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GGally/inst/doc/rd.Rmd0000644000176200001440000000112113006440707014220 0ustar liggesusers--- title: "GGally" subtitle: "Extension to 'ggplot2'" author: "Barret Schloerke" copyright: "Barret Schloerke" output: packagedocs::package_docs_rd: toc: true toc_collapse: true redirect: http://ggobi.github.io/ggally/rd.html vignette: | %\VignetteIndexEntry{GGally_rd} %\VignetteEngine{packagedocs::redirect} navpills: |
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  • --- ```{r global_options, include=FALSE} # R output pre blocks are styled by default to indicate output knitr::opts_chunk$set( comment = NA, cache = TRUE, fig.height = 8, fig.width = 10 ) suppressMessages(suppressWarnings(library(GGally))) # shorthand for rd_link() - see ?packagedocs::rd_link for more information rdl <- function(x) packagedocs::rd_link(deparse(substitute(x))) ``` # GGally Welcome to the GGally documentation page. The following topic sections are alphabetically sorted. # GGally::ggcoef The purpose of this function is to quickly plot the coefficients of a model. #### *Joseph Larmarange* #### *May 16, 2016* ## Quick coefficients plot To work automatically, this function requires the `r rdl(broom)` package. Simply call `r rdl(ggcoef)` with a model object. It could be the result of `r rdl(lm)`, `r rdl(glm)` or any other model covered by `r rdl(broom)` and its `r rdl(tidy)` method^[See http://www.rdocumentation.org/packages/broom.]. ```{r ggcoef-reg} reg <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width, data = iris) ggcoef(reg) ``` In the case of a logistic regression (or any other model for which coefficients are usually exponentiated), simply indicated `exponentiate = TRUE`. Note that a logarithmic scale will be used for the x-axis. ```{r ggcoef-titanic} d <- as.data.frame(Titanic) log.reg <- glm(Survived ~ Sex + Age + Class, family = binomial, data = d, weights = d$Freq) ggcoef(log.reg, exponentiate = TRUE) ``` ## Customizing the plot You can use `conf.int`, `vline` and `exclude_intercept` to display or not confidence intervals as error bars, a vertical line for `x = 0` (or `x = 1` if coeffcients are exponentiated) and the intercept. ```{r ggcoef-reg-custom} ggcoef(reg, vline = FALSE, conf.int = FALSE, exclude_intercept = TRUE) ``` See the help page of `r rdl(ggcoef)` for the full list of arguments that could be used to personalize how error bars and the vertical line are plotted. ```{r ggcoef-full-args} ggcoef( log.reg, exponentiate = TRUE, vline_color = "red", vline_linetype = "solid", errorbar_color = "blue", errorbar_height = .25 ) ``` Additional parameters will be passed to `r rdl(geom_point)`. ```{r ggcoef-log.reg} ggcoef(log.reg, exponentiate = TRUE, color = "purple", size = 5, shape = 18) ``` Finally, you can also customize the aesthetic mapping of the points. ```{r ggcoef-aes} library(ggplot2) ggcoef(log.reg, exponentiate = TRUE, mapping = aes(x = estimate, y = term, size = p.value)) + scale_size_continuous(trans = "reverse") ``` ## Custom data frame You can also pass a custom data frame to `r rdl(ggcoef)`. The following variables are expected: - `term` (except if you customize the mapping) - `estimate` (except if you customize the mapping) - `conf.low` and `conf.high` (only if you want to display error bars) ```{r ggcoef-data-frame} cust <- data.frame( term = c("male vs. female", "30-49 vs. 18-29", "50+ vs. 18-29", "urban vs. rural"), estimate = c(.456, 1.234, 1.897, 1.003), conf.low = c(.411, 1.042, 1.765, 0.678), conf.high = c(.498, 1.564, 2.034, 1.476), variable = c("sex", "age", "age", "residence") ) cust$term <- factor(cust$term, cust$term) ggcoef(cust, exponentiate = TRUE) ggcoef( cust, exponentiate = TRUE, mapping = aes(x = estimate, y = term, colour = variable), size = 5 ) ``` # GGally::ggduo #### *Barret Schloerke* #### *July 4, 2016* The purpose of this function is to display two grouped data in a plot matrix. This is useful for canonical correlation analysis, multiple time series analysis, and regression analysis. ## Canonical Correlation Analysis This example is derived from `` R Data Analysis Examples | Canonical Correlation Analysis. UCLA: Institute for Digital Research and Education. from http://www.stats.idre.ucla.edu/r/dae/canonical-correlation-analysis (accessed May 22, 2017). `` `` Example 1. A researcher has collected data on three psychological variables, four academic variables (standardized test scores) and gender for 600 college freshman. She is interested in how the set of psychological variables relates to the academic variables and gender. In particular, the researcher is interested in how many dimensions (canonical variables) are necessary to understand the association between the two sets of variables." `` ```{r ggduo-cca} data(psychademic) str(psychademic) (psych_variables <- attr(psychademic, "psychology")) (academic_variables <- attr(psychademic, "academic")) ``` First, look at the within correlation using `r rdl(ggpairs)`. ```{r ggduo-within} ggpairs(psychademic, psych_variables, title = "Within Psychological Variables") ggpairs(psychademic, academic_variables, title = "Within Academic Variables") ``` Next, look at the between correlation using `r rdl(ggduo)`. ```{r ggduo-between} ggduo( psychademic, psych_variables, academic_variables, types = list(continuous = "smooth_lm"), title = "Between Academic and Psychological Variable Correlation", xlab = "Psychological", ylab = "Academic" ) ``` Since ggduo does not have a upper section to display the correlation values, we may use a custom function to add the information in the continuous plots. The strips may be removed as each group name may be recovered in the outer axis labels. ```{r ggduo-lm} lm_with_cor <- function(data, mapping, ..., method = "pearson") { x <- eval(mapping$x, data) y <- eval(mapping$y, data) cor <- cor(x, y, method = method) ggally_smooth_lm(data, mapping, ...) + ggplot2::geom_label( data = data.frame( x = min(x, na.rm = TRUE), y = max(y, na.rm = TRUE), lab = round(cor, digits = 3) ), mapping = ggplot2::aes(x = x, y = y, label = lab), hjust = 0, vjust = 1, size = 5, fontface = "bold", inherit.aes = FALSE # do not inherit anything from the ... ) } ggduo( psychademic, rev(psych_variables), academic_variables, mapping = aes(color = sex), types = list(continuous = wrap(lm_with_cor, alpha = 0.25)), showStrips = FALSE, title = "Between Academic and Psychological Variable Correlation", xlab = "Psychological", ylab = "Academic", legend = c(5,2) ) + theme(legend.position = "bottom") ``` ## Multiple Time Series Analysis While displaying multiple time series vertically over time, such as ``+ facet_grid(time ~ .)``, `r rdl(ggduo)` can handle both continuous and discrete data. `r rdl(ggplot2)` does not mix discrete and continuous data on the same axis. ```{r ggduo-mtsa} library(ggplot2) data(pigs) pigs_dt <- pigs[-(2:3)] # remove year and quarter pigs_dt$profit_group <- as.numeric(pigs_dt$profit > mean(pigs_dt$profit)) qplot( time, value, data = reshape::melt.data.frame(pigs_dt, "time"), geom = c("smooth", "point") ) + facet_grid(variable ~ ., scales = "free_y") ``` Instead, we may use `ggts` to display the data. `ggts` changes the default behavior of ggduo of `columnLabelsX` to equal `NULL` and allows for mixed variable types. ```{r ggduo-mtsa-group} # make the profit group as a factor value profit_groups <- c( "1" = "high", "0" = "low" ) pigs_dt$profit_group <- factor( profit_groups[as.character(pigs_dt$profit_group)], levels = unname(profit_groups), ordered = TRUE ) ggts(pigs_dt, "time", 2:7) # remove the binwidth warning pigs_types <- list( comboHorizontal = wrap(ggally_facethist, binwidth = 1) ) ggts(pigs_dt, "time", 2:7, types = pigs_types) # add color and legend pigs_mapping <- aes(color = profit_group) ggts(pigs_dt, pigs_mapping, "time", 2:7, types = pigs_types, legend = c(6,1)) ``` Produce more meaningful labels, add a legend, and remove profit group strips. ```{r ggduo-mtsa-pretty} pm <- ggts( pigs_dt, pigs_mapping, 1, 2:7, types = pigs_types, legend = c(6,1), columnLabelsY = c( "number of\nfirst birth sows", "sell price over\nfeed cost", "sell count over\nheard size", "meat head count", "breading\nheard size", "profit\ngroup" ), showStrips = FALSE ) + labs(fill = "profit group") + theme( legend.position = "bottom", strip.background = element_rect( fill = "transparent", color = "grey80" ) ) pm ``` ## Regression Analysis Since `r rdl(ggduo)` may take custom functions just like `r rdl(ggpairs)`, we will make a custom function that displays the residuals with a red line at 0 and all other y variables will receive a simple linear regression plot. Note: the marginal residuals are calculated before plotting and the y_range is found to display all residuals on the same scale. ```{r ggduo-reg-swiss} swiss <- datasets::swiss # add a 'fake' column swiss$Residual <- seq_len(nrow(swiss)) # calculate all residuals prior to display residuals <- lapply(swiss[2:6], function(x) { summary(lm(Fertility ~ x, data = swiss))$residuals }) # calculate a consistent y range for all residuals y_range <- range(unlist(residuals)) # custom function to display continuous data. If the y variable is "Residual", do custom work. lm_or_resid <- function(data, mapping, ..., line_color = "red", line_size = 1) { if (as.character(mapping$y) != "Residual") { return(ggally_smooth_lm(data, mapping, ...)) } # make residual data to display resid_data <- data.frame( x = data[[as.character(mapping$x)]], y = residuals[[as.character(mapping$x)]] ) ggplot(data = data, mapping = mapping) + geom_hline(yintercept = 0, color = line_color, size = line_size) + ylim(y_range) + geom_point(data = resid_data, mapping = aes(x = x, y = y), ...) } # plot the data ggduo( swiss, 2:6, c(1,7), types = list(continuous = lm_or_resid) ) # change line to be thicker and blue and the points to be slightly transparent ggduo( swiss, 2:6, c(1,7), types = list( continuous = wrap(lm_or_resid, alpha = 0.7, line_color = "blue", line_size = 3 ) ) ) ``` # GGally::glyphs #### *Hadley Wickham, Charlotte Wickham, Di Cook, Heike Hofmann* #### *Nov 6, 2015* This function rearranges data to be able to construct a glyph plot ```{r glyphs-basic-usage, fig.height=7, fig.width=7} library(ggplot2) data(nasa) temp.gly <- glyphs(nasa, "long", "day", "lat", "surftemp", height=2.5) ggplot(temp.gly, ggplot2::aes(gx, gy, group = gid)) + add_ref_lines(temp.gly, color = "grey90") + add_ref_boxes(temp.gly, color = "grey90") + geom_path() + theme_bw() + labs(x = "", y = "") ``` This shows a glyphplot of monthly surface temperature for 6 years over Central America. You can see differences from one location to another, that in large areas temperature doesn't change much. There are large seasonal trends in the top left over land. Rescaling in different ways puts emphasis on different components, see the examples in the referenced paper. And with ggplot2 you can make a map of the geographic area underlying the glyphs. ## References Wickham, H., Hofmann, H., Wickham, C. and Cook, D. (2012) Glyph-maps for Visually Exploring Temporal Patterns in Climate Data and Models, **Environmetrics**, *23*(5):151-182. # GGally::ggmatrix #### *Barret Schloerke* #### *Oct 29, 2015* `r rdl(ggmatrix)` is a function for managing multiple plots in a matrix-like layout. It was designed to adapt to any number of columns and rows. This allows for very customized plot matrices. ## Generic Example The examples below use plots labeled 1 to 6 to distinguish where the plots are being placed. ```{r ggmatrix_genExample} plotList <- list() for (i in 1:6) { plotList[[i]] <- ggally_text(paste("Plot #", i, sep = "")) } # bare minimum of plotList, nrow, and ncol pm <- ggmatrix(plotList, 2, 3) pm # provide more information pm <- ggmatrix( plotList, nrow = 2, ncol = 3, xAxisLabels = c("A", "B", "C"), yAxisLabels = c("D", "E"), title = "Matrix Title" ) pm # display plots in column order pm <- ggmatrix( plotList, nrow = 2, ncol = 3, xAxisLabels = c("A", "B", "C"), yAxisLabels = c("D", "E"), title = "Matrix Title", byrow = FALSE ) pm ``` ## Matrix Subsetting Individual plots may be retrieved from the plot matrix and can be placed in the plot matrix. ```{r ggmatrix_place} pm <- ggmatrix( plotList, nrow = 2, ncol = 3, xAxisLabels = c("A", "B", "C"), yAxisLabels = c("D", "E"), title = "Matrix Title" ) pm p2 <- pm[1,2] p3 <- pm[1,3] p2 p3 pm[1,2] <- p3 pm[1,3] <- p2 pm ``` ## Themes ```{r ggmatrix_theme} library(ggplot2) pm <- ggmatrix( plotList, nrow = 2, ncol = 3, xAxisLabels = c("A", "B", "C"), yAxisLabels = c("D", "E"), title = "Matrix Title", byrow = FALSE ) pm <- pm + theme_bw() pm ``` ## Axis Control The X and Y axis have booleans to turn on/off the individual plot's axes on the bottom and left sides of the plot matrix. To save time, `showAxisPlotLabels` can be set to override `showXAxisPlotLabels` and `showYAxisPlotLabels`. ```{r ggmatrix_axisControl} pm <- ggmatrix( plotList, nrow = 2, ncol = 3, xAxisLabels = c("A", "B", "C"), yAxisLabels = c("D", "E"), title = "No Left Plot Axis", showYAxisPlotLabels = FALSE ) pm pm <- ggmatrix( plotList, nrow = 2, ncol = 3, xAxisLabels = c("A", "B", "C"), yAxisLabels = c("D", "E"), title = "No Bottom Plot Axis", showXAxisPlotLabels = FALSE ) pm pm <- ggmatrix( plotList, nrow = 2, ncol = 3, xAxisLabels = c("A", "B", "C"), yAxisLabels = c("D", "E"), title = "No Plot Axes", showAxisPlotLabels = FALSE ) pm ``` ## Strips Control By default, the plots in the top row and the right most column will display top-side and right-side strips respectively (`showStrips = NULL`). If all strips need to appear in each plot, `showStrips` may be set to `TRUE`. If all strips should not be displayed, `showStrips` may be set to `FALSE`. ```{r ggmatrix_stripControl} data(tips, package = "reshape") plotList <- list( qplot(total_bill, tip, data = subset(tips, smoker == "No" & sex == "Female")) + facet_grid(time ~ day), qplot(total_bill, tip, data = subset(tips, smoker == "Yes" & sex == "Female")) + facet_grid(time ~ day), qplot(total_bill, tip, data = subset(tips, smoker == "No" & sex == "Male")) + facet_grid(time ~ day), qplot(total_bill, tip, data = subset(tips, smoker == "Yes" & sex == "Male")) + facet_grid(time ~ day) ) pm <- ggmatrix( plotList, nrow = 2, ncol = 2, yAxisLabels = c("Female", "Male"), xAxisLabels = c("Non Smoker", "Smoker"), title = "Total Bill vs Tip", showStrips = NULL # default ) pm pm <- ggmatrix( plotList, nrow = 2, ncol = 2, yAxisLabels = c("Female", "Male"), xAxisLabels = c("Non Smoker", "Smoker"), title = "Total Bill vs Tip", showStrips = TRUE ) pm pm <- ggmatrix( plotList, nrow = 2, ncol = 2, yAxisLabels = c("Female", "Male"), xAxisLabels = c("Non Smoker", "Smoker"), title = "Total Bill vs Tip", showStrips = FALSE ) pm ``` # GGally::ggnetworkmap #### *Amos Elberg* #### *January 10, 2015* `r rdl(ggnetworkmap)` is a function for plotting elegant maps using `r rdl(ggplot2)`. It builds on `r rdl(ggnet)` by allowing to draw a network over a map, and is particularly intended for use with `r rdl(ggmap)`. ## Example: US airports This example is based on a [tutorial by Nathan Yau at Flowing Data](http://flowingdata.com/2011/05/11/how-to-map-connections-with-great-circles/). ```{r ggnetworkmap-init} suppressMessages(library(network)) suppressMessages(library(sna)) suppressMessages(library(maps)) suppressMessages(library(ggplot2)) airports <- read.csv("http://datasets.flowingdata.com/tuts/maparcs/airports.csv", header = TRUE) rownames(airports) <- airports$iata # select some random flights set.seed(1234) flights <- data.frame( origin = sample(airports[200:400, ]$iata, 200, replace = TRUE), destination = sample(airports[200:400, ]$iata, 200, replace = TRUE) ) # convert to network flights <- network(flights, directed = TRUE) # add geographic coordinates flights %v% "lat" <- airports[ network.vertex.names(flights), "lat" ] flights %v% "lon" <- airports[ network.vertex.names(flights), "long" ] # drop isolated airports delete.vertices(flights, which(degree(flights) < 2)) # compute degree centrality flights %v% "degree" <- degree(flights, gmode = "digraph") # add random groups flights %v% "mygroup" <- sample(letters[1:4], network.size(flights), replace = TRUE) # create a map of the USA usa <- ggplot(map_data("usa"), aes(x = long, y = lat)) + geom_polygon(aes(group = group), color = "grey65", fill = "#f9f9f9", size = 0.2) delete.vertices(flights, which(flights %v% "lon" < min(usa$data$long))) delete.vertices(flights, which(flights %v% "lon" > max(usa$data$long))) delete.vertices(flights, which(flights %v% "lat" < min(usa$data$lat))) delete.vertices(flights, which(flights %v% "lat" > max(usa$data$lat))) # overlay network data to map ggnetworkmap(usa, flights, size = 4, great.circles = TRUE, node.group = mygroup, segment.color = "steelblue", ring.group = degree, weight = degree) ``` ## Example: Twitter spambots This next example uses data from a Twitter spam community identified while exploring and trying to clear-up a group of tweets. After coloring the nodes based on their centrality, the odd structure stood out clearly. ```{r ggnetworkmap-data, eval=FALSE} data(twitter_spambots) ``` ```{r ggnetworkmap-world} # create a world map world <- fortify(map("world", plot = FALSE, fill = TRUE)) world <- ggplot(world, aes(x = long, y = lat)) + geom_polygon(aes(group = group), color = "grey65", fill = "#f9f9f9", size = 0.2) # view global structure ggnetworkmap(world, twitter_spambots) ``` Is the network really concentrated in the U.S.? Probably not. One of the odd things about the network, is a much higher proportion of the users gave locations that could be geocoded, than Twitter users generally. Let's see the network topology ```{r ggnetworkmap-topology} ggnetworkmap(net = twitter_spambots, arrow.size = 0.5) ``` Coloring nodes according to degree centrality can highlight network structures. ```{r ggnetworkmap-color} # compute indegree and outdegree centrality twitter_spambots %v% "indegree" <- degree(twitter_spambots, cmode = "indegree") twitter_spambots %v% "outdegree" <- degree(twitter_spambots, cmode = "outdegree") ggnetworkmap(net = twitter_spambots, arrow.size = 0.5, node.group = indegree, ring.group = outdegree, size = 4) + scale_fill_continuous("Indegree", high = "red", low = "yellow") + labs(color = "Outdegree") ``` Some Twitter attributes have been included as vertex attributes. ```{r ggnetworkmap-twitter-attr} # show some vertex attributes associated with each account ggnetworkmap(net = twitter_spambots, arrow.size = 0.5, node.group = followers, ring.group = friends, size = 4, weight = indegree, label.nodes = TRUE, vjust = -1.5) + scale_fill_continuous("Followers", high = "red", low = "yellow") + labs(color = "Friends") + scale_color_continuous(low = "lightgreen", high = "darkgreen") ``` # GGally::ggnostic #### *Barret Schloerke* #### *Oct 1, 2016* `r rdl(ggnostic)` is a display wrapper to `r rdl(ggduo)` that displays full model diagnostics for each given explanatory variable. By default, `r rdl(ggduo)` displays the residuals, leave-one-out model sigma value, leverage points, and Cook's distance against each explanatory variable. The rows of the plot matrix can be expanded to include fitted values, standard error of the fitted values, standardized residuals, and any of the response variables. If the model is a linear model, stars are added according to the anova significance of each explanatory variable. Most diagnostic plots contain reference line(s) to help determine if the model is fitting properly * **residuals:** * Type key: `".resid"` * A solid line is located at the expected value of 0 with dashed lines at the 95% confidence interval. ($0 \pm 1.96 * \sigma$) * Plot function: `r rdl(ggally_nostic_resid)`. * See also `r rdl(stats::residuals)` * **standardized residuals:** * Type key: `".std.resid"` * Same as residuals, except the standardized residuals equal the regular residuals divided by sigma. The dashed lines are located at $0 \pm 1.96 * 1$. * Plot function: `r rdl(ggally_nostic_std_resid)`. * See also `r rdl(stats::rstandard)` * **leave-one-out model sigma:** * Type key: `".sigma"` * A solid line is located at the full model's sigma value. * Plot function: `r rdl(ggally_nostic_sigma)`. * See also `r rdl(stats::influence)`'s value on `sigma` * **leverage points:** * Type key: `".hat"` * The expected value for the diagonal of a hat matrix is $p / n$. Points are considered leverage points if they are large than $2 * p / n$, where the higher line is drawn. * Plot function: `r rdl(ggally_nostic_hat)`. * See also `r rdl(stats::influence)`'s value on `hat` * **Cook's distance:** * Type key: `".cooksd"` * Points that are larger than $4 / n$ line are considered highly influential points. Plot function: `r rdl(ggally_nostic_cooksd)`. See also `r rdl(stats::cooks.distance)` * **fitted points:** * Type key: `".fitted"` * No reference lines by default. * Default plot function: `r rdl(ggally_points)`. * See also `r rdl(stats::predict)` * **standard error of fitted points**: * Type key: `".se.fit"` * No reference lines by default. * Plot function: `r rdl(ggally_nostic_se_fit)`. * See also `r rdl(stats::fitted)` * **response variables:** * Type key: (response name in data.frame) * No reference lines by default. * Default plot function: `r rdl(ggally_points)`. ## Life Expectancy Model Fitting Looking at the dataset `r rdl(datasets::state.x77)`, we will fit a multiple regression model for Life Expectancy. ```{r life_model} # make a data.frame and fix column names state <- as.data.frame(state.x77) colnames(state)[c(4, 6)] <- c("Life.Exp", "HS.Grad") str(state) # fit full model model <- lm(Life.Exp ~ ., data = state) # reduce to "best fit" model with model <- step(model, trace = FALSE) summary(model) ``` Next, we look at the variables for any high (|value| > 0.8) correlation values and general interaction behavior. ```{r nostic_scatmat} # look at variables for high correlation (none) ggscatmat(state, columns = c("Population", "Murder", "HS.Grad", "Frost")) ``` All variables appear to be ok. Next, we look at the model diagnostics. ```{r nostic_diag} # look at model diagnostics ggnostic(model) ``` * The residuals appear to be normally distributed. There are a couple residual outliers, but 2.5 outliers are expected. * There are 5 leverage points according the diagonal of the hat matrix * There are 2 leverage points according to Cook's distance. One is **much** larger than the other. Let's remove the largest data point first to try and define a better model. ```{r nostic_no_hawaii} # very high life expectancy state[11, ] state_no_hawaii <- state[-11, ] model_no_hawaii <- lm(Life.Exp ~ Population + Murder + HS.Grad + Frost, data = state_no_hawaii) ggnostic(model_no_hawaii) ``` There are no more outrageous Cook's distance values. The model without Hawaii appears to be a good fitting model. ```{r nostic_summary} summary(model) summary(model_no_hawaii) ``` Since there is only a marginal improvement by removing Hawaii, the original model should be used to explain life expectancy. ## Full diagnostic plot matrix example The following lines of code will display different modle diagnostic plot matrices for the same statistical model. The first one is of the default settings. The second adds color according to the ``species``. Finally, the third displays all possible columns and uses `r rdl(ggally_smooth)` to display the fitted points and response variables. ```{r nostic_flea} flea_model <- step(lm(head ~ ., data = flea), trace = FALSE) summary(flea_model) # default output ggnostic(flea_model) # color'ed output ggnostic(flea_model, mapping = ggplot2::aes(color = species)) # full color'ed output ggnostic( flea_model, mapping = ggplot2::aes(color = species), columnsY = c("head", ".fitted", ".se.fit", ".resid", ".std.resid", ".hat", ".sigma", ".cooksd"), continuous = list(default = ggally_smooth, .fitted = ggally_smooth) ) ``` # GGally::ggpairs #### *Barret Schloerke* #### *Oct 29, 2015* `r rdl(ggpairs)` is a special form of a `r rdl(ggmatrix)` that produces a pairwise comparison of multivariate data. By default, `r rdl(ggpairs)` provides two different comparisons of each pair of columns and displays either the density or count of the respective variable along the diagonal. With different parameter settings, the diagonal can be replaced with the axis values and variable labels. There are many hidden features within ggpairs. Please take a look at the examples below to get the most out of ggpairs. ## Columns and Mapping The `columns` displayed default to all columns of the provided `data`. To subset to only a few columns, use the `columns` parameter. ```{r ggpairs_columns} data(tips, package = "reshape") pm <- ggpairs(tips) pm ## too many plots for this example. ## reduce the columns being displayed ## these two lines of code produce the same plot matrix pm <- ggpairs(tips, columns = c(1, 6, 2)) pm <- ggpairs(tips, columns = c("total_bill", "time", "tip"), columnLabels = c("Total Bill", "Time of Day", "Tip")) pm ``` Aesthetics can be applied to every subplot with the `mapping` parameter. ```{r ggpairs_mapping} library(ggplot2) pm <- ggpairs(tips, mapping = aes(color = sex), columns = c("total_bill", "time", "tip")) pm ``` Since the plots are default plots (or are helper functions from GGally), the aesthetic color is altered to be appropriate. Looking at the example above, 'tip' vs 'total_bill' (pm[3,1]) needs the `color` aesthetic, while 'time' vs 'total_bill' needs the `fill` aesthetic. If custom functions are supplied, no aesthetic alterations will be done. ## Matrix Sections There are three major sections of the pairwise matrix: `lower`, `upper`, and `diag`. The `lower` and `upper` may contain three plot types: `continuous`, `combo`, and `discrete`. The 'diag' only contains either `continuous` or `discrete`. * `continuous`: both X and Y are continuous variables * `combo`: one X and Y variable is discrete while the other is continuous * `discrete`: both X and Y are discrete variables To make adjustments to each section, a list of information may be supplied. The list can be comprised of the following elements: * `continuous`: * a character string representing the tail end of a `ggally_NAME` function, or a custom function * current valid `upper$continuous` and `lower$continuous` character strings: `'points'`, `'smooth'`, `'density'`, `'cor'`, `'blank'` * current valid `diag$continuous` character strings: `'densityDiag'`, `'barDiag'`, `'blankDiag'` * `combo`: * a character string representing the tail end of a `ggally_NAME` function, or a custom function. (not applicable for a `diag` list) * current valid `upper$combo` and `lower$combo` character strings: `'box'`, `'dot'`, `'facethist'`, `'facetdensity'`, `'denstrip'`, `'blank'` * `discrete`: * a character string representing the tail end of a `ggally_NAME` function, or a custom function * current valid `upper$discrete` and `lower$discrete` character strings: `'ratio'`, `'facetbar'`, `'blank'` * current valid `diag$discrete` character strings: `'barDiag'`, `'blankDiag'` * `mapping`: if mapping is provided, only the section's mapping will be overwritten ```{r ggpairs_section} library(ggplot2) pm <- ggpairs( tips, columns = c("total_bill", "time", "tip"), lower = list( continuous = "smooth", combo = "facetdensity", mapping = aes(color = time) ) ) pm ``` A section list may be set to the character string `"blank"` or `NULL` if the section should be skipped when printed. ```{r ggpairs_blank} pm <- ggpairs( tips, columns = c("total_bill", "time", "tip"), upper = "blank", diag = NULL ) pm ``` ## Custom Functions The `ggally_NAME` functions do not provide all graphical options. Instead of supplying a character string to a `continuous`, `combo`, or `discrete` element within `upper`, `lower`, or `diag`, a custom function may be given. The custom function should follow the api of ```{r ggally_custom_function} custom_function <- function(data, mapping, ...){ # produce ggplot2 object here } ``` There is no requirement to what happens within the function, as long as a ggplot2 object is returned. ```{r ggpairs_custom_function} my_bin <- function(data, mapping, ..., low = "#132B43", high = "#56B1F7") { ggplot(data = data, mapping = mapping) + geom_bin2d(...) + scale_fill_gradient(low = low, high = high) } pm <- ggpairs( tips, columns = c("total_bill", "time", "tip"), lower = list( continuous = my_bin ) ) pm ``` ## Function Wrapping The examples above use default parameters to each of the subplots. One of the immediate parameters to be set it `binwidth`. This parameters is only needed in the lower, combination plots where one variable is continuous while the other variable is discrete. To change the default parameter `binwidth` setting, we will `r rdl(wrap)` the function. `r rdl(wrap)` first parameter should be a character string or a custom function. The remaining parameters supplied to wrap will be supplied to the function at run time. ```{r ggpairs_wrap} pm <- ggpairs( tips, columns = c("total_bill", "time", "tip"), lower = list( combo = wrap("facethist", binwidth = 1), continuous = wrap(my_bin, binwidth = c(5, 0.5), high = "red") ) ) pm ``` To get finer control over parameters, please look into custom functions. ## Plot Matrix Subsetting Please look at the [vignette for ggmatrix](#ggallyggmatrix) on plot matrix manipulations. Small ggpairs example: ```{r ggpairs_matrix} pm <- ggpairs(tips, columns = c("total_bill", "time", "tip")) # retrieve the third row, first column plot p <- pm[3,1] p <- p + aes(color = time) p pm[3,1] <- p pm ``` ## Themes Please look at the [vignette for ggmatrix](#ggallyggmatrix) on plot matrix manipulations. Small ggpairs example: ```{r ggpairs_theme} pmBW <- pm + theme_bw() pmBW ``` ## References John W Emerson, Walton A Green, Barret Schloerke, Jason Crowley, Dianne Cook, Heike Hofmann, Hadley Wickham. **[The Generalized Pairs Plot](http://vita.had.co.nz/papers/gpp.html)**. *Journal of Computational and Graphical Statistics*, vol. 22, no. 1, pp. 79-91, 2012. # GGally::ggscatmat #### *Di Cook, Mengjia Ni* #### *Nov 6, 2015* The primary function is `r rdl(ggscatmat)`. It is similar to `r rdl(ggpairs)` but only works for purely numeric multivariate data. It is faster than ggpairs, because less choices need to be made. It creates a matrix with scatterplots in the lower diagonal, densities on the diagonal and correlations written in the upper diagonal. Syntax is to enter the dataset, the columns that you want to plot, a color column, and an alpha level. ```{r ggscatmat-basic-usage, fig.height=7, fig.width=7} data(flea) ggscatmat(flea, columns = 2:4, color="species", alpha=0.8) ``` In this plot, you can see that the three different species vary a little from each other in these three variables. Heptapot (blue) has smaller values on the variable "tars1" than the other two. The correlation between the three variables is similar for all species. ## References John W Emerson, Walton A Green, Barret Schloerke, Jason Crowley, Dianne Cook, Heike Hofmann, Hadley Wickham. **[The Generalized Pairs Plot](http://vita.had.co.nz/papers/gpp.html)**. *Journal of Computational and Graphical Statistics*, vol. 22, no. 1, pp. 79-91, 2012. # GGally::ggsurv #### *Edwin Thoen* #### *April, 4, 2016* This function produces Kaplan-Meier plots using `r rdl(ggplot2)`. As a first argument, `r rdl(ggsurv)` needs a `r rdl(survfit)` object, created by the `r rdl(survival)` package. Default settings differ for single stratum and multiple strata objects. ## Single Stratum ```{r basic-usage, fig.height=7, fig.width=7} require(ggplot2) require(survival) require(scales) data(lung, package = "survival") sf.lung <- survival::survfit(Surv(time, status) ~ 1, data = lung) ggsurv(sf.lung) ``` ## Multiple Stratum The legend color positions matches the survival order or each stratum, where the stratums that end at a lower value or time have a position that is lower in the legend. ```{r ggsurv-multiple} sf.sex <- survival::survfit(Surv(time, status) ~ sex, data = lung) pl.sex <- ggsurv(sf.sex) pl.sex ``` ## Alterations Since a ggplot2 object is returned, plot objects may be altered after the original creation. ### Adjusting the legend ```{r ggsurv-legend} pl.sex + ggplot2::guides(linetype = FALSE) + ggplot2::scale_colour_discrete( name = 'Sex', breaks = c(1, 2), labels = c('Male', 'Female') ) ``` ### Adjust the limits ```{r ggsurv-limits} data(kidney, package = "survival") sf.kid <- survival::survfit(Surv(time, status) ~ disease, data = kidney) pl.kid <- ggsurv(sf.kid, plot.cens = FALSE) pl.kid # Zoom in to first 80 days pl.kid + ggplot2::coord_cartesian(xlim = c(0, 80), ylim = c(0.45, 1)) ``` ### Add text and remove the legend ```{r ggsurv-text} pl.kid + ggplot2::annotate( "text", label = c("PKD", "Other", "GN", "AN"), x = c(90, 125, 5, 60), y = c(0.8, 0.65, 0.55, 0.30), size = 5, colour = scales::hue_pal( h = c(0, 360) + 15, c = 100, l = 65, h.start = 0, direction = 1 )(4) ) + ggplot2::guides(color = FALSE, linetype = FALSE) ``` GGally/inst/doc/docs.html0000644000176200001440000000112313277320367015000 0ustar liggesusers GGally

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    GGally/tests/0000755000176200001440000000000013017123421012565 5ustar liggesusersGGally/tests/testthat.R0000644000176200001440000000007013001231535014544 0ustar liggesuserslibrary(testthat) library(GGally) test_check("GGally") GGally/tests/testthat/0000755000176200001440000000000013277410307014437 5ustar liggesusersGGally/tests/testthat/test-utils.R0000644000176200001440000000070713277311163016703 0ustar liggesusers context("utils") test_that("require_namespaces", { if ("survival" %in% loadedNamespaces()) unloadNamespace("survival") expect_false("package:survival" %in% search()) suppressMessages(require_namespaces(c("survival"))) expect_false("package:survival" %in% search()) expect_false(is.null(getNamespace("survival"))) expect_error( suppressWarnings(suppressMessages( require_namespaces("DOES_NOT_EXIST_qweqweqweqwe") )) ) }) GGally/tests/testthat/test-ggcorr.R0000644000176200001440000000476413276725426017047 0ustar liggesusers context("ggcorr") # nba <- read.csv("http://datasets.flowingdata.com/ppg2008.csv") data(flea) test_that("limits", { print(ggcorr(flea[, -1])) print(ggcorr(flea[, -1], limits = TRUE)) print(ggcorr(flea[, -1], limits = FALSE)) print(ggcorr(flea[, -1], limits = NULL)) print(ggcorr(flea[, -1], limits = c(-5, 5))) print(ggcorr(flea[, -1], limits = c(-0.5, 0.5))) expect_true(TRUE) }) test_that("examples", { # Default output. p <- ggcorr(flea[, -1]) expect_equal(length(p$layers), 2) # Labelled output, with coefficient transparency. p <- ggcorr(flea[, -1], label = TRUE, label_alpha = TRUE, name = "") expect_equal(length(p$layers), 3) # Custom options. p <- ggcorr( flea[, -1], geom = "circle", max_size = 6, size = 3, hjust = 0.75, nbreaks = 6, angle = -45, palette = "PuOr" # colorblind safe, photocopy-able ) expect_equal(length(p$layers), 3) p <- ggcorr(flea[, -1], label = TRUE, name = "") expect_equal(length(p$layers), 3) # test other combinations of geoms + color scales ggcorr(flea[, -1], nbreaks = 4, palette = "PuOr") ggcorr(flea[, -1], nbreaks = 4, geom = "circle") ggcorr(flea[, -1], geom = "text") ggcorr(flea[, -1], geom = "text", limits = FALSE) ggcorr(flea[, -1], nbreaks = 4, geom = "text") ggcorr(flea[, -1], nbreaks = 4, palette = "PuOr", geom = "text") ggcorr(flea[, -1], label = TRUE, label_alpha = 0.5) }) test_that("non-numeric data", { expect_warning(ggcorr(flea), "not numeric") }) test_that("null midpoint", { expect_message(ggcorr(flea[, -1], midpoint = NULL), "Color gradient") }) test_that("further options", { ggcorr(flea[, -1], geom = "circle") ggcorr(flea[, -1], geom = "circle", limits = FALSE) ggcorr(flea[, -1], geom = "tile", nbreaks = 3) ggcorr(flea[, -1], geom = "tile", limits = FALSE) expect_error(ggcorr(flea[, -1], layout.exp = "a"), "incorrect layout.exp") expect_silent({ ggcorr(flea[, -1], layout.exp = 1) }) }) test_that("data.matrix", { p <- ggcorr(data.matrix(flea[, -1])) expect_equal(length(p$layers), 2) }) test_that("cor_matrix", { p <- ggcorr(data = NULL, cor_matrix = cor(flea[, -1], use = "pairwise")) expect_equal(length(p$layers), 2) }) test_that("other geoms", { expect_error(ggcorr(flea[, -1], geom = "hexbin"), "incorrect geom") expect_silent({ ggcorr(flea[, -1], geom = "blank") }) }) test_that("backwards compatibility", { expect_silent({ ggcorr(flea[, -1], method = "everything") }) }) GGally/tests/testthat/test-wrap.R0000644000176200001440000000213013001231535016471 0ustar liggesusers context("wrap") test_that("errors", { fn <- ggally_points # named params expect_error(wrap(fn, NA), "all parameters") expect_error(wrap(fn, y = TRUE, 5), "all parameters") # named params to wrapp expect_error(wrapp(fn, list(5)), "'params' must") expect_error(wrapp(fn, table(1:10, 1:10)), "'params' must") expect_error(wrapp(fn, list(A = 4, 5)), "'params' must") # if the character fn doesn't exist expect_error(wrap("does not exist", A = 5), "The following") expect_error(wrapp("does not exist", list(A = 5)), "The following") }) test_that("wrap", { (regularPlot <- ggally_points( iris, ggplot2::aes(Sepal.Length, Sepal.Width), size = 5, color = "red" )) # Wrap ggally_points to have parameter values size = 5 and color = 'red' w_ggally_points <- wrap(ggally_points, size = 5, color = "red") (wrappedPlot <- w_ggally_points( iris, ggplot2::aes(Sepal.Length, Sepal.Width) )) # Double check the aes parameters are the same for the geom_point layer expect_true(identical(regularPlot$layers[[1]]$aes_params, wrappedPlot$layers[[1]]$aes_params)) }) GGally/tests/testthat/test-gg-plots.R0000644000176200001440000001253213277311163017276 0ustar liggesusers context("gg-plots") data(tips, package = "reshape") data(nasa) nas <- subset(nasa, x <= 2 & y == 1) expect_print <- function(x) { testthat::expect_silent(print(x)) } test_that("denstrip", { expect_message( suppressWarnings(print(ggally_denstrip(tips, mapping = aes_string("sex", "tip")))), "`stat_bin()` using `bins = 30`", fixed = TRUE ) expect_message( suppressWarnings(print(ggally_denstrip(tips, mapping = aes_string("tip", "sex")))), "`stat_bin()` using `bins = 30`", fixed = TRUE ) }) test_that("density", { p <- ggally_density( tips, mapping = ggplot2::aes_string(x = "total_bill", y = "tip", fill = "..level..") ) + ggplot2::scale_fill_gradient(breaks = c(0.05, 0.1, 0.15, 0.2)) expect_equal(p$labels$fill, "level") }) test_that("cor", { expect_warning( ggally_cor(tips, mapping = ggplot2::aes_string(x = "total_bill", y = "tip"), use = "NOTFOUND"), "correlation 'use' not found" ) ti <- tips class(ti) <- c("NOTFOUND", "data.frame") p <- ggally_cor(ti, ggplot2::aes(x = total_bill, y = tip, color = day), use = "complete.obs") expect_equal(mapping_string(get("mapping", envir = p$layers[[2]])$colour), "labelp") p <- ggally_cor( ti, ggplot2::aes(x = total_bill, y = tip, color = I("blue")), use = "complete.obs" ) expect_equal(mapping_string(get("mapping", envir = p$layers[[1]])$colour), "I(\"blue\")") expect_err <- function(..., msg = NULL) { expect_error( ggally_cor( ti, ggplot2::aes(x = total_bill, y = tip), ... ), msg ) } expect_err(corAlignPercent = 0.9, "'corAlignPercent' is deprecated") expect_err(corMethod = "pearson", "'corMethod' is deprecated") expect_err(corUse = "complete.obs", "'corUse' is deprecated") expect_print(ggally_cor(ti, ggplot2::aes(x = total_bill, y = tip, color = I("green")))) ti3 <- ti2 <- ti ti2[2, "total_bill"] <- NA ti3[2, "total_bill"] <- NA ti3[3, "tip"] <- NA ti3[4, "total_bill"] <- NA ti3[4, "tip"] <- NA expect_warn <- function(data, msg) { expect_warning( ggally_cor(data, ggplot2::aes(x = total_bill, y = tip)), msg ) } expect_warn(ti2, "Removing 1 row that") expect_warn(ti3, "Removed 3 rows containing") expect_error( ggally_cor( ti, ggplot2::aes(x = total_bill, y = tip, color = size) ), "ggally_cor: mapping color column" ) expect_silent( ggally_cor( ti, ggplot2::aes(x = total_bill, y = tip, color = as.factor(size)) ) ) }) test_that("diagAxis", { p <- ggally_diagAxis(iris, ggplot2::aes(x = Petal.Width)) pDat1 <- get("data", envir = p$layers[[2]]) attr(pDat1, "out.attrs") <- NULL testDt1 <- data.frame( xPos = c(0.076, 0.076, 0.076, 0.076, 0.076, 0.076, 0.500, 1.000, 1.500, 2.000, 2.500), yPos = c(0.500, 1.000, 1.500, 2.000, 2.500, 0.076, 0.076, 0.076, 0.076, 0.076, 0.076), lab = as.character(c(0.5, 1, 1.5, 2, 2.5, 0, 0.5, 1, 1.5, 2, 2.5)), hjust = c(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.5, 0.5, 0.5, 0.5), vjust = c(0.5, 0.5, 0.5, 0.5, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), stringsAsFactors = FALSE ) rownames(testDt1) <- 2:12 expect_equal(pDat1, testDt1) p <- ggally_diagAxis(iris, ggplot2::aes(x = Species)) pDat2 <- get("data", envir = p$layers[[2]]) attr(pDat2, "out.attrs") <- NULL testDt2 <- data.frame( x = c(0.125, 0.500, 0.875), y = c(0.875, 0.500, 0.125), lab = c("setosa", "versicolor", "virginica") ) expect_equal(pDat2, testDt2) expect_error({ ggally_diagAxis(iris, mapping = ggplot2::aes(y = Sepal.Length)) }, "mapping\\$x is null.") # nolint }) test_that("dates", { class(nas) <- c("NOTFOUND", "data.frame") p <- ggally_cor(nas, ggplot2::aes(x = date, y = ozone)) expect_equal(get("aes_params", envir = p$layers[[1]])$label, "Corr:\n0.278") p <- ggally_cor(nas, ggplot2::aes(y = date, x = ozone)) expect_equal(get("aes_params", envir = p$layers[[1]])$label, "Corr:\n0.278") p <- ggally_barDiag(nas, ggplot2::aes(x = date)) expect_equal(mapping_string(p$mapping$x), "date") expect_equal(p$labels$y, "count") }) test_that("rescale", { p <- ggally_densityDiag(tips, mapping = ggplot2::aes(x = day), rescale = FALSE) expect_true(p$labels$y == "density") expect_print(p) p <- ggally_densityDiag(tips, mapping = ggplot2::aes(x = day), rescale = TRUE) expect_true(! identical(p$labels$y, "density")) expect_print(p) p <- ggally_barDiag(tips, mapping = ggplot2::aes(x = tip), binwidth = 0.25, rescale = FALSE) expect_true(p$labels$y == "count") expect_print(p) p <- ggally_barDiag(tips, mapping = ggplot2::aes(x = tip), binwidth = 0.25, rescale = TRUE) expect_true(! identical(p$labels$y, "count")) expect_print(p) }) test_that("shrink", { p <- ggally_smooth_loess(iris, mapping = ggplot2::aes(Sepal.Width, Petal.Length)) expect_true(!is.null(p$coordinates$limits$y)) expect_print(p) p <- ggally_smooth_loess(iris, mapping = ggplot2::aes(Sepal.Width, Petal.Length), shrink = FALSE) expect_true(is.null(p$coordinates$limits$y)) expect_print(p) }) test_that("smooth_se", { p <- ggally_smooth_loess(iris, mapping = ggplot2::aes(Sepal.Width, Petal.Length), se = TRUE) expect_equal(p$layers[[2]]$stat_params$se, TRUE) expect_print(p) p <- ggally_smooth_loess(iris, mapping = ggplot2::aes(Sepal.Width, Petal.Length), se = FALSE) expect_equal(p$layers[[2]]$stat_params$se, FALSE) expect_print(p) }) GGally/tests/testthat/test-ggsurv.R0000644000176200001440000001162413276725426017072 0ustar liggesusers context("ggsurv") suppressMessages(require(survival)) suppressMessages(require(scales)) data(lung, package = "survival") data(kidney, package = "survival") sf.lung <- survival::survfit(Surv(time, status) ~ 1, data = lung) sf.kid <- survival::survfit(Surv(time, status) ~ disease, data = kidney) expect_print <- function(x) { testthat::expect_silent(print(x)) } test_that("single", { a <- ggsurv(sf.lung) expect_equivalent(mapping_string(a$mapping$x), "time") expect_equivalent(mapping_string(a$mapping$y), "surv") expect_true(is.null(a$labels$group)) expect_true(is.null(a$labels$colour)) expect_true(is.null(a$labels$linetype)) }) test_that("multiple", { a <- ggsurv(sf.kid) expect_equivalent(mapping_string(a$mapping$x), "time") expect_equivalent(mapping_string(a$mapping$y), "surv") expect_true(!is.null(a$labels$group)) expect_true(!is.null(a$labels$colour)) expect_true(!is.null(a$labels$linetype)) }) test_that("adjust plot", { a <- ggsurv(sf.kid, plot.cens = FALSE) expect_equivalent(length(a$layers), 1) a <- ggsurv(sf.kid, plot.cens = TRUE) expect_equivalent(length(a$layers), 2) }) test_that("stops", { noCensor <- subset(lung, status == 1) lungNoCensor <- survival::survfit(Surv(time, status) ~ 1, data = noCensor) # check that the surv.col and lty.est are of the correct length expect_error(ggsurv(lungNoCensor, surv.col = c("black", "red"))) expect_error(ggsurv(lungNoCensor, lty.est = 1:2)) # must have censor to plot expect_error(ggsurv(lungNoCensor, plot.cens = TRUE)) noCensor <- subset(kidney, status == 1) kidneyNoCensor <- survival::survfit(Surv(time, status) ~ disease, data = noCensor) # check that the surv.col and lty.est are of the correct length. should be 4 expect_error(ggsurv(kidneyNoCensor, surv.col = c("black", "red", "blue"))) expect_error(ggsurv(kidneyNoCensor, lty.est = 1:3)) # must have censor to plot expect_error(ggsurv(kidneyNoCensor, plot.cens = TRUE)) # must have censor to plot expect_silent( ggsurv(sf.kid, CI = TRUE, surv.col = c("black", "red", "blue", "green")) ) expect_silent( ggsurv(sf.kid, CI = TRUE, lty.est = 1:4) ) ggsurv(sf.kid, CI = TRUE, surv.col = "red") }) test_that("back.white", { sf.lung <- survival::survfit(Surv(time, status) ~ 1, data = lung) sf.kid <- survival::survfit(Surv(time, status) ~ disease, data = kidney) a <- ggsurv(sf.lung, back.white = FALSE) expect_true(length(a$theme) == 0) a <- ggsurv(sf.lung, back.white = TRUE) expect_true(length(a$theme) != 0) a <- ggsurv(sf.kid, back.white = FALSE) expect_true(length(a$theme) == 0) a <- ggsurv(sf.kid, back.white = TRUE) expect_true(length(a$theme) != 0) }) test_that("surv.col", { ggsurv(sf.lung, surv.col = "red") ggsurv(sf.kid, surv.col = "red") ggsurv(sf.kid, surv.col = c("black", "red", "blue", "green")) ggsurv(sf.kid, lty.est = 1) ggsurv(sf.kid, lty.est = 1:4) expect_true("idk how to test it happened" != "fail") }) test_that("CI", { a <- ggsurv(sf.lung, CI = FALSE) b <- ggsurv(sf.lung, CI = TRUE) expect_equivalent(length(b$layers) - length(a$layers), 2) a <- ggsurv(sf.kid, CI = FALSE) b <- ggsurv(sf.kid, CI = TRUE) expect_equivalent(length(b$layers) - length(a$layers), 2) }) test_that("multiple colors", { expect_print(ggsurv(sf.kid, plot.cens = TRUE)) expect_warning({ ggsurv(sf.kid, plot.cens = TRUE, cens.col = c("red", "blue")) }, "Color scales for censored points") # nolint expect_silent({ print( ggsurv(sf.kid, plot.cens = TRUE, cens.col = "blue") ) }) cusotm_color <- c("green", "blue", "purple", "orange") expect_silent({ print( ggsurv(sf.kid, plot.cens = TRUE, cens.col = cusotm_color) ) }) expect_warning({ ggsurv( sf.kid, plot.cens = TRUE, cens.col = cusotm_color, cens.shape = c(1, 2) ) }, "The length of the censored shapes") # nolint expect_silent({ print( ggsurv( sf.kid, plot.cens = TRUE, cens.col = cusotm_color, cens.shape = c(1, 2, 3, 4) ) ) }) }) test_that("cens.size", { a <- ggsurv(sf.lung) b <- ggsurv(sf.lung, cens.size = 5) expect_true(a$layers[[4]]$aes_params$size == 2) expect_true(b$layers[[4]]$aes_params$size != 2) a <- ggsurv(sf.kid) b <- ggsurv(sf.lung, cens.size = 5) expect_true(a$layers[[2]]$aes_params$size == 2) expect_true(b$layers[[2]]$aes_params$size != 2) }) # 881 R/ggsurv.r 231 231 0 # 883 R/ggsurv.r 242 242 0 # 884 R/ggsurv.r 247 249 0 # 885 R/ggsurv.r 248 248 0 # 886 R/ggsurv.r 251 255 0 # 887 R/ggsurv.r 252 252 0 # 888 R/ggsurv.r 254 254 0 # 889 R/ggsurv.r 256 258 0 # 890 R/ggsurv.r 263 263 0 # 891 R/ggsurv.r 274 274 0 GGally/tests/testthat/test-ggnetworkmap.R0000644000176200001440000001672713277311163020261 0ustar liggesusers context("ggnetworkmap") if ("package:igraph" %in% search()) { detach("package:igraph") } rq <- function(...) { require(..., quietly = TRUE) } rq(network) rq(sna) rq(maps) rq(ggplot2) rq(intergraph) # test igraph conversion # first 500 rows of http://datasets.flowingdata.com/tuts/maparcs/airports.csv # avoids downloading the dataset to test the package airports <- read.csv("data/airports.csv", header = TRUE) rownames(airports) <- airports$iata # select some random flights set.seed(1234) flights <- data.frame( origin = sample(airports[200:400, ]$iata, 200, replace = TRUE), destination = sample(airports[200:400, ]$iata, 200, replace = TRUE) ) # convert to network flights <- network(flights, directed = TRUE) # add geographic coordinates flights %v% "lat" <- airports[ network.vertex.names(flights), "lat" ] # nolint flights %v% "lon" <- airports[ network.vertex.names(flights), "long" ] # nolint # drop isolated airports delete.vertices(flights, which(degree(flights) < 2)) # compute degree centrality flights %v% "degree" <- degree(flights, gmode = "digraph") # add random groups flights %v% "mygroup" <- sample(letters[1:4], network.size(flights), replace = TRUE) # create a map of the USA usa <- ggplot(map_data("usa"), aes(x = long, y = lat)) + geom_polygon(aes(group = group), color = "grey65", fill = "#f9f9f9", size = 0.2) test_that("basic drawing", { # no map p <- ggnetworkmap(net = flights, size = 2) expect_true(is.null(nrow(p$data))) # overlay network data to map p <- ggnetworkmap(usa, flights, size = 2) expect_false(is.null(nrow(p$data))) }) test_that("great circles", { p <- ggnetworkmap(usa, flights, size = 2, great.circles = TRUE) expect_equal(length(p$layers), 3) expect_equal(get("aes_params", envir = p$layers[[3]])$colour, "black") }) test_that("node groups", { p <- ggnetworkmap(usa, flights, size = 2, great.circles = TRUE, node.group = degree) expect_equal(length(p$layers), 3) expect_true(is.null(get("aes_params", envir = p$layers[[3]])$colour)) expect_equal(mapping_string(get("mapping", envir = p$layers[[3]])$colour), ".ngroup") p <- ggnetworkmap(usa, flights, size = 2, great.circles = TRUE, node.color = "red") expect_equal(mapping_string(get("aes_params", envir = p$layers[[3]])$colour), "\"red\"") }) test_that("ring groups", { p <- ggnetworkmap(usa, flights, size = 2, great.circles = TRUE, node.group = degree, ring.group = mygroup) expect_equal(length(p$layers), 3) expect_true(is.null(get("aes_params", envir = p$layers[[3]])$colour)) expect_equal(mapping_string(get("mapping", envir = p$layers[[3]])$colour), ".rgroup") expect_equal(mapping_string(get("mapping", envir = p$layers[[3]])$fill), ".ngroup") }) test_that("segment color", { p <- ggnetworkmap(usa, flights, size = 2, great.circles = TRUE, node.group = degree, ring.group = mygroup, segment.color = "cornflowerblue" ) expect_equal(length(p$layers), 3) expect_true(is.null(get("aes_params", envir = p$layers[[3]])$colour)) expect_equal(mapping_string(get("mapping", envir = p$layers[[3]])$colour), ".rgroup") expect_equal(mapping_string(get("mapping", envir = p$layers[[3]])$fill), ".ngroup") expect_equal( mapping_string(get("aes_params", envir = p$layers[[2]])$colour), "\"cornflowerblue\"" ) }) test_that("weight", { p <- ggnetworkmap(usa, flights, size = 2, great.circles = TRUE, node.group = degree, ring.group = mygroup, segment.color = "cornflowerblue", weight = degree ) expect_equal(length(p$layers), 3) expect_true(is.null(get("aes_params", envir = p$layers[[3]])$colour)) expect_equal(mapping_string(get("mapping", envir = p$layers[[3]])$colour), ".rgroup") expect_equal(mapping_string(get("mapping", envir = p$layers[[3]])$fill), ".ngroup") expect_equal( mapping_string(get("aes_params", envir = p$layers[[2]])$colour), "\"cornflowerblue\"" ) expect_equal(mapping_string(get("mapping", envir = p$layers[[3]])$size), ".weight") }) test_that("labels", { p <- ggnetworkmap(usa, flights, size = 2, great.circles = TRUE, node.group = degree, ring.group = mygroup, segment.color = "cornflowerblue", weight = degree, label.nodes = TRUE) expect_equal(length(p$layers), 4) expect_true(is.null(get("aes_params", envir = p$layers[[3]])$colour)) expect_equal(mapping_string(get("mapping", envir = p$layers[[3]])$colour), ".rgroup") expect_equal(mapping_string(get("mapping", envir = p$layers[[3]])$fill), ".ngroup") expect_equal( mapping_string(get("aes_params", envir = p$layers[[2]])$colour), "\"cornflowerblue\"" ) expect_equal(mapping_string(get("mapping", envir = p$layers[[3]])$size), ".weight") expect_equal(mapping_string(get("mapping", envir = p$layers[[4]])$label), ".label") expect_true(is.null(get("aes_params", envir = p$layers[[2]])$arrow)) }) test_that("arrows", { p <- ggnetworkmap(usa, flights, size = 2, great.circles = TRUE, node.group = degree, ring.group = mygroup, segment.color = "cornflowerblue", weight = degree, label.nodes = TRUE, arrow.size = 0.2) expect_equal(length(p$layers), 4) expect_true(is.null(get("aes_params", envir = p$layers[[3]])$colour)) expect_equal(mapping_string(get("mapping", envir = p$layers[[3]])$colour), ".rgroup") expect_equal(mapping_string(get("mapping", envir = p$layers[[3]])$fill), ".ngroup") expect_equal( mapping_string(get("aes_params", envir = p$layers[[2]])$colour), "\"cornflowerblue\"" ) expect_equal(mapping_string(get("mapping", envir = p$layers[[3]])$size), ".weight") expect_equal(mapping_string(get("mapping", envir = p$layers[[4]])$label), ".label") # look at geom_params for arrow info expect_true(is.list(get("geom_params", envir = p$layers[[2]])$arrow)) }) test_that("labels", { expect_error(ggnetworkmap(usa, flights, label.nodes = c("A", "B"))) testLabels <- paste("L", 1:network.size(flights), sep = "") # does logical check p <- ggnetworkmap(usa, flights, label.nodes = testLabels) ## PROBLEM HERE: why would vertex.names be equal to testLabels? ## expect_equal(get("data", p$layers[[4]])$.label, testLabels) # does vertex.names check p <- ggnetworkmap(usa, flights, label.nodes = TRUE) expect_true(!is.null(get("data", p$layers[[4]])$.label)) # does id check flights2 <- flights flights2 %v% "id" <- testLabels p <- ggnetworkmap(usa, flights2, label.nodes = TRUE) expect_true(!is.null(get("data", p$layers[[4]])$.label)) }) ### --- test arrow.size test_that("arrow.size", { expect_error(ggnetworkmap(net = flights, arrow.size = -1), "incorrect arrow.size") expect_warning(ggnetworkmap(net = network(as.matrix(flights), directed = FALSE), arrow.size = 1), "arrow.size ignored") }) ### --- test network coercion test_that("network coercion", { expect_warning( ggnetworkmap(net = network(matrix(1, nrow = 2, ncol = 2), loops = TRUE)), "self-loops" ) expect_error(ggnetworkmap(net = 1:2), "network object") expect_error(ggnetworkmap(net = network(data.frame(1:2, 3:4), hyper = TRUE)), "hyper graphs") expect_error( ggnetworkmap(net = network(data.frame(1:2, 3:4), multiple = TRUE)), "multiplex graphs" ) }) ### --- test igraph functionality test_that("igraph conversion", { if (requireNamespace("igraph", quietly = TRUE)) { library(igraph) n <- asIgraph(flights) p <- ggnetworkmap(net = n) expect_equal(length(p$layers), 2) } }) expect_true(TRUE) GGally/tests/testthat/test-crosstalk.R0000644000176200001440000000172513277315152017553 0ustar liggesusers context("crosstalk") test_that("crosstalk works with ggduo and ggpairs", { skip_if_not_installed("crosstalk") sd <- crosstalk::SharedData$new(iris[1:4]) expect_silent({ pm <- ggpairs(sd) }) expect_error({ pm <- ggpairs(sd, 3:5) }, "Make sure your numeric" ) expect_error({ pm <- ggpairs(sd, c("Petal.Length", "Petal.Width", crosstalk_key())) }, "Columns in 'columns' not" ) expect_silent({ pm <- ggduo(sd) }) expect_error({ pm <- ggduo(sd, c(1:2, 5), 3:5) }, "Make sure your numeric 'columnsX'" ) expect_error({ pm <- ggduo( sd, c("Sepal.Length", "Sepal.Width", crosstalk_key()), c("Petal.Length", "Petal.Width") ) }, "Columns in 'columnsX' not" ) expect_error({ pm <- ggduo( sd, c("Sepal.Length", "Sepal.Width"), c("Petal.Length", "Petal.Width", crosstalk_key()) ) }, "Columns in 'columnsY' not" ) }) GGally/tests/testthat/test-ggparcoord.R0000644000176200001440000002306613277311163017675 0ustar liggesusers context("ggparcoord") set.seed(123) data(diamonds, package = "ggplot2") diamonds.samp <- diamonds[sample(1:dim(diamonds)[1], 100), ] iris2 <- iris iris2$alphaLevel <- c("setosa" = 0.2, "versicolor" = 0.3, "virginica" = 0)[iris2$Species] test_that("stops", { # basic parallel coordinate plot, using default settings # ggparcoord(data = diamonds.samp, columns = c(1, 5:10)) # this time, color by diamond cut expect_error( ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = NULL, order = "anyClass"), "can't use the 'order' methods " ) expect_error( ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = NULL, order = "allClass"), "can't use the 'order' methods " ) expect_error( ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = c(1, 2)), "invalid value for 'groupColumn'" ) expect_error( ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 1i), "invalid value for 'groupColumn'" ) expect_error( ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, scale = "notValid"), "invalid value for 'scale'" ) expect_error( ggparcoord( data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, centerObsID = nrow(diamonds.samp) + 10 ), "invalid value for 'centerObsID'" ) expect_error( ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, missing = "notValid"), "invalid value for 'missing'" ) expect_error( ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, order = "notValid"), "invalid value for 'order'" ) expect_error( ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, order = 1i), "invalid value for 'order'" ) expect_error( ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, showPoints = 1), "invalid value for 'showPoints'" ) expect_error( ggparcoord( data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, alphaLines = "notAColumn" ), "'alphaLines' column is missing in data" ) tmpDt <- diamonds.samp tmpDt$price[1] <- NA range(tmpDt$price) expect_error( ggparcoord( data = tmpDt, columns = c(1, 5:10), groupColumn = 2, alphaLines = "price" ), "missing data in 'alphaLines' column" ) expect_error( ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, alphaLines = "price"), "invalid value for 'alphaLines' column; max range " ) expect_error( ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, alphaLines = -0.1), "invalid value for 'alphaLines'; must be a scalar value" ) expect_error( ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, alphaLines = 1.1), "invalid value for 'alphaLines'; must be a scalar value" ) expect_error( ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, boxplot = 1), "invalid value for 'boxplot'" ) expect_error( ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, shadeBox = c(1, 2)), "invalid value for 'shadeBox'; must be a single color" ) expect_error( ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, shadeBox = "notacolor"), "invalid value for 'shadeBox'; must be a valid R color" ) expect_error( ggparcoord(diamonds.samp, columns = c(1, 5:10), groupColumn = 2, splineFactor = NULL), "invalid value for 'splineFactor'" ) }) test_that("alphaLines", { p <- ggparcoord( data = iris2, columns = 1:4, groupColumn = 5, order = "anyClass", showPoints = TRUE, title = "Parallel Coordinate Plot for the Iris Data", alphaLines = "alphaLevel" ) expect_equal(length(p$layers), 2) expect_equivalent(mapping_string(get("mapping", envir = p$layers[[1]])$alpha), "alphaLevel") }) test_that("splineFactor", { ## Use splines on values, rather than lines (all produce the same result) columns <- c(1, 5:10) p1 <- ggparcoord(diamonds.samp, columns, groupColumn = 2, splineFactor = TRUE) p2 <- ggparcoord(diamonds.samp, columns, groupColumn = 2, splineFactor = 3) splineFactor <- length(columns) * 3 p3 <- ggparcoord(diamonds.samp, columns, groupColumn = 2, splineFactor = I(splineFactor)) pList <- list(p1, p2, p3) for (p in pList) { expect_equivalent(mapping_string(get("mapping", envir = p$layers[[1]])$x), "spline.x") expect_equivalent(mapping_string(get("mapping", envir = p$layers[[1]])$y), "spline.y") tmp <- unique(as.numeric(get("data", envir = p$layers[[1]])$ggally_splineFactor)) expect_true( (tmp == 3) || (tmp == 21) ) } p <- ggparcoord( data = iris2, columns = 1:4, groupColumn = 5, splineFactor = 3, alphaLines = "alphaLevel" ) expect_equal(mapping_string(get("mapping", p$layers[[1]])$alpha), "alphaLevel") p <- ggparcoord( data = iris2, columns = 1:4, groupColumn = 5, splineFactor = 3, showPoints = TRUE ) expect_equal(length(p$layers), 2) expect_equal(mapping_string(get("mapping", p$layers[[1]])$x), "spline.x") expect_equal(mapping_string(get("mapping", p$layers[[2]])$y), "value") }) test_that("groupColumn", { ds2 <- diamonds.samp ds2$color <- mapping_string(ds2$color) # column 3 has a character # column 4 has a factor p <- ggparcoord(data = ds2, columns = c(1, 3:10), groupColumn = 2) expect_true("color" %in% levels(p$data$variable)) expect_true("clarity" %in% levels(p$data$variable)) expect_true(is.numeric(p$data$value)) expect_equal(mapping_string(p$mapping$colour), colnames(ds2)[2]) p <- ggparcoord( data = ds2, columns = c( "carat", "color", "clarity", "depth", "table", "price", "x", "y", "z" ), order = c(1, 3:10), groupColumn = "cut" ) expect_true("color" %in% levels(p$data$variable)) expect_true("clarity" %in% levels(p$data$variable)) expect_true(is.numeric(p$data$value)) expect_equal(levels(p$data$cut), levels(ds2$cut)) # group column is a regular column ## factor p <- ggparcoord(data = ds2, columns = c(1, 3:10), groupColumn = 4) expect_true("clarity" %in% levels(p$data$variable)) ## character p <- ggparcoord(data = ds2, columns = c(1, 3:10), groupColumn = 3) expect_true("color" %in% levels(p$data$variable)) ## numeric p <- ggparcoord(data = ds2, columns = c(1, 3:10), groupColumn = 1) expect_true("carat" %in% levels(p$data$variable)) }) test_that("scale", { for (scale in c("std", "robust", "uniminmax", "globalminmax", "center", "centerObs")) { p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, scale = scale) } expect_true(TRUE) }) test_that("missing", { ds2 <- diamonds.samp ds2[3, 1] <- NA for (missing in c("exclude", "mean", "median", "min10", "random")) { p <- ggparcoord(data = ds2, columns = c(1, 5:10), groupColumn = 2, missing = missing) } expect_true(TRUE) }) test_that("order", { if (requireNamespace("scagnostics", quietly = TRUE)) { for (ordering in c("Outlying", "Skewed", "Clumpy", "Sparse", "Striated", "Convex", "Skinny", "Stringy", "Monotonic")) { p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, order = ordering) expect_true(all(levels(p$data) != c("carat", "depth", "table", "price", "x", "y", "z"))) } } for (ordering in c("skewness", "allClass", "anyClass")) { p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, order = ordering) expect_true(all(levels(p$data) != c("carat", "depth", "table", "price", "x", "y", "z"))) } }) test_that("basic", { # no color supplied p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10)) expect_true(is.null(p$mapping$colour)) # color supplied p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2) expect_false(is.null(p$mapping$colour)) # title supplied ttl <- "Parallel Coord. Plot of Diamonds Data" p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10), title = ttl) expect_equal(p$labels$title, ttl) col <- "blue" p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10), shadeBox = col) expect_equal(length(p$layers), 2) expect_equal(get("aes_params", envir = p$layers[[1]])$colour, col) p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10), mapping = ggplot2::aes(size = 1)) expect_equal(length(p$layers), 1) expect_equal(p$mapping$size, 1) }) test_that("size", { p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10), mapping = ggplot2::aes(size = gear)) expect_equal(mapping_string(p$mapping$size), "gear") p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10)) + ggplot2::aes(size = gear) expect_equal(mapping_string(p$mapping$size), "gear") }) test_that("columns containing only a single value do not cause an scaling error", { df <- data.frame(obs = 1:5, var1 = sample(10, 5), var2 = rep(3, 5)) # no scaling expect_silent(ggparcoord(data = df, columns = 1:3, scale = "globalminmax")) # requires scaling, must not throw an errror due to scaling the single values (to NaN) expect_silent(ggparcoord(data = df, columns = 1:3, scale = "uniminmax")) df2 <- data.frame(df, var3 = factor(c("a", "b", "c", "a", "c"))) # requires scaling, must not throw an errror due to scaling the single values (to NaN) expect_silent(ggparcoord(data = df2, columns = 1:4, scale = "uniminmax")) df3 <- data.frame(df2, var4 = factor(c("d", "d", "d", "d", "d"))) expect_silent(ggparcoord(data = df3, columns = 1:4, scale = "uniminmax")) expect_silent(ggparcoord(data = df3, columns = 1:4, scale = "robust")) expect_silent(ggparcoord(data = df3, columns = 1:4, scale = "std")) }) GGally/tests/testthat/data/0000755000176200001440000000000013277311163015350 5ustar liggesusersGGally/tests/testthat/data/airports.csv0000644000176200001440000010545013277311163017735 0ustar liggesusers"iata","airport","city","state","country","lat","long" "00M","Thigpen ","Bay Springs","MS","USA",31.95376472,-89.23450472 "00R","Livingston Municipal","Livingston","TX","USA",30.68586111,-95.01792778 "00V","Meadow Lake","Colorado Springs","CO","USA",38.94574889,-104.5698933 "01G","Perry-Warsaw","Perry","NY","USA",42.74134667,-78.05208056 "01J","Hilliard Airpark","Hilliard","FL","USA",30.6880125,-81.90594389 "01M","Tishomingo County","Belmont","MS","USA",34.49166667,-88.20111111 "02A","Gragg-Wade ","Clanton","AL","USA",32.85048667,-86.61145333 "02C","Capitol","Brookfield","WI","USA",43.08751,-88.17786917 "02G","Columbiana County","East Liverpool","OH","USA",40.67331278,-80.64140639 "03D","Memphis Memorial","Memphis","MO","USA",40.44725889,-92.22696056 "04M","Calhoun County","Pittsboro","MS","USA",33.93011222,-89.34285194 "04Y","Hawley Municipal","Hawley","MN","USA",46.88384889,-96.35089861 "05C","Griffith-Merrillville ","Griffith","IN","USA",41.51961917,-87.40109333 "05F","Gatesville - City/County","Gatesville","TX","USA",31.42127556,-97.79696778 "05U","Eureka","Eureka","NV","USA",39.60416667,-116.0050597 "06A","Moton Municipal","Tuskegee","AL","USA",32.46047167,-85.68003611 "06C","Schaumburg","Chicago/Schaumburg","IL","USA",41.98934083,-88.10124278 "06D","Rolla Municipal","Rolla","ND","USA",48.88434111,-99.62087694 "06M","Eupora Municipal","Eupora","MS","USA",33.53456583,-89.31256917 "06N","Randall ","Middletown","NY","USA",41.43156583,-74.39191722 "06U","Jackpot/Hayden ","Jackpot","NV","USA",41.97602222,-114.6580911 "07C","Dekalb County","Auburn","IN","USA",41.30716667,-85.06433333 "07F","Gladewater Municipal","Gladewater","TX","USA",32.52883861,-94.97174556 "07G","Fitch H Beach","Charlotte","MI","USA",42.57450861,-84.81143139 "07K","Central City Municipal","Central City","NE","USA",41.11668056,-98.05033639 "08A","Wetumpka Municipal","Wetumpka","AL","USA",32.52943944,-86.32822139 "08D","Stanley Municipal","Stanley","ND","USA",48.30079861,-102.4063514 "08K","Harvard State","Harvard","NE","USA",40.65138528,-98.07978667 "08M","Carthage-Leake County","Carthage","MS","USA",32.76124611,-89.53007139 "09A","Butler-Choctaw County","Butler","AL","USA",32.11931306,-88.1274625 "09J","Jekyll Island","Jekyll Island","GA","USA",31.07447222,-81.42777778 "09K","Sargent Municipal","Sargent","NE","USA",41.63695083,-99.34038139 "09M","Charleston Municipal","Charleston","MS","USA",33.99150222,-90.078145 "09W","South Capitol Street","Washington","DC","USA",38.86872333,-77.00747583 "0A3","Smithville Municipal","Smithville","TN","USA",35.98531194,-85.80931806 "0A8","Bibb County","Centreville","AL","USA",32.93679056,-87.08888306 "0A9","Elizabethton Municipal","Elizabethton","TN","USA",36.37094306,-82.17374111 "0AK","Pilot Station","Pilot Station","AK","USA",61.93396417,-162.8929358 "0B1","Col. Dyke ","Bethel","ME","USA",44.42506444,-70.80784778 "0B4","Hartington Municipal","Hartington","NE","USA",42.60355556,-97.25263889 "0B5","Turners Falls","Montague","MA","USA",42.59136361,-72.52275472 "0B7","Warren-Sugar Bush","Warren","VT","USA",44.11672722,-72.82705806 "0B8","Elizabeth ","Fishers Island","NY","USA",41.25130806,-72.03161139 "0C0","Dacy","Chicago/Harvard","IL","USA",42.40418556,-88.63343222 "0C4","Pender Municipal","Pender","NE","USA",42.11388722,-96.72892556 "0D1","South Haven Municipal","South Haven","MI","USA",42.35083333,-86.25613889 "0D8","Gettysburg Municipal","Gettysburg","SD","USA",44.98730556,-99.9535 "0E0","Moriarty","Moriarty","NM","USA",34.98560639,-106.0094661 "0E8","Crownpoint","Crownpoint","NM","USA",35.71765889,-108.2015961 "0F2","Bowie Municipal","Bowie","TX","USA",33.60166667,-97.77556 "0F4","Loup City Municipal","Loup City","NE","USA",41.29028694,-98.99064278 "0F7","Fountainhead Lodge Airpark","Eufaula","OK","USA",35.38898833,-95.60165111 "0F8","William R Pogue Municipal","Sand Springs","OK","USA",36.17528,-96.15181028 "0F9","Tishomingo Airpark","Tishomingo","OK","USA",34.19592833,-96.67555694 "0G0","North Buffalo Suburban","Lockport","NY","USA",43.10318389,-78.70334583 "0G3","Tecumseh Municipal","Tecumseh","NE","USA",40.39944417,-96.17139694 "0G6","Williams County","Bryan","OH","USA",41.46736111,-84.50655556 "0G7","Finger Lakes Regional","Seneca Falls","NY","USA",42.88062278,-76.78162028 "0H1","Trego Wakeeney ","Wakeeney","KS","USA",39.0044525,-99.89289917 "0I8","Cynthiana-Harrison County","Cynthiana","KY","USA",38.36674167,-84.28410056 "0J0","Abbeville Municipal","Abbeville","AL","USA",31.60016778,-85.23882222 "0J4","Florala Municipal","Florala","AL","USA",31.04247361,-86.31156111 "0J6","Headland Municipal","Headland","AL","USA",31.364895,-85.30965556 "0K7","Humboldt Municipal","Humboldt","IA","USA",42.7360825,-94.24524167 "0L5","Goldfield","Goldfield","NV","USA",37.71798833,-117.2384119 "0L7","Jean","Jean","NV","USA",35.76827222,-115.3296378 "0L9","Echo Bay","Overton","NV","USA",36.31108972,-114.4638672 "0M0","Dumas Municipal","Dumas","AR","USA",33.8845475,-91.53429111 "0M1","Scott ","Parsons","TN","USA",35.63778,-88.127995 "0M4","Benton County","Camden","TN","USA",36.01122694,-88.12328833 "0M5","Humphreys County","Waverly","TN","USA",36.11659972,-87.73815889 "0M6","Panola County","Batesville","MS","USA",34.36677444,-89.90008917 "0M8","Byerley","Lake Providence","LA","USA",32.82587917,-91.187665 "0O3","Calaveras Co-Maury Rasmussen ","San Andreas","CA","USA",38.14611639,-120.6481733 "0O4","Corning Municipal","Corning","CA","USA",39.94376806,-122.1713781 "0O5","University","Davis","CA","USA",38.53146222,-121.7864906 "0Q5","Shelter Cove","Shelter Cove","CA","USA",40.02764333,-124.0733639 "0Q6","Shingletown","Shingletown","CA","USA",40.52210111,-121.8177683 "0R0","Columbia-Marion County","Columbia","MS","USA",31.29700806,-89.81282944 "0R1","Atmore Municipal","Atmore","AL","USA",31.01621528,-87.44675972 "0R3","Abbeville Chris Crusta Memorial","Abbeville","LA","USA",29.97576083,-92.08415167 "0R4","Concordia Parish","Vidalia","LA","USA",31.56683278,-91.50011889 "0R5","David G Joyce","Winnfield","LA","USA",31.96366222,-92.66026056 "0R7","Red River","Coushatta","LA","USA",31.99071694,-93.30739306 "0S7","Dorothy Scott","Oroville","WA","USA",48.958965,-119.4119622 "0S9","Jefferson County International","Port Townsend","WA","USA",48.04981361,-122.8012792 "0V2","Harriet Alexander ","Salida","CO","USA",38.53916389,-106.0458483 "0V3","Pioneer Village ","Minden","NE","USA",40.5149125,-98.94565083 "0V4","Brookneal/Campbell County","Brookneal","VA","USA",37.14172222,-79.01638889 "0V6","Mission Sioux","Mission","SD","USA",43.30694778,-100.6281936 "0V7","Kayenta","Kayenta","AZ","USA",36.70972139,-110.2367978 "10C","Galt","Chicago/Greenwood/Wonderlake","IL","USA",42.40266472,-88.37588917 "10D","Winsted Municipal","Winsted","MN","USA",44.94996278,-94.0669175 "10G","Holmes County","Millersburg","OH","USA",40.53716667,-81.95436111 "10N","Wallkill","Wallkill","NY","USA",41.62787111,-74.13375583 "10U","Owyhee","Owyhee","NV","USA",41.95323306,-116.1876014 "11A","Clayton Municipal","Clayton","AL","USA",31.88329917,-85.48491361 "11D","Clarion Cty","Clarion","PA","USA",41.22581222,-79.44098972 "11IS","Schaumburg Heliport","Chicago/Schaumburg","IL","USA",42.04808278,-88.05257194 "11J","Early County","Blakely","GA","USA",31.39698611,-84.89525694 "11R","Brenham Municipal","Brenham","TX","USA",30.219,-96.37427778 "12C","Rochelle Municipal","Rochelle","IL","USA",41.89300139,-89.07829 "12D","Tower Municipal","Tower","MN","USA",47.81833333,-92.29166667 "12J","Brewton Municipal","Brewton","AL","USA",31.05126306,-87.06796833 "12K","Superior Municipal","Superior","NE","USA",40.04636111,-98.06011111 "12Y","Le Sueur Municipal","Le Sueur","MN","USA",44.43746472,-93.91274083 "13C","Lakeview","Lakeview","MI","USA",43.45213722,-85.26480333 "13K","Eureka Municipal","Eureka","KS","USA",37.8515825,-96.29169806 "13N","Trinca","Andover","NJ","USA",40.96676444,-74.78016556 "14J","Carl Folsom","Elba","AL","USA",31.40988861,-86.08883583 "14M","Hollandale Municipal","Hollandale","MS","USA",33.18262167,-90.83065444 "14Y","Todd Field ","Long Prairie","MN","USA",45.89857556,-94.87391 "15F","Haskell Municipal","Haskell","TX","USA",33.19155556,-99.71793056 "15J","Cook County","Adel","GA","USA",31.13780556,-83.45308333 "15M","Luka ","Luka","MS","USA",34.7723125,-88.16587444 "15Z","McCarthy 2","McCarthy","AK","USA",61.43706083,-142.9037372 "16A","Nunapitchuk","Nunapitchuk","AK","USA",60.90582833,-162.4391158 "16G","Seneca County","Tiffin","OH","USA",41.09405556,-83.2125 "16J","Dawson Municipal","Dawson","GA","USA",31.74328472,-84.419285 "16S","Myrtle Creek Municipal","Myrtle Creek","OR","USA",42.99845056,-123.3095092 "17G","Port Bucyrus-Crawford County","Bucyrus","OH","USA",40.78141667,-82.97469444 "17J","Donalsonville Municipal","Donalsonville","GA","USA",31.00694444,-84.87761111 "17K","Boise City","Boise City","OK","USA",36.77430028,-102.5104364 "17M","Magee Municipal","Magee","MS","USA",31.86127139,-89.80285361 "17N","Cross Keys","Cross Keys","NJ","USA",39.70547583,-75.03300306 "17Z","Manokotak","Manokotak","AK","USA",58.98896583,-159.0499739 "18A","Franklin County","Canon","GA","USA",34.34010472,-83.13348333 "18I","McCreary County ","Pine Knot","KY","USA",36.69591306,-84.39160389 "19A","Jackson County","Jefferson","GA","USA",34.17402472,-83.56066528 "19M","C A Moore","Lexington","MS","USA",33.12546111,-90.02555694 "19N","Camden","Berlin","NJ","USA",39.77842056,-74.94780389 "19P","Port Protection SPB","Port Protection","AK","USA",56.32880417,-133.6100844 "1A3","Martin Campbell ","Copperhill","TN","USA",35.01619111,-84.34631083 "1A5","Macon County","Franklin","NC","USA",35.222595,-83.41904389 "1A6","Middlesboro-Bell County","Middlesboro","KY","USA",36.6106375,-83.73741611 "1A7","Jackson County","Gainesboro","TN","USA",36.39728139,-85.64164278 "1A9","Autauga County","Prattville","AL","USA",32.438775,-86.51044778 "1B0","Dexter Regional","Dexter","ME","USA",45.00839444,-69.23976722 "1B1","Columbia Cty","Hudson","NY","USA",42.29130028,-73.71031944 "1B3","Fair Haven","Fair Haven","VT","USA",43.61534389,-73.27455556 "1B9","Mansfield Municipal","Mansfield","MA","USA",42.00013306,-71.19677139 "1C5","Clow","Chicago/Plainfield","IL","USA",41.69597444,-88.12923056 "1D1","Milbank Municipal","Milbank","SD","USA",45.23053806,-96.56596556 "1D2","Canton -Plymouth - Mettetal","Plymouth","MI","USA",42.35003667,-83.45826833 "1D3","Platte Municipal","Platte","SD","USA",43.40332833,-98.82952972 "1D6","Hector Municipal","Hector","MN","USA",44.73107278,-94.71471333 "1D7","Webster Municipal","Webster","SD","USA",45.29329111,-97.51369889 "1D8","Redfield Municipal","Redfield","SD","USA",44.86247611,-98.52953972 "1F0","Downtown Ardmore","Ardmore","OK","USA",34.14698917,-97.12265194 "1F1","Lake Murray State Park","Overbrook","OK","USA",34.07509694,-97.10667917 "1F4","Madill Municipal","Madill","OK","USA",34.14040194,-96.81203222 "1F9","Bridgeport Municipal","Bridgeport","TX","USA",33.17533333,-97.82838889 "1G0","Wood County","Bowling Green","OH","USA",41.391,-83.63013889 "1G3","Kent State University","Kent","OH","USA",41.15186167,-81.41658306 "1G4","Grand Canyon West","Peach Springs","AZ","USA",35.99221,-113.8166164 "1G5","Freedom ","Medina","OH","USA",41.13144444,-81.76491667 "1G6","Michael ","Cicero","NY","USA",43.18166667,-76.12777778 "1H0","Creve Coeur","St Louis","MO","USA",38.72752,-90.50830417 "1H2","Effingham County Memorial","Effingham","IL","USA",39.07045083,-88.53351972 "1H3","Linn State Tech. College","Linn","MO","USA",38.47149444,-91.81531667 "1H8","Casey Municipal","Casey","IL","USA",39.30250917,-88.00406194 "1I5","Freehold","Freehold","NY","USA",42.36425,-74.06596806 "1I9","Delphi Municipal","Delphi","IN","USA",40.54281417,-86.68167194 "1J0","Tri-County","Bonifay","FL","USA",30.84577778,-85.60138889 "1K2","Lindsay Municipal","Lindsay","OK","USA",34.85007333,-97.58642028 "1K4","David J. Perry","Goldsby","OK","USA",35.1550675,-97.47039389 "1K5","Waynoka Municipal","Waynoka","OK","USA",36.56670028,-98.85231333 "1K9","Satanta Municipal","Satanta","KS","USA",37.45419111,-100.9921119 "1L0","St. John the Baptist Parish","Reserve","LA","USA",30.08720833,-90.58266528 "1L1","Lincoln Co","Panaca","NV","USA",37.78746444,-114.4216567 "1L7","Escalante Municipal","Escalante","UT","USA",37.74532639,-111.5701653 "1L9","Parowan","Parowan","UT","USA",37.85969694,-112.816055 "1M1","North Little Rock Municipal","No Lit Rock","AR","USA",34.83398056,-92.25792778 "1M2","Belzoni Municipal","Belzoni","MS","USA",33.14518056,-90.51528472 "1M4","Posey ","Haleyville","AL","USA",34.28034806,-87.60044139 "1M5","Portland Municipal","Portland","TN","USA",36.59287528,-86.47691028 "1M7","Fulton","Fulton","KY","USA",36.52589417,-88.91561611 "1MO","Mountain Grove Memorial","Mountain Grove","MO","USA",37.12071889,-92.311245 "1N2","Spadaro ","East Moriches","NY","USA",40.82787639,-72.74871083 "1N4","Woodbine Muni ","Woodbine","NJ","USA",39.21915,-74.794765 "1N7","Blairstown","Blairstown","NJ","USA",40.97114556,-74.99747556 "1N9","Allentown Queen City Muni","Allentown","PA","USA",40.57027778,-75.48830556 "1ND3","Hamry ","Kindred","ND","USA",46.6485775,-97.00564306 "1O1","Grandfield Municipal","Grandfield","OK","USA",34.23758944,-98.74200917 "1O2","Lampson ","Lakeport","CA","USA",38.99017472,-122.8997175 "1O3","Lodi","Lodi","CA","USA",38.20241667,-121.2684167 "1O4","Thomas Municipal","Thomas","OK","USA",35.73338222,-98.73063833 "1O6","Dunsmuir Municipal-Mott","Dunsmuir","CA","USA",41.26320889,-122.2719528 "1R1","Jena","Jena","LA","USA",31.671005,-92.15846722 "1R7","Brookhaven-Lincoln County","Brookhaven","MS","USA",31.6058475,-90.40931583 "1R8","Bay Minette Municipal","Bay Minette","AL","USA",30.87046278,-87.81738167 "1S0","Pierce County ","Puyallup","WA","USA",47.10391667,-122.2871944 "1S3","Tillitt ","Forsyth","MT","USA",46.27110639,-106.6239206 "1S5","Sunnyside Municipal","Sunnyside","WA","USA",46.32763139,-119.9705964 "1S6","Priest River Muni","Priest River","ID","USA",48.19018611,-116.9093644 "1U7","Bear Lake County","Paris","ID","USA",42.24714972,-111.33826 "1V0","Navajo State Park ","Navajo Dam","NM","USA",36.80833833,-107.6514444 "1V2","Grant County ","Hyannis","NE","USA",42.00942944,-101.7693439 "1V5","Boulder Muni","Boulder","CO","USA",40.03942972,-105.2258217 "1V6","Fremont County","Canon City","CO","USA",38.42838111,-105.1054994 "1V9","Blake ","Delta","CO","USA",38.78539722,-108.0636611 "20A","Robbins ","Oneonta","AL","USA",33.97231972,-86.37942722 "20M","Macon Municipal","Macon","MS","USA",33.13345889,-88.53559806 "20N","Kingston-Ulster","Kingston","NY","USA",41.9852525,-73.96409722 "20U","Beach","Beach","ND","USA",46.92362444,-103.9785389 "20V","McElroy Airfield","Kremmling","CO","USA",40.05367972,-106.3689467 "21D","Lake Elmo","St Paul","MN","USA",44.99748861,-92.85568111 "21F","Jacksboro Municipal","Jacksboro","TX","USA",33.228725,-98.14671083 "22B","Mountain Meadow Airstrip","Burlington","CT","USA",41.77287528,-73.01121667 "22I","Vinton County","McArthur","OH","USA",39.328125,-82.44182167 "22M","Pontotoc County","Pontotoc","MS","USA",34.27593833,-89.03839694 "22N","Carbon Cty-Jake Arner Memorial","Lehighton","PA","USA",40.80950889,-75.76149639 "23J","Herlong","Jacksonville","FL","USA",30.27778889,-81.80594722 "23M","Clarke County","Quitman","MS","USA",32.08487111,-88.73893389 "23N","Bayport Aerodrome","Bayport","NY","USA",40.75843139,-73.05372083 "23R","Devine Municipal","Devine","TX","USA",29.1384075,-98.94189028 "24A","Jackson County","Sylva","NC","USA",35.3168625,-83.20936806 "24J","Suwannee County","Live Oak","FL","USA",30.30105583,-83.02318778 "24N","Jicarilla Apache Nation","Dulce","NM","USA",36.828535,-106.8841914 "25J","Cuthbert-Randolph","Cuthbert","GA","USA",31.70016583,-84.82492194 "25M","Ripley ","Ripley","MS","USA",34.72226778,-89.01504944 "25R","International","Edinburg","TX","USA",26.44201083,-98.12945306 "26A","Ashland/Lineville","Ashland/Lineville","AL","USA",33.28761417,-85.80412861 "26N","Ocean City Muni cipal","Ocean City","NJ","USA",39.26347222,-74.60747222 "26R","Jackson County","Edna/Ganado","TX","USA",29.00101,-96.58194667 "26U","McDermitt State","McDermitt","OR","USA",42.00211083,-117.7231972 "27A","Elbert County-Patz ","Elberton","GA","USA",34.09519722,-82.81586417 "27D","Myers ","Canby","MN","USA",44.72801889,-96.26309972 "27J","Newberry Municipal","Newberry","SC","USA",34.30927778,-81.63972222 "27K","Georgetown-Scott County","Georgetown","KY","USA",38.23442528,-84.43468667 "28J","Kay Larkin","Palatka","FL","USA",29.65863889,-81.68855556 "29D","Grove City","Grove City","PA","USA",41.14597611,-80.16592194 "29G","Portage County","Ravenna","OH","USA",41.210195,-81.25163083 "29S","Gardiner","Gardiner","MT","USA",45.04993556,-110.7466008 "2A0","Mark Anton","Dayton","TN","USA",35.48624611,-84.93109722 "2A1","Jamestown Municipal","Jamestown","TN","USA",36.34970833,-84.94664472 "2A3","Larsen Bay","Larsen Bay","AK","USA",57.53510667,-153.9784169 "2A9","Kotlik","Kotlik","AK","USA",63.03116111,-163.5299278 "2AK","Lime Village","Lime Village","AK","USA",61.35848528,-155.4403508 "2B3","Parlin ","Newport","NH","USA",43.38812944,-72.18925417 "2B7","Pittsfield Municipal","Pittsfield","ME","USA",44.76852778,-69.37441667 "2B9","Post Mills","Post Mills","VT","USA",43.884235,-72.25370333 "2D1","Barber","Alliance","OH","USA",40.97089139,-81.09981889 "2D5","Oakes Municipal","Oakes","ND","USA",46.17301972,-98.07987556 "2F5","Lamesa Municipal","Lamesa","TX","USA",32.75627778,-101.9194722 "2F6","Skiatook Municipal","Skiatook","OK","USA",36.357035,-96.01138556 "2F7","Commerce Municipal","Commerce","TX","USA",33.29288889,-95.89641806 "2F8","Morehouse Memorial","Bastrop","LA","USA",32.75607944,-91.88057194 "2G2","Jefferson County Airpark","Steubenville","OH","USA",40.35944306,-80.70007806 "2G3","Connellsville","Connellsville","PA","USA",39.95893667,-79.65713306 "2G4","Garrett County","Oakland","MD","USA",39.58027778,-79.33941667 "2G9","Somerset County","Somerset","PA","USA",40.03911111,-79.01455556 "2H0","Shelby County","Shelbyville","IL","USA",39.41042861,-88.8454325 "2H2","Aurora Memorial Municipal","Aurora","MO","USA",36.96230778,-93.69531111 "2I0","Madisonville Municipal","Madisonville","KY","USA",37.35502778,-87.39963889 "2I5","Chanute","Rantoul","IL","USA",40.29355556,-88.14236111 "2IS","Airglades","Clewiston","FL","USA",26.74200972,-81.04978917 "2J2","Liberty County","Hinesville","GA","USA",31.78461111,-81.64116667 "2J3","Louisville Municipal","Louisville","GA","USA",32.98654083,-82.38568139 "2J5","Millen","Millen","GA","USA",32.89376972,-81.96511583 "2J9","Quincy Municipal","Quincy","FL","USA",30.59786111,-84.55741667 "2K3","Stanton County Municipal","Johnson","KS","USA",37.58271111,-101.73281 "2K4","Scott ","Mangum","OK","USA",34.89172583,-99.52675667 "2K5","Telida","Telida","AK","USA",63.39387278,-153.2689733 "2M0","Princeton-Caldwell County","Princeton","KY","USA",37.11560444,-87.85556944 "2M2","Lawrenceburg Municipal","Lawrenceburg","TN","USA",35.2343025,-87.25793222 "2M3","Sallisaw Municipal","Sallisaw","OK","USA",35.43816667,-94.80277778 "2M4","G. V. Montgomery","Forest","MS","USA",32.35347778,-89.48867944 "2M8","Charles W. Baker","Millington","TN","USA",35.27897583,-89.93147611 "2O1","Gansner ","Quincy","CA","USA",39.94378056,-120.9468983 "2O3","Angwin-Parrett ","Angwin","CA","USA",38.57851778,-122.4352572 "2O6","Chowchilla","Chowchilla","CA","USA",37.11244417,-120.2468406 "2O7","Independence","Independence","CA","USA",36.81382111,-118.2050956 "2O8","Hinton Municipal","Hinton","OK","USA",35.50592472,-98.34236111 "2P2","Washington Island","Washington Island","WI","USA",45.38620833,-86.92448056 "2Q3","Yolo Co-Davis/Woodland/Winters","Davis/Woodland/Winters","CA","USA",38.5790725,-121.8566322 "2R0","Waynesboro Municipal","Waynesboro","MS","USA",31.64599472,-88.63475667 "2R4","Peter Prince ","Milton","FL","USA",30.63762083,-86.99365278 "2R5","St Elmo","St Elmo","AL","USA",30.50190833,-88.27511667 "2R9","Karnes County","Kenedy","TX","USA",28.8250075,-97.86558333 "2S1","Vashon Municipal","Vashon","WA","USA",47.45815333,-122.4773506 "2S6","Sportsman Airpark","Newberg","OR","USA",45.29567333,-122.9553783 "2S7","Chiloquin State","Chiloquin","OR","USA",42.58319167,-121.8761261 "2S8","Wilbur","Wilbur","WA","USA",47.75320639,-118.7438936 "2T1","Muleshoe Municipal","Muleshoe","TX","USA",34.18513639,-102.6410981 "2V1","Stevens ","Pagosa Springs","CO","USA",37.277505,-107.0558742 "2V2","Vance Brand","Longmont","CO","USA",40.16367139,-105.1630369 "2V5","Wray Municipal","Wray","CO","USA",40.10032333,-102.24096 "2V6","Yuma Municipal","Yuma","CO","USA",40.10415306,-102.7129869 "2W5","Maryland","Indian Head","MD","USA",38.60053667,-77.07296917 "2W6","Captain Walter Francis Duke Regional ","Leonardtown","MD","USA",38.31536111,-76.55011111 "2Y3","Yakutat SPB","Yakutat","AK","USA",59.5624775,-139.7410994 "2Y4","Rockwell City Municipal","Rockwell City","IA","USA",42.38748056,-94.61803333 "31F","Gaines County","Seminole","TX","USA",32.67535389,-102.652685 "32M","Norfolk","Norfolk","MA","USA",42.12787528,-71.37033556 "32S","Stevensville","Stevensville","MT","USA",46.52511111,-114.0528056 "33J","Geneva Municipal","Geneva","AL","USA",31.05527778,-85.88033333 "33M","Water Valley ","Water Valley","MS","USA",34.16677639,-89.68619722 "33N","Delaware Airpark","Dover","DE","USA",39.21837556,-75.59642667 "33S","Pru ","Ritzville","WA","USA",47.12487194,-118.3927539 "34A","Laurens County","Laurens","SC","USA",34.50705556,-81.94719444 "35A","Union County, Troy Shelton ","Union","SC","USA",34.68680111,-81.64121167 "35D","Padgham ","Allegan","MI","USA",42.53098278,-85.82513556 "35S","Wasco State","Wasco","OR","USA",45.58944444,-120.6741667 "36K","Lakin","Lakin","KS","USA",37.96946389,-101.2554472 "36S","Happy Camp","Happy Camp","CA","USA",41.79067944,-123.3889444 "36U","Heber City Municipal/Russ McDonald ","Heber","UT","USA",40.48180556,-111.4288056 "37T","Calico Rock-Izard County","Calico Rock","AR","USA",36.16565278,-92.14523611 "37W","Harnett County","Erwin","NC","USA",35.37880028,-78.73362917 "38A","Shaktoolik","Shaktoolik","AK","USA",64.36263194,-161.2025369 "38S","Deer Lodge-City-County","Deer Lodge","MT","USA",46.38881583,-112.7669842 "38U","Wayne Wonderland","Loa","UT","USA",38.36247972,-111.5960164 "39N","Princeton","Princeton","NJ","USA",40.39834833,-74.65760361 "3A0","Grove Hill Municipal","Grove Hill","AL","USA",31.68932389,-87.7613875 "3A1","Folsom ","Cullman","AL","USA",34.26870833,-86.85833611 "3A2","New Tazewell Municipal","Tazewell","TN","USA",36.41008417,-83.55546167 "3A3","Anson County","Wadesboro","NC","USA",35.02397611,-80.08127333 "3AU","Augusta Municipal","Augusta","KS","USA",37.67162778,-97.07787222 "3B0","Southbridge Municipal","Southbridge","MA","USA",42.10092806,-72.03840833 "3B1","Greenville Municipal","Greenville","ME","USA",45.46302778,-69.55161111 "3B2","Marshfield","Marshfield","MA","USA",42.09824111,-70.67212083 "3B9","Chester","Chester","CT","USA",41.38390472,-72.50589444 "3BS","Jack Barstow","Midland","MI","USA",43.66291528,-84.261325 "3CK","Lake In The Hills","Lake In The Hills","IL","USA",42.20680306,-88.32304028 "3CM","James Clements Municipal","Bay City","MI","USA",43.54691667,-83.89550222 "3CU","Cable Union","Cable","WI","USA",46.19424889,-91.24640972 "3D2","Ephraim/Gibraltar","Ephraim","WI","USA",45.13535778,-87.18586556 "3D4","Frankfort Dow Memorial","Frankfort","MI","USA",44.62506389,-86.20061944 "3F3","De Soto Parish","Mansfield","LA","USA",32.07345972,-93.76551889 "3F4","Vivian","Vivian","LA","USA",32.86133333,-94.01015361 "3F7","Jones Memorial","Bristow","OK","USA",35.80685278,-96.42185556 "3FM","Fremont Municipal","Fremont","MI","USA",43.43890528,-85.99478 "3FU","Faulkton Municipal","Faulkton","SD","USA",45.03191861,-99.11566417 "3G3","Wadsworth Municipal","Wadsworth","OH","USA",41.00158222,-81.75513111 "3G4","Ashland County","Ashland","OH","USA",40.90297222,-82.25563889 "3G7","Williamson/Sodus","Williamson","NY","USA",43.23472222,-77.12097222 "3GM","Grand Haven Memorial Airpark","Grand Haven","MI","USA",43.03404639,-86.1981625 "3I2","Mason County","Point Pleasant","WV","USA",38.91463889,-82.09858333 "3I7","Phillipsburg","Phillipsburg","OH","USA",39.91344194,-84.40030889 "3J1","Ridgeland","Ridgeland","SC","USA",32.49268694,-80.99233028 "3J7","Greene County Airpark","Greensboro","GA","USA",33.59766667,-83.139 "3JC","Freeman ","Junction City","KS","USA",39.04327556,-96.84328694 "3K3","Syracuse-Hamilton County Municipal","Syracuse","KS","USA",37.99167972,-101.7462822 "3K6","St Louis-Metro East","Troy/Marine/St. Louis","IL","USA",38.73290861,-89.80656722 "3K7","Mark Hoard Memorial","Leoti","KS","USA",38.45696333,-101.3532161 "3LC","Logan County","Lincoln","IL","USA",40.15847222,-89.33497222 "3LF","Litchfield Municipal","Litchfield","IL","USA",39.16635306,-89.67489694 "3M7","Lafayette Municipal","Lafayette","TN","USA",36.518375,-86.05828083 "3M8","North Pickens ","Reform","AL","USA",33.38900611,-88.00557806 "3M9","Warren Municipal","Warren","AR","USA",33.56044333,-92.08538861 "3MY","Mt. Hawley Auxiliary","Peoria","IL","USA",40.79525917,-89.6134025 "3N6","Old Bridge","Old Bridge","NJ","USA",40.32988667,-74.34678694 "3N8","Mahnomen County ","Mahnomen","MN","USA",47.25996056,-95.92809778 "3ND0","Northwood Municipal","Northwood","ND","USA",47.72423333,-97.59042222 "3O1","Gustine","Gustine","CA","USA",37.26271722,-120.9632586 "3O3","Municipal","Purcell","OK","USA",34.97979444,-97.38586167 "3O4","Sayre Municipal","Sayre","OK","USA",35.16755222,-99.65787361 "3O5","Walters Municipal","Walters","OK","USA",34.37258444,-98.40588583 "3O7","Hollister Municipal","Hollister","CA","USA",36.89334528,-121.4102706 "3O9","Grand Lake Regional","Afton","OK","USA",36.5775775,-94.86190028 "3R0","Beeville Municipal","Beeville","TX","USA",28.36455528,-97.79208194 "3R1","Bay City Municipal","Bay City","TX","USA",28.973255,-95.86345528 "3R2","Le Gros Memorial","Crowley","LA","USA",30.16173611,-92.48396111 "3R4","Hart","Many","LA","USA",31.54489667,-93.48645306 "3R7","Jennings","Jennings","LA","USA",30.24269333,-92.67344778 "3S4","Illinois Valley","Illinois Valley (Cave Junction)","OR","USA",42.10372417,-123.6822911 "3S8","Grants Pass","Grants Pass","OR","USA",42.51011722,-123.3879894 "3S9","Condon State-Pauling ","Condon","OR","USA",45.24651889,-120.1664233 "3SG","Harry W Browne","Saginaw - H.Browne","MI","USA",43.43341028,-83.86245833 "3SQ","St Charles","St Charles","MO","USA",38.84866139,-90.50011833 "3T3","Boyceville Municipal ","Boyceville","WI","USA",45.042185,-92.0293475 "3T5","Fayette Regional Air Center","La Grange","TX","USA",29.90930556,-96.9505 "3TR","Jerry Tyler Memorial","Niles","MI","USA",41.83590806,-86.22517611 "3U3","Bowman ","Anaconda","MT","USA",46.15313278,-112.86784 "3U7","Benchmark","Benchmark","MT","USA",47.48133194,-112.8697678 "3U8","Big Sandy","Big Sandy","MT","USA",48.16247972,-110.1132631 "3V4","Fort Morgan Municipal","Fort Morgan","CO","USA",40.33423194,-103.8039508 "3WO","Shawano Municipal","Shawano","WI","USA",44.78777778,-88.56152444 "3Y2","George L Scott Municipal","West Union","IA","USA",42.98508917,-91.79060417 "3Y3","Winterset Madison County","Winterset","IA","USA",41.36276778,-94.02106194 "3Z9","Haines SPB","Haines","AK","USA",59.23495111,-135.4407181 "40J","Perry-Foley","Perry","FL","USA",30.06927778,-83.58058333 "40N","Chester Cty-G O Carlson","Coatesville","PA","USA",39.97897222,-75.86547222 "40U","Manila","Manila","UT","USA",40.98607,-109.6784811 "41U","Manti-Ephraim","Manti","UT","USA",39.32912833,-111.6146397 "42A","Melbourne Municipal","Melbourne","AR","USA",36.07079222,-91.82914667 "42C","White Cloud","White Cloud","MI","USA",43.55974139,-85.77421944 "42J","Keystone Airpark","Keystone Heights","FL","USA",29.84475,-82.04752778 "42S","Poplar","Poplar","MT","USA",48.11595861,-105.1821928 "43A","Montgomery County","Star","NC","USA",35.38819528,-79.79281667 "44B","Dover/Foxcroft","Dover-Foxcroft","ME","USA",45.18338806,-69.2328225 "44N","Sky Acres","Millbrook","NY","USA",41.70742861,-73.73802889 "45J","Rockingham-Hamlet","Rockingham","NC","USA",34.89107083,-79.75905806 "45OH","North Bass Island","North Bass Island","OH","USA",41.71932528,-82.82196917 "45R","Kountz - Hawthorne ","Kountze/Silsbee","TX","USA",30.33633806,-94.25754361 "46A","Blairsville","Blairsville","GA","USA",34.85508722,-83.996855 "46D","Carrington Municipal","Carrington","ND","USA",47.45111111,-99.15111111 "46N","Sky Park","Red Hook","NY","USA",41.98458333,-73.83596556 "47A","Cherokee County","Canton","GA","USA",34.31058333,-84.42391667 "47J","Cheraw Municipal","Cheraw","SC","USA",34.71258333,-79.95794444 "47N","Central Jersey Regional","Manville","NJ","USA",40.52438417,-74.59839194 "47V","Curtis Municipal","Curtis","NE","USA",40.63750778,-100.4712539 "48A","Cochran","Cochran","GA","USA",32.39936111,-83.27591667 "48D","Clare Municipal","Clare","MI","USA",43.83111111,-84.74133333 "48I","Braxton County","Sutton","WV","USA",38.68704444,-80.65176083 "48K","Ness City Municipal","Ness City","KS","USA",38.47110278,-99.90806667 "48S","Harlem","Harlem","MT","USA",48.56666472,-108.7729339 "48V","Tri-County","Erie","CO","USA",40.010225,-105.047975 "49A","Gilmer County","Ellijay","GA","USA",34.62786417,-84.52492889 "49T","Downtown Heliport","Dallas","TX","USA",32.77333333,-96.80027778 "49X","Chemehuevi Valley","Chemehuevi Valley","CA","USA",34.52751083,-114.4310697 "49Y","Fillmore County","Preston","MN","USA",43.67676,-92.17973444 "4A2","Atmautluak","Atmautluak","AK","USA",60.86674556,-162.2731389 "4A4","Cornelius-Moore ","Cedartown","GA","USA",34.01869444,-85.14647222 "4A5","Marshall-Searcy County","Marshall","AR","USA",35.89893667,-92.65588611 "4A6","Scottsboro Municipal","Scottsboro","AL","USA",34.68897278,-86.0058125 "4A7","Clayton County","Hampton","GA","USA",33.38911111,-84.33236111 "4A9","Isbell ","Fort Payne","AL","USA",34.4728925,-85.72221722 "4B0","South Albany","South Bethlehem","NY","USA",42.56072611,-73.83395639 "4B1","Duanesburg","Duanesburg","NY","USA",42.75840889,-74.13290472 "4B6","Ticonderoga Muni","Ticonderoga","NY","USA",43.87700278,-73.41317639 "4B7","Schroon Lake","Schroon Lake","NY","USA",43.86256083,-73.74262972 "4B8","Robertson ","Plainville","CT","USA",41.69037667,-72.8648225 "4B9","Simsbury Tri-Town","Simsbury","CT","USA",41.91676389,-72.77731778 "4C8","Albia Municipal","Albia","IA","USA",40.99445361,-92.76297194 "4D0","Abrams Municipal","Grandledge","MI","USA",42.77420167,-84.73309806 "4D9","Alma Municipal","Alma","NE","USA",40.11389972,-99.34565306 "4F2","Panola County-Sharpe ","Carthage","TX","USA",32.17608333,-94.29880556 "4F4","Gilmer-Upshur County","Gilmer","TX","USA",32.699,-94.94886111 "4G1","Greenville Muni","Greenville","PA","USA",41.44683167,-80.39126167 "4G2","Hamburg Inc.","Hamburg","NY","USA",42.7008925,-78.91475694 "4G5","Monroe County","Woodsfield","OH","USA",39.77904472,-81.10277222 "4G6","Hornell Muni","Hornell","NY","USA",42.38214444,-77.6821125 "4G7","Fairmont Muni","Fairmont","WV","USA",39.44816667,-80.16702778 "4I0","Mingo County","Williamson","WV","USA",37.68760139,-82.26097306 "4I3","Knox County","Mount Vernon","OH","USA",40.32872222,-82.52377778 "4I7","Putnam County","Greencastle","IN","USA",39.63359556,-86.8138325 "4I9","Morrow County","Mt. Gilead","OH","USA",40.52452778,-82.85005556 "4J1","Brantley County","Nahunta","GA","USA",31.21272417,-81.90539083 "4J2","Berrien County","Nashville","GA","USA",31.21255556,-83.22627778 "4J5","Quitman-Brooks County","Quitman","GA","USA",30.80575139,-83.58654889 "4J6","St Marys","St Marys","GA","USA",30.75468028,-81.55731917 "4K0","Pedro Bay","Pedro Bay","AK","USA",59.78960972,-154.1238331 "4K5","Ouzinkie","Ouzinkie","AK","USA",57.92287611,-152.5005111 "4K6","Bloomfield Municipal","Bloomfield","IA","USA",40.73210556,-92.42826889 "4KA","Tununak","Tununak","AK","USA",60.57559667,-165.2731272 "4M1","Carroll County","Berryville","AR","USA",36.38340333,-93.61685667 "4M3","Carlisle Municipal","Carlisle","AR","USA",34.80823,-91.71205083 "4M4","Clinton Municipal","Clinton","AR","USA",35.59785528,-92.45182472 "4M7","Russellville-Logan County","Russellville","KY","USA",36.79991667,-86.81016667 "4M8","Clarendon Municipal","Clarendon","AR","USA",34.64870694,-91.39457111 "4M9","Corning Municipal","Corning","AR","USA",36.40423139,-90.64792639 "4N1","Greenwood Lake","West Milford","NJ","USA",41.12854806,-74.34584611 "4O3","Blackwell-Tonkawa Municipal","Blackwell-Tonkawa","OK","USA",36.74511583,-97.34959972 "4O4","McCurtain County Regional","Idabel","OK","USA",33.909325,-94.85835278 "4O5","Cherokee Municipal","Cherokee","OK","USA",36.78336306,-98.35035083 "4PH","Polacca","Polacca","AZ","USA",35.79167222,-110.4234653 "4R1","I H Bass Jr Memorial","Lumberton","MS","USA",31.01546028,-89.48256556 "4R3","Jackson Municipal","Jackson","AL","USA",31.47210861,-87.89472083 "4R4","Fairhope Municipal","Fairhope","AL","USA",30.4621125,-87.87801972 "4R5","Madeline Island","La Pointe","WI","USA",46.78865556,-90.75866944 "4R7","Eunice","Eunice","LA","USA",30.46628389,-92.42379917 "4R9","Dauphin Island","Dauphin Island","AL","USA",30.26048083,-88.12749972 "4S1","Gold Beach Muni","Gold Beach","OR","USA",42.41344444,-124.4242742 "4S2","Hood River","Hood River","OR","USA",45.67261833,-121.5364625 "4S3","Joseph State","Joseph","OR","USA",45.35709583,-117.2532244 "4S9","Portland-Mulino","Mulino (Portland)","OR","USA",45.21632417,-122.5900839 "4SD","Reno/Stead","Reno","NV","USA",39.66738111,-119.8754169 "4T6","Mid-Way","Midlothian-Waxahachie","TX","USA",32.45609722,-96.91240972 "4U3","Liberty County","Chester","MT","USA",48.51072222,-110.9908639 "4U6","Circle Town County","Circle","MT","USA",47.41861972,-105.5619431 "4V0","Rangely","Rangely","CO","USA",40.09469917,-108.7612172 "4V1","Johnson ","Walsenburg","CO","USA",37.69640056,-104.7838747 "4V9","Antelope County","Neligh","NE","USA",42.11222889,-98.0386775 "4W1","Elizabethtown Municipal","Elizabethtown","NC","USA",34.60183722,-78.57973306 "4Z4","Holy Cross","Holy Cross","AK","USA",62.18829583,-159.7749503 "4Z7","Hyder SPB","Hyder","AK","USA",55.90331972,-130.0067031 "50I","Kentland Municipal","Kentland","IN","USA",40.75873222,-87.42821917 "50J","Berkeley County","Moncks Corner","SC","USA",33.18605556,-80.03563889 "50K","Pawnee City Municipal","Pawnee City","NE","USA",40.11611111,-96.19445278 "50R","Lockhart Municipal","Lockhart","TX","USA",29.85033333,-97.67241667 "51D","Edgeley Municipal ","Edgeley","ND","USA",46.34858333,-98.73555556 "51Z","Minto (New)","Minto","AK","USA",65.14370889,-149.3699647 "52A","Madison Municipal","Madison","GA","USA",33.61212528,-83.46044333 "52E","Timberon ","Timberon","NM","USA",32.63388889,-105.6863889 "52J","Lee County","Bishopville","SC","USA",34.24459889,-80.23729333 "53A","Dr. C.P. Savage, Sr.","Montezuma","GA","USA",32.302,-84.00747222 "53K","Osage City Municipal","Osage City","KS","USA",38.63334222,-95.80859806 "54J","Defuniak Springs","Defuniak Springs","FL","USA",30.7313,-86.15160833 "55D","Grayling Army Airfield","Grayling","MI","USA",44.68032028,-84.72886278 "55J","Fernandina Beach Municipal","Fernandina Beach","FL","USA",30.61170083,-81.462345 "55S","Packwood","Packwood","WA","USA",46.60400083,-121.6778664 "56D","Wyandot County","Upper Sandusky","OH","USA",40.88336139,-83.3145325 "56M","Warsaw Municipal","Warsaw","MO","USA",38.34688889,-93.345425 "56S","Seaside Municipal","Seaside","OR","USA",46.01649694,-123.9054167 "57B","Islesboro","Islesboro","ME","USA",44.30285556,-68.91058722 "57C","East Troy Municipal","East Troy","WI","USA",42.79711111,-88.3725 "59B","Newton ","Jackman","ME","USA",45.63199111,-70.24728944 "5A4","Okolona Mun.