\n", "The result of the method doesn't contain the p column.\n", "Note that, tests such as tukey_hsd or games_howell_test handle p-value adjustement ", "internally; they only return the p.adj.", call. = FALSE ) } } if(!("p" %in% colnames(stat.test))){ # Case when method is tukey or games-howel, etc # doesn't return p but p.adj stat.test <- stat.test %>% tibble::add_column(p = NA, .before = "p.adj") stat.label <- gsub(pattern = "p.format", replacement = "p.adj.format", stat.label) } sy <- symnum.args stat.test <- stat.test %>% rstatix::add_x_position(x = "x", group = "group", dodge = dodge) %>% rstatix::add_significance(p.col = "p", cutpoints = sy$cutpoints, symbols = sy$symbols) %>% rstatix::add_significance(p.col = "p.adj", cutpoints = sy$cutpoints, symbols = sy$symbols) %>% rstatix::p_format(p, p.adj, new.col = TRUE, accuracy = 1e-4) %>% add_stat_n() %>% keep_only_tbl_df_classes() %>% add_stat_label(label = stat.label) if(!is.null(ref.group)){ if(!(ref.group %in% c(".all.", "all"))){ # when comparisons is done against reference group if(remove.bracket) stat.test$xmin <- stat.test$xmax } } # Hide NS if hide.ns not null stat.test <- rstatix::remove_ns(stat.test, col = hide.ns) # Grouped bracket colors # When data is grouped by legend variable and # stat test is computed between x variable groups # labels are stacked on y-axis. labels group ids are group (legend var) if(is.grouped.plots){ if(grouping.var == "group" & "colour" %in% colnames(data)){ color.data <- data %>% select(.data$group, .data$colour) %>% distinct(.data$group, .keep_all = TRUE) stat.test <- stat.test %>% dplyr::left_join(color.data, by = "group") } } # Bracket groups, used for spacing vertically brackets bracket.group <- 1 if(nrow(stat.test) > 1) bracket.group <- 1:nrow(stat.test) if(is.grouped.plots){ if(grouping.var == "x"){ nb.comparisons.by.group <- stat.test %>% rstatix::df_group_by(vars = grouping.var) %>% dplyr::summarise(n = dplyr::n()) bracket.group <- unlist(purrr::map(nb.comparisons.by.group$n, seq)) } else if(grouping.var == "group" & bracket.group.by == "legend.var"){ stat.test <- stat.test %>% dplyr::arrange(.data$group1, .data$group2) } } # Parameters for customizing brackets group <- 1 if(nrow(stat.test) > 1) group <- 1:nrow(stat.test) stat.test <- stat.test %>% mutate( group = group, bracket.group = bracket.group, step.increase = step.increase, bracket.nudge.y = bracket.nudge.y, bracket.shorten = bracket.shorten ) # Bracket x positions bracket.shorten <- stat.test$bracket.shorten/2 xmin <- as.numeric(stat.test$xmin) + bracket.shorten xmax <- as.numeric(stat.test$xmax) - bracket.shorten # Bracket y positions yrange <- scales$y$range$range y.scale.range <- yrange[2] - yrange[1] bracket.nudge.y <- stat.test$bracket.nudge.y step.increase <- stat.test$step.increase if(is.null(y.position)){ y.position <- yrange[2] + y.scale.range * bracket.nudge.y + y.scale.range * step.increase * (bracket.group-1) } else if(length(y.position) == 1) { y.position <- y.position + y.scale.range * bracket.nudge.y + y.scale.range * step.increase * (bracket.group-1) } else if(length(y.position) >= length(stat.test$group)){ y.position <- y.position[stat.test$group] } if("tip.length" %in% colnames(stat.test)){ tip.length <- rep(stat.test$tip.length, each=2) } # Preparing bracket data stat.test <- dplyr::bind_rows(stat.test, stat.test, stat.test) stat.test$x <- c(xmin, xmin, xmax) stat.test$xend = c(xmin, xmax, xmax) stat.test$y <- c(y.position - y.scale.range*tip.length[seq_len(length(tip.length))%% 2 == 1], y.position, y.position) stat.test$yend <- c(y.position, y.position, y.position-y.scale.range*tip.length[seq_len(length(tip.length))%% 2 == 0]) stat.test %>% select(-step.increase, -bracket.nudge.y, -bracket.shorten) } ) #' @rdname geom_pwc #' @export geom_pwc <- function(mapping = NULL, data = NULL, stat = "pwc", method = "wilcox_test", method.args = list(), ref.group = NULL, label = "p.format", y.position = NULL, group.by = NULL, dodge = 0.8, stack = FALSE, step.increase = 0.12, tip.length = 0.03, bracket.nudge.y = 0.05, bracket.shorten = 0, bracket.group.by = c("x.var", "legend.var"), size = 0.3, label.size = 3.88, family="", vjust = 0, hjust = 0.5, p.adjust.method = "holm", p.adjust.by = c("group", "panel"), symnum.args = list(), hide.ns = FALSE, remove.bracket = FALSE, position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, parse = FALSE, ...) { p.adjust.by <- match.arg(p.adjust.by) if(missing(parse) & is_plotmath_expression(label)){ parse <- TRUE } bracket.group.by <- match.arg(bracket.group.by) if(is.null(group.by)) group.by <- "x.var" if(stack){ # for stacked bar plots/line plots dodge <- 0 if (group.by == "x.var") remove.bracket <- TRUE } if(!is.null(ref.group)){ if(ref.group %in% c("all", ".all.")){ if(missing(step.increase)) step.increase <- 0 remove.bracket <- TRUE } } # Keep legend variable in memory, we'll be useful to guess ref.group id # related question: https://stackoverflow.com/questions/63640543/how-to-access-to-a-legend-group-id-inside-a-ggplot2-extension legend.var <- aes_get_group(mapping) if(!is.null(legend.var)) mapping$legend.var <- as.name(legend.var) ggplot2::layer( stat = stat, geom = GeomPwc, mapping = mapping, data = data, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list( method = method, method.args = method.args, ref.group = ref.group, stat.label = label, y.position = y.position, group.by = group.by, dodge = dodge, bracket.nudge.y = bracket.nudge.y, bracket.shorten = bracket.shorten, bracket.group.by = bracket.group.by, step.increase = step.increase, tip.length = tip.length, size = size, label.size = label.size, family = family, na.rm = na.rm, hjust = hjust, vjust = vjust, p.adjust.method = p.adjust.method, p.adjust.by = p.adjust.by, symnum.args = fortify_signif_symbols_encoding(symnum.args), hide.ns = hide.ns, remove.bracket = remove.bracket, parse = parse, ... ) ) } GeomPwc <- ggplot2::ggproto("GeomPwc", ggplot2::Geom, required_aes = c("x", "xend", "y", "yend", "label"), default_aes = ggplot2::aes( shape = 19, colour = "black", label.size = 3.88, angle = NA, hjust = 0.5, vjust = 0, alpha = NA, family = "", fontface = 1, lineheight = 1.2, linetype=1, size = 0.3, legend.var = NA ), # draw_key = function(...){grid::nullGrob()}, # for legend: draw_key = draw_key_path, draw_panel = function(data, panel_params, coord, parse = FALSE, remove.bracket = FALSE) { coords <- coord$transform(data, panel_params) coord.flip <- inherits(coord, "CoordFlip") if(remove.bracket){ text_grob <- get_text_grob(data, coords, coord.flip = coord.flip, parse = parse) return(text_grob) } grid::gList( get_text_grob(data, coords, coord.flip = coord.flip, parse = parse), grid::segmentsGrob( coords$x, coords$y, default.units = "native", coords$xend, coords$yend, gp = grid::gpar( col = scales::alpha(coords$colour, coords$alpha), lty = coords$linetype, lwd = coords$size * ggplot2::.pt ) ) ) } ) # Text grob ------------------------------ get_text_grob <- function(data, coords, coord.flip = FALSE, parse = FALSE){ lab <- as.character(data$label) if(parse) lab <- parse_as_expression(lab) label_coords <- get_pwc_label_coords(coords, coord.flip = coord.flip ) grid::textGrob( lab, x = label_coords$x, y = label_coords$y, default.units = "native", hjust = coords$hjust, vjust = coords$vjust, rot = label_coords$angle, gp = grid::gpar( col = scales::alpha(coords$colour, coords$alpha), fontsize = coords$label.size * ggplot2::.pt, fontfamily = coords$family, fontface = coords$fontface, lineheight = coords$lineheight ) ) } # Source: https://github.com/tidyverse/ggplot2/issues/2864 parse_as_expression <- function(text) { stopifnot(is.character(text)) out <- vector("expression", length(text)) for (i in seq_along(text)) { expr <- parse(text = text[[i]]) out[[i]] <- if (length(expr) == 0) NA else expr[[1]] } out } # Compute the coordinates of pairwise comparison labels get_pwc_label_coords <- function(coords, coord.flip = FALSE){ get_label_x <- function(coords){mean(c(coords$x[1], utils::tail(coords$xend, n=1)))} get_label_y <- function(coords){max(c(coords$y, coords$yend))+0.01} get_label_angle <- function(coords, coord.flip = FALSE){ label.angle <- coords$angle[1] if(coord.flip & is.na(label.angle)) label.angle <- -90 if(is.na(label.angle)) label.angle <- 0 label.angle[1] } if(coord.flip){ get_label_x <- function(coords){max(c(coords$x, coords$xend))+0.01} get_label_y <- function(coords){mean(c(coords$y[1], utils::tail(coords$yend, n=1)))} } if(!("group" %in% colnames(coords))){ coords$group <- 1 } data <- NULL coords %>% rstatix::df_nest_by(vars = c("group", "bracket.group")) %>% dplyr::transmute( x = unlist(map(data, get_label_x)), y = unlist(map(data, get_label_y)), angle = unlist(map(data, get_label_angle, coord.flip = coord.flip)) ) } # Statistical tests -------------------------------------------------- # Get the pairwise comparison stat function # returns a list(method, method.args) get_pwc_stat_function <- function(method, method.args){ if(is.character(method)){ # replace t.test by t_test, etc method <- gsub(pattern = ".", replacement = "_", method, fixed = TRUE) if(method == "wilcoxon") method <- "wilcox_test" else if(method == "emmeans") method <- "emmeans_test" else if(method == "games_howell") method <- "games_howell_test" allowed.methods <- c( "wilcox_test", "wilcoxon", "t_test", "sign_test", "dunn_test", "emmeans_test", "emmeans", "tukey_hsd", "games_howell_test" ) if(!(method %in% allowed.methods)){ stop( "Unknown stat method: '", method, "'. ", "Allowed method names are: ", paste(allowed.methods, collapse = ","), call. = FALSE ) } # Ignoring p.adjust.method for tukey_hsd and games_howell # because they come with internal adjustment method if(method %in% c("tukey_hsd", "games_howell_test")) method.args$p.adjust.method <- NULL stat_func <- switch( method, wilcox_test = rstatix::wilcox_test, t_test = rstatix::t_test, sign_test = rstatix::sign_test, dunn_test = rstatix::dunn_test, emmeans_test = rstatix::emmeans_test, tukey_hsd = function(data, formula, ...){rstatix::tukey_hsd(data, formula, ...)}, games_howell_test = rstatix::games_howell_test, games_howell = rstatix::games_howell_test ) } else if(is.function(method)){ stat_func <- method } else{ stop("The argument method is required.") } list( method = stat_func, method.args = method.args ) } # Get ref group rank id for comparison against reference ------------------------------------- # param scales: ggproto scales # param data: ggproto data # param ref.group: group name # Returns 1 (for the first group), 2 for the second group get_ref_group_id <- function(scales, data, ref.group = NULL, is.comparisons.between.legend.grps = FALSE){ if(is.null(ref.group)) return(NULL) else if(ref.group %in% c(".all.", "all")) return(ref.group) # Grouped plots if(is.comparisons.between.legend.grps){ if(is.character(ref.group)){ if("legend.var" %in% colnames(data)){ ref.group <- get_group_id(data$legend.var, ref.group) if(is.na(ref.group)) stop("The ref.group ('", ref.group, "') doesn't exist.", call. = FALSE) } else{ stop( "ref.group should be a numeric value indicating the rank of the ", "legend group to be used as the reference. \n", "For example, use ref.group = 1 when the first group is the reference; \n", "use ref.group = 2 when the second group is the reference and so on.", call. = FALSE ) } } } # Basic plot: comparison between x-axis groups else if(is.character(ref.group)){ ref.group <- scales$x$map(ref.group) %>% as.character() } as.character(ref.group) } # x a factor # group a level get_group_id <- function(x, group){ ids <- x %>% as.factor() %>% as.numeric() names(ids) <- x unname(ids[group]) %>% unique() } ggpubr/R/ggpubr_args.R0000644000176200001440000000635614332553655014413 0ustar liggesusers#' ggpubr General Arguments Description #' #'@param data a data frame #'@param x character string containing the name of x variable. #'@param y character vector containing one or more variables to plot #'@param combine logical value. Default is FALSE. Used only when y is a vector #' containing multiple variables to plot. If TRUE, create a multi-panel plot by #' combining the plot of y variables. #'@param merge logical or character value. Default is FALSE. Used only when y is #' a vector containing multiple variables to plot. If TRUE, merge multiple y #' variables in the same plotting area. Allowed values include also "asis" #' (TRUE) and "flip". If merge = "flip", then y variables are used as x tick #' labels and the x variable is used as grouping variable. #'@param color outline color. #'@param fill fill color. #'@param palette the color palette to be used for coloring or filling by groups. #' Allowed values include "grey" for grey color palettes; brewer palettes e.g. #' "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and #' scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", #' "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty". #'@param linetype line types. #'@param size Numeric value (e.g.: size = 1). change the size of points and #' outlines. #'@param select character vector specifying which items to display. #'@param remove character vector specifying which items to remove from the plot. #'@param order character vector specifying the order of items. #'@param add character vector for adding another plot element (e.g.: dot plot or #' error bars). Allowed values are one or the combination of: "none", #' "dotplot", "jitter", "boxplot", "point", "mean", "mean_se", "mean_sd", #' "mean_ci", "mean_range", "median", "median_iqr", "median_mad", #' "median_range"; see ?desc_statby for more details. #'@param add.params parameters (color, shape, size, fill, linetype) for the #' argument 'add'; e.g.: add.params = list(color = "red"). #'@param error.plot plot type used to visualize error. Allowed values are one of #' c("pointrange", "linerange", "crossbar", "errorbar", "upper_errorbar", #' "lower_errorbar", "upper_pointrange", "lower_pointrange", "upper_linerange", #' "lower_linerange"). Default value is "pointrange" or "errorbar". Used only #' when add != "none" and add contains one "mean_*" or "med_*" where "*" = sd, #' se, .... #'@param font.label a list which can contain the combination of the following elements: the size #' (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and #' the color (e.g.: "red") of labels. For example font.label = list(size = 14, #' face = "bold", color ="red"). To specify only the size and the style, use font.label = #' list(size = 14, face = "plain"). #' @param title plot main title. #' @param xlab character vector specifying x axis labels. Use xlab #' = FALSE to hide xlab. #' @param ylab character vector specifying y axis labels. Use ylab = FALSE to #' hide ylab. #'@param ggtheme function, ggplot2 theme name. Default value is theme_pubr(). #' Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), #' theme_minimal(), theme_classic(), theme_void(), .... #' @name ggpubr_args #' @rdname ggpubr_args NULL ggpubr/R/ggline.R0000644000176200001440000002623214342705003013334 0ustar liggesusers#' @include utilities.R ggpar.R NULL #' Line plot #' @description Create a line plot. #' @inheritParams ggboxplot #' @inheritParams ggplot2::geom_line #' @param x,y x and y variables for drawing. #' @param group grouping variable to connect points by line. #' Allowed values are 1 (for one line, one group) or a character vector specifying #' the name of the grouping variable (case of multiple lines). #' @param numeric.x.axis logical. If TRUE, x axis will be treated as numeric. Default is FALSE. #' @param color line colors. #' @param linetype line type. #' @param plot_type plot type. Allowed values are one of "b" for both line and point; #' "l" for line only; and "p" for point only. Default is "b". #' @param shape point shapes. #' @param stroke point stroke. Used only for shapes 21-24 to control the thickness of points border. #' @param show.line.label logical value. If TRUE, shows line labels. #' @param point.size point size. #' @param point.color point color. #' @param ... other arguments to be passed to geom_dotplot. #' #' #' @details The plot can be easily customized using the function ggpar(). Read #' ?ggpar for changing: \itemize{ \item main title and axis labels: main, #' xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis #' scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: #' palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, #' labels and position: legend = "right" \item plot orientation : orientation #' = c("vertical", "horizontal", "reverse") } #' @seealso \code{\link{ggpar}}, \code{\link{ggbarplot}} #' @examples #' # Data #' df <- data.frame(dose=c("D0.5", "D1", "D2"), #' len=c(4.2, 10, 29.5)) #' print(df) #' #' # Basic plot #' # +++++++++++++++++++++++++++ #' ggline(df, x = "dose", y = "len") #' #' #' # Plot with multiple groups #' # +++++++++++++++++++++ #' #' # Create some data #' df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3), #' dose=rep(c("D0.5", "D1", "D2"),2), #' len=c(6.8, 15, 33, 4.2, 10, 29.5)) #' print(df2) #' #' # Plot "len" by "dose" and #' # Change line types and point shapes by a second groups: "supp" #' ggline(df2, "dose", "len", #' linetype = "supp", shape = "supp") #' #' #' # Change colors #' # +++++++++++++++++++++ #' #' # Change color by group: "supp" #' # Use custom color palette #' ggline(df2, "dose", "len", #' linetype = "supp", shape = "supp", #' color = "supp", palette = c("#00AFBB", "#E7B800")) #' #' #' # Add points and errors #' # ++++++++++++++++++++++++++ #' #' # Data: ToothGrowth data set we'll be used. #' df3 <- ToothGrowth #' head(df3, 10) #' #' # It can be seen that for each group we have #' # different values #' ggline(df3, x = "dose", y = "len") #' #' # Visualize the mean of each group #' ggline(df3, x = "dose", y = "len", #' add = "mean") #' #' # Add error bars: mean_se #' # (other values include: mean_sd, mean_ci, median_iqr, ....) #' # Add labels #' ggline(df3, x = "dose", y = "len", add = "mean_se") #' #' # Change error.plot to "pointrange" #' ggline(df3, x = "dose", y = "len", #' add = "mean_se", error.plot = "pointrange") #' #' # Add jitter points and errors (mean_se) #' ggline(df3, x = "dose", y = "len", #' add = c("mean_se", "jitter")) #' #' # Add dot and errors (mean_se) #' ggline(df3, x = "dose", y = "len", #' add = c("mean_se", "dotplot"), color = "steelblue") #' #' # Add violin and errors (mean_se) #' ggline(df3, x = "dose", y = "len", #' add = c("mean_se", "violin"), color = "steelblue") #' #' # Multiple groups with error bars #' # ++++++++++++++++++++++ #' #' ggline(df3, x = "dose", y = "len", color = "supp", #' add = "mean_se", palette = c("#00AFBB", "#E7B800")) #' #' # Add jitter #' ggline(df3, x = "dose", y = "len", color = "supp", #' add = c("mean_se", "jitter"), palette = c("#00AFBB", "#E7B800")) #' #' # Add dot plot #' ggline(df3, x = "dose", y = "len", color = "supp", #' add = c("mean_se", "dotplot"), palette = c("#00AFBB", "#E7B800")) # #' #' #' @export ggline<- function(data, x, y, group = 1, numeric.x.axis = FALSE, combine = FALSE, merge = FALSE, color = "black", palette = NULL, linetype = "solid", plot_type = c("b", "l", "p"), size = 0.5, shape = 19, stroke = NULL, point.size = size, point.color = color, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, select = NULL, remove = NULL, order = NULL, add = "none", add.params = list(), error.plot = "errorbar", label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, show.line.label = FALSE, position = "identity", ggtheme = theme_pubr(), ...) { # Default options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .opts <- list( group = group, numeric.x.axis = numeric.x.axis, combine = combine, merge = merge, color = color, palette = palette, linetype = linetype, plot_type = plot_type, size = size, shape = shape, stroke = stroke, point.size = point.size, point.color = point.color, title = title, xlab = xlab, ylab = ylab, facet.by = facet.by, panel.labs = panel.labs, short.panel.labs = short.panel.labs, select = select , remove = remove, order = order, add = add, add.params = add.params, error.plot = error.plot, label = label, font.label = font.label, label.select = label.select, repel = repel, label.rectangle = label.rectangle, show.line.label = show.line.label, position = position, ggtheme = ggtheme, ...) if(!missing(data)) .opts$data <- data if(!missing(x)) .opts$x <- x if(!missing(y)) .opts$y <- y # User options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .user.opts <- as.list(match.call(expand.dots = TRUE)) .user.opts[[1]] <- NULL # Remove the function name # keep only user arguments for(opt.name in names(.opts)){ if(is.null(.user.opts[[opt.name]])) .opts[[opt.name]] <- NULL } .opts$fun <- ggline_core .opts$fun_name <- "ggline" if(missing(ggtheme) & (!is.null(facet.by) | combine)) .opts$ggtheme <- theme_pubr(border = TRUE) p <- do.call(.plotter, .opts) if(.is_list(p) & length(p) == 1) p <- p[[1]] return(p) } ggline_core <- function(data, x, y, group = 1, numeric.x.axis = FALSE, color = "black", fill = "white", palette = NULL, linetype = "solid", plot_type = c("b", "l", "p"), size = 0.5, shape = 19, stroke = NULL, point.size = size, point.color = color, title = NULL, xlab = NULL, ylab = NULL, select = NULL, order = NULL, facet.by = NULL, add = "none", add.params = list(), error.plot = "errorbar", show.line.label = FALSE, font.label = list(size = 11, color = "black"), repel = FALSE, label.rectangle = FALSE, position = "identity", ggtheme = theme_pubr(), ...) { xx <- .select_vec(data, x) if(inherits(xx, c("character", "numeric")) & !numeric.x.axis) data[[x]] <- .select_vec(data, x) %>% as.factor() error.plot = error.plot[1] plot_type <- match.arg(plot_type) if("none" %in% add) add <- "none" grouping.vars <- intersect(c(x, color, linetype, group, facet.by), names(data)) . <- NULL # static summaries for computing mean/median and adding errors if(is.null(add.params$fill)) add.params$fill <- "white" add.params <- .check_add.params(add, add.params, error.plot, data, color, fill = "white", ...) if(any(.summary_functions() %in% add)) { data_sum <- desc_statby(data, measure.var = y, grps = grouping.vars) summary.funcs <- intersect(.summary_functions(), add) if(length(summary.funcs) > 1) stop("Only one summary function is allowed. ", "Choose one of ", .collapse(.summary_functions(), sep = ", ")) .center <- summary.funcs %>% strsplit("_", fixed = TRUE) %>% unlist() %>% .[1] add <- setdiff(add, .center) names(data_sum)[which(names(data_sum) == .center)] <- y # data_sum[, x] <- as.factor(data_sum[, x]) if(inherits(xx, c("character", "numeric")) & !numeric.x.axis) data_sum[, x] <- .select_vec(data_sum, x) %>% as.factor() } else data_sum <- data .cols <- unique(c(color, linetype, group)) if(any(.cols %in% names(data))){ .in <- which(.cols %in% names(data)) group <- .cols[.in] if(is.null(add.params$group)) add.params$group <- group[1] } p <- ggplot(data, create_aes(list(x = x, y = y))) # Add other geom or summary #::::::::::::::::::::::::::::::::::::::: add.params <- add.params %>% .add_item(error.plot = error.plot, position = position, p_geom = "geom_line") # First add geom if any p <- add.params %>% .add_item(p = p, add = setdiff(add, .summary_functions())) %>% do.call(ggadd, .) # Then add summary statistics p <- add.params %>% .add_item(p = p, size = size, add = intersect(add, .summary_functions())) %>% do.call(ggadd, .) # add.params <- add.params %>% # .add_item(p = p, add = add, error.plot = error.plot, # position = "identity", p_geom = "geom_line") # p <- do.call(ggadd, add.params) # Main plot if(plot_type %in% c("b", "l")){ line_args <- geom_exec(NULL, data = data_sum, stat = "identity", color = color, linetype = linetype, position = position, size = size) mapping <- line_args$mapping mapping[["group"]] <- group option <- line_args$option option[["mapping"]] <- create_aes(mapping) p <- p + do.call(geom_line, option) } if(plot_type %in% c("p", "b")){ p <- p + geom_exec(geom_point, data = data_sum, color = point.color, shape = shape, size = 1.2+point.size, stroke = stroke, position = position) # Adjust shape when ngroups > 6, to avoid ggplot warnings p <-.scale_point_shape(p, data_sum, shape) } # Color palette user.add.color <- list(...)