-Richard M. Stovall ","Okolona","MS","USA",34.01580528,-88.72618944 GGally/tests/testthat/test-ggnet2.R0000644000176200001440000002076013140471254016727 0ustar liggesusers context("ggnet2") if ("package:igraph" %in% search()) { detach("package:igraph") } rq <- function(...) { require(..., quietly = TRUE) } rq(network) # network objects rq(sna) # placement and centrality rq(ggplot2) # grammar of graphics rq(grid) # arrows rq(scales) # sizing rq(intergraph) # test igraph conversion rq(RColorBrewer) # test ColorBrewer palettes test_that("examples", { ### --- start: documented examples # random adjacency matrix x <- 10 ndyads <- x * (x - 1) density <- x / ndyads m <- matrix(0, nrow = x, ncol = x) dimnames(m) <- list(letters[ 1:x ], letters[ 1:x ]) m[ row(m) != col(m) ] <- runif(ndyads) < density m # random undirected network n <- network::network(m, directed = FALSE) n ggnet2(n, label = TRUE) # ggnet2(n, label = TRUE, shape = 15) # ggnet2(n, label = TRUE, shape = 15, color = "black", label.color = "white") # add vertex attribute x <- network.vertex.names(n) # nolint x <- ifelse(x %in% c("a", "e", "i"), "vowel", "consonant") n %v% "phono" <- x ggnet2(n, color = "phono") ggnet2(n, color = "phono", palette = c("vowel" = "gold", "consonant" = "grey")) ggnet2(n, shape = "phono", color = "phono") # random groups n %v% "group" <- sample(LETTERS[1:3], 10, replace = TRUE) ggnet2(n, color = "group", palette = "Set2") # random weights n %e% "weight" <- sample(1:3, network.edgecount(n), replace = TRUE) ggnet2(n, edge.size = "weight", edge.label = "weight") # Padgett's Florentine wedding data data(flo, package = "network") flo ggnet2(flo, label = TRUE) ggnet2(flo, label = TRUE, label.trim = 4, vjust = -1, size = 3, color = 1) # ggnet2(flo, label = TRUE, size = 12, color = "white") ### --- end: documented examples # test node assignment errors expect_error(ggnet2(n, color = NA)) expect_error(ggnet2(n, color = -1)) expect_error(ggnet2(n, color = rep("red", network.size(n) - 1))) # test node assignment ggnet2(n, color = rep("red", network.size(n))) # test node assignment errors expect_error(ggnet2(n, edge.color = NA)) expect_error(ggnet2(n, edge.color = -1)) expect_error(ggnet2(n, edge.color = rep("red", network.edgecount(n) - 1))) # test edge assignment ggnet2(n, edge.color = rep("red", network.edgecount(n))) # ggnet2(n, edge.color = "weight") # test mode = c("x", "y") ggnet2(n, mode = matrix(1, ncol = 2, nrow = 10)) n %v% "x" <- sample(1:10) n %v% "y" <- sample(1:10) ggnet2(n, mode = c("x", "y")) expect_error(ggnet2(n, mode = c("xx", "yy")), "not found") expect_error(ggnet2(n, mode = c("phono", "phono")), "not numeric") expect_error(ggnet2(n, mode = matrix(1, ncol = 2, nrow = 9)), "coordinates length") # test arrow.size expect_error(ggnet2(n, arrow.size = -1), "incorrect arrow.size") expect_warning(ggnet2(n, arrow.size = 1), "arrow.size ignored") # test arrow.gap suppressWarnings(expect_error( ggnet(n, arrow.size = 12, arrow.gap = -1), "incorrect arrow.gap" )) suppressWarnings(expect_warning( ggnet(n, arrow.size = 12, arrow.gap = 0.1), "arrow.gap ignored" # network is undirected; arrow.gap ignored )) suppressWarnings(expect_warning( ggnet(n, arrow.size = 12, arrow.gap = 0.1), "arrow.size ignored" # network is undirected; arrow.size ignored )) m <- network::network(m, directed = TRUE) ggnet2(m, arrow.size = 12, arrow.gap = 0.05) # test max_size expect_error(ggnet2(n, max_size = NA), "incorrect max_size") # test na.rm expect_error(ggnet2(n, na.rm = 1:2), "incorrect na.rm") expect_error(ggnet2(n, na.rm = "xyz"), "not found") n %v% "missing" <- ifelse(n %v% "phono" == "vowel", NA, n %v% "phono") expect_message(ggnet2(n, na.rm = "missing"), "removed") n %v% "missing" <- NA expect_warning(ggnet2(n, na.rm = "missing"), "removed all nodes") # test size = "degree" ggnet2(n, size = "degree") # test size.min expect_error(ggnet2(n, size = "degree", size.min = -1), "incorrect size.min") expect_message(ggnet2(n, size = "degree", size.min = 1), "size.min removed") expect_warning(ggnet2(n, size = "abc", size.min = 1), "not numeric") expect_warning(ggnet2(n, size = 4, size.min = 5), "removed all nodes") # test size.max expect_error(ggnet2(n, size = "degree", size.max = -1), "incorrect size.max") expect_message(ggnet2(n, size = "degree", size.max = 99), "size.max removed") expect_warning(ggnet2(n, size = "abc", size.max = 1), "not numeric") expect_warning(ggnet2(n, size = 4, size.max = 3), "removed all nodes") # test size.cut ggnet2(n, size = 1:10, size.cut = 3) ggnet2(n, size = 1:10, size.cut = TRUE) expect_error(ggnet2(n, size = 1:10, size.cut = NA), "incorrect size.cut") expect_error(ggnet2(n, size = 1:10, size.cut = "xyz"), "incorrect size.cut") expect_warning(ggnet2(n, size = "abc", size.cut = 3), "not numeric") expect_warning(ggnet2(n, size = 1, size.cut = 3), "ignored") # test alpha.palette ggnet2(n, alpha = "phono", alpha.palette = c("vowel" = 1, "consonant" = 0.5)) ggnet2(n, alpha = factor(1:10)) expect_error( ggnet2(n, alpha = "phono", alpha.palette = c("vowel" = 1)), "no alpha.palette value" ) # test color.palette # ggnet2(n, color = "phono", color.palette = c("vowel" = 1, "consonant" = 2)) ggnet2(n, color = factor(1:10)) ggnet2(n, color = "phono", palette = "Set1") # only 2 groups, palette has min. 3 expect_error(ggnet2(n, color = factor(1:10), palette = "Set1"), "too many node groups") expect_error( ggnet2(n, color = "phono", color.palette = c("vowel" = 1)), "no color.palette value" ) # test shape.palette ggnet2(n, shape = "phono", shape.palette = c("vowel" = 15, "consonant" = 19)) expect_warning(ggnet2(n, shape = factor(1:10)), "discrete values") expect_error( ggnet2(n, shape = "phono", shape.palette = c("vowel" = 1)), "no shape.palette value" ) # test size.palette ggnet2(n, size = "phono", size.palette = c("vowel" = 1, "consonant" = 2)) ggnet2(n, size = factor(1:10)) expect_error(ggnet2(n, size = "phono", size.palette = c("vowel" = 1)), "no size.palette value") # test node.label ggnet2(n, label = sample(letters, 10)) ggnet2(n, label = "phono") # test label.alpha expect_error(ggnet2(n, label = TRUE, label.alpha = "xyz"), "incorrect label.alpha") # test label.color expect_error(ggnet2(n, label = TRUE, label.color = "xyz"), "incorrect label.color") # test label.size expect_error(ggnet2(n, label = TRUE, label.size = "xyz"), "incorrect label.size") # test label.trim expect_error(ggnet2(n, label = TRUE, label.trim = "xyz"), "incorrect label.trim") ggnet2(n, label = TRUE, label.trim = toupper) # test mode expect_error(ggnet2(n, mode = "xyz"), "unsupported") expect_error(ggnet2(n, mode = letters[1:3]), "incorrect mode") # test edge.node shared colors ggnet2(n, color = "phono", edge.color = c("color", "grey")) # test edge.color expect_error(ggnet2(n, edge.color = "xyz"), "incorrect edge.color") # test edge.label.alpha expect_error( ggnet2(n, edge.label = "xyz", edge.label.alpha = "xyz"), "incorrect edge.label.alpha" ) # test edge.label.color expect_error( ggnet2(n, edge.label = "xyz", edge.label.color = "xyz"), "incorrect edge.label.color" ) # test edge.label.size expect_error(ggnet2(n, edge.label = "xyz", edge.label.size = "xyz"), "incorrect edge.label.size") # test edge.size expect_error(ggnet2(n, edge.size = "xyz"), "incorrect edge.size") # test layout.exp expect_error(ggnet2(n, layout.exp = "xyz")) ggnet2(n, layout.exp = 0.1) ### --- test bipartite functionality # weighted adjacency matrix bip <- data.frame( event1 = c(1, 2, 1, 0), event2 = c(0, 0, 3, 0), event3 = c(1, 1, 0, 4), row.names = letters[1:4] ) # weighted bipartite network bip <- network( bip, matrix.type = "bipartite", ignore.eval = FALSE, names.eval = "weights" ) # test bipartite mode ggnet2(bip, color = "mode") ### --- test network coercion expect_warning(ggnet2(network(matrix(1, nrow = 2, ncol = 2), loops = TRUE)), "self-loops") expect_error(ggnet2(1:2), "network object") expect_error(ggnet2(network(data.frame(1:2, 3:4), hyper = TRUE)), "hyper graphs") expect_error(ggnet2(network(data.frame(1:2, 3:4), multiple = TRUE)), "multiplex graphs") ### --- test igraph functionality if (requireNamespace("igraph", quietly = TRUE)) { library(igraph) # test igraph conversion p <- ggnet2(asIgraph(n), color = "group") expect_null(p$guides$colour) # test igraph degree ggnet2(n, size = "degree") expect_true(TRUE) } }) GGally/tests/testthat/test-ggmatrix_add.R0000644000176200001440000000210713114357267020176 0ustar liggesusers context("ggmatrix_add") data(tips, package = "reshape") test_that("add", { pm <- ggpairs(tips) expect_true(is.null(pm$title)) expect_true(is.null(pm$xlab)) expect_true(is.null(pm$ylab)) pm1 <- pm + labs(title = "my title", x = "x label", y = "y label") expect_equivalent(pm1$title, "my title") expect_equivalent(pm1$xlab, "x label") expect_equivalent(pm1$ylab, "y label") expect_true(is.null(pm$gg)) # first add pm2 <- pm + ggplot2::theme_bw() expect_true(! is.null(pm2$gg)) # second to nth add pm3 <- pm + ggplot2::theme_bw() expect_true(! is.null(pm3$gg)) # badd add expect_error(pm + ggplot2::geom_abline(), "'ggmatrix' does not know how to add") }) test_that("add_list", { pm <- ggpairs(tips, 1:2) pm1 <- pm + list( ggplot2::labs(x = "x title"), ggplot2::labs(title = "list title") ) expect_equal(pm1$xlab, "x title") expect_equal(pm1$title, "list title") }) test_that("v1_ggmatrix_theme", { pm <- ggpairs(tips, 1:2) pm1 <- pm + v1_ggmatrix_theme() expect_true(is.null(pm$gg)) expect_true(!is.null(pm1$gg)) }) GGally/tests/testthat/test-gglegend.R0000644000176200001440000000453313276725426017332 0ustar liggesusers context("gglegend") expect_print <- function(p, ...) { testthat::expect_silent(print(p)) } test_that("examples", { library(ggplot2) histPlot <- ggplot(diamonds, aes(price, fill = cut)) + geom_histogram(binwidth = 500) (right <- histPlot) (bottom <- histPlot + theme(legend.position = "bottom")) (top <- histPlot + theme(legend.position = "top")) (left <- histPlot + theme(legend.position = "left")) expect_legend <- function(p) { plotLegend <- grab_legend(p) expect_true(inherits(plotLegend, "gtable")) expect_true(inherits(plotLegend, "gTree")) expect_true(inherits(plotLegend, "grob")) expect_print(plotLegend) } expect_legend(right) expect_legend(bottom) expect_legend(top) expect_legend(left) }) test_that("legend", { # display regular plot expect_print( ggally_points(iris, ggplot2::aes(Sepal.Length, Sepal.Width, color = Species)) ) # Make a function that will only print the legend points_legend <- gglegend(ggally_points) expect_print(points_legend( iris, ggplot2::aes(Sepal.Length, Sepal.Width, color = Species) )) # produce the sample legend plot, but supply a string that 'wrap' understands same_points_legend <- gglegend("points") expect_identical( attr(attr(points_legend, "fn"), "original_fn"), attr(attr(same_points_legend, "fn"), "original_fn") ) # Complicated examples custom_legend <- wrap(gglegend("points"), size = 6) p <- custom_legend( iris, ggplot2::aes(Sepal.Length, Sepal.Width, color = Species) ) expect_print(p) expect_true(inherits(p, "gtable")) expect_true(inherits(p, "gTree")) expect_true(inherits(p, "grob")) # Use within ggpairs expect_silent({ pm <- ggpairs( iris, 1:2, mapping = ggplot2::aes(color = Species), upper = list(continuous = gglegend("points")) ) print(pm) }) # Use within ggpairs expect_silent({ pm <- ggpairs( iris, 1:2, mapping = ggplot2::aes(color = Species) ) pm[1, 2] <- points_legend(iris, ggplot2::aes(Sepal.Width, Sepal.Length, color = Species)) print(pm) }) }) test_that("plotNew", { points_legend <- gglegend(ggally_points) expect_print(points_legend( iris, ggplot2::aes(Sepal.Length, Sepal.Width, color = Species) )) expect_print(points_legend( iris, ggplot2::aes(Sepal.Length, Sepal.Width, color = Species) ), plotNew = TRUE) }) GGally/tests/testthat/test-ggscatmat.R0000644000176200001440000000165413001231535017504 0ustar liggesusers context("ggscatmat") data(flea) test_that("example", { flea2 <- flea flea2$species2 <- as.character(flea2$species) expect_warning(p <- ggscatmat(flea2, c(1:3)), "Factor variables are omitted in plot") expect_warning(p <- ggscatmat(flea2, c(2:3, 8)), "Factor variables are omitted in plot") expect_true(is.null(p$labels$colour)) # print(p) p <- ggscatmat(flea, columns = 2:4, color = "species") expect_true(!is.null(p$labels$colour)) # print(p) }) test_that("corMethod", { expect_silent({ p <- ggscatmat(flea, columns = 2:3, corMethod = "pearson") p <- ggscatmat(flea, columns = 2:3, corMethod = "rsquare") }) }) test_that("stops", { expect_error(ggscatmat(flea, columns = c(1, 2)), "Not enough numeric variables to") expect_error(ggscatmat(flea, columns = c(1, 1, 1)), "All of your variables are factors") expect_error(scatmat(flea, columns = c(1, 1, 1)), "All of your variables are factors") }) GGally/tests/testthat/test-ggnostic.R0000644000176200001440000000473513277311163017365 0ustar liggesuserscontext("ggnostic") expect_print <- function(p) { testthat::expect_silent({ print(p) }) } test_that("fn_switch", { fn1 <- function(data, mapping, ...) { return(1) } fn2 <- function(data, mapping, ...) { return(2) } fn3 <- function(data, mapping, ...) { return(3) } fn5 <- function(data, mapping, ...) { return(5) } fn <- fn_switch(list(A = fn1, B = fn2, C = fn3), "value") dummy_dt <- data.frame(A = rnorm(100), B = rnorm(100), C = rnorm(100)) chars <- c("A", "B", "C") for (i in 1:3) { mapping <- ggplot2::aes_string(value = chars[i]) expect_equal(fn(dummy_dt, mapping), i) } fn <- fn_switch(list(A = fn1, default = fn5), "value") expect_equal(fn(dummy_dt, ggplot2::aes_string(value = "A")), 1) expect_equal(fn(dummy_dt, ggplot2::aes_string(value = "B")), 5) expect_equal(fn(dummy_dt, ggplot2::aes_string(value = "C")), 5) fn <- fn_switch(list(A = fn1), "value") expect_equal(fn(dummy_dt, ggplot2::aes_string(value = "A")), 1) expect_error(fn(dummy_dt, ggplot2::aes_string(value = "B")), "function could not be found") }) test_that("model_beta_label", { mod <- lm(mpg ~ wt + qsec + am, mtcars) expect_equal(model_beta_label(mod), c("wt***", "qsec***", "am*")) expect_equal(model_beta_label(mod, lmStars = FALSE), c("wt", "qsec", "am")) }) test_that("ggnostic mtcars", { mtc <- mtcars; mtc$am <- c("0" = "automatic", "1" = "manual")[as.character(mtc$am)]; mod <- lm(mpg ~ wt + qsec + am, data = mtc); continuous_type <- list( .resid = wrap(ggally_nostic_resid, method = "loess"), .std.resid = wrap(ggally_nostic_std_resid, method = "loess") ) pm <- ggnostic( mod, mapping = ggplot2::aes(), columnsY = c("mpg", ".fitted", ".se.fit", ".resid", ".std.resid", ".sigma", ".hat", ".cooksd"), continuous = continuous_type, progress = FALSE ) expect_print(pm) pm <- ggnostic( mod, mapping = ggplot2::aes(color = am), legend = c(1, 3), continuous = continuous_type, progress = FALSE ) expect_print(pm) }) test_that("error checking", { get_cols <- function(cols) { match_nostic_columns( cols, c("mpg", broom_columns()), "columnsY" ) } expect_equivalent( get_cols(c(".resid", ".sig", ".hat", ".c")), c(".resid", ".sigma", ".hat", ".cooksd") ) expect_error( get_cols(c( "not_there", ".fitted", ".se.fit", ".resid", ".std.resid", ".sigma", ".hat", ".cooksd" )), "Could not match 'columnsY'" ) }) GGally/tests/testthat/test-ggfacet.R0000644000176200001440000000204313277311163017136 0ustar liggesuserscontext("ggfacet") expect_print <- function(p) { testthat::expect_silent(print(p)) } if (requireNamespace("chemometrics", quietly = TRUE)) { data(NIR, package = "chemometrics") NIR_sub <- data.frame(NIR$yGlcEtOH, NIR$xNIR[, 1:3]) test_that("warnings", { expect_warning( ggfacet(iris, columnsX = 1:5, columnsY = 1), "1 factor variables are being removed from X columns" ) expect_warning( ggfacet(iris, columnsX = 1, columnsY = 1:5), "1 factor variables are being removed from Y columns" ) }) test_that("generally works", { # factor variables expect_print( ggfacet( NIR_sub, columnsY = 1:2, columnsX = 3:5, fn = ggally_smooth_loess ) ) }) test_that("generally works", { # factor variables expect_print( ggfacet( NIR_sub, columnsY = 1:2, columnsX = 3:5, fn = ggally_smooth_loess ) ) expect_print( ggts(pigs, "time", c("gilts", "profit", "s_per_herdsz", "production", "herdsz")) ) }) } GGally/tests/testthat/test-ggcoef.R0000644000176200001440000000112413277311163016767 0ustar liggesusers context("ggcoef") suppressMessages(require(broom)) test_that("example", { expect_print <- function(x) { expect_silent(print(x)) } reg <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width, data = iris) expect_print(ggcoef(reg)) skip_if_not_installed("MASS") d <- as.data.frame(Titanic) reg2 <- glm(Survived ~ Sex + Age + Class, family = binomial, data = d, weights = d$Freq) expect_print(ggcoef(reg2, exponentiate = TRUE)) expect_print(ggcoef( reg2, exponentiate = TRUE, exclude_intercept = TRUE, errorbar_height = .2, color = "blue" )) }) GGally/tests/testthat/test-ggsave.R0000644000176200001440000000044713001231535017005 0ustar liggesusers context("ggsave") test_that("ggsave", { pm <- ggpairs(iris, 1:2) test_file <- "test.pdf" on.exit({ unlink(test_file) }) expect_true(!file.exists(test_file)) expect_silent({ ggsave(test_file, pm, width = 7, height = 7) }) expect_true(file.exists(test_file)) }) GGally/tests/testthat/test-ggnet.R0000644000176200001440000001605313277311163016650 0ustar liggesusers context("ggnet") if ("package:igraph" %in% search()) { detach("package:igraph") } rq <- function(...) { suppressMessages(require(..., quietly = TRUE)) } rq(network) # network objects rq(sna) # placement and centrality rq(ggplot2) # grammar of graphics rq(grid) # arrows rq(scales) # sizing rq(intergraph) # test igraph conversion test_that("examples", { ### --- start: documented examples set.seed(54321) # random adjacency matrix x <- 10 ndyads <- x * (x - 1) density <- x / ndyads m <- matrix(0, nrow = x, ncol = x) dimnames(m) <- list(letters[ 1:x ], letters[ 1:x ]) m[ row(m) != col(m) ] <- runif(ndyads) < density m # random undirected network n <- network::network(m, directed = FALSE) n ggnet(n, label = TRUE, alpha = 1, color = "white", segment.color = "black") # random groups g <- sample(letters[ 1:3 ], 10, replace = TRUE) # color palette p <- c("a" = "steelblue", "b" = "forestgreen", "c" = "tomato") p <- ggnet(n, node.group = g, node.color = p, label = TRUE, color = "white") expect_equal(length(p$layers), 3) expect_true(!is.null(p$mapping$colour)) ### --- end: documented examples ### --- test deprecations # test mode = "geo" xy <- gplot.layout.circle(n) # nolint n %v% "lon" <- xy[, 1] n %v% "lat" <- xy[, 2] expect_warning(ggnet(n, mode = "geo"), "deprecated") # test names = c(x, y) expect_warning(ggnet(n, names = c("a", "b")), "deprecated") # test quantize.weights expect_warning(ggnet(n, quantize.weights = TRUE)) # test subset.threshold expect_warning(ggnet(n, subset.threshold = 2)) # test top8.nodes expect_warning(ggnet(n, top8.nodes = TRUE)) # test trim.labels expect_warning(ggnet(n, trim.labels = TRUE)) # # test subset.threshold by removing all nodes # expect_warning( # expect_error( # ggnet(n, subset.threshold = 11), # "NA/NaN/Inf" # ), # "NaNs produced" # ) # # p <- ggnet(n, mode = "geo") # expect_equal(p$data$X1, xy[, 1]) # expect_equal(p$data$X2, xy[, 2]) # test user-submitted weights ggnet(n, weight = sample(1:2, 10, replace = TRUE)) # test segment.label x <- sample(letters, network.edgecount(n)) p <- ggnet(n, segment.label = x) expect_true(mapping_string(p$layers[[2]]$mapping$x) == "midX") expect_true(mapping_string(p$layers[[2]]$mapping$y) == "midY") # test weight.cut n %v% "weights" <- 1:10 ggnet(n, weight.method = "weights", weight.cut = TRUE) ### --- test errors in set_node expect_error(ggnet(n, group = NA), "incorrect") expect_error(ggnet(n, group = 1:3), "incorrect") expect_error(ggnet(n, label = TRUE, label.size = -10:-1), "incorrect") expect_error(ggnet(n, size = "phono"), "incorrect") ggnet(n, group = "weights") ### --- test errors in set_edges expect_error(ggnet(n, segment.label = NA), "incorrect") expect_error(ggnet(n, segment.label = 1:3), "incorrect") expect_error(ggnet(n, segment.label = -11:-1), "incorrect") # unnecessary # expect_error(ggnet(n, size = "phono"), "incorrect") n %e% "weights" <- sample(1:2, network.edgecount(n), replace = TRUE) ggnet(n, segment.label = "weights") ggnet(n, segment.label = "a") ### --- test mode = c(x, y) ggnet(n, mode = matrix(1, ncol = 2, nrow = 10)) ggnet(n, mode = c("lon", "lat")) expect_error(ggnet(n, mode = c("xx", "yy")), "not found") n %v% "abc" <- "abc" expect_error(ggnet(n, mode = c("abc", "abc")), "not numeric") expect_error(ggnet(n, mode = matrix(1, ncol = 2, nrow = 9)), "coordinates length") ### --- test arrow.size expect_error(ggnet(n, arrow.size = -1), "incorrect arrow.size") expect_warning(ggnet(n, arrow.size = 1), "arrow.size ignored") ### --- test arrow.gap suppressWarnings(expect_error( ggnet(n, arrow.size = 12, arrow.gap = -1), "incorrect arrow.gap" )) suppressWarnings(expect_warning( ggnet(n, arrow.size = 12, arrow.gap = 0.1), "arrow.gap ignored" # network is undirected; arrow.gap ignored )) suppressWarnings(expect_warning( ggnet(n, arrow.size = 12, arrow.gap = 0.1), "arrow.size ignored" # network is undirected; arrow.size ignored )) m <- network::network(m, directed = TRUE) ggnet(m, arrow.size = 12, arrow.gap = 0.05) ### --- test degree centrality ggnet(n, weight = "degree") ### --- test weight.min, weight.max and weight.cut # test weight.min expect_error(ggnet(n, weight = "degree", weight.min = -1), "incorrect weight.min") expect_message(ggnet(n, weight = "degree", weight.min = 1), "weight.min removed") expect_warning(ggnet(n, weight = "degree", weight.min = 99), "removed all nodes") # test weight.max expect_error(ggnet(n, weight = "degree", weight.max = -1), "incorrect weight.max") expect_message(ggnet(n, weight = "degree", weight.max = 99), "weight.max removed") expect_warning(ggnet(n, weight = 1:10, weight.max = 0.5), "removed all nodes") expect_error(ggnet(n, weight = "abc"), "incorrect weight.method") # test weight.cut expect_error(ggnet(n, weight.cut = NA), "incorrect weight.cut") expect_error(ggnet(n, weight.cut = "a"), "incorrect weight.cut") expect_warning(ggnet(n, weight.cut = 3), "weight.cut ignored") ggnet(n, weight = "degree", weight.cut = 3) ### --- test node.group and node.color expect_warning(ggnet(n, group = 1:10, node.color = "blue"), "unequal length") ### --- test node labels and label sizes ggnet(n, label = letters[ 1:10 ], color = "white") ggnet(n, label = "abc", color = "white", label.size = 4, size = 12) expect_error(ggnet(n, label = letters[ 1:10 ], label.size = "abc"), "incorrect label.size") ### --- test node placement expect_error(ggnet(n, mode = "xyz"), "unsupported") expect_error(ggnet(n, mode = letters[1:3]), "incorrect mode") ### --- test label.trim expect_error(ggnet(n, label = TRUE, label.trim = "xyz"), "incorrect label.trim") ggnet(n, label = TRUE, color = "white", label.trim = 1) ggnet(n, label = TRUE, color = "white", label.trim = toupper) ### --- test layout.exp expect_error(ggnet(n, layout.exp = "xyz")) ggnet(n, layout.exp = 0.1) ### --- test bipartite functionality # weighted adjacency matrix bip <- data.frame( event1 = c(1, 2, 1, 0), event2 = c(0, 0, 3, 0), event3 = c(1, 1, 0, 4), row.names = letters[1:4] ) # weighted bipartite network bip <- network( bip, matrix.type = "bipartite", ignore.eval = FALSE, names.eval = "weights" ) # test bipartite mode ggnet(bip, group = "mode") ### --- test network coercion expect_warning(ggnet(network(matrix(1, nrow = 2, ncol = 2), loops = TRUE)), "self-loops") expect_error(ggnet(1:2), "network object") expect_error(ggnet(network(data.frame(1:2, 3:4), hyper = TRUE)), "hyper graphs") expect_error(ggnet(network(data.frame(1:2, 3:4), multiple = TRUE)), "multiplex graphs") ### --- test igraph functionality if (requireNamespace("igraph", quietly = TRUE)) { library(igraph) # test igraph conversion p <- ggnet(asIgraph(n)) expect_null(p$guides$colour) expect_equal(length(p$layers), 2) # test igraph degree ggnet(n, weight = "degree") expect_true(TRUE) } }) GGally/tests/testthat/test-gglyph.R0000644000176200001440000000710013276725426017041 0ustar liggesusers context("gglyph") data(nasa) nasaLate <- nasa[ nasa$date >= as.POSIXct("1998-01-01") & nasa$lat >= 20 & nasa$lat <= 40 & nasa$long >= -80 & nasa$long <= -60 , ] do_glyph <- function(...) { glyphs( nasaLate, # no lint "long", "day", "lat", "surftemp", height = 2.37, width = 2.38, ... ) } do_gg <- function(dt) { ggplot2::ggplot(dt, ggplot2::aes(gx, gy, group = gid)) + add_ref_lines(dt, color = "red", size = 0.5) + add_ref_boxes(dt, color = "blue") + ggplot2::geom_path() + ggplot2::theme_bw() + ggplot2::labs(x = "", y = "") + ggplot2::xlim(-80, -60) + ggplot2::ylim(20, 40) } test_that("examples", { dt <- do_glyph() expect_true(all(c("gx", "gy", "gid") %in% names(dt))) expect_true(all(names(nasaLate) %in% names(dt))) p <- do_gg(dt) expect_equal(length(p$layers), 3) expect_equal(as.character(get("aes_params", envir = p$layers[[1]])$colour), "red") expect_equal(as.character(get("aes_params", envir = p$layers[[2]])$colour), "blue") }) test_that("message", { expect_message(glyphs(nasaLate, "long", "day", "lat", "surftemp", height = 1), "Using width 2.38") expect_message(glyphs(nasaLate, "long", "day", "lat", "surftemp", width = 1), "Using height 2.37") }) test_that("scales", { dt <- do_glyph(x_scale = log) dt$dayLog <- dt$day dt$day <- NULL dtm <- merge(dt, nasaLate) expect_true(all(dtm$dayLog == log(dtm$day))) dt <- do_glyph(y_scale = log) dt$surftempLog <- dt$surftemp dt$surftemp <- NULL dtm <- merge(dt, nasaLate) expect_true(all(dtm$surftempLog == log(dtm$surftemp))) for (scale_fn in c(range01, max1, mean0, min0, rescale01, rescale11)) { dt <- do_glyph(y_scale = scale_fn) dt$surftempScaled <- dt$surftemp dt$surftemp <- NULL dtm <- merge(dt, nasaLate) expect_true(all(dtm$surftempScaled != dtm$surftemp)) } for (scale_fn in c(rescale01, rescale11)) { scale_fn2 <- function(x) { scale_fn(x, xlim = c(1 / 4, 3 / 4)) } dt <- do_glyph(y_scale = scale_fn2) dt$surftempScaled <- dt$surftemp dt$surftemp <- NULL dtm <- merge(dt, nasaLate) expect_true(all(dtm$surftempScaled != dtm$surftemp)) } }) test_that("polar", { dt <- do_glyph(polar = TRUE) expect_equal(attr(dt, "polar"), TRUE) # idk how to test that polar happened p <- do_gg(dt) expect_equal(length(p$layers), 3) }) test_that("fill", { dt <- do_glyph() # idk how to test that polar happened do_gg_fill <- function(...){ ggplot2::ggplot(dt, ggplot2::aes(gx, gy, group = gid)) + add_ref_lines(dt, color = "red", size = 0.5) + add_ref_boxes(dt, color = "blue", ...) + ggplot2::geom_path() + ggplot2::theme_bw() + ggplot2::labs(x = "", y = "") + ggplot2::xlim(-80, -60) + ggplot2::ylim(20, 40) } p <- do_gg_fill(fill = "green") expect_equal(mapping_string(get("aes_params", envir = p$layers[[2]])$fill), "\"green\"") p <- do_gg_fill(var_fill = "gid") expect_equal(mapping_string(get("mapping", envir = p$layers[[2]])$fill), "fill") }) test_that("print", { dt <- do_glyph() txt <- capture.output(print(dt)) expect_equal(txt[length(txt) - 2], "Cartesian glyphplot: ") expect_equal(txt[length(txt) - 1], " Size: [2.38, 2.37]") expect_equal(txt[length(txt) - 0], " Major axes: long, lat" ) dt <- do_glyph(polar = TRUE) txt <- capture.output(print(dt)) expect_equal(txt[length(txt) - 2], "Polar glyphplot: ") expect_equal(txt[length(txt) - 1], " Size: [2.38, 2.37]") expect_equal(txt[length(txt) - 0], " Major axes: long, lat" ) txt <- capture.output(print(rel(0.95))) expect_equal(txt, "[1] 0.95 *") }) GGally/tests/testthat/test-zzz_ggpairs.R0000644000176200001440000005173213277311163020120 0ustar liggesusers context("ggpairs") data(tips, package = "reshape") expect_print <- function(p) { testthat::expect_silent(print(p)) } facethistBindwidth1 <- list(combo = wrap("facethist", binwidth = 1)) facethistBindwidth1Duo <- list( comboHorizontal = wrap("facethist", binwidth = 1), comboVertical = wrap("facethist", binwidth = 1) ) test_that("structure", { expect_null <- function(x) { expect_true(is.null(x)) } expect_obj <- function(x) { expect_is(x$data, "data.frame") expect_is(x$plots, "list") expect_equivalent(length(x$plots), ncol(tips) ^ 2) expect_null(x$title) expect_null(x$xlab) expect_null(x$ylab) expect_is(x$xAxisLabels, "character") expect_is(x$yAxisLabels, "character") expect_is(x$showXAxisPlotLabels, "logical") expect_is(x$showYAxisPlotLabels, "logical") expect_null(x$legend) expect_is(x$byrow, "logical") expect_null(x$gg) expect_true("gg" %in% names(x)) } expect_obj(ggduo(tips)) expect_obj(ggpairs(tips)) }) test_that("columns", { expect_obj <- function(pm, columnsX, columnsY) { expect_equivalent(length(pm$plots), length(columnsX) * length(columnsY)) expect_equivalent(pm$xAxisLabels, columnsX) expect_equivalent(pm$yAxisLabels, columnsY) expect_equivalent(pm$ncol, length(columnsX)) expect_equivalent(pm$nrow, length(columnsY)) } columnsUsed <- c("total_bill", "tip", "sex") pm <- ggpairs(tips, columns = columnsUsed) expect_obj(pm, columnsUsed, columnsUsed) columnsX <- c("total_bill", "tip", "sex") columnsY <- c("smoker", "day", "time", "size") pm <- ggduo(tips, columnsX, columnsY) expect_obj(pm, columnsX, columnsY) }) test_that("column labels", { expect_obj <- function(pm, columnLabelsX, columnLabelsY) { expect_equivalent(pm$xAxisLabels, columnLabelsX) expect_equivalent(pm$yAxisLabels, columnLabelsY) } columnTitles <- c("A", "B", "C") pm <- ggpairs(tips, 1:3, columnLabels = columnTitles) expect_obj(pm, columnTitles, columnTitles) columnTitles <- c("Total Bill %", "Tip 123456", "Sex ( /a asdf)") pm <- ggpairs(tips, 1:3, columnLabels = columnTitles) expect_obj(pm, columnTitles, columnTitles) columnLabelsX <- c("Total Bill %", "Tip 123456", "Sex ( /a asdf)") columnLabelsY <- c("Smoker !#@", "Day 678", "1", "NULL") pm <- ggduo(tips, 1:3, 4:7, columnLabelsX = columnLabelsX, columnLabelsY = columnLabelsY) expect_obj(pm, columnLabelsX, columnLabelsY) }) test_that("character", { expect_obj <- function(pm) { expect_true(is.factor(pm$data$sex)) expect_true(is.factor(pm$data$smoker)) } tips2 <- tips tips2$sex <- as.character(tips2$sex) tips2$smoker <- as.character(tips2$smoker) expect_obj(ggpairs(tips2)) expect_obj(ggduo(tips2)) }) test_that("upper/lower/diag = blank", { columnsUsed <- 1:3 au <- ggpairs(tips, columnsUsed, upper = "blank") ad <- ggpairs(tips, columnsUsed, diag = "blank") al <- ggpairs(tips, columnsUsed, lower = "blank") for (i in 1:3) { for (j in 1:3) { if (i < j) { expect_true( is_blank_plot(au[i, j])) expect_false( is_blank_plot(ad[i, j])) expect_false( is_blank_plot(al[i, j])) } if (i > j) { expect_false( is_blank_plot(au[i, j])) expect_false( is_blank_plot(ad[i, j])) expect_true( is_blank_plot(al[i, j])) } if (i == j) { expect_false( is_blank_plot(au[i, j])) expect_true( is_blank_plot(ad[i, j])) expect_false( is_blank_plot(al[i, j])) } } } a <- ggpairs(tips, columnsUsed) a[1, 1] <- ggplot2::qplot(total_bill, data = tips) expect_false(is_blank_plot(a[1, 1])) }) test_that("stops", { expect_warning({ pm <- ggpairs(tips, axisLabels = "not_a_chosen", lower = facethistBindwidth1) }, "'axisLabels' not in ") # nolint expect_warning({ pm <- ggduo(tips, axisLabels = "not_a_chosen", types = facethistBindwidth1Duo) }, "'axisLabels' not in ") # nolint expect_warning({ pm <- ggpairs(tips, color = "sex") }, "Extra arguments: ") # nolint expect_warning({ pm <- ggduo(tips, 2:3, 2:3, types = list(combo = "facetdensity")) }, "Setting:\n\ttypes") # nolint expect_error({ ggpairs(tips, columns = c("tip", "day", "not in tips")) }, "Columns in 'columns' not found in data") # nolint expect_error({ ggduo(tips, columnsX = c("tip", "day", "not in tips"), columnsY = "smoker") }, "Columns in 'columnsX' not found in data") # nolint expect_error({ ggduo(tips, columnsX = c("tip", "day", "smoker"), columnsY = "not in tips") }, "Columns in 'columnsY' not found in data") # nolint expect_warning({ pm <- ggpairs(tips, legends = TRUE) }, "'legends' will be deprecated") # nolint expect_error({ ggpairs(tips, params = c(size = 2)) }, "'params' is a deprecated") # nolint expect_error( { ggpairs(tips, columns = 1:10) }, "Make sure your numeric 'columns' values are less than or equal to") # nolint expect_error( { ggduo(tips, columnsX = 1:10) }, "Make sure your numeric 'columnsX' values are less than or equal to") # nolint expect_error( { ggduo(tips, columnsY = 1:10) }, "Make sure your numeric 'columnsY' values are less than or equal to") # nolint expect_error({ ggpairs(tips, columns = -5:5) }, "Make sure your numeric 'columns' values are positive") # nolint expect_error({ ggduo(tips, columnsX = -5:5) }, "Make sure your numeric 'columnsX' values are positive") # nolint expect_error({ ggduo(tips, columnsY = -5:5) }, "Make sure your numeric 'columnsY' values are positive") # nolint expect_error({ ggpairs(tips, columns = (2:10) / 2) }, "Make sure your numeric 'columns' values are integers") # nolint expect_error({ ggduo(tips, columnsX = (2:10) / 2) }, "Make sure your numeric 'columnsX' values are integers") # nolint expect_error({ ggduo(tips, columnsY = (2:10) / 2) }, "Make sure your numeric 'columnsY' values are integers") # nolint expect_error({ ggpairs(tips, columns = 1:3, columnLabels = c("A", "B", "C", "Extra")) }, "The length of the 'columnLabels' does not match the length of the 'columns'") # nolint expect_error({ ggduo(tips, columnsX = 1:3, columnLabelsX = c("A", "B", "C", "Extra")) }, "The length of the 'columnLabelsX' does not match the length of the 'columnsX'") # nolint expect_error({ ggduo(tips, columnsY = 1:3, columnLabelsY = c("A", "B", "C", "Extra")) }, "The length of the 'columnLabelsY' does not match the length of the 'columnsY'") # nolint expect_error({ ggpairs(tips, upper = c("not_a_list")) }, "'upper' is not a list") # nolint expect_error({ ggpairs(tips, diag = c("not_a_list")) }, "'diag' is not a list") # nolint expect_error({ ggpairs(tips, lower = c("not_a_list")) }, "'lower' is not a list") # nolint expect_error({ ggduo(tips, types = c("not_a_list")) }, "'types' is not a list") # nolint # # couldn't get correct error message # # variables: 'colour' have non standard format: 'total_bill + tip'. # expect_error({ # ggpairs(tips, mapping = ggplot2::aes(color = total_bill + tip)) # }, "variables\\: \"colour\" have non standard format") # nolint # expect_error({ # ggduo(tips, mapping = ggplot2::aes(color = total_bill + tip)) # }, "variables\\: \"colour\" have non standard format") # nolint errorString <- "'aes_string' is a deprecated element" expect_error({ ggpairs(tips, upper = list(aes_string = ggplot2::aes(color = day))) }, errorString) # nolint expect_error({ ggpairs(tips, lower = list(aes_string = ggplot2::aes(color = day))) }, errorString) # nolint expect_error({ ggpairs(tips, diag = list(aes_string = ggplot2::aes(color = day))) }, errorString) # nolint expect_error({ ggduo(tips, types = list(aes_string = ggplot2::aes(color = day))) }, errorString) # nolint expect_diag_warn <- function(key, value) { warnString <- str_c("Changing diag\\$", key, " from '", value, "' to '", value, "Diag'") diagObj <- list() diagObj[[key]] <- value expect_warning({ pm <- ggpairs(tips, diag = diagObj) }, warnString ) } # diag # continuous # densityDiag # barDiag # blankDiag # discrete # barDiag # blankDiag expect_diag_warn("continuous", "density") expect_diag_warn("continuous", "bar") expect_diag_warn("continuous", "blank") expect_diag_warn("discrete", "bar") expect_diag_warn("discrete", "blank") }) test_that("cardinality", { expect_silent(stop_if_high_cardinality(tips, 1:ncol(tips), NULL)) expect_silent(stop_if_high_cardinality(tips, 1:ncol(tips), FALSE)) expect_error( stop_if_high_cardinality(tips, 1:ncol(tips), "not numeric"), "'cardinality_threshold' should" ) expect_error( stop_if_high_cardinality(tips, 1:ncol(tips), 2), "Column 'day' has more levels" ) }) test_that("blank types", { columnsUsed <- 1:3 pmUpper <- ggpairs(tips, columnsUsed, upper = "blank", lower = facethistBindwidth1) pmDiag <- ggpairs(tips, columnsUsed, diag = "blank", lower = facethistBindwidth1) pmLower <- ggpairs(tips, columnsUsed, lower = "blank") for (i in columnsUsed) { for (j in columnsUsed) { if (i < j) { # upper expect_true(is_blank_plot(pmUpper[i, j])) expect_false(is_blank_plot(pmDiag[i, j])) expect_false(is_blank_plot(pmLower[i, j])) } else if ( i > j) { # lower expect_false(is_blank_plot(pmUpper[i, j])) expect_false(is_blank_plot(pmDiag[i, j])) expect_true(is_blank_plot(pmLower[i, j])) } else { # diag expect_false(is_blank_plot(pmUpper[i, j])) expect_true(is_blank_plot(pmDiag[i, j])) expect_false(is_blank_plot(pmLower[i, j])) } } } columnsUsedX <- 1:3 columnsUsedY <- 4:5 pmDuo <- ggduo(tips, columnsUsedX, columnsUsedY, types = "blank") for (i in seq_along(columnsUsedX)) { for (j in seq_along(columnsUsedY)) { expect_true(is_blank_plot(pmDuo[j, i])) } } }) test_that("axisLabels", { expect_obj <- function(pm, axisLabel) { expect_true(is.null(pm$showStrips)) if (axisLabel == "show") { expect_true(pm$showXAxisPlotLabels) expect_true(pm$showYAxisPlotLabels) expect_false(is.null(pm$xAxisLabels)) expect_false(is.null(pm$yAxisLabels)) } else if (axisLabel == "internal") { for (i in 1:(pm$ncol)) { p <- pm[i, i] expect_true(inherits(p$layers[[1]]$geom, "GeomText")) expect_true(inherits(p$layers[[2]]$geom, "GeomText")) expect_equal(length(p$layers), 2) } expect_false(pm$showXAxisPlotLabels) expect_false(pm$showYAxisPlotLabels) expect_true(is.null(pm$xAxisLabels)) expect_true(is.null(pm$yAxisLabels)) } else if (axisLabel == "none") { expect_false(pm$showXAxisPlotLabels) expect_false(pm$showYAxisPlotLabels) expect_false(is.null(pm$xAxisLabels)) expect_false(is.null(pm$yAxisLabels)) } expect_print(pm) } fn <- function(axisLabels) { pm <- ggpairs( iris, c(3, 4, 5, 1), upper = "blank", lower = facethistBindwidth1, axisLabels = axisLabels, title = str_c("axisLabels = ", axisLabels), progress = FALSE ) pm } for (axisLabels in c("show", "internal", "none")) { expect_obj(fn(axisLabels), axisLabels) } plots <- ggpairs(iris, 1:3)$plots for (val in c(TRUE, FALSE)) { pm <- ggmatrix( plots, 3, 3, showAxisPlotLabels = val ) expect_equal(pm$showXAxisPlotLabels, val) expect_equal(pm$showYAxisPlotLabels, val) } fn <- function(axisLabels) { a <- ggduo( iris, c(4, 5), c(5, 1), types = facethistBindwidth1Duo, axisLabels = axisLabels, title = str_c("axisLabels = ", axisLabels) ) a } for (axisLabels in c("show", "none")) { expect_obj(fn(axisLabels), axisLabels) } }) test_that("strips and axis", { # axis should line up with left side strips pm <- ggpairs( tips, c(3, 1, 4), showStrips = TRUE, title = "Axis should line up even if strips are present", lower = list(combo = wrap("facethist", binwidth = 1)) ) expect_print(pm) # default behavior. tested in other places # expect_silent({ # pm <- ggpairs(tips, c(3, 1, 4), showStrips = FALSE) # print(pm) # }) }) test_that("dates", { startDt <- as.POSIXct("2000-01-01", tz = "UTC") endDt <- as.POSIXct("2000-04-01", tz = "UTC") dts <- seq(startDt, endDt, 86400) # 86400 = as.numeric(ddays(1)) x <- data.frame( date = dts, x1 = rnorm(length(dts)), x2 = rnorm(length(dts)), cat = sample(c("a", "b", "c"), length(dts), replace = TRUE) ) class(x) <- c("NOT_data.frame", "data.frame") a <- ggpairs( x, c(2, 1, 4, 3), mapping = ggplot2::aes(color = cat), lower = "blank", diag = list(continuous = "densityDiag"), upper = list(continuous = "cor") ) p <- a[1, 2] expect_true(inherits(p$layers[[1]]$geom, "GeomText")) expect_true(inherits(p$layers[[2]]$geom, "GeomText")) expect_equal(length(p$layers), 2) a <- ggpairs( x, c(2, 1, 4, 3), mapping = ggplot2::aes(color = cat), lower = "blank", diag = list(continuous = "barDiag"), upper = list(continuous = "cor") ) p <- a[1, 1] expect_true(inherits(p$layers[[1]]$geom, "GeomBar")) expect_equal(length(p$layers), 1) }) test_that("mapping", { pm <- ggpairs(tips, mapping = 1:3) expect_equal(pm$xAxisLabels, names(tips)[1:3]) pm <- ggpairs(tips, columns = 1:3) expect_equal(pm$xAxisLabels, names(tips)[1:3]) expect_error({ ggpairs(tips, columns = 1:3, mapping = 1:3) }, "'mapping' should not be numeric") # nolint }) test_that("user functions", { p0 <- ggally_points(tips, ggplot2::aes(x = total_bill, y = tip)) pm1 <- ggpairs(tips, 1:2, lower = list(continuous = "points")) p1 <- pm1[2, 1] expect_equivalent(p0, p1) pm2 <- ggpairs(tips, 1:2, lower = list(continuous = ggally_points)) p2 <- pm2[2, 1] expect_equivalent(p0, p2) }) test_that("NA data", { expect_is_na_plot <- function(p) { expect_true(identical(as.character(p$data$label), "NA")) expect_true(inherits(p$layers[[1]]$geom, "GeomText")) expect_equivalent(length(p$layers), 1) } expect_not_na_plot <- function(p) { expect_false(identical(as.character(p$data$label), "NA")) } expect_is_blank <- function(p) { expect_true(is_blank_plot(p)) } dd <- data.frame(x = c(1:5, rep(NA, 5)), y = c(rep(NA, 5), 2:6), z = 1:10, w = NA) pm <- ggpairs(dd) test_pm <- function(pm, na_mat) { for (i in 1:4) { for (j in 1:4) { if (na_mat[i, j]) { expect_is_na_plot(pm[i, j]) } else { if (j == 3 & i < 3) { expect_warning({ p <- pm[i, j] }, "Removed 5 rows" ) } else { p <- pm[i, j] } expect_not_na_plot(p) } } } } na_mat <- matrix(FALSE, ncol = 4, nrow = 4) na_mat[1, 2] <- TRUE na_mat[2, 1] <- TRUE na_mat[1:4, 4] <- TRUE na_mat[4, 1:4] <- TRUE test_pm(pm, na_mat) }) test_that("strip-top and strip-right", { data(tips, package = "reshape") double_strips <- function(data, mapping, ...) { dt <- count(data, c(mapping_string(mapping$x), mapping_string(mapping$y))) ggplot2::qplot( xmin = 0.25, xmax = 0.75, ymin = 1, ymax = freq, data = dt, geom = "rect" ) + ggplot2::facet_grid(paste0(mapping_string(mapping$y), " ~ ", mapping_string(mapping$x))) + ggplot2::scale_x_continuous(breaks = 0.5, labels = NULL) } pm <- ggpairs( tips, 3:6, lower = "blank", diag = "blank", upper = list(discrete = double_strips), progress = FALSE ) expect_print(pm) pm <- ggpairs( tips, 3:6, lower = "blank", diag = "blank", upper = list(discrete = double_strips), showStrips = TRUE, progress = FALSE ) expect_print(pm) }) test_that("subtypes", { # list of the different plot types to check # continuous # points # smooth # smooth_loess # density # cor # blank # combo # box # dot plot # facethist # facetdensity # denstrip # blank # discrete # ratio # facetbar # blank gn <- function(x) { fnName <- attr(x, "name") ifnull(fnName, x) } ggpairs_fn1 <- function(title, types, diag, ...) { ggpairs( tips, 1:4, axisLabels = "show", title = paste( "upper = c(cont = ", gn(types$continuous), ", combo = ", gn(types$combo), ", discrete = ", gn(types$discrete), "); diag = c(cont = ", gn(diag$continuous), ", discrete = ", gn(diag$discrete), ")", sep = ""), upper = types, lower = types, diag = diag, progress = FALSE, ... ) + ggplot2::theme(plot.title = ggplot2::element_text(size = 9)) } ggpairs_fn2 <- function(...) { ggpairs_fn1(..., mapping = ggplot2::aes(color = day), legend = c(1, 3)) } ggduo_fn1 <- function(title, types, diag, ...) { types$comboHorizontal <- types$combo types$comboVertical <- types$combo types$combo <- NULL ggduo( tips, 1:3, 1:4, axisLabels = "show", title = paste( "types = c(cont = ", gn(types$continuous), ", combo = ", gn(types$comboHorizontal), ", discrete = ", gn(types$discrete), ")", sep = ""), types = types, progress = FALSE, ... ) + ggplot2::theme(plot.title = ggplot2::element_text(size = 9)) } ggduo_fn2 <- function(...) { ggduo_fn1(..., mapping = ggplot2::aes(color = day), legend = 3) + theme(legend.position = "bottom") } # re ordered the subs so that density can have no binwidth param conSubs <- list("density", "points", "smooth", "smooth_loess", "cor", "blank") comSubs <- list( "box", "dot", "box_no_facet", "dot_no_facet", wrap("facethist", binwidth = 1), "facetdensity", wrap("denstrip", binwidth = 1), "blank" ) disSubs <- list("ratio", "facetbar", "blank") conDiagSubs <- c("densityDiag", wrap("barDiag", binwidth = 1), "blankDiag") disDiagSubs <- c("barDiag", "blankDiag") # for (fn in list(ggpairs_fn1, ggpairs_fn2, ggduo_fn1, ggduo_fn2)) { for (fn_num in 1:4) { fn <- list(ggpairs_fn1, ggpairs_fn2, ggduo_fn1, ggduo_fn2)[[fn_num]] for (i in 1:6) { conSub <- if (i <= length(conSubs)) conSubs[[i]] else "blank" comSub <- if (i <= length(comSubs)) comSubs[[i]] else "blank" disSub <- if (i <= length(disSubs)) disSubs[[i]] else "blank" diagConSub <- if (i <= length(conDiagSubs)) conDiagSubs[[i]] else "blankDiag" diagDisSub <- if (i <= length(disDiagSubs)) disDiagSubs[[i]] else "blankDiag" # print(list( # fn_num = fn_num, # types = list( # continuous = conSub, # combo = comSub, # discrete = disSub # ), # diag = list( # continuous = diagConSub, # discrete = diagDisSub # ) # )) # expect_silent({ pm <- fn( types = list( continuous = conSub, combo = comSub, discrete = disSub ), diag = list( continuous = diagConSub, discrete = diagDisSub ) ) }) if (grepl("/Users/barret/", getwd(), fixed = TRUE)) { # only if on personal machine, do viz test expect_print(pm) } } } expect_error({ ggpairs(tips, 1:2, lower = "blank", diag = "blank", upper = list(continuous = "BAD_TYPE")) }) }) # pm <- ggpairs(tips, upper = "blank") # # pm # # Custom Example # pm <- ggpairs( # tips[, c(1, 3, 4, 2)], # upper = list(continuous = "density", combo = "box"), # lower = list(continuous = "points", combo = "dot") # ) # # pm # # Use sample of the diamonds data # data(diamonds, package="ggplot2") # diamonds.samp <- diamonds[sample(1:dim(diamonds)[1], 200), ] # # Custom Example # pm <- ggpairs( # diamonds.samp[, 1:5], # upper = list(continuous = "density", combo = "box"), # lower = list(continuous = "points", combo = "dot"), # color = "cut", # alpha = 0.4, # title = "Diamonds" # ) # # pm # # Will plot four "Incorrect Plots" # bad_plots <- ggpairs( # tips[, 1:3], # upper = list(continuous = "wrongType1", combo = "wrongType2"), # lower = list(continuous = "IDK1", combo = "IDK2", discrete = "mosaic"), # ) # # bad_plots # # Only Variable Labels on the diagonal (no axis labels) # pm <- ggpairs(tips[, 1:3], axisLabels="internal") # # pm # # Only Variable Labels on the outside (no axis labels) # pm <- ggpairs(tips[, 1:3], axisLabels="none") # # pm # # Custom Examples # custom_car <- ggpairs(mtcars[, c("mpg", "wt", "cyl")], upper = "blank", title = "Custom Example") # #' # ggplot example taken from example(geom_text) # #' plot <- ggplot2::ggplot(mtcars, ggplot2::aes(x=wt, y=mpg, label=rownames(mtcars))) # #' plot <- plot + # #' ggplot2::geom_text(ggplot2::aes(colour=factor(cyl)), size = 3) + # #' ggplot2::scale_colour_discrete(l=40) # #' custom_car <- putPlot(custom_car, plot, 1, 2) # #' personal_plot <- ggally_text( # #' "ggpairs allows you\nto put in your\nown plot.\nLike that one.\n <---" # #' ) # #' custom_car <- putPlot(custom_car, personal_plot, 1, 3) # #' # custom_car GGally/tests/testthat/test-ggmatrix_getput.R0000644000176200001440000000240613001231535020740 0ustar liggesusers context("ggmatrix_getput") data(tips, package = "reshape") test_that("stops", { pm <- ggpairs(tips) p <- ggally_blankDiag() expect_error(pm["total_bill", 1], "'i' may only be a single") expect_error(pm[1, "total_bill"], "'j' may only be a single") expect_error(pm["total_bill", 1] <- p, "'i' may only be a single") expect_error(pm[1, "total_bill"] <- p, "'j' may only be a single") pm <- ggduo(tips, 1:3, 1:4) expect_error(pm[0, 1], "'i' may only be in the range") expect_error(pm[1, 0], "'j' may only be in the range") expect_error(pm[5, 1], "'i' may only be in the range") expect_error(pm[1, 4], "'j' may only be in the range") for (i in 1:4) { for (j in 1:3) { expect_silent({ p <- pm[i, j] }) } } }) test_that("get", { a <- ggpairs( tips, 1:4, axisLabels = "show" ) p <- a[2, 1] expect_equal(p$labels$x, "total_bill") expect_equal(p$labels$y, "tip") # test odd input and retrieve it a[2, 1] <- 1:4 expect_error({ a[2, 1] }, "unknown plot object type") # nolint }) test_that("put", { a <- ggpairs( tips, 1:4, axisLabels = "show" ) txt <- "My Custom Plot" a[2, 1] <- ggally_text(txt) p <- a[2, 1] expect_equal(get("aes_params", envir = p$layers[[1]])$label, txt) }) GGally/tests/testthat/test-ggmatrix.R0000644000176200001440000000706313277311163017367 0ustar liggesusers context("ggmatrix") data(tips, package = "reshape") expect_print <- function(x) { testthat::expect_silent(print(x)) } test_that("stops", { expect_error(ggmatrix(plots = matrix(), nrow = 2, ncol = 3), "'plots' must be a list()") expect_error(ggmatrix(plots = list(), nrow = "2", ncol = 3), "'nrow' must be a numeric value") expect_error(ggmatrix(plots = list(), nrow = 2, ncol = "3"), "'ncol' must be a numeric value") expect_error( ggmatrix(plots = list(), nrow = c(2, 3), ncol = 3), "'nrow' must be a single numeric value" ) expect_error( ggmatrix(plots = list(), nrow = 2, ncol = c(2, 3)), "'ncol' must be a single numeric value" ) }) test_that("expression labels", { chars <- c("col1", "col2") exprs <- c("alpha[0]", "gamma[x + y ^ z]") expect_print(ggpairs(tips, 1:2, columnLabels = exprs, labeller = "label_parsed")) expect_error(print(ggpairs(tips, 1:2, columnLabels = expression(alpha, beta))), "xAxisLabels") }) test_that("byrow", { plotList <- list() for (i in 1:6) { p <- ggally_text(paste("Plot #", i, sep = "")) p$ggally_check_val <- i plotList[[i]] <- p } a <- ggmatrix( plotList, 2, 3, c("A", "B", "C"), c("D", "E"), byrow = TRUE ) k <- 1 for (i in 1:2) { for (j in 1:3) { expect_equal(a[i, j]$ggally_check_val, k) k <- k + 1 } } a <- ggmatrix( plotList, 2, 3, c("A", "B", "C"), c("D", "E"), byrow = FALSE ) k <- 1 for (j in 1:3) { for (i in 1:2) { expect_equal(a[i, j]$ggally_check_val, k) k <- k + 1 } } a }) test_that("missing plot", { plotList <- list() for (i in c(1, 3, 5)) { p <- ggally_text(paste("Plot #", i, sep = "")) p$ggally_check_val <- i plotList[[i]] <- p } a <- ggmatrix( plotList, 2, 3, c("A", "B", "C"), c("D", "E"), byrow = TRUE ) # reaches code where there are more cells than plots print(a) expect_equal(a[1, 1]$ggally_check_val, 1) expect_equal(a[1, 3]$ggally_check_val, 3) expect_equal(a[2, 2]$ggally_check_val, 5) }) test_that("str.ggmatrix", { pm <- ggpairs(tips, 1:3, upper = "blank") pm[1, 1] <- pm[1, 1] txt <- capture.output({ str(pm) }) expect_true(any(str_detect(txt, "Custom str.ggmatrix output:"))) txt <- capture.output({ str(pm, raw = TRUE) }) expect_false(any(str_detect(txt, "Custom str.ggmatrix output:"))) }) test_that("blank", { pm <- ggpairs(tips, 1:2) pm[1, 2] <- "blank" expect_print(pm) pm[2, 1] <- NULL expect_print(pm) expect_equal(length(pm$plots), 4) expect_error({ pm[2, 2] <- "not blank" }, "character values \\(besides 'blank'\\)") # nolint }) test_that("proportions", { pm <- ggpairs(iris, 1:2, mapping = ggplot2::aes(color = Species)) pm[2, 2] <- pm[2, 2] + ggplot2::coord_flip() pm2 <- ggmatrix( data = iris, pm$plots, ncol = 2, nrow = 2, xProportions = c(2, 1), yProportions = c(1, 2), title = "big plot, small marginals" ) expect_print(pm2) # turn on progress for a quick plot # TODO - turn test back on when it uses message properly # testthat::expect_message(print(pm2, progress = TRUE)) }) test_that("ggmatrix_gtable progress", { pm <- ggpairs(iris, 1:2) expect_silent({ pg <- ggmatrix_gtable(pm) }) expect_warning({ ggmatrix_gtable(pm, progress = TRUE) }) expect_warning({ ggmatrix_gtable(pm, progress_format = "asdfasdf :plot_i") }) }) # # printShowStrips <- c(TRUE, FALSE) # if (i <= length(printShowStrips)) { # printShowStrip <- printShowStrips[i] # } else { # printShowStrip <- NULL # } # GGally/NAMESPACE0000644000176200001440000000610113277311162012651 0ustar liggesusers# Generated by roxygen2: do not edit by hand S3method("+",gg) S3method("[",ggmatrix) S3method("[",glyphplot) S3method("[<-",ggmatrix) S3method(grid.draw,ggmatrix) S3method(print,ggmatrix) S3method(print,glyphplot) S3method(print,legend_guide_box) S3method(str,ggmatrix) export(add_ref_boxes) export(add_ref_lines) export(brew_colors) export(broomify) export(eval_data_col) export(fn_switch) export(getPlot) export(ggally_barDiag) export(ggally_blank) export(ggally_blankDiag) export(ggally_box) export(ggally_box_no_facet) export(ggally_cor) export(ggally_density) export(ggally_densityDiag) export(ggally_denstrip) export(ggally_diagAxis) export(ggally_dot) export(ggally_dot_and_box) export(ggally_dot_no_facet) export(ggally_facetbar) export(ggally_facetdensity) export(ggally_facetdensitystrip) export(ggally_facethist) export(ggally_na) export(ggally_naDiag) export(ggally_nostic_cooksd) export(ggally_nostic_hat) export(ggally_nostic_resid) export(ggally_nostic_se_fit) export(ggally_nostic_sigma) export(ggally_nostic_std_resid) export(ggally_points) export(ggally_ratio) export(ggally_smooth) export(ggally_smooth_lm) export(ggally_smooth_loess) export(ggally_text) export(ggcoef) export(ggcorr) export(ggduo) export(ggfacet) export(gglegend) export(ggmatrix) export(ggmatrix_gtable) export(ggmatrix_progress) export(ggnet) export(ggnet2) export(ggnetworkmap) export(ggnostic) export(ggpairs) export(ggparcoord) export(ggscatmat) export(ggsurv) export(ggts) export(glyphplot) export(glyphs) export(grab_legend) export(is.glyphplot) export(is_character_column) export(is_horizontal) export(lowertriangle) export(mapping_color_to_fill) export(mapping_string) export(mapping_swap_x_y) export(max1) export(mean0) export(min0) export(model_beta_label) export(model_beta_variables) export(model_response_variables) export(print_if_interactive) export(putPlot) export(range01) export(rescale01) export(rescale11) export(scatmat) export(uppertriangle) export(v1_ggmatrix_theme) export(wrap) export(wrap_fn_with_param_arg) export(wrap_fn_with_params) export(wrapp) import(RColorBrewer) import(ggplot2) import(plyr) import(utils) importFrom(grDevices,colorRampPalette) importFrom(grDevices,gray.colors) importFrom(grid,gpar) importFrom(grid,grid.draw) importFrom(grid,grid.layout) importFrom(grid,grid.newpage) importFrom(grid,grid.rect) importFrom(grid,grid.text) importFrom(grid,popViewport) importFrom(grid,pushViewport) importFrom(grid,seekViewport) importFrom(grid,upViewport) importFrom(grid,viewport) importFrom(gtable,gtable_filter) importFrom(reshape,melt) importFrom(reshape,melt.data.frame) importFrom(reshape,melt.default) importFrom(rlang,"%||%") importFrom(stats,anova) importFrom(stats,complete.cases) importFrom(stats,cor) importFrom(stats,lm) importFrom(stats,mad) importFrom(stats,median) importFrom(stats,na.omit) importFrom(stats,pf) importFrom(stats,qnorm) importFrom(stats,quantile) importFrom(stats,sd) importFrom(stats,spline) importFrom(stats,symnum) importFrom(stats,terms) importFrom(stats,time) importFrom(utils,capture.output) importFrom(utils,head) importFrom(utils,installed.packages) importFrom(utils,str) GGally/NEWS.md0000644000176200001440000002226713277315152012546 0ustar liggesusersGGally 1.3.3 ---------------- `ggpairs` and `ggduo` * Become ggplot2 v2.2.2 compliant (#266) * When retrieving functions with wrap, `ggally_*` functions do not require the GGally namespace (#269) * Exported `eval_data_col`, `mapping_string`, and `mapping_swap_x_y` (5d157f6) * Exported `is_horizontal` and `is_character_column` (#270) * Logical values are now treated as discrete (#272) `ggmatrix` * `progress` parameter added to ggmatrix (and appropriate parent functions). Allows for `TRUE`, `FALSE`, `NULL`, and `function(pm){...}` (#271) `ggnostic` * Cooks distance cutoff is now at F_{p, n - p}(0.5) (#274) `ggnet2` * Replaced loading packages with loading namespaces(#262) `ggally_smooth` * Added `shrink` and `se` parameters to `ggally_smooth` (#247) `ggcoef` * Added `sort` parameter to sort by beta values (#273) `ggparcoord` * Fixed bug where x axis breaks and labels did not appear when `splineFactor = TRUE` (#279) GGally 1.3.2 ----------------- `ggpairs` and `ggduo` * Removed warning where pure numeric names gave a warning (#238, @lepennec) * Fixed ordering issue with horizontal boxplots (#239) `ggparcoord` * Fixed missing `x` aes requirement when shadebox is provided (#237, @treysp) Package * Made igraph a non required dependency for tests (#240) GGally 1.3.1 ----------------- Added new dataset `psychademic` * See `?psychademic` for more details * (And updated the broken UCLA links) Added original ggmatrix theme * added function to set theme to have clear strip background and rearrange the strip positions * added parameter `switch` to ggmatrix (and friends) to allow for strip repositioning. See `?ggplot::facet_grid` for more documentation on `switch` (#223, #224) `ggsurv` error reporting * removed a one error check that is covered in other places (#222) `+.gg` * allow to add a list of items to a ggmatrix (#228) `ggmatrix.print` * fix strip issues with ggplot2 name update GGally 1.3.0 ----------------- `ggmatrix.print` - massive update! * Now prints with a ggplot2 facet'ed structure * Column titles are now placed in the strip of a plot matrix * If there are 16 plots or more, a progress bar is displayed automatically (if interactive). Please look at the documentation for `ggmatrix_gtable` more details. `ggmatrix` legend * A legend may be added with the `legend` parameter in `ggduo`, `ggpairs`, and `ggmatrix` * May specify a (length two) numeric plot coordinate * May specify a (length one) numeric plot position * May specify a legend object retrieved from `grab_legend` `ggnostic` - New function! * Produces a `ggmatrix` of diagnostic plots from a model object * Uses broom to retrieve model information * Each column of the plot matrix is a predictor variable. The rows can display the response variables, fitted points, residuals, standardized residuals, leave one out model sigma values, diagonals of the hat matrix, and cook's distance for each point. `ggfacet` - New function! * Produces single ggplot2 object * interface is very similar to `ggduo` and `ggpairs` `fn_switch` - New function! * Provide many functions in a list but only call one function at run time according to a mapping value * Useful for `ggnostic` for different behavior depending on the y variable * Allows for a 'default' value for the default switch case `ggmatrix` - allow custom labellers for facet labels * Added labeller parameter which is supplied to `ggplot2::facet_grid()` * Allows for labels with plotmath expressions `ggmatrix` and `ggplot2::last_plot()` * If a `ggmatrix` object is printed, `ggplot2::last_plot()` will return the plot matrix `ggmatrix` and ggplot2 labels * `ggplot2::labs` `+`'ed to a ggmatrix object * `ggplot2::xlab` and `ggplot2::ylab` may be `+`'ed to a ggmatrix object * `ggplot2::ggtitle` `+`'ed to a ggmatrix object * (anything that returns a class of "labels" may be added to a ggmatrix object) `ggmatrix` and `ggplot2::ggsave()` * `ggsave` now works with `ggmatrix` objects `ggpairs` and `ggduo` check for cardinality (#197) * Before creating a ggmatrix object, a check is made for character/factor columns * If there are more than 15 (default) unique combinations, an error is thrown. * Setting `cardinality_threshold` parameter to a higher value can fix the problem (knowing single cell plots may take more time to produce) * Setting `cardinality_threshold` parameter to `NULL` can stop the check `ggmatrix` plot proportions * `ggmatrix` can set the plot proportions with the parameters `xProportions` and `yProportions` * These will change the relative size of the plot panels produced. `ggally_cor` colour aesthetic * color must be a non-numeric value `ggsurv` * added boolean to allow for legend to not be sorted * fixed bug where censored points with custom color didn't match properly (#185) Vignettes * vignettes are now displayed using `packagedocs`. More info at http://hafen.github.io/packagedocs/ `ggally_box_no_facet` and `ggally_dot_no_facet` * New methods added as defaults to pair with new ggmatrix print method GGally 1.2.0 ----------------- install requirements * relaxed install requirements on grid (5d06dfc, d57469a, 933bb14, 73b314d) ggduo - New! * plot two grouped data in a plot matrix (#173) * helpful for plotting two sets of columns, multivariate analysis, and canonical correlation analysis * be sure to check out the examples! ggally_smooth_loess - New! * uses the loess method with drawing a line (1552f96) ggally_smooth_lm - New! * uses the lm method with drawing a line (1552f96) * alias of ggally_smooth ggmatrix.print * fixed bug strips where causing spacing issue when printing axis labels (174630d) ggnetworkmap * fixed bug where checking for the package 'intergraph' couldn't be reached ggsurv * changed default of plotting multiple censored data color to match the survival line package testing * added many more tests! GGally 1.1.0 ----------------- ggcoef - New! * plot model coefficients with broom and ggplot2 PR#162 * Plotting model coefficients (http://www.r-statistics.com/2010/07/visualization-of-regression-coefficients-in-r/) gglegend - New! * pull out the legend of a plot which can also be used in ggpairs PR#155, PR#169 ggally_densityDiag * fixed bug where '...' was not respected (d0fe633) ggally_smooth * added 'method' parameter (411213c) ggally_ratio * Does not call ggfluctuation2 anymore. PR#165 ggcorr * fixed issue with unnamed correlation matrix used as input PR#146 * fixed issue undesired shifting when layout.exp was > 0 PR#171 ggfluctuation2 * is being deprecated. Please use ggally_ratio instead PR#165 ggnetworkmap * fixed issue with overlaying network on a world map PR#157 ggparcoord * Fixed odd bug where a list was trying to be forced as a double PR#162 ggpairs * Fixed improperly rotated axes with ggally_ratio PR#165 ggscatmat * added 'corMethod' parameter for use in upper triangle PR#145 ggsurv * size.est and size.ci parameters added PR#153 * ordering changed to reflect survival time PR#147 * added a vignette PR#154 wrap * documentation updated PR#152 * changes default behavior only. If an argument is supplied, the argument will take precedence github chat * https://gitter.im/ggobi/ggally is the place to visit for general questions. travis-ci * cache packages for faster checking * install covr and lintr from github for testing purposes GGally 1.0.1 ----------------- ggparcoord * fix handling of factor group variable PR#131 ggscatmat * force all char columns to factors PR#134 print.ggmatrix * add boolean for grid.newpage ggmatrix print method PR#126 GGally 1.0.0 ----------------- ggplot2 * GGally has been upgraded to run on the latest ggplot2 v1.1.0. PR#109 New functions * ggmatrix. Make a generic matrix of ggplot2 plots * ggnetworkmap. Plot a network with ggplot2 suitable for overlay on a ggmap::map ggplot, or other ggplot * ggnet2. Function for plotting network objects using ggplot2, with additional control over graphical parameters that are not supported by the ggnet function Vignettes * glyph - new! * ggmatrix - new! * ggnetworkmap - new! * ggpairs - new! * ggscatmat - new! ggmatrix * allows for bracket notation when getting or setting plots. PR#61 * full control over axis labels and axis text. PR#107, PR#111 ggpairs * is now wrapper to ggmatrix * takes in 'wrapped' functions. This better handles the case of many different parameters being supplied to different plot types. PR#90 * dates are better handled in ggpairs. Still room for improvement for default behavior, but they do not cause errors. PR#58, PR#59 * displays a 'NA' plot when all or a combination of the data is NA. PR#119 ggcorr * legend title expressions may be used. PR#55 * handles objects that may be coerced into a data.frame PR#70 gglyph * changed geom_line to geom_path in gglyph. Fixes ordering issue. PR#51 ggparcoord * remaining columns are passed through so aesthetics may be added later. PR#54 * fixed parcoord ordering issues with odd names. PR#106 * fixed scaling when unique length equals 1. PR#122 ggsurv * color censored marks the same color as the line. PR#74 * allow for different censored color marks. PR#113 ggally_density * add fake data points to extend the limits of the stat_density2d. PR#114 ggally_na * new plot type! 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  • --- ```{r global_options, include=FALSE} # R output pre blocks are styled by default to indicate output knitr::opts_chunk$set( comment = NA, cache = TRUE, fig.height = 8, fig.width = 10 ) suppressMessages(suppressWarnings(library(GGally))) # shorthand for rd_link() - see ?packagedocs::rd_link for more information rdl <- function(x) packagedocs::rd_link(deparse(substitute(x))) ``` # GGally Welcome to the GGally documentation page. The following topic sections are alphabetically sorted. # GGally::ggcoef The purpose of this function is to quickly plot the coefficients of a model. #### *Joseph Larmarange* #### *May 16, 2016* ## Quick coefficients plot To work automatically, this function requires the `r rdl(broom)` package. Simply call `r rdl(ggcoef)` with a model object. It could be the result of `r rdl(lm)`, `r rdl(glm)` or any other model covered by `r rdl(broom)` and its `r rdl(tidy)` method^[See http://www.rdocumentation.org/packages/broom.]. ```{r ggcoef-reg} reg <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width, data = iris) ggcoef(reg) ``` In the case of a logistic regression (or any other model for which coefficients are usually exponentiated), simply indicated `exponentiate = TRUE`. Note that a logarithmic scale will be used for the x-axis. ```{r ggcoef-titanic} d <- as.data.frame(Titanic) log.reg <- glm(Survived ~ Sex + Age + Class, family = binomial, data = d, weights = d$Freq) ggcoef(log.reg, exponentiate = TRUE) ``` ## Customizing the plot You can use `conf.int`, `vline` and `exclude_intercept` to display or not confidence intervals as error bars, a vertical line for `x = 0` (or `x = 1` if coeffcients are exponentiated) and the intercept. ```{r ggcoef-reg-custom} ggcoef(reg, vline = FALSE, conf.int = FALSE, exclude_intercept = TRUE) ``` See the help page of `r rdl(ggcoef)` for the full list of arguments that could be used to personalize how error bars and the vertical line are plotted. ```{r ggcoef-full-args} ggcoef( log.reg, exponentiate = TRUE, vline_color = "red", vline_linetype = "solid", errorbar_color = "blue", errorbar_height = .25 ) ``` Additional parameters will be passed to `r rdl(geom_point)`. ```{r ggcoef-log.reg} ggcoef(log.reg, exponentiate = TRUE, color = "purple", size = 5, shape = 18) ``` Finally, you can also customize the aesthetic mapping of the points. ```{r ggcoef-aes} library(ggplot2) ggcoef(log.reg, exponentiate = TRUE, mapping = aes(x = estimate, y = term, size = p.value)) + scale_size_continuous(trans = "reverse") ``` ## Custom data frame You can also pass a custom data frame to `r rdl(ggcoef)`. The following variables are expected: - `term` (except if you customize the mapping) - `estimate` (except if you customize the mapping) - `conf.low` and `conf.high` (only if you want to display error bars) ```{r ggcoef-data-frame} cust <- data.frame( term = c("male vs. female", "30-49 vs. 18-29", "50+ vs. 18-29", "urban vs. rural"), estimate = c(.456, 1.234, 1.897, 1.003), conf.low = c(.411, 1.042, 1.765, 0.678), conf.high = c(.498, 1.564, 2.034, 1.476), variable = c("sex", "age", "age", "residence") ) cust$term <- factor(cust$term, cust$term) ggcoef(cust, exponentiate = TRUE) ggcoef( cust, exponentiate = TRUE, mapping = aes(x = estimate, y = term, colour = variable), size = 5 ) ``` # GGally::ggduo #### *Barret Schloerke* #### *July 4, 2016* The purpose of this function is to display two grouped data in a plot matrix. This is useful for canonical correlation analysis, multiple time series analysis, and regression analysis. ## Canonical Correlation Analysis This example is derived from `` R Data Analysis Examples | Canonical Correlation Analysis. UCLA: Institute for Digital Research and Education. from http://www.stats.idre.ucla.edu/r/dae/canonical-correlation-analysis (accessed May 22, 2017). `` `` Example 1. A researcher has collected data on three psychological variables, four academic variables (standardized test scores) and gender for 600 college freshman. She is interested in how the set of psychological variables relates to the academic variables and gender. In particular, the researcher is interested in how many dimensions (canonical variables) are necessary to understand the association between the two sets of variables." `` ```{r ggduo-cca} data(psychademic) str(psychademic) (psych_variables <- attr(psychademic, "psychology")) (academic_variables <- attr(psychademic, "academic")) ``` First, look at the within correlation using `r rdl(ggpairs)`. ```{r ggduo-within} ggpairs(psychademic, psych_variables, title = "Within Psychological Variables") ggpairs(psychademic, academic_variables, title = "Within Academic Variables") ``` Next, look at the between correlation using `r rdl(ggduo)`. ```{r ggduo-between} ggduo( psychademic, psych_variables, academic_variables, types = list(continuous = "smooth_lm"), title = "Between Academic and Psychological Variable Correlation", xlab = "Psychological", ylab = "Academic" ) ``` Since ggduo does not have a upper section to display the correlation values, we may use a custom function to add the information in the continuous plots. The strips may be removed as each group name may be recovered in the outer axis labels. ```{r ggduo-lm} lm_with_cor <- function(data, mapping, ..., method = "pearson") { x <- eval(mapping$x, data) y <- eval(mapping$y, data) cor <- cor(x, y, method = method) ggally_smooth_lm(data, mapping, ...) + ggplot2::geom_label( data = data.frame( x = min(x, na.rm = TRUE), y = max(y, na.rm = TRUE), lab = round(cor, digits = 3) ), mapping = ggplot2::aes(x = x, y = y, label = lab), hjust = 0, vjust = 1, size = 5, fontface = "bold", inherit.aes = FALSE # do not inherit anything from the ... ) } ggduo( psychademic, rev(psych_variables), academic_variables, mapping = aes(color = sex), types = list(continuous = wrap(lm_with_cor, alpha = 0.25)), showStrips = FALSE, title = "Between Academic and Psychological Variable Correlation", xlab = "Psychological", ylab = "Academic", legend = c(5,2) ) + theme(legend.position = "bottom") ``` ## Multiple Time Series Analysis While displaying multiple time series vertically over time, such as ``+ facet_grid(time ~ .)``, `r rdl(ggduo)` can handle both continuous and discrete data. `r rdl(ggplot2)` does not mix discrete and continuous data on the same axis. ```{r ggduo-mtsa} library(ggplot2) data(pigs) pigs_dt <- pigs[-(2:3)] # remove year and quarter pigs_dt$profit_group <- as.numeric(pigs_dt$profit > mean(pigs_dt$profit)) qplot( time, value, data = reshape::melt.data.frame(pigs_dt, "time"), geom = c("smooth", "point") ) + facet_grid(variable ~ ., scales = "free_y") ``` Instead, we may use `ggts` to display the data. `ggts` changes the default behavior of ggduo of `columnLabelsX` to equal `NULL` and allows for mixed variable types. ```{r ggduo-mtsa-group} # make the profit group as a factor value profit_groups <- c( "1" = "high", "0" = "low" ) pigs_dt$profit_group <- factor( profit_groups[as.character(pigs_dt$profit_group)], levels = unname(profit_groups), ordered = TRUE ) ggts(pigs_dt, "time", 2:7) # remove the binwidth warning pigs_types <- list( comboHorizontal = wrap(ggally_facethist, binwidth = 1) ) ggts(pigs_dt, "time", 2:7, types = pigs_types) # add color and legend pigs_mapping <- aes(color = profit_group) ggts(pigs_dt, pigs_mapping, "time", 2:7, types = pigs_types, legend = c(6,1)) ``` Produce more meaningful labels, add a legend, and remove profit group strips. ```{r ggduo-mtsa-pretty} pm <- ggts( pigs_dt, pigs_mapping, 1, 2:7, types = pigs_types, legend = c(6,1), columnLabelsY = c( "number of\nfirst birth sows", "sell price over\nfeed cost", "sell count over\nheard size", "meat head count", "breading\nheard size", "profit\ngroup" ), showStrips = FALSE ) + labs(fill = "profit group") + theme( legend.position = "bottom", strip.background = element_rect( fill = "transparent", color = "grey80" ) ) pm ``` ## Regression Analysis Since `r rdl(ggduo)` may take custom functions just like `r rdl(ggpairs)`, we will make a custom function that displays the residuals with a red line at 0 and all other y variables will receive a simple linear regression plot. Note: the marginal residuals are calculated before plotting and the y_range is found to display all residuals on the same scale. ```{r ggduo-reg-swiss} swiss <- datasets::swiss # add a 'fake' column swiss$Residual <- seq_len(nrow(swiss)) # calculate all residuals prior to display residuals <- lapply(swiss[2:6], function(x) { summary(lm(Fertility ~ x, data = swiss))$residuals }) # calculate a consistent y range for all residuals y_range <- range(unlist(residuals)) # custom function to display continuous data. If the y variable is "Residual", do custom work. lm_or_resid <- function(data, mapping, ..., line_color = "red", line_size = 1) { if (as.character(mapping$y) != "Residual") { return(ggally_smooth_lm(data, mapping, ...)) } # make residual data to display resid_data <- data.frame( x = data[[as.character(mapping$x)]], y = residuals[[as.character(mapping$x)]] ) ggplot(data = data, mapping = mapping) + geom_hline(yintercept = 0, color = line_color, size = line_size) + ylim(y_range) + geom_point(data = resid_data, mapping = aes(x = x, y = y), ...) } # plot the data ggduo( swiss, 2:6, c(1,7), types = list(continuous = lm_or_resid) ) # change line to be thicker and blue and the points to be slightly transparent ggduo( swiss, 2:6, c(1,7), types = list( continuous = wrap(lm_or_resid, alpha = 0.7, line_color = "blue", line_size = 3 ) ) ) ``` # GGally::glyphs #### *Hadley Wickham, Charlotte Wickham, Di Cook, Heike Hofmann* #### *Nov 6, 2015* This function rearranges data to be able to construct a glyph plot ```{r glyphs-basic-usage, fig.height=7, fig.width=7} library(ggplot2) data(nasa) temp.gly <- glyphs(nasa, "long", "day", "lat", "surftemp", height=2.5) ggplot(temp.gly, ggplot2::aes(gx, gy, group = gid)) + add_ref_lines(temp.gly, color = "grey90") + add_ref_boxes(temp.gly, color = "grey90") + geom_path() + theme_bw() + labs(x = "", y = "") ``` This shows a glyphplot of monthly surface temperature for 6 years over Central America. You can see differences from one location to another, that in large areas temperature doesn't change much. There are large seasonal trends in the top left over land. Rescaling in different ways puts emphasis on different components, see the examples in the referenced paper. And with ggplot2 you can make a map of the geographic area underlying the glyphs. ## References Wickham, H., Hofmann, H., Wickham, C. and Cook, D. (2012) Glyph-maps for Visually Exploring Temporal Patterns in Climate Data and Models, **Environmetrics**, *23*(5):151-182. # GGally::ggmatrix #### *Barret Schloerke* #### *Oct 29, 2015* `r rdl(ggmatrix)` is a function for managing multiple plots in a matrix-like layout. It was designed to adapt to any number of columns and rows. This allows for very customized plot matrices. ## Generic Example The examples below use plots labeled 1 to 6 to distinguish where the plots are being placed. ```{r ggmatrix_genExample} plotList <- list() for (i in 1:6) { plotList[[i]] <- ggally_text(paste("Plot #", i, sep = "")) } # bare minimum of plotList, nrow, and ncol pm <- ggmatrix(plotList, 2, 3) pm # provide more information pm <- ggmatrix( plotList, nrow = 2, ncol = 3, xAxisLabels = c("A", "B", "C"), yAxisLabels = c("D", "E"), title = "Matrix Title" ) pm # display plots in column order pm <- ggmatrix( plotList, nrow = 2, ncol = 3, xAxisLabels = c("A", "B", "C"), yAxisLabels = c("D", "E"), title = "Matrix Title", byrow = FALSE ) pm ``` ## Matrix Subsetting Individual plots may be retrieved from the plot matrix and can be placed in the plot matrix. ```{r ggmatrix_place} pm <- ggmatrix( plotList, nrow = 2, ncol = 3, xAxisLabels = c("A", "B", "C"), yAxisLabels = c("D", "E"), title = "Matrix Title" ) pm p2 <- pm[1,2] p3 <- pm[1,3] p2 p3 pm[1,2] <- p3 pm[1,3] <- p2 pm ``` ## Themes ```{r ggmatrix_theme} library(ggplot2) pm <- ggmatrix( plotList, nrow = 2, ncol = 3, xAxisLabels = c("A", "B", "C"), yAxisLabels = c("D", "E"), title = "Matrix Title", byrow = FALSE ) pm <- pm + theme_bw() pm ``` ## Axis Control The X and Y axis have booleans to turn on/off the individual plot's axes on the bottom and left sides of the plot matrix. To save time, `showAxisPlotLabels` can be set to override `showXAxisPlotLabels` and `showYAxisPlotLabels`. ```{r ggmatrix_axisControl} pm <- ggmatrix( plotList, nrow = 2, ncol = 3, xAxisLabels = c("A", "B", "C"), yAxisLabels = c("D", "E"), title = "No Left Plot Axis", showYAxisPlotLabels = FALSE ) pm pm <- ggmatrix( plotList, nrow = 2, ncol = 3, xAxisLabels = c("A", "B", "C"), yAxisLabels = c("D", "E"), title = "No Bottom Plot Axis", showXAxisPlotLabels = FALSE ) pm pm <- ggmatrix( plotList, nrow = 2, ncol = 3, xAxisLabels = c("A", "B", "C"), yAxisLabels = c("D", "E"), title = "No Plot Axes", showAxisPlotLabels = FALSE ) pm ``` ## Strips Control By default, the plots in the top row and the right most column will display top-side and right-side strips respectively (`showStrips = NULL`). If all strips need to appear in each plot, `showStrips` may be set to `TRUE`. If all strips should not be displayed, `showStrips` may be set to `FALSE`. ```{r ggmatrix_stripControl} data(tips, package = "reshape") plotList <- list( qplot(total_bill, tip, data = subset(tips, smoker == "No" & sex == "Female")) + facet_grid(time ~ day), qplot(total_bill, tip, data = subset(tips, smoker == "Yes" & sex == "Female")) + facet_grid(time ~ day), qplot(total_bill, tip, data = subset(tips, smoker == "No" & sex == "Male")) + facet_grid(time ~ day), qplot(total_bill, tip, data = subset(tips, smoker == "Yes" & sex == "Male")) + facet_grid(time ~ day) ) pm <- ggmatrix( plotList, nrow = 2, ncol = 2, yAxisLabels = c("Female", "Male"), xAxisLabels = c("Non Smoker", "Smoker"), title = "Total Bill vs Tip", showStrips = NULL # default ) pm pm <- ggmatrix( plotList, nrow = 2, ncol = 2, yAxisLabels = c("Female", "Male"), xAxisLabels = c("Non Smoker", "Smoker"), title = "Total Bill vs Tip", showStrips = TRUE ) pm pm <- ggmatrix( plotList, nrow = 2, ncol = 2, yAxisLabels = c("Female", "Male"), xAxisLabels = c("Non Smoker", "Smoker"), title = "Total Bill vs Tip", showStrips = FALSE ) pm ``` # GGally::ggnetworkmap #### *Amos Elberg* #### *January 10, 2015* `r rdl(ggnetworkmap)` is a function for plotting elegant maps using `r rdl(ggplot2)`. It builds on `r rdl(ggnet)` by allowing to draw a network over a map, and is particularly intended for use with `r rdl(ggmap)`. ## Example: US airports This example is based on a [tutorial by Nathan Yau at Flowing Data](http://flowingdata.com/2011/05/11/how-to-map-connections-with-great-circles/). ```{r ggnetworkmap-init} suppressMessages(library(network)) suppressMessages(library(sna)) suppressMessages(library(maps)) suppressMessages(library(ggplot2)) airports <- read.csv("http://datasets.flowingdata.com/tuts/maparcs/airports.csv", header = TRUE) rownames(airports) <- airports$iata # select some random flights set.seed(1234) flights <- data.frame( origin = sample(airports[200:400, ]$iata, 200, replace = TRUE), destination = sample(airports[200:400, ]$iata, 200, replace = TRUE) ) # convert to network flights <- network(flights, directed = TRUE) # add geographic coordinates flights %v% "lat" <- airports[ network.vertex.names(flights), "lat" ] flights %v% "lon" <- airports[ network.vertex.names(flights), "long" ] # drop isolated airports delete.vertices(flights, which(degree(flights) < 2)) # compute degree centrality flights %v% "degree" <- degree(flights, gmode = "digraph") # add random groups flights %v% "mygroup" <- sample(letters[1:4], network.size(flights), replace = TRUE) # create a map of the USA usa <- ggplot(map_data("usa"), aes(x = long, y = lat)) + geom_polygon(aes(group = group), color = "grey65", fill = "#f9f9f9", size = 0.2) delete.vertices(flights, which(flights %v% "lon" < min(usa$data$long))) delete.vertices(flights, which(flights %v% "lon" > max(usa$data$long))) delete.vertices(flights, which(flights %v% "lat" < min(usa$data$lat))) delete.vertices(flights, which(flights %v% "lat" > max(usa$data$lat))) # overlay network data to map ggnetworkmap(usa, flights, size = 4, great.circles = TRUE, node.group = mygroup, segment.color = "steelblue", ring.group = degree, weight = degree) ``` ## Example: Twitter spambots This next example uses data from a Twitter spam community identified while exploring and trying to clear-up a group of tweets. After coloring the nodes based on their centrality, the odd structure stood out clearly. ```{r ggnetworkmap-data, eval=FALSE} data(twitter_spambots) ``` ```{r ggnetworkmap-world} # create a world map world <- fortify(map("world", plot = FALSE, fill = TRUE)) world <- ggplot(world, aes(x = long, y = lat)) + geom_polygon(aes(group = group), color = "grey65", fill = "#f9f9f9", size = 0.2) # view global structure ggnetworkmap(world, twitter_spambots) ``` Is the network really concentrated in the U.S.? Probably not. One of the odd things about the network, is a much higher proportion of the users gave locations that could be geocoded, than Twitter users generally. Let's see the network topology ```{r ggnetworkmap-topology} ggnetworkmap(net = twitter_spambots, arrow.size = 0.5) ``` Coloring nodes according to degree centrality can highlight network structures. ```{r ggnetworkmap-color} # compute indegree and outdegree centrality twitter_spambots %v% "indegree" <- degree(twitter_spambots, cmode = "indegree") twitter_spambots %v% "outdegree" <- degree(twitter_spambots, cmode = "outdegree") ggnetworkmap(net = twitter_spambots, arrow.size = 0.5, node.group = indegree, ring.group = outdegree, size = 4) + scale_fill_continuous("Indegree", high = "red", low = "yellow") + labs(color = "Outdegree") ``` Some Twitter attributes have been included as vertex attributes. ```{r ggnetworkmap-twitter-attr} # show some vertex attributes associated with each account ggnetworkmap(net = twitter_spambots, arrow.size = 0.5, node.group = followers, ring.group = friends, size = 4, weight = indegree, label.nodes = TRUE, vjust = -1.5) + scale_fill_continuous("Followers", high = "red", low = "yellow") + labs(color = "Friends") + scale_color_continuous(low = "lightgreen", high = "darkgreen") ``` # GGally::ggnostic #### *Barret Schloerke* #### *Oct 1, 2016* `r rdl(ggnostic)` is a display wrapper to `r rdl(ggduo)` that displays full model diagnostics for each given explanatory variable. By default, `r rdl(ggduo)` displays the residuals, leave-one-out model sigma value, leverage points, and Cook's distance against each explanatory variable. The rows of the plot matrix can be expanded to include fitted values, standard error of the fitted values, standardized residuals, and any of the response variables. If the model is a linear model, stars are added according to the anova significance of each explanatory variable. Most diagnostic plots contain reference line(s) to help determine if the model is fitting properly * **residuals:** * Type key: `".resid"` * A solid line is located at the expected value of 0 with dashed lines at the 95% confidence interval. ($0 \pm 1.96 * \sigma$) * Plot function: `r rdl(ggally_nostic_resid)`. * See also `r rdl(stats::residuals)` * **standardized residuals:** * Type key: `".std.resid"` * Same as residuals, except the standardized residuals equal the regular residuals divided by sigma. The dashed lines are located at $0 \pm 1.96 * 1$. * Plot function: `r rdl(ggally_nostic_std_resid)`. * See also `r rdl(stats::rstandard)` * **leave-one-out model sigma:** * Type key: `".sigma"` * A solid line is located at the full model's sigma value. * Plot function: `r rdl(ggally_nostic_sigma)`. * See also `r rdl(stats::influence)`'s value on `sigma` * **leverage points:** * Type key: `".hat"` * The expected value for the diagonal of a hat matrix is $p / n$. Points are considered leverage points if they are large than $2 * p / n$, where the higher line is drawn. * Plot function: `r rdl(ggally_nostic_hat)`. * See also `r rdl(stats::influence)`'s value on `hat` * **Cook's distance:** * Type key: `".cooksd"` * Points that are larger than $4 / n$ line are considered highly influential points. Plot function: `r rdl(ggally_nostic_cooksd)`. See also `r rdl(stats::cooks.distance)` * **fitted points:** * Type key: `".fitted"` * No reference lines by default. * Default plot function: `r rdl(ggally_points)`. * See also `r rdl(stats::predict)` * **standard error of fitted points**: * Type key: `".se.fit"` * No reference lines by default. * Plot function: `r rdl(ggally_nostic_se_fit)`. * See also `r rdl(stats::fitted)` * **response variables:** * Type key: (response name in data.frame) * No reference lines by default. * Default plot function: `r rdl(ggally_points)`. ## Life Expectancy Model Fitting Looking at the dataset `r rdl(datasets::state.x77)`, we will fit a multiple regression model for Life Expectancy. ```{r life_model} # make a data.frame and fix column names state <- as.data.frame(state.x77) colnames(state)[c(4, 6)] <- c("Life.Exp", "HS.Grad") str(state) # fit full model model <- lm(Life.Exp ~ ., data = state) # reduce to "best fit" model with model <- step(model, trace = FALSE) summary(model) ``` Next, we look at the variables for any high (|value| > 0.8) correlation values and general interaction behavior. ```{r nostic_scatmat} # look at variables for high correlation (none) ggscatmat(state, columns = c("Population", "Murder", "HS.Grad", "Frost")) ``` All variables appear to be ok. Next, we look at the model diagnostics. ```{r nostic_diag} # look at model diagnostics ggnostic(model) ``` * The residuals appear to be normally distributed. There are a couple residual outliers, but 2.5 outliers are expected. * There are 5 leverage points according the diagonal of the hat matrix * There are 2 leverage points according to Cook's distance. One is **much** larger than the other. Let's remove the largest data point first to try and define a better model. ```{r nostic_no_hawaii} # very high life expectancy state[11, ] state_no_hawaii <- state[-11, ] model_no_hawaii <- lm(Life.Exp ~ Population + Murder + HS.Grad + Frost, data = state_no_hawaii) ggnostic(model_no_hawaii) ``` There are no more outrageous Cook's distance values. The model without Hawaii appears to be a good fitting model. ```{r nostic_summary} summary(model) summary(model_no_hawaii) ``` Since there is only a marginal improvement by removing Hawaii, the original model should be used to explain life expectancy. ## Full diagnostic plot matrix example The following lines of code will display different modle diagnostic plot matrices for the same statistical model. The first one is of the default settings. The second adds color according to the ``species``. Finally, the third displays all possible columns and uses `r rdl(ggally_smooth)` to display the fitted points and response variables. ```{r nostic_flea} flea_model <- step(lm(head ~ ., data = flea), trace = FALSE) summary(flea_model) # default output ggnostic(flea_model) # color'ed output ggnostic(flea_model, mapping = ggplot2::aes(color = species)) # full color'ed output ggnostic( flea_model, mapping = ggplot2::aes(color = species), columnsY = c("head", ".fitted", ".se.fit", ".resid", ".std.resid", ".hat", ".sigma", ".cooksd"), continuous = list(default = ggally_smooth, .fitted = ggally_smooth) ) ``` # GGally::ggpairs #### *Barret Schloerke* #### *Oct 29, 2015* `r rdl(ggpairs)` is a special form of a `r rdl(ggmatrix)` that produces a pairwise comparison of multivariate data. By default, `r rdl(ggpairs)` provides two different comparisons of each pair of columns and displays either the density or count of the respective variable along the diagonal. With different parameter settings, the diagonal can be replaced with the axis values and variable labels. There are many hidden features within ggpairs. Please take a look at the examples below to get the most out of ggpairs. ## Columns and Mapping The `columns` displayed default to all columns of the provided `data`. To subset to only a few columns, use the `columns` parameter. ```{r ggpairs_columns} data(tips, package = "reshape") pm <- ggpairs(tips) pm ## too many plots for this example. ## reduce the columns being displayed ## these two lines of code produce the same plot matrix pm <- ggpairs(tips, columns = c(1, 6, 2)) pm <- ggpairs(tips, columns = c("total_bill", "time", "tip"), columnLabels = c("Total Bill", "Time of Day", "Tip")) pm ``` Aesthetics can be applied to every subplot with the `mapping` parameter. ```{r ggpairs_mapping} library(ggplot2) pm <- ggpairs(tips, mapping = aes(color = sex), columns = c("total_bill", "time", "tip")) pm ``` Since the plots are default plots (or are helper functions from GGally), the aesthetic color is altered to be appropriate. Looking at the example above, 'tip' vs 'total_bill' (pm[3,1]) needs the `color` aesthetic, while 'time' vs 'total_bill' needs the `fill` aesthetic. If custom functions are supplied, no aesthetic alterations will be done. ## Matrix Sections There are three major sections of the pairwise matrix: `lower`, `upper`, and `diag`. The `lower` and `upper` may contain three plot types: `continuous`, `combo`, and `discrete`. The 'diag' only contains either `continuous` or `discrete`. * `continuous`: both X and Y are continuous variables * `combo`: one X and Y variable is discrete while the other is continuous * `discrete`: both X and Y are discrete variables To make adjustments to each section, a list of information may be supplied. The list can be comprised of the following elements: * `continuous`: * a character string representing the tail end of a `ggally_NAME` function, or a custom function * current valid `upper$continuous` and `lower$continuous` character strings: `'points'`, `'smooth'`, `'density'`, `'cor'`, `'blank'` * current valid `diag$continuous` character strings: `'densityDiag'`, `'barDiag'`, `'blankDiag'` * `combo`: * a character string representing the tail end of a `ggally_NAME` function, or a custom function. (not applicable for a `diag` list) * current valid `upper$combo` and `lower$combo` character strings: `'box'`, `'dot'`, `'facethist'`, `'facetdensity'`, `'denstrip'`, `'blank'` * `discrete`: * a character string representing the tail end of a `ggally_NAME` function, or a custom function * current valid `upper$discrete` and `lower$discrete` character strings: `'ratio'`, `'facetbar'`, `'blank'` * current valid `diag$discrete` character strings: `'barDiag'`, `'blankDiag'` * `mapping`: if mapping is provided, only the section's mapping will be overwritten ```{r ggpairs_section} library(ggplot2) pm <- ggpairs( tips, columns = c("total_bill", "time", "tip"), lower = list( continuous = "smooth", combo = "facetdensity", mapping = aes(color = time) ) ) pm ``` A section list may be set to the character string `"blank"` or `NULL` if the section should be skipped when printed. ```{r ggpairs_blank} pm <- ggpairs( tips, columns = c("total_bill", "time", "tip"), upper = "blank", diag = NULL ) pm ``` ## Custom Functions The `ggally_NAME` functions do not provide all graphical options. Instead of supplying a character string to a `continuous`, `combo`, or `discrete` element within `upper`, `lower`, or `diag`, a custom function may be given. The custom function should follow the api of ```{r ggally_custom_function} custom_function <- function(data, mapping, ...){ # produce ggplot2 object here } ``` There is no requirement to what happens within the function, as long as a ggplot2 object is returned. ```{r ggpairs_custom_function} my_bin <- function(data, mapping, ..., low = "#132B43", high = "#56B1F7") { ggplot(data = data, mapping = mapping) + geom_bin2d(...) + scale_fill_gradient(low = low, high = high) } pm <- ggpairs( tips, columns = c("total_bill", "time", "tip"), lower = list( continuous = my_bin ) ) pm ``` ## Function Wrapping The examples above use default parameters to each of the subplots. One of the immediate parameters to be set it `binwidth`. This parameters is only needed in the lower, combination plots where one variable is continuous while the other variable is discrete. To change the default parameter `binwidth` setting, we will `r rdl(wrap)` the function. `r rdl(wrap)` first parameter should be a character string or a custom function. The remaining parameters supplied to wrap will be supplied to the function at run time. ```{r ggpairs_wrap} pm <- ggpairs( tips, columns = c("total_bill", "time", "tip"), lower = list( combo = wrap("facethist", binwidth = 1), continuous = wrap(my_bin, binwidth = c(5, 0.5), high = "red") ) ) pm ``` To get finer control over parameters, please look into custom functions. ## Plot Matrix Subsetting Please look at the [vignette for ggmatrix](#ggallyggmatrix) on plot matrix manipulations. Small ggpairs example: ```{r ggpairs_matrix} pm <- ggpairs(tips, columns = c("total_bill", "time", "tip")) # retrieve the third row, first column plot p <- pm[3,1] p <- p + aes(color = time) p pm[3,1] <- p pm ``` ## Themes Please look at the [vignette for ggmatrix](#ggallyggmatrix) on plot matrix manipulations. Small ggpairs example: ```{r ggpairs_theme} pmBW <- pm + theme_bw() pmBW ``` ## References John W Emerson, Walton A Green, Barret Schloerke, Jason Crowley, Dianne Cook, Heike Hofmann, Hadley Wickham. **[The Generalized Pairs Plot](http://vita.had.co.nz/papers/gpp.html)**. *Journal of Computational and Graphical Statistics*, vol. 22, no. 1, pp. 79-91, 2012. # GGally::ggscatmat #### *Di Cook, Mengjia Ni* #### *Nov 6, 2015* The primary function is `r rdl(ggscatmat)`. It is similar to `r rdl(ggpairs)` but only works for purely numeric multivariate data. It is faster than ggpairs, because less choices need to be made. It creates a matrix with scatterplots in the lower diagonal, densities on the diagonal and correlations written in the upper diagonal. Syntax is to enter the dataset, the columns that you want to plot, a color column, and an alpha level. ```{r ggscatmat-basic-usage, fig.height=7, fig.width=7} data(flea) ggscatmat(flea, columns = 2:4, color="species", alpha=0.8) ``` In this plot, you can see that the three different species vary a little from each other in these three variables. Heptapot (blue) has smaller values on the variable "tars1" than the other two. The correlation between the three variables is similar for all species. ## References John W Emerson, Walton A Green, Barret Schloerke, Jason Crowley, Dianne Cook, Heike Hofmann, Hadley Wickham. **[The Generalized Pairs Plot](http://vita.had.co.nz/papers/gpp.html)**. *Journal of Computational and Graphical Statistics*, vol. 22, no. 1, pp. 79-91, 2012. # GGally::ggsurv #### *Edwin Thoen* #### *April, 4, 2016* This function produces Kaplan-Meier plots using `r rdl(ggplot2)`. As a first argument, `r rdl(ggsurv)` needs a `r rdl(survfit)` object, created by the `r rdl(survival)` package. Default settings differ for single stratum and multiple strata objects. ## Single Stratum ```{r basic-usage, fig.height=7, fig.width=7} require(ggplot2) require(survival) require(scales) data(lung, package = "survival") sf.lung <- survival::survfit(Surv(time, status) ~ 1, data = lung) ggsurv(sf.lung) ``` ## Multiple Stratum The legend color positions matches the survival order or each stratum, where the stratums that end at a lower value or time have a position that is lower in the legend. ```{r ggsurv-multiple} sf.sex <- survival::survfit(Surv(time, status) ~ sex, data = lung) pl.sex <- ggsurv(sf.sex) pl.sex ``` ## Alterations Since a ggplot2 object is returned, plot objects may be altered after the original creation. ### Adjusting the legend ```{r ggsurv-legend} pl.sex + ggplot2::guides(linetype = FALSE) + ggplot2::scale_colour_discrete( name = 'Sex', breaks = c(1, 2), labels = c('Male', 'Female') ) ``` ### Adjust the limits ```{r ggsurv-limits} data(kidney, package = "survival") sf.kid <- survival::survfit(Surv(time, status) ~ disease, data = kidney) pl.kid <- ggsurv(sf.kid, plot.cens = FALSE) pl.kid # Zoom in to first 80 days pl.kid + ggplot2::coord_cartesian(xlim = c(0, 80), ylim = c(0.45, 1)) ``` ### Add text and remove the legend ```{r ggsurv-text} pl.kid + ggplot2::annotate( "text", label = c("PKD", "Other", "GN", "AN"), x = c(90, 125, 5, 60), y = c(0.8, 0.65, 0.55, 0.30), size = 5, colour = scales::hue_pal( h = c(0, 360) + 15, c = 100, l = 65, h.start = 0, direction = 1 )(4) ) + ggplot2::guides(color = FALSE, linetype = FALSE) ``` GGally/vignettes/ggcoef.html0000644000176200001440000000057013001231535015554 0ustar liggesusers

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    GGally/README.md0000644000176200001440000000341113012362115012701 0ustar liggesusers# [GGally](http://ggobi.github.io/ggally): Extension to [ggplot2](http://docs.ggplot2.org/current/) Master: [![Build Status](https://travis-ci.org/ggobi/ggally.png?branch=master)](https://travis-ci.org/ggobi/ggally) [![codecov.io](https://codecov.io/github/ggobi/ggally/coverage.svg?branch=master)](https://codecov.io/github/ggobi/ggally?branch=master) [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/GGally)](https://cran.r-project.org/package=GGally) [![](http://cranlogs.r-pkg.org/badges/GGally)](https://cran.r-project.org/package=GGally) [![DOI](https://zenodo.org/badge/22529/ggobi/ggally.svg)](https://zenodo.org/badge/latestdoi/22529/ggobi/ggally) Dev: [![Build Status](https://travis-ci.org/ggobi/ggally.png?branch=dev)](https://travis-ci.org/ggobi/ggally) [![codecov.io](https://codecov.io/github/ggobi/ggally/coverage.svg?branch=dev)](https://codecov.io/github/ggobi/ggally?branch=dev) [![Join the chat at https://gitter.im/ggobi/ggally](https://badges.gitter.im/ggobi/ggally.svg)](https://gitter.im/ggobi/ggally?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [ggplot2](http://docs.ggplot2.org/current/) is a plotting system for R based on the grammar of graphics. [GGally](https://ggobi.github.io/ggally) extends ggplot2 by adding several functions to reduce the complexity of combining geoms with transformed data. Some of these functions include a pairwise plot matrix, a scatterplot plot matrix, a parallel coordinates plot, a survival plot, and several functions to plot networks. ## Installation To install this package from Github or [CRAN](https://cran.r-project.org/package=GGally), do the following from the R console: ```r # Github library(devtools) install_github("ggobi/ggally") ``` ```r # CRAN install.packages("GGally") ``` GGally/MD50000644000176200001440000002316213277410307011751 0ustar liggesusersb19a685a259045ededeb5865a8058519 *DESCRIPTION bbf5b9dcdd709f4ddb73e912be28c4c8 *NAMESPACE 6ef9a29ae945281b2d76d61f20888dc4 *NEWS.md 61058af96e533ce5ff7e656b8399d17f *R/data-australia-pisa-2012.R 040a3753cbc5934d39c690be2df943ae *R/data-flea.R 2ed6f649e2b7ed9b3e9c289c18090dd3 *R/data-happy.R 1a857c1deb41349c1238f3fa93e38fa7 *R/data-nasa.R 53bf3cbe28642ebaa5499f14cc23ae52 *R/data-pigs.R 8e06efe84025094992bd469e708538c9 *R/data-psychademic.R f0212d247c0c0145a7f37da9e39820f3 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Extension to 'ggplot2' Type: Package LazyLoad: yes LazyData: true URL: https://ggobi.github.io/ggally, https://github.com/ggobi/ggally BugReports: https://github.com/ggobi/ggally/issues Authors@R: c( person("Barret", "Schloerke", role = c("aut", "cre"), email = "schloerke@gmail.com", comment = "author for ggpairs, ggduo, ggnostic, ggts, ggfacet, and ggally_*. Contributor for all functions."), person("Jason", "Crowley", role = "aut", email = "crowley.jason.s@gmail.com", comment = "ggparcoord"), person("Di", "Cook", role = c("aut", "ths"), email = "dicook@monash.edu", comment = "ggscatmat, gglyph"), person("Heike", "Hofmann", role = "ths", email = "hofmann@iastate.edu"), person("Hadley", "Wickham", role = "ths", email = "h.wickham@gmail.com"), person("Francois", "Briatte", role = "aut", email = "f.briatte@gmail.com", comment = "ggcorr, ggnet, ggnet2"), person("Moritz", "Marbach", role = "aut", email = "mmarbach@mail.uni-mannheim.de", comment = "ggnet, ggnet2"), person("Edwin", "Thoen", role = "aut", email = "edwinthoen@gmail.com", comment = "ggsurv"), person("Amos", "Elberg", role = "aut", email = "amos.elberg@gmail.com", comment = "ggnetworkmap"), person("Joseph", "Larmarange", role = "aut", email = "joseph@larmarange.net", comment = "ggcoef")) Description: The R package 'ggplot2' is a plotting system based on the grammar of graphics. 'GGally' extends 'ggplot2' by adding several functions to reduce the complexity of combining geometric objects with transformed data. Some of these functions include a pairwise plot matrix, a two group pairwise plot matrix, a parallel coordinates plot, a survival plot, and several functions to plot networks. Depends: R (>= 3.1), ggplot2 (> 2.2.0) Imports: grDevices, grid, gtable (>= 0.2.0), plyr (>= 1.8.3), progress, RColorBrewer, reshape (>= 0.8.5), utils, rlang Suggests: broom (>= 0.4.0), chemometrics, geosphere (>= 1.5-1), igraph (>= 1.0.1), intergraph (>= 2.0-2), maps (>= 3.1.0), mapproj, network (>= 1.12.0), scagnostics, scales (>= 0.4.0), sna (>= 2.3-2), survival, packagedocs (>= 0.4.0), rmarkdown, roxygen2, testthat, crosstalk RoxygenNote: 6.0.1 VignetteBuilder: packagedocs SystemRequirements: openssl NeedsCompilation: no Packaged: 2018-05-17 15:34:16 UTC; barret Author: Barret Schloerke [aut, cre] (author for ggpairs, ggduo, ggnostic, ggts, ggfacet, and ggally_*. Contributor for all functions.), Jason Crowley [aut] (ggparcoord), Di Cook [aut, ths] (ggscatmat, gglyph), Heike Hofmann [ths], Hadley Wickham [ths], Francois Briatte [aut] (ggcorr, ggnet, ggnet2), Moritz Marbach [aut] (ggnet, ggnet2), Edwin Thoen [aut] (ggsurv), Amos Elberg [aut] (ggnetworkmap), Joseph Larmarange [aut] (ggcoef) Maintainer: Barret Schloerke Repository: CRAN Date/Publication: 2018-05-17 23:31:19 UTC GGally/man/0000755000176200001440000000000013277320370012210 5ustar liggesusersGGally/man/add_ref_lines.Rd0000644000176200001440000000104713114357267015264 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gglyph.R \name{add_ref_lines} \alias{add_ref_lines} \title{Add reference lines for each cell of the glyphmap.} \usage{ add_ref_lines(data, color = "white", size = 1.5, ...) } \arguments{ \item{data}{A glyphmap structure.} \item{color}{Set the color to draw in, default is "white"} \item{size}{Set the line size, default is 1.5} \item{...}{other arguments passed onto \code{\link[ggplot2]{geom_line}}} } \description{ Add reference lines for each cell of the glyphmap. } GGally/man/happy.Rd0000644000176200001440000000337613114357267013636 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data-happy.R \docType{data} \name{happy} \alias{happy} \title{Data related to happiness from the General Social Survey, 1972-2006.} \format{A data frame with 51020 rows and 10 variables} \usage{ data(happy) } \description{ This data extract is taken from Hadley Wickham's \code{productplots} package. The original description follows, with minor edits. } \details{ The data is a small sample of variables related to happiness from the General Social Survey (GSS). The GSS is a yearly cross-sectional survey of Americans, run from 1972. We combine data for 25 years to yield 51,020 observations, and of the over 5,000 variables, we select nine related to happiness: \itemize{ \item age. age in years: 18--89. \item degree. highest education: lt high school, high school, junior college, bachelor, graduate. \item finrela. relative financial status: far above, above average, average, below average, far below. \item happy. happiness: very happy, pretty happy, not too happy. \item health. health: excellent, good, fair, poor. \item marital. marital status: married, never married, divorced, widowed, separated. \item sex. sex: female, male. \item wtsall. probability weight. 0.43--6.43. } } \references{ Smith, Tom W., Peter V. Marsden, Michael Hout, Jibum Kim. \emph{General Social Surveys, 1972-2006}. [machine-readable data file]. Principal Investigator, Tom W. Smith; Co-Principal Investigators, Peter V. Marsden and Michael Hout, NORC ed. Chicago: National Opinion Research Center, producer, 2005; Storrs, CT: The Roper Center for Public Opinion Research, University of Connecticut, distributor. 1 data file (57,061 logical records) and 1 codebook (3,422 pp). } \keyword{datasets} GGally/man/str.ggmatrix.Rd0000644000176200001440000000110313114357267015130 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggpairs_internal_plots.R \name{str.ggmatrix} \alias{str.ggmatrix} \title{ggmatrix structure} \usage{ \method{str}{ggmatrix}(object, ..., raw = FALSE) } \arguments{ \item{object}{ggmatrix object to be viewed} \item{...}{passed on to the default str method} \item{raw}{boolean to determine if the plots should be converted to text or kept as original objects} } \description{ View the condensed version of the ggmatrix object. The attribute "class" is ALWAYS altered to "_class" to avoid recursion. } GGally/man/lowertriangle.Rd0000644000176200001440000000154613114357267015370 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggscatmat.R \name{lowertriangle} \alias{lowertriangle} \title{lowertriangle - rearrange dataset as the preparation of ggscatmat function} \usage{ lowertriangle(data, columns = 1:ncol(data), color = NULL) } \arguments{ \item{data}{a data matrix. Should contain numerical (continuous) data.} \item{columns}{an option to choose the column to be used in the raw dataset. Defaults to \code{1:ncol(data)}} \item{color}{an option to choose a factor variable to be grouped with. Defaults to \code{(NULL)}} } \description{ function for making the melted dataset used to plot the lowertriangle scatterplots. } \examples{ data(flea) head(lowertriangle(flea, columns= 2:4)) head(lowertriangle(flea)) head(lowertriangle(flea, color="species")) } \author{ Mengjia Ni, Di Cook \email{dicook@monash.edu} } GGally/man/print.ggmatrix.Rd0000644000176200001440000000136013277311163015454 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggmatrix_print.R \name{print.ggmatrix} \alias{print.ggmatrix} \title{Print ggmatrix object} \usage{ \method{print}{ggmatrix}(x, newpage = is.null(vp), vp = NULL, ...) } \arguments{ \item{x}{plot to display} \item{newpage}{draw new (empty) page first?} \item{vp}{viewport to draw plot in} \item{...