$user.add.color if(is.null(user.add.color)) user.add.color <- "" if(.is_color(user.add.color) & !is.numeric(group)){ ngroup <- nlevels(.select_vec(data_sum, group)) palette <- rep(user.add.color, ngroup) } if(show.line.label & !is.numeric(group)){ xval <- .select_vec(data_sum, x) last.xval <- .levels(xval) %>% utils::tail(1) groupval <- .select_vec(data_sum, group) label.data <- subset(data_sum, xval == last.xval) font.label <- .parse_font(font.label) p <- font.label %>% .add_item(data = label.data, x = x, y = y, label = group, repel = repel, label.rectangle = label.rectangle, ggtheme = ggtheme, ggp = p) %>% do.call(ggtext, .) } p <- ggpar(p, palette = palette, ggtheme = ggtheme, title = title, xlab = xlab, ylab = ylab,...) p } ggpubr/R/stat_compare_means.R0000644000176200001440000003516214370060424015737 0ustar liggesusers#' @include utilities.R utilities_label.R NULL #'Add Mean Comparison P-values to a ggplot #'@description Add mean comparison p-values to a ggplot, such as box blots, dot #' plots and stripcharts. #'@inheritParams ggplot2::layer #'@inheritParams compare_means #'@param method a character string indicating which method to be used for #' comparing means. #'@param method.args a list of additional arguments used for the test method. #' For example one might use \code{method.args = list(alternative = "greater")} #' for wilcoxon test. #'@param comparisons A list of length-2 vectors. The entries in the vector are #' either the names of 2 values on the x-axis or the 2 integers that correspond #' to the index of the groups of interest, to be compared. #'@param hide.ns logical value. If TRUE, hide ns symbol when displaying #' significance levels. #'@param label character string specifying label type. Allowed values include #' "p.signif" (shows the significance levels), "p.format" (shows the formatted #' p value). #'@param label.sep a character string to separate the terms. Default is ", ", to #' separate the correlation coefficient and the p.value. #'@param label.x.npc,label.y.npc can be \code{numeric} or \code{character} #' vector of the same length as the number of groups and/or panels. If too #' short they will be recycled. \itemize{ \item If \code{numeric}, value should #' be between 0 and 1. Coordinates to be used for positioning the label, #' expressed in "normalized parent coordinates". \item If \code{character}, #' allowed values include: i) one of c('right', 'left', 'center', 'centre', #' 'middle') for x-axis; ii) and one of c( 'bottom', 'top', 'center', 'centre', #' 'middle') for y-axis.} #'@param vjust move the text up or down relative to the bracket. #'@param tip.length numeric vector with the fraction of total height that the #' bar goes down to indicate the precise column. Default is 0.03. Can be of #' same length as the number of comparisons to adjust specifically the tip #' lenth of each comparison. For example tip.length = c(0.01, 0.03). #' #' If too short they will be recycled. #'@param label.x,label.y \code{numeric} Coordinates (in data units) to be used #' for absolute positioning of the label. If too short they will be recycled. #'@param bracket.size Width of the lines of the bracket. #'@param step.increase numeric vector with the increase in fraction of total #' height for every additional comparison to minimize overlap. #'@param ... other arguments to pass to \code{\link[ggplot2]{geom_text}} or #' \code{\link[ggplot2:geom_text]{geom_label}}. #'@param na.rm If FALSE (the default), removes missing values with a warning. If #' TRUE silently removes missing values. #'@seealso \code{\link{compare_means}} #' @examples #' # Load data #' data("ToothGrowth") #' head(ToothGrowth) #' #' # Two independent groups #' #::::::::::::::::::::::::::::::::::::::::::::::::: #' p <- ggboxplot(ToothGrowth, x = "supp", y = "len", #' color = "supp", palette = "npg", add = "jitter") #' #' # Add p-value #' p + stat_compare_means() #' # Change method #' p + stat_compare_means(method = "t.test") #' #' # Paired samples #' #::::::::::::::::::::::::::::::::::::::::::::::::: #' ggpaired(ToothGrowth, x = "supp", y = "len", #' color = "supp", line.color = "gray", line.size = 0.4, #' palette = "npg")+ #' stat_compare_means(paired = TRUE) #' #' # More than two groups #' #::::::::::::::::::::::::::::::::::::::::::::::::: #' # Pairwise comparisons: Specify the comparisons you want #' my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") ) #' ggboxplot(ToothGrowth, x = "dose", y = "len", #' color = "dose", palette = "npg")+ #' # Add pairwise comparisons p-value #' stat_compare_means(comparisons = my_comparisons, label.y = c(29, 35, 40))+ #' stat_compare_means(label.y = 45) # Add global Anova p-value #' #' # Multiple pairwise test against a reference group #' ggboxplot(ToothGrowth, x = "dose", y = "len", #' color = "dose", palette = "npg")+ #' stat_compare_means(method = "anova", label.y = 40)+ # Add global p-value #' stat_compare_means(aes(label = after_stat(p.signif)), #' method = "t.test", ref.group = "0.5") #' #' # Multiple grouping variables #' #::::::::::::::::::::::::::::::::::::::::::::::::: #' # Box plot facetted by "dose" #'p <- ggboxplot(ToothGrowth, x = "supp", y = "len", #' color = "supp", palette = "npg", #' add = "jitter", #' facet.by = "dose", short.panel.labs = FALSE) #'# Use only p.format as label. Remove method name. #'p + stat_compare_means( #' aes(label = paste0("p = ", after_stat(p.format))) #') #' #'@export stat_compare_means <- function(mapping = NULL, data = NULL, method = NULL, paired = FALSE, method.args = list(), ref.group = NULL, comparisons = NULL, hide.ns = FALSE, label.sep = ", ", label = NULL, label.x.npc = "left", label.y.npc = "top", label.x = NULL, label.y = NULL, vjust = 0, tip.length = 0.03, bracket.size = 0.3, step.increase = 0, symnum.args = list(), geom = "text", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...) { if(!is.null(comparisons)){ method.info <- .method_info(method) method <- method.info$method method.args <- .add_item(method.args, paired = paired) pms <- list(...) size <- ifelse(is.null(pms$size), 3.88, pms$size) color <- ifelse(is.null(pms$color), "black", pms$color) map_signif_level <- FALSE if(is.null(label)) label <- "p.format" if(.is_p.signif_in_mapping(mapping) | (label %in% "p.signif")) { map_signif_level <- c("****"=0.0001, "***"=0.001, "**"=0.01, "*"=0.05, "ns"=Inf) if(hide.ns) map_signif_level <- .hide_ns(map_signif_level) } if(!.is_empty(symnum.args)){ symnum.args.isok <- length(symnum.args$cutpoints == length(symnum.args$symbols)) if(!symnum.args.isok) stop("Incorrect format detected in symnum.args. ", "Check the documentation.") map_signif_level <- symnum.args$cutpoints[-1] # the first element is 0 (the minimum p-value) names(map_signif_level) <- symnum.args$symbols if(hide.ns) map_signif_level <- .hide_ns(map_signif_level) } if(missing(step.increase)){ step.increase <- ifelse(is.null(label.y), 0.12, 0) } ggsignif::geom_signif(comparisons = comparisons, y_position = label.y, test = method, test.args = method.args, step_increase = step.increase, size = bracket.size, textsize = size, color = color, map_signif_level = map_signif_level, tip_length = tip.length, data = data, vjust = vjust) } else{ mapping <- .update_mapping(mapping, label) layer( stat = StatCompareMeans, data = data, mapping = mapping, geom = geom, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(label.x.npc = label.x.npc , label.y.npc = label.y.npc, label.x = label.x, label.y = label.y, label.sep = label.sep, method = method, method.args = method.args, paired = paired, ref.group = ref.group, symnum.args = symnum.args, hide.ns = hide.ns, na.rm = na.rm, vjust = vjust,...) ) } } StatCompareMeans<- ggproto("StatCompareMeans", Stat, required_aes = c("x", "y"), default_aes = aes(hjust = ..hjust.., vjust = ..vjust..), compute_panel = function(data, scales, method, method.args, paired, ref.group, symnum.args, hide.ns, label.x.npc, label.y.npc, label.x, label.y, label.sep) { . <- x <- NULL .is.multiple.grouping.vars <- !all(data$x == data$group) if(!is.null(ref.group)) { if(ref.group != ".all.") ref.group <- scales$x$map(ref.group) } # Guess the number of group to be compared #:::::::::::::::::::::::::::::::::::::::::::::::::: if(.is.multiple.grouping.vars) x.levels <- .levels(data$group) else x.levels <- .levels(data$x) two.groups <- length(x.levels) == 2 | !is.null(ref.group) multi.groups <- length(x.levels) > 2 # Guess the test to be performed #:::::::::::::::::::::::::::::::::::::::::::::::::: if(two.groups & is.null(method)) method <- "wilcox.test" else if(multi.groups & is.null(method)) method <- "kruskal.test" # Perform group comparisons #:::::::::::::::::::::::::::::::::::::::::::::::::: if(!is.null(ref.group)) ref.group <- as.character(ref.group) method.args <- method.args %>% .add_item(data = data, method = method, paired = paired, ref.group = ref.group, symnum.args = symnum.args) if(.is.multiple.grouping.vars){ method.args <- method.args %>% .add_item(formula = y ~ group, group.by = "x") .test <- do.call(compare_means, method.args) } else{ method.args <- method.args %>% .add_item(formula = y ~ x) .test <- do.call(compare_means, method.args) } pvaltxt <- ifelse(.test$p < 2.2e-16, "p < 2.2e-16", paste("p =", signif(.test$p, 2))) .test$label <- paste(.test$method, pvaltxt, sep = label.sep) # Options for label positioning #:::::::::::::::::::::::::::::::::::::::::::::::::: label.opts <- list(data = data, scales = scales, label.x.npc = label.x.npc, label.y.npc = label.y.npc, label.x = label.x, label.y = label.y, symnum.args = symnum.args, .by = "panel" ) if(.is.multiple.grouping.vars){ if(is.null(label.x) & length(label.x.npc) == 1) label.opts$label.x <- .test$x .label.pms <- label.opts %>% .add_item(group.ids = .test$x) %>% do.call(.label_params_by_group, .) # Returns a data frame with label: x, y, hjust, vjust # .test <- dplyr::select(.test, -x) .label.pms <- dplyr::select(.label.pms, -x) } else{ .label.pms <- label.opts %>% do.call(.label_params, .) %>% # Returns a data frame with label: x, y, hjust, vjust dplyr::mutate(hjust = 0.2) } if(!is.null(ref.group)){ group.ids <- as.numeric(.test$group2) if(!is.null(label.y) & ref.group != ".all."){ if(length(label.y) == length(group.ids)) label.opts$label.y <- c(0, label.y) } .label.pms <- label.opts %>% .add_item(group.ids = group.ids) %>% do.call(.label_params_by_group, .) } res <- cbind(.test, .label.pms) if(!is.null(ref.group)){ # Set label x value to group names other.group.index <- as.numeric(res$group2) res$x <- scales$x$range$range[other.group.index ] res <- res %>% dplyr::mutate(hjust = 0.5) } if(hide.ns){ p.signif <- res$p.signif p.format <- res$p.format p.signif[p.signif == "ns"] <- " " res$p.signif <- p.signif } res } ) # Check if p.signif is in mapping .is_p.signif_in_mapping <- function(mapping){ res <- FALSE if(!is.null(mapping)){ if(!is.null(mapping$label)){ .label <- rlang::as_label(mapping$label) res <- grepl(pattern = "p\\.signif", .label) } } return(res) } # Update mapping with label .update_mapping <- function (mapping, label){ allowed.label <- list( "p.signif" = quote(ggplot2::after_stat(p.signif)), "..p.signif.." = quote(ggplot2::after_stat(p.signif)), "p.format" = quote(ggplot2::after_stat(paste0("p = ", p.format))), "..p.format.." = quote(ggplot2::after_stat(paste0("p = ", p.format))), "p" = quote(ggplot2::after_stat(paste0("p = ", p.format))), "..p.." = quote(ggplot2::after_stat(paste0("p = ", p.format))) ) if(!is.null(label)){ if(!label %in% names(allowed.label) ) stop("Allowed values for label are: ", .collapse(names(allowed.label) , sep = ", ")) } if(!is.null(mapping) & is.character(label)){ mapping$label <- allowed.label[[label]] } else if(is.character(label)){ mapping <- aes() mapping$label <- allowed.label[[label]] } convert_label_dotdot_notation_to_after_stat(mapping) } # Hide NS in map_signif_level .hide_ns <- function(x){ n <- names(x) ns.pos <- which(n == "ns" | n == "NS") if(!.is_empty(ns.pos)) n[ns.pos] = " " names(x) <- n x } # The dot-dot notation (`..p.signif..`) was deprecated in ggplot2 3.4.0. # after_stat(p.signif) should be used. This function makes automatic # conversion if user specified ..p.signif.. convert_label_dotdot_notation_to_after_stat <- function(mapping){ if(!is.null(mapping) ){ label <- mapping$label if(!is.null(mapping$label)){ label <- rlang::as_label(mapping$label) label <- gsub( pattern = "..p.signif..", replacement = "ggplot2::after_stat(p.signif)", x = label, fixed = TRUE ) label <- gsub( pattern = "..p.format..", replacement = "ggplot2::after_stat(p.format)", x = label, fixed = TRUE ) label <- gsub( pattern = "..p..", replacement = "ggplot2::after_stat(p)", x = label, fixed = TRUE ) mapping$label <- parse(text = label)[[1]] } } mapping } ggpubr/R/set_palette.R0000644000176200001440000000452314332553655014414 0ustar liggesusers#' @include utilities_color.R NULL #'Set Color Palette #' #'@description \itemize{ \item \code{change_palette(), set_palette()}: Change #'both color and fill palettes. \item \code{color_palette()}: change color #'palette only. \item \code{fill_palette()}: change fill palette only. #' #'} #'@inheritParams get_palette #'@param p a ggplot #'@param ... other arguments passed to ggplot2 scale_color_xxx() and #' scale_fill_xxx() functions. #' #'@seealso \link{get_palette}. #' #' #'@examples #'# Load data #'data("ToothGrowth") #'df <- ToothGrowth #' #'# Basic plot #'p <- ggboxplot(df, x = "dose", y = "len", #' color = "dose") #'p #' #'# Change the color palette #' set_palette(p, "jco") #'@name set_palette #'@rdname set_palette #'@export set_palette <- function(p, palette){ p + .ggcolor(palette)+ .ggfill(palette) } #'@rdname set_palette #'@export change_palette <- function(p, palette){ set_palette(p, palette) } #'@rdname set_palette #'@export color_palette <- function(palette = NULL, ...) { brewerpal <- .brewerpal() ggscipal <- .ggscipal() res <- NULL if (is.null(palette)) palette <- "" if (length(palette) == 1) { if (palette %in% brewerpal) ggplot2::scale_color_brewer(..., palette = palette) else if (palette %in% ggscipal) .scale_color_ggsci(palette = palette) else if (palette == "grey") ggplot2::scale_color_grey(..., start = 0.8, end = 0.2) else if (palette == "hue") ggplot2::scale_color_hue(...) else if(.is_color(palette)) ggplot2::scale_color_manual(..., values = palette) } else if (palette[1] != "") ggplot2::scale_color_manual(..., values = palette) } #'@rdname set_palette #'@export fill_palette <- function(palette = NULL, ...){ brewerpal <- .brewerpal() ggscipal <- .ggscipal() res <- NULL if (is.null(palette)) palette <- "" if (length(palette) == 1) { if (palette %in% brewerpal) ggplot2::scale_fill_brewer(..., palette = palette) else if (palette %in% ggscipal) .scale_fill_ggsci(palette = palette) else if (palette == "grey") ggplot2::scale_fill_grey(..., start = 0.8, end = 0.2) else if (palette == "hue") ggplot2::scale_fill_hue(...) else if(.is_color(palette)) ggplot2::scale_fill_manual(..., values = palette) } else if (palette[1] != "") ggplot2::scale_fill_manual(..., values = palette) } ggpubr/R/gene_citation.R0000644000176200001440000000245114332553655014711 0ustar liggesusers#'Gene Citation Index #' #'@description Contains the mean citation index of 66 genes obtained by #' assessing PubMed abstracts and annotations using two key words i) Gene name #' + b cell differentiation and ii) Gene name + plasma cell differentiation. #'@name gene_citation #'@docType data #'@usage data("gene_citation") #'@format A data frame with 66 rows and 2 columns. \describe{ #' \item{\code{gene}}{gene names} \item{\code{citation_index}}{mean citation index} } #' #' @examples #' data(gene_citation) #' #'# Some key genes of interest to be highlighted #'key.gns <- c("MYC", "PRDM1", "CD69", "IRF4", "CASP3", "BCL2L1", "MYB", "BACH2", "BIM1", "PTEN", #' "KRAS", "FOXP1", "IGF1R", "KLF4", "CDK6", "CCND2", "IGF1", "TNFAIP3", "SMAD3", "SMAD7", #' "BMPR2", "RB1", "IGF2R", "ARNT") #'# Density distribution #'ggdensity(gene_citation, x = "citation_index", y = "..count..", #' xlab = "Number of citation", #' ylab = "Number of genes", #' fill = "lightgray", color = "black", #' label = "gene", label.select = key.gns, repel = TRUE, #' font.label = list(color= "citation_index"), #' xticks.by = 20, # Break x ticks by 20 #' gradient.cols = c("blue", "red"), #' legend = "bottom", #' legend.title = "" # Hide legend title #' ) #' NULL ggpubr/R/reexports.R0000644000176200001440000000027514332553655014136 0ustar liggesusers#' @importFrom dplyr mutate #' @export dplyr::mutate #' @importFrom dplyr group_by #' @export dplyr::group_by #' @importFrom rstatix get_summary_stats #' @export rstatix::get_summary_stats ggpubr/R/background_image.R0000644000176200001440000000165314332553655015365 0ustar liggesusers#' @include utilities.R NULL #'Add Background Image to ggplot2 #'@description Add background image to ggplot2. #'@param raster.img raster object to display, as returned by the function #' \code{readPNG()}[in \code{png} package] and \code{readJPEG()} [in \code{jpeg} package]. #'@author Alboukadel Kassambara #' @examples #' \dontrun{ #'install.packages("png") #' #'# Import the image #'img.file <- system.file(file.path("images", "background-image.png"), #' package = "ggpubr") #'img <- png::readPNG(img.file) #' #'# Plot with background image #'ggplot(iris, aes(Species, Sepal.Length))+ #' background_image(img)+ #' geom_boxplot(aes(fill = Species), color = "white")+ #' fill_palette("jco") #' } #' #'@export background_image <- function(raster.img){ annotation_raster(raster.img, xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf) } ggpubr/R/ggpubr-package.R0000644000176200001440000000257014343207174014754 0ustar liggesusers#' @keywords internal #' #' @details #' #' General resources: #' #'\itemize{ #' \item \href{https://rpkgs.datanovia.com/ggpubr/}{ggpubr documentation} #' \item \href{https://www.datanovia.com/en/blog/tag/ggpubr/}{ggpubr tutorials} #'} #' #' @section Package options: #' #' \describe{ #' \item{ggpubr.parse_aes}{logical indicating whether to parse or not aesthetics variables names. #' Default is \code{TRUE}. For example, if you want \code{ggpubr} to handle non-standard column names, like \code{A-A}, #' without parsing, then set this option to \code{FALSE} using \code{options(ggpubr.parse_aes = FALSE)}.} #' \item{ggpubr.null_device}{A function that creates an appropriate null device. #' These include: \code{\link[cowplot:pdf_null_device]{cowplot::pdf_null_device}}, #' \code{\link[cowplot:png_null_device]{cowplot::png_null_device}}, #' \code{\link[cowplot:cairo_null_device]{cowplot::cairo_null_device}} and #' \code{\link[cowplot:agg_null_device]{cowplot::agg_null_device}}. Default is #' \code{\link[cowplot:pdf_null_device]{cowplot::pdf_null_device}}. This is used in #' function like \code{\link{as_ggplot}()}, which needs to open a graphics #' device to render ggplot objects into grid graphics objects. This function is #' used to open null device for avoiding the display of unnecessary blank page #' when calling \code{\link{ggarrange}()} or \code{\link{as_ggplot}()}} } #' "_PACKAGE" ggpubr/R/ggtexttable.R0000644000176200001440000011427714350533257014421 0ustar liggesusers#' @include utilities.R NULL #' Draw a Textual Table #' @description Draw a textual table. \itemize{ \item \code{ggtexttable()}: draw #' a textual table. \item \code{ttheme()}: customize table theme. \item #' \code{rownames_style(), colnames_style(), tbody_style()}: helper functions #' to customize the table row names, column names and body. #' \item \code{table_cell_font()}: access to a table cell for changing the text font (size and face). #' \item \code{table_cell_bg()}: access to a table cell for changing the background (fill, color, linewidth). #' \item \code{tab_cell_crossout()}: cross out a table cell. #' \item \code{tab_ncol(), tab_nrow()}: returns, respectively, the number of columns and rows in a ggtexttable. #' \item \code{tab_add_hline()}: Creates horizontal lines or separators at the top or the bottom side of a given specified row. #' \item \code{tab_add_vline()}: Creates vertical lines or separators at the right or the left side of a given specified column. #' \item \code{tab_add_border(), tbody_add_border(), thead_add_border()}: Add borders to table; tbody is for table body and thead is for table head. #' \item \code{tab_add_title(),tab_add_footnote()}: Add title, subtitle and footnote to a table. #' } #'@inheritParams gridExtra::tableGrob #'@param x a \code{data.frame} or \code{matrix}. #'@param theme a list, as returned by the function \code{ttheme()}, defining the #' parameters of the table theme. Allowed values include one of \code{ttheme()} #' and \code{ttheme_clean()}. #'@param base_style character string the table style/theme. The available themes #' are illustrated in the #' \href{https://rpkgs.datanovia.com/ggpubr/files/ggtexttable-theme.pdf}{ggtexttable-theme.pdf} #' file. Allowed values include one of \code{c("default", "blank", "classic", #' "minimal", "light", "lBlack", "lBlue", "lRed", "lGreen", "lViolet", "lCyan", #' "lOrange", "lBlackWhite", "lBlueWhite", "lRedWhite", "lGreenWhite", #' "lVioletWhite", "lCyanWhite", "lOrangeWhite", "mBlack", "mBlue", "mRed", #' "mGreen", "mViolet", "mCyan", "mOrange", "mBlackWhite", "mBlueWhite", #' "mRedWhite", "mGreenWhite", "mVioletWhite", "mCyanWhite", "mOrangeWhite" )}. #' Note that, l = "light"; m = "medium". #'@param colnames.style a list, as returned by the function #' \code{colnames_style()}, defining the style of the table column names. #' Considered only when \code{base_size = "default"}. #'@param rownames.style a list, as returned by the function #' \code{rownames_style()}, defining the style of the table row names. #' Considered only when \code{base_size = "default"}. #'@param tbody.style a list, as returned by the function \code{tbody_style()}, #' defining the style of the table body. Considered only when \code{base_size = #' "default"}. #'@param color,face,size text font color, face and size, respectively. Allowed #' values for face include c("plain", "bold", "italic", "bold.italic"). #'@param fill background color. #'@param linewidth,linecolor line width and color, respectively. #'@param alpha numeric value specifying fill color transparency. #' Value should be in [0, 1], where 0 is full transparency and 1 is no transparency. #' @param at.row a numeric vector of row indexes; for example \code{at.row = c(1, 2)}. #' @param row.side row side to which the horinzotal line should be added. Can be one of \code{c("bottom", "top")}. #' @param from.column integer indicating the column from which to start drawing the horizontal line. #' @param to.column integer indicating the column to which the horizontal line should end. #' @param linetype line type #' @param at.column a numeric vector of column indexes; for example \code{at.column = c(1, 2)}. #' @param column.side column side to which the vertical line should be added. Can be one of \code{c("left", "right")}. #' @param from.row integer indicating the row from which to start drawing the horizontal line. #' @param to.row integer indicating the row to which the vertical line should end. #'@param ... extra parameters for text justification, e.g.: hjust and x. Default #' is "centre" for the body and header, and "right" for the row names. Left #' justification: \code{hjust = 0, x = 0.1}. Right justification: \code{hjust = 1, x = 0.9}. #'@return an object of class ggplot. #' #' #' @examples #' # data #' df <- head(iris) #' #' # Default table #' # Remove row names using rows = NULL #' ggtexttable(df, rows = NULL) #' #' # Text justification for individual cells/rows/columns (#335) #' # First column is left justified i.e., hjust = 0 , x = 0.1 #' # Remaining columns are right justified i.e., hjust = 1 , x = 0.9 #' table_theme <- ttheme( #' tbody.style = tbody_style( #' hjust = as.vector(matrix(c(0, 1, 1, 1, 1), ncol = 5, nrow = nrow(df), byrow = TRUE)), #' x = as.vector(matrix(c(.1, .9, .9,.9, .9), ncol = 5, nrow = nrow(df), byrow = TRUE)) #' ) #' ) #' ggtexttable(df, rows = NULL, theme = table_theme) #' #' # Blank theme #' ggtexttable(df, rows = NULL, theme = ttheme("blank")) #' #' # light theme #' ggtexttable(df, rows = NULL, theme = ttheme("light")) #' #' # Column names border only #' ggtexttable(df, rows = NULL, theme = ttheme("blank")) %>% #' tab_add_hline(at.row = 1:2, row.side = "top", linewidth = 2) #' #' # classic theme #' ggtexttable(df, rows = NULL, theme = ttheme("classic")) #' #' # minimal theme #' ggtexttable(df, rows = NULL, theme = ttheme("minimal")) #' #' # Medium blue (mBlue) theme #' ggtexttable(df, rows = NULL, theme = ttheme("mBlue")) #' #' #' # Customize the table as you want #' ggtexttable(df, rows = NULL, #' theme = ttheme( #' colnames.style = colnames_style(color = "white", fill = "#8cc257"), #' tbody.style = tbody_style(color = "black", fill = c("#e8f3de", "#d3e8bb")) #' ) #' ) #' #' # Use RColorBrewer palette #' # Provide as many fill color as there are rows in the table body, here nrow = 6 #'ggtexttable(df, #' theme = ttheme( #' colnames.style = colnames_style(fill = "white"), #' tbody.style = tbody_style(fill = get_palette("RdBu", 6)) #' ) #') #' #'# Text justification #'#:::::::::::::::::::::::::::::::::::::::::::::: #'# Default is "centre" for the body and header, and "right" for the row names. #'# Left justification: hjust=0, x=0.1 #'# Right justification: hjust=1, x=0.9 #'tbody.style = tbody_style(color = "black", #' fill = c("#e8f3de", "#d3e8bb"), hjust=1, x=0.9) #'ggtexttable(head(iris), rows = NULL, #' theme = ttheme( #' colnames.style = colnames_style(color = "white", fill = "#8cc257"), #' tbody.style = tbody.style #' ) #') #' #'# Access and modify the font and #'# the background of table cells #'# ::::::::::::::::::::::::::::::::::::::::::::: #'tab <- ggtexttable(head(iris), rows = NULL, #' theme = ttheme("classic")) #'tab <- table_cell_font(tab, row = 3, column = 2, #' face = "bold") #'tab <- table_cell_bg(tab, row = 4, column = 3, linewidth = 5, #' fill="darkolivegreen1", color = "darkolivegreen4") #'tab #' #'# Change table cells background and font for column 3, #'# Spaning from row 2 to the last row in the data #'tab <- ggtexttable(df, rows = NULL, theme = ttheme("classic")) #'tab %>% #' table_cell_bg(row = 2:tab_nrow(tab), column = 3, fill = "darkblue") %>% #' table_cell_font(row = 2:tab_nrow(tab), column = 3, face = "italic", color = "white") #' #'# Add separators and borders #'# ::::::::::::::::::::::::::::::::::::::::::::::::::: #'# Table with blank theme #'tab <- ggtexttable(df, theme = ttheme("blank"), rows = NULL) #'# Add horizontal and vertical lines #'tab %>% #' tab_add_hline(at.row = c(1, 2), row.side = "top", linewidth = 3, linetype = 1) %>% #' tab_add_hline(at.row = c(7), row.side = "bottom", linewidth = 3, linetype = 1) %>% #' tab_add_vline(at.column = 2:tab_ncol(tab), column.side = "left", from.row = 2, linetype = 2) #' #'# Add borders to table body and header #'# Cross out some cells #'tab %>% #' tbody_add_border() %>% #' thead_add_border() %>% #' tab_cell_crossout( #' row = c(2, 4), column = 3, linecolor = "red", #' reduce.size.by = 0.6 #' ) #' #'# Add titles andd footnote #'# ::::::::::::::::::::::::::::::::::::::::::::::::::: #'# Add titles and footnote #'# Wrap subtitle into multiple lines using strwrap() #'main.title <- "Edgar Anderson's Iris Data" #'subtitle <- paste0( #' "This famous (Fisher's or Anderson's) iris data set gives the measurements", #' " in centimeters of the variables sepal length and width and petal length and width,", #' " respectively, for 50 flowers from each of 3 species of iris.", #' " The species are Iris setosa, versicolor, and virginica." #') %>% #' strwrap(width = 80) %>% #' paste(collapse = "\n") #' #'tab <- ggtexttable(head(iris), theme = ttheme("light")) #'tab %>% #' tab_add_title(text = subtitle, face = "plain", size = 10) %>% #' tab_add_title(text = main.title, face = "bold", padding = unit(0.1, "line")) %>% #' tab_add_footnote(text = "*Table created using ggpubr", size = 10, face = "italic") #' #' #' # Combine density plot and summary table #'#::::::::::::::::::::::::::::::::::::: #'# Density plot of "Sepal.Length" #'density.p <- ggdensity(iris, x = "Sepal.Length", #' fill = "Species", palette = "jco") #' #'# Draw the summary table of Sepal.Length #'# Descriptive statistics by groups #'stable <- desc_statby(iris, measure.var = "Sepal.Length", #' grps = "Species") #'stable <- stable[, c("Species", "length", "mean", "sd")] #'stable.p <- ggtexttable(stable, rows = NULL, #' theme = ttheme("mOrange")) #' #'# Arrange the plots on the same page #'ggarrange(density.p, stable.p, #' ncol = 1, nrow = 2, #' heights = c(1, 0.5)) #' #'@rdname ggtexttable #'@export ggtexttable <- function(x, rows = rownames(x), cols = colnames(x), vp = NULL, theme = ttheme(), ...) { style <- attr(theme, "style") res <- gridExtra::tableGrob(x, rows = rows, cols = cols, vp = vp, theme = theme, ...) if(style == "minimal"){ # minimal = blank theme + left vertical line res <- tab_add_vline( res, at.column = 2:tab_ncol(res), column.side = "left", from.row = 2, to.row = tab_nrow(res) ) } else if(style == "light"){ # light = blank theme + horizontal line res <- tab_add_hline( res, at.row = c(1, tab_nrow(res)), row.side = "bottom", linewidth = 1.5, linetype = 1, from.column = 1 ) } .grob <- res res <- as_ggplot(res) attr(res, "ggtexttableGrob") <- .grob return(res) } #' @export #' @rdname ggtexttable ttheme <- function(base_style = "default", base_size = 11, base_colour = "black", padding = unit(c(4, 4), "mm"), colnames.style = colnames_style(size = base_size), rownames.style = rownames_style(size = base_size), tbody.style = tbody_style(size = base_size) ) { style <- tstyle(base_style, size = base_size) if(!is.null(style)){ if(missing(colnames.style)) colnames.style <- style$colnames.style if(missing(rownames.style)) rownames.style <- style$rownames.style if(missing(tbody.style)) tbody.style <- style$tbody.style } .ttheme <- gridExtra::ttheme_default(base_size = base_size, base_colour = base_colour, padding = padding) .ttheme$colhead <- do.call(.add_item, c(list(.list = .ttheme$colhead), colnames.style)) .ttheme$rowhead <- do.call(.add_item, c(list(.list = .ttheme$rowhead), rownames.style)) .ttheme$core <- do.call(.add_item, c(list(.list = .ttheme$core), tbody.style)) attr(.ttheme, "style") <- base_style .ttheme } #' @export #' @rdname ggtexttable colnames_style <- function(color = "black", face = "bold", size = 12, fill = "grey80", linewidth = 1, linecolor = "white", parse = FALSE, ...) { list( fg_params = list(parse = parse, col = color, fontface = face, fontsize = size) %>% .add_item(...), # Accept extra parameters bg_params = list(fill = fill, lwd = linewidth, col = linecolor)) } #' @export #' @rdname ggtexttable rownames_style <- function(color = "black", face = "italic", size = 12, fill = NA, linewidth = 1, linecolor = "white", parse = FALSE, ...) { list( fg_params = list(parse = parse, col = color, fontface = face, fontsize = size, hjust = 1, x = 0.95) %>% .add_item(...), # Accept extra parameters bg_params = list(fill = fill, lwd = linewidth, col = linecolor)) } #' @export #' @rdname ggtexttable tbody_style <- function(color = "black", face = "plain", size = 12, fill = c("grey95", "grey90"), linewidth = 1, linecolor = "white", parse = FALSE, ...) { list( fg_params = list(parse = parse, col = color, fontface = face, fontsize = size)%>% .add_item(...), # Accept extra parameters bg_params = list(fill = fill, lwd = linewidth, col = linecolor)) } #' @export #' @rdname ggtexttable #' @param tab an object from \code{ggtexttable} or from \code{gridExtra::tableGrob()}. #' @param row,column an integer specifying the row and the column numbers for the cell of interest. table_cell_font <- function(tab, row, column, face = NULL, size = NULL, color = NULL) { tabGrob <- get_tablegrob(tab) cells <- expand.grid(row = row, column = column) for(i in 1:nrow(cells)){ tc <- .find_cell(tabGrob, cells$row[i], cells$column[i], "core-fg") tabGrob$grobs[tc][[1]][["gp"]] <- grid::gpar(fontface = face, fontsize = size, col = color) } tab_return_same_class_as_input(tabGrob, input = tab) } #' @export #' @rdname ggtexttable table_cell_bg <- function(tab, row, column, fill = NULL, color = NULL, linewidth = NULL, alpha = NULL) { tabGrob <- get_tablegrob(tab) cells <- expand.grid(row = row, column = column) for(i in 1:nrow(cells)){ tc <- .find_cell(tabGrob, cells$row[i], cells$column[i], "core-bg") tabGrob$grobs[tc][[1]][["gp"]] <- grid::gpar( fill = fill, col = color, lwd = linewidth, alpha = alpha ) } tab_return_same_class_as_input(tabGrob, input = tab) } .find_cell <- function(tab, row, column, name="core-fg"){ l <- tab$layout which(l$t==row & l$l==column & l$name==name) } #' @export #' @rdname ggtexttable #' @param reduce.size.by Numeric value in [0, 1] to reduce the size by. tab_cell_crossout <- function(tab, row, column, linetype = 1, linewidth = 1, linecolor = "black", reduce.size.by = 0){ required_package("gtable") tabgrob <- get_tablegrob(tab) crosses <- replicate( n = length(row), tab_cross(linetype = linetype, linewidth = linewidth, linecolor = linecolor, reduce.size.by = reduce.size.by), simplify = FALSE ) tabgrob <- gtable::gtable_add_grob( tabgrob, grobs = crosses, t = row, b = row, l = column, r = column ) tab_return_same_class_as_input(tabgrob, input = tab) } tab_cross <- function(linetype = 1, linewidth = 1, linecolor = "black", reduce.size.by = 0){ reduce <- reduce.size.by/2 grid::grobTree( grid::segmentsGrob( # diagonal line ul -> lr x0 = unit(0+reduce,"npc"), y0 = unit(1-reduce,"npc"), x1 = unit(1-reduce,"npc"), y1 = unit(0+reduce,"npc"), gp = grid::gpar( lty = linetype, lwd = linewidth, col = linecolor, reduce = reduce) ), grid::segmentsGrob( # diagonal line ll -> ur x0 = unit(0+reduce,"npc"), y0 = unit(0+reduce,"npc"), x1 = unit(1-reduce,"npc"), y1 = unit(1-reduce,"npc"), gp = grid::gpar( lty = linetype, lwd = linewidth, col = linecolor) ) ) } #' @export #' @rdname ggtexttable tab_ncol <- function(tab){ ncol(get_tablegrob(tab)) } #' @export #' @rdname ggtexttable tab_nrow <- function(tab){ nrow(get_tablegrob(tab)) } #' @export #' @rdname ggtexttable tab_add_hline <- function(tab, at.row = 2:tab_nrow(tab), row.side = c("bottom", "top"), from.column = 1, to.column = tab_ncol(tab), linetype = 1, linewidth = 1, linecolor = "black"){ required_package("gtable") row.side <- match.arg(row.side) tabgrob <- get_tablegrob(tab) separators <- replicate( n = length(at.row), tab_hline(row.side = row.side, linetype = linetype, linewidth = linewidth, linecolor = linecolor), simplify = FALSE ) tabgrob <- gtable::gtable_add_grob( tabgrob, grobs = separators, t = at.row, b = at.row, l = from.column, r = to.column ) tab_return_same_class_as_input(tabgrob, input = tab) } # Create hline at the top or the bottom side of a given row tab_hline <- function(row.side = c("bottom", "top"), linetype = 1, linewidth = 1, linecolor = "black"){ row.side <- match.arg(row.side) y0 <- y1 <- unit(0, "npc") if(row.side == "top") y0 <- y1 <- unit(1, "npc") grid::segmentsGrob( x0 = unit(0, "npc"), x1 = unit(1,"npc"), y0 = y0, y1 = y1, gp = grid::gpar( lty = linetype, lwd = linewidth, col = linecolor) ) } #' @export #' @rdname ggtexttable tab_add_vline <- function(tab, at.column = 2:tab_ncol(tab), column.side = c("left", "right"), from.row = 1, to.row = tab_nrow(tab), linetype = 1, linewidth = 1, linecolor = "black"){ required_package("gtable") column.side <- match.arg(column.side) tabgrob <- get_tablegrob(tab) separators <- replicate( n = length(at.column), tab_vline(column.side = column.side, linetype = linetype, linewidth = linewidth, linecolor = linecolor), simplify = FALSE ) tabgrob <- gtable::gtable_add_grob( tabgrob, grobs = separators, t = from.row, b = to.row, l = at.column, r = at.column ) tab_return_same_class_as_input(tabgrob, input = tab) } # Create vline at the left or the right side of a given column tab_vline <- function(column.side = c("left", "right"), linetype = 1, linewidth = 1, linecolor = "black"){ column.side <- match.arg(column.side) x0 <- x1 <- unit(0, "npc") if(column.side == "right") x0 <- x1 <- unit(1, "npc") grid::segmentsGrob( x0 = x0, x1 = x1, y0 = unit(0, "npc"), y1 = unit(1, "npc"), gp = grid::gpar( lty = linetype, lwd = linewidth, col = linecolor) ) } #' @export #' @rdname ggtexttable tab_add_border <- function(tab, from.row = 2, to.row = tab_nrow(tab), from.column = 1, to.column = tab_ncol(tab), linetype = 1, linewidth = 1, linecolor = "black"){ required_package("gtable") tabgrob <- get_tablegrob(tab) border <- grid::rectGrob( gp = grid::gpar(fill = NA, lty = linetype, lwd = linewidth, col = linecolor) ) tabgrob <- gtable::gtable_add_grob( tabgrob, grobs = border, t = from.row, b = to.row, l = from.column, r = to.column ) tab_return_same_class_as_input(tabgrob, input = tab) } #' @export #' @rdname ggtexttable tbody_add_border <- function(tab, from.row = 2, to.row = tab_nrow(tab), from.column = 1, to.column = tab_ncol(tab), linetype = 1, linewidth = 1, linecolor = "black"){ tab_add_border( tab, from.row = from.row, to.row = to.row, from.column = from.column, to.column = to.column, linetype = linetype, linewidth = linewidth, linecolor = linecolor ) } #' @export #' @rdname ggtexttable thead_add_border <- function(tab, from.row = 1, to.row = 1, from.column = 1, to.column = tab_ncol(tab), linetype = 1, linewidth = 1, linecolor = "black"){ tab_add_border( tab, from.row = from.row, to.row = to.row, from.column = from.column, to.column = to.column, linetype = linetype, linewidth = linewidth, linecolor = linecolor ) } #' @export #' @rdname ggtexttable #' @param text text to be added as title or footnote. tab_add_title <- function(tab, text, face = NULL, size = NULL, color = NULL, family = NULL, padding = unit(1.5,"line"), just = "left", hjust = NULL, vjust = NULL){ required_package("gtable") tabgrob <- get_tablegrob(tab) text <- grid::textGrob( text, x = 0.02, just = just, hjust = hjust, vjust = vjust, gp = grid::gpar(fontsize = size, fontface = face, fontfamily = family, col = color) ) # Add row at the top tabgrob <- gtable::gtable_add_rows( tabgrob, heights = grid::grobHeight(text) + padding, pos = 0 ) tabgrob <- gtable::gtable_add_grob( tabgrob, list(text), t = 1, b = 1, l = 1, r = ncol(tabgrob) ) tab_return_same_class_as_input(tabgrob, input = tab) } #' @export #' @rdname ggtexttable #' @param text text to be added as title or footnote. #' @param family font family #' @param just The justification of the text relative to its (x, y) location. If #' there are two values, the first value specifies horizontal justification #' and the second value specifies vertical justification. Possible string #' values are: "left", "right", "centre", "center", "bottom", and "top". For #' numeric values, 0 means left (bottom) alignment and 1 means right (top) #' alignment. #' @param hjust A numeric vector specifying horizontal justification. If #' specified, overrides the just setting. #' @param vjust A numeric vector specifying vertical justification. If #' specified, overrides the just setting. #' tab_add_footnote <- function(tab, text, face = NULL, size = NULL, color = NULL, family = NULL, padding = unit(1.5,"line"), just = "right", hjust = NULL, vjust = NULL){ required_package("gtable") tabgrob <- get_tablegrob(tab) text <- grid::textGrob( text, x = 0.95, just = just, hjust = hjust, vjust = vjust, gp = grid::gpar(fontsize = size, fontface = face, fontfamily = family, col = color) ) # Add row at the bottom tabgrob <- gtable::gtable_add_rows( tabgrob, heights = grid::grobHeight(text) + padding, pos = -1 ) tabgrob <- gtable::gtable_add_grob( tabgrob, list(text), t = nrow(tabgrob), b = nrow(tabgrob), l = 1, r = ncol(tabgrob) ) tab_return_same_class_as_input(tabgrob, input = tab) } #:::::::::::::::::::::::::::::::::::::::: # Helper function #:::::::::::::::::::::::::::::::::::::::: is_ggtexttable <- function(tab){ !is.null(attr(tab, "ggtexttableGrob")) } is_tablegrob <- function(tab){ inherits(tab, "gtable") & inherits(tab, "grob") } # Transform a table grob in ggtexttable like object as_ggtexttable <- function(tabgrob){ res <- as_ggplot(tabgrob) attr(res, "ggtexttableGrob") <- tabgrob res } # Extract tableGrob from ggtexttable() get_tablegrob <- function(tab){ if(is_ggtexttable(tab)){ tabgrob <- attr(tab, "ggtexttableGrob") } else if(is_tablegrob(tab)){ tabgrob <- tab } else{ stop("tab should be an object from either ggpubr::ggtexttable() or gridExtra::tableGrob().") } tabgrob } # Return the same class as the input data, # which can be either ggtextable or a gridExtra::tableGrob tab_return_same_class_as_input <- function(tabgrob, input){ if(is_ggtexttable(input)){ return(as_ggtexttable(tabgrob)) } else if(is_tablegrob(input)){ return(tabgrob) } tabgrob } # Define table style tstyle <- function(pal, size = 12){ allowed.palettes = c("default", "blank", "classic", "minimal", "light", "lBlack", "lBlue", "lRed", "lGreen", "lViolet", "lCyan", "lOrange", "lBlackWhite", "lBlueWhite", "lRedWhite", "lGreenWhite", "lVioletWhite", "lCyanWhite", "lOrangeWhite", "mBlack", "mBlue", "mRed", "mGreen", "mViolet", "mCyan", "mOrange", "mBlackWhite", "mBlueWhite", "mRedWhite", "mGreenWhite", "mVioletWhite", "mCyanWhite", "mOrangeWhite" ) if(!(pal %in% allowed.palettes )) stop(pal, " is not a supported palette") style <- switch(pal, blank = list( colnames.style = colnames_style(fill = NA, linecolor = NA, size = size), rownames.style = rownames_style(fill = NA, linecolor = NA, size = size), tbody.style = tbody_style(fill = NA, linecolor = NA, size = size) ), classic = list( colnames.style = colnames_style(fill = NA, linecolor = "black", size = size), rownames.style = rownames_style(fill = NA, linecolor = NA, size = size), tbody.style = tbody_style(fill = NA, linecolor = "black", size = size) ), minimal = list( colnames.style = colnames_style(fill = NA, linecolor = NA, size = size), rownames.style = rownames_style(fill = NA, linecolor = NA, size = size), tbody.style = tbody_style(fill = NA, linecolor = NA, size = size) ), light = list( colnames.style = colnames_style(fill = NA, linecolor = NA, size = size), rownames.style = rownames_style(fill = NA, linecolor = NA, size = size), tbody.style = tbody_style(fill = NA, linecolor = NA, size = size) ), lBlack = list( colnames.style = colnames_style( color = "black", fill = "white", size = size, linecolor = NA, linewidth = 1), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = c("#D9D9D9", "white"), linecolor = NA, size = size) ), lBlue = list( colnames.style = colnames_style( color = "#2F5D94", fill = "white", linecolor = NA, linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "#2F5D94", fill = c("#DAE6F2", "white"), linecolor = NA, size = size) ), lRed = list( colnames.style = colnames_style( color = "#A3262A", fill = "white", linecolor = NA, linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "#A3262A", fill = c("#F7DBDA", "white"), linecolor = NA, size = size) ), lGreen = list( colnames.style = colnames_style( color = "#7F993A", fill = "white", linecolor = NA, linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "#7F993A", fill = c("#E8F3DE", "white"), linecolor = NA, size = size) ), lViolet = list( colnames.style = colnames_style( color = "#67417B", fill = "white", linecolor = NA, linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "#67417B", fill = c("#E6DEEC", "white"), linecolor = NA, size = size) ), lCyan = list( colnames.style = colnames_style( color = "#00889E", fill = "white", linecolor = NA, linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "#00889E", fill = c("#D5EFF4", "white"), linecolor = NA, size = size) ), lOrange = list( colnames.style = colnames_style( color = "#F15F00", fill = "white", linecolor = NA, linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "#F15F00", fill = c("#FBE5D4", "white"), linecolor = NA, size = size) ), lBlackWhite = list( colnames.style = colnames_style( color = "white", fill = "black", size = size, linecolor = "black", linewidth = 1), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = "white", linecolor = "black", size = size) ), lBlueWhite = list( colnames.style = colnames_style( color = "white", fill = "#477DC0", size = size, linecolor = "#477DC0", linewidth = 1), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = "white", linecolor = "#477DC0", size = size) ), lRedWhite = list( colnames.style = colnames_style( color = "white", fill = "#D04042", size = size, linecolor = "#D04042", linewidth = 1), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = "white", linecolor = "#D04042", size = size) ), lGreenWhite = list( colnames.style = colnames_style( color = "white", fill = "#8CC257", size = size, linecolor = "#8CC257", linewidth = 1), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = "white", linecolor = "#8CC257", size = size) ), lVioletWhite = list( colnames.style = colnames_style( color = "white", fill = "#895AA3", size = size, linecolor = "#895AA3", linewidth = 1), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = "white", linecolor = "#895AA3", size = size) ), lCyanWhite = list( colnames.style = colnames_style( color = "white", fill = "#00AEC9", size = size, linecolor = "#00AEC9", linewidth = 1), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = "white", linecolor = "#00AEC9", size = size) ), lOrangeWhite = list( colnames.style = colnames_style( color = "white", fill = "#FB8F2D", size = size, linecolor = "#FB8F2D", linewidth = 1), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = "white", linecolor = "#FB8F2D", size = size) ), mBlack = list( colnames.style = colnames_style( color = "white", fill = "black", size = size, linecolor = "white", linewidth = 1), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = c("#A6A6A6", "#D9D9D9"), linecolor = "white", size = size) ), mBlue = list( colnames.style = colnames_style( color = "white", fill = "#477DC0", linecolor = "white", linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = c("#B5CBE5", "#DAE6F2"), linecolor = "white", size = size) ), mRed = list( colnames.style = colnames_style( color = "white", fill = "#D04042", linecolor = "white", linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = c("#EFB4B5", "#F7DBDA"), linecolor = "white", size = size) ), mGreen = list( colnames.style = colnames_style( color = "white", fill = "#8CC257", linecolor = "white", linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = c("#D3E8BB", "#E8F3DE"), linecolor = "white", size = size) ), mViolet = list( colnames.style = colnames_style( color = "white", fill = "#895AA3", linecolor = "white", linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = c("#D0BDDA", "#E6DEEC"), linecolor = "white", size = size) ), mCyan = list( colnames.style = colnames_style( color = "white", fill = "#00AEC9", linecolor = "white", linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = c("#ADDFEA", "#D5EFF4"), linecolor = "white", size = size) ), mOrange = list( colnames.style = colnames_style( color = "white", fill = "#EC8C2C", linecolor = "white", linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = c("#FDD2AF", "#FFE9D7"), linecolor = "white", size = size) ), mBlackWhite = list( colnames.style = colnames_style( color = "white", fill = "black", size = size, linecolor = "black", linewidth = 1), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = c("#A6A6A6", "white"), linecolor = "black", size = size) ), mBlueWhite = list( colnames.style = colnames_style( color = "white", fill = "#477DC0", linecolor = "#477DC0", linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = c("#B5CBE5", "white"), linecolor = "#477DC0", size = size) ), mRedWhite = list( colnames.style = colnames_style( color = "white", fill = "#D04042", linecolor = "#D04042", linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = c("#EFB4B5", "white"), linecolor = "#D04042", size = size) ), mGreenWhite = list( colnames.style = colnames_style( color = "white", fill = "#8CC257", linecolor = "#8CC257", linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = c("#D3E8BB", "white"), linecolor = "#8CC257", size = size) ), mVioletWhite = list( colnames.style = colnames_style( color = "white", fill = "#895AA3", linecolor = "#895AA3", linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = c("#D0BDDA", "white"), linecolor = "#895AA3", size = size) ), mCyanWhite = list( colnames.style = colnames_style( color = "white", fill = "#00AEC9", linecolor = "#00AEC9", linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = c("#ADDFEA", "white"), linecolor = "#00AEC9", size = size) ), mOrangeWhite = list( colnames.style = colnames_style( color = "white", fill = "#EC8C2C", linecolor = "#EC8C2C", linewidth = 1, size = size), rownames.style = rownames_style(size = size), tbody.style = tbody_style(color = "black", fill = c("#FDD2AF", "white"), linecolor = "#EC8C2C", size = size) ) , NULL ) style } ggpubr/R/stat_stars.R0000644000176200001440000000305414332553655014270 0ustar liggesusers#' @include utilities.R NULL #' Add Stars to a Scatter Plot #' @description Create a star plot by drawing segments from group centroid to each points. #' @inheritParams ggplot2::layer #' @param ... other arguments to pass to \code{\link[ggplot2]{geom_segment}}. #' @param na.rm If FALSE (the default), removes missing values with a warning. #' If TRUE silently removes missing values. #' @seealso \code{\link{ggscatter}} #' @examples #' # Load data #' data("mtcars") #' df <- mtcars #' df$cyl <- as.factor(df$cyl) #' #' # Scatter plot with ellipses and group mean points #' ggscatter(df, x = "wt", y = "mpg", #' color = "cyl", shape = "cyl", #' mean.point = TRUE, ellipse = TRUE)+ #' stat_stars(aes(color = cyl)) #' #' @export stat_stars <- function(mapping = NULL, data = NULL, geom = "segment", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...) { layer( stat = StatStars, data = data, mapping = mapping, geom = geom, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(na.rm = na.rm, ...) ) } StatStars<- ggproto("StatStars", Stat, required_aes = c("x", "y"), compute_group = function(data, scales) { x.mean <- mean(data$x, na.rm = TRUE) y.mean <- mean(data$y, na.rm = TRUE) res <- data %>% mutate(x = x.mean, y = y.mean) res$xend <- data$x res$yend <- data$y res } ) ggpubr/R/axis_scale.R0000644000176200001440000000531714332553655014220 0ustar liggesusers#'Change Axis Scale: log2, log10 and more #' #'@description Change axis scale. #'\itemize{ #'\item \code{xscale}: change x axis scale. #'\item \code{yscale}: change y axis scale. #'} #'@param .scale axis scale. Allowed values are one of c("none", "log2", "log10", #' "sqrt", "percent", "dollar", "scientific"); e.g.: .scale="log2". #'@param .format ogical value. If TRUE, axis tick mark labels will be formatted #' when .scale = "log2" or "log10". #' #' #'@examples #'# Basic scatter plots #'data(cars) #'p <- ggscatter(cars, x = "speed", y = "dist") #'p #' #'# Set log scale #'p + yscale("log2", .format = TRUE) #'@name axis_scale #'@rdname axis_scale #'@export xscale <- function(.scale, .format = FALSE) { .x <- NULL if(.format & .scale %in% c("percent", "dollar", "scientific")) .format <- FALSE if(.format){ if(.scale == "log2"){ scale_x_continuous(trans = scales::log2_trans(), breaks = scales::trans_breaks("log2", function(x) 2^x), labels = scales::trans_format("log2", scales::math_format(2^.x))) } else if(.scale == "log10"){ scale_x_continuous(trans = scales::log10_trans(), breaks = scales::trans_breaks("log10", function(x) 10^x), labels = scales::trans_format("log10", scales::math_format(10^.x))) } } else if(.scale %in% c("log2", "log10")){ scale_x_continuous(trans = .scale) } else{ switch(.scale, percent = scale_x_continuous(labels = scales::percent), dollar = scale_x_continuous(labels = scales::dollar), scientific = scale_x_continuous(labels = scales::scientific) ) } } #'@rdname axis_scale #'@export yscale <- function(.scale, .format = FALSE) { .x <- NULL if(.format & .scale %in% c("percent", "dollar", "scientific")) .format <- FALSE if(.format){ if(.scale == "log2"){ scale_y_continuous(trans = scales::log2_trans(), breaks = scales::trans_breaks("log2", function(x) 2^x), labels = scales::trans_format("log2", scales::math_format(2^.x))) } else if(.scale == "log10"){ scale_y_continuous(trans = scales::log10_trans(), breaks = scales::trans_breaks("log10", function(x) 10^x), labels = scales::trans_format("log10", scales::math_format(10^.x))) } } else if(.scale %in% c("log2", "log10")){ scale_y_continuous(trans = .scale) } else{ switch(.scale, percent = scale_y_continuous(labels = scales::percent), dollar = scale_y_continuous(labels = scales::dollar), scientific = scale_y_continuous(labels = scales::scientific) ) } } ggpubr/R/stat_friedman_test.R0000644000176200001440000001171614332576562015766 0ustar liggesusers#' @include utilities.R utils_stat_test_label.R stat_anova_test.R utils-aes.R NULL #'Add Friedman Test P-values to a GGPlot #'@description Add automatically Friedman test p-values to a ggplot, such as #' box blots, dot plots and stripcharts. #'@inheritParams ggplot2::layer #'@inheritParams stat_pvalue_manual #'@inheritParams stat_anova_test #' #'@section Computed variables: \itemize{ #' \item{statistic}: the value of the test statistic (Chi-squared). #' \item{df}: the degrees of freedom of the approximate chi-squared distribution of the test statistic. #' \item{p}: p-value. #' \item{p.adj}: Adjusted p-values. #' \item{p.signif}: P-value significance. #' \item{p.adj.signif}: Adjusted p-value significance. #' \item{p.format}: Formated p-value. #' \item{p.adj.format}: Formated adjusted p-value. #' \item{n}: number of samples. #' } #' #' @examples #' # Data preparation #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' # Transform `dose` into factor variable #' df <- ToothGrowth #' df$dose <- as.factor(df$dose) #' df$id <- as.factor(c(rep(1:10, 3), rep(11:20, 3))) #' # Add a random grouping variable #' set.seed(123) #' df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20))) #' df$len <- ifelse(df$group == "grp2", df$len+2, df$len) #' df$len <- ifelse(df$group == "grp3", df$len+7, df$len) #' head(df, 3) #' #' #' # Basic boxplot #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' # Create a basic boxplot #' # Add 5% and 10% space to the plot bottom and the top, respectively #' bxp <- ggboxplot(df, x = "dose", y = "len") + #' scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) #' #' # Add the p-value to the boxplot #' bxp + stat_friedman_test(aes(wid = id)) #' #' # Change the label position #' # Using coordinates in data units #' bxp + stat_friedman_test(aes(wid = id), label.x = "1", label.y = 10, hjust = 0) #' #' # Format the p-value differently #' custom_p_format <- function(p) { #' rstatix::p_format(p, accuracy = 0.0001, digits = 3, leading.zero = FALSE) #' } #' bxp + stat_friedman_test( #' aes(wid = id), #' label = "Friedman test, italic(p) = {custom_p_format(p)}{p.signif}" #' ) #' #' # Show a detailed label in italic #' bxp + stat_friedman_test(aes(wid = id), label = "as_detailed_italic") #' #' #' # Faceted plots #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' # Create a ggplot facet #' df$id <- rep(1:10,6) #' bxp <- ggboxplot(df, x = "dose", y = "len", facet.by = "supp") + #' scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) #' # Add p-values #' bxp + stat_friedman_test(aes(wid = id)) #' #' #' # Grouped plots #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' df$id <- rep(1:10,6) #' bxp <- ggboxplot(df, x = "dose", y = "len", color = "supp", palette = "jco") #' #' # For each legend group, computes tests within x variable groups #' bxp + stat_friedman_test(aes(wid = id, group = supp, color = supp), within = "x") #' #' # For each x-position, computes tests within legend variable groups #' bxp + stat_friedman_test( #' aes(wid = id, group = supp, color = supp), #' within = "group", label = "p = {p.format}" #' ) #' #' @export stat_friedman_test <- function(mapping = NULL, data = NULL, wid = NULL, group.by = NULL, label = "{method}, p = {p.format}", label.x.npc = "left", label.y.npc = "top", label.x = NULL, label.y = NULL, step.increase = 0.1, p.adjust.method = "holm", significance = list(), geom = "text", position = "identity", na.rm = FALSE, show.legend = FALSE, inherit.aes = TRUE, parse = FALSE, ...) { label <- get_friedman_test_label_template(label) if(missing(parse) & is_plotmath_expression(label)){ parse <- TRUE } if(!is.null(wid)){ if(!is.null(mapping)) mapping$wid <- as.name(wid) else mapping <- create_aes(list(wid = wid)) } layer( stat = StatCompareMultipleMeans, data = data, mapping = mapping, geom = geom, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list( method = "friedman_test", method.args = list(), group.by = group.by, correction = "none", na.rm = na.rm, stat.label = label, label.x.npc = label.x.npc , label.y.npc = label.y.npc, label.x = label.x, label.y = label.y, parse = parse, is.group.specified = is_group_aes_specified(mapping), step.increase = step.increase, p.adjust.method = p.adjust.method, significance = fortify_signif_symbols_encoding(significance), ... ) ) } get_friedman_test_label_template <- function(label){ switch( label, as_italic = "{method}, italic(p) = {p.format}", as_detailed = "{method}, X2({df}) = {statistic}, p = {p.format}, n = {n}", as_detailed_italic = "{method}, italic(chi)^2 ({df}) = {statistic}, italic(p) = {p.format}, italic(n) = {n}", as_detailed_expression = "{method}, italic(chi)^2({df}) = {statistic}, italic(p) = {p.format}, italic(n) = {n}", label ) } ggpubr/R/facet.R0000644000176200001440000001342214342705172013155 0ustar liggesusers#' @include utilities.R NULL #'Facet a ggplot into Multiple Panels #'@description Create multi-panel plots of a data set grouped by one or two #' grouping variables. Wrapper around \code{\link[ggplot2]{facet_wrap}} #'@param p a ggplot #'@param facet.by character vector, of length 1 or 2, specifying grouping #' variables for faceting the plot into multiple panels. Should be in the data. #'@param nrow,ncol Number of rows and columns in the panel. Used only when the #' data is faceted by one grouping variable. #'@param scales should axis scales of panels be fixed ("fixed", the default), #' free ("free"), or free in one dimension ("free_x", "free_y"). #'@param short.panel.labs logical value. Default is TRUE. If TRUE, create short #' labels for panels by omitting variable names; in other words panels will be #' labelled only by variable grouping levels. #'@param labeller Character vector. An alternative to the argument #' \code{short.panel.labs}. Possible values are one of "label_both" (panel #' labelled by both grouping variable names and levels) and "label_value" #' (panel labelled with only grouping levels). #'@param panel.labs a list of one or two character vectors to modify facet panel #' labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies #' the labels for the "sex" variable. For two grouping variables, you can use #' for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", #' "Lev", "Lev2") ). #'@param panel.labs.background a list to customize the background of panel #' labels. Should contain the combination of the following elements: \itemize{ #' \item \code{color, linetype, size}: background line color, type and size #' \item \code{fill}: background fill color. } For example, #' panel.labs.background = list(color = "blue", fill = "pink", linetype = #' "dashed", size = 0.5). #'@param panel.labs.font a list of aestheics indicating the size (e.g.: 14), the #' face/style (e.g.: "plain", "bold", "italic", "bold.italic") and the color #' (e.g.: "red") and the orientation angle (e.g.: 45) of panel labels. #'@param panel.labs.font.x,panel.labs.font.y same as panel.labs.font but for #' only x and y direction, respectively. #'@param strip.position (used only in \code{facet_wrap()}). By default, the #' labels are displayed on the top of the plot. Using \code{strip.position} it #' is possible to place the labels on either of the four sides by setting #' \code{strip.position = c("top", "bottom", "left", "right")} #'@param ... not used #' @examples #' p <- ggboxplot(ToothGrowth, x = "dose", y = "len", #' color = "supp") #' print(p) #' #' facet(p, facet.by = "supp") #' #' # Customize #' facet(p + theme_bw(), facet.by = "supp", #' short.panel.labs = FALSE, # Allow long labels in panels #' panel.labs.background = list(fill = "steelblue", color = "steelblue") #' ) #'@name facet #'@rdname facet #'@export facet <- function(p, facet.by, nrow = NULL, ncol = NULL, scales = "fixed", short.panel.labs = TRUE, labeller = "label_value", panel.labs = NULL, panel.labs.background = list(color = NULL, fill = NULL), panel.labs.font = list(face = NULL, color = NULL, size = NULL, angle = NULL), panel.labs.font.x = panel.labs.font, panel.labs.font.y = panel.labs.font, strip.position = "top", ... ) { if(length(facet.by) > 2) stop("facet.by should be of length 1 or 2.") if(!missing(labeller)){ if(labeller == "label_value") short.panel.labs = TRUE else if(labeller == "label_both") short.panel.labs = FALSE else stop("Don't support the following labeller: ", labeller, call. = FALSE) } panel.labs.background <- .compact(panel.labs.background) panel.labs.font.x <- .compact(panel.labs.font.x) panel.labs.font.y <- .compact(panel.labs.font.y) .labeller <- "label_value" if(!is.null(panel.labs)){ .labeller <- .create_labeller(p$data, panel.labs) } else if(!short.panel.labs) { .labeller <- label_both } if(length(facet.by) == 1){ facet.formula <- paste0("~", glue::backtick(facet.by)) %>% stats::as.formula() p <- p + facet_wrap(facet.formula, nrow = nrow, ncol = ncol, scales = scales, labeller = .labeller, strip.position = strip.position) } else if(length(facet.by) == 2){ facet.formula <- paste(glue::backtick(facet.by), collapse = " ~ ") %>% stats::as.formula() p <- p + facet_grid(facet.formula, scales = scales, labeller = .labeller) } if(!.is_empty(panel.labs.background)) p <- p + theme(strip.background = do.call(element_rect, panel.labs.background)) if(!.is_empty(panel.labs.font.x)) p <- p + theme(strip.text.x = do.call(element_text, panel.labs.font.x)) if(!.is_empty(panel.labs.font.y)) p <- p + theme(strip.text.y = do.call(element_text, panel.labs.font.y)) p } # Create labeller to rename panel labels .create_labeller <- function(data, panel.labs = NULL) { if(is.null(panel.labs)) return(NULL) if(!is.null(panel.labs) & !.is_list(panel.labs)) stop("Argument panel.labs should be a list. Read the documentation.") if(is.null(names(panel.labs))) stop("panel.labs should be a named list. ", "Ex: panel.labs = list(sex = c('Male', 'Female') )") variables <- names(panel.labs) . <- NULL .labels <- list() for(variable in variables){ current.levels <- .levels(data[[variable]]) provided.levels <- panel.labs[[variable]] if(length(current.levels) != length(provided.levels)){ stop("The number of ", variable, " levels in panel.labs ", "and in the data are different.") } names(provided.levels) <- current.levels .labels[[variable]] <- provided.levels } if(!.is_empty(.labels)) do.call(ggplot2::labeller, .labels) else return(NULL) } ggpubr/R/geom_exec.R0000644000176200001440000000607114340626116014026 0ustar liggesusers#' Execute ggplot2 functions #' @description A helper function used by ggpubr functions to execute any geom_* #' functions in ggplot2. Useful only when you want to call a geom_* function #' without carrying about the arguments to put in aes(). Basic users of ggpubr #' don't need this function. #' @param geomfunc a ggplot2 function (e.g.: geom_point) #' @param data a data frame to be used for mapping #' @param position Position adjustment, either as a string, or the result of a #' call to a position adjustment function. #' @param ... arguments accepted by the function #' @return return a plot if geomfunc!=Null or a list(option, mapping) if #' geomfunc = NULL. #' @examples #' \dontrun{ #' ggplot() + geom_exec(geom_point, data = mtcars, #' x = "mpg", y = "wt", size = "cyl", color = "cyl") #' } #' @export geom_exec <- function (geomfunc = NULL, data = NULL, position = NULL, ...) { params <- list(...) mapping <- list() # option to pass to mapping aes() option <- list() # option to the geom_*() allowed_options <- c( # general "x", "y", "color", "colour", "linetype", "fill", "size", "shape", "width", "alpha", "na.rm", "lwd", "pch", "cex", "position", "stat", "geom", "show.legend", "inherit.aes", "fun.args", "fontface", # point "stroke", # boxplot "outlier.colour", "outlier.shape", "outlier.size", "outlier.stroke", "notch", "notchwidth", "varwidth", # dot plot "binwidth", "binaxis", "method", "binpositions", "stackdir", "stackratio", "dotsize", # Violin "trim", "draw_quantiles", "scale", # error "ymin", "ymax", "xmin", "xmax", # text "label", "hjust", "vjust", "fontface", "angle", "family", "parse", # text.repel "segment.size", "force", "max.overlaps", "seed", # smooth "se", "level", "fullrange", "conf.int.level", # straightline "xintercept", "yintercept", # histograms "bins", "weight", # rug "sides", # segment "arrow", "xend", "yend", # stat_summary, "fun.data", "fun.y", "fun.ymin", "fun.ymax", # bracket "y.position", "tip.length", "label.size", "step.increase", "bracket.nudge.y", "bracket.shorten", "coord.flip" ) columns <- colnames(data) for (key in names(params)) { value <- params[[key]] if (is.null(value)) { } else if (unlist(value)[1] %in% columns & key %in% allowed_options) { mapping[[key]] <- value } else if (key %in% allowed_options) { option[[key]] <- value } else if (key =="group") { mapping[[key]] <- value # for line plot } else if(key == "step.group.by"){ # for geom_bracket, value are variable name. # but this parameter is an option not an aes option[[key]] <- value } # else warnings("Don't know '", key, "'") } if (!is.null(position)) option[["position"]] <- position option[["data"]] <- data if(is.null(geomfunc)){ res <- list(option = option, mapping = mapping) } else{ option[["mapping"]] <- create_aes(mapping) res <- do.call(geomfunc, option) } res } ggpubr/R/stat_central_tendency.R0000644000176200001440000000716114332553655016460 0ustar liggesusers#' @include utilities.R NULL #' Add Central Tendency Measures to a GGPLot #' #' @description Add central tendency measures (mean, median, mode) to density #' and histogram plots created using ggplots. #' #' Note that, normally, the mode is used for categorical data where we wish to #' know which is the most common category. Therefore, we can have have two or #' more values that share the highest frequency. This might be problematic for #' continuous variable. #' #' For continuous variable, we can consider using mean or median as the #' measures of the central tendency. #' @inheritParams ggplot2::layer #' @param ... other arguments to pass to \code{\link[ggplot2:geom_path]{geom_line}}. #' @param na.rm If FALSE (the default), removes missing values with a warning. #' If TRUE silently removes missing values. #' @param type the type of central tendency measure to be used. Possible values #' include: \code{"mean", "median", "mode"}. #' @seealso \code{\link{ggdensity}} #' @examples #' # Simple density plot #' data("mtcars") #' ggdensity(mtcars, x = "mpg", fill = "red") + #' scale_x_continuous(limits = c(-1, 50)) + #' stat_central_tendency(type = "mean", linetype = "dashed") #' #' # Color by groups #' data(iris) #' ggdensity(iris, "Sepal.Length", color = "Species") + #' stat_central_tendency(aes(color = Species), type = "median", linetype = 2) #' #' # Use geom = "point" for central tendency #' data(iris) #' ggdensity(iris, "Sepal.Length", color = "Species") + #' stat_central_tendency( #' aes(color = Species), type = "median", #' geom = "point", size = 4 #' ) #' #' # Facet #' ggdensity(iris, "Sepal.Length", facet.by = "Species") + #' stat_central_tendency(type = "mean", color = "red", linetype = 2) + #' stat_central_tendency(type = "median", color = "blue", linetype = 2) #' #' @export stat_central_tendency <- function(mapping = NULL, data = NULL, geom = c("line", "point"), position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, type = c("mean", "median", "mode"), ...) { type <- match.arg(type) geom <- match.arg(geom) if(is.null(mapping)){ mapping <- ggplot2::aes(y = NULL) }else{ mapping["y"] <- list(NULL) } layer( stat = StatCentralTendency, data = data, mapping = mapping, geom = geom, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(na.rm = na.rm, type = type, geom = geom, ...) ) } StatCentralTendency <- ggproto("StatCentralTendency", Stat, required_aes = c("x"), compute_group = function(data, scales, type, geom) { center.func <- switch (type, mean = mean, median = stats::median, mode = .get_mode ) .center <- center.func(data$x, na.rm = TRUE) n <- length(.center) if(geom == "line"){ results <- data.frame(x = rep(.center, each = 2),y = c(-Inf, Inf)) if(n >= 2) results$group <- rep(1:n, each = 2) } else if(geom == "point") { results <- data.frame(x = .center, y = -Inf) } results } ) # from rstatix .get_mode <- function (x, na.rm = TRUE) { if(na.rm) x <- stats::na.omit(x) .x <- factor(x) .table <- table(.x) .max <- max(.table) if (all(.table == .max)) { .mode <- NA } else { .mode <- names(.table)[.table == .max] } if (is.numeric(x)) { .mode <- as.numeric(.mode) } .mode } ggpubr/R/get_coord.R0000644000176200001440000000552714332576562014057 0ustar liggesusers#' Checks and Returns Data Coordinates from Multiple Input Options #' #' @description Checks and returns selected coordinates from multiple input #' options, which can be either data (x-y) coordinates or npc (normalized #' parent coordinates). #' #' Helper function internally used in \code{ggpubr} function to guess the type #' of coordinates specified by the user. For example, in the function #' \code{stat_cor()}, users can specify either the option \code{label.x} (data #' coordinates) or \code{label.x.npc} (npc coordinates); those coordinates are #' passed to \code{get_coord()}, which will make some checking and then return #' a unique coordinates for the label position. #' @inheritParams as_npc #' @param data.ranges a numeric vector of length 2 containing the data ranges #' (minimum and the maximum). Should be specified only when \code{coord = #' NULL} and \code{npc} is specified. Used to convert \code{npc} to data #' coordinates. Considered only when the argument \code{npc} is specified. #' @param coord data coordinates (i.e., either x or y coordinates). #' @param npc numeric (in [0-1]) or character vector of coordinates. If #' character, should be one of c('right', 'left', 'bottom', 'top', 'center', #' 'centre', 'middle'). Note that, the \code{data.ranges}, \code{step} and #' \code{margin.npc}, arguments are considered only when \code{npc} is #' specified. The option \code{npc} is ignored when the argument \code{coord} is specified. #' @return a numeric vector representing data coordinates. #' @seealso \code{\link{as_npc}}, \code{\link{npc_to_data_coord}}. #' @examples #' # If npc is specified, it is converted into data coordinates #' get_coord(data.ranges = c(2, 20), npc = "left") #' get_coord(data.ranges = c(2, 20), npc = 0.1) #' #' # When coord is specified, no transformation is performed #' # because this is assumed to be a data coordinate #' get_coord(coord = 5) #' #' # For grouped plots #' res_top <- get_coord( #' data.ranges = c(4.2, 36.4), group = c(1, 2, 3), #' npc = "top", step = -0.1, margin.npc = 0 #' ) #' res_top #' #' @rdname get_coord #' @export get_coord <- function(group = 1L, data.ranges = NULL, coord = NULL, npc = "left", step = 0.1, margin.npc = 0.05){ if(!is.null(coord)){ if(!is.numeric(group)){ stop("get_coord: 'group' should be numeric. ", "Current class is: ", class(group)) } # If coords are too short, they are recycled. coord <- ifelse(length(coord) >= group, coord[group], coord[1]) return(coord) } else if(!is.null(npc)){ if(is.null(data.ranges)){ stop("Specify the option data.ranges", call. = FALSE) } npc <- ifelse(length(npc) >= group, npc[group], npc[1]) coord <- as_npc( npc, group = group, step = step, margin.npc = margin.npc ) %>% npc_to_data_coord(data.ranges) } coord } ggpubr/R/ggtext.R0000644000176200001440000002256714342651060013403 0ustar liggesusers#' @include utilities.R ggpar.R stat_chull.R stat_conf_ellipse.R NULL #' Text #' @description Add text to a plot. #' @inheritParams ggscatter #' @param data a data frame #' @param x,y x and y variables for drawing. #' @param label the name of the column containing point labels. Can be also a #' character vector with length = nrow(data). #' @param color text font color. #' @param size text font size. #' @param face text font style. Allowed values are one of c("plain", "bold", #' "italic", "bold.italic"). #' @param parse If \code{TRUE}, the labels will be parsed into expressions and #' displayed as described in \code{?plotmath}. #' @param family character vector specifying font family. #' @param show.legend logical. Should text be included in the legends? NA, the #' default, includes if any aesthetics are mapped. FALSE never includes, and #' TRUE always includes. #' @param label.select can be of two formats: \itemize{ \item a character vector #' specifying some labels to show. \item a list containing one or the #' combination of the following components: \itemize{ \item \code{top.up} and #' \code{top.down}: to display the labels of the top up/down points. For #' example, \code{label.select = list(top.up = 10, top.down = 4)}. \item #' \code{criteria}: to filter, for example, by x and y variabes values, use #' this: \code{label.select = list(criteria = "`y` > 2 & `y` < 5 & `x` \%in\% #' c('A', 'B')")}. } } #' @param repel a logical value, whether to use ggrepel to avoid overplotting #' text labels or not. #' @param label.rectangle logical value. If TRUE, add rectangle underneath the #' text, making it easier to read. #' @param grouping.vars grouping variables to sort the data by, when the user #' wants to display the top n up/down labels. #' @param position Position adjustment, either as a string, or the result of a #' call to a position adjustment function. #' @param ggp a ggplot. If not NULL, points are added to an existing plot. #' @param ... other arguments to be passed to \code{\link{ggpar}}. #' @details The plot can be easily customized using the function ggpar(). Read #' ?ggpar for changing: \itemize{ \item main title and axis labels: main, #' xlab, ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis #' scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: #' palette = "Dark2" or palette = c("gray", "blue", "red") \item legend title, #' labels and position: legend = "right" } #' @seealso \code{\link{ggpar}} #' @examples #' # Load data #' data("mtcars") #' df <- mtcars #' df$cyl <- as.factor(df$cyl) #' df$name <- rownames(df) #' head(df[, c("wt", "mpg", "cyl")], 3) #' #' # Textual annotation #' # +++++++++++++++++ #' ggtext(df, x = "wt", y = "mpg", #' color = "cyl", palette = c("#00AFBB", "#E7B800", "#FC4E07"), #' label = "name", repel = TRUE) #' #' # Add rectangle around label #' ggtext(df, x = "wt", y = "mpg", #' color = "cyl", palette = c("#00AFBB", "#E7B800", "#FC4E07"), #' label = "name", repel = TRUE, label.rectangle = TRUE) #' #' #' @export ggtext <- function(data, x = NULL, y = NULL, label = NULL, color = "black", palette = NULL, size = 11, face = "plain", family = "", show.legend = NA, label.select = NULL, repel = FALSE, label.rectangle = FALSE, parse = FALSE, grouping.vars = NULL, position = "identity", ggp = NULL, ggtheme = theme_pubr(), ...) { . <- NULL .dots <- list(...) data <- as.data.frame(data) if(length(label) >1){ if(length(label) != nrow(data)) stop("The argument label should be a column name or a vector of length = nrow(data). ", "It seems that length(label) != nrow(data)") else data$label.xx <- label label <- "label.xx" } if(is.null(ggp)) p <- ggplot(data, create_aes(list(x = x, y = y))) else p <- ggp # Add textual annotation # ++++++ alpha <- 1 if(!is.null(list(...)$alpha)) alpha <- list(...)$alpha if(is.null(label)) return(p) lab_data <- data # Special case for density plot and histogram # y is calculated as ..count.. or ..density.. # we should estimate label y from ggplot2 output .is_density_plot <- y[1] %in% c("..count..", "..density..") if(.is_density_plot){ lab_data <- .hist_label_data(p, grouping.vars = list(...)$facet.by, x = x) y <- "lab.y" # hist.data <- ggplot_build(p)$data[[1]][, c("x", "y", "count", "density")] # hist.x <- hist.data$x # hist.y <- hist.data$y # break.x <- c(0, hist.x) %>% unique() # label.break <- 1:(length(break.x)-1) # lab.y <- .select_vec(data, x) %>% # cut(breaks = break.x, labels = label.break) %>% # hist.y[.] # lab_data$lab.y <- lab.y # y <- "lab.y" # lab_data <- lab_data %>% dplyr::filter(!is.na(lab.y)) } # Select some labels to show if(!is.null(label.select)){ lab_data <- .get_label_data (lab_data, x, y, label = label, label.select = label.select, grouping.vars = grouping.vars) } if(repel){ max.overlaps = getOption("ggrepel.max.overlaps", default = Inf) ggfunc <- ggrepel::geom_text_repel if(label.rectangle) ggfunc <- ggrepel::geom_label_repel p <- p + geom_exec(ggfunc, data = lab_data, x = x, y = y, label = label, fontface = face, family = family, show.legend = show.legend, size = size/3, color = color, alpha = alpha, parse = parse, box.padding = unit(0.35, "lines"), point.padding = unit(0.3, "lines"), force = 1, segment.size = 0.2, seed = 123, max.overlaps = max.overlaps ) } else{ ggfunc <- geom_text vjust <- -0.7 hjust <- NULL if(label.rectangle) { ggfunc <- geom_label vjust <- -0.4 } vjust <- ifelse(is.null(.dots$vjust), vjust, .dots$vjust) if(!is.null(.dots$hjust)) hjust <- .dots$hjust p <- p + geom_exec(ggfunc, data = lab_data, x = x, y = y, color = color, label = label, fontface = face, family = family, show.legend = show.legend, size = size/3, color = color, parse = parse, vjust = vjust, hjust = hjust, alpha = alpha, position = position) } #p <- ggpar(p, palette = palette, ggtheme = ggtheme, ...) if(family != "") p <- p + theme(text = element_text(family = family)) p } # data: data frame # x, y: x and y variables # label: label columns # label.select: select some labels. Can be a character vector, or a list # with the following components (top.up, top.down) # grouping.vars grouping variables .get_label_data <- function(data, x, y, label = NULL, label.select = NULL, grouping.vars = NULL) { if(.is_list(label.select)){ expected.components = c("top.up", "top.down", "criteria") if(!any(expected.components %in% names(label.select))) stop("If label.select is a list, it should contain one or the combination ", "of the following element: ", .collapse(expected.components, sep = ", ")) } data <- as.data.frame(data) if(is.null(label)) lab_data <- NULL else if(is.null(label.select)) lab_data <- data else if(.is_list(label.select)){ lab_data <- data top_up <- top_down <- . <- NULL if(!is.null(label.select$top.up)) top_up <- .top_up(data, x, y, n = label.select$top.up, grouping.vars = grouping.vars) if(!is.null(label.select$top.down)) top_down <- .top_down(data, x, y, n = label.select$top.down, grouping.vars = grouping.vars) if(!is.null(top_up) | !is.null(top_down)) lab_data <- rbind(top_up, top_down) if(!is.null(label.select$criteria)){ criteria <- gsub("`y`", y, label.select$criteria) %>% gsub("`x`", x, .) lab_data <- dplyr::filter(lab_data, !!rlang::parse_expr(criteria)) } } else lab_data <- subset(data, data[[label]] %in% label.select, drop = FALSE) return(lab_data) } # Get histogram/density label y coord from ggplot output # grouping.vars : facet variables # x: x variable name .hist_label_data <- function(p, grouping.vars = NULL, x = NULL){ . <- NULL #x <- .mapping(p) %>%.$x hist.data <- ggplot_build(p)$data[[1]] data <- p$data if(is.null(grouping.vars)){ return(.hist_label_y(hist.data, data, x )) } data <- p$data %>% df_nest_by(vars = grouping.vars) hist.data <- hist.data %>% df_nest_by(vars = "PANEL") %>% .$data data <- data %>% mutate(hist.data = hist.data) lab.data <- purrr::map2(data$hist.data, data$data, .hist_label_y, x) data <- data %>% mutate(lab.data = lab.data ) %>% df_select(vars = c( "lab.data", grouping.vars)) %>% tidyr::unnest() data } # Get histogram/density label y coord from ggplot output # hist.data: histogram data. ggplot_build(p)$data[[1]] # data: data frame # x: x variable name .hist_label_y <- function(hist.data, data, x ){ . <- NULL hist.x <- hist.data$x hist.y <- hist.data$y break.x <- c(0, hist.x) %>% unique() label.break <- 1:(length(break.x)-1) lab.y <- .select_vec(data, x) %>% cut(breaks = break.x, labels = label.break) %>% hist.y[.] data$lab.y <- lab.y data <- data %>% dplyr::filter(!is.na(lab.y)) data } ggpubr/R/ggparagraph.R0000644000176200001440000000547614337533653014377 0ustar liggesusers#' @include utilities.R NULL #'Draw a Paragraph of Text #'@description Draw a paragraph o text. Splits a long text into multiple lines #' (by inserting line breaks) so that the output will fit within the current #' viewport. #'@param text the text to plot. #'@param color font color, example: color = "black" #'@param size font size, example: size = 12 #'@param face font face. Allowed values are one of "plain", "italic", "bold", #' "bold.italic". #' @param family font family #' @param lineheight Line height, example: lineheight = 2. #'@author Alboukadel Kassambara #' @examples #' # Density plot #'density.p <- ggdensity(iris, x = "Sepal.Length", #' fill = "Species", palette = "jco") #' #'# Text plot #'text <- paste("iris data set gives the measurements in cm", #' "of the variables sepal length and width", #' "and petal length and width, respectively,", #' "for 50 flowers from each of 3 species of iris.", #' "The species are Iris setosa, versicolor, and virginica.", sep = " ") #'text.p <- ggparagraph(text, face = "italic", size = 12) #' #'# Arrange the plots on the same page #'ggarrange(density.p, text.p, #' ncol = 1, nrow = 2, #' heights = c(1, 0.3)) #' #'@export ggparagraph <- function(text, color = NULL, size = NULL, face = NULL, family = NULL, lineheight = NULL) { style <- grid::gpar(col = color, fontsize = size, fontface = face, fontfamily = family, lineheight = lineheight) p <- grid::grob(text = text, cl = "splitText", gp = style) p <- as_ggplot(p) return(p) } # Helper functions # Extracted from RGraphics package # ::::::::::::::::::::::::::::::::::::::::::: splitString <- function(text) { strings <- strsplit(text, " ")[[1]] if (length(strings) < 2) return(text) newstring <- strings[1] linewidth <- grid::stringWidth(newstring) gapwidth <- grid::stringWidth(" ") availwidth <- grid::convertWidth(unit(1, "npc"), "in", valueOnly=TRUE) for (i in 2:length(strings)) { width <- grid::stringWidth(strings[i]) if (grid::convertWidth(linewidth + gapwidth + width, "in", valueOnly=TRUE) < availwidth) { sep <- " " linewidth <- linewidth + gapwidth + width } else { sep <- "\n" linewidth <- width } newstring <- paste(newstring, strings[i], sep=sep) } newstring } #' @method drawDetails splitText #' @rdname ggparagraph #' @param x a grid grob #' @param recording a logical value indicating whether a grob is being added to #' the display list or redrawn from the display list. #' @export drawDetails.splitText <- function(x, recording) { grid::grid.text(splitString(x$text), x=0, y=1, just=c("left", "top")) } ggpubr/R/stat_anova_test.R0000644000176200001440000005751314333301370015272 0ustar liggesusers#' @include utilities.R utils_stat_test_label.R NULL #'Add Anova Test P-values to a GGPlot #'@description Adds automatically one-way and two-way ANOVA test p-values to a #' ggplot, such as box blots, dot plots and stripcharts. #'@inheritParams ggplot2::layer #'@inheritParams stat_pvalue_manual #'@param method ANOVA test methods. Possible values are one of #' \code{c("one_way", "one_way_repeated", "two_way", "two_way_repeated", #' "two_way_mixed")}. #'@param wid (factor) column name containing individuals/subjects identifier. #' Should be unique per individual. Required only for repeated measure tests #' (\code{"one_way_repeated", "two_way_repeated", "friedman_test", etc}). #'@param group.by (optional) character vector specifying the grouping variable; #' it should be used only for grouped plots. Possible values are : \itemize{ #' \item \code{"x.var"}: Group by the x-axis variable and perform the test #' between legend groups. In other words, the p-value is compute between legend #' groups at each x position \item \code{"legend.var"}: Group by the legend #' variable and perform the test between x-axis groups. In other words, the #' test is performed between the x-groups for each legend level. } #'@param type the type of sums of squares for ANOVA. Allowed values are either #' 1, 2 or 3. \code{type = 2} is the default because this will yield identical #' ANOVA results as type = 1 when data are balanced but type = 2 will #' additionally yield various assumption tests where appropriate. When the data #' are unbalanced the \code{type = 3} is used by popular commercial softwares #' including SPSS. #'@param effect.size the effect size to compute and to show in the ANOVA #' results. Allowed values can be either "ges" (generalized eta squared) or #' "pes" (partial eta squared) or both. Default is "ges". #'@param error (optional) for a linear model, an lm model object from which the #' overall error sum of squares and degrees of freedom are to be calculated. #' Read more in \code{\link[car]{Anova}()} documentation. #'@param correction character. Used only in repeated measures ANOVA test to #' specify which correction of the degrees of freedom should be reported for #' the within-subject factors. Possible values are: \itemize{ \item{"GG"}: #' applies Greenhouse-Geisser correction to all within-subjects factors even if #' the assumption of sphericity is met (i.e., Mauchly's test is not #' significant, p > 0.05). \item{"HF"}: applies Hyunh-Feldt correction to all #' within-subjects factors even if the assumption of sphericity is met, #' \item{"none"}: returns the ANOVA table without any correction and #' \item{"auto"}: apply automatically GG correction to only within-subjects #' factors violating the sphericity assumption (i.e., Mauchly's test p-value is #' significant, p <= 0.05). } #'@param p.adjust.method method for adjusting p values (see #' \code{\link[stats]{p.adjust}}). Has impact only in a situation, where #' multiple pairwise tests are performed; or when there are multiple grouping #' variables. Allowed values include "holm", "hochberg", "hommel", #' "bonferroni", "BH", "BY", "fdr", "none". If you don't want to adjust the p #' value (not recommended), use p.adjust.method = "none". #'@param significance a list of arguments specifying the signifcance cutpoints #' and symbols. For example, \code{significance <- list(cutpoints = c(0, #' 0.0001, 0.001, 0.01, 0.05, Inf), symbols = c("****", "***", "**", "*", #' "ns"))}. #' #' In other words, we use the following convention for symbols indicating #' statistical significance: \itemize{ \item \code{ns}: p > 0.05 \item #' \code{*}: p <= 0.05 \item \code{**}: p <= 0.01 \item \code{***}: p <= 0.001 #' \item \code{****}: p <= 0.0001 } #'@param label character string specifying label. Can be: \itemize{ \item the #' column containing the label (e.g.: \code{label = "p"} or \code{label = #' "p.adj"}), where \code{p} is the p-value. Other possible values are #' \code{"p.signif", "p.adj.signif", "p.format", "p.adj.format"}. \item an #' expression that can be formatted by the \code{\link[glue]{glue}()} package. #' For example, when specifying \code{label = "Anova, p = \{p\}"}, the #' expression \{p\} will be replaced by its value. \item a combination of #' plotmath expressions and glue expressions. You may want some of the #' statistical parameter in italic; for example:\code{label = "Anova, italic(p) #' = {p}"}. \item a constant: \code{label = "as_italic"}: display statistical #' parameters in italic; \code{label = "as_detailed"}: detailed plain text; #' \code{label = "as_detailed_expression"} or \code{label = #' "as_detailed_italic"}: detailed plotmath expression. Statistical parameters #' will be displayed in italic.}. #'@param label.x.npc,label.y.npc can be \code{numeric} or \code{character} #' vector of the same length as the number of groups and/or panels. If too #' short they will be recycled. \itemize{ \item If \code{numeric}, value should #' be between 0 and 1. Coordinates to be used for positioning the label, #' expressed in "normalized parent coordinates". \item If \code{character}, #' allowed values include: i) one of c('right', 'left', 'center', 'centre', #' 'middle') for x-axis; ii) and one of c( 'bottom', 'top', 'center', 'centre', #' 'middle') for y-axis.} #'@param parse If TRUE, the labels will be parsed into expressions and displayed #' as described in \code{?plotmath}. #'@param label.x,label.y \code{numeric} Coordinates (in data units) to be used #' for absolute positioning of the label. If too short they will be recycled. #'@param step.increase numeric value in with the increase in fraction of total #' height for every additional comparison to minimize overlap. The step value #' can be negative to reverse the order of groups. #'@param ... other arguments to pass to #' \code{\link[ggplot2:geom_text]{geom_text}}, such as:\itemize{ \item #' \code{hjust}: horizontal justification of the text. Move the text left or #' right and \item \code{vjust}: vertical justification of the text. Move the #' text up or down. } #'@param na.rm If FALSE (the default), removes missing values with a warning. If #' TRUE silently removes missing values. #' #'@section Computed variables: \itemize{ \item{DFn}: Degrees of Freedom in the #' numerator (i.e. DF effect). \item{DFd}: Degrees of Freedom in the #' denominator (i.e., DF error). \item{ges}: Generalized Eta-Squared measure of #' effect size. Computed only when the option \code{effect.size = "ges"}. #' \item{pes}: Partial Eta-Squared measure of effect size. Computed only when #' the option \code{effect.size = "pes"}. \item{F}: F-value. \item{p}: p-value. #' \item{p.adj}: Adjusted p-values. \item{p.signif}: P-value significance. #' \item{p.adj.signif}: Adjusted p-value significance. \item{p.format}: #' Formated p-value. \item{p.adj.format}: Formated adjusted p-value. \item{n}: #' number of samples. } #' #' @examples #' # Data preparation #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' # Transform `dose` into factor variable #' df <- ToothGrowth #' df$dose <- as.factor(df$dose) #' # Add individuals id #' df$id <- rep(1:10, 6) #' # Add a random grouping variable #' set.seed(123) #' df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20))) #' df$len <- ifelse(df$group == "grp2", df$len+2, df$len) #' df$len <- ifelse(df$group == "grp3", df$len+7, df$len) #' head(df, 3) #' #' #' # Basic boxplot #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' # Create a basic boxplot #' # Add 5% and 10% space to the plot bottom and the top, respectively #' bxp <- ggboxplot(df, x = "dose", y = "len") + #' scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) #' #' # Add the p-value to the boxplot #' bxp + stat_anova_test() #' #'\dontrun{ #' # Change the label position #' # Using coordinates in data units #' bxp + stat_anova_test(label.x = "1", label.y = 10, hjust = 0) #' } #' #' # Format the p-value differently #' custom_p_format <- function(p) { #' rstatix::p_format(p, accuracy = 0.0001, digits = 3, leading.zero = FALSE) #' } #' bxp + stat_anova_test( #' label = "Anova, italic(p) = {custom_p_format(p)}{p.signif}" #' ) #' #' # Show a detailed label in italic #' bxp + stat_anova_test(label = "as_detailed_italic") #' #' #' # Faceted plots #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' # Create a ggplot facet #' bxp <- ggboxplot(df, x = "dose", y = "len", facet.by = "supp") + #' scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) #' # Add p-values #' bxp + stat_anova_test() #' #' #' # Grouped plots #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' bxp2 <- ggboxplot(df, x = "group", y = "len", color = "dose", palette = "npg") #' #' # For each x-position, computes tests between legend groups #' bxp2 + stat_anova_test(aes(group = dose), label = "p = {p.format}{p.signif}") #' #' # For each legend group, computes tests between x variable groups #' bxp2 + stat_anova_test(aes(group = dose, color = dose), group.by = "legend.var") #' #' #'\dontrun{ #' # Two-way ANOVA: Independent measures #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' # Visualization: box plots with p-values #' # Two-way interaction p-values between x and legend (group) variables #' bxp3 <- ggboxplot( #' df, x = "supp", y = "len", #' color = "dose", palette = "jco" #' ) #' bxp3 + stat_anova_test(aes(group = dose), method = "two_way") #' #' # One-way repeatead measures ANOVA #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' df$id <- as.factor(c(rep(1:10, 3), rep(11:20, 3))) #' ggboxplot(df, x = "dose", y = "len") + #' stat_anova_test(method = "one_way_repeated", wid = "id") #' #' # Two-way repeatead measures ANOVA #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' df$id <- as.factor(rep(1:10, 6)) #' ggboxplot(df, x = "dose", y = "len", color = "supp", palette = "jco") + #' stat_anova_test(aes(group = supp), method = "two_way_repeated", wid = "id") #' #' # Grouped one-way repeated measures ANOVA #' ggboxplot(df, x = "dose", y = "len", color = "supp", palette = "jco") + #' stat_anova_test(aes(group = supp, color = supp), #' method = "one_way_repeated", wid = "id", group.by = "legend.var") #' } #'@export stat_anova_test <- function(mapping = NULL, data = NULL, method = c("one_way", "one_way_repeated", "two_way", "two_way_repeated", "two_way_mixed"), wid = NULL, group.by = NULL, type = NULL, effect.size = "ges", error = NULL, correction = c("auto", "GG", "HF", "none"), label = "{method}, p = {p.format}", label.x.npc = "left", label.y.npc = "top", label.x = NULL, label.y = NULL, step.increase = 0.1, p.adjust.method = "holm", significance = list(), geom = "text", position = "identity", na.rm = FALSE, show.legend = FALSE, inherit.aes = TRUE, parse = FALSE, ...) { label <- get_anova_test_label_template(label) if(effect.size == "pes"){ label <- gsub(pattern = "ges", replacement = "pes", x = label, fixed = TRUE) label <- gsub(pattern = "eta2[g]", replacement = "eta2[p]", x = label, fixed = TRUE) } if(missing(parse) & is_plotmath_expression(label)){ parse <- TRUE } correction <- match.arg(correction) method <- match.arg(method) if(!is.null(wid)){ if(!is.null(mapping)) mapping$wid <- as.name(wid) else mapping <- create_aes(list(wid = wid)) } layer( stat = StatCompareMultipleMeans, data = data, mapping = mapping, geom = geom, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list( method = method, method.args = list(type = type, effect.size = effect.size, error = error), group.by = group.by, correction = correction, na.rm = na.rm, stat.label = label, label.x.npc = label.x.npc , label.y.npc = label.y.npc, label.x = label.x, label.y = label.y, parse = parse, is.group.specified = is_group_aes_specified(mapping), step.increase = step.increase, p.adjust.method = p.adjust.method, significance = fortify_signif_symbols_encoding(significance), ... ) ) } StatCompareMultipleMeans <- ggproto("StatCompareMultipleMeans", Stat, required_aes = c("x", "y"), default_aes = aes(wid = NULL), setup_params = function(data, params){ # Initialize parameters methods_for_repeated_measures <- c( "one_way_repeated", "two_way_repeated", "two_way_mixed", "friedman_test" ) if(params$method %in% methods_for_repeated_measures){ if(is.null(data$wid)){ stop( "The argument `wid` (sample id) is required for repeated measures tests.\n", "Specify it using wid = 'id_col_name', where id_col_name is the column containing ids.", call. = FALSE ) } } return(params) }, compute_panel = function(data, scales, method, method.args, group.by, correction, p.adjust.method, stat.label, label.x.npc, label.y.npc, label.x, label.y, significance, is.group.specified, step.increase){ p <- p.adj <- x <- NULL if(method %in% c("one_way_repeated", "friedman_test")){ # One-way repeated measures ANOVA if(!is.group.specified) data$group <- data$x } df <- data is.grouped.plots <- contains_multiple_grouping_vars(df) # Statistical test parameters # test_args <- get_test_args(data, between, within) test_args <- get_test_args(data, method = method, group.by = group.by) wid <- test_args$wid between <- test_args$between within <- test_args$within group <- test_args$group nb_vars <- unique(c(between, within)) %>% length() is_two_way <- nb_vars >= 2 # label group id. Used to shift coordinates to avoid overlaps. x.group <- y.group <- 1 # Run statistical tests df <- df %>% rstatix::convert_as_factor(vars = c(between, within)) if(!is.null(group)){ df <- df %>% rstatix::df_group_by(vars = group) } anova_methods <- c("one_way", "one_way_repeated", "two_way", "two_way_repeated", "two_way_mixed") if(method %in% anova_methods){ method.args <- method.args %>% .add_item(data = df, dv = "y", between = between, within = within, wid = wid) stat.test <- do.call(rstatix::anova_test, method.args) %>% rstatix::get_anova_table(correction = correction) method.name <- "Anova" } else if(method == "kruskal_test"){ .formula <- paste0("y ~", between) %>% as.formula() method.name <- "Kruskal-Wallis" stat.test <- rstatix::kruskal_test(df, .formula) stat.test$statistic <- round(stat.test$statistic, 2) } else if(method == "welch_anova_test"){ .formula <- paste0("y ~", between) %>% as.formula() method.name <- "Welch Anova" stat.test <- rstatix::welch_anova_test(df, .formula) stat.test$statistic <- round(stat.test$statistic, 2) } else if(method == "friedman_test"){ if(is.null(within)) stop("The argument 'within' is required.") if(is.null(wid)) stop("The argument 'wid' is required.") .formula <- paste0("y ~", within, " | ", wid) %>% as.formula() method.name <- "Friedman test" stat.test <- rstatix::friedman_test(df, .formula) stat.test$statistic <- round(stat.test$statistic, 2) } else stop("Don't support the method: ", method, call. = FALSE) # Prepare the output for visualization if(is_two_way){ stat.test <- get_interaction_row(stat.test) } # Grouped tests else if(!is.null(group)){ # When data is grouped by x variable and # stat test computed between legend groups at each x position # labels group ids are x if(group == "x"){ x.group <- stat.test$x if(is.null(label.x) & length(label.x.npc) == 1) label.x <- stat.test$x } # When data is grouped by legend variable and # stat test is computed between x variable groups # labels are stacked on y-axis. labels group ids are group (legend var) else if(group == "group"){ if("colour" %in% colnames(df)) stat.test$colour <- .levels(df$colour) y.group <- stat.test$group } } sy <- significance stat.test <- stat.test %>% rstatix::adjust_pvalue(method = p.adjust.method) %>% rstatix::add_significance(p.col = "p", cutpoints = sy$cutpoints, symbols = sy$symbols) %>% rstatix::add_significance(p.col = "p.adj", cutpoints = sy$cutpoints, symbols = sy$symbols) %>% rstatix::p_format(p, p.adj, new.col = TRUE, accuracy = 1e-4) %>% add_stat_n() %>% keep_only_tbl_df_classes() %>% mutate(method = method.name) %>% add_stat_label(label = stat.label) stat.test$x <- get_coord( data.ranges = scales$x$dimension(), coord = label.x, npc = label.x.npc, margin.npc = 0, group = x.group ) stat.test$y <- get_coord( data.ranges = scales$y$dimension(), coord = label.y, npc = label.y.npc, margin.npc = 0, group = y.group, step = -step.increase ) # set label horizontal and vertical justification stat.test <- set_label_hvjust(stat.test, data = df, group) stat.test } ) # Returns arguments for the tests # data: layer data get_test_args <- function(data, method = "one_way", group.by = NULL){ # Sample within id (considered for repeated measures) wid <- between <- within <- group <- NULL if("wid" %in% colnames(data)) wid <- "wid" # Grouped or not grouped plots is_grouped_plots <- data %>% contains_multiple_grouping_vars() if(method %in% c("one_way", "kruskal_test", "welch_anova_test")){ between <- "x" if(is_grouped_plots){ if(is.null(group.by)) group.by = "x.var" # Group by x axis variable and compute test between legend variable groups if(group.by == "x.var") { group <- "x" between <-"group" } # Group by legend variable and compute test between x axis groups else if (group.by == "legend.var") { group <- "group" between <- "x" } } } else if(method %in% c("one_way_repeated", "friedman_test")){ within <- "x" if(is_grouped_plots){ if(is.null(group.by)) group.by = "x.var" # Group by x axis variable and compute test between legend variable groups if(group.by == "x.var") { group <- "x" within <-"group" } # Group by legend variable and compute test between x axis groups else if (group.by == "legend.var") { group <- "group" within <- "x" } } } else if(method == "two_way"){ between <- c("x", "group") } else if(method == "two_way_repeated"){ within <- c("x", "group") } else if(method == "two_way_mixed"){ # Auto-detect between- and within-subjects variables # ID should be unique for one of the grouping variable # It's unique for the within-ss variable when data is in a wide format wid.freq.group <- data %>% dplyr::group_by(.data$group) %>% dplyr::count(.data$wid) %>% dplyr::pull(.data$n) %>% unique() wid.freq.x <- data %>% dplyr::group_by(.data$x) %>% dplyr::count(.data$wid) %>% dplyr::pull(.data$n) %>% unique() if(!(all(wid.freq.group == 1) | all(wid.freq.x == 1))){ stop("This is not a mixed design. Check your data.", call. = FALSE) } if(all(wid.freq.group == 1)){ within <- "group" between <- "x" } else if(all(wid.freq.x == 1)){ within <- "x" between <- "group" } } # end two-way-mixed list(dv = "y", between = between, within = within, wid = wid, group = group) } # Take the last row of the ANOVA table # Corresponds to the interaction in the two-way model. # In one-way: there is only one row get_interaction_row <- function(stat.test){ utils::tail(stat.test, 1) } get_anova_test_label_template <- function(label){ switch( label, as_italic = "{method}, italic(p) = {p.format}", as_detailed = "{method}, F({DFn}, {DFd}) = {F}, eta2[g] = {ges}, p = {p.format}, n = {n}", as_detailed_italic = "{method}, italic(F)({DFn}, {DFd}) = {F}, eta2[g] = {ges}, italic(p) = {p.format}, italic(n) = {n}", as_detailed_expression = "{method}, italic(F)({DFn}, {DFd}) = {F}, eta2[g] = {ges}, italic(p) = {p.format}, italic(n) = {n}", label ) } # set label horizontal and vertical justification # group: grouping variable; either "x" or "group" set_label_hvjust <- function(stat.test, data, group){ # Adapt hjust and x position for grouped plots hjust <- 0.2 vjust <- 0 if(!is.null(group)){ if(group == "x"){ # Center label at each x position hjust <- 0.5 } else if(group == "group"){ n <- length(stat.test$group) dodge <- 0.8 if(all(stat.test$x == 1)){ # Move labels to the left stat.test$x <- round(((dodge - dodge*n) / (2*n)) + stat.test$x, 2) } } } # Two-way anova: move label to the left else if("Effect" %in% colnames(stat.test)){ if(all(stat.test$Effect == "x:group") & all(stat.test$x == 1)){ n <- length(unique(data$group)) dodge <- 0.8 stat.test$x <- round(((dodge - dodge*n) / (2*n)) + stat.test$x, 2) } } stat.test <- stat.test %>% tibble::add_column(hjust = hjust, vjust = vjust) stat.test } ggpubr/R/create_aes.R0000644000176200001440000000547614334106575014203 0ustar liggesusers#' @include utilities.R NULL #' Create Aes Mapping from a List #' #' @description Create aes mapping to make programming easy with ggplot2. #' @param .list a list of aesthetic arguments; for example .list = list(x = "dose", y = "len", color = "dose"). #' @param parse logical. If TRUE, parse the input as an expression. #' #' @examples #' # Simple aes creation #' create_aes(list(x = "Sepal.Length", y = "Petal.Length" )) #' #' # Parse an expression #' x <- "log2(Sepal.Length)" #' y <- "log2(Petal.Length)" #' create_aes(list(x = x, y = y ), parse = TRUE) #' #' # Create a ggplot #' mapping <- create_aes(list(x = x, y = y ), parse = TRUE) #' ggplot(iris, mapping) + #' geom_point() #' #' @export create_aes <- function(.list, parse = TRUE){ if(missing(parse)){ parse <- base::getOption("ggpubr.parse_aes", default = TRUE) } if(parse){ return(create_aes.parse(.list)) } else{ return(create_aes.name(.list)) } } # Ref: https://rpubs.com/hadley/97970 # Parse as name. If x_var is "a + b", as.name() will turn it into a variable called `a + b`. create_aes.name <- function(.list){ .list <- .list %>% purrr::map(function(x) to_name(x)) do.call(ggplot2::aes, .list) } # Parse an expression. If x_var is "a + b", parse() will turn it into the function call a + b create_aes.parse <- function(.list){ .list <- .list %>% purrr:: map(function(x) parse_expression(x)) do.call(ggplot2::aes, .list) } parse_expression <- function(x){ if(is_parsable_aes(x)){ # if contains space, just add backsticks using as.name() if(contains_space(x)){ if(!is_math_string(x)) return(as.name(x)) } x <- parse(text = x)[[1]] } x } to_name <- function(x){ if(is_parsable_aes(x)){ x <- as.name(x) } x } # Check if parsable aesthetic is_parsable_aes <- function(x){ is.character(x) & (!is_numeric_char(x)) & (length(x) == 1) } # Check if x is a numeric string "1", "2" # return TRUE for "1", "2", etc is_numeric_char <- function(x){ if(is.character(x)) res <- grepl("^[[:digit:]]+$", x) else res <- FALSE res } # Fortify variable name---------------------------- fortify_variable_name <- function(x){ if(contains_space(x)){ if(!is_math_string(x)){ } } } # Check if string contains space contains_space <- function(x){ grepl("\\s", x) } extract_text_inside_parenthesis <- function(x){ regmatches(x, gregexpr("(?<=\\().*?(?=\\))", x, perl=TRUE))[[1]] } # Check if text contains mathematical operators is_math_string <- function(x){ # operators <- unlist(lapply( c("Arith","Compare","Math"), methods::getGroupMembers )) operators <- c("+", "-", "*", "^", "%%", "%/%", "/", "==", ">", "<", "!=", "<=", ">=") contains_math_operators <- unlist(lapply(operators, grepl, x, fixed = TRUE)) contains_parentheses <- grepl(pattern = "\\(.*\\)", x) any(c(contains_math_operators, contains_parentheses)) } ggpubr/R/utils_stat_test_label.R0000644000176200001440000001346614332576562016504 0ustar liggesusers# check symnum.args -------------------------------- fortify_signif_symbols_encoding <- function(symnum.args = list()){ if(.is_empty(symnum.args)){ symnum.args <- list( cutpoints = c(0, 1e-04, 0.001, 0.01, 0.05, Inf), symbols = c("****", "***", "**", "*", "ns") ) } else { symnum.args.isok <- (length(symnum.args$symbols) == length(symnum.args$cutpoints)-1) if(!symnum.args.isok) stop( "Incorrect format detected in 'symnum.args'. ", "Check the documentation. ", "length(symbols) should be length(cutpoints)-1", call. = FALSE ) } symnum.args } # Check user specified label ----------------------------- # Check if is glue package expression is_glue_expression <- function(label){ grepl("\\{|\\}", label, perl = TRUE) } # Check if label is a plotmath expression contains_plotmath_symbols <- function(label){ grepl("==|italic\\s?\\(|bold\\s?\\(|bolditalic\\s?\\(", label) } starts_with_list <- function(label){ grepl("^list\\s?\\(", label) } is_plotmath_expression <- function(label){ starts_with_list(label) | contains_plotmath_symbols(label) } # Fortify label -------------------- # if label is plotmath expression, then # fortify it in case users miss something contains_p_signif <- function(label){ any(grepl("p*\\.signif", label)) } contains_twoequal_signs <- function(label){ any(grepl("==", label)) } replace_simple_by_double_equals <- function(label){ if(!contains_twoequal_signs(label)){ label <- gsub("=", "==", label) } label } escape_psignif_asteriks <- function(label){ # Escaping asteriks (special plotmath symbols) in p.signif or p.adj.signif by adding quote label <- gsub(pattern = "\\}\\{(p.*.signif)\\}", replacement = "}*`{\\1}`", x = label) # p signif preceded with space label <- gsub(pattern = "~\\{(p.*.signif)\\}", replacement = "~`{\\1}`", x = label) label <- gsub(pattern = "=+?\\s+?\\{(p.*.signif)}", replacement = "== `{\\1}`", x = label) label } # Get statistical test label to be displayed ------------------- # stat.test: statistical test output # description: the description of the stat test, for example: "Anova" # label: can be p, p.signif, p.adj or glue expression add_stat_label <- function (stat.test, label = NULL){ is_plotmath <- FALSE if(is.null(label)){ stat.test$label <- add_p(stat.test$p.format) } else{ is_plotmath <- is_plotmath_expression(label) if(is_plotmath){ label <- fortify_plotmath(label) } if(is_glue_expression(label)){ stat.test <- stat.test %>% mutate(label = glue(label)) } else { if(!(label %in% colnames(stat.test))){ stop( "Can't find the value of the argument label ('", label, "') in the computed ", "statistical test table.", call. = FALSE ) } stat.test$label <- as.character(stat.test[[label]]) } } label <- gsub(pattern = "=+(\\s+)?<", replacement = "<\\1", stat.test$label ) if(is_plotmath){ label <- replace_simple_by_double_equals(label) label <- gsub(pattern = "\\s", replacement = "~", label) label <- gsub(pattern = "~==~", replacement = "~`=`~", label ) label <- gsub(pattern = "~<~", replacement = "~`<`~", label ) # Make sure that decimal values will be displayed asis in character when parsed by ggplot # Add quote around numeric values label <- gsub("([0-9.-]+)", "'\\1'", label) # Escape minus in text label <- gsub("Kruskal'-'Wallis", "'Kruskal-Wallis'", label) } stat.test$label <- label stat.test } fortify_plotmath <- function(label){ label <- gsub(pattern = "~", replacement = " ", x = label, fixed = TRUE) if(!starts_with_list(label)) label <- paste0("list(", label, ")") if(contains_p_signif(label)){ # Escape p signif stars label <- gsub(pattern = "\\}\\{p.signif\\}", replacement = "}*`{p.signif}`", x = label) label <- gsub(pattern = "\\}\\{p.adj.signif\\}", replacement = "}*`{p.adj.signif}`", x = label) # Escape p signif stars preceded by space label <- gsub(pattern = "\\s\\{p.signif\\}", replacement = " `{p.signif}`", x = label) label <- gsub(pattern = "\\s\\{p.adj.signif\\}", replacement = " `{p.adj.signif}`", x = label) # Escape p signif stars preceded by equal signs label <- gsub(pattern = "=(\\s+)?\\{p.signif}", replacement = "=\\1`{p.signif}`", x = label) label <- gsub(pattern = "=(\\s+)?\\{p.adj.signif}", replacement = "=\\1`{p.adj.signif}`", x = label) } label <- gsub(pattern = "eta2[g]", replacement = "eta[g]^2", x = label, fixed = TRUE) label <- gsub(pattern = "eta2[p]", replacement = "eta[p]^2", x = label, fixed = TRUE) label } # Add p prefix # add_p(0.05) --> p = 0.05 # add_p("<0.05") --> p < 0.05 add_p <-function(label){ contain.inf.symbol <- grepl("<", label) label2 <- paste0("p", " = ", label) if(sum(contain.inf.symbol) > 0){ # no need to add = label2[contain.inf.symbol] <- paste0("p", label[contain.inf.symbol]) } # Add space before and after inf symbol label2 <- gsub(pattern = "<", replacement = " < ", label2) label2 } # Add statistical test number of samples add_stat_n <- function(stat.test){ stat.test$n <- rstatix::get_n(stat.test) stat.test } # gg layer data checking -------------------------- # Check whether there is multiple grouping variables # This is the case for grouped plots contains_multiple_grouping_vars <- function(data){ !all(data$x == data$group) } # Check if group variable is specified in aes is_group_aes_specified <- function(mapping){ answer <- FALSE if(is.null(mapping)) { answer <- FALSE } else if(!is.null(mapping$group)) { answer <- TRUE } answer } # Manipulating statistical test outputs ----------------- keep_only_tbl_df_classes <- function(x){ toremove <- setdiff(class(x), c("tbl_df", "tbl", "data.frame")) if(length(toremove) > 0){ class(x) <- setdiff(class(x), toremove) } x } ggpubr/R/stat_welch_anova_test.R0000644000176200001440000001122514332576562016462 0ustar liggesusers#' @include utilities.R utils_stat_test_label.R stat_anova_test.R NULL #'Add Welch One-Way ANOVA Test P-values to a GGPlot #'@description Add Welch one-way ANOVA test p-values to a ggplot, such as #' box blots, dot plots and stripcharts. #'@inheritParams ggplot2::layer #'@inheritParams stat_pvalue_manual #'@inheritParams stat_anova_test #' #'@section Computed variables: \itemize{ #' \item{statistic}: the value of the test statistic (F-value) #' \item{DFn}: Degrees of Freedom in the numerator (i.e. DF effect) #' \item{DFd}: Degrees of Freedom in the denominator (i.e., DF error) #' \item{p}: p-value. #' \item{p.adj}: Adjusted p-values. #' \item{p.signif}: P-value significance. #' \item{p.adj.signif}: Adjusted p-value significance. #' \item{p.format}: Formated p-value. #' \item{p.adj.format}: Formated adjusted p-value. #' \item{n}: number of samples. #' } #' #' @examples #' # Data preparation #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' # Transform `dose` into factor variable #' df <- ToothGrowth #' df$dose <- as.factor(df$dose) #' # Add a random grouping variable #' set.seed(123) #' df$group <- sample(factor(rep(c("grp1", "grp2", "grp3"), 20))) #' df$len <- ifelse(df$group == "grp2", df$len+2, df$len) #' df$len <- ifelse(df$group == "grp3", df$len+7, df$len) #' head(df, 3) #' #' #' # Basic boxplot #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' # Create a basic boxplot #' # Add 5% and 10% space to the plot bottom and the top, respectively #' bxp <- ggboxplot(df, x = "dose", y = "len") + #' scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) #' #' # Add the p-value to the boxplot #' bxp + stat_welch_anova_test() #' #' # Change the label position #' # Using coordinates in data units #' bxp + stat_welch_anova_test(label.x = "1", label.y = 10, hjust = 0) #' #' # Format the p-value differently #' custom_p_format <- function(p) { #' rstatix::p_format(p, accuracy = 0.0001, digits = 3, leading.zero = FALSE) #' } #' bxp + stat_welch_anova_test( #' label = "Welch Anova, italic(p) = {custom_p_format(p)}{p.signif}" #' ) #' #' # Show a detailed label in italic #' bxp + stat_welch_anova_test(label = "as_detailed_italic") #' #' #' # Faceted plots #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' # Create a ggplot facet #' bxp <- ggboxplot(df, x = "dose", y = "len", facet.by = "supp") + #' scale_y_continuous(expand = expansion(mult = c(0.05, 0.1))) #' # Add p-values #' bxp + stat_welch_anova_test() #' #' #' # Grouped plots #' #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #' bxp2 <- ggboxplot(df, x = "group", y = "len", color = "dose", palette = "npg") #' #' # For each x-position, computes tests between legend groups #' bxp2 + stat_welch_anova_test(aes(group = dose), label = "p = {p.format}{p.signif}") #' #' # For each legend group, computes tests between x variable groups #' bxp2 + stat_welch_anova_test(aes(group = dose, color = dose), group.by = "legend.var") #' #' @export stat_welch_anova_test <- function(mapping = NULL, data = NULL, group.by = NULL, label = "{method}, p = {p.format}", label.x.npc = "left", label.y.npc = "top", label.x = NULL, label.y = NULL, step.increase = 0.1, p.adjust.method = "holm", significance = list(), geom = "text", position = "identity", na.rm = FALSE, show.legend = FALSE, inherit.aes = TRUE, parse = FALSE, ...) { label <- get_welch_anova_test_label_template(label) if(missing(parse) & is_plotmath_expression(label)){ parse <- TRUE } layer( stat = StatCompareMultipleMeans, data = data, mapping = mapping, geom = geom, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list( method = "welch_anova_test", method.args = list(), group.by = group.by, correction = "none", na.rm = na.rm, stat.label = label, label.x.npc = label.x.npc , label.y.npc = label.y.npc, label.x = label.x, label.y = label.y, parse = parse, is.group.specified = is_group_aes_specified(mapping), step.increase = step.increase, p.adjust.method = p.adjust.method, significance = fortify_signif_symbols_encoding(significance), ... ) ) } get_welch_anova_test_label_template <- function(label){ switch( label, as_italic = "{method}, italic(p) = {p.format}", as_detailed = "{method}, F({DFn}, {DFd}) = {statistic}, p = {p.format}, n = {n}", as_detailed_italic = "{method}, italic(F)({DFn}, {DFd}) = {F}, italic(p) = {p.format}, italic(n) = {n}", as_detailed_expression = "{method}, italic(F)({DFn}, {DFd}) = {F}, italic(p) = {p.format}, italic(n) = {n}", label ) } ggpubr/R/font.R0000644000176200001440000000742514332553655013055 0ustar liggesusers#'Change the Appearance of Titles and Axis Labels #' #'@description Change the appearance of the main title, subtitle, caption, axis #' labels and text, as well as the legend title and texts. Wrapper around #' \code{\link[ggplot2:element]{element_text}()}. #' #'@param object character string specifying the plot components. Allowed values #' include: \itemize{ \item \code{"title"} for the main title \item #' \code{"subtitle"} for the plot subtitle \item \code{"caption"} for the plot #' caption \item \code{"legend.title"} for the legend title \item #' \code{"legend.text"} for the legend text \item \code{"x", "xlab", or "x.title"} #' for x axis label \item \code{"y", "ylab", or "y.title"} for y axis label \item #' \code{"xy", "xylab", "xy.title" or "axis.title"} for both x and y axis #' labels \item \code{"x.text"} for x axis texts (x axis tick labels) \item #' \code{"y.text"} for y axis texts (y axis tick labels) \item \code{"xy.text"} #' or \code{"axis.text"} for both x and y axis texts } #' #'@param size numeric value specifying the font size, (e.g.: \code{size = 12}). #'@param color character string specifying the font color, (e.g.: \code{color = #' "red"}). #'@param face the font face or style. Allowed values include one of #' \code{"plain", "bold", "italic", "bold.italic"}, (e.g.: \code{face = #' "bold.italic"}). #'@param family the font family. #'@param ... other arguments to pass to the function #' \code{\link[ggplot2:element]{element_text}()}. #' #'@examples #'# Load data #'data("ToothGrowth") #' #'# Basic plot #'p <- ggboxplot(ToothGrowth, x = "dose", y = "len", color = "dose", #' title = "Box Plot created with ggpubr", #' subtitle = "Length by dose", #' caption = "Source: ggpubr", #' xlab ="Dose (mg)", ylab = "Teeth length") #'p #' #'# Change the appearance of titles and labels #'p + #' font("title", size = 14, color = "red", face = "bold.italic")+ #' font("subtitle", size = 10, color = "orange")+ #' font("caption", size = 10, color = "orange")+ #' font("xlab", size = 12, color = "blue")+ #' font("ylab", size = 12, color = "#993333")+ #' font("xy.text", size = 12, color = "gray", face = "bold") #' #'# Change the appearance of legend title and texts #'p + #' font("legend.title", color = "blue", face = "bold")+ #' font("legend.text", color = "red") #' #'@export font <- function(object, size = NULL, color = NULL, face = NULL, family = NULL, ...){ elmt <- element_text(size = size, color = color, face = face, family = family, ...) switch(object, title = theme(plot.title = elmt), subtitle = theme(plot.subtitle = elmt), caption = theme(plot.caption = elmt), x = theme(axis.title.x = elmt), xlab = theme(axis.title.x = elmt), x.title = theme(axis.title.x = elmt), y = theme(axis.title.y = elmt), ylab = theme(axis.title.y = elmt), y.title = theme(axis.title.y = elmt), xy = theme(axis.title.x = elmt, axis.title.y = elmt), xylab = theme(axis.title.x = elmt, axis.title.y = elmt), xy.title = theme(axis.title.x = elmt, axis.title.y = elmt), axis.title = theme(axis.title.x = elmt, axis.title.y = elmt), legendtitle = theme(legend.title = elmt), legend.title = theme(legend.title = elmt), legendtext = theme(legend.text = elmt), legend.text = theme(legend.text = elmt), # Axis tick labels x.text = theme(axis.text.x = elmt), y.text = theme(axis.text.y = elmt), xy.text = theme(axis.text.x = elmt, axis.text.y = elmt), yxtext = theme(axis.text.x = elmt, axis.text.y = elmt), axis.text = theme(axis.text.x = elmt, axis.text.y = elmt), stop("Don't support ", object) ) } ggpubr/R/ggpubr_options.R0000644000176200001440000000040514343207174015131 0ustar liggesusers#' Global Options for GGPubr #' @description Displays allowed global options in ggpubr. #' @examples #' #' ggpubr_options() #' #' @export ggpubr_options <- function(){ list( ggpubr.parse_aes = TRUE, ggpubr.null_device = cowplot::pdf_null_device ) } ggpubr/R/get_palette.R0000644000176200001440000000631614332553655014402 0ustar liggesusers#'Generate Color Palettes #' #'@description Generate a palette of k colors from ggsci palettes, RColorbrewer #' palettes and custom color palettes. Useful to extend RColorBrewer and ggsci to support more colors. #'@param palette Color palette. Allowed values include: \itemize{ \item #' \strong{Grey color palettes}: "grey" or "gray"; \item \strong{RColorBrewer #' palettes}, see \code{\link[RColorBrewer:ColorBrewer]{brewer.pal}} and details section. #' Examples of palette names include: "RdBu", "Blues", "Dark2", "Set2", ...; #' \item \strong{Custom color palettes}. For example, palette = c("#00AFBB", #' "#E7B800", "#FC4E07"); \item \strong{ggsci scientific journal palettes}, #' e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and #' "rickandmorty". } #'@param k the number of colors to generate. #'@details #'\strong{RColorBrewer palettes}: To display all available color #' palettes, type this in R:RColorBrewer::display.brewer.all(). Color palette #' names include: #' #' \itemize{ \item \strong{Sequential palettes}, suited to ordered data that #' progress from low to high. Palette names include: Blues BuGn BuPu GnBu #' Greens Greys Oranges OrRd PuBu PuBuGn PuRd Purples RdPu Reds YlGn YlGnBu #' YlOrBr YlOrRd. \item \strong{Diverging palettes}:Gradient colors. Names #' include: BrBG PiYG PRGn PuOr RdBu RdGy RdYlBu RdYlGn Spectral. \item #' \strong{Qualitative palettes}: Best suited to representing nominal or #' categorical data. Names include: Accent, Dark2, Paired, Pastel1, Pastel2, #' Set1, Set2, Set3. #' } #' #'@return Returns a vector of color palettes. #' #'@examples #'data("iris") #' iris$Species2 <- factor(rep(c(1:10), each = 15)) #' #'# Generate a gradient of 10 colors #'ggscatter(iris, x = "Sepal.Length", y = "Petal.Length", #' color = "Species2", #' palette = get_palette(c("#00AFBB", "#E7B800", "#FC4E07"), 10)) #' #'# Scatter plot with default color palette #'ggscatter(iris, x = "Sepal.Length", y = "Petal.Length", #' color = "Species") #' #'# RColorBrewer color palettes #'ggscatter(iris, x = "Sepal.Length", y = "Petal.Length", #' color = "Species", palette = get_palette("Dark2", 3)) #' #' # ggsci color palettes #'ggscatter(iris, x = "Sepal.Length", y = "Petal.Length", #' color = "Species", palette = get_palette("npg", 3)) #' #' # Custom color palette #'ggscatter(iris, x = "Sepal.Length", y = "Petal.Length", #' color = "Species", #' palette = c("#00AFBB", "#E7B800", "#FC4E07")) #' #' # Or use this #'ggscatter(iris, x = "Sepal.Length", y = "Petal.Length", #' color = "Species", #' palette = get_palette(c("#00AFBB", "#FC4E07"), 3)) #' #' #' #' #' #'@export get_palette <- function(palette = "default", k) { # Check if RColorBrewer, ggsci, hue or grey/gray color palettes if(.is_col_palette(palette)){ if(palette %in% .brewerpal()) .get_brewer_pal(palette, k) else if(palette %in% .ggscipal()) .get_ggsci_pal(palette, k) else if(palette %in% c("default", "hue")){ hues <- seq(15, 375, length = k + 1) grDevices::hcl(h = hues, l = 65, c = 100, alpha = 1)[1:k] } # Grey color palette else if(palette %in% c("grey", "gray")){ grDevices::grey.colors(k, start = 0.2, end = 0.8) } } else grDevices::colorRampPalette(palette)(k) } ggpubr/R/ggdonutchart.R0000644000176200001440000001160114345714554014570 0ustar liggesusers#'@include utilities.R ggpar.R ggpie.R NULL #'Donut chart #'@description Create a donut chart. #'@inheritParams ggboxplot #'@inheritParams ggplot2::geom_errorbar #'@param x variable containing values for drawing. #'@param label variable specifying the label of each slice. #'@param lab.pos character specifying the position for labels. Allowed values #' are "out" (for outside) or "in" (for inside). #'@param lab.adjust numeric value, used to adjust label position when lab.pos = #' "in". Increase or decrease this value to see the effect. #'@param lab.font a vector of length 3 indicating respectively the size (e.g.: #' 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the #' color (e.g.: "red") of label font. For example \emph{lab.font= c(4, "bold", #' "red")}. #'@param font.family character vector specifying font family. #'@param color,fill outline and fill colors. #'@param ... other arguments to be passed to be passed to ggpar(). #'@details The plot can be easily customized using the function ggpar(). Read #' ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, #' ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis #' scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette #' = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels #' and position: legend = "right" \item plot orientation : orientation = #' c("vertical", "horizontal", "reverse") } #'@seealso \code{\link{ggpar}}, \code{\link{ggpie}} #' @examples #' #' # Data: Create some data #' # +++++++++++++++++++++++++++++++ #' #' df <- data.frame( #' group = c("Male", "Female", "Child"), #' value = c(25, 25, 50)) #' #' head(df) #' #' #' # Basic pie charts #' # ++++++++++++++++++++++++++++++++ #' #' ggdonutchart(df, "value", label = "group") #' #' #' # Change color #' # ++++++++++++++++++++++++++++++++ #' #' # Change fill color by group #' # set line color to white #' # Use custom color palette #' ggdonutchart(df, "value", label = "group", #' fill = "group", color = "white", #' palette = c("#00AFBB", "#E7B800", "#FC4E07") ) #' #' #' # Change label #' # ++++++++++++++++++++++++++++++++ #' #' # Show group names and value as labels #' labs <- paste0(df$group, " (", df$value, "%)") #' ggdonutchart(df, "value", label = labs, #' fill = "group", color = "white", #' palette = c("#00AFBB", "#E7B800", "#FC4E07")) #' #' # Change the position and font color of labels #' ggdonutchart(df, "value", label = labs, #' lab.pos = "in", lab.font = "white", #' fill = "group", color = "white", #' palette = c("#00AFBB", "#E7B800", "#FC4E07")) #' #' #' #'@export ggdonutchart <- function( data, x, label = x, lab.pos = c("out", "in"), lab.adjust = 0, lab.font = c(4, "plain", "black"), font.family = "", color = "black", fill = "white", palette = NULL, size = NULL, ggtheme = theme_pubr(), ... ) { lab.pos <- match.arg(lab.pos) lab.font <- .parse_font(lab.font) %>% .check_pie_labfont() # We should order the data in desc order. Because, # in stacked bar plot the order of factor levels are reversed # Very important to have the label in the right place group.vars <- intersect(c(fill, color), names(data)) if(length(group.vars) != 0){ group.val <- dplyr::pull(data, group.vars[1]) data <- data %>% dplyr::arrange(dplyr::desc(group.val)) } # Label y coordinates when placed inside slices .x <- dplyr::pull(data, x) data <- data %>% dplyr::mutate( .lab.ypos. = cumsum(.x) -0.5*.x -lab.adjust ) if(length(label) > 1 & length(label) != nrow(data)) stop("label should be of the same length as data") else if(length(label) > 1){ # 1. Add label column data <- data %>% dplyr::mutate(.label. = label) label <- ".label." } p <- ggplot(data, create_aes(list(x = 2, y = x))) + geom_exec( geom_bar, data, stat = "identity", fill = fill, color = color, size = size ) p <- ggpar( p, palette = palette, ggtheme = ggtheme, font.family = font.family, ... ) + coord_polar( theta = "y", start = 0, clip = "off" ) + ggtheme + .remove_axis() # Annotate pie slice #::::::::::::::::::::::::::::::::::: if(!is.null(label)){ # Label each slice at the middle of the slice if(lab.pos == "out"){ p <- p + scale_y_continuous( breaks = cumsum(.x) - .x/2, labels = dplyr::pull(data, label) ) + ggplot2::theme(axis.text.x = element_text( face = lab.font$face, color = lab.font$color, size = 2.5*lab.font$size, family = font.family )) } # Compute the cumulative sum as label ypos if(lab.pos == "in"){ p <- p + geom_text( create_aes(list(y = ".lab.ypos.", label = label)), size = lab.font$size, fontface = lab.font$face, colour = lab.font$color, family = font.family )+ clean_theme() } } p + xlim(0.5, 2.5) } ggpubr/R/grids.R0000644000176200001440000000227414332553655013214 0ustar liggesusers#'Add Grids to a ggplot #' #'@description Add grids to ggplot. #'@param axis axis for which grid should be added. Allowed values include \code{c("xy", "x", "y")}. #'@param color grid line color. #'@param size numeric value specifying grid line size. #'@param linetype line type. An integer (0:8), a name (blank, solid, dashed, #' dotted, dotdash, longdash, twodash). Sess \code{\link{show_line_types}}. #' #' #'@examples #'# Load data #'data("ToothGrowth") #' #'# Basic plot #'p <- ggboxplot(ToothGrowth, x = "dose", y = "len") #'p #' #'# Add border #' p + grids(linetype = "dashed") #'@export grids <- function(axis = c("xy", "x", "y"), color = "grey92", size = NULL, linetype = NULL) { axis <- match.arg(axis) grid.major <- element_line(color = color, size = size, linetype = linetype) grid.minor <- element_line(color = color, size = 0.25, linetype = linetype) switch(axis, xy = theme(panel.grid.major = grid.major, panel.grid.minor = grid.minor), x = theme(panel.grid.major.x = grid.major, panel.grid.minor.x = grid.minor), y = theme(panel.grid.major.y = grid.major, panel.grid.minor.y = grid.minor) ) } ggpubr/R/ggviolin.R0000644000176200001440000001500114342647721013711 0ustar liggesusers#' @include utilities.R ggpar.R NULL #'Violin plot #'@description Create a violin plot with error bars. Violin plots are similar to #'box plots, except that they also show the kernel probability density of the #'data at different values. #'@inheritParams ggboxplot #'@param width violin width. #'@param alpha color transparency. Values should be between 0 and 1. #'@inheritParams ggplot2::geom_violin #' @param ... other arguments to be passed to #' \code{\link[ggplot2]{geom_violin}}, \code{\link{ggpar}} and #' \code{\link{facet}}. #'@details The plot can be easily customized using the function ggpar(). Read #' ?ggpar for changing: \itemize{ \item main title and axis labels: main, xlab, #' ylab \item axis limits: xlim, ylim (e.g.: ylim = c(0, 30)) \item axis #' scales: xscale, yscale (e.g.: yscale = "log2") \item color palettes: palette #' = "Dark2" or palette = c("gray", "blue", "red") \item legend title, labels #' and position: legend = "right" \item plot orientation : orientation = #' c("vertical", "horizontal", "reverse") } #'@seealso \code{\link{ggpar}} #' @examples #' # Load data #' data("ToothGrowth") #' df <- ToothGrowth #' #' # Basic plot #' # +++++++++++++++++++++++++++ #'ggviolin(df, x = "dose", y = "len") #' # Change the plot orientation: horizontal #'ggviolin(df, "dose", "len", orientation = "horiz") #' #' # Add summary statistics #' # ++++++++++++++++++++++++++ #' # Draw quantiles #' ggviolin(df, "dose", "len", add = "none", #' draw_quantiles = 0.5) #' #' # Add box plot #' ggviolin(df, x = "dose", y = "len", #' add = "boxplot") #' #'ggviolin(df, x = "dose", y = "len", #' add = "dotplot") #' #' # Add jitter points and #' # change point shape by groups ("dose") #'ggviolin(df, x = "dose", y = "len", #' add = "jitter", shape = "dose") #' #' #' # Add mean_sd + jittered points #' ggviolin(df, x = "dose", y = "len", #' add = c("jitter", "mean_sd")) #' #' # Change error.plot to "crossbar" #' ggviolin(df, x = "dose", y = "len", #' add = "mean_sd", error.plot = "crossbar") #' #' #' # Change colors #' # +++++++++++++++++++++++++++ #' # Change outline and fill colors #' ggviolin(df, "dose", "len", #' color = "black", fill = "gray") #' #' # Change outline colors by groups: dose #' # Use custom color palette and add boxplot #' ggviolin(df, "dose", "len", color = "dose", #' palette = c("#00AFBB", "#E7B800", "#FC4E07"), #' add = "boxplot") #' #' # Change fill color by groups: dose #' # add boxplot with white fill color #' ggviolin(df, "dose", "len", fill = "dose", #' palette = c("#00AFBB", "#E7B800", "#FC4E07"), #' add = "boxplot", add.params = list(fill = "white")) #' #' #' # Plot with multiple groups #' # +++++++++++++++++++++ #' # fill or color box plot by a second group : "supp" #'ggviolin(df, "dose", "len", color = "supp", #' palette = c("#00AFBB", "#E7B800"), add = "boxplot") #' #'@export ggviolin <- function(data, x, y, combine = FALSE, merge = FALSE, color = "black", fill = "white", palette = NULL, alpha = 1, title = NULL, xlab = NULL, ylab = NULL, facet.by = NULL, panel.labs = NULL, short.panel.labs = TRUE, linetype = "solid", trim = FALSE, size = NULL, width = 1, draw_quantiles = NULL, select = NULL, remove = NULL, order = NULL, add = "mean_se", add.params = list(), error.plot = "pointrange", label = NULL, font.label = list(size = 11, color = "black"), label.select = NULL, repel = FALSE, label.rectangle = FALSE, position = position_dodge(0.8), ggtheme = theme_pubr(), ...) { # Default options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .opts <- list( combine = combine, merge = merge, color = color, fill = fill, palette = palette, alpha = alpha, title = title, xlab = xlab, ylab = ylab, facet.by = facet.by, panel.labs = panel.labs, short.panel.labs = short.panel.labs, linetype = linetype, trim = trim, size = size, width = width, draw_quantiles = draw_quantiles, select = select , remove = remove, order = order, add = add, add.params = add.params, error.plot = error.plot, label = label, font.label = font.label, label.select = label.select, repel = repel, label.rectangle = label.rectangle, position = position, ggtheme = ggtheme, ...) if(!missing(data)) .opts$data <- data if(!missing(x)) .opts$x <- x if(!missing(y)) .opts$y <- y # User options #::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: .user.opts <- as.list(match.call(expand.dots = TRUE)) .user.opts[[1]] <- NULL # Remove the function name # keep only user arguments for(opt.name in names(.opts)){ if(is.null(.user.opts[[opt.name]])) .opts[[opt.name]] <- NULL } .opts$fun <- ggviolin_core if(missing(ggtheme) & (!is.null(facet.by) | combine)) .opts$ggtheme <- theme_pubr(border = TRUE) if(missing(ggtheme) & !is.null(facet.by)) .opts$ggtheme <- theme_pubr(border = TRUE) p <- do.call(.plotter, .opts) if(.is_list(p) & length(p) == 1) p <- p[[1]] return(p) } ggviolin_core <- function(data, x, y, color = "black", fill = "white", palette = NULL, alpha = 1, title = NULL, xlab = NULL, ylab = NULL, linetype = "solid", trim = FALSE, size = NULL, width = 1, draw_quantiles = NULL, add = "mean_se", add.params = list(), error.plot = "pointrange", ggtheme = theme_pubr(), position = position_dodge(0.8), ...) { if(!is.factor(data[[x]])) data[[x]] <- as.factor(data[[x]]) pms <- .violin_params(...) p <- ggplot(data, create_aes(list(x = x, y = y))) + geom_exec(geom_violin, data = data, color = color, fill = fill, linetype = linetype, trim = trim, size = size, width = width, alpha = alpha, position = position, draw_quantiles = draw_quantiles, stat = pms$stat, scale = pms$scale) # Add #+++++++++++++++++++ if(is.null(add.params$group)){ if(fill %in% names(data)) add.params$group <- fill } add.params <- .check_add.params(add, add.params, error.plot, data, color, fill, ...) %>% .add_item(p = p, add = add, error.plot = error.plot, position = position) p <- do.call(ggadd, add.params) %>% ggpar(palette = palette, ggtheme = ggtheme, title = title, xlab = xlab, ylab = ylab,...) p } ggpubr/NEWS.md0000644000176200001440000010424214367517046012656 0ustar liggesusers# ggpubr 0.6.0 ## New features - New function `ggadjust_pvalue()` added to adjust p-values produced by `geom_pwc()` on a ggplot (#522). - New data added: `gene_expression` - Global options: New available package options: `ggpubr.null_device`, which value should be a function that creates an appropriate null device. These include: `cowplot::pdf_null_device`, `cowplot::png_null_device`, `cowplot::cairo_null_device` and `cowplot::agg_null_device`. Default is `cowplot::pdf_null_device`. This is used in function like `as_ggplot()`, which needs to open a graphics device to render ggplot objects into grid graphics objects. This function is used to open null device for avoiding the display of unnecessary blank page when calling `ggarrange()` or `as_ggplot()` (#306 and #158). The default option can be changed using, for example, `options(ggpubr.null_device = cowplot::png_null_device)`. ## Major changes - `gadd()`: Restoring back random state after setting seed when adding jittered points. To do so, the seed number is just passed to `position_jitter()` and `position_jitterdodge()`, which preserve the initial random state ( #177 and #349) . - `ggpubr` requires now a version of `ggrepel >= 0.9.2.9999`, which restores now the initial random state after set.seed(). see https://github.com/slowkow/ggrepel/issues/228 - `ggpubr` requires now a version of `cowplot >= 1.1.1` ## Minor changes - `ggtexttable()`: doc updated with another example; text justification for individual cells/rows/columns (#335). - `ggpie()`: setting the default of `clip = "off"` in `coord_polar()` so that `ggpie()` does not crop labels (#429) - `as_ggplot()`: using null_device to avoid blank page #306 and #158 - `ggarrange()`: using null_device to avoid blank page #306 and #158 - Indexing variable in a data frame: using df[[x]] instead of df[, x] to make sure that the result is a vector even if the `df` is a tibble. - `ggexport()`: support added for graphics device svg (#469) - `ggpie()` and `ggdonutchart()` now fully reacts to the option `lab.font` (#502) - Replacing deprecated `gather_()` in both internal (`.check_data()`) and exported functions (`compare_means()`) (#513) - `stat_compare_means()`: The dot-dot notation (`..p.signif..`) was deprecated in ggplot2 3.4.0; `after_stat(p.signif)` should be used; updated so that `..p.signif..` is automatically converted into `after_stat()` format without warning for bacward compatibility. - Enable faceting by column names with spaces (#391) - Licence changed to GPL (>= 2) (#482) - `desc_statby()` doc updated to clarify the difference between SD (standard deviation) and SE (standard error) (#492) - The message `geom_smooth() using formula 'y ~ x'` is now turned off in `ggscatter()`(#488) ## Bug fixes - `ggtext()`: fix warning "`filter_()` was deprecated in dplyr 0.7.0". - `ggqqplot()`: the argument `conf.int` is taken into account now when specified (#524). - `ggqqplot()`: Fixing the warning: "The following aesthetics were dropped during statistical transformation: sample" (#523) - Requiring `rstatix v >=0.7.1.999` for preserving factor class in `emmeans_test()` (#386) - `ggmaplot()`: Suppressing ggmaplot warning: *Unlabeled data points (too many overlaps). Consider increasing max.overlaps* (#520) - `compare_means()`: works now when the grouping variable levels contain the key words group2 or group1 (#450) - `ggparagraph()` : fixing bug about minimum paragraph length (#408) - `ggexport()`: the verbose argument is now considered when specifyed by user (#474) # ggpubr 0.5.0 ## New features - New functions `stat_anova_test()`, `stat_kruskal_test()`, `stat_welch_anova_test()`, `stat_friedman_test()` and `geom_pwc()` added. These are flexible functions to add p-values onto ggplot with more options. The function `geom_pwc()` is for adding pairwise comparisons p-values to a ggplot; supportted statistical methods include "wilcox_test", "t_test", "sign_test", "dunn_test", "emmeans_test", "tukey_hsd" and "games_howell_test". - New functions to convert character vector coordinates into NPC (normalized parent coordinates) and data coordinates: `as_npc()`, `npc_to_data_coordinates()` and `get_coord()`. - Global options: - New function `ggpubr_options()` to display allowed global options in ggpubr - New available package options: `ggpubr.parse_aes`. logical indicating whether to parse or not the aesthetics variables names. Default is `TRUE`. For example, if you want ggpubr to handle non-standard column names, like A-A, without parsing, then set this option to FALSE using `options(ggpubr.parse_aes = FALSE)`. ## Minor changes - Minimum rstatix version needed is set to 0.7.1 - Minimum ggplot2 version needed is set to 3.4.0 - `stat_conf_ellipse`: ensure stat returns a data.frame for compatibility with ggplot2 v>=3.4.0 - `stat_compare_means()`: - Unit tests added - Updated to use `after_stat(p.signif)` as the dot-dot notation (`..p.signif..`) was deprecated in ggplot2 3.4.0 (#509). - `ggdensity()` and `gghistogram()`: dot-dot notation (`..density..`, `..count..`) replaced by `after_stat(density)` and `after_stat(count)`, respectively for compatibility with ggplot2 3.4.0. - `create_aes()`: - Default is now to parse its input, which can be an expression (#348). If you want ggpubr to handle non-standard column names (#229), like A-A, without parsing, then set this option to FALSE using `options(ggpubr.parse_aes = FALSE)`. - Supports space in column names like "Dimension 1" - Unittest added - Arguments (`digits` and `table.font.size`) added to `ggsummarystats()` for changing the summary table decimal place and text size (#341). - In `stat_pvalue_manual()` the argument `hide.ns` can be either a logical value (TRUE or FALSE) or a character value ("p" or "p.adj" for filtering out non significant by p-value or adjusted p-values). - Now, the x-axis tick label names correctly align with the corresponding ticks when the rotation angle of the texts is set to 90. This is automatically achieved by setting internally `vjust = 0.5` (#301). - `Capital NS.` is no longer displayed by `stat_compare_means()` (#171) - Unit tests added for`ggshistogram()` to make sure that it works when: - using `after_stat()`, - using after_stat() with trailing space inside parentheses. - Unit tests added for`ggscatter()` to make sure that: - it works when there is spaces in variable names - it can handle non-standard column names when `ggpubr.parse_aes` global option is set to FALSE (#229) # ggpubr 0.4.0 ## New features - New functions added to customize `ggtexttable()` (#125, #129 and #283): - `tab_cell_crossout()`: cross out a table cell. - `tab_ncol(), tab_nrow()`: returns, respectively, the number of columns and rows in a ggtexttable. - `tab_add_hline()`: Creates horizontal lines or separators at the top or the bottom side of a given specified row. - `tab_add_vline()`: Creates vertical lines or separators at the right or the left side of a given specified column. - `tab_add_border(), tbody_add_border(), thead_add_border()`: Add borders to table; tbody is for table body and thead is for table head. - `tab_add_title()` and `tab_add_footnote()` to add titles and footnotes (#243). - ggpubr functions updated to handle non-standard column names, for example ("A-A") (#229). - New function `create_aes()` added to create aes mapping from a list. Makes programming easy with ggplot2 (#229). - New argument `coord.flip` added to support adding p-values onto horizontal ggplots (#179). When adding the p-values to a horizontal ggplot (generated using `coord_flip()`), you need to specify the option `coord.flip = TRUE`. - New errorbar functions - `median_hilow_()` and `median_q1q3()` - added ([@davidlorenz, #209](https://github.com/kassambara/ggpubr/issues/209)): - `median_hilow_()`: computes the sample median and a selected pair of outer quantiles having equal tail areas. This function is a reformatted version of `Hmisc::smedian.hilow()`. The confidence limits are computed as follow: `lower.limits = (1-ci)/2` percentiles; `upper.limits = (1+ci)/2` percentiles. By default (`ci = 0.95`), the 2.5th and the 97.5th percentiles are used as the lower and the upper confidence limits, respectively. If you want to use the 25th and the 75th percentiles as the confidence limits, then specify `ci = 0.5` or use the function `median_q1q3()`. - `median_q1q3()`: computes the sample median and, the 25th and 75th percentiles. Wrapper around the function median_hilow_() using ci = 0.5. - New function `get_breaks()` added to easily create breaks for numeric axes. Can be used to increase the number of x and y ticks by specifying the option `n`. It's also possible to control axis breaks by specifying a step between ticks. For example, if by = 5, a tick mark is shown on every 5 ([@Chitanda-Satou, #258](https://github.com/kassambara/ggpubr/issues/258)). ## Major changes - The following enhancement has been added to `ggscatterhist()` ([@juliechevalier, #176](https://github.com/kassambara/ggpubr/issues/176)): - the output of `ggscatterhist()` is now a list of ggplots, containing the main scatter plot (`sp`) and the marginal plots (`xplot` and `yplot`), which can be customized by the end user using the standard ggplot verbs - An S3 printing method is now available for an object of class ggscatterhist. The printing method displays the arranged final figure. ## Minor changes - Now, when creating a box plot with error bars, color and fill argiments are taken into account in the errorbar function (#105). - New argument `alternative` supported in `stat_cor()` (#276). - New argument `position` in `ggline()` to make position "dodged" (#52). - New argument `outlier.shape` in ggboxplot(). Default is 19. To hide outlier, specify outlier.shape = NA. When jitter is added, then outliers will be automatically hidden. - Sorting can be now disabled in `ggdotchart()` using the option `sorting = "none"` (#115, #223). - New argument `weight` added in `gghistogram()` for creating a weighted histogram (#215) - Now `ggscaterhist()` takes into account the argument `position` in `margin.params` when marginal plot is a histogram (#286). - `ggbarplot()` enhanced to better handle the creation of dodged bar plots combined with jitter points ([@aherholt, #176](https://github.com/kassambara/ggpubr/issues/282)) - New argument `bracket.shorten` added in `stat_pvalue_manual()` and `geom_bracket()`. a small numeric value in [0-1] for shortening the with of bracket (#285). - New argument `bracket.nudge.y` added in `stat_pvalue_manual()` and `geom_bracket()`. Vertical adjustment to nudge brackets by. Useful to move up or move down the bracket. If positive value, brackets will be moved up; if negative value, brackets are moved down ([#281](https://github.com/kassambara/ggpubr/issues/281)). - New argument `numeric.x.axis` added in `ggerrorplot()`; logical value, If TRUE, x axis will be treated as numeric. Default is FALSE ([#280](https://github.com/kassambara/ggpubr/issues/280)). - The option `width` is now considered in `ggadd()` for plotting error bars ([#278](https://github.com/kassambara/ggpubr/issues/278)). - New argument `linetype` in `ggpaired()`. - `geom_exec()` used in `ggpaired()` to add lines between paired points. - `ggmaplot()` now supports two input formats (#198): 1. baseMean | log2FoldChange|padj: Here, we'll use log2(baseMean) as the x-axis variable 2. baseMeanLog2 | log2FoldChange|padj: here, baseMeanLog2 is assumed to be the mean of logged values; so we'll use it as x-axis variable without any transformation. - new arguments added in `ggmaplot()`: - `alpha` for controlling point transparency/density ([@apcamargo, #152](https://github.com/kassambara/ggpubr/issues/152)). - `label.select` to select specific genes to show on the plot ([@apastore, #70](https://github.com/kassambara/ggpubr/issues/70)) - In `ggadd()` the `fill` argument is considered for jitter points only when the point shape is in 21:25 ([@atakanekiz, #148](https://github.com/kassambara/ggpubr/issues/148)). - New argument `parse` added in `ggscatter()` and in `ggtext()`. If TRUE, the labels will be parsed into expressions and displayed as described in ?plotmath (#250). - New argument `stroke` supported in `ggscatter()` and in `ggline()`. Used only for shapes 21-24 to control the thickness of points border ([@bioguy2018, #258](https://github.com/kassambara/ggpubr/issues/236)). - the `stat_cor()` function code has been simplified. New arguments `p.accuracy` and `r.accuracy` added; a real value specifying the number of decimal places of precision for the p-value and the correlation coefficient, respectively. Default is NULL. Use (e.g.) 0.01 to show 2 decimal places of precision ([@garthtarr, #186](https://github.com/kassambara/ggpubr/issues/186), [@raedevan6, #114](https://github.com/kassambara/ggpubr/issues/114), [#270](https://github.com/kassambara/ggpubr/issues/270)). ## Bug fixes - `annotate_figure()` manual updated to show how to use of superscript/subscript in the axis labels (#165). - `ggtextable()` now supports further customization when theme is specified (#283). - the argument `font.family` is now correctly handled by `ggscatter()` (#149) - `ggpar()` arguments are correctly applied using `ggpie()` (#277). - `ggscatter()`: When `conf.int = FALSE`, fill color is set to "lightgray" for the regression line confidence band ([@zhan6073, #111](https://github.com/kassambara/ggpubr/issues/111)). - Now, `gghistogram()` supports the paramter `yticks.by` ([@Chitanda-Satou, #258](https://github.com/kassambara/ggpubr/issues/258)). # ggpubr 0.3.0 ## New features - New functions: - `ggsummarystats()` to create a GGPLOT with summary stats table under the plot ( [#251](https://github.com/kassambara/ggpubr/pull/251)). - `clean_table_theme()` to clean the the theme of a table, such as those created by `ggsummarytable()` - `ggbarplot()` now supports stacked barplots with error bars ([#245](https://github.com/kassambara/ggpubr/pull/245)). ## Minor changes - New arguments: - `vjsut` in `stat_compare_means()` to move the text up or down relative to the bracket. - `type` in `geom_bracket()` to specify label type. Can be "text" or "expression" (for parsing plotmath expression); [#253](https://github.com/kassambara/ggpubr/issues/253). - `labeller` to the function `facet()` - `position` in `get_legend()` to specify legend position - `legend.grob` in `ggarrange()` to specify a common legend you want to add onto the combined plot. - Maintenance adaptation to dplyr new version by removing deprecated functions, such as group_by_, select_, arrange_, etc ## Bug fixes - Now, Barplots are correctly labelled when custom labels are specified by users ([@sekharcu, #234](https://github.com/kassambara/ggpubr/issues/234)) # ggpubr 0.2.5 ## Minor changes - New arguments `cor.coef.name` in the function `stat_cor()`. Can be one of "R" (pearson coef), "rho" (spearman coef) and "tau" (kendall coef). Uppercase and lowercase are allowed ([@andhamel, #216](https://github.com/kassambara/ggpubr/issues/228)). - New arguments `digits, r.digits, p.digits` in the function `stat_cor()`. Integer indicating the number of decimal places (round) or significant digits (signif) to be used for the correlation coefficient and the p-value ([@raedevan6, #216](https://github.com/kassambara/ggpubr/issues/114)). - `compare_means()` adapted to tidyr v>= 1.0.0 by specifying cols in the unnest() function ([@Youguang, #216](https://github.com/kassambara/ggpubr/issues/216)). # ggpubr 0.2.4 ## Minor change - unnest adapted to tidyr 1.0.0 - `stat_pvalue_manual()` can now handle an rstatix test result containing only one group column. # ggpubr 0.2.3 ## New features - New function `stat_central_tendency()` to add central tendency measures (mean, median, mode) to density and histogram plots - New function `stat_overlay_normal_density()` to overlay normal density plot (with the same mean and SD) to the density distribution of 'x'. ## Minor changes - The option `exact = FALSE` is no longer used when computing correlation in `stat_cor()` ([@tiagochst, #205](https://github.com/kassambara/ggpubr/issues/205)) ## Bug fixes - `ggpie()` keeps now the default order of labels ([@WortJohn, #203](https://github.com/kassambara/ggpubr/pull/203)) # ggpubr 0.2.2 ## New fatures - New function `geom_bracket()` for adding brackets with label annotation to a ggplot. Helpers for adding p-value or significance levels to a plot. ## Minor changes - `compare_means()` has been adapted to tidyr v1.0.0 ([@jennybc, #196](https://github.com/kassambara/ggpubr/pull/196)) - `geom_exec()` now handles `geom_bracket()` arguments - New arguments `vjust`, `hide.ns`, `step.increase`, `step.group.by`, `color` and `linetype` added in `stat_pvalue_manual()` - `stat_pvalue_manual()` can now guess automatically the significance label column. - New argument `show.legend` added to `ggadd()` and `add_summary()` functions. ## Bug fixes - Bug fixes in `gghistogram()`. Works now when the x variable is R keyword, such as var, mean, etc. ([#192](https://github.com/kassambara/ggpubr/issues/192)) - In `ggline()`, error bars now react automatically to grouping by line type ([#191](https://github.com/kassambara/ggpubr/issues/191)) # ggpubr 0.2.1 ## Minor changes - New arguments `step.increase` added in `stat_compare_means()` to avoid overlap between brackets. - In `stat_pvalue_manual()` x axis variable is no longer automatically converted into factor. If your x variable is a factor, make sure that it is converted into factor. - `stat_pvalue_manual()` can automatically handle the output of rstatix tests - `ggbarplot()` and `ggviolin()` now automatically create error bars by groups when users forget the option `add.params = list(group = )` ([#183](https://github.com/kassambara/ggpubr/issues/183)). - Now, `ggarrange()` works when either `ncol = 1` or `nrow = 1` ([@GegznaV, #141](https://github.com/kassambara/ggpubr/issues/144). - When method = "wilcox.test", the function `compare_means()` set automatically the option `exact = FALSE`. This is no longer the case ([@stemicha, #141](https://github.com/kassambara/ggpubr/issues/141). - `stat_pvalue_manual()` now supports dodged grouped plots ([@emcnerny, #104](https://github.com/kassambara/ggpubr/issues/104)). - the argument `position` is now handled by `ggdotplot()` ([@Adam-JJJJJ, #178](https://github.com/kassambara/ggpubr/issues/178)) ## Bug fixes - Adding points works now for barplots grouped by fill color ([@elenichri](https://github.com/kassambara/ggpubr/issues/173)) - `label.sep` argument works now in `ggscatter()` and `stat_cor()` ([@sbbmu, #150](https://github.com/kassambara/ggpubr/issues/150)) - Fix in `ggscatter()` to avoid freezing when the `add` argument is incorrect ([@atakanekiz, #135](https://github.com/kassambara/ggpubr/issues/180)). # ggpubr 0.2 ## Bug fixes - P-value for multiple comparisons by group (stat_compare_means()) are now correctly displayed ([@elisheva100, #135](https://github.com/kassambara/ggpubr/issues/135)). # ggpubr 0.1.9 ## Minor changes - ggsci palettes have been updated to add new palettes: nejm, jama, ucscgb, d3, locuszoom, igv, startrek, tron, futurama, simpsons ([@cbrueffer, #118](https://github.com/kassambara/ggpubr/pull/127) ## Bug fixes - The option `ref.group` was only considered when the grouping variable contains more than two levels. In that case, each level is compared against the specified reference group. Now, `ref.group` option is also considereded in two samples mean comparisons ([@OwenDonohoe, #118](https://github.com/kassambara/ggpubr/issues/118)) - Now, `ggqqplot()` reacts to the argument `conf.int.level` ([@vsluydts, #123](https://github.com/kassambara/ggpubr/issues/123) - Added error bar color is now inherited from the main plot ([@JesseRop, #109](https://github.com/kassambara/ggpubr/issues/109) # ggpubr 0.1.8 ## New features - New arguments `bxp.errorbar` added to `ggboxplot()` for adding error bars at the top of the box plots ([@j3ypi, #105](https://github.com/kassambara/ggpubr/issues/105). - New function `stat_pvalue_manual()` for adding p-values generated elswhere ([@achamess, #81](https://github.com/kassambara/ggpubr/issues/81), [@grst, #65](https://github.com/kassambara/ggpubr/issues/65)). ## Minor changes - `alpha`option added to `ggviolin()` [@mtmatter, #77](https://github.com/kassambara/ggpubr/pull/77) - New argument `bracket.size` added to `stat_compare_means()` [@mtmatter, #43](https://github.com/kassambara/ggpubr/issues/43) - Now, the function `stat_cor()` supports R^2 as an option [@philament, #32](https://github.com/kassambara/ggpubr/issues/32) - New argument `position` added in `gghistogram()`. Allowed values include "identity", "stack", "dodge". - New argument `ci` added in `ggerrorplot()` [@abrar-alshaer, #94](https://github.com/kassambara/ggpubr/issues/94) ## Bug fixes - Now, `ggscatter()` can remove the letter 'a' from the legend, when the argument `show.legend.text = FALSE` specified [@atsyplenkov, #106](https://github.com/kassambara/ggpubr/issues/106). - Now, adding a `size` option to ggscatter `add.params` is supported [@retrogenomics, #94](https://github.com/kassambara/ggpubr/issues/53). # ggpubr 0.1.7 ## New features - New function `ggdonutchart()` added. ## Minor changes - Significance levels can be now customized and passed to `stat_compare_means()` ([@jaison75, #45](https://github.com/kassambara/ggpubr/issues/30)). - Editing pdf size is now supported in `ggexport()` ([@JauntyJJS, #45](https://github.com/kassambara/ggpubr/issues/63)). ## Bug fixes - In `ggscatterhist()` the x variable was plotted two times, on both the plot x & y margins, instead of having, as expected, a) the x variable on the main plot x margin and 2) the y variable on the main plot y margin. This has been now fixed. - In previous version, `ggdotchart()` sorted automatically within groups when the `color` argument is specified, even when groups = NULL. This default behaviour has been now removed. Sorting withi groups is performed only when the argument `group` is specified ([@sfeds, #90](https://github.com/kassambara/ggpubr/issues/90)). - Now, `yticks.by` and `xticks.by` work with NAs ([@j3ypi, #89](https://github.com/kassambara/ggpubr/issues/89)). # ggpubr 0.1.6 ## New features - New function `ggballoonplot()` added to visualize a contingency table. - `ggdotchart()` can be now used to plot multiple groups with `position = position_dodge()` ([@ManuelSpinola, #45](https://github.com/kassambara/ggpubr/issues/45)). - New function `ggscatterhist()` to create a scatter plot with marginal histograms, density plots and box plots. - New theme `theme_pubclean()`: a clean theme without axis lines, to direct more attention to the data. - New arguments in `ggarrange()` to customize plot labels ([@G-Thomson, #41](https://github.com/kassambara/ggpubr/issues/38)): - font.label - label.x and label.y - hjust and vjust - New argument `method.args` added to `stat_compare_means()`. A list of additional arguments used for the test method. For example one might use method.args = list(alternative = "greater") for wilcoxon test ([@Nicktz, #41](https://github.com/kassambara/ggpubr/issues/41)). - New argument `symnum.args` added to `stat_compare_means()`. A list of arguments to pass to the function symnum for symbolic number coding of p-values. For example, `symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1), symbols = c("****", "***", "**", "*", "ns"))` - New functions `table_cell_font()` and `table_cell_bg()` to easily access and change the text font and the background of `ggtexttable()` cells ([@ProbleMaker, #29](https://github.com/kassambara/ggpubr/issues/29)). - New argument `numeric.x.axis` in `ggline()`. logical. If TRUE, x axis will be treated as numeric. Default is FALSE. ([@mdphan, #35](https://github.com/kassambara/ggpubr/issues/35)) - New argument `lab.nb.digits` in `ggbarplot()`. Integer indicating the number of decimal places (round) to be used ([#28](https://github.com/kassambara/ggpubr/issues/28)). Example: lab.nb.digits = 2. - New argument `tip.length` in `stat_compare_means()`. Numeric vector with the fraction of total height that the bar goes down to indicate the precise column. Default is 0.03. Can be of same length as the number of comparisons to adjust specifically the tip lenth of each comparison. For example tip.length = c(0.01, 0.03). ## Minor changes - Now `get_legend()` returns NULL when the plot doesn't have legend. ## Bug fixes - Now data argument are supported in `stat_compare_means()` when the option comparisons are specified ([@emcnerny, #48](https://github.com/kassambara/ggpubr/issues/48)) - Now `compare_means()` returns the same p-values as `stat_compare_means()` ([@wydty, #15](https://github.com/kassambara/ggpubr/issues/34)). - `stat_compare_means()` now reacts to label = "p.format" when comparisons specified ([#28](https://github.com/kassambara/ggpubr/issues/28)). - Now, the p.values are displayed correctly when ref.group is not the first group ([@sehufnkjesktgna, #15](https://github.com/kassambara/ggpubr/issues/27)). # ggpubr 0.1.5 ## Minor changes - In `ggpar()`, now `legend.title` can be either a character vector, e.g.: legend.title = "Species" or a list, `legend.title = list(color = "Species", linetype = "Species", shape = "Species")`. - New argument `ellipse.border.remove` in `ggscatter()` to remove ellipse border lines. ```r ggscatter(mtcars, x = "mpg", y = "wt", color = "cyl", ellipse = TRUE, mean.point = TRUE, ellipse.border.remove = TRUE) ``` - In `ggscatter`(), the argument `mean.point` now reacts to fill color. - Support for text justification added in `ggtexttable()` ([@cj-wilson, #15](https://github.com/kassambara/ggpubr/issues/18)) - The function `ggpie()` can now display japanese texts. New argument `font.family` in `ggpie`() and in `ggpar()` ([@tomochan001, #15](https://github.com/kassambara/ggpubr/issues/15)). - Using time on x axis works know with `ggline()` and `ggbarplot()` ([@jcpsantiago, #15](https://github.com/kassambara/ggpubr/issues/17)). ## Bug fixes - `stat_compare_means()` now reacts to `hide.ns` properly. - `drawDetails.splitText()` exported so that the function `ggparagraph()` works properly. - Now, ggpubr functions accept expression for label text - In `ggbarplot()`, now labels correspond to the true size of bars ([@tdelhomme, #15](https://github.com/kassambara/ggpubr/issues/15)). - `stat_compare_means()` now keep the default order of factor levels ([@RoKant, #12](https://github.com/kassambara/ggpubr/issues/12)). # ggpubr 0.1.4 ## New features - New helper functions: - `gradient_color()` and `gradient_color()`: change gradient color and fill palettes. - `clean_theme()`: remove axis lines, ticks, texts and titles. - `get_legend()`: to extract the legend labels from a ggplot object. - `as_ggplot()`: Transform the output of `gridExtra::arrangeGrob()` and `gridExtra::grid.arrange()` to a an object of class ggplot. - `ggtexttable()`: to draw a textual table. - `ggparagraph()`: to draw a paragraph of text. - fill_palette() and color_palette() to change the fill and color palette, respectively. - `annotate_figure()` to annotate (arranged) ggplots. - `text_grob()` to create easily a customized text graphical object. - `background_image()` to add a background image to a ggplot. - New theme function `theme_transparent()` to create a ggplot with transparent background. ## Minor changes - In `gghistogram()`, density curve and rug react to the fill color. - `ggarrange()`: - New argument `àlign` to specify whether graphs in the grid should be horizontally ("h") or vertically ("v") aligned. - New argument `legend` to remove or specify the legend position when arranging multiple plots. - New argument `common.legend` to create a common unique legend for multiple plots. # ggpubr 0.1.3 ## New features - New functions: - `ggarrange()` to arrange multiple ggplots on the same page. - `ggexport()` to export one or multiple ggplots to a file (pdf, eps, png, jpeg). - `ggpaired()` to plot paired data. - `compare_means()` to compare the means of two or multiple groups. Returns a data frame. - `stat_compare_means()` to add p-values and significance levels to plots. - `stat_cor()` to add correlation coefficients with p-values to a scatter plot. - `stat_stars()` to add stars to a scatter plot. - Now, the argument `y` can be a character vector of multiple variables to plot at once. This might be useful in genomic fields to plot the gene expression levels of multiple genes at once. see `ggboxplot()`, `ggdotplot()`, `ggstripchart()`, `ggviolin()`, `ggbarplot()` and `ggline`. - The argument `x` can be a vector of multiple variables in `gghistogram()`, `ggdensity()`, `ggecdf()` and `ggqqplot()`. - New functions to edit ggplot graphical parameters: - `font()` to change the appearance of titles and labels. - `rotate_x_text()` and `rotate_y_text()` to rotate x and y axis texts. - `rotate()` to rotate a ggplot for creating horizontal plot. - `set_palette()` or `change_palette()` to change a ggplot color palette. - `border()` to add/change border lines around a ggplot. - `bgcolor()` to change ggplot panel background color. - `rremove()` to remove a specific component from a ggplot. - `grids()` to add grid lines. - `xscale()` and `yscale()` to change axis scale. - New helper functions: - `facet()` added to create multi-panel plots ([#5](https://github.com/kassambara/ggpubr/issues/5)). - `add_summary()` to add summary statistics. - `ggadd()` to add summary statistics or a geometry onto a ggplot. - New data set added: `gene_citation` - New arguments in `ggpar()`: `x.text.angle` and `y.text.angle` ## Major changes - New arguments in ggpubr functions, see `ggboxplot()`, `ggdotplot()`, `ggstripchart()`, `ggviolin()`, `ggbarplot()` and `ggline`: - `combine` added to combine multiple y variables on the same graph. - `merge` to merge multiple y variables in the same ploting area. - `select` to select which item to display. - `remove` to remove a specific item from a plot. - `order` to order plot items. - `label, font.label, label.select, repel, label.rectangle` to add and customize labels - `facet.by, panel.labs and short.panel.labs`: support for faceting and customization of plot panels - New argument `grouping.vars` in `ggtext()`. Grouping variables to sort the data by, when the user wants to display the top n up/down labels. - New arguments in `theme_pubr()`: - border, - margin, - legend, - x.text.angle ## Minor changes - Now, the argument `palette` Can be also a numeric vector of length(groups); in this case a basic color palette is created using the function `grDevices::palette()`. ## Bug fixes - Now, `ggpar()` reacts to palette when length(palette) = 1 and palette is a color name [#3](https://github.com/kassambara/ggpubr/issues/3). - `ggmaplot()` now handles situations, where there is only upregulated, or downlegulated gnes. # ggpubr 0.1.2 ## New features - New function `get_palette()` to generate a palette of k colors from ggsci palettes, RColorbrewer palettes and custom color palettes. Useful to extend RColorBrewer and ggsci to support more colors. ## Minor changes - Now the `ggpar()` function can handle a list of ggplots. - Now the default legend position is `right`. - New argument `show.legend.text` in the `ggscatter()` function. Use show.legend.text = FALSE to hide text in the legend. - New arguments `title, submain, subtitle, caption, font.submain, font.subtitle, font.caption` in the `ggpar()` function. - New argument `font.family` in `ggscatter()`. ## Bug fixed - The mean within group for `ggdensity` (`gghistogram`) are now shown if data have NA values [@chunkaowang, #1](https://github.com/kassambara/ggpubr/issues/1) # ggpubr 0.1.1 ## New features - New function `ggtext()` for textual annotation. - New argument star.plot in `ggscatter()`. A logical value. If TRUE, a star plot is generated. - New helper function `geom_exec()`. A helper function used by ggpubr functions to execute any geom_xx functions in ggplot2. 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Classic theme with axis lines. ## Minor changes - `ggboxplot()`, `ggviolin()`, `ggdotplot()`, `ggstripchart()`, `gghistogram()`, `ggdensity()`, `ggecdf()` and `ggqqplot()` can now handle one single numeric vector. ``` # Example ggboxplot(iris$Sepal.Length) ``` - Now, in `gghistogram()`, when add_density = TRUE, y scale remains = "..count..". - Now, default theme changed to theme_classic2() - Default point size and line size set to NULL # ggpubr 0.1.0 ## Plot one variable - X: Continuous - ggdensity(): Density plot - gghistogram(): Histogram plot - ggecdf(): Empirical cumulative density function - ggqqplot(): QQ plots ## Plot two variables - X & Y: Discrete X and Continuous Y - ggboxplot(): Box plot - ggviolin(): Violin plot - ggdotplot(): Dot plot - ggstripchart(): Stripchart (jitter) - ggbarplot(): Bar plot - ggline(): Line plot - ggerrorplot(): Error plot - ggpie(): Pie chart - ggdotchart(): Cleveland's dot plots ## Plot two continuous variables - ggscatter(): Scatter plot ## 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