}{arguments passed onto \code{\link{ggmatrix_gtable}}} } \description{ Print method taken from \code{ggplot2:::print.ggplot} and altered for a ggmatrix object } \examples{ data(tips, package = "reshape") pMat <- ggpairs(tips, c(1,3,2), mapping = ggplot2::aes_string(color = "sex")) pMat # calls print(pMat), which calls print.ggmatrix(pMat) } \author{ Barret Schloerke } GGally/man/ggnet.Rd0000644000176200001440000002165413114357267013620 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggnet.R \name{ggnet} \alias{ggnet} \title{ggnet - Plot a network with ggplot2} \usage{ ggnet(net, mode = "fruchtermanreingold", layout.par = NULL, layout.exp = 0, size = 9, alpha = 1, weight = "none", weight.legend = NA, weight.method = weight, weight.min = NA, weight.max = NA, weight.cut = FALSE, group = NULL, group.legend = NA, node.group = group, node.color = NULL, node.alpha = alpha, segment.alpha = alpha, segment.color = "grey50", segment.label = NULL, segment.size = 0.25, arrow.size = 0, arrow.gap = 0, arrow.type = "closed", label = FALSE, label.nodes = label, label.size = size/2, label.trim = FALSE, legend.size = 9, legend.position = "right", names = c("", ""), quantize.weights = FALSE, subset.threshold = 0, top8.nodes = FALSE, trim.labels = FALSE, ...) } \arguments{ \item{net}{an object of class \code{\link[network]{network}}, or any object that can be coerced to this class, such as an adjacency or incidence matrix, or an edge list: see \link[network]{edgeset.constructors} and \link[network]{network} for details. If the object is of class \code{\link[igraph:igraph-package]{igraph}} and the \code{\link[intergraph:intergraph-package]{intergraph}} package is installed, it will be used to convert the object: see \code{\link[intergraph]{asNetwork}} for details.} \item{mode}{a placement method from those provided in the \code{\link[sna]{sna}} package: see \link[sna:gplot.layout]{gplot.layout} for details. Also accepts the names of two numeric vertex attributes of \code{net}, or a matrix of numeric coordinates, in which case the first two columns of the matrix are used. Defaults to the Fruchterman-Reingold force-directed algorithm.} \item{layout.par}{options to be passed to the placement method, as listed in \link[sna]{gplot.layout}. Defaults to \code{NULL}.} \item{layout.exp}{a multiplier to expand the horizontal axis if node labels get clipped: see \link[scales]{expand_range} for details. Defaults to \code{0} (no expansion).} \item{size}{size of the network nodes. If the nodes are weighted, their area is proportionally scaled up to the size set by \code{size}. Defaults to \code{9}.} \item{alpha}{a level of transparency for nodes, vertices and arrows. Defaults to \code{1}.} \item{weight}{the weighting method for the nodes, which might be a vertex attribute or a vector of size values. Also accepts \code{"indegree"}, \code{"outdegree"}, \code{"degree"} or \code{"freeman"} to size the nodes by their unweighted degree centrality (\code{"degree"} and \code{"freeman"} are equivalent): see \code{\link[sna]{degree}} for details. All node weights must be positive. Defaults to \code{"none"} (no weighting).} \item{weight.legend}{the name to assign to the legend created by \code{weight}. Defaults to \code{NA} (no name).} \item{weight.method}{see \code{weight}} \item{weight.min}{whether to subset the network to nodes with a minimum size, based on the values of \code{weight}. Defaults to \code{NA} (preserves all nodes).} \item{weight.max}{whether to subset the network to nodes with a maximum size, based on the values of \code{weight}. Defaults to \code{NA} (preserves all nodes).} \item{weight.cut}{whether to cut the size of the nodes into a certain number of quantiles. Accepts \code{TRUE}, which tries to cut the sizes into quartiles, or any positive numeric value, which tries to cut the sizes into that many quantiles. If the size of the nodes do not contain the specified number of distinct quantiles, the largest possible number is used. See \code{\link[stats]{quantile}} and \code{\link[base]{cut}} for details. Defaults to \code{FALSE} (does nothing).} \item{group}{the groups of the nodes, either as a vector of values or as a vertex attribute. If set to \code{mode} on a bipartite network, the nodes will be grouped as \code{"actor"} if they belong to the primary mode and \code{"event"} if they belong to the secondary mode.} \item{group.legend}{the name to assign to the legend created by \code{group}.} \item{node.group}{see \code{group}} \item{node.color}{a vector of character strings to color the nodes with, holding as many colors as there are levels in \code{node.group}. Defaults to \code{NULL}, which will assign grayscale colors to each group.} \item{node.alpha}{transparency of the nodes. Inherits from \code{alpha}.} \item{segment.alpha}{the level of transparency of the edges. Defaults to \code{alpha}, which defaults to \code{1}.} \item{segment.color}{the color of the edges, as a color value, a vector of color values, or as an edge attribute containing color values. Defaults to \code{"grey50"}.} \item{segment.label}{the labels to plot at the middle of the edges, as a single value, a vector of values, or as an edge attribute. Defaults to \code{NULL} (no edge labels).} \item{segment.size}{the size of the edges, in points, as a single numeric value, a vector of values, or as an edge attribute. Defaults to \code{0.25}.} \item{arrow.size}{the size of the arrows for directed network edges, in points. See \code{\link[grid]{arrow}} for details. Defaults to \code{0} (no arrows).} \item{arrow.gap}{a setting aimed at improving the display of edge arrows by plotting slightly shorter edges. Accepts any value between \code{0} and \code{1}, where a value of \code{0.05} will generally achieve good results when the size of the nodes is reasonably small. Defaults to \code{0} (no shortening).} \item{arrow.type}{the type of the arrows for directed network edges. See \code{\link[grid]{arrow}} for details. Defaults to \code{"closed"}.} \item{label}{whether to label the nodes. If set to \code{TRUE}, nodes are labeled with their vertex names. If set to a vector that contains as many elements as there are nodes in \code{net}, nodes are labeled with these. If set to any other vector of values, the nodes are labeled only when their vertex name matches one of these values. Defaults to \code{FALSE} (no labels).} \item{label.nodes}{see \code{label}} \item{label.size}{the size of the node labels, in points, as a numeric value, a vector of numeric values, or as a vertex attribute containing numeric values. Defaults to \code{size / 2} (half the maximum node size), which defaults to \code{6}.} \item{label.trim}{whether to apply some trimming to the node labels. Accepts any function that can process a character vector, or a strictly positive numeric value, in which case the labels are trimmed to a fixed-length substring of that length: see \code{\link[base]{substr}} for details. Defaults to \code{FALSE} (does nothing).} \item{legend.size}{the size of the legend symbols and text, in points. Defaults to \code{9}.} \item{legend.position}{the location of the plot legend(s). Accepts all \code{legend.position} values supported by \code{\link[ggplot2]{theme}}. Defaults to \code{"right"}.} \item{names}{deprecated: see \code{group.legend} and \code{size.legend}} \item{quantize.weights}{deprecated: see \code{weight.cut}} \item{subset.threshold}{deprecated: see \code{weight.min}} \item{top8.nodes}{deprecated: this functionality was experimental and has been removed entirely from \code{ggnet}} \item{trim.labels}{deprecated: see \code{label.trim}} \item{...}{other arguments passed to the \code{geom_text} object that sets the node labels: see \code{\link[ggplot2]{geom_text}} for details.} } \description{ Function for plotting network objects using ggplot2, now replaced by the \code{\link{ggnet2}} function, which provides additional control over plotting parameters. Please visit \url{http://github.com/briatte/ggnet} for the latest version of ggnet2, and \url{https://briatte.github.io/ggnet} for a vignette that contains many examples and explanations. } \details{ The degree centrality measures that can be produced through the \code{weight} argument will take the directedness of the network into account, but will be unweighted. To compute weighted network measures, see the \code{tnet} package by Tore Opsahl (\code{help("tnet", package = "tnet")}). } \examples{ library(network) # random adjacency matrix x <- 10 ndyads <- x * (x - 1) density <- x / ndyads m <- matrix(0, nrow = x, ncol = x) dimnames(m) <- list(letters[ 1:x ], letters[ 1:x ]) m[ row(m) != col(m) ] <- runif(ndyads) < density m # random undirected network n <- network::network(m, directed = FALSE) n ggnet(n, label = TRUE, alpha = 1, color = "white", segment.color = "black") # random groups g <- sample(letters[ 1:3 ], 10, replace = TRUE) # color palette p <- c("a" = "steelblue", "b" = "forestgreen", "c" = "tomato") ggnet(n, node.group = g, node.color = p, label = TRUE, color = "white") # edge arrows on a directed network ggnet(network(m, directed = TRUE), arrow.gap = 0.05, arrow.size = 10) } \seealso{ \code{\link{ggnet2}} in this package, \code{\link[sna]{gplot}} in the \code{\link[sna]{sna}} package, and \code{\link[network]{plot.network}} in the \code{\link[network]{network}} package } \author{ Moritz Marbach and Francois Briatte, with help from Heike Hoffmann, Pedro Jordano and Ming-Yu Liu } GGally/man/ggally_denstrip.Rd0000644000176200001440000000151713114357267015677 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_denstrip} \alias{ggally_denstrip} \title{Plots a tile plot with facets} \usage{ ggally_denstrip(data, mapping, ...) } \arguments{ \item{data}{data set using} \item{mapping}{aesthetics being used} \item{...}{other arguments being sent to stat_bin} } \description{ Make Tile Plot as densely as possible. } \examples{ data(tips, package = "reshape") ggally_denstrip(tips, mapping = ggplot2::aes(x = total_bill, y = sex)) ggally_denstrip(tips, mapping = ggplot2::aes_string(x = "total_bill", y = "sex")) ggally_denstrip( tips, mapping = ggplot2::aes_string(x = "sex", y = "tip", binwidth = "0.2") ) + ggplot2::scale_fill_gradient(low = "grey80", high = "black") } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/ggally_density.Rd0000644000176200001440000000212613114357267015523 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_density} \alias{ggally_density} \title{Plots the Scatter Density Plot} \usage{ ggally_density(data, mapping, ...) } \arguments{ \item{data}{data set using} \item{mapping}{aesthetics being used} \item{...}{parameters sent to either stat_density2d or geom_density2d} } \description{ Make a scatter density plot from a given data. } \details{ The aesthetic "fill" determines whether or not stat_density2d (filled) or geom_density2d (lines) is used. } \examples{ data(tips, package = "reshape") ggally_density(tips, mapping = ggplot2::aes(x = total_bill, y = tip)) ggally_density(tips, mapping = ggplot2::aes_string(x = "total_bill", y = "tip")) ggally_density( tips, mapping = ggplot2::aes_string(x = "total_bill", y = "tip", fill = "..level..") ) ggally_density( tips, mapping = ggplot2::aes_string(x = "total_bill", y = "tip", fill = "..level..") ) + ggplot2::scale_fill_gradient(breaks = c(0.05, 0.1, 0.15, 0.2)) } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/model_terms.Rd0000644000176200001440000000154713114364223015013 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggnostic.R \name{model_response_variables} \alias{model_response_variables} \alias{model_beta_variables} \alias{model_beta_label} \title{Model term names} \usage{ model_response_variables(model, data = broom::augment(model)) model_beta_variables(model, data = broom::augment(model)) model_beta_label(model, data = broom::augment(model), lmStars = TRUE) } \arguments{ \item{model}{model in question} \item{data}{equivalent to \code{broom::augment(model)}} \item{lmStars}{boolean that determines if stars are added to labels} } \value{ character vector of names } \description{ Retrieve either the response variable names, the beta variable names, or beta variable names. If the model is an object of class 'lm', by default, the beta variable names will include anova significance stars. } GGally/man/brew_colors.Rd0000644000176200001440000000046413114357267015030 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggnostic.R \name{brew_colors} \alias{brew_colors} \title{RColorBrewer Set1 colors} \usage{ brew_colors(col) } \arguments{ \item{col}{standard color name used to retrieve hex color value} } \description{ RColorBrewer Set1 colors } GGally/man/gg-add.Rd0000644000176200001440000000226713164043077013632 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggpairs_add.R \name{+.gg} \alias{+.gg} \title{Modify a ggmatrix object by adding an ggplot2 object to all plots} \usage{ \method{+}{gg}(e1, e2) } \arguments{ \item{e1}{An object of class \code{ggplot} or \code{theme}} \item{e2}{A component to add to \code{e1}} } \description{ This operator allows you to add ggplot2 objects to a ggmatrix object. } \details{ If the first object is an object of class \code{ggmatrix}, you can add the following types of objects, and it will return a modified ggplot object. \itemize{ \item \code{theme}: update plot theme } The \code{+} operator completely replaces elements with elements from e2. } \examples{ data(tips, package = "reshape") pm <- ggpairs(tips[, 2:3]) ## change to black and white theme pm + ggplot2::theme_bw() ## change to linedraw theme # pm + ggplot2::theme_linedraw() ## change to custom theme # pm + ggplot2::theme(panel.background = ggplot2::element_rect(fill = "lightblue")) ## add a list of information extra <- list(ggplot2::theme_bw(), ggplot2::labs(caption = "My caption!")) pm + extra } \seealso{ \code{\link[ggplot2]{+.gg}} and \code{\link[ggplot2]{theme}} } GGally/man/ggally_nostic_se_fit.Rd0000644000176200001440000000243513114357267016677 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggnostic.R \name{ggally_nostic_se_fit} \alias{ggally_nostic_se_fit} \title{ggnostic - fitted value standard error} \usage{ ggally_nostic_se_fit(data, mapping, ..., lineColor = brew_colors("grey"), linePosition = NULL) } \arguments{ \item{data, mapping, ..., lineColor}{parameters supplied to \code{\link{ggally_nostic_line}}} \item{linePosition}{base comparison for a perfect fit} } \value{ ggplot2 plot object } \description{ A function to display \code{stats::\link[stats]{predict}}'s standard errors } \details{ As stated in \code{stats::\link[stats]{predict}} documentation: If the logical 'se.fit' is 'TRUE', standard errors of the predictions are calculated. If the numeric argument 'scale' is set (with optional ''df'), it is used as the residual standard deviation in the computation of the standard errors, otherwise this is extracted from the model fit. Since the se.fit is \code{TRUE} and scale is unset by default, the standard errors are extracted from the model fit. A base line of 0 is added to give reference to a perfect fit. } \examples{ dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars)) ggally_nostic_se_fit(dt, ggplot2::aes(wt, .se.fit)) } \seealso{ \code{stats::\link[stats]{influence}} } GGally/man/ggally_nostic_line.Rd0000644000176200001440000000254213114364223016342 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggnostic.R \name{ggally_nostic_line} \alias{ggally_nostic_line} \title{ggnostic -background line with geom} \usage{ ggally_nostic_line(data, mapping, ..., linePosition = NULL, lineColor = "red", lineSize = 0.5, lineAlpha = 1, lineType = 1, continuous_geom = ggplot2::geom_point, combo_geom = ggplot2::geom_boxplot, mapColorToFill = TRUE) } \arguments{ \item{data, mapping}{supplied directly to \code{ggplot2::\link[ggplot2]{ggplot}(data, mapping)}} \item{...}{parameters supplied to \code{continuous_geom} or \code{combo_geom}} \item{linePosition, lineColor, lineSize, lineAlpha, lineType}{parameters supplied to \code{ggplot2::\link[ggplot2]{geom_line}}} \item{continuous_geom}{ggplot2 geom that is executed after the line is (possibly) added and if the x data is continuous} \item{combo_geom}{ggplot2 geom that is executed after the line is (possibly) added and if the x data is discrete} \item{mapColorToFill}{boolean to determine if combo plots should cut the color mapping to the fill mapping} } \value{ ggplot2 plot object } \description{ If a non-null \code{linePosition} value is given, a line will be drawn before the given \code{continuous_geom} or \code{combo_geom} is added to the plot. } \details{ Functions with a color in their name have different default color behavior. } GGally/man/ggally_facetbar.Rd0000644000176200001440000000132613114364223015602 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_facetbar} \alias{ggally_facetbar} \title{Plots the Bar Plots Faceted by Conditional Variable} \usage{ ggally_facetbar(data, mapping, ...) } \arguments{ \item{data}{data set using} \item{mapping}{aesthetics being used} \item{...}{other arguments are sent to geom_bar} } \description{ X variables are plotted using \code{geom_bar} and faceted by the Y variable. } \examples{ data(tips, package = "reshape") ggally_facetbar(tips, ggplot2::aes(x = sex, y = smoker, fill = time)) ggally_facetbar(tips, ggplot2::aes(x = smoker, y = sex, fill = time)) } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/fn_switch.Rd0000644000176200001440000000232313114364223014456 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggnostic.R \name{fn_switch} \alias{fn_switch} \title{Function switch} \usage{ fn_switch(types, mapping_val = "y") } \arguments{ \item{types}{list of functions that follow the ggmatrix function standard: \code{function(data, mapping, ...){ #make ggplot2 object }}. One key should be a 'default' key for a default switch case.} \item{mapping_val}{mapping value to switch on. Defaults to the 'y' variable of the aesthetics list.} } \description{ Function that allows you to call different functions based upon an aesthetic variable value. } \examples{ ggnostic_continuous_fn <- fn_switch(list( default = ggally_points, .fitted = ggally_points, .se.fit = ggally_nostic_se_fit, .resid = ggally_nostic_resid, .hat = ggally_nostic_hat, .sigma = ggally_nostic_sigma, .cooksd = ggally_nostic_cooksd, .std.resid = ggally_nostic_std_resid )) ggnostic_combo_fn <- fn_switch(list( default = ggally_box_no_facet, fitted = ggally_box_no_facet, .se.fit = ggally_nostic_se_fit, .resid = ggally_nostic_resid, .hat = ggally_nostic_hat, .sigma = ggally_nostic_sigma, .cooksd = ggally_nostic_cooksd, .std.resid = ggally_nostic_std_resid )) } GGally/man/ggally_dot_and_box.Rd0000644000176200001440000000160113114357267016321 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_dot_and_box} \alias{ggally_dot_and_box} \title{Plots either Box Plot or Dot Plots} \usage{ ggally_dot_and_box(data, mapping, ..., boxPlot = TRUE) } \arguments{ \item{data}{data set using} \item{mapping}{aesthetics being used} \item{...}{parameters passed to either geom_jitter or geom_boxplot} \item{boxPlot}{boolean to decide to plot either box plots (TRUE) or dot plots (FALSE)} } \description{ Place box plots or dot plots on the graph } \examples{ data(tips, package = "reshape") ggally_dot_and_box( tips, mapping = ggplot2::aes(x = total_bill, y = sex, color = sex), boxPlot = TRUE ) ggally_dot_and_box( tips, mapping = ggplot2::aes(x = total_bill, y = sex, color = sex), boxPlot = FALSE ) } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/ggally_nostic_cooksd.Rd0000644000176200001440000000224713277311163016704 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggnostic.R \name{ggally_nostic_cooksd} \alias{ggally_nostic_cooksd} \title{ggnostic - Cook's distance} \usage{ ggally_nostic_cooksd(data, mapping, ..., linePosition = pf(0.5, length(attr(data, "var_x")), nrow(data) - length(attr(data, "var_x"))), lineColor = brew_colors("grey"), lineType = 2) } \arguments{ \item{data, mapping, ..., lineColor, lineType}{parameters supplied to \code{\link{ggally_nostic_line}}} \item{linePosition}{4 / n is the general cutoff point for Cook's Distance} } \value{ ggplot2 plot object } \description{ A function to display \code{stats::\link[stats]{cooks.distance}}. } \details{ A line is added at F_{p, n - p}(0.5) to display the general cutoff point for Cook's Distance. Reference: Michael H. Kutner, Christopher J. Nachtsheim, John Neter, and William Li. Applied linear statistical models. The McGraw-Hill / Irwin series operations and decision sciences. McGraw-Hill Irwin, 2005, p. 403 } \examples{ dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars)) ggally_nostic_cooksd(dt, ggplot2::aes(wt, .cooksd)) } \seealso{ \code{stats::\link[stats]{cooks.distance}} } GGally/man/print_if_interactive.Rd0000644000176200001440000000053413114357267016715 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{print_if_interactive} \alias{print_if_interactive} \title{Print if not CRAN} \usage{ print_if_interactive(p) } \arguments{ \item{p}{plot to be displayed} } \description{ Small function to print a plot if the R session is interactive or in a travis build } GGally/man/glyphplot.Rd0000644000176200001440000000214513114357267014530 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gglyph.R \name{glyphplot} \alias{glyphplot} \alias{is.glyphplot} \alias{[.glyphplot} \alias{print.glyphplot} \title{Glyph plot class} \usage{ glyphplot(data, width, height, polar, x_major, y_major) is.glyphplot(x) \method{[}{glyphplot}(x, ...) \method{print}{glyphplot}(x, ...) } \arguments{ \item{data}{A data frame containing variables named in \code{x_major}, \code{x_minor}, \code{y_major} and \code{y_minor}.} \item{height, width}{The height and width of each glyph. Defaults to 95\% of the \code{\link[ggplot2]{resolution}} of the data. Specify the width absolutely by supplying a numeric vector of length 1, or relative to the} \item{polar}{A logical of length 1, specifying whether the glyphs should be drawn in polar coordinates. Defaults to \code{FALSE}.} \item{x_major, y_major}{The name of the variable (as a string) for the major x and y axes. Together, the} \item{x}{glyphplot to be printed} \item{...}{ignored} } \description{ Glyph plot class } \author{ Di Cook \email{dicook@monash.edu}, Heike Hofmann, Hadley Wickham } GGally/man/skewness.Rd0000644000176200001440000000061313114357267014346 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggparcoord.R \name{skewness} \alias{skewness} \title{Sample skewness} \usage{ skewness(x) } \arguments{ \item{x}{numeric vector} } \value{ sample skewness of \code{x} } \description{ Calculate the sample skewness of a vector while ignoring missing values. } \author{ Jason Crowley \email{crowley.jason.s@gmail.com} } GGally/man/eval_data_col.Rd0000644000176200001440000000101213276725426015260 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{eval_data_col} \alias{eval_data_col} \title{Evaluate data column} \usage{ eval_data_col(data, aes_col) } \arguments{ \item{data}{data set to evaluate the data with} \item{aes_col}{Single value from an \code{ggplot2::\link[ggplot2]{aes}(...)} object} } \value{ Aes mapping with the x and y values switched } \description{ Evaluate data column } \examples{ mapping <- ggplot2::aes(Petal.Length) eval_data_col(iris, mapping$x) } GGally/man/psychademic.Rd0000644000176200001440000000174213114364223014767 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data-psychademic.R \docType{data} \name{psychademic} \alias{psychademic} \title{UCLA canonical correlation analysis data} \format{A data frame with 600 rows and 8 variables} \usage{ data(psychademic) } \description{ This data contains 600 observations on eight variables } \details{ \itemize{ \item locus_of_control - psychological \item self_concept - psychological \item motivation - psychological. Converted to four character groups \item read - academic \item write - academic \item math - academic \item science - academic \item female - academic. Dropped from original source \item sex - academic. Added as a character version of female column } } \references{ R Data Analysis Examples | Canonical Correlation Analysis. UCLA: Institute for Digital Research and Education. from http://www.stats.idre.ucla.edu/r/dae/canonical-correlation-analysis (accessed May 22, 2017). } \keyword{datasets} GGally/man/scatmat.Rd0000644000176200001440000000171013114357267014137 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggscatmat.R \name{scatmat} \alias{scatmat} \title{scatmat - plot the lowertriangle plots and density plots of the scatter plot matrix.} \usage{ scatmat(data, columns = 1:ncol(data), color = NULL, alpha = 1) } \arguments{ \item{data}{a data matrix. Should contain numerical (continuous) data.} \item{columns}{an option to choose the column to be used in the raw dataset. Defaults to \code{1:ncol(data)}} \item{color}{an option to group the dataset by the factor variable and color them by different colors. Defaults to \code{NULL}} \item{alpha}{an option to set the transparency in scatterplots for large data. Defaults to \code{1}.} } \description{ function for making scatterplots in the lower triangle and diagonal density plots. } \examples{ data(flea) scatmat(flea, columns=2:4) scatmat(flea, columns= 2:4, color="species") } \author{ Mengjia Ni, Di Cook \email{dicook@monash.edu} } GGally/man/ggally_points.Rd0000644000176200001440000000142413114357267015360 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_points} \alias{ggally_points} \title{Plots the Scatter Plot} \usage{ ggally_points(data, mapping, ...) } \arguments{ \item{data}{data set using} \item{mapping}{aesthetics being used} \item{...}{other arguments are sent to geom_point} } \description{ Make a scatter plot with a given data set. } \examples{ data(mtcars) ggally_points(mtcars, mapping = ggplot2::aes(x = disp, y = hp)) ggally_points(mtcars, mapping = ggplot2::aes_string(x = "disp", y = "hp")) ggally_points( mtcars, mapping = ggplot2::aes_string( x = "disp", y = "hp", color = "as.factor(cyl)", size = "gear" ) ) } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/ggally_nostic_std_resid.Rd0000644000176200001440000000153413114357267017405 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggnostic.R \name{ggally_nostic_std_resid} \alias{ggally_nostic_std_resid} \title{ggnostic - standardized residuals} \usage{ ggally_nostic_std_resid(data, mapping, ..., sigma = 1) } \arguments{ \item{data, mapping, ...}{parameters supplied to \code{\link{ggally_nostic_resid}}} \item{sigma}{sigma value for the \code{pVal} percentiles. Set to 1 for standardized residuals} } \value{ ggplot2 plot object } \description{ If non-null \code{pVal} and \code{sigma} values are given, confidence interval lines will be added to the plot at the specified \code{pVal} locations of a N(0, 1) distribution. } \examples{ dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars)) ggally_nostic_std_resid(dt, ggplot2::aes(wt, .std.resid)) } \seealso{ \code{stats::\link[stats]{rstandard}} } GGally/man/ggally_nostic_sigma.Rd0000644000176200001440000000233413114357267016524 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggnostic.R \name{ggally_nostic_sigma} \alias{ggally_nostic_sigma} \title{ggnostic - leave one out model sigma} \usage{ ggally_nostic_sigma(data, mapping, ..., lineColor = brew_colors("grey"), linePosition = attr(data, "broom_glance")$sigma) } \arguments{ \item{data, mapping, ..., lineColor}{parameters supplied to \code{\link{ggally_nostic_line}}} \item{linePosition}{line that is drawn in the background of the plot. Defaults to the overall model's sigma value.} } \value{ ggplot2 plot object } \description{ A function to display \code{stats::\link[stats]{influence}}'s sigma value. } \details{ As stated in \code{stats::\link[stats]{influence}} documentation: sigma: a vector whose i-th element contains the estimate of the residual standard deviation obtained when the i-th case is dropped from the regression. (The approximations needed for GLMs can result in this being 'NaN'.) A line is added to display the overall model's sigma value. This gives a baseline for comparison } \examples{ dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars)) ggally_nostic_sigma(dt, ggplot2::aes(wt, .sigma)) } \seealso{ \code{stats::\link[stats]{influence}} } GGally/man/mapping_string.Rd0000644000176200001440000000062313276725426015533 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{mapping_string} \alias{mapping_string} \title{Aes name} \usage{ mapping_string(aes_col) } \arguments{ \item{aes_col}{Single value from \code{ggplot2::\link[ggplot2]{aes}(...)}} } \value{ character string } \description{ Aes name } \examples{ mapping <- ggplot2::aes(Petal.Length) mapping_string(mapping$x) } GGally/man/ggally_barDiag.Rd0000644000176200001440000000141213114364223015360 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_barDiag} \alias{ggally_barDiag} \title{Plots the Bar Plots by Using Diagonal} \usage{ ggally_barDiag(data, mapping, ..., rescale = FALSE) } \arguments{ \item{data}{data set using} \item{mapping}{aesthetics being used} \item{...}{other arguments are sent to geom_bar} \item{rescale}{boolean to decide whether or not to rescale the count output. Only applies to numeric data} } \description{ Plots the bar plots by using Diagonal. } \examples{ data(tips, package = "reshape") ggally_barDiag(tips, mapping = ggplot2::aes(x = day)) ggally_barDiag(tips, mapping = ggplot2::aes(x = tip), binwidth = 0.25) } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/ggally_facethist.Rd0000644000176200001440000000131213114357267016012 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_facethist} \alias{ggally_facethist} \title{Plots the Histograms by Faceting} \usage{ ggally_facethist(data, mapping, ...) } \arguments{ \item{data}{data set using} \item{mapping}{aesthetics being used} \item{...}{parameters sent to stat_bin()} } \description{ Make histograms by displaying subsets of the data in different panels. } \examples{ data(tips, package = "reshape") ggally_facethist(tips, mapping = ggplot2::aes(x = tip, y = sex)) ggally_facethist(tips, mapping = ggplot2::aes_string(x = "tip", y = "sex"), binwidth = 0.1) } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/find_plot_type.Rd0000644000176200001440000000110713114357267015522 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/find-combo.R \name{find_plot_type} \alias{find_plot_type} \title{Find Plot Types} \usage{ find_plot_type(col1Name, col2Name, type1, type2, isAllNa, allowDiag) } \arguments{ \item{col1Name}{x column name} \item{col2Name}{y column name} \item{type1}{x column type} \item{type2}{y column type} \item{isAllNa}{is.na(data)} \item{allowDiag}{allow for diag values to be returned} } \description{ Retrieves the type of plot for the specific columns } \author{ Barret Schloerke \email{schloerke@gmail.com} } GGally/man/ggscatmat.Rd0000644000176200001440000000216713114357267014464 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggscatmat.R \name{ggscatmat} \alias{ggscatmat} \title{ggscatmat - a traditional scatterplot matrix for purely quantitative variables} \usage{ ggscatmat(data, columns = 1:ncol(data), color = NULL, alpha = 1, corMethod = "pearson") } \arguments{ \item{data}{a data matrix. Should contain numerical (continuous) data.} \item{columns}{an option to choose the column to be used in the raw dataset. Defaults to \code{1:ncol(data)}.} \item{color}{an option to group the dataset by the factor variable and color them by different colors. Defaults to \code{NULL}.} \item{alpha}{an option to set the transparency in scatterplots for large data. Defaults to \code{1}.} \item{corMethod}{method argument supplied to \code{\link[stats]{cor}}} } \description{ This function makes a scatterplot matrix for quantitative variables with density plots on the diagonal and correlation printed in the upper triangle. } \examples{ data(flea) ggscatmat(flea, columns = 2:4) ggscatmat(flea, columns = 2:4, color = "species") } \author{ Mengjia Ni, Di Cook \email{dicook@monash.edu} } GGally/man/australia_PISA2012.Rd0000644000176200001440000000504313114357267015614 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data-australia-pisa-2012.R \docType{data} \name{australia_PISA2012} \alias{australia_PISA2012} \title{Programme for International Student Assesment (PISA) 2012 Data for Australia} \format{A data frame with 8247 rows and 32 variables} \source{ \url{http://www.oecd.org/pisa/pisaproducts/database-cbapisa2012.htm} } \usage{ data(australia_PISA2012) } \description{ About PISA } \details{ The Programme for International Student Assessment (PISA) is a triennial international survey which aims to evaluate education systems worldwide by testing the skills and knowledge of 15-year-old students. To date, students representing more than 70 economies have participated in the assessment. While 65 economies took part in the 2012 study, this data set only contains information from the country of Australia. \itemize{ \item gender : Factor w/ 2 levels "female","male": 1 1 2 2 2 1 1 1 2 1 ... \item age : Factor w/ 4 levels "4","5","6","7": 2 2 2 4 3 1 2 2 2 2 ... \item homework : num 5 5 9 3 2 3 4 3 5 1 ... \item desk : num 1 0 1 1 1 1 1 1 1 1 ... \item room : num 1 1 1 1 1 1 1 1 1 1 ... \item study : num 1 1 1 1 1 1 1 1 1 1 ... \item computer : num 1 1 1 1 1 1 1 1 1 1 ... \item software : num 1 1 1 1 1 1 1 1 1 1 ... \item internet : num 1 1 1 1 1 1 1 1 1 1 ... \item literature : num 0 0 1 0 1 1 1 1 1 0 ... \item poetry : num 0 0 1 0 1 1 0 1 1 1 ... \item art : num 1 0 1 0 1 1 0 1 1 1 ... \item textbook : num 1 1 1 1 1 0 1 1 1 1 ... \item dictionary : num 1 1 1 1 1 1 1 1 1 1 ... \item dishwasher : num 1 1 1 1 0 1 1 1 1 1 ... \item PV1MATH : num 562 565 602 520 613 ... \item PV2MATH : num 569 557 594 507 567 ... \item PV3MATH : num 555 553 552 501 585 ... \item PV4MATH : num 579 538 526 521 596 ... \item PV5MATH : num 548 573 619 547 603 ... \item PV1READ : num 582 617 650 554 605 ... \item PV2READ : num 571 572 608 560 557 ... \item PV3READ : num 602 560 594 517 627 ... \item PV4READ : num 572 564 575 564 597 ... \item PV5READ : num 585 565 620 572 598 ... \item PV1SCIE : num 583 627 668 574 639 ... \item PV2SCIE : num 579 600 665 612 635 ... \item PV3SCIE : num 593 574 620 571 666 ... \item PV4SCIE : num 567 582 592 598 700 ... \item PV5SCIE : num 587 625 656 662 670 ... \item SENWGT_STU : num 0.133 0.133 0.141 0.141 0.141 ... \item possessions: num 10 8 12 9 11 11 10 12 12 11 ... } } \keyword{datasets} GGally/man/ggnetworkmap.Rd0000644000176200001440000001665413277311163015220 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggnetworkmap.R \name{ggnetworkmap} \alias{ggnetworkmap} \title{ggnetworkmap - Plot a network with ggplot2 suitable for overlay on a ggmap:: map ggplot, or other ggplot} \usage{ ggnetworkmap(gg, net, size = 3, alpha = 0.75, weight, node.group, node.color = NULL, node.alpha = NULL, ring.group, segment.alpha = NULL, segment.color = "grey", great.circles = FALSE, segment.size = 0.25, arrow.size = 0, label.nodes = FALSE, label.size = size/2, ...) } \arguments{ \item{gg}{an object of class \code{ggplot}.} \item{net}{an object of class \code{\link[network]{network}}, or any object that can be coerced to this class, such as an adjacency or incidence matrix, or an edge list: see \link[network]{edgeset.constructors} and \link[network]{network} for details. If the object is of class \code{\link[igraph:igraph-package]{igraph}} and the \code{\link[intergraph:intergraph-package]{intergraph}} package is installed, it will be used to convert the object: see \code{\link[intergraph]{asNetwork}} for details.} \item{size}{size of the network nodes. Defaults to 3. If the nodes are weighted, their area is proportionally scaled up to the size set by \code{size}.} \item{alpha}{a level of transparency for nodes, vertices and arrows. Defaults to 0.75.} \item{weight}{if present, the unquoted name of a vertex attribute in \code{data}. Otherwise nodes are unweighted.} \item{node.group}{\code{NULL}, the default, or the unquoted name of a vertex attribute that will be used to determine the color of each node.} \item{node.color}{If \code{node.group} is null, a character string specifying a color.} \item{node.alpha}{transparency of the nodes. Inherits from \code{alpha}.} \item{ring.group}{if not \code{NULL}, the default, the unquoted name of a vertex attribute that will be used to determine the color of each node border.} \item{segment.alpha}{transparency of the vertex links. Inherits from \code{alpha}} \item{segment.color}{color of the vertex links. Defaults to \code{"grey"}.} \item{great.circles}{whether to draw edges as great circles using the \code{geosphere} package. Defaults to \code{FALSE}} \item{segment.size}{size of the vertex links, as a vector of values or as a single value. Defaults to 0.25.} \item{arrow.size}{size of the vertex arrows for directed network plotting, in centimeters. Defaults to 0.} \item{label.nodes}{label nodes with their vertex names attribute. If set to \code{TRUE}, all nodes are labelled. Also accepts a vector of character strings to match with vertex names.} \item{label.size}{size of the labels. Defaults to \code{size / 2}.} \item{...}{other arguments supplied to geom_text for the node labels. Arguments pertaining to the title or other items can be achieved through ggplot2 methods.} } \description{ This is a descendent of the original \code{ggnet} function. \code{ggnet} added the innovation of plotting the network geographically. However, \code{ggnet} needed to be the first object in the ggplot chain. \code{ggnetworkmap} does not. If passed a \code{ggplot} object as its first argument, such as output from \code{ggmap}, \code{ggnetworkmap} will plot on top of that chart, looking for vertex attributes \code{lon} and \code{lat} as coordinates. Otherwise, \code{ggnetworkmap} will generate coordinates using the Fruchterman-Reingold algorithm. } \details{ This is a function for plotting graphs generated by \code{network} or \code{igraph} in a more flexible and elegant manner than permitted by ggnet. The function does not need to be the first plot in the ggplot chain, so the graph can be plotted on top of a map or other chart. Segments can be straight lines, or plotted as great circles. Note that the great circles feature can produce odd results with arrows and with vertices beyond the plot edges; this is a ggplot2 limitation and cannot yet be fixed. Nodes can have two color schemes, which are then plotted as the center and ring around the node. The color schemes are selected by adding scale_fill_ or scale_color_ just like any other ggplot2 plot. If there are no rings, scale_color sets the color of the nodes. If there are rings, scale_color sets the color of the rings, and scale_fill sets the color of the centers. Note that additional arguments in the ... are passed to geom_text for plotting labels. } \examples{ # small function to display plots only if it's interactive p_ <- GGally::print_if_interactive invisible(lapply(c("ggplot2", "maps", "network", "sna"), base::library, character.only = TRUE)) ## Example showing great circles on a simple map of the USA ## http://flowingdata.com/2011/05/11/how-to-map-connections-with-great-circles/ \donttest{ airports <- read.csv("http://datasets.flowingdata.com/tuts/maparcs/airports.csv", header = TRUE) rownames(airports) <- airports$iata # select some random flights set.seed(1234) flights <- data.frame( origin = sample(airports[200:400, ]$iata, 200, replace = TRUE), destination = sample(airports[200:400, ]$iata, 200, replace = TRUE) ) # convert to network flights <- network(flights, directed = TRUE) # add geographic coordinates flights \%v\% "lat" <- airports[ network.vertex.names(flights), "lat" ] flights \%v\% "lon" <- airports[ network.vertex.names(flights), "long" ] # drop isolated airports delete.vertices(flights, which(degree(flights) < 2)) # compute degree centrality flights \%v\% "degree" <- degree(flights, gmode = "digraph") # add random groups flights \%v\% "mygroup" <- sample(letters[1:4], network.size(flights), replace = TRUE) # create a map of the USA usa <- ggplot(map_data("usa"), aes(x = long, y = lat)) + geom_polygon(aes(group = group), color = "grey65", fill = "#f9f9f9", size = 0.2) # overlay network data to map p <- ggnetworkmap( usa, flights, size = 4, great.circles = TRUE, node.group = mygroup, segment.color = "steelblue", ring.group = degree, weight = degree ) p_(p) ## Exploring a community of spambots found on Twitter ## Data by Amos Elberg: see ?twitter_spambots for details data(twitter_spambots) # create a world map world <- fortify(map("world", plot = FALSE, fill = TRUE)) world <- ggplot(world, aes(x = long, y = lat)) + geom_polygon(aes(group = group), color = "grey65", fill = "#f9f9f9", size = 0.2) # view global structure p <- ggnetworkmap(world, twitter_spambots) p_(p) # domestic distribution p <- ggnetworkmap(net = twitter_spambots) p_(p) # topology p <- ggnetworkmap(net = twitter_spambots, arrow.size = 0.5) p_(p) # compute indegree and outdegree centrality twitter_spambots \%v\% "indegree" <- degree(twitter_spambots, cmode = "indegree") twitter_spambots \%v\% "outdegree" <- degree(twitter_spambots, cmode = "outdegree") p <- ggnetworkmap( net = twitter_spambots, arrow.size = 0.5, node.group = indegree, ring.group = outdegree, size = 4 ) + scale_fill_continuous("Indegree", high = "red", low = "yellow") + labs(color = "Outdegree") p_(p) # show some vertex attributes associated with each account p <- ggnetworkmap( net = twitter_spambots, arrow.size = 0.5, node.group = followers, ring.group = friends, size = 4, weight = indegree, label.nodes = TRUE, vjust = -1.5 ) + scale_fill_continuous("Followers", high = "red", low = "yellow") + labs(color = "Friends") + scale_color_continuous(low = "lightgreen", high = "darkgreen") p_(p) } } \author{ Amos Elberg \email{amos.elberg@gmail.com}. Original by Moritz Marbach \email{mmarbach@mail.uni-mannheim.de}, Francois Briatte \email{f.briatte@gmail.com} } GGally/man/ggcoef.Rd0000644000176200001440000000456413277311163013742 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggcoef.R \name{ggcoef} \alias{ggcoef} \title{ggcoef - Plot Model Coefficients with broom and ggplot2} \usage{ ggcoef(x, mapping = aes_string(y = "term", x = "estimate"), conf.int = TRUE, conf.level = 0.95, exponentiate = FALSE, exclude_intercept = FALSE, vline = TRUE, vline_intercept = "auto", vline_color = "gray50", vline_linetype = "dotted", vline_size = 1, errorbar_color = "gray25", errorbar_height = 0, errorbar_linetype = "solid", errorbar_size = 0.5, sort = c("none", "ascending", "decending"), ...) } \arguments{ \item{x}{a model object to be tidied with \code{\link[broom]{tidy}} or a data frame (see Details)} \item{mapping}{default aesthetic mapping} \item{conf.int}{display confidence intervals as error bars?} \item{conf.level}{level of confidence intervals (passed to \code{\link[broom]{tidy}} if \code{x} is not a data frame)} \item{exponentiate}{if \code{TRUE}, x-axis will be logarithmic (also passed to \code{\link[broom]{tidy}} if \code{x} is not a data frame)} \item{exclude_intercept}{should the intercept be excluded from the plot?} \item{vline}{print a vertical line?} \item{vline_intercept}{\code{xintercept} for the vertical line. \code{"auto"} for \code{x = 0} (or \code{x = 1} if {exponentiate} is \code{TRUE})} \item{vline_color}{color of the vertical line} \item{vline_linetype}{line type of the vertical line} \item{vline_size}{size of the vertical line} \item{errorbar_color}{color of the error bars} \item{errorbar_height}{height of the error bars} \item{errorbar_linetype}{line type of the error bars} \item{errorbar_size}{size of the error bars} \item{sort}{\code{"none"} (default) do not sort, \code{"ascending"} sort by increasing coefficient value, or \code{"decending"} sort by decreasing coefficient value} \item{...}{additional arguments sent to \code{\link[ggplot2]{geom_point}}} } \description{ Plot the coefficients of a model with \pkg{broom} and \pkg{ggplot2}. } \examples{ library(broom) reg <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width, data = iris) ggcoef(reg) \donttest{d <- as.data.frame(Titanic) reg2 <- glm(Survived ~ Sex + Age + Class, family = binomial, data = d, weights = d$Freq) ggcoef(reg2, exponentiate = TRUE) ggcoef( reg2, exponentiate = TRUE, exclude_intercept = TRUE, errorbar_height = .2, color = "blue", sort = "ascending" )} } GGally/man/mapping_swap_x_y.Rd0000644000176200001440000000073213276725426016057 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{mapping_swap_x_y} \alias{mapping_swap_x_y} \title{Swap x and y mapping} \usage{ mapping_swap_x_y(mapping) } \arguments{ \item{mapping}{output of \code{ggplot2::\link[ggplot2]{aes}(...)}} } \value{ Aes mapping with the x and y values switched } \description{ Swap x and y mapping } \examples{ mapping <- ggplot2::aes(Petal.Length, Sepal.Width) mapping mapping_swap_x_y(mapping) } GGally/man/pigs.Rd0000644000176200001440000000224513114357267013451 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data-pigs.R \docType{data} \name{pigs} \alias{pigs} \title{United Kingdom Pig Production} \format{A data frame with 48 rows and 8 variables} \usage{ data(pigs) } \description{ This data contains about the United Kingdom Pig Production from the book 'Data' by Andrews and Herzberg. The original data can be on Statlib: http://lib.stat.cmu.edu/datasets/Andrews/T62.1 } \details{ The time variable has been added from a combination of year and quarter \itemize{ \item time year + (quarter - 1) / 4 \item year year of production \item quarter quarter of the year of production \item gilts number of sows giving birth for the first time \item profit ratio of price to an index of feed price \item s_per_herdsz ratio of the number of breeding pigs slaughtered to the total breeding herd size \item production number of pigs slaughtered that were reared for meat \item herdsz breeding herd size } } \references{ Andrews, David F., and Agnes M. Herzberg. Data: a collection of problems from many fields for the student and research worker. Springer Science & Business Media, 2012. } \keyword{datasets} GGally/man/plot_types.Rd0000644000176200001440000000064213114357267014710 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/find-combo.R \name{plot_types} \alias{plot_types} \title{Plot Types} \usage{ plot_types(data, columnsX, columnsY, allowDiag = TRUE) } \arguments{ \item{data}{data set to be used} } \description{ Retrieves the type of plot that should be used for all combinations } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{internal} GGally/man/nasa.Rd0000644000176200001440000000166113114357267013432 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data-nasa.R \docType{data} \name{nasa} \alias{nasa} \title{Data from the Data Expo JSM 2006.} \format{A data frame with 41472 rows and 17 variables} \usage{ data(nasa) } \description{ This data was provided by NASA for the competition. } \details{ The data shows 6 years of monthly measurements of a 24x24 spatial grid from Central America: \itemize{ \item time integer specifying temporal order of measurements \item x, y, lat, long spatial location of measurements. \item cloudhigh, cloudlow, cloudmid, ozone, pressure, surftemp, temperature are the various satellite measurements. \item date, day, month, year specifying the time of measurements. \item id unique ide for each spatial position. } } \references{ Murrell, P. (2010) The 2006 Data Expo of the American Statistical Association. Computational Statistics, 25:551-554. } \keyword{datasets} GGally/man/ggpairs.Rd0000644000176200001440000002223613277311163014140 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggpairs.R \name{ggpairs} \alias{ggpairs} \title{ggpairs - A ggplot2 generalized pairs plot} \usage{ ggpairs(data, mapping = NULL, columns = 1:ncol(data), title = NULL, upper = list(continuous = "cor", combo = "box_no_facet", discrete = "facetbar", na = "na"), lower = list(continuous = "points", combo = "facethist", discrete = "facetbar", na = "na"), diag = list(continuous = "densityDiag", discrete = "barDiag", na = "naDiag"), params = NULL, ..., xlab = NULL, ylab = NULL, axisLabels = c("show", "internal", "none"), columnLabels = colnames(data[columns]), labeller = "label_value", switch = NULL, showStrips = NULL, legend = NULL, cardinality_threshold = 15, progress = NULL, legends = stop("deprecated")) } \arguments{ \item{data}{data set using. Can have both numerical and categorical data.} \item{mapping}{aesthetic mapping (besides \code{x} and \code{y}). See \code{\link[ggplot2]{aes}()}. If \code{mapping} is numeric, \code{columns} will be set to the \code{mapping} value and \code{mapping} will be set to \code{NULL}.} \item{columns}{which columns are used to make plots. Defaults to all columns.} \item{title, xlab, ylab}{title, x label, and y label for the graph} \item{upper}{see Details} \item{lower}{see Details} \item{diag}{see Details} \item{params}{deprecated. Please see \code{\link{wrap_fn_with_param_arg}}} \item{...}{deprecated. Please use \code{mapping}} \item{axisLabels}{either "show" to display axisLabels, "internal" for labels in the diagonal plots, or "none" for no axis labels} \item{columnLabels}{label names to be displayed. Defaults to names of columns being used.} \item{labeller}{labeller for facets. See \code{\link[ggplot2]{labellers}}. Common values are \code{"label_value"} (default) and \code{"label_parsed"}.} \item{switch}{switch parameter for facet_grid. See \code{ggplot2::\link[ggplot2]{facet_grid}}. By default, the labels are displayed on the top and right of the plot. If \code{"x"}, the top labels will be displayed to the bottom. If \code{"y"}, the right-hand side labels will be displayed to the left. Can also be set to \code{"both"}} \item{showStrips}{boolean to determine if each plot's strips should be displayed. \code{NULL} will default to the top and right side plots only. \code{TRUE} or \code{FALSE} will turn all strips on or off respectively.} \item{legend}{May be the two objects described below or the default \code{NULL} value. The legend position can be moved by using ggplot2's theme element \code{pm + theme(legend.position = "bottom")} \describe{\item{a numeric vector of length 2}{provides the location of the plot to use the legend for the plot matrix's legend. Such as \code{legend = c(3,5)} which will use the legend from the plot in the third row and fifth column}\item{a single numeric value}{provides the location of a plot according to the display order. Such as \code{legend = 3} in a plot matrix with 2 rows and 5 columns displayed by column will return the plot in position \code{c(1,2)}}\item{a object from \code{\link{grab_legend}()}}{a predetermined plot legend that will be displayed directly}}} \item{cardinality_threshold}{maximum number of levels allowed in a character / factor column. Set this value to NULL to not check factor columns. Defaults to 15} \item{progress}{\code{NULL} (default) for a progress bar in interactive sessions with more than 15 plots, \code{TRUE} for a progress bar, \code{FALSE} for no progress bar, or a function that accepts at least a plot matrix and returns a new \code{progress::\link[progress]{progress_bar}}. See \code{\link{ggmatrix_progress}}.} \item{legends}{deprecated} } \value{ ggmatrix object that if called, will print } \description{ Make a matrix of plots with a given data set } \details{ \code{upper} and \code{lower} are lists that may contain the variables 'continuous', 'combo', 'discrete', and 'na'. Each element of the list may be a function or a string. If a string is supplied, it must implement one of the following options: \describe{ \item{continuous}{exactly one of ('points', 'smooth', 'smooth_loess', 'density', 'cor', 'blank'). This option is used for continuous X and Y data.} \item{combo}{exactly one of ('box', 'box_no_facet', 'dot', 'dot_no_facet', 'facethist', 'facetdensity', 'denstrip', 'blank'). This option is used for either continuous X and categorical Y data or categorical X and continuous Y data.} \item{discrete}{exactly one of ('facetbar', 'ratio', 'blank'). This option is used for categorical X and Y data.} \item{na}{exactly one of ('na', 'blank'). This option is used when all X data is \code{NA}, all Y data is \code{NA}, or either all X or Y data is \code{NA}.} } \code{diag} is a list that may only contain the variables 'continuous', 'discrete', and 'na'. Each element of the diag list is a string implementing the following options: \describe{ \item{continuous}{exactly one of ('densityDiag', 'barDiag', 'blankDiag'). This option is used for continuous X data.} \item{discrete}{exactly one of ('barDiag', 'blankDiag'). This option is used for categorical X and Y data.} \item{na}{exactly one of ('naDiag', 'blankDiag'). This option is used when all X data is \code{NA}.} } If 'blank' is ever chosen as an option, then ggpairs will produce an empty plot. If a function is supplied as an option to \code{upper}, \code{lower}, or \code{diag}, it should implement the function api of \code{function(data, mapping, ...){#make ggplot2 plot}}. If a specific function needs its parameters set, \code{\link{wrap}(fn, param1 = val1, param2 = val2)} the function with its parameters. } \examples{ # small function to display plots only if it's interactive p_ <- GGally::print_if_interactive ## Quick example, with and without colour data(flea) ggpairs(flea, columns = 2:4) pm <- ggpairs(flea, columns = 2:4, ggplot2::aes(colour=species)) p_(pm) # Note: colour should be categorical, else you will need to reset # the upper triangle to use points instead of trying to compute corr data(tips, package = "reshape") pm <- ggpairs(tips[, 1:3]) p_(pm) pm <- ggpairs(tips, 1:3, columnLabels = c("Total Bill", "Tip", "Sex")) p_(pm) pm <- ggpairs(tips, upper = "blank") p_(pm) ## Plot Types # Change default plot behavior pm <- ggpairs( tips[, c(1, 3, 4, 2)], upper = list(continuous = "density", combo = "box_no_facet"), lower = list(continuous = "points", combo = "dot_no_facet") ) p_(pm) # Supply Raw Functions (may be user defined functions!) pm <- ggpairs( tips[, c(1, 3, 4, 2)], upper = list(continuous = ggally_density, combo = ggally_box_no_facet), lower = list(continuous = ggally_points, combo = ggally_dot_no_facet) ) p_(pm) # Use sample of the diamonds data data(diamonds, package="ggplot2") diamonds.samp <- diamonds[sample(1:dim(diamonds)[1], 1000), ] # Different aesthetics for different plot sections and plot types pm <- ggpairs( diamonds.samp[, 1:5], mapping = ggplot2::aes(color = cut), upper = list(continuous = wrap("density", alpha = 0.5), combo = "box_no_facet"), lower = list(continuous = wrap("points", alpha = 0.3), combo = wrap("dot_no_facet", alpha = 0.4)), title = "Diamonds" ) p_(pm) ## Axis Label Variations # Only Variable Labels on the diagonal (no axis labels) pm <- ggpairs(tips[, 1:3], axisLabels="internal") p_(pm) # Only Variable Labels on the outside (no axis labels) pm <- ggpairs(tips[, 1:3], axisLabels="none") p_(pm) ## Facet Label Variations # Default: df_x <- rnorm(100) df_y <- df_x + rnorm(100, 0, 0.1) df <- data.frame(x = df_x, y = df_y, c = sqrt(df_x^2 + df_y^2)) pm <- ggpairs( df, columnLabels = c("alpha[foo]", "alpha[bar]", "sqrt(alpha[foo]^2 + alpha[bar]^2)") ) p_(pm) # Parsed labels: pm <- ggpairs( df, columnLabels = c("alpha[foo]", "alpha[bar]", "sqrt(alpha[foo]^2 + alpha[bar]^2)"), labeller = "label_parsed" ) p_(pm) ## Plot Insertion Example custom_car <- ggpairs(mtcars[, c("mpg", "wt", "cyl")], upper = "blank", title = "Custom Example") # ggplot example taken from example(geom_text) plot <- ggplot2::ggplot(mtcars, ggplot2::aes(x=wt, y=mpg, label=rownames(mtcars))) plot <- plot + ggplot2::geom_text(ggplot2::aes(colour=factor(cyl)), size = 3) + ggplot2::scale_colour_discrete(l=40) custom_car[1, 2] <- plot personal_plot <- ggally_text( "ggpairs allows you\\nto put in your\\nown plot.\\nLike that one.\\n <---" ) custom_car[1, 3] <- personal_plot p_(custom_car) ## Remove binwidth warning from ggplot2 # displays warning about picking a better binwidth pm <- ggpairs(tips, 2:3) p_(pm) # no warning displayed pm <- ggpairs(tips, 2:3, lower = list(combo = wrap("facethist", binwidth = 0.5))) p_(pm) # no warning displayed with user supplied function pm <- ggpairs(tips, 2:3, lower = list(combo = wrap(ggally_facethist, binwidth = 0.5))) p_(pm) } \references{ John W Emerson, Walton A Green, Barret Schloerke, Jason Crowley, Dianne Cook, Heike Hofmann, Hadley Wickham. The Generalized Pairs Plot. Journal of Computational and Graphical Statistics, vol. 22, no. 1, pp. 79-91, 2012. } \seealso{ wrap v1_ggmatrix_theme } \author{ Barret Schloerke \email{schloerke@gmail.com}, Jason Crowley \email{crowley.jason.s@gmail.com}, Di Cook \email{dicook@iastate.edu}, Heike Hofmann \email{hofmann@iastate.edu}, Hadley Wickham \email{h.wickham@gmail.com} } \keyword{hplot} GGally/man/add_and_overwrite_aes.Rd0000644000176200001440000000137513140471254017012 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggpairs.R \name{add_and_overwrite_aes} \alias{add_and_overwrite_aes} \title{Add new aes} \usage{ add_and_overwrite_aes(current, new) } \value{ aes_ output } \description{ Add new aesthetics to a previous aes. } \examples{ data(diamonds, package="ggplot2") diamonds.samp <- diamonds[sample(1:dim(diamonds)[1], 1000), ] pm <- ggpairs(diamonds.samp, columns = 5:7, mapping = ggplot2::aes(color = color), upper = list(continuous = "cor", mapping = ggplot2::aes_string(color = "clarity")), lower = list(continuous = "cor", mapping = ggplot2::aes_string(color = "cut")), title = "Diamonds Sample" ) str(pm) } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{internal} GGally/man/uppertriangle.Rd0000644000176200001440000000167013114357267015371 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggscatmat.R \name{uppertriangle} \alias{uppertriangle} \title{uppertriangle - rearrange dataset as the preparation of ggscatmat function} \usage{ uppertriangle(data, columns = 1:ncol(data), color = NULL, corMethod = "pearson") } \arguments{ \item{data}{a data matrix. Should contain numerical (continuous) data.} \item{columns}{an option to choose the column to be used in the raw dataset. Defaults to \code{1:ncol(data)}} \item{color}{an option to choose a factor variable to be grouped with. Defaults to \code{(NULL)}} \item{corMethod}{method argument supplied to \code{\link[stats]{cor}}} } \description{ function for making the dataset used to plot the uppertriangle plots. } \examples{ data(flea) head(uppertriangle(flea, columns=2:4)) head(uppertriangle(flea)) head(uppertriangle(flea, color="species")) } \author{ Mengjia Ni, Di Cook \email{dicook@monash.edu} } GGally/man/is_horizontal.Rd0000644000176200001440000000140013276725426015370 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{is_horizontal} \alias{is_horizontal} \alias{is_character_column} \title{Check if plot is horizontal} \usage{ is_horizontal(data, mapping, val = "y") is_character_column(data, mapping, val = "y") } \arguments{ \item{data}{data used in ggplot2 plot} \item{mapping}{ggplot2 \code{aes()} mapping} \item{val}{key to retrieve from \code{mapping}} } \value{ Boolean determining if the data is a character-like data } \description{ Check if plot is horizontal } \examples{ is_horizontal(iris, ggplot2::aes(Sepal.Length, Species)) # TRUE is_horizontal(iris, ggplot2::aes(Sepal.Length, Species), "x") # FALSE is_horizontal(iris, ggplot2::aes(Sepal.Length, Sepal.Width)) # FALSE } GGally/man/ggally_smooth.Rd0000644000176200001440000000236113277311163015351 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_smooth} \alias{ggally_smooth} \alias{ggally_smooth_loess} \alias{ggally_smooth_lm} \title{Plots the Scatter Plot with Smoothing} \usage{ ggally_smooth(data, mapping, ..., method = "lm", se = TRUE, shrink = TRUE) ggally_smooth_loess(data, mapping, ...) ggally_smooth_lm(data, mapping, ...) } \arguments{ \item{data}{data set using} \item{mapping}{aesthetics being used} \item{...}{other arguments to add to geom_point} \item{method, se}{parameters supplied to \code{\link[ggplot2]{geom_smooth}}} \item{shrink}{boolean to determine if y range is reduced to range of points or points and error ribbon} } \description{ Add a smoothed condition mean with a given scatter plot. } \details{ Y limits are reduced to match original Y range with the goal of keeping the Y axis the same across plots. } \examples{ data(tips, package = "reshape") ggally_smooth(tips, mapping = ggplot2::aes(x = total_bill, y = tip)) ggally_smooth(tips, mapping = ggplot2::aes_string(x = "total_bill", y = "tip")) ggally_smooth(tips, mapping = ggplot2::aes_string(x = "total_bill", y = "tip", color = "sex")) } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/add_ref_boxes.Rd0000644000176200001440000000131013114357267015263 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gglyph.R \name{add_ref_boxes} \alias{add_ref_boxes} \title{Add reference boxes around each cell of the glyphmap.} \usage{ add_ref_boxes(data, var_fill = NULL, color = "white", size = 0.5, fill = NA, ...) } \arguments{ \item{data}{A glyphmap structure.} \item{var_fill}{Variable name to use to set the fill color} \item{color}{Set the color to draw in, default is "white"} \item{size}{Set the line size, default is 0.5} \item{fill}{fill value used if \code{var_fill} is \code{NULL}} \item{...}{other arguments passed onto \code{\link[ggplot2]{geom_rect}}} } \description{ Add reference boxes around each cell of the glyphmap. } GGally/man/is_date.Rd0000644000176200001440000000041513114357267014114 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/find-combo.R \name{is_date} \alias{is_date} \title{Check if object is a date} \usage{ is_date(x) } \arguments{ \item{x}{vector} } \description{ Check if object is a date } \keyword{internal} GGally/man/twitter_spambots.Rd0000644000176200001440000000155613114357267016125 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data-twitter_spambots.R \docType{data} \name{twitter_spambots} \alias{twitter_spambots} \title{Twitter spambots} \format{An object of class \code{network} with 120 edges and 94 vertices.} \usage{ data(twitter_spambots) } \description{ A network of spambots found on Twitter as part of a data mining project. } \details{ Each node of the network is identified by the Twitter screen name of the account and further carries five vertex attributes: \itemize{ \item location user's location, as provided by the user \item lat latitude, based on the user's location \item lon longitude, based on the user's location \item followers number of Twitter accounts that follow this account \item friends number of Twitter accounts followed by the account } } \author{ Amos Elberg } \keyword{datasets} GGally/man/ggmatrix.Rd0000644000176200001440000001114213277311163014320 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggmatrix.R \name{ggmatrix} \alias{ggmatrix} \title{ggmatrix - A ggplot2 Matrix} \usage{ ggmatrix(plots, nrow, ncol, xAxisLabels = NULL, yAxisLabels = NULL, title = NULL, xlab = NULL, ylab = NULL, byrow = TRUE, showStrips = NULL, showAxisPlotLabels = TRUE, showXAxisPlotLabels = TRUE, showYAxisPlotLabels = TRUE, labeller = NULL, switch = NULL, xProportions = NULL, yProportions = NULL, progress = NULL, data = NULL, gg = NULL, legend = NULL) } \arguments{ \item{plots}{list of plots to be put into matrix} \item{nrow, ncol}{number of rows and columns} \item{xAxisLabels, yAxisLabels}{strip titles for the x and y axis respectively. Set to \code{NULL} to not be displayed} \item{title, xlab, ylab}{title, x label, and y label for the graph. Set to \code{NULL} to not be displayed} \item{byrow}{boolean that determines whether the plots should be ordered by row or by column} \item{showStrips}{boolean to determine if each plot's strips should be displayed. \code{NULL} will default to the top and right side plots only. \code{TRUE} or \code{FALSE} will turn all strips on or off respectively.} \item{showAxisPlotLabels, showXAxisPlotLabels, showYAxisPlotLabels}{booleans that determine if the plots axis labels are printed on the X (bottom) or Y (left) part of the plot matrix. If \code{showAxisPlotLabels} is set, both \code{showXAxisPlotLabels} and \code{showYAxisPlotLabels} will be set to the given value.} \item{labeller}{labeller for facets. See \code{\link[ggplot2]{labellers}}. Common values are \code{"label_value"} (default) and \code{"label_parsed"}.} \item{switch}{switch parameter for facet_grid. See \code{ggplot2::\link[ggplot2]{facet_grid}}. By default, the labels are displayed on the top and right of the plot. If \code{"x"}, the top labels will be displayed to the bottom. If \code{"y"}, the right-hand side labels will be displayed to the left. Can also be set to \code{"both"}} \item{xProportions, yProportions}{Value to change how much area is given for each plot. Either \code{NULL} (default), numeric value matching respective length, or \code{grid::\link[grid]{unit}} object with matching respective length} \item{progress}{\code{NULL} (default) for a progress bar in interactive sessions with more than 15 plots, \code{TRUE} for a progress bar, \code{FALSE} for no progress bar, or a function that accepts at least a plot matrix and returns a new \code{progress::\link[progress]{progress_bar}}. See \code{\link{ggmatrix_progress}}.} \item{data}{data set using. This is the data to be used in place of 'ggally_data' if the plot is a string to be evaluated at print time} \item{gg}{ggplot2 theme objects to be applied to every plot} \item{legend}{May be the two objects described below or the default \code{NULL} value. The legend position can be moved by using ggplot2's theme element \code{pm + theme(legend.position = "bottom")} \describe{\item{a numeric vector of length 2}{provides the location of the plot to use the legend for the plot matrix's legend. Such as \code{legend = c(3,5)} which will use the legend from the plot in the third row and fifth column}\item{a single numeric value}{provides the location of a plot according to the display order. Such as \code{legend = 3} in a plot matrix with 2 rows and 5 columns displayed by column will return the plot in position \code{c(1,2)}}\item{a object from \code{\link{grab_legend}()}}{a predetermined plot legend that will be displayed directly}}} } \description{ Make a generic matrix of ggplot2 plots. } \section{Memory usage}{ Now that the print.ggmatrix method uses a large gtable object, rather than print each plot independently, memory usage may be of concern. From small tests, memory usage flutters around \code{object.size(data) * 0.3 * length(plots)}. So, for a 80Mb random noise dataset with 100 plots, about 2.4 Gb of memory needed to print. For the 3.46 Mb diamonds dataset with 100 plots, about 100 Mb of memory was needed to print. The benefits of using the ggplot2 format greatly outweigh the price of about 20% increase in memory usage from the prior ad-hoc print method. } \examples{ # small function to display plots only if it's interactive p_ <- GGally::print_if_interactive plotList <- list() for (i in 1:6) { plotList[[i]] <- ggally_text(paste("Plot #", i, sep = "")) } pm <- ggmatrix( plotList, 2, 3, c("A", "B", "C"), c("D", "E"), byrow = TRUE ) p_(pm) pm <- ggmatrix( plotList, 2, 3, xAxisLabels = c("A", "B", "C"), yAxisLabels = NULL, byrow = FALSE, showXAxisPlotLabels = FALSE ) p_(pm) } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/glyphs.Rd0000644000176200001440000000340213114357267014011 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gglyph.R \name{glyphs} \alias{glyphs} \title{Create the data needed to generate a glyph plot.} \usage{ glyphs(data, x_major, x_minor, y_major, y_minor, polar = FALSE, height = ggplot2::rel(0.95), width = ggplot2::rel(0.95), y_scale = identity, x_scale = identity) } \arguments{ \item{data}{A data frame containing variables named in \code{x_major}, \code{x_minor}, \code{y_major} and \code{y_minor}.} \item{x_major, x_minor, y_major, y_minor}{The name of the variable (as a string) for the major and minor x and y axes. Together, each unique} \item{polar}{A logical of length 1, specifying whether the glyphs should be drawn in polar coordinates. Defaults to \code{FALSE}.} \item{height, width}{The height and width of each glyph. Defaults to 95\% of the \code{\link[ggplot2]{resolution}} of the data. Specify the width absolutely by supplying a numeric vector of length 1, or relative to the} \item{y_scale, x_scale}{The scaling function to be applied to each set of minor values within a grid cell. Defaults to \code{\link{identity}} so that no scaling is performed.} } \description{ Create the data needed to generate a glyph plot. } \examples{ data(nasa) nasaLate <- nasa[ nasa$date >= as.POSIXct("1998-01-01") & nasa$lat >= 20 & nasa$lat <= 40 & nasa$long >= -80 & nasa$long <= -60 , ] temp.gly <- glyphs(nasaLate, "long", "day", "lat", "surftemp", height=2.5) ggplot2::ggplot(temp.gly, ggplot2::aes(gx, gy, group = gid)) + add_ref_lines(temp.gly, color = "grey90") + add_ref_boxes(temp.gly, color = "grey90") + ggplot2::geom_path() + ggplot2::theme_bw() + ggplot2::labs(x = "", y = "") } \author{ Di Cook \email{dicook@monash.edu}, Heike Hofmann, Hadley Wickham } GGally/man/ggally_cor.Rd0000644000176200001440000000247113114364223014620 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_cor} \alias{ggally_cor} \title{Correlation from the Scatter Plot} \usage{ ggally_cor(data, mapping, alignPercent = 0.6, method = "pearson", use = "complete.obs", corAlignPercent = NULL, corMethod = NULL, corUse = NULL, ...) } \arguments{ \item{data}{data set using} \item{mapping}{aesthetics being used} \item{alignPercent}{right align position of numbers. Default is 60 percent across the horizontal} \item{method}{\code{method} supplied to cor function} \item{use}{\code{use} supplied to cor function} \item{corAlignPercent}{deprecated. Use parameter \code{alignPercent}} \item{corMethod}{deprecated. Use parameter \code{method}} \item{corUse}{deprecated. Use parameter \code{use}} \item{...}{other arguments being supplied to geom_text} } \description{ Estimate correlation from the given data. } \examples{ data(tips, package = "reshape") ggally_cor(tips, mapping = ggplot2::aes_string(x = "total_bill", y = "tip")) ggally_cor( tips, mapping = ggplot2::aes(x = total_bill, y = tip), size = 15, colour = I("red") ) ggally_cor( tips, mapping = ggplot2::aes_string(x = "total_bill", y = "tip", color = "sex"), size = 5 ) } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/ggally_nostic_resid.Rd0000644000176200001440000000307513114364223016523 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggnostic.R \name{ggally_nostic_resid} \alias{ggally_nostic_resid} \title{ggnostic - residuals} \usage{ ggally_nostic_resid(data, mapping, ..., linePosition = 0, lineColor = brew_colors("grey"), lineSize = 0.5, lineAlpha = 1, lineType = 1, lineConfColor = brew_colors("grey"), lineConfSize = lineSize, lineConfAlpha = lineAlpha, lineConfType = 2, pVal = c(0.025, 0.975), sigma = attr(data, "broom_glance")$sigma, se = TRUE, method = "auto") } \arguments{ \item{data, mapping, ...}{parameters supplied to \code{\link{ggally_nostic_line}}} \item{linePosition, lineColor, lineSize, lineAlpha, lineType}{parameters supplied to \code{ggplot2::\link[ggplot2]{geom_line}}} \item{lineConfColor, lineConfSize, lineConfAlpha, lineConfType}{parameters supplied to the confidence interval lines} \item{pVal}{percentiles of a N(0, sigma) distribution to be drawn} \item{sigma}{sigma value for the \code{pVal} percentiles} \item{se}{boolean to determine if the confidence intervals should be displayed} \item{method}{parameter supplied to \code{ggplot2::\link[ggplot2]{geom_smooth}}. Defaults to \code{"auto"}} } \value{ ggplot2 plot object } \description{ If non-null \code{pVal} and \code{sigma} values are given, confidence interval lines will be added to the plot at the specified \code{pVal} percentiles of a N(0, sigma) distribution. } \examples{ dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars)) ggally_nostic_resid(dt, ggplot2::aes(wt, .resid)) } \seealso{ \code{stats::\link[stats]{residuals}} } GGally/man/plotting_data_type.Rd0000644000176200001440000000045013114357267016375 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/find-combo.R \name{plotting_data_type} \alias{plotting_data_type} \title{Get plotting data type} \usage{ plotting_data_type(x) } \arguments{ \item{x}{vector} } \description{ Get plotting data type } \keyword{internal} GGally/man/ggmatrix_gtable.Rd0000644000176200001440000000146513277311163015645 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggmatrix_gtable.R \name{ggmatrix_gtable} \alias{ggmatrix_gtable} \title{Print ggmatrix object} \usage{ ggmatrix_gtable(pm, ..., progress = NULL, progress_format = formals(ggmatrix_progress)$format) } \arguments{ \item{pm}{ggmatrix object to be plotted} \item{...}{ignored} \item{progress, progress_format}{Please use the 'progress' parameter in your ggmatrix-like function. See \code{\link{ggmatrix_progress}} for a few examples. These parameters will soon be deprecated.} } \description{ Specialized method to print the ggmatrix object- } \examples{ data(tips, package = "reshape") pm <- ggpairs(tips, c(1,3,2), mapping = ggplot2::aes_string(color = "sex")) ggmatrix_gtable(pm) } \author{ Barret Schloerke \email{schloerke@gmail.com} } GGally/man/ggally_ratio.Rd0000644000176200001440000000220413114357267015157 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_ratio} \alias{ggally_ratio} \title{Plots a mosaic plot} \usage{ ggally_ratio(data, mapping = do.call(ggplot2::aes_string, as.list(colnames(data)[1:2])), ..., floor = 0, ceiling = NULL) } \arguments{ \item{data}{data set using} \item{mapping}{aesthetics being used. Only x and y will used and both are required} \item{...}{passed to \code{\link[ggplot2]{geom_tile}(...)}} \item{floor}{don't display cells smaller than this value} \item{ceiling}{max value to scale frequencies. If any frequency is larger than the ceiling, the fill color is displayed darker than other rectangles} } \description{ Plots the mosaic plot by using fluctuation. } \examples{ data(tips, package = "reshape") ggally_ratio(tips, ggplot2::aes(sex, day)) ggally_ratio(tips, ggplot2::aes(sex, day)) + ggplot2::coord_equal() # only plot tiles greater or equal to 20 and scale to a max of 50 ggally_ratio( tips, ggplot2::aes(sex, day), floor = 20, ceiling = 50 ) + ggplot2::theme(aspect.ratio = 4/2) } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/singleClassOrder.Rd0000644000176200001440000000172313114357267015752 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggparcoord.R \name{singleClassOrder} \alias{singleClassOrder} \title{Order axis variables} \usage{ singleClassOrder(classVar, axisVars, specClass = NULL) } \arguments{ \item{classVar}{class variable (vector from original dataset)} \item{axisVars}{variables to be plotted as axes (data frame)} \item{specClass}{character string matching to level of \code{classVar}; instead of looking for separation between any class and the rest, will only look for separation between this class and the rest} } \value{ character vector of names of axisVars ordered such that the first variable has the most separation between one of the classes and the rest, and the last variable has the least (as measured by F-statistics from an ANOVA) } \description{ Order axis variables by separation between one class and the rest (most separation to least). } \author{ Jason Crowley \email{crowley.jason.s@gmail.com} } GGally/man/ggnostic.Rd0000644000176200001440000001276613277311163014330 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggnostic.R \name{ggnostic} \alias{ggnostic} \title{ggnostic - Plot matrix of statistical model diagnostics} \usage{ ggnostic(model, ..., columnsX = attr(data, "var_x"), columnsY = c(".resid", ".sigma", ".hat", ".cooksd"), columnLabelsX = attr(data, "var_x_label"), columnLabelsY = gsub("\\\\.", " ", gsub("^\\\\.", "", columnsY)), xlab = "explanatory variables", ylab = "diagnostics", title = paste(deparse(model$call, width.cutoff = 500L), collapse = "\\n"), continuous = list(default = ggally_points, .fitted = ggally_points, .se.fit = ggally_nostic_se_fit, .resid = ggally_nostic_resid, .hat = ggally_nostic_hat, .sigma = ggally_nostic_sigma, .cooksd = ggally_nostic_cooksd, .std.resid = ggally_nostic_std_resid), combo = list(default = ggally_box_no_facet, fitted = ggally_box_no_facet, .se.fit = ggally_nostic_se_fit, .resid = ggally_nostic_resid, .hat = ggally_nostic_hat, .sigma = ggally_nostic_sigma, .cooksd = ggally_nostic_cooksd, .std.resid = ggally_nostic_std_resid), discrete = list(default = ggally_ratio, .fitted = ggally_ratio, .se.fit = ggally_ratio, .resid = ggally_ratio, .hat = ggally_ratio, .sigma = ggally_ratio, .cooksd = ggally_ratio, .std.resid = ggally_ratio), progress = NULL, data = broomify(model)) } \arguments{ \item{model}{statistical model object such as output from \code{stats::\link[stats]{lm}} or \code{stats::\link[stats]{glm}}} \item{...}{arguments passed directly to \code{\link{ggduo}}} \item{columnsX}{columns to be displayed in the plot matrix. Defaults to the predictor columns of the \code{model}} \item{columnsY}{rows to be displayed in the plot matrix. Defaults to residuals, leave one out sigma value, diagonal of the hat matrix, and Cook's Distance. The possible values are the response variables in the model and the added columns provided by \code{broom::\link[broom]{augment}(model)}. See details for more information.} \item{columnLabelsX, columnLabelsY}{column and row labels to display in the plot matrix} \item{xlab, ylab, title}{plot matrix labels passed directly to \code{\link{ggmatrix}}} \item{continuous, combo, discrete}{list of functions for each y variable. See details for more information.} \item{progress}{\code{NULL} (default) for a progress bar in interactive sessions with more than 15 plots, \code{TRUE} for a progress bar, \code{FALSE} for no progress bar, or a function that accepts at least a plot matrix and returns a new \code{progress::\link[progress]{progress_bar}}. See \code{\link{ggmatrix_progress}}.} \item{data}{data defaults to a 'broomify'ed model object. This object will contain information about the X variables, Y variables, and multiple broom outputs. See \code{\link{broomify}(model)} for more information} } \description{ ggnostic - Plot matrix of statistical model diagnostics } \section{`columnsY`}{ \code{broom::\link[broom]{augment}()} collects data from the supplied model and returns a data.frame with the following columns (taken directly from broom documentation). These columns are the only allowed values in the \code{columnsY} parameter to \code{ggnostic}. \describe{ \item{.resid}{Residuals} \item{.hat}{Diagonal of the hat matrix} \item{.sigma}{Estimate of residual standard deviation when corresponding observation is dropped from model} \item{.cooksd}{Cooks distance, \code{\link[stats]{cooks.distance}}} \item{.fitted}{Fitted values of model} \item{.se.fit}{Standard errors of fitted values} \item{.std.resid}{Standardized residuals} \item{response variable name}{The response variable in the model may be added. Such as \code{"mpg"} in the model \code{lm(mpg ~ ., data = mtcars)}} } } \section{`continuous`, `combo`, `discrete` types}{ Similar to \code{\link{ggduo}} and \code{\link{ggpairs}}, functions may be supplied to display the different column types. However, since the Y rows are fixed, each row has it's own corresponding function in each of the plot types: continuous, combo, and discrete. Each plot type list can have keys that correspond to the \code{broom::\link[broom]{augment}()} output: \code{".fitted"}, \code{".resid"}, \code{".std.resid"}, \code{".sigma"}, \code{".se.fit"}, \code{".hat"}, \code{".cooksd"}. An extra key, \code{"default"}, is used to plot the response variables of the model if they are included. Having a function for each diagnostic allows for very fine control over the diagnostics plot matrix. The functions for each type list are wrapped into a switch function that calls the function corresponding to the y variable being plotted. These switch functions are then passed directly to the \code{types} parameter in \code{\link{ggduo}}. } \examples{ # small function to display plots only if it's interactive p_ <- GGally::print_if_interactive data(mtcars) # use mtcars dataset and alter the 'am' column to display actual name values mtc <- mtcars mtc$am <- c("0" = "automatic", "1" = "manual")[as.character(mtc$am)] # step the complete model down to a smaller model mod <- stats::step(stats::lm(mpg ~ ., data = mtc), trace = FALSE) # display using defaults pm <- ggnostic(mod) p_(pm) # color by am value pm <- ggnostic(mod, mapping = ggplot2::aes(color = am)) p_(pm) # turn resid smooth error ribbon off pm <- ggnostic(mod, continuous = list(.resid = wrap("nostic_resid", se = FALSE))) p_(pm) ## plot residuals vs fitted in a ggpairs plot matrix dt <- broomify(mod) pm <- ggpairs( dt, c(".fitted", ".resid"), columnLabels = c("fitted", "residuals"), lower = list(continuous = ggally_nostic_resid) ) p_(pm) } GGally/man/ggcorr.Rd0000644000176200001440000001343613114357267013776 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggcorr.R \name{ggcorr} \alias{ggcorr} \title{ggcorr - Plot a correlation matrix with ggplot2} \usage{ ggcorr(data, method = c("pairwise", "pearson"), cor_matrix = NULL, nbreaks = NULL, digits = 2, name = "", low = "#3B9AB2", mid = "#EEEEEE", high = "#F21A00", midpoint = 0, palette = NULL, geom = "tile", min_size = 2, max_size = 6, label = FALSE, label_alpha = FALSE, label_color = "black", label_round = 1, label_size = 4, limits = c(-1, 1), drop = is.null(limits) || identical(limits, FALSE), layout.exp = 0, legend.position = "right", legend.size = 9, ...) } \arguments{ \item{data}{a data frame or matrix containing numeric (continuous) data. If any of the columns contain non-numeric data, they will be dropped with a warning.} \item{method}{a vector of two character strings. The first value gives the method for computing covariances in the presence of missing values, and must be (an abbreviation of) one of \code{"everything"}, \code{"all.obs"}, \code{"complete.obs"}, \code{"na.or.complete"} or \code{"pairwise.complete.obs"}. The second value gives the type of correlation coefficient to compute, and must be one of \code{"pearson"}, \code{"kendall"} or \code{"spearman"}. See \code{\link[stats]{cor}} for details. Defaults to \code{c("pairwise", "pearson")}.} \item{cor_matrix}{the named correlation matrix to use for calculations. Defaults to the correlation matrix of \code{data} when \code{data} is supplied.} \item{nbreaks}{the number of breaks to apply to the correlation coefficients, which results in a categorical color scale. See 'Note'. Defaults to \code{NULL} (no breaks, continuous scaling).} \item{digits}{the number of digits to show in the breaks of the correlation coefficients: see \code{\link[base]{cut}} for details. Defaults to \code{2}.} \item{name}{a character string for the legend that shows the colors of the correlation coefficients. Defaults to \code{""} (no legend name).} \item{low}{the lower color of the gradient for continuous scaling of the correlation coefficients. Defaults to \code{"#3B9AB2"} (blue).} \item{mid}{the midpoint color of the gradient for continuous scaling of the correlation coefficients. Defaults to \code{"#EEEEEE"} (very light grey).} \item{high}{the upper color of the gradient for continuous scaling of the correlation coefficients. Defaults to \code{"#F21A00"} (red).} \item{midpoint}{the midpoint value for continuous scaling of the correlation coefficients. Defaults to \code{0}.} \item{palette}{if \code{nbreaks} is used, a ColorBrewer palette to use instead of the colors specified by \code{low}, \code{mid} and \code{high}. Defaults to \code{NULL}.} \item{geom}{the geom object to use. Accepts either \code{"tile"}, \code{"circle"}, \code{"text"} or \code{"blank"}.} \item{min_size}{when \code{geom} has been set to \code{"circle"}, the minimum size of the circles. Defaults to \code{2}.} \item{max_size}{when \code{geom} has been set to \code{"circle"}, the maximum size of the circles. Defaults to \code{6}.} \item{label}{whether to add correlation coefficients to the plot. Defaults to \code{FALSE}.} \item{label_alpha}{whether to make the correlation coefficients increasingly transparent as they come close to 0. Also accepts any numeric value between \code{0} and \code{1}, in which case the level of transparency is set to that fixed value. Defaults to \code{FALSE} (no transparency).} \item{label_color}{the color of the correlation coefficients. Defaults to \code{"grey75"}.} \item{label_round}{the decimal rounding of the correlation coefficients. Defaults to \code{1}.} \item{label_size}{the size of the correlation coefficients. Defaults to \code{4}.} \item{limits}{bounding of color scaling for correlations, set \code{limits = NULL} or \code{FALSE} to remove} \item{drop}{if using \code{nbreaks}, whether to drop unused breaks from the color scale. Defaults to \code{FALSE} (recommended).} \item{layout.exp}{a multiplier to expand the horizontal axis to the left if variable names get clipped. Defaults to \code{0} (no expansion).} \item{legend.position}{where to put the legend of the correlation coefficients: see \code{\link[ggplot2]{theme}} for details. Defaults to \code{"bottom"}.} \item{legend.size}{the size of the legend title and labels, in points: see \code{\link[ggplot2]{theme}} for details. Defaults to \code{9}.} \item{...}{other arguments supplied to \code{\link[ggplot2]{geom_text}} for the diagonal labels.} } \description{ Function for making a correlation matrix plot, using ggplot2. The function is directly inspired by Tian Zheng and Yu-Sung Su's \code{corrplot} function in the 'arm' package. Please visit \url{http://github.com/briatte/ggcorr} for the latest version of \code{ggcorr}, and see the vignette at \url{https://briatte.github.io/ggcorr/} for many examples of how to use it. } \note{ Recommended values for the \code{nbreaks} argument are \code{3} to \code{11}, as values above 11 are visually difficult to separate and are not supported by diverging ColorBrewer palettes. } \examples{ # Basketball statistics provided by Nathan Yau at Flowing Data. dt <- read.csv("http://datasets.flowingdata.com/ppg2008.csv") # Default output. ggcorr(dt[, -1]) # Labelled output, with coefficient transparency. ggcorr(dt[, -1], label = TRUE, label_alpha = TRUE) # Custom options. ggcorr( dt[, -1], name = expression(rho), geom = "circle", max_size = 10, min_size = 2, size = 3, hjust = 0.75, nbreaks = 6, angle = -45, palette = "PuOr" # colorblind safe, photocopy-able ) # Supply your own correlation matrix ggcorr( data = NULL, cor_matrix = cor(dt[, -1], use = "pairwise") ) } \seealso{ \code{\link[stats]{cor}} and \code{corrplot} in the \code{arm} package. } \author{ Francois Briatte, with contributions from Amos B. Elberg and Barret Schloerke } GGally/man/gglegend.Rd0000644000176200001440000000300213114364223014241 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggmatrix_legend.R \name{gglegend} \alias{gglegend} \title{Plot only legend of plot function} \usage{ gglegend(fn) } \arguments{ \item{fn}{this value is passed directly to an empty \code{\link{wrap}} call. Please see \code{?\link{wrap}} for more details.} } \value{ a function that when called with arguments will produce the legend of the plotting function supplied. } \description{ Plot only legend of plot function } \examples{ # display regular plot ggally_points(iris, ggplot2::aes(Sepal.Length, Sepal.Width, color = Species)) # Make a function that will only print the legend points_legend <- gglegend(ggally_points) points_legend(iris, ggplot2::aes(Sepal.Length, Sepal.Width, color = Species)) # produce the sample legend plot, but supply a string that 'wrap' understands same_points_legend <- gglegend("points") identical( attr(attr(points_legend, "fn"), "original_fn"), attr(attr(same_points_legend, "fn"), "original_fn") ) # Complicated examples custom_legend <- wrap(gglegend("points"), size = 6) custom_legend(iris, ggplot2::aes(Sepal.Length, Sepal.Width, color = Species)) # Use within ggpairs pm <- ggpairs( iris, 1:2, mapping = ggplot2::aes(color = Species), upper = list(continuous = gglegend("points")) ) # pm # Place a legend in a specific location pm <- ggpairs(iris, 1:2, mapping = ggplot2::aes(color = Species)) # Make the legend pm[1,2] <- points_legend(iris, ggplot2::aes(Sepal.Width, Sepal.Length, color = Species)) pm } GGally/man/getPlot.Rd0000644000176200001440000000120013114364223014101 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggpairs_getput.R \name{getPlot} \alias{getPlot} \alias{[.ggmatrix} \title{getPlot} \usage{ getPlot(pm, i, j) \method{[}{ggmatrix}(pm, i, j, ...) } \arguments{ \item{pm}{ggmatrix object to select from} \item{i}{row from the top} \item{j}{column from the left} \item{...}{ignored} } \description{ Retrieves the ggplot object at the desired location. } \examples{ data(tips, package = "reshape") plotMatrix2 <- ggpairs(tips[, 3:2], upper = list(combo = "denstrip")) plotMatrix2[1, 2] } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/rescale01.Rd0000644000176200001440000000071513114357267014266 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gglyph.R \name{rescale01} \alias{rescale01} \alias{range01} \alias{max1} \alias{mean0} \alias{min0} \alias{rescale01} \alias{rescale11} \title{Rescaling functions} \usage{ range01(x) max1(x) mean0(x) min0(x) rescale01(x, xlim = NULL) rescale11(x, xlim = NULL) } \arguments{ \item{x}{numeric vector} \item{xlim}{value used in \code{range}} } \description{ Rescaling functions } GGally/man/putPlot.Rd0000644000176200001440000000236113114357267014155 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggpairs_getput.R \name{putPlot} \alias{putPlot} \alias{[<-.ggmatrix} \title{Put Plot} \usage{ putPlot(pm, value, i, j) \method{[}{ggmatrix}(pm, i, j, ...) <- value } \arguments{ \item{pm}{ggally object to be altered} \item{value}{ggplot object to be placed} \item{i}{row from the top} \item{j}{column from the left} \item{...}{ignored} } \description{ Function to place your own plot in the layout. } \examples{ custom_car <- ggpairs(mtcars[, c("mpg", "wt", "cyl")], upper = "blank", title = "Custom Example") # ggplot example taken from example(geom_text) plot <- ggplot2::ggplot(mtcars, ggplot2::aes(x=wt, y=mpg, label=rownames(mtcars))) plot <- plot + ggplot2::geom_text(ggplot2::aes(colour=factor(cyl)), size = 3) + ggplot2::scale_colour_discrete(l=40) custom_car[1, 2] <- plot personal_plot <- ggally_text( "ggpairs allows you\\nto put in your\\nown plot.\\nLike that one.\\n <---" ) custom_car[1, 3] <- personal_plot # custom_car # remove plots after creating a plot matrix custom_car[2,1] <- NULL custom_car[3,1] <- "blank" # the same as storing null custom_car[3,2] <- NULL custom_car } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/ggts.Rd0000644000176200001440000000112713114357267013451 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggnostic.R \name{ggts} \alias{ggts} \title{Multiple Time Series} \usage{ ggts(..., columnLabelsX = NULL, xlab = "time") } \arguments{ \item{...}{supplied directly to \code{\link{ggduo}}} \item{columnLabelsX}{remove top strips for the X axis by default} \item{xlab}{defaults to "time"} } \value{ ggmatrix object } \description{ GGally implementation of ts.plot. Wraps around the ggduo function and removes the column strips } \examples{ ggts(pigs, "time", c("gilts", "profit", "s_per_herdsz", "production", "herdsz")) } GGally/man/v1_ggmatrix_theme.Rd0000644000176200001440000000075113114357267016121 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggpairs_add.R \name{v1_ggmatrix_theme} \alias{v1_ggmatrix_theme} \title{Modify a ggmatrix object by adding an ggplot2 object to all plots} \usage{ v1_ggmatrix_theme() } \description{ Modify a ggmatrix object by adding an ggplot2 object to all plots } \examples{ ggpairs(iris, 1:2) + v1_ggmatrix_theme() # move the column names to the left and bottom ggpairs(iris, 1:2, switch = "both") + v1_ggmatrix_theme() } GGally/man/ggally_na.Rd0000644000176200001440000000122313114357267014437 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_na} \alias{ggally_na} \alias{ggally_naDiag} \title{NA plot} \usage{ ggally_na(data = NULL, mapping = NULL, size = 10, color = "grey20", ...) ggally_naDiag(...) } \arguments{ \item{data}{ignored} \item{mapping}{ignored} \item{size}{size of the geom_text 'NA'} \item{color}{color of the geom_text 'NA'} \item{...}{other arguments sent to geom_text} } \description{ Draws a large \code{NA} in the middle of the plotting area. This plot is useful when all X or Y data is \code{NA} } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/get_x_axis_labels.Rd0000644000176200001440000000055513114357267016165 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{get_x_axis_labels} \alias{get_x_axis_labels} \title{Get x axis labels} \usage{ get_x_axis_labels(p, xRange) } \arguments{ \item{p}{plot object} \item{xRange}{range of x values} } \description{ Retrieves x axis labels from the plot object directly. } \keyword{internal} GGally/man/ggally_blank.Rd0000644000176200001440000000067713114357267015144 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_blank} \alias{ggally_blank} \alias{ggally_blankDiag} \title{Blank} \usage{ ggally_blank(...) ggally_blankDiag(...) } \arguments{ \item{...}{other arguments ignored} } \description{ Draws nothing. } \details{ Makes a "blank" ggplot object that will only draw white space } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/ggally_facetdensity.Rd0000644000176200001440000000140213114357267016522 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_facetdensity} \alias{ggally_facetdensity} \title{Plots the density plots by faceting} \usage{ ggally_facetdensity(data, mapping, ...) } \arguments{ \item{data}{data set using} \item{mapping}{aesthetics being used} \item{...}{other arguments being sent to stat_density} } \description{ Make density plots by displaying subsets of the data in different panels. } \examples{ data(tips, package = "reshape") ggally_facetdensity(tips, mapping = ggplot2::aes(x = total_bill, y = sex)) ggally_facetdensity( tips, mapping = ggplot2::aes_string(y = "total_bill", x = "sex", color = "sex") ) } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/wrap.Rd0000644000176200001440000000661113114364223013447 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggpairs_internal_plots.R \name{wrap_fn_with_param_arg} \alias{wrap_fn_with_param_arg} \alias{wrapp} \alias{wrap} \alias{wrap_fn_with_params} \title{Wrap a function with different parameter values} \usage{ wrap_fn_with_param_arg(funcVal, params = NULL, funcArgName = deparse(substitute(funcVal))) wrapp(funcVal, params = NULL, funcArgName = deparse(substitute(funcVal))) wrap(funcVal, ..., funcArgName = deparse(substitute(funcVal))) wrap_fn_with_params(funcVal, ..., funcArgName = deparse(substitute(funcVal))) } \arguments{ \item{funcVal}{function that the \code{params} will be applied to. The function should follow the api of \code{function(data, mapping, ...)\{\}}. \code{funcVal} is allowed to be a string of one of the \code{ggally_NAME} functions, such as \code{"points"} for \code{ggally_points} or \code{"facetdensity"} for \code{ggally_facetdensity}.} \item{params}{named vector or list of parameters to be applied to the \code{funcVal}} \item{funcArgName}{name of function to be displayed} \item{...}{named parameters to be supplied to \code{wrap_fn_with_param_arg}} } \value{ a \code{function(data, mapping, ...)\{\}} that will wrap the original function with the parameters applied as arguments } \description{ Wraps a function with the supplied parameters to force different default behavior. This is useful for functions that are supplied to ggpairs. It allows you to change the behavior of one function, rather than creating multiple functions with different parameter settings. } \details{ \code{wrap} is identical to \code{wrap_fn_with_params}. These function take the new parameters as arguments. \code{wrapp} is identical to \code{wrap_fn_with_param_arg}. These functions take the new parameters as a single list. The \code{params} and \code{fn} attributes are there for debugging purposes. If either attribute is altered, the function must be re-wrapped to have the changes take effect. } \examples{ # small function to display plots only if it's interactive p_ <- GGally::print_if_interactive # example function that prints 'val' fn <- function(data, mapping, val = 2) { print(val) } fn(data = NULL, mapping = NULL) # 2 # wrap function to change default value 'val' to 5 instead of 2 wrapped_fn1 <- wrap(fn, val = 5) wrapped_fn1(data = NULL, mapping = NULL) # 5 # you may still supply regular values wrapped_fn1(data = NULL, mapping = NULL, val = 3) # 3 # wrap function to change 'val' to 5 using the arg list wrapped_fn2 <- wrap_fn_with_param_arg(fn, params = list(val = 5)) wrapped_fn2(data = NULL, mapping = NULL) # 5 # change parameter settings in ggpairs for a particular function ## Goal output: regularPlot <- ggally_points( iris, ggplot2::aes(Sepal.Length, Sepal.Width), size = 5, color = "red" ) p_(regularPlot) # Wrap ggally_points to have parameter values size = 5 and color = 'red' w_ggally_points <- wrap(ggally_points, size = 5, color = "red") wrappedPlot <- w_ggally_points( iris, ggplot2::aes(Sepal.Length, Sepal.Width) ) p_(wrappedPlot) # Double check the aes parameters are the same for the geom_point layer identical(regularPlot$layers[[1]]$aes_params, wrappedPlot$layers[[1]]$aes_params) # Use a wrapped function in ggpairs pm <- ggpairs(iris, 1:3, lower = list(continuous = wrap(ggally_points, size = 5, color = "red"))) p_(pm) pm <- ggpairs(iris, 1:3, lower = list(continuous = w_ggally_points)) p_(pm) } GGally/man/ggparcoord.Rd0000644000176200001440000001763613114357267014650 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggparcoord.R \name{ggparcoord} \alias{ggparcoord} \title{ggparcoord - A ggplot2 Parallel Coordinate Plot} \usage{ ggparcoord(data, columns = 1:ncol(data), groupColumn = NULL, scale = "std", scaleSummary = "mean", centerObsID = 1, missing = "exclude", order = columns, showPoints = FALSE, splineFactor = FALSE, alphaLines = 1, boxplot = FALSE, shadeBox = NULL, mapping = NULL, title = "") } \arguments{ \item{data}{the dataset to plot} \item{columns}{a vector of variables (either names or indices) to be axes in the plot} \item{groupColumn}{a single variable to group (color) by} \item{scale}{method used to scale the variables (see Details)} \item{scaleSummary}{if scale=="center", summary statistic to univariately center each variable by} \item{centerObsID}{if scale=="centerObs", row number of case plot should univariately be centered on} \item{missing}{method used to handle missing values (see Details)} \item{order}{method used to order the axes (see Details)} \item{showPoints}{logical operator indicating whether points should be plotted or not} \item{splineFactor}{logical or numeric operator indicating whether spline interpolation should be used. Numeric values will multiplied by the number of columns, \code{TRUE} will default to cubic interpolation, \code{\link[base]{AsIs}} to set the knot count directly and \code{0}, \code{FALSE}, or non-numeric values will not use spline interpolation.} \item{alphaLines}{value of alpha scaler for the lines of the parcoord plot or a column name of the data} \item{boxplot}{logical operator indicating whether or not boxplots should underlay the distribution of each variable} \item{shadeBox}{color of underlaying box which extends from the min to the max for each variable (no box is plotted if shadeBox == NULL)} \item{mapping}{aes string to pass to ggplot object} \item{title}{character string denoting the title of the plot} } \value{ ggplot object that if called, will print } \description{ A function for plotting static parallel coordinate plots, utilizing the \code{ggplot2} graphics package. } \details{ \code{scale} is a character string that denotes how to scale the variables in the parallel coordinate plot. Options: \itemize{ \item{\code{std}}{: univariately, subtract mean and divide by standard deviation} \item{\code{robust}}{: univariately, subtract median and divide by median absolute deviation} \item{\code{uniminmax}}{: univariately, scale so the minimum of the variable is zero, and the maximum is one} \item{\code{globalminmax}}{: no scaling is done; the range of the graphs is defined by the global minimum and the global maximum} \item{\code{center}}{: use \code{uniminmax} to standardize vertical height, then center each variable at a value specified by the \code{scaleSummary} param} \item{\code{centerObs}}{: use \code{uniminmax} to standardize vertical height, then center each variable at the value of the observation specified by the \code{centerObsID} param} } \code{missing} is a character string that denotes how to handle missing missing values. Options: \itemize{ \item{\code{exclude}}{: remove all cases with missing values} \item{\code{mean}}{: set missing values to the mean of the variable} \item{\code{median}}{: set missing values to the median of the variable} \item{\code{min10}}{: set missing values to 10\% below the minimum of the variable} \item{\code{random}}{: set missing values to value of randomly chosen observation on that variable} } \code{order} is either a vector of indices or a character string that denotes how to order the axes (variables) of the parallel coordinate plot. Options: \itemize{ \item{\code{(default)}}{: order by the vector denoted by \code{columns}} \item{\code{(given vector)}}{: order by the vector specified} \item{\code{anyClass}}{: order variables by their separation between any one class and the rest (as opposed to their overall variation between classes). This is accomplished by calculating the F-statistic for each class vs. the rest, for each axis variable. The axis variables are then ordered (decreasing) by their maximum of k F-statistics, where k is the number of classes.} \item{\code{allClass}}{: order variables by their overall F statistic (decreasing) from an ANOVA with \code{groupColumn} as the explanatory variable (note: it is required to specify a \code{groupColumn} with this ordering method). Basically, this method orders the variables by their variation between classes (most to least).} \item{\code{skewness}}{: order variables by their sample skewness (most skewed to least skewed)} \item{\code{Outlying}}{: order by the scagnostic measure, Outlying, as calculated by the package \code{scagnostics}. Other scagnostic measures available to order by are \code{Skewed}, \code{Clumpy}, \code{Sparse}, \code{Striated}, \code{Convex}, \code{Skinny}, \code{Stringy}, and \code{Monotonic}. Note: To use these methods of ordering, you must have the \code{scagnostics} package loaded.} } } \examples{ # small function to display plots only if it's interactive p_ <- GGally::print_if_interactive # use sample of the diamonds data for illustrative purposes data(diamonds, package="ggplot2") diamonds.samp <- diamonds[sample(1:dim(diamonds)[1], 100), ] # basic parallel coordinate plot, using default settings p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10)) p_(p) # this time, color by diamond cut p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2) p_(p) # underlay univariate boxplots, add title, use uniminmax scaling p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, scale = "uniminmax", boxplot = TRUE, title = "Parallel Coord. Plot of Diamonds Data") p_(p) # utilize ggplot2 aes to switch to thicker lines p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, title ="Parallel Coord. Plot of Diamonds Data", mapping = ggplot2::aes(size = 1)) + ggplot2::scale_size_identity() p_(p) # basic parallel coord plot of the msleep data, using 'random' imputation and # coloring by diet (can also use variable names in the columns and groupColumn # arguments) data(msleep, package="ggplot2") p <- ggparcoord(data = msleep, columns = 6:11, groupColumn = "vore", missing = "random", scale = "uniminmax") p_(p) # center each variable by its median, using the default missing value handler, # 'exclude' p <- ggparcoord(data = msleep, columns = 6:11, groupColumn = "vore", scale = "center", scaleSummary = "median") p_(p) # with the iris data, order the axes by overall class (Species) separation using # the anyClass option p <- ggparcoord(data = iris, columns = 1:4, groupColumn = 5, order = "anyClass") p_(p) # add points to the plot, add a title, and use an alpha scalar to make the lines # transparent p <- ggparcoord(data = iris, columns = 1:4, groupColumn = 5, order = "anyClass", showPoints = TRUE, title = "Parallel Coordinate Plot for the Iris Data", alphaLines = 0.3) p_(p) # color according to a column iris2 <- iris iris2$alphaLevel <- c("setosa" = 0.2, "versicolor" = 0.3, "virginica" = 0)[iris2$Species] p <- ggparcoord(data = iris2, columns = 1:4, groupColumn = 5, order = "anyClass", showPoints = TRUE, title = "Parallel Coordinate Plot for the Iris Data", alphaLines = "alphaLevel") p_(p) ## Use splines on values, rather than lines (all produce the same result) columns <- c(1, 5:10) p <- ggparcoord(diamonds.samp, columns, groupColumn = 2, splineFactor = TRUE) p_(p) p <- ggparcoord(diamonds.samp, columns, groupColumn = 2, splineFactor = 3) p_(p) splineFactor <- length(columns) * 3 p <- ggparcoord(diamonds.samp, columns, groupColumn = 2, splineFactor = I(splineFactor)) p_(p) } \author{ Jason Crowley \email{crowley.jason.s@gmail.com}, Barret Schloerke \email{schloerke@gmail.com}, Di Cook \email{dicook@iastate.edu}, Heike Hofmann \email{hofmann@iastate.edu}, Hadley Wickham \email{h.wickham@gmail.com} } GGally/man/ggnet2.Rd0000644000176200001440000003234613114357267013702 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggnet2.R \name{ggnet2} \alias{ggnet2} \title{ggnet2 - Plot a network with ggplot2} \usage{ ggnet2(net, mode = "fruchtermanreingold", layout.par = NULL, layout.exp = 0, alpha = 1, color = "grey75", shape = 19, size = 9, max_size = 9, na.rm = NA, palette = NULL, alpha.palette = NULL, alpha.legend = NA, color.palette = palette, color.legend = NA, shape.palette = NULL, shape.legend = NA, size.palette = NULL, size.legend = NA, size.zero = FALSE, size.cut = FALSE, size.min = NA, size.max = NA, label = FALSE, label.alpha = 1, label.color = "black", label.size = max_size/2, label.trim = FALSE, node.alpha = alpha, node.color = color, node.label = label, node.shape = shape, node.size = size, edge.alpha = 1, edge.color = "grey50", edge.lty = "solid", edge.size = 0.25, edge.label = NULL, edge.label.alpha = 1, edge.label.color = label.color, edge.label.fill = "white", edge.label.size = max_size/2, arrow.size = 0, arrow.gap = 0, arrow.type = "closed", legend.size = 9, legend.position = "right", ...) } \arguments{ \item{net}{an object of class \code{\link[network]{network}}, or any object that can be coerced to this class, such as an adjacency or incidence matrix, or an edge list: see \link[network]{edgeset.constructors} and \link[network]{network} for details. If the object is of class \code{\link[igraph:igraph-package]{igraph}} and the \code{\link[intergraph:intergraph-package]{intergraph}} package is installed, it will be used to convert the object: see \code{\link[intergraph]{asNetwork}} for details.} \item{mode}{a placement method from those provided in the \code{\link[sna]{sna}} package: see \link[sna:gplot.layout]{gplot.layout} for details. Also accepts the names of two numeric vertex attributes of \code{net}, or a matrix of numeric coordinates, in which case the first two columns of the matrix are used. Defaults to the Fruchterman-Reingold force-directed algorithm.} \item{layout.par}{options to be passed to the placement method, as listed in \link[sna]{gplot.layout}. Defaults to \code{NULL}.} \item{layout.exp}{a multiplier to expand the horizontal axis if node labels get clipped: see \link[scales]{expand_range} for details. Defaults to \code{0} (no expansion).} \item{alpha}{the level of transparency of the edges and nodes, which might be a single value, a vertex attribute, or a vector of values. Also accepts \code{"mode"} on bipartite networks (see 'Details'). Defaults to \code{1} (no transparency).} \item{color}{the color of the nodes, which might be a single value, a vertex attribute, or a vector of values. Also accepts \code{"mode"} on bipartite networks (see 'Details'). Defaults to \code{grey75}.} \item{shape}{the shape of the nodes, which might be a single value, a vertex attribute, or a vector of values. Also accepts \code{"mode"} on bipartite networks (see 'Details'). Defaults to \code{19} (solid circle).} \item{size}{the size of the nodes, in points, which might be a single value, a vertex attribute, or a vector of values. Also accepts \code{"indegree"}, \code{"outdegree"}, \code{"degree"} or \code{"freeman"} to size the nodes by their unweighted degree centrality (\code{"degree"} and \code{"freeman"} are equivalent): see \code{\link[sna]{degree}} for details. All node sizes must be strictly positive. Also accepts \code{"mode"} on bipartite networks (see 'Details'). Defaults to \code{9}.} \item{max_size}{the \emph{maximum} size of the node when \code{size} produces nodes of different sizes, in points. Defaults to \code{9}.} \item{na.rm}{whether to subset the network to nodes that are \emph{not} missing a given vertex attribute. If set to any vertex attribute of \code{net}, the nodes for which this attribute is \code{NA} will be removed. Defaults to \code{NA} (does nothing).} \item{palette}{the palette to color the nodes, when \code{color} is not a color value or a vector of color values. Accepts named vectors of color values, or if \code{\link[RColorBrewer]{RColorBrewer}} is installed, any ColorBrewer palette name: see \code{\link[RColorBrewer]{brewer.pal}} and \url{http://colorbrewer2.org/} for details. Defaults to \code{NULL}, which will create an array of grayscale color values if \code{color} is not a color value or a vector of color values.} \item{alpha.palette}{the palette to control the transparency levels of the nodes set by \code{alpha} when the levels are not numeric values. Defaults to \code{NULL}, which will create an array of alpha transparency values if \code{alpha} is not a numeric value or a vector of numeric values.} \item{alpha.legend}{the name to assign to the legend created by \code{alpha} when its levels are not numeric values. Defaults to \code{NA} (no name).} \item{color.palette}{see \code{palette}} \item{color.legend}{the name to assign to the legend created by \code{palette}. Defaults to \code{NA} (no name).} \item{shape.palette}{the palette to control the shapes of the nodes set by \code{shape} when the shapes are not numeric values. Defaults to \code{NULL}, which will create an array of shape values if \code{shape} is not a numeric value or a vector of numeric values.} \item{shape.legend}{the name to assign to the legend created by \code{shape} when its levels are not numeric values. Defaults to \code{NA} (no name).} \item{size.palette}{the palette to control the sizes of the nodes set by \code{size} when the sizes are not numeric values.} \item{size.legend}{the name to assign to the legend created by \code{size}. Defaults to \code{NA} (no name).} \item{size.zero}{whether to accept zero-sized nodes based on the value(s) of \code{size}. Defaults to \code{FALSE}, which ensures that zero-sized nodes are still shown in the plot and its size legend.} \item{size.cut}{whether to cut the size of the nodes into a certain number of quantiles. Accepts \code{TRUE}, which tries to cut the sizes into quartiles, or any positive numeric value, which tries to cut the sizes into that many quantiles. If the size of the nodes do not contain the specified number of distinct quantiles, the largest possible number is used. See \code{\link[stats]{quantile}} and \code{\link[base]{cut}} for details. Defaults to \code{FALSE} (does nothing).} \item{size.min}{whether to subset the network to nodes with a minimum size, based on the values of \code{size}. Defaults to \code{NA} (preserves all nodes).} \item{size.max}{whether to subset the network to nodes with a maximum size, based on the values of \code{size}. Defaults to \code{NA} (preserves all nodes).} \item{label}{whether to label the nodes. If set to \code{TRUE}, nodes are labeled with their vertex names. If set to a vector that contains as many elements as there are nodes in \code{net}, nodes are labeled with these. If set to any other vector of values, the nodes are labeled only when their vertex name matches one of these values. Defaults to \code{FALSE} (no labels).} \item{label.alpha}{the level of transparency of the node labels, as a numeric value, a vector of numeric values, or as a vertex attribute containing numeric values. Defaults to \code{1} (no transparency).} \item{label.color}{the color of the node labels, as a color value, a vector of color values, or as a vertex attribute containing color values. Defaults to \code{"black"}.} \item{label.size}{the size of the node labels, in points, as a numeric value, a vector of numeric values, or as a vertex attribute containing numeric values. Defaults to \code{max_size / 2} (half the maximum node size), which defaults to \code{4.5}.} \item{label.trim}{whether to apply some trimming to the node labels. Accepts any function that can process a character vector, or a strictly positive numeric value, in which case the labels are trimmed to a fixed-length substring of that length: see \code{\link[base]{substr}} for details. Defaults to \code{FALSE} (does nothing).} \item{node.alpha}{see \code{alpha}} \item{node.color}{see \code{color}} \item{node.label}{see \code{label}} \item{node.shape}{see \code{shape}} \item{node.size}{see \code{size}} \item{edge.alpha}{the level of transparency of the edges. Defaults to the value of \code{alpha}, which defaults to \code{1}.} \item{edge.color}{the color of the edges, as a color value, a vector of color values, or as an edge attribute containing color values. Defaults to \code{"grey50"}.} \item{edge.lty}{the linetype of the edges, as a linetype value, a vector of linetype values, or as an edge attribute containing linetype values. Defaults to \code{"solid"}.} \item{edge.size}{the size of the edges, in points, as a numeric value, a vector of numeric values, or as an edge attribute containing numeric values. All edge sizes must be strictly positive. Defaults to \code{0.25}.} \item{edge.label}{the labels to plot at the middle of the edges, as a single value, a vector of values, or as an edge attribute. Defaults to \code{NULL} (no edge labels).} \item{edge.label.alpha}{the level of transparency of the edge labels, as a numeric value, a vector of numeric values, or as an edge attribute containing numeric values. Defaults to \code{1} (no transparency).} \item{edge.label.color}{the color of the edge labels, as a color value, a vector of color values, or as an edge attribute containing color values. Defaults to \code{label.color}, which defaults to \code{"black"}.} \item{edge.label.fill}{the background color of the edge labels. Defaults to \code{"white"}.} \item{edge.label.size}{the size of the edge labels, in points, as a numeric value, a vector of numeric values, or as an edge attribute containing numeric values. All edge label sizes must be strictly positive. Defaults to \code{max_size / 2} (half the maximum node size), which defaults to \code{4.5}.} \item{arrow.size}{the size of the arrows for directed network edges, in points. See \code{\link[grid]{arrow}} for details. Defaults to \code{0} (no arrows).} \item{arrow.gap}{a setting aimed at improving the display of edge arrows by plotting slightly shorter edges. Accepts any value between \code{0} and \code{1}, where a value of \code{0.05} will generally achieve good results when the size of the nodes is reasonably small. Defaults to \code{0} (no shortening).} \item{arrow.type}{the type of the arrows for directed network edges. See \code{\link[grid]{arrow}} for details. Defaults to \code{"closed"}.} \item{legend.size}{the size of the legend symbols and text, in points. Defaults to \code{9}.} \item{legend.position}{the location of the plot legend(s). Accepts all \code{legend.position} values supported by \code{\link[ggplot2]{theme}}. Defaults to \code{"right"}.} \item{...}{other arguments passed to the \code{geom_text} object that sets the node labels: see \code{\link[ggplot2]{geom_text}} for details.} } \description{ Function for plotting network objects using ggplot2, with additional control over graphical parameters that are not supported by the \code{\link{ggnet}} function. Please visit \url{http://github.com/briatte/ggnet} for the latest version of ggnet2, and \url{https://briatte.github.io/ggnet} for a vignette that contains many examples and explanations. } \details{ The degree centrality measures that can be produced through the \code{size} argument will take the directedness of the network into account, but will be unweighted. To compute weighted network measures, see the \code{tnet} package by Tore Opsahl (\code{help("tnet", package = "tnet")}). The nodes of bipartite networks can be mapped to their mode by passing the \code{"mode"} argument to any of \code{alpha}, \code{color}, \code{shape} and \code{size}, in which case the nodes of the primary mode will be mapped as \code{"actor"}, and the nodes of the secondary mode will be mapped as \code{"event"}. } \examples{ library(network) # random adjacency matrix x <- 10 ndyads <- x * (x - 1) density <- x / ndyads m <- matrix(0, nrow = x, ncol = x) dimnames(m) <- list(letters[ 1:x ], letters[ 1:x ]) m[ row(m) != col(m) ] <- runif(ndyads) < density m # random undirected network n <- network::network(m, directed = FALSE) n ggnet2(n, label = TRUE) ggnet2(n, label = TRUE, shape = 15) ggnet2(n, label = TRUE, shape = 15, color = "black", label.color = "white") # add vertex attribute x = network.vertex.names(n) x = ifelse(x \%in\% c("a", "e", "i"), "vowel", "consonant") n \%v\% "phono" = x ggnet2(n, color = "phono") ggnet2(n, color = "phono", palette = c("vowel" = "gold", "consonant" = "grey")) ggnet2(n, shape = "phono", color = "phono") if (require(RColorBrewer)) { # random groups n \%v\% "group" <- sample(LETTERS[1:3], 10, replace = TRUE) ggnet2(n, color = "group", palette = "Set2") } # random weights n \%e\% "weight" <- sample(1:3, network.edgecount(n), replace = TRUE) ggnet2(n, edge.size = "weight", edge.label = "weight") # edge arrows on a directed network ggnet2(network(m, directed = TRUE), arrow.gap = 0.05, arrow.size = 10) # Padgett's Florentine wedding data data(flo, package = "network") flo ggnet2(flo, label = TRUE) ggnet2(flo, label = TRUE, label.trim = 4, vjust = -1, size = 3, color = 1) ggnet2(flo, label = TRUE, size = 12, color = "white") } \seealso{ \code{\link{ggnet}} in this package, \code{\link[sna]{gplot}} in the \code{\link[sna]{sna}} package, and \code{\link[network]{plot.network}} in the \code{\link[network]{network}} package } \author{ Moritz Marbach and Francois Briatte, with help from Heike Hoffmann, Pedro Jordano and Ming-Yu Liu } GGally/man/ggally_densityDiag.Rd0000644000176200001440000000142613114357267016312 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_densityDiag} \alias{ggally_densityDiag} \title{Plots the Density Plots by Using Diagonal} \usage{ ggally_densityDiag(data, mapping, ..., rescale = FALSE) } \arguments{ \item{data}{data set using} \item{mapping}{aesthetics being used.} \item{...}{other arguments sent to stat_density} \item{rescale}{boolean to decide whether or not to rescale the count output} } \description{ Plots the density plots by using Diagonal. } \examples{ data(tips, package = "reshape") ggally_densityDiag(tips, mapping = ggplot2::aes(x = total_bill)) ggally_densityDiag(tips, mapping = ggplot2::aes(x = total_bill, color = day)) } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/ggally_dot.Rd0000644000176200001440000000207213114357267014632 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_dot} \alias{ggally_dot} \alias{ggally_dot_no_facet} \title{Plots the Box Plot with Dot} \usage{ ggally_dot(data, mapping, ...) ggally_dot_no_facet(data, mapping, ...) } \arguments{ \item{data}{data set using} \item{mapping}{aesthetics being used} \item{...}{other arguments being supplied to geom_jitter} } \description{ Add jittering with the box plot. \code{ggally_dot_no_facet} will be a single panel plot, while \code{ggally_dot} will be a faceted plot } \examples{ data(tips, package = "reshape") ggally_dot(tips, mapping = ggplot2::aes(x = total_bill, y = sex)) ggally_dot(tips, mapping = ggplot2::aes_string(x = "total_bill", y = "sex")) ggally_dot( tips, mapping = ggplot2::aes_string(y = "total_bill", x = "sex", color = "sex") ) ggally_dot( tips, mapping = ggplot2::aes_string(y = "total_bill", x = "sex", color = "sex", shape = "sex") ) + ggplot2::scale_shape(solid=FALSE) } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/scag_order.Rd0000644000176200001440000000117613114357267014621 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggparcoord.R \name{scag_order} \alias{scag_order} \title{Find order of variables} \usage{ scag_order(scag, vars, measure) } \arguments{ \item{scag}{\code{scagnostics} object} \item{vars}{character vector of the variables to be ordered} \item{measure}{scagnostics measure to order according to} } \value{ character vector of variable ordered according to the given scagnostic measure } \description{ Find order of variables based on a specified scagnostic measure by maximizing the index values of that measure along the path. } \author{ Barret Schloerke } GGally/man/broomify.Rd0000644000176200001440000000162713114357267014340 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggnostic.R \name{broomify} \alias{broomify} \title{Broomify a model} \usage{ broomify(model, lmStars = TRUE) } \arguments{ \item{model}{model to be sent to \code{broom::\link[broom]{augment}}, \code{broom::\link[broom]{glance}}, and \code{broom::\link[broom]{tidy}}} \item{lmStars}{boolean that determines if stars are added to labels} } \value{ broom::augmented data frame with the broom::glance data.frame and broom::tidy data.frame as 'broom_glance' and 'broom_tidy' attributes respectively. \code{var_x} and \code{var_y} variables are also added as attributes } \description{ broom::augment a model and add broom::glance and broom::tidy output as attributes. X and Y variables are also added. } \examples{ data(mtcars) model <- stats::lm(mpg ~ wt + qsec + am, data = mtcars) broomified_model <- broomify(model) str(broomified_model) } GGally/man/ggally_nostic_hat.Rd0000644000176200001440000000335313276725426016207 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggnostic.R \name{ggally_nostic_hat} \alias{ggally_nostic_hat} \title{ggnostic - leverage points} \usage{ ggally_nostic_hat(data, mapping, ..., linePosition = 2 * sum(eval_data_col(data, mapping$y))/nrow(data), lineColor = brew_colors("grey"), lineSize = 0.5, lineAlpha = 1, lineType = 2, avgLinePosition = sum(eval_data_col(data, mapping$y))/nrow(data), avgLineColor = brew_colors("grey"), avgLineSize = lineSize, avgLineAlpha = lineAlpha, avgLineType = 1) } \arguments{ \item{data, mapping, ...}{supplied directly to \code{\link{ggally_nostic_line}}} \item{linePosition, lineColor, lineSize, lineAlpha, lineType}{parameters supplied to \code{ggplot2::\link[ggplot2]{geom_line}} for the cutoff line} \item{avgLinePosition, avgLineColor, avgLineSize, avgLineAlpha, avgLineType}{parameters supplied to \code{ggplot2::\link[ggplot2]{geom_line}} for the average line} } \value{ ggplot2 plot object } \description{ A function to display stats::influence's hat information against a given explanatory variable. } \details{ As stated in \code{stats::\link[stats]{influence}} documentation: hat: a vector containing the diagonal of the 'hat' matrix. The diagonal elements of the 'hat' matrix describe the influence each response value has on the fitted value for that same observation. A suggested "cutoff" line is added to the plot at a height of 2 * p / n and an expected line at a height of p / n. If either \code{linePosition} or \code{avgLinePosition} is \code{NULL}, the respective line will not be drawn. } \examples{ dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars)) ggally_nostic_hat(dt, ggplot2::aes(wt, .hat)) } \seealso{ \code{stats::\link[stats]{influence}} } GGally/man/ggsurv.Rd0000644000176200001440000000653513114357267014032 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggsurv.R \name{ggsurv} \alias{ggsurv} \title{Survival curves with ggplot2} \usage{ ggsurv(s, CI = "def", plot.cens = TRUE, surv.col = "gg.def", cens.col = "gg.def", lty.est = 1, lty.ci = 2, size.est = 0.5, size.ci = size.est, cens.size = 2, cens.shape = 3, back.white = FALSE, xlab = "Time", ylab = "Survival", main = "", order.legend = TRUE) } \arguments{ \item{s}{an object of class \code{survfit}} \item{CI}{should a confidence interval be plotted? Defaults to \code{TRUE} for single stratum objects and \code{FALSE} for multiple strata objects.} \item{plot.cens}{mark the censored observations?} \item{surv.col}{colour of the survival estimate. Defaults to black for one stratum, and to the default \code{ggplot2} colours for multiple strata. Length of vector with colour names should be either 1 or equal to the number of strata.} \item{cens.col}{colour of the points that mark censored observations.} \item{lty.est}{linetype of the survival curve(s). Vector length should be either 1 or equal to the number of strata.} \item{lty.ci}{linetype of the bounds that mark the 95\% CI.} \item{size.est}{line width of the survival curve} \item{size.ci}{line width of the 95\% CI} \item{cens.size}{point size of the censoring points} \item{cens.shape}{shape of the points that mark censored observations.} \item{back.white}{if TRUE the background will not be the default grey of \code{ggplot2} but will be white with borders around the plot.} \item{xlab}{the label of the x-axis.} \item{ylab}{the label of the y-axis.} \item{main}{the plot label.} \item{order.legend}{boolean to determine if the legend display should be ordered by final survival time} } \value{ An object of class \code{ggplot} } \description{ This function produces Kaplan-Meier plots using \code{ggplot2}. As a first argument it needs a \code{survfit} object, created by the \code{survival} package. Default settings differ for single stratum and multiple strata objects. } \examples{ if (require(survival) && require(scales)) { data(lung, package = "survival") sf.lung <- survival::survfit(Surv(time, status) ~ 1, data = lung) ggsurv(sf.lung) # Multiple strata examples sf.sex <- survival::survfit(Surv(time, status) ~ sex, data = lung) pl.sex <- ggsurv(sf.sex) pl.sex # Adjusting the legend of the ggsurv fit pl.sex + ggplot2::guides(linetype = FALSE) + ggplot2::scale_colour_discrete( name = 'Sex', breaks = c(1,2), labels = c('Male', 'Female') ) # We can still adjust the plot after fitting data(kidney, package = "survival") sf.kid <- survival::survfit(Surv(time, status) ~ disease, data = kidney) pl.kid <- ggsurv(sf.kid, plot.cens = FALSE) pl.kid # Zoom in to first 80 days pl.kid + ggplot2::coord_cartesian(xlim = c(0, 80), ylim = c(0.45, 1)) # Add the diseases names to the plot and remove legend pl.kid + ggplot2::annotate( "text", label = c("PKD", "Other", "GN", "AN"), x = c(90, 125, 5, 60), y = c(0.8, 0.65, 0.55, 0.30), size = 5, colour = scales::hue_pal( h = c(0, 360) + 15, c = 100, l = 65, h.start = 0, direction = 1 )(4) ) + ggplot2::guides(color = FALSE, linetype = FALSE) } } \author{ Edwin Thoen \email{edwinthoen@gmail.com} } GGally/man/ggmatrix_progress.Rd0000644000176200001440000000163713277311163016254 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggmatrix_progress.R \name{ggmatrix_progress} \alias{ggmatrix_progress} \title{ggmatrix default progress bar} \usage{ ggmatrix_progress(format = " plot: [:plot_i,:plot_j] [:bar]:percent est::eta ", clear = TRUE, show_after = 0, ...) } \arguments{ \item{format, clear, show_after, ...}{parameters supplied directly to \code{progress::\link[progress]{progress_bar}$new()}} } \value{ function that accepts a plot matrix as the first argument and \code{...} for future expansion. Internally, the plot matrix is used to determine the total number of plots for the progress bar. } \description{ ggmatrix default progress bar } \examples{ p_ <- GGally::print_if_interactive pm <- ggpairs(iris, 1:2, progress = ggmatrix_progress()) p_(pm) # does not clear after finishing pm <- ggpairs(iris, 1:2, progress = ggmatrix_progress(clear = FALSE)) p_(pm) } GGally/man/ggally_diagAxis.Rd0000644000176200001440000000224413114357267015576 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_diagAxis} \alias{ggally_diagAxis} \title{Internal Axis Labeling Plot for ggpairs} \usage{ ggally_diagAxis(data, mapping, label = mapping$x, labelSize = 5, labelXPercent = 0.5, labelYPercent = 0.55, labelHJust = 0.5, labelVJust = 0.5, gridLabelSize = 4, ...) } \arguments{ \item{data}{dataset being plotted} \item{mapping}{aesthetics being used (x is the variable the plot will be made for)} \item{label}{title to be displayed in the middle. Defaults to \code{mapping$x}} \item{labelSize}{size of variable label} \item{labelXPercent}{percent of horizontal range} \item{labelYPercent}{percent of vertical range} \item{labelHJust}{hjust supplied to label} \item{labelVJust}{vjust supplied to label} \item{gridLabelSize}{size of grid labels} \item{...}{other arguments for geom_text} } \description{ This function is used when \code{axisLabels == "internal"}. } \examples{ data(tips, package = "reshape") ggally_diagAxis(tips, ggplot2::aes(x=tip)) ggally_diagAxis(tips, ggplot2::aes(x=sex)) } \author{ Jason Crowley \email{crowley.jason.s@gmail.com} and Barret Schloerke } GGally/man/column_is_character.Rd0000644000176200001440000000111713114357267016510 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggparcoord.R \name{column_is_character} \alias{column_is_character} \alias{column_is_factor} \title{Get vector of variable types from data frame} \usage{ column_is_character(df) column_is_factor(df) } \arguments{ \item{df}{data frame to extract variable types from} } \value{ character vector with variable types, with names corresponding to the variable names from df } \description{ Get vector of variable types from data frame } \author{ Jason Crowley \email{crowley.jason.s@gmail.com} } \keyword{internal} GGally/man/ggally_facetdensitystrip.Rd0000644000176200001440000000137613114357267017616 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_facetdensitystrip} \alias{ggally_facetdensitystrip} \title{Plots a density plot with facets or a tile plot with facets} \usage{ ggally_facetdensitystrip(data, mapping, ..., den_strip = FALSE) } \arguments{ \item{data}{data set using} \item{mapping}{aesthetics being used} \item{...}{other arguments being sent to either geom_histogram or stat_density} \item{den_strip}{boolean to decide whether or not to plot a density strip(TRUE) or a facet density(FALSE) plot.} } \description{ Make Tile Plot as densely as possible. } \examples{ example(ggally_facetdensity) example(ggally_denstrip) } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/ggally_box.Rd0000644000176200001440000000176013114357267014637 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_box} \alias{ggally_box} \alias{ggally_box_no_facet} \title{Plots the Box Plot} \usage{ ggally_box(data, mapping, ...) ggally_box_no_facet(data, mapping, ...) } \arguments{ \item{data}{data set using} \item{mapping}{aesthetics being used} \item{...}{other arguments being supplied to geom_boxplot} } \description{ Make a box plot with a given data set. \code{ggally_box_no_facet} will be a single panel plot, while \code{ggally_box} will be a faceted plot } \examples{ data(tips, package = "reshape") ggally_box(tips, mapping = ggplot2::aes(x = total_bill, y = sex)) ggally_box(tips, mapping = ggplot2::aes_string(x = "total_bill", y = "sex")) ggally_box( tips, mapping = ggplot2::aes_string(y = "total_bill", x = "sex", color = "sex"), outlier.colour = "red", outlier.shape = 13, outlier.size = 8 ) } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot} GGally/man/grab_legend.Rd0000644000176200001440000000174713114357267014746 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggmatrix_legend.R \name{grab_legend} \alias{grab_legend} \alias{print.legend_guide_box} \title{Grab the legend and print it as a plot} \usage{ grab_legend(p) \method{print}{legend_guide_box}(x, ..., plotNew = FALSE) } \arguments{ \item{p}{ggplot2 plot object} \item{x}{legend object that has been grabbed from a ggplot2 object} \item{...}{ignored} \item{plotNew}{boolean to determine if the `grid.newpage()` command and a new blank rectangle should be printed} } \description{ Grab the legend and print it as a plot } \examples{ library(ggplot2) histPlot <- qplot( x = Sepal.Length, data = iris, fill = Species, geom = "histogram", binwidth = 1/4 ) (right <- histPlot) (bottom <- histPlot + theme(legend.position = "bottom")) (top <- histPlot + theme(legend.position = "top")) (left <- histPlot + theme(legend.position = "left")) grab_legend(right) grab_legend(bottom) grab_legend(top) grab_legend(left) } GGally/man/ggduo.Rd0000644000176200001440000002144513277311163013612 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggpairs.R \name{ggduo} \alias{ggduo} \title{ggduo - A ggplot2 generalized pairs plot for two columns sets of a data.frame} \usage{ ggduo(data, mapping = NULL, columnsX = 1:ncol(data), columnsY = 1:ncol(data), title = NULL, types = list(continuous = "smooth_loess", comboVertical = "box_no_facet", comboHorizontal = "facethist", discrete = "ratio"), axisLabels = c("show", "none"), columnLabelsX = colnames(data[columnsX]), columnLabelsY = colnames(data[columnsY]), labeller = "label_value", switch = NULL, xlab = NULL, ylab = NULL, showStrips = NULL, legend = NULL, cardinality_threshold = 15, progress = NULL, legends = stop("deprecated")) } \arguments{ \item{data}{data set using. Can have both numerical and categorical data.} \item{mapping}{aesthetic mapping (besides \code{x} and \code{y}). See \code{\link[ggplot2]{aes}()}. If \code{mapping} is numeric, \code{columns} will be set to the \code{mapping} value and \code{mapping} will be set to \code{NULL}.} \item{columnsX, columnsY}{which columns are used to make plots. Defaults to all columns.} \item{title, xlab, ylab}{title, x label, and y label for the graph} \item{types}{see Details} \item{axisLabels}{either "show" to display axisLabels or "none" for no axis labels} \item{columnLabelsX, columnLabelsY}{label names to be displayed. Defaults to names of columns being used.} \item{labeller}{labeller for facets. See \code{\link[ggplot2]{labellers}}. Common values are \code{"label_value"} (default) and \code{"label_parsed"}.} \item{switch}{switch parameter for facet_grid. See \code{ggplot2::\link[ggplot2]{facet_grid}}. By default, the labels are displayed on the top and right of the plot. If \code{"x"}, the top labels will be displayed to the bottom. If \code{"y"}, the right-hand side labels will be displayed to the left. Can also be set to \code{"both"}} \item{showStrips}{boolean to determine if each plot's strips should be displayed. \code{NULL} will default to the top and right side plots only. \code{TRUE} or \code{FALSE} will turn all strips on or off respectively.} \item{legend}{May be the two objects described below or the default \code{NULL} value. The legend position can be moved by using ggplot2's theme element \code{pm + theme(legend.position = "bottom")} \describe{\item{a numeric vector of length 2}{provides the location of the plot to use the legend for the plot matrix's legend. Such as \code{legend = c(3,5)} which will use the legend from the plot in the third row and fifth column}\item{a single numeric value}{provides the location of a plot according to the display order. Such as \code{legend = 3} in a plot matrix with 2 rows and 5 columns displayed by column will return the plot in position \code{c(1,2)}}\item{a object from \code{\link{grab_legend}()}}{a predetermined plot legend that will be displayed directly}}} \item{cardinality_threshold}{maximum number of levels allowed in a character / factor column. Set this value to NULL to not check factor columns. Defaults to 15} \item{progress}{\code{NULL} (default) for a progress bar in interactive sessions with more than 15 plots, \code{TRUE} for a progress bar, \code{FALSE} for no progress bar, or a function that accepts at least a plot matrix and returns a new \code{progress::\link[progress]{progress_bar}}. See \code{\link{ggmatrix_progress}}.} \item{legends}{deprecated} } \description{ Make a matrix of plots with a given data set with two different column sets } \details{ \code{types} is a list that may contain the variables 'continuous', 'combo', 'discrete', and 'na'. Each element of the list may be a function or a string. If a string is supplied, it must implement one of the following options: \describe{ \item{continuous}{exactly one of ('points', 'smooth', 'smooth_loess', 'density', 'cor', 'blank'). This option is used for continuous X and Y data.} \item{comboHorizontal}{exactly one of ('box', 'box_no_facet', 'dot', 'dot_no_facet', 'facethist', 'facetdensity', 'denstrip', 'blank'). This option is used for either continuous X and categorical Y data or categorical X and continuous Y data.} \item{comboVertical}{exactly one of ('box', 'box_no_facet', 'dot', 'dot_no_facet', 'facethist', 'facetdensity', 'denstrip', 'blank'). This option is used for either continuous X and categorical Y data or categorical X and continuous Y data.} \item{discrete}{exactly one of ('facetbar', 'ratio', 'blank'). This option is used for categorical X and Y data.} \item{na}{exactly one of ('na', 'blank'). This option is used when all X data is \code{NA}, all Y data is \code{NA}, or either all X or Y data is \code{NA}.} } If 'blank' is ever chosen as an option, then ggduo will produce an empty plot. If a function is supplied as an option, it should implement the function api of \code{function(data, mapping, ...){#make ggplot2 plot}}. If a specific function needs its parameters set, \code{\link{wrap}(fn, param1 = val1, param2 = val2)} the function with its parameters. } \examples{ # small function to display plots only if it's interactive p_ <- GGally::print_if_interactive data(baseball, package = "plyr") # Keep players from 1990-1995 with at least one at bat # Add how many singles a player hit # (must do in two steps as X1b is used in calculations) dt <- transform( subset(baseball, year >= 1990 & year <= 1995 & ab > 0), X1b = h - X2b - X3b - hr ) # Add # the player's batting average, # the player's slugging percentage, # and the player's on base percentage # Make factor a year, as each season is discrete dt <- transform( dt, batting_avg = h / ab, slug = (X1b + 2*X2b + 3*X3b + 4*hr) / ab, on_base = (h + bb + hbp) / (ab + bb + hbp), year = as.factor(year) ) pm <- ggduo( dt, c("year", "g", "ab", "lg"), c("batting_avg", "slug", "on_base"), mapping = ggplot2::aes(color = lg) ) # Prints, but # there is severe over plotting in the continuous plots # the labels could be better # want to add more hitting information p_(pm) # address overplotting issues and add a title pm <- ggduo( dt, c("year", "g", "ab", "lg"), c("batting_avg", "slug", "on_base"), columnLabelsX = c("year", "player game count", "player at bat count", "league"), columnLabelsY = c("batting avg", "slug \%", "on base \%"), title = "Baseball Hitting Stats from 1990-1995", mapping = ggplot2::aes(color = lg), types = list( # change the shape and add some transparency to the points continuous = wrap("smooth_loess", alpha = 0.50, shape = "+") ), showStrips = FALSE ); p_(pm) # Example derived from: ## R Data Analysis Examples | Canonical Correlation Analysis. UCLA: Institute for Digital ## Research and Education. ## from http://www.stats.idre.ucla.edu/r/dae/canonical-correlation-analysis ## (accessed May 22, 2017). # "Example 1. A researcher has collected data on three psychological variables, four # academic variables (standardized test scores) and gender for 600 college freshman. # She is interested in how the set of psychological variables relates to the academic # variables and gender. In particular, the researcher is interested in how many # dimensions (canonical variables) are necessary to understand the association between # the two sets of variables." data(psychademic) summary(psychademic) (psych_variables <- attr(psychademic, "psychology")) (academic_variables <- attr(psychademic, "academic")) ## Within correlation p_(ggpairs(psychademic, columns = psych_variables)) p_(ggpairs(psychademic, columns = academic_variables)) ## Between correlation loess_with_cor <- function(data, mapping, ..., method = "pearson") { x <- eval(mapping$x, data) y <- eval(mapping$y, data) cor <- cor(x, y, method = method) ggally_smooth_loess(data, mapping, ...) + ggplot2::geom_label( data = data.frame( x = min(x, na.rm = TRUE), y = max(y, na.rm = TRUE), lab = round(cor, digits = 3) ), mapping = ggplot2::aes(x = x, y = y, label = lab), hjust = 0, vjust = 1, size = 5, fontface = "bold", inherit.aes = FALSE # do not inherit anything from the ... ) } pm <- ggduo( psychademic, rev(psych_variables), academic_variables, types = list(continuous = loess_with_cor), showStrips = FALSE ) suppressWarnings(p_(pm)) # ignore warnings from loess # add color according to sex pm <- ggduo( psychademic, mapping = ggplot2::aes(color = sex), rev(psych_variables), academic_variables, types = list(continuous = loess_with_cor), showStrips = FALSE, legend = c(5,2) ) suppressWarnings(p_(pm)) # add color according to sex pm <- ggduo( psychademic, mapping = ggplot2::aes(color = motivation), rev(psych_variables), academic_variables, types = list(continuous = loess_with_cor), showStrips = FALSE, legend = c(5,2) ) + ggplot2::theme(legend.position = "bottom") suppressWarnings(p_(pm)) } GGally/man/flea.Rd0000644000176200001440000000176313114357267013422 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data-flea.R \docType{data} \name{flea} \alias{flea} \title{Historical data used for classification examples.} \format{A data frame with 74 rows and 7 variables} \usage{ data(flea) } \description{ This data contains physical measurements on three species of flea beetles. } \details{ \itemize{ \item species Ch. concinna, Ch. heptapotamica, Ch. heikertingeri \item tars1 width of the first joint of the first tarsus in microns \item tars2 width of the second joint of the first tarsus in microns \item head the maximal width of the head between the external edges of the eyes in 0.01 mm \item aede1 the maximal width of the aedeagus in the fore-part in microns \item aede2 the front angle of the aedeagus (1 unit = 7.5 degrees) \item aede3 the aedeagus width from the side in microns } } \references{ Lubischew, A. A. (1962), On the Use of Discriminant Functions in Taxonomy, Biometrics 18:455-477. } \keyword{datasets} GGally/man/ggfacet.Rd0000644000176200001440000000427713277311163014111 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggfacet.R \name{ggfacet} \alias{ggfacet} \title{ggfacet - single ggplot2 plot matrix with facet_grid} \usage{ ggfacet(data, mapping = NULL, columnsX = 1:ncol(data), columnsY = 1:ncol(data), fn = ggally_points, ..., columnLabelsX = names(data[columnsX]), columnLabelsY = names(data[columnsY]), xlab = NULL, ylab = NULL, title = NULL, scales = "free") } \arguments{ \item{data}{data.frame that contains all columns to be displayed. This data will be melted before being passed into the function \code{fn}} \item{mapping}{aesthetic mapping (besides \code{x} and \code{y}). See \code{\link[ggplot2]{aes}()}} \item{columnsX}{columns to be displayed in the plot matrix} \item{columnsY}{rows to be displayed in the plot matrix} \item{fn}{function to be executed. Similar to \code{\link{ggpairs}} and \code{\link{ggduo}}, the function may either be a string identifier or a real function that \code{\link{wrap}} understands.} \item{...}{extra arguments passed directly to \code{fn}} \item{columnLabelsX, columnLabelsY}{column and row labels to display in the plot matrix} \item{xlab, ylab, title}{plot matrix labels} \item{scales}{parameter supplied to \code{ggplot2::\link[ggplot2]{facet_grid}}. Default behavior is \code{"free"}} } \description{ ggfacet - single ggplot2 plot matrix with facet_grid } \examples{ # Small function to display plots only if it's interactive p_ <- GGally::print_if_interactive if (requireNamespace("chemometrics", quietly = TRUE)) { data(NIR, package = "chemometrics") NIR_sub <- data.frame(NIR$yGlcEtOH, NIR$xNIR[,1:3]) str(NIR_sub) x_cols <- c("X1115.0", "X1120.0", "X1125.0") y_cols <- c("Glucose", "Ethanol") # using ggduo directly p <- ggduo(NIR_sub, x_cols, y_cols, types = list(continuous = "points")) p_(p) # using ggfacet p <- ggfacet(NIR_sub, x_cols, y_cols) p_(p) # add a smoother p <- ggfacet(NIR_sub, x_cols, y_cols, fn = 'smooth_loess') p_(p) # same output p <- ggfacet(NIR_sub, x_cols, y_cols, fn = ggally_smooth_loess) p_(p) # Change scales to be the same in for every row and for every column p <- ggfacet(NIR_sub, x_cols, y_cols, scales = "fixed") p_(p) } } GGally/man/mapping_color_to_fill.Rd0000644000176200001440000000055613114357267017053 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggpairs.R \name{mapping_color_to_fill} \alias{mapping_color_to_fill} \title{Aesthetic Mapping Color Fill} \usage{ mapping_color_to_fill(current) } \arguments{ \item{current}{the current aesthetics} } \description{ Replace the fill with the color and make color NULL. } \keyword{internal} GGally/man/require_namespaces.Rd0000644000176200001440000000053413276725426016366 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{require_namespaces} \alias{require_namespaces} \title{Loads package namespaces} \usage{ require_namespaces(pkgs) } \arguments{ \item{pkgs}{vector of character values} } \description{ Loads package namespaces or yells at user... loudly } \keyword{internal} GGally/man/is_blank_plot.Rd0000644000176200001440000000072013114357267015323 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ggmatrix_print.R \name{is_blank_plot} \alias{is_blank_plot} \title{Is Blank Plot? Find out if the plot equals a blank plot} \usage{ is_blank_plot(p) } \description{ Is Blank Plot? Find out if the plot equals a blank plot } \examples{ GGally:::is_blank_plot(ggally_blank()) GGally:::is_blank_plot(ggally_points(mtcars, ggplot2::aes_string(x = "disp", y = "hp"))) } \keyword{internal} GGally/man/ggally_text.Rd0000644000176200001440000000165713140471254015030 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gg-plots.R \name{ggally_text} \alias{ggally_text} \title{Text Plot} \usage{ ggally_text(label, mapping = ggplot2::aes(color = "black"), xP = 0.5, yP = 0.5, xrange = c(0, 1), yrange = c(0, 1), ...) } \arguments{ \item{label}{text that you want to appear} \item{mapping}{aesthetics that don't relate to position (such as color)} \item{xP}{horizontal position percentage} \item{yP}{vertical position percentage} \item{xrange}{range of the data around it. Only nice to have if plotting in a matrix} \item{yrange}{range of the data around it. Only nice to have if plotting in a matrix} \item{...}{other arguments for geom_text} } \description{ Plot text for a plot. } \examples{ ggally_text("Example 1") ggally_text("Example\\nTwo", mapping = ggplot2::aes(size = 15), color = I("red")) } \author{ Barret Schloerke \email{schloerke@gmail.com} } \keyword{hplot}

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"gam" from mgcv omits '.sigma') #' Broomify a model #' #' broom::augment a model and add broom::glance and broom::tidy output as attributes. X and Y variables are also added. #' #' @param model model to be sent to \code{broom::\link[broom]{augment}}, \code{broom::\link[broom]{glance}}, and \code{broom::\link[broom]{tidy}} #' @param lmStars boolean that determines if stars are added to labels #' @return broom::augmented data frame with the broom::glance data.frame and broom::tidy data.frame as 'broom_glance' and 'broom_tidy' attributes respectively. \code{var_x} and \code{var_y} variables are also added as attributes #' @export #' @examples #' data(mtcars) #' model <- stats::lm(mpg ~ wt + qsec + am, data = mtcars) #' broomified_model <- broomify(model) #' str(broomified_model) broomify <- function(model, lmStars = TRUE) { if (inherits(model, "broomify")) { return(model) } require_namespaces("broom") broom_glance_info <- broom::glance(model) broom_tidy_coef <- broom::tidy(model) broom_augment_rows <- broom::augment(model) attr(broom_augment_rows, "broom_glance") <- broom_glance_info attr(broom_augment_rows, "broom_tidy") <- broom_tidy_coef attr(broom_augment_rows, "var_x") <- model_beta_variables(data = broom_augment_rows) attr(broom_augment_rows, "var_y") <- model_response_variables(data = broom_augment_rows) attr(broom_augment_rows, "var_x_label") <- model_beta_label( model, data = broom_augment_rows, lmStars ) class(broom_augment_rows) <- c(class(broom_augment_rows), "broomify") return(broom_augment_rows) } model_variables <- function(model, data = broom::augment(model)) { augment_names <- names(data) augment_names <- augment_names[!grepl("^\\.", augment_names)] } #' Model term names #' #' Retrieve either the response variable names, the beta variable names, or beta variable names. If the model is an object of class 'lm', by default, the beta variable names will include anova significance stars. #' #' @param model model in question #' @param data equivalent to \code{broom::augment(model)} #' @param lmStars boolean that determines if stars are added to labels #' @return character vector of names #' @rdname model_terms #' @export #' @importFrom stats terms model_response_variables <- function(model, data = broom::augment(model)) { model_variables(model = model, data = data)[1] } #' @rdname model_terms #' @export model_beta_variables <- function(model, data = broom::augment(model)) { model_variables(model = model, data = data)[-1] } #' @importFrom stats symnum beta_stars <- function(p_val) { unclass(symnum( p_val, corr = FALSE, na = FALSE, cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", "**", "*", ".", " ") )) } #' @export #' @rdname model_terms #' @importFrom stats anova model_beta_label <- function(model, data = broom::augment(model), lmStars = TRUE) { beta_vars <- model_beta_variables(model, data = data) if ( (! identical(class(model), "lm")) || (!isTRUE(lmStars))) { return(beta_vars) } # for lm models only tidy_anova <- broom::tidy(anova(model)) tidy_anova <- tidy_anova[tidy_anova$term %in% beta_vars, ] p_vals <- tidy_anova$p.value names(p_vals) <- tidy_anova$term p_vals <- p_vals[beta_vars] x_labs <- paste(beta_vars, beta_stars(p_vals), sep = "") gsub("\\s+$", "", x_labs) } broom_columns <- function() { c(".fitted", ".se.fit", ".resid", ".hat", ".sigma", ".cooksd", ".std.resid") } #' RColorBrewer Set1 colors #' #' @param col standard color name used to retrieve hex color value #' @import RColorBrewer #' @export brew_colors <- function(col) { brew_cols <- RColorBrewer::brewer.pal(n = 9, "Set1") names(brew_cols) <- c( "red", "blue", "green", "purple", "orange", "yellow", "brown", "pink", "grey" ) brew_cols <- as.list(brew_cols) ret <- brew_cols[[col]] if (is.null(ret)) { stop(paste("color '", col, "' not found in: c(", paste(names(brew_cols), collapse = ", "), ")", sep = "")) } ret } #' ggnostic -background line with geom #' #' If a non-null \code{linePosition} value is given, a line will be drawn before the given \code{continuous_geom} or \code{combo_geom} is added to the plot. #' #' Functions with a color in their name have different default color behavior. #' #' @param data,mapping supplied directly to \code{ggplot2::\link[ggplot2]{ggplot}(data, mapping)} #' @param ... parameters supplied to \code{continuous_geom} or \code{combo_geom} #' @param linePosition,lineColor,lineSize,lineAlpha,lineType parameters supplied to \code{ggplot2::\link[ggplot2]{geom_line}} #' @param continuous_geom ggplot2 geom that is executed after the line is (possibly) added and if the x data is continuous #' @param combo_geom ggplot2 geom that is executed after the line is (possibly) added and if the x data is discrete #' @param mapColorToFill boolean to determine if combo plots should cut the color mapping to the fill mapping #' @return ggplot2 plot object #' @rdname ggally_nostic_line ggally_nostic_line <- function( data, mapping, ..., linePosition = NULL, lineColor = "red", lineSize = 0.5, lineAlpha = 1, lineType = 1, continuous_geom = ggplot2::geom_point, combo_geom = ggplot2::geom_boxplot, mapColorToFill = TRUE ) { x_is_character <- is_character_column(data, mapping, "x") if (x_is_character & isTRUE(mapColorToFill)) { mapping <- mapping_color_to_fill(mapping) } p <- ggplot(data = data, mapping = mapping) if (!is.null(linePosition)) { p <- p + geom_hline( yintercept = linePosition, color = lineColor, size = lineSize, alpha = lineAlpha, linetype = lineType ) } if (x_is_character) { p <- p + combo_geom(...) } else { p <- p + continuous_geom(...) } p } #' ggnostic - residuals #' #' If non-null \code{pVal} and \code{sigma} values are given, confidence interval lines will be added to the plot at the specified \code{pVal} percentiles of a N(0, sigma) distribution. #' #' @param data,mapping,... parameters supplied to \code{\link{ggally_nostic_line}} #' @param linePosition,lineColor,lineSize,lineAlpha,lineType parameters supplied to \code{ggplot2::\link[ggplot2]{geom_line}} #' @param lineConfColor,lineConfSize,lineConfAlpha,lineConfType parameters supplied to the confidence interval lines #' @param pVal percentiles of a N(0, sigma) distribution to be drawn #' @param sigma sigma value for the \code{pVal} percentiles #' @param se boolean to determine if the confidence intervals should be displayed #' @param method parameter supplied to \code{ggplot2::\link[ggplot2]{geom_smooth}}. Defaults to \code{"auto"} #' @return ggplot2 plot object #' @seealso \code{stats::\link[stats]{residuals}} #' @export #' @importFrom stats qnorm #' @examples #' dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars)) #' ggally_nostic_resid(dt, ggplot2::aes(wt, .resid)) ggally_nostic_resid <- function( data, mapping, ..., linePosition = 0, lineColor = brew_colors("grey"), lineSize = 0.5, lineAlpha = 1, lineType = 1, lineConfColor = brew_colors("grey"), lineConfSize = lineSize, lineConfAlpha = lineAlpha, lineConfType = 2, pVal = c(0.025, 0.975), sigma = attr(data, "broom_glance")$sigma, se = TRUE, method = "auto" ) { if (!is.null(linePosition) & !is.null(pVal) & !is.null(sigma)) { scaled_sigmas <- qnorm(pVal, lower.tail = TRUE, sd = sigma) linePosition <- c(linePosition, linePosition + scaled_sigmas) lineColor <- c(lineColor, lineConfColor, lineConfColor) lineType <- c(lineType, lineConfType, lineConfType) lineSize <- c(lineSize, lineConfSize, lineConfSize) lineAlpha <- c(lineAlpha, lineConfAlpha, lineConfAlpha) } p <- ggally_nostic_line( data, mapping, ..., linePosition = linePosition, lineColor = lineColor, lineType = lineType, lineSize = lineSize, lineAlpha = lineAlpha ) if (! is_character_column(data, mapping, "x")) { p <- p + geom_smooth(se = se, method = method) } p + coord_cartesian( ylim = range( c(linePosition, eval_data_col(data, mapping$y)), na.rm = TRUE ) ) } #' ggnostic - standardized residuals #' #' If non-null \code{pVal} and \code{sigma} values are given, confidence interval lines will be added to the plot at the specified \code{pVal} locations of a N(0, 1) distribution. #' #' @param data,mapping,... parameters supplied to \code{\link{ggally_nostic_resid}} #' @param sigma sigma value for the \code{pVal} percentiles. Set to 1 for standardized residuals #' @seealso \code{stats::\link[stats]{rstandard}} #' @return ggplot2 plot object #' @rdname ggally_nostic_std_resid #' @export #' @examples #' dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars)) #' ggally_nostic_std_resid(dt, ggplot2::aes(wt, .std.resid)) ggally_nostic_std_resid <- function( data, mapping, ..., sigma = 1 ) { ggally_nostic_resid( data, mapping, ..., sigma = sigma ) } #' ggnostic - fitted value standard error #' #' A function to display \code{stats::\link[stats]{predict}}'s standard errors #' #' @details #' As stated in \code{stats::\link[stats]{predict}} documentation: #' #' If the logical 'se.fit' is 'TRUE', standard errors of the predictions are calculated. If the numeric argument 'scale' is set (with optional ''df'), it is used as the residual standard deviation in the computation of the standard errors, otherwise this is extracted from the model fit. #' #' Since the se.fit is \code{TRUE} and scale is unset by default, the standard errors are extracted from the model fit. #' #' A base line of 0 is added to give reference to a perfect fit. #' #' @param data,mapping,...,lineColor parameters supplied to \code{\link{ggally_nostic_line}} #' @param linePosition base comparison for a perfect fit #' @seealso \code{stats::\link[stats]{influence}} #' @return ggplot2 plot object #' @export #' @examples #' dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars)) #' ggally_nostic_se_fit(dt, ggplot2::aes(wt, .se.fit)) ggally_nostic_se_fit <- function( data, mapping, ..., lineColor = brew_colors("grey"), linePosition = NULL ) { ggally_nostic_line( data, mapping, ..., lineColor = lineColor, linePosition = linePosition ) } #' ggnostic - leave one out model sigma #' #' A function to display \code{stats::\link[stats]{influence}}'s sigma value. #' #' @details #' As stated in \code{stats::\link[stats]{influence}} documentation: #' #' sigma: a vector whose i-th element contains the estimate of the residual standard deviation obtained when the i-th case is dropped from the regression. (The approximations needed for GLMs can result in this being 'NaN'.) #' #' A line is added to display the overall model's sigma value. This gives a baseline for comparison #' #' @param data,mapping,...,lineColor parameters supplied to \code{\link{ggally_nostic_line}} #' @param linePosition line that is drawn in the background of the plot. Defaults to the overall model's sigma value. #' @seealso \code{stats::\link[stats]{influence}} #' @return ggplot2 plot object #' @export #' @examples #' dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars)) #' ggally_nostic_sigma(dt, ggplot2::aes(wt, .sigma)) ggally_nostic_sigma <- function( data, mapping, ..., lineColor = brew_colors("grey"), linePosition = attr(data, "broom_glance")$sigma ) { ggally_nostic_line( data, mapping, ..., lineColor = lineColor, linePosition = linePosition ) } #' ggnostic - Cook's distance #' #' A function to display \code{stats::\link[stats]{cooks.distance}}. #' #' @details #' A line is added at F_{p, n - p}(0.5) to display the general cutoff point for Cook's Distance. #' #' Reference: Michael H. Kutner, Christopher J. Nachtsheim, John Neter, and William Li. Applied linear statistical models. The McGraw-Hill / Irwin series operations and decision sciences. McGraw-Hill Irwin, 2005, p. 403 #' #' @param data,mapping,...,lineColor,lineType parameters supplied to \code{\link{ggally_nostic_line}} #' @param linePosition 4 / n is the general cutoff point for Cook's Distance #' @seealso \code{stats::\link[stats]{cooks.distance}} #' @return ggplot2 plot object #' @rdname ggally_nostic_cooksd #' @export #' @importFrom stats pf #' @examples #' dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars)) #' ggally_nostic_cooksd(dt, ggplot2::aes(wt, .cooksd)) ggally_nostic_cooksd <- function( data, mapping, ..., linePosition = pf(0.5, length(attr(data, "var_x")), nrow(data) - length(attr(data, "var_x"))), lineColor = brew_colors("grey"), lineType = 2 ) { ggally_nostic_line( data, mapping, ..., linePosition = linePosition, lineColor = lineColor, lineType = lineType ) } #' ggnostic - leverage points #' #' A function to display stats::influence's hat information against a given explanatory variable. #' #' @details #' As stated in \code{stats::\link[stats]{influence}} documentation: #' #' hat: a vector containing the diagonal of the 'hat' matrix. #' #' The diagonal elements of the 'hat' matrix describe the influence each response value has on the fitted value for that same observation. #' #' A suggested "cutoff" line is added to the plot at a height of 2 * p / n and an expected line at a height of p / n. #' If either \code{linePosition} or \code{avgLinePosition} is \code{NULL}, the respective line will not be drawn. #' #' @param data,mapping,... supplied directly to \code{\link{ggally_nostic_line}} #' @param linePosition,lineColor,lineSize,lineAlpha,lineType parameters supplied to \code{ggplot2::\link[ggplot2]{geom_line}} for the cutoff line #' @param avgLinePosition,avgLineColor,avgLineSize,avgLineAlpha,avgLineType parameters supplied to \code{ggplot2::\link[ggplot2]{geom_line}} for the average line #' @seealso \code{stats::\link[stats]{influence}} #' @return ggplot2 plot object #' @export #' @examples #' dt <- broomify(stats::lm(mpg ~ wt + qsec + am, data = mtcars)) #' ggally_nostic_hat(dt, ggplot2::aes(wt, .hat)) ggally_nostic_hat <- function( data, mapping, ..., linePosition = 2 * sum(eval_data_col(data, mapping$y)) / nrow(data), lineColor = brew_colors("grey"), lineSize = 0.5, lineAlpha = 1, lineType = 2, avgLinePosition = sum(eval_data_col(data, mapping$y)) / nrow(data), avgLineColor = brew_colors("grey"), avgLineSize = lineSize, avgLineAlpha = lineAlpha, avgLineType = 1 ) { if (is.null(linePosition)) { lineColor <- lineSize <- lineAlpha <- lineType <- NULL } if (is.null(avgLinePosition)) { avgLineColor <- avgLineSize <- avgLineAlpha <- avgLineType <- NULL } ggally_nostic_line( data, mapping, ..., linePosition = c(linePosition, avgLinePosition), lineColor = c(lineColor, avgLineColor), lineSize = c(lineSize, avgLineSize), lineType = c(lineType, avgLineType), lineAlpha = c(lineAlpha, avgLineAlpha) ) } #' Function switch #' #' Function that allows you to call different functions based upon an aesthetic variable value. #' #' @param types list of functions that follow the ggmatrix function standard: \code{function(data, mapping, ...){ #make ggplot2 object }}. One key should be a 'default' key for a default switch case. #' @param mapping_val mapping value to switch on. Defaults to the 'y' variable of the aesthetics list. #' @export #' @examples #' ggnostic_continuous_fn <- fn_switch(list( #' default = ggally_points, #' .fitted = ggally_points, #' .se.fit = ggally_nostic_se_fit, #' .resid = ggally_nostic_resid, #' .hat = ggally_nostic_hat, #' .sigma = ggally_nostic_sigma, #' .cooksd = ggally_nostic_cooksd, #' .std.resid = ggally_nostic_std_resid #' )) #' #' ggnostic_combo_fn <- fn_switch(list( #' default = ggally_box_no_facet, #' fitted = ggally_box_no_facet, #' .se.fit = ggally_nostic_se_fit, #' .resid = ggally_nostic_resid, #' .hat = ggally_nostic_hat, #' .sigma = ggally_nostic_sigma, #' .cooksd = ggally_nostic_cooksd, #' .std.resid = ggally_nostic_std_resid #' )) fn_switch <- function( types, mapping_val = "y" ) { function(data, mapping, ...) { var <- mapping_string(mapping[[mapping_val]]) fn <- ifnull(types[[var]], types[["default"]]) if (is.null(fn)) { stop(str_c( "function could not be found for ", mapping_val, " or 'default'. ", "Please include one of these two keys as a function." )) } fn(data = data, mapping = mapping, ...) } } check_and_set_nostic_types <- function( types, default, .fitted, .resid, .std.resid, # nolint .sigma, .se.fit, # nolint .hat, .cooksd ) { types_names <- names(types) set_type_value <- function(name, value) { if (is.null(types[[name]])) { # value is not set if (! (name %in% types_names)) { # set suggested fn types[[name]] <<- value } else { # does not plot displayed types[[name]] <<- ggally_blank } } } set_type_value("default", default) set_type_value(".fitted", .fitted) set_type_value(".resid", .resid) set_type_value(".std.resid", .std.resid) # nolint set_type_value(".sigma", .sigma) set_type_value(".se.fit", .se.fit) # nolint set_type_value(".hat", .hat) set_type_value(".cooksd", .cooksd) types } #' ggnostic - Plot matrix of statistical model diagnostics #' #' #' @section `columnsY`: #' \code{broom::\link[broom]{augment}()} collects data from the supplied model and returns a data.frame with the following columns (taken directly from broom documentation). These columns are the only allowed values in the \code{columnsY} parameter to \code{ggnostic}. #' #' \describe{ #' \item{.resid}{Residuals} #' \item{.hat}{Diagonal of the hat matrix} #' \item{.sigma}{Estimate of residual standard deviation when #' corresponding observation is dropped from model} #' \item{.cooksd}{Cooks distance, \code{\link[stats]{cooks.distance}}} #' \item{.fitted}{Fitted values of model} #' \item{.se.fit}{Standard errors of fitted values} #' \item{.std.resid}{Standardized residuals} #' \item{response variable name}{The response variable in the model may be added. Such as \code{"mpg"} in the model \code{lm(mpg ~ ., data = mtcars)}} #' } #' #' @section `continuous`, `combo`, `discrete` types: #' Similar to \code{\link{ggduo}} and \code{\link{ggpairs}}, functions may be supplied to display the different column types. However, since the Y rows are fixed, each row has it's own corresponding function in each of the plot types: continuous, combo, and discrete. Each plot type list can have keys that correspond to the \code{broom::\link[broom]{augment}()} output: \code{".fitted"}, \code{".resid"}, \code{".std.resid"}, \code{".sigma"}, \code{".se.fit"}, \code{".hat"}, \code{".cooksd"}. An extra key, \code{"default"}, is used to plot the response variables of the model if they are included. Having a function for each diagnostic allows for very fine control over the diagnostics plot matrix. The functions for each type list are wrapped into a switch function that calls the function corresponding to the y variable being plotted. These switch functions are then passed directly to the \code{types} parameter in \code{\link{ggduo}}. #' #' @param model statistical model object such as output from \code{stats::\link[stats]{lm}} or \code{stats::\link[stats]{glm}} #' @param ... arguments passed directly to \code{\link{ggduo}} #' @param columnsX columns to be displayed in the plot matrix. Defaults to the predictor columns of the \code{model} #' @param columnsY rows to be displayed in the plot matrix. Defaults to residuals, leave one out sigma value, diagonal of the hat matrix, and Cook's Distance. The possible values are the response variables in the model and the added columns provided by \code{broom::\link[broom]{augment}(model)}. See details for more information. #' @param columnLabelsX,columnLabelsY column and row labels to display in the plot matrix #' @param xlab,ylab,title plot matrix labels passed directly to \code{\link{ggmatrix}} #' @param continuous,combo,discrete list of functions for each y variable. See details for more information. #' @template ggmatrix-progress #' @param data data defaults to a 'broomify'ed model object. This object will contain information about the X variables, Y variables, and multiple broom outputs. See \code{\link{broomify}(model)} for more information #' @export #' @examples #' # small function to display plots only if it's interactive #' p_ <- GGally::print_if_interactive #' data(mtcars) #' #' # use mtcars dataset and alter the 'am' column to display actual name values #' mtc <- mtcars #' mtc$am <- c("0" = "automatic", "1" = "manual")[as.character(mtc$am)] #' #' # step the complete model down to a smaller model #' mod <- stats::step(stats::lm(mpg ~ ., data = mtc), trace = FALSE) #' #' # display using defaults #' pm <- ggnostic(mod) #' p_(pm) #' #' # color by am value #' pm <- ggnostic(mod, mapping = ggplot2::aes(color = am)) #' p_(pm) #' #' # turn resid smooth error ribbon off #' pm <- ggnostic(mod, continuous = list(.resid = wrap("nostic_resid", se = FALSE))) #' p_(pm) #' #' #' ## plot residuals vs fitted in a ggpairs plot matrix #' dt <- broomify(mod) #' pm <- ggpairs( #' dt, c(".fitted", ".resid"), #' columnLabels = c("fitted", "residuals"), #' lower = list(continuous = ggally_nostic_resid) #' ) #' p_(pm) ggnostic <- function( model, ..., columnsX = attr(data, "var_x"), # columnsY = c(".fitted", ".se.fit", ".resid", ".std.resid", ".sigma", ".hat", ".cooksd"), columnsY = c(".resid", ".sigma", ".hat", ".cooksd"), columnLabelsX = attr(data, "var_x_label"), columnLabelsY = gsub("\\.", " ", gsub("^\\.", "", columnsY)), xlab = "explanatory variables", ylab = "diagnostics", title = paste(deparse(model$call, width.cutoff = 500L), collapse = "\n"), continuous = list( default = ggally_points, .fitted = ggally_points, .se.fit = ggally_nostic_se_fit, .resid = ggally_nostic_resid, .hat = ggally_nostic_hat, .sigma = ggally_nostic_sigma, .cooksd = ggally_nostic_cooksd, .std.resid = ggally_nostic_std_resid ), combo = list( default = ggally_box_no_facet, fitted = ggally_box_no_facet, .se.fit = ggally_nostic_se_fit, .resid = ggally_nostic_resid, .hat = ggally_nostic_hat, .sigma = ggally_nostic_sigma, .cooksd = ggally_nostic_cooksd, .std.resid = ggally_nostic_std_resid ), discrete = list( default = ggally_ratio, .fitted = ggally_ratio, .se.fit = ggally_ratio, .resid = ggally_ratio, .hat = ggally_ratio, .sigma = ggally_ratio, .cooksd = ggally_ratio, .std.resid = ggally_ratio ), progress = NULL, data = broomify(model) ) { continuous_types <- check_and_set_nostic_types( continuous, default = ggally_nostic_line, .fitted = ggally_nostic_line, .se.fit = ggally_nostic_se_fit, .resid = ggally_nostic_resid, .hat = ggally_nostic_hat, .sigma = ggally_nostic_sigma, .cooksd = ggally_nostic_cooksd, .std.resid = ggally_nostic_std_resid ) combo_types <- check_and_set_nostic_types( combo, default = ggally_nostic_line, .fitted = ggally_nostic_line, .se.fit = ggally_nostic_se_fit, .resid = ggally_nostic_resid, .hat = ggally_nostic_hat, .sigma = ggally_nostic_sigma, .cooksd = ggally_nostic_cooksd, .std.resid = ggally_nostic_std_resid ) discrete_types <- check_and_set_nostic_types( discrete, default = ggally_ratio, .fitted = ggally_ratio, .se.fit = ggally_ratio, .resid = ggally_ratio, .hat = ggally_ratio, .sigma = ggally_ratio, .cooksd = ggally_ratio, .std.resid = ggally_ratio ) continuous_fn <- fn_switch(continuous_types, "y") combo_fn <- fn_switch(combo_types, "y") discrete_fn <- fn_switch(discrete_types, "y") columnsX <- match_nostic_columns(columnsX, attr(data, "var_x"), "columnsX") columnsY <- match_nostic_columns( columnsY, c(attr(data, "var_y"), broom_columns()), "columnsY" ) ggduo( data = data, columnsX = columnsX, columnsY = columnsY, columnLabelsX = columnLabelsX, columnLabelsY = columnLabelsY, types = list( continuous = continuous_fn, comboVertical = combo_fn, comboHorizontal = combo_fn, discrete = discrete_fn ), ..., progress = progress, title = title, xlab = xlab, ylab = ylab ) } # https://github.com/ggobi/ggobi/blob/master/data/pigs.xml #' Multiple Time Series #' #' GGally implementation of ts.plot. Wraps around the ggduo function and removes the column strips #' @param ... supplied directly to \code{\link{ggduo}} #' @param columnLabelsX remove top strips for the X axis by default #' @param xlab defaults to "time" #' @return ggmatrix object #' @export #' @examples #' ggts(pigs, "time", c("gilts", "profit", "s_per_herdsz", "production", "herdsz")) ggts <- function( ..., columnLabelsX = NULL, xlab = "time" ) { pm <- ggduo( ..., # remove the "time" strip columnLabelsX = columnLabelsX, xlab = xlab ) pm } # if (!is.null(group)) { # column_type <- unlist(lapply( # data[setdiff(names(data), broom_columns())], # plotting_data_type # )) # is_discrete <- column_type[column_type == "discrete"] # group_names <- names(is_discrete) # } else { # group_names <- deparse(mapping$group) # } # # line_mapping <- mapping # line_mapping[c("x", "y", "xend", "yend")] <- # aes_string(x = "xmin", y = "ymin", xend = "xmax", yend = "ymax") # # color_group <- c(group_names) # # if (!is.null(mapping$colour)) { # # set the colors to the mapping colors # color_group[length(color_group) + 1] <- deparse(mapping$colour) # } else { # # set the default color to the line color # line_mapping$colour <- I(lineColor) # } # # color_group <- unique(color_group) # # print(line_mapping) # print(color_group) # hline_data <- ddply( # data, color_group, # function(subsetDt) { # ret <- data.frame( # ymax = mean(subsetDt[[deparse(mapping$y)]], na.rm = TRUE), # ymin = mean(subsetDt[[deparse(mapping$y)]], na.rm = TRUE), # xmin = min(subsetDt[[deparse(mapping$x)]], na.rm = TRUE), # xmax = max(subsetDt[[deparse(mapping$x)]], na.rm = TRUE) # ) # # transfer the unique columns that need to be there # for (col in color_group) { # ret[[col]] <- unique(subsetDt[[col]]) # } # ret # } # ) match_nostic_columns <- function(columns, choices, name) { column_matches <- pmatch(columns, choices, nomatch = NA, duplicates.ok = TRUE) if (any(is.na(column_matches))) { stop(paste( "Could not match '", name, "': c(", paste("'", columns[is.na(column_matches)], "'", collapse = ", ", sep = ""), ") to choices: c(", paste("'", choices, "'", collapse = ", ", sep = ""), ")", sep = "" )) } columns <- choices[column_matches] columns } GGally/R/ggpairs.R0000644000176200001440000010344213277311163013421 0ustar liggesusers# list of the different plot types to check # continuous # points # smooth # smooth_loess # density # cor # blank # combo # box # box_no_facet # dot # dot_no_facet # facethist # facetdensity # denstrip # blank # discrete # ratio # facetbar # blank # diag # continuous # densityDiag # barDiag # blankDiag # discrete # barDiag # blankDiag crosstalk_key <- function() { ".crossTalkKey" } fortify_SharedData <- function(model, data, ...) { key <- model$key() set <- model$groupName() data <- model$origData() # need a consistent name so we know how to access it in ggplotly() # MUST be added last. can NOT be done first data[[crosstalk_key()]] <- key structure(data, set = set) } fix_data <- function(data) { if (inherits(data, "SharedData")) { data <- fortify_SharedData(data) } data <- fortify(data) data <- as.data.frame(data) for (i in 1:dim(data)[2] ) { if (is.character(data[[i]])) { data[[i]] <- as.factor(data[[i]]) } } data } fix_data_slim <- function(data, isSharedData) { if (isSharedData) { data[[crosstalk_key()]] <- NULL } data } fix_column_values <- function( data, columns, columnLabels, columnsName, columnLabelsName, isSharedData = FALSE ) { colnamesData <- colnames(data) if (is.character(columns)) { colNumValues <- lapply(columns, function(colName){ which(colnamesData == colName) }) isFound <- as.logical(unlist(lapply(colNumValues, length))) if (any(!isFound)) { stop( "Columns in '", columnsName, "' not found in data: c(", str_c(str_c("'", columns[!isFound], "'"), collapse = ", "), "). Choices: c('", paste(colnamesData, collapse = "', '"), "')" ) } columns <- unlist(colNumValues) } if (any(columns > ncol(data))) { stop( "Make sure your numeric '", columnsName, "'", " values are less than or equal to ", ncol(data), ".\n", "\t", columnsName, " = c(", str_c(columns, collapse = ", "), ")" ) } if (any(columns < 1)) { stop( "Make sure your numeric '", columnsName, "' values are positive.", "\n", "\t", columnsName, " = c(", paste(columns, collapse = ", "), ")" ) } if (any( (columns %% 1) != 0)) { stop( "Make sure your numeric '", columnsName, "' values are integers.", "\n", "\t", columnsName, " = c(", paste(columns, collapse = ", "), ")" ) } if (!is.null(columnLabels)) { if (length(columnLabels) != length(columns)) { stop( "The length of the '", columnLabelsName, "'", " does not match the length of the '", columnsName, "' being used.", " Labels: c('", paste(columnLabels, collapse = ", "), "')\n", " Columns: c('", paste(columns, collapse = ", "), "')" ) } } columns } warn_deprecated <- function(is_supplied, title) { if (is_supplied) { warning(paste( "'", title, "' will be deprecated in future versions. Please remove it from your code", sep = "" )) } } stop_if_bad_mapping <- function(mapping) { if (is.numeric(mapping)) { stop( "'mapping' should not be numeric", " unless 'columns' is missing from function call." ) } } warn_if_args_exist <- function(args) { if (length(args) > 0) { argNames <- names(args) warning(str_c( "Extra arguments: ", str_c(shQuote(argNames), collapse = ", "), " are being ignored.", " If these are meant to be aesthetics, submit them using the", " 'mapping' variable within ggpairs with ggplot2::aes or ggplot2::aes_string." )) } } fix_axis_label_choice <- function(axisLabels, axisLabelChoices) { if (length(axisLabels) > 1) { axisLabels <- axisLabels[1] } axisLabelChoice <- pmatch(axisLabels, axisLabelChoices) if (is.na(axisLabelChoice)) { warning(str_c( "'axisLabels' not in c(", str_c(str_c("'", axisLabelChoices, "'"), collapse = ", "), "). Reverting to '", axisLabelChoices[1], "'" )) axisLabelChoice <- 1 } axisLabels <- axisLabelChoices[axisLabelChoice] } stop_if_high_cardinality <- function(data, columns, threshold) { if (is.null(threshold)) { return() } if (identical(threshold, FALSE)) { return() } if (!is.numeric(threshold)) { stop("'cardinality_threshold' should be a numeric or NULL") } for (col in names(data[columns])) { data_col <- data[[col]] if (!is.numeric(data_col)) { level_length <- length(levels(data_col)) if (level_length > threshold) { stop( "Column '", col, "' has more levels (", level_length, ")", " than the threshold (", threshold, ") allowed.\n", "Please remove the column or increase the 'cardinality_threshold' parameter. Increasing the cardinality_threshold may produce long processing times" # nolint ) } } } } #' ggduo - A ggplot2 generalized pairs plot for two columns sets of a data.frame #' #' Make a matrix of plots with a given data set with two different column sets #' #' @details #' \code{types} is a list that may contain the variables #' 'continuous', 'combo', 'discrete', and 'na'. Each element of the list may be a function or a string. If a string is supplied, it must implement one of the following options: #'\describe{ #' \item{continuous}{exactly one of ('points', 'smooth', 'smooth_loess', 'density', 'cor', 'blank'). This option is used for continuous X and Y data.} #' \item{comboHorizontal}{exactly one of ('box', 'box_no_facet', 'dot', 'dot_no_facet', 'facethist', 'facetdensity', 'denstrip', 'blank'). This option is used for either continuous X and categorical Y data or categorical X and continuous Y data.} #' \item{comboVertical}{exactly one of ('box', 'box_no_facet', 'dot', 'dot_no_facet', 'facethist', 'facetdensity', 'denstrip', 'blank'). This option is used for either continuous X and categorical Y data or categorical X and continuous Y data.} #' \item{discrete}{exactly one of ('facetbar', 'ratio', 'blank'). This option is used for categorical X and Y data.} #' \item{na}{exactly one of ('na', 'blank'). This option is used when all X data is \code{NA}, all Y data is \code{NA}, or either all X or Y data is \code{NA}.} #'} #' #' If 'blank' is ever chosen as an option, then ggduo will produce an empty plot. #' #' If a function is supplied as an option, it should implement the function api of \code{function(data, mapping, ...){#make ggplot2 plot}}. If a specific function needs its parameters set, \code{\link{wrap}(fn, param1 = val1, param2 = val2)} the function with its parameters. #' #' @export #' @param data data set using. Can have both numerical and categorical data. #' @param mapping aesthetic mapping (besides \code{x} and \code{y}). See \code{\link[ggplot2]{aes}()}. If \code{mapping} is numeric, \code{columns} will be set to the \code{mapping} value and \code{mapping} will be set to \code{NULL}. #' @param columnsX,columnsY which columns are used to make plots. Defaults to all columns. #' @param title,xlab,ylab title, x label, and y label for the graph #' @param types see Details #' @param axisLabels either "show" to display axisLabels or "none" for no axis labels #' @param columnLabelsX,columnLabelsY label names to be displayed. Defaults to names of columns being used. #' @template ggmatrix-labeller-param #' @template ggmatrix-switch-param #' @param showStrips boolean to determine if each plot's strips should be displayed. \code{NULL} will default to the top and right side plots only. \code{TRUE} or \code{FALSE} will turn all strips on or off respectively. #' @template ggmatrix-legend-param #' @param cardinality_threshold maximum number of levels allowed in a character / factor column. Set this value to NULL to not check factor columns. Defaults to 15 #' @template ggmatrix-progress #' @param legends deprecated #' @export #' @examples #' # small function to display plots only if it's interactive #' p_ <- GGally::print_if_interactive #' #' data(baseball, package = "plyr") #' #' # Keep players from 1990-1995 with at least one at bat #' # Add how many singles a player hit #' # (must do in two steps as X1b is used in calculations) #' dt <- transform( #' subset(baseball, year >= 1990 & year <= 1995 & ab > 0), #' X1b = h - X2b - X3b - hr #' ) #' # Add #' # the player's batting average, #' # the player's slugging percentage, #' # and the player's on base percentage #' # Make factor a year, as each season is discrete #' dt <- transform( #' dt, #' batting_avg = h / ab, #' slug = (X1b + 2*X2b + 3*X3b + 4*hr) / ab, #' on_base = (h + bb + hbp) / (ab + bb + hbp), #' year = as.factor(year) #' ) #' #' #' pm <- ggduo( #' dt, #' c("year", "g", "ab", "lg"), #' c("batting_avg", "slug", "on_base"), #' mapping = ggplot2::aes(color = lg) #' ) #' # Prints, but #' # there is severe over plotting in the continuous plots #' # the labels could be better #' # want to add more hitting information #' p_(pm) #' #' # address overplotting issues and add a title #' pm <- ggduo( #' dt, #' c("year", "g", "ab", "lg"), #' c("batting_avg", "slug", "on_base"), #' columnLabelsX = c("year", "player game count", "player at bat count", "league"), #' columnLabelsY = c("batting avg", "slug %", "on base %"), #' title = "Baseball Hitting Stats from 1990-1995", #' mapping = ggplot2::aes(color = lg), #' types = list( #' # change the shape and add some transparency to the points #' continuous = wrap("smooth_loess", alpha = 0.50, shape = "+") #' ), #' showStrips = FALSE #' ); #' #' p_(pm) #' #' #' #' # Example derived from: #' ## R Data Analysis Examples | Canonical Correlation Analysis. UCLA: Institute for Digital #' ## Research and Education. #' ## from http://www.stats.idre.ucla.edu/r/dae/canonical-correlation-analysis #' ## (accessed May 22, 2017). #' # "Example 1. A researcher has collected data on three psychological variables, four #' # academic variables (standardized test scores) and gender for 600 college freshman. #' # She is interested in how the set of psychological variables relates to the academic #' # variables and gender. In particular, the researcher is interested in how many #' # dimensions (canonical variables) are necessary to understand the association between #' # the two sets of variables." #' data(psychademic) #' summary(psychademic) #' #' (psych_variables <- attr(psychademic, "psychology")) #' (academic_variables <- attr(psychademic, "academic")) #' #' ## Within correlation #' p_(ggpairs(psychademic, columns = psych_variables)) #' p_(ggpairs(psychademic, columns = academic_variables)) #' #' ## Between correlation #' loess_with_cor <- function(data, mapping, ..., method = "pearson") { #' x <- eval(mapping$x, data) #' y <- eval(mapping$y, data) #' cor <- cor(x, y, method = method) #' ggally_smooth_loess(data, mapping, ...) + #' ggplot2::geom_label( #' data = data.frame( #' x = min(x, na.rm = TRUE), #' y = max(y, na.rm = TRUE), #' lab = round(cor, digits = 3) #' ), #' mapping = ggplot2::aes(x = x, y = y, label = lab), #' hjust = 0, vjust = 1, #' size = 5, fontface = "bold", #' inherit.aes = FALSE # do not inherit anything from the ... #' ) #' } #' pm <- ggduo( #' psychademic, #' rev(psych_variables), academic_variables, #' types = list(continuous = loess_with_cor), #' showStrips = FALSE #' ) #' suppressWarnings(p_(pm)) # ignore warnings from loess #' #' # add color according to sex #' pm <- ggduo( #' psychademic, #' mapping = ggplot2::aes(color = sex), #' rev(psych_variables), academic_variables, #' types = list(continuous = loess_with_cor), #' showStrips = FALSE, #' legend = c(5,2) #' ) #' suppressWarnings(p_(pm)) #' #' #' # add color according to sex #' pm <- ggduo( #' psychademic, #' mapping = ggplot2::aes(color = motivation), #' rev(psych_variables), academic_variables, #' types = list(continuous = loess_with_cor), #' showStrips = FALSE, #' legend = c(5,2) #' ) + #' ggplot2::theme(legend.position = "bottom") #' suppressWarnings(p_(pm)) # # # # pm <- ggduo( # dt, # c("year", "g", "ab", "lg", "lg"), # c("batting_avg", "slug", "on_base", "hit_type"), # columnLabelsX = c("year", "player game count", "player at bat count", "league", ""), # columnLabelsY = c("batting avg", "slug %", "on base %", "hit type"), # title = "Baseball Hitting Stats from 1990-1995 (player strike in 1994)", # mapping = aes(color = year), # types = list( # continuous = wrap("smooth_loess", alpha = 0.50, shape = "+"), # comboHorizontal = wrap(display_hit_type_combo, binwidth = 15), # discrete = wrap(display_hit_type_discrete, color = "black", size = 0.15) # ), # showStrips = FALSE # ); # # # make the 5th column blank, except for the legend # pm[1,5] <- NULL # pm[2,5] <- grab_legend(pm[2,1]) # pm[3,5] <- NULL # pm[4,5] <- NULL # # pm # # ggduo( # australia_PISA2012, # c("gender", "age", "homework", "possessions"), # c("PV1MATH", "PV2MATH", "PV3MATH", "PV4MATH", "PV5MATH"), # types = list( # continuous = "points", # combo = "box", # discrete = "ratio" # ) # ) # # ggduo( # australia_PISA2012, # c("gender", "age", "homework", "possessions"), # c("PV1MATH", "PV2MATH", "PV3MATH", "PV4MATH", "PV5MATH"), # mapping = ggplot2::aes(color = gender), # types = list( # continuous = wrap("smooth", alpha = 0.25, method = "loess"), # combo = "box", # discrete = "ratio" # ) # ) # # ggduo(australia_PISA2012, c("gender", "age", "homework", "possessions"), c("PV1MATH", "PV1READ", "PV1SCIE"), types = list(continuous = "points", combo = "box", discrete = "ratio")) # ggduo(australia_PISA2012, c("gender", "age", "homework", "possessions"), c("PV1MATH", "PV1READ", "PV1SCIE"), types = list(continuous = wrap("smooth", alpha = 0.25, method = "loess"), combo = "box", discrete = "ratio"), mapping = ggplot2::aes(color = gender)) ggduo <- function( data, mapping = NULL, columnsX = 1:ncol(data), columnsY = 1:ncol(data), title = NULL, types = list( continuous = "smooth_loess", comboVertical = "box_no_facet", comboHorizontal = "facethist", discrete = "ratio" ), axisLabels = c("show", "none"), columnLabelsX = colnames(data[columnsX]), columnLabelsY = colnames(data[columnsY]), labeller = "label_value", switch = NULL, xlab = NULL, ylab = NULL, showStrips = NULL, legend = NULL, cardinality_threshold = 15, progress = NULL, legends = stop("deprecated") ) { warn_deprecated(!missing(legends), "legends") isSharedData <- inherits(data, "SharedData") data_ <- fix_data(data) data <- fix_data_slim(data_, isSharedData) # fix args if ( !missing(mapping) & !is.list(mapping) & !missing(columnsX) & missing(columnsY) ) { columnsY <- columnsX columnsX <- mapping mapping <- NULL } stop_if_bad_mapping(mapping) columnsX <- fix_column_values(data, columnsX, columnLabelsX, "columnsX", "columnLabelsX") columnsY <- fix_column_values(data, columnsY, columnLabelsY, "columnsY", "columnLabelsY") stop_if_high_cardinality(data, columnsX, cardinality_threshold) stop_if_high_cardinality(data, columnsY, cardinality_threshold) types <- check_and_set_ggpairs_defaults( "types", types, continuous = "smooth_loess", discrete = "ratio", na = "na", isDuo = TRUE ) if (!is.null(types[["combo"]])) { warning(str_c( "\nSetting:\n", "\ttypes$comboHorizontal <- types$combo\n", "\ttypes$comboVertical <- types$combo" )) types$comboHorizontal <- types$combo types$comboVertical <- types$combo types$combo <- NULL } if (is.null(types[["comboVertical"]])) { types$comboVertical <- "box_no_facet" } if (is.null(types[["comboHorizontal"]])) { types$comboHorizontal <- "facethist" } axisLabels <- fix_axis_label_choice(axisLabels, c("show", "none")) # get plot type information dataTypes <- plot_types(data, columnsX, columnsY, allowDiag = FALSE) ggduoPlots <- lapply(seq_len(nrow(dataTypes)), function(i) { plotType <- dataTypes[i, "plotType"] # posX <- dataTypes[i, "posX"] # posY <- dataTypes[i, "posY"] xColName <- dataTypes[i, "xVar"] yColName <- dataTypes[i, "yVar"] sectionAes <- add_and_overwrite_aes( add_and_overwrite_aes( aes_(x = as.name(xColName), y = as.name(yColName)), mapping ), types$mapping ) if (plotType == "combo") { if (dataTypes[i, "isVertical"]) { plotTypesList <- list(combo = types$comboVertical) } else { plotTypesList <- list(combo = types$comboHorizontal) } } else { plotTypesList <- types } args <- list(types = plotTypesList, sectionAes = sectionAes) plot_fn <- ggmatrix_plot_list(plotType) plotObj <- do.call(plot_fn, args) return(plotObj) }) plotMatrix <- ggmatrix( plots = ggduoPlots, byrow = TRUE, nrow = length(columnsY), ncol = length(columnsX), xAxisLabels = columnLabelsX, yAxisLabels = columnLabelsY, labeller = labeller, switch = switch, showStrips = showStrips, showXAxisPlotLabels = identical(axisLabels, "show"), showYAxisPlotLabels = identical(axisLabels, "show"), title = title, xlab = xlab, ylab = ylab, data = data_, gg = NULL, progress = progress, legend = legend ) plotMatrix } ### Example removed due to not using facet labels anymore # #Sequence to show how to change label size # make_small_strip <- function(plot_matrix, from_top, from_left, new_size = 7){ # up <- from_left > from_top # p <- getPlot(plot_matrix, from_top, from_left) # if(up) # p <- p + opts(strip.text.x = element_text(size = new_size)) # else # p <- p + opts(strip.text.y = element_text(angle = -90, size = new_size)) # # putPlot(plot_matrix, p, from_top, from_left) # } # small_label_diamond <- make_small_strip(diamondMatrix, 2, 1) # small_label_diamond <- make_small_strip(small_label_diamond, 1, 2) # small_label_diamond <- make_small_strip(small_label_diamond, 2, 2) # #small_label_diamond # now with much smaller strip text #' ggpairs - A ggplot2 generalized pairs plot #' #' Make a matrix of plots with a given data set #' #' @details #' \code{upper} and \code{lower} are lists that may contain the variables #' 'continuous', 'combo', 'discrete', and 'na'. Each element of the list may be a function or a string. If a string is supplied, it must implement one of the following options: #'\describe{ #' \item{continuous}{exactly one of ('points', 'smooth', 'smooth_loess', 'density', 'cor', 'blank'). This option is used for continuous X and Y data.} #' \item{combo}{exactly one of ('box', 'box_no_facet', 'dot', 'dot_no_facet', 'facethist', 'facetdensity', 'denstrip', 'blank'). This option is used for either continuous X and categorical Y data or categorical X and continuous Y data.} #' \item{discrete}{exactly one of ('facetbar', 'ratio', 'blank'). This option is used for categorical X and Y data.} #' \item{na}{exactly one of ('na', 'blank'). This option is used when all X data is \code{NA}, all Y data is \code{NA}, or either all X or Y data is \code{NA}.} #'} #' #' \code{diag} is a list that may only contain the variables 'continuous', 'discrete', and 'na'. Each element of the diag list is a string implementing the following options: #'\describe{ #' \item{continuous}{exactly one of ('densityDiag', 'barDiag', 'blankDiag'). This option is used for continuous X data.} #' \item{discrete}{exactly one of ('barDiag', 'blankDiag'). This option is used for categorical X and Y data.} #' \item{na}{exactly one of ('naDiag', 'blankDiag'). This option is used when all X data is \code{NA}.} #'} #' #' If 'blank' is ever chosen as an option, then ggpairs will produce an empty plot. #' #' If a function is supplied as an option to \code{upper}, \code{lower}, or \code{diag}, it should implement the function api of \code{function(data, mapping, ...){#make ggplot2 plot}}. If a specific function needs its parameters set, \code{\link{wrap}(fn, param1 = val1, param2 = val2)} the function with its parameters. #' #' @export #' @seealso wrap v1_ggmatrix_theme #' @param data data set using. Can have both numerical and categorical data. #' @param mapping aesthetic mapping (besides \code{x} and \code{y}). See \code{\link[ggplot2]{aes}()}. If \code{mapping} is numeric, \code{columns} will be set to the \code{mapping} value and \code{mapping} will be set to \code{NULL}. #' @param columns which columns are used to make plots. Defaults to all columns. #' @param title,xlab,ylab title, x label, and y label for the graph #' @param upper see Details #' @param lower see Details #' @param diag see Details #' @param params deprecated. Please see \code{\link{wrap_fn_with_param_arg}} #' @param ... deprecated. Please use \code{mapping} #' @param axisLabels either "show" to display axisLabels, "internal" for labels in the diagonal plots, or "none" for no axis labels #' @param columnLabels label names to be displayed. Defaults to names of columns being used. #' @template ggmatrix-labeller-param #' @template ggmatrix-switch-param #' @param showStrips boolean to determine if each plot's strips should be displayed. \code{NULL} will default to the top and right side plots only. \code{TRUE} or \code{FALSE} will turn all strips on or off respectively. #' @template ggmatrix-legend-param #' @param cardinality_threshold maximum number of levels allowed in a character / factor column. Set this value to NULL to not check factor columns. Defaults to 15 #' @template ggmatrix-progress #' @param legends deprecated #' @keywords hplot #' @import ggplot2 #' @references John W Emerson, Walton A Green, Barret Schloerke, Jason Crowley, Dianne Cook, Heike Hofmann, Hadley Wickham. The Generalized Pairs Plot. Journal of Computational and Graphical Statistics, vol. 22, no. 1, pp. 79-91, 2012. #' @author Barret Schloerke \email{schloerke@@gmail.com}, Jason Crowley \email{crowley.jason.s@@gmail.com}, Di Cook \email{dicook@@iastate.edu}, Heike Hofmann \email{hofmann@@iastate.edu}, Hadley Wickham \email{h.wickham@@gmail.com} #' @return ggmatrix object that if called, will print #' @examples #' # small function to display plots only if it's interactive #' p_ <- GGally::print_if_interactive #' #' #' ## Quick example, with and without colour #' data(flea) #' ggpairs(flea, columns = 2:4) #' pm <- ggpairs(flea, columns = 2:4, ggplot2::aes(colour=species)) #' p_(pm) #' # Note: colour should be categorical, else you will need to reset #' # the upper triangle to use points instead of trying to compute corr #' #' data(tips, package = "reshape") #' pm <- ggpairs(tips[, 1:3]) #' p_(pm) #' pm <- ggpairs(tips, 1:3, columnLabels = c("Total Bill", "Tip", "Sex")) #' p_(pm) #' pm <- ggpairs(tips, upper = "blank") #' p_(pm) #' #' ## Plot Types #' # Change default plot behavior #' pm <- ggpairs( #' tips[, c(1, 3, 4, 2)], #' upper = list(continuous = "density", combo = "box_no_facet"), #' lower = list(continuous = "points", combo = "dot_no_facet") #' ) #' p_(pm) #' # Supply Raw Functions (may be user defined functions!) #' pm <- ggpairs( #' tips[, c(1, 3, 4, 2)], #' upper = list(continuous = ggally_density, combo = ggally_box_no_facet), #' lower = list(continuous = ggally_points, combo = ggally_dot_no_facet) #' ) #' p_(pm) #' #' # Use sample of the diamonds data #' data(diamonds, package="ggplot2") #' diamonds.samp <- diamonds[sample(1:dim(diamonds)[1], 1000), ] #' #' # Different aesthetics for different plot sections and plot types #' pm <- ggpairs( #' diamonds.samp[, 1:5], #' mapping = ggplot2::aes(color = cut), #' upper = list(continuous = wrap("density", alpha = 0.5), combo = "box_no_facet"), #' lower = list(continuous = wrap("points", alpha = 0.3), combo = wrap("dot_no_facet", alpha = 0.4)), #' title = "Diamonds" #' ) #' p_(pm) #' #' ## Axis Label Variations #' # Only Variable Labels on the diagonal (no axis labels) #' pm <- ggpairs(tips[, 1:3], axisLabels="internal") #' p_(pm) #' # Only Variable Labels on the outside (no axis labels) #' pm <- ggpairs(tips[, 1:3], axisLabels="none") #' p_(pm) #' #' ## Facet Label Variations #' # Default: #' df_x <- rnorm(100) #' df_y <- df_x + rnorm(100, 0, 0.1) #' df <- data.frame(x = df_x, y = df_y, c = sqrt(df_x^2 + df_y^2)) #' pm <- ggpairs( #' df, #' columnLabels = c("alpha[foo]", "alpha[bar]", "sqrt(alpha[foo]^2 + alpha[bar]^2)") #' ) #' p_(pm) #' # Parsed labels: #' pm <- ggpairs( #' df, #' columnLabels = c("alpha[foo]", "alpha[bar]", "sqrt(alpha[foo]^2 + alpha[bar]^2)"), #' labeller = "label_parsed" #' ) #' p_(pm) #' #' ## Plot Insertion Example #' custom_car <- ggpairs(mtcars[, c("mpg", "wt", "cyl")], upper = "blank", title = "Custom Example") #' # ggplot example taken from example(geom_text) #' plot <- ggplot2::ggplot(mtcars, ggplot2::aes(x=wt, y=mpg, label=rownames(mtcars))) #' plot <- plot + #' ggplot2::geom_text(ggplot2::aes(colour=factor(cyl)), size = 3) + #' ggplot2::scale_colour_discrete(l=40) #' custom_car[1, 2] <- plot #' personal_plot <- ggally_text( #' "ggpairs allows you\nto put in your\nown plot.\nLike that one.\n <---" #' ) #' custom_car[1, 3] <- personal_plot #' p_(custom_car) #' #' ## Remove binwidth warning from ggplot2 #' # displays warning about picking a better binwidth #' pm <- ggpairs(tips, 2:3) #' p_(pm) #' # no warning displayed #' pm <- ggpairs(tips, 2:3, lower = list(combo = wrap("facethist", binwidth = 0.5))) #' p_(pm) #' # no warning displayed with user supplied function #' pm <- ggpairs(tips, 2:3, lower = list(combo = wrap(ggally_facethist, binwidth = 0.5))) #' p_(pm) ggpairs <- function( data, mapping = NULL, columns = 1:ncol(data), title = NULL, upper = list(continuous = "cor", combo = "box_no_facet", discrete = "facetbar", na = "na"), lower = list(continuous = "points", combo = "facethist", discrete = "facetbar", na = "na"), diag = list(continuous = "densityDiag", discrete = "barDiag", na = "naDiag"), params = NULL, ..., xlab = NULL, ylab = NULL, axisLabels = c("show", "internal", "none"), columnLabels = colnames(data[columns]), labeller = "label_value", switch = NULL, showStrips = NULL, legend = NULL, cardinality_threshold = 15, progress = NULL, legends = stop("deprecated") ){ warn_deprecated(!missing(legends), "legends") warn_if_args_exist(list(...)) stop_if_params_exist(params) isSharedData <- inherits(data, "SharedData") data_ <- fix_data(data) data <- fix_data_slim(data_, isSharedData) if ( !missing(mapping) & !is.list(mapping) & missing(columns) ) { columns <- mapping mapping <- NULL } stop_if_bad_mapping(mapping) columns <- fix_column_values(data, columns, columnLabels, "columns", "columnLabels") stop_if_high_cardinality(data, columns, cardinality_threshold) upper <- check_and_set_ggpairs_defaults( "upper", upper, continuous = "cor", combo = "box_no_facet", discrete = "facetbar", na = "na" ) lower <- check_and_set_ggpairs_defaults( "lower", lower, continuous = "points", combo = "facethist", discrete = "facetbar", na = "na" ) diag <- check_and_set_ggpairs_defaults( "diag", diag, continuous = "densityDiag", discrete = "barDiag", na = "naDiag", isDiag = TRUE ) axisLabels <- fix_axis_label_choice(axisLabels, c("show", "internal", "none")) # get plot type information dataTypes <- plot_types(data, columns, columns, allowDiag = TRUE) # make internal labels on the diag axis if (identical(axisLabels, "internal")) { dataTypes$plotType[dataTypes$posX == dataTypes$posY] <- "label" } ggpairsPlots <- lapply(seq_len(nrow(dataTypes)), function(i) { plotType <- dataTypes[i, "plotType"] posX <- dataTypes[i, "posX"] posY <- dataTypes[i, "posY"] xColName <- dataTypes[i, "xVar"] yColName <- dataTypes[i, "yVar"] if (posX > posY) { types <- upper } else if (posX < posY) { types <- lower } else { types <- diag } sectionAes <- add_and_overwrite_aes( add_and_overwrite_aes( aes_(x = as.name(xColName), y = as.name(yColName)), mapping ), types$mapping ) args <- list(types = types, sectionAes = sectionAes) if (plotType == "label") { args$label <- columnLabels[posX] } plot_fn <- ggmatrix_plot_list(plotType) p <- do.call(plot_fn, args) return(p) }) plotMatrix <- ggmatrix( plots = ggpairsPlots, byrow = TRUE, nrow = length(columns), ncol = length(columns), xAxisLabels = (if (axisLabels == "internal") NULL else columnLabels), yAxisLabels = (if (axisLabels == "internal") NULL else columnLabels), labeller = labeller, switch = switch, showStrips = showStrips, showXAxisPlotLabels = identical(axisLabels, "show"), showYAxisPlotLabels = identical(axisLabels, "show"), title = title, xlab = xlab, ylab = ylab, data = data_, gg = NULL, progress = progress, legend = legend ) plotMatrix } #' Add new aes #' #' Add new aesthetics to a previous aes. #' #' @keywords internal #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @return aes_ output #' @import ggplot2 #' @rdname add_and_overwrite_aes #' @examples #' data(diamonds, package="ggplot2") #' diamonds.samp <- diamonds[sample(1:dim(diamonds)[1], 1000), ] #' pm <- ggpairs(diamonds.samp, columns = 5:7, #' mapping = ggplot2::aes(color = color), #' upper = list(continuous = "cor", mapping = ggplot2::aes_string(color = "clarity")), #' lower = list(continuous = "cor", mapping = ggplot2::aes_string(color = "cut")), #' title = "Diamonds Sample" #' ) #' str(pm) #' add_and_overwrite_aes <- function(current, new) { if (length(new) >= 1) { for (i in 1:length(new)) { current[names(new)[i]] <- new[i] } } for (curName in names(current)) { if (is.null(current[[curName]])) { current[[curName]] <- NULL } } current } #' Aesthetic Mapping Color Fill #' #' Replace the fill with the color and make color NULL. #' #' @param current the current aesthetics #' @keywords internal #' @export mapping_color_to_fill <- function(current) { if (is.null(current)) { return(aes()) } currentNames <- names(current) color <- c("color", "colour") if (any(color %in% currentNames) && "fill" %in% currentNames) { # do nothing } else if (any(color %in% currentNames)) { # fill <- current[["fill" %in% currentNames]] # col <- current[[color %in% currentNames]] # current <- add_and_overwrite_aes(current, aes_string(fill = col, color = NA)) current$fill <- current$colour current$colour <- NULL } # if(!is.null(mapping$colour) && !is.null(mapping$fill)) { # # do nothing # } else if(!is.null(mapping$colour)) { # } current } set_to_blank_list_if_blank <- function( val, combo = TRUE, blank = "blank", isDuo = FALSE ) { isBlank <- is.null(val) if (!isBlank) { isBlank <- (!is.list(val) && (val == blank || val == "blank")) } if (isBlank) { val <- list() val$continuous <- blank if (combo) { val$combo <- blank } if (isDuo) { val$comboVertical <- blank val$comboHorizontal <- blank } val$discrete <- blank val$na <- blank } val } check_and_set_ggpairs_defaults <- function( name, obj, continuous = NULL, combo = NULL, discrete = NULL, na = NULL, isDiag = FALSE, isDuo = FALSE ) { blankVal <- ifelse(isDiag, "blankDiag", "blank") obj <- set_to_blank_list_if_blank( obj, combo = ! isDiag & ! isDuo, blank = blankVal, isDuo = isDuo ) if (!is.list(obj)) { stop("'", name, "' is not a list") } stop_if_params_exist(obj$params) if (is.null(obj$continuous) && (!is.null(continuous))) { obj$continuous <- continuous } if (is.null(obj$combo) && (!is.null(combo))) { obj$combo <- combo } if (is.null(obj$discrete) && (!is.null(discrete))) { obj$discrete <- discrete } if (is.null(obj$na) && (!is.null(na))) { obj$na <- na } if (! is.null(obj$aes_string)) { stop( "'aes_string' is a deprecated element for the section ", name, ".\n", "Please use 'mapping' instead. " ) } if (isDiag) { for (key in c("continuous", "discrete", "na")) { val <- obj[[key]] if (is.character(val)) { if (! str_detect(val, "Diag$")) { newVal <- paste(val, "Diag", sep = "") warning(paste( "Changing diag$", key, " from '", val, "' to '", newVal, "'", sep = "" )) obj[[key]] <- newVal } } } } obj } get_subtype_name <- function(.subType) { fn <- wrapp(.subType) ret <- attr(fn, "name") if (ret == ".subType") { ret <- "custom_function" } ret } stop_if_params_exist <- function(params) { if (! is.null(params)) { stop( "'params' is a deprecated argument. ", "Please 'wrap' the function to supply arguments. ", "help(\"wrap\", package = \"GGally\")" ) } } #diamondMatrix <- ggpairs( # diamonds, # columns = 8:10, # upper = list(points = "scatterplot", aes_string = aes_string(color = "cut")), # lower = list(points = "scatterplot", aes_string = aes_string(color = "cut")), # diag = "blank", ## color = "color", # title = "Diamonds" #) #if(TRUE) #{ # #d <- diamonds[runif(floor(nrow(diamonds)/10), 0, nrow(diamonds)), ] # #diamondMatrix <- ggpairs( # d, # columns = 8:10, # upper = list(continuous = "points", aes_string = aes_string(color = "clarity")), # lower = list(continuous = "points", aes_string = aes_string(color = "cut")), # diag = "blank", ## color = "color", # title = "Diamonds" #) # # #m <- mtcars ##m$vs <- as.factor(m$vs) ##m$cyl <- as.factor(m$cyl) ##m$qsec <- as.factor(m$qsec) #carsMatrix <- ggpairs( # mtcars, # columns = c(1, 3, 4), # upper = list(continuous = "points", aes_string = aes_string(shape = "cyl", size = 5)), # lower = list(continuous = "points", aes_string = aes_string(size = "cyl")), # diag = "blank", # color = "cyl", # title = "mtcars", #) # # # carsMatrix <- ggpairs( # mtcars, # columns = c(1, 3, 4), # upper = list(aes_string = aes_string(shape = "as.factor(cyl)", size = 5)), # lower = list(aes_string = aes_string(size = "as.factor(cyl)")), # diag = "blank", # color = "cyl", # title = "Custom Cars", # ) # # #} GGally/R/ggpairs_internal_plots.R0000644000176200001440000002175113276725426016552 0ustar liggesusers #' Wrap a function with different parameter values #' #' Wraps a function with the supplied parameters to force different default behavior. This is useful for functions that are supplied to ggpairs. It allows you to change the behavior of one function, rather than creating multiple functions with different parameter settings. #' #' \code{wrap} is identical to \code{wrap_fn_with_params}. These function take the new parameters as arguments. #' #' \code{wrapp} is identical to \code{wrap_fn_with_param_arg}. These functions take the new parameters as a single list. #' #' The \code{params} and \code{fn} attributes are there for debugging purposes. If either attribute is altered, the function must be re-wrapped to have the changes take effect. #' #' @param funcVal function that the \code{params} will be applied to. The function should follow the api of \code{function(data, mapping, ...)\{\}}. \code{funcVal} is allowed to be a string of one of the \code{ggally_NAME} functions, such as \code{"points"} for \code{ggally_points} or \code{"facetdensity"} for \code{ggally_facetdensity}. #' @param ... named parameters to be supplied to \code{wrap_fn_with_param_arg} #' @param params named vector or list of parameters to be applied to the \code{funcVal} #' @param funcArgName name of function to be displayed #' @return a \code{function(data, mapping, ...)\{\}} that will wrap the original function with the parameters applied as arguments #' @export #' @rdname wrap #' @examples #' # small function to display plots only if it's interactive #' p_ <- GGally::print_if_interactive #' #' # example function that prints 'val' #' fn <- function(data, mapping, val = 2) { #' print(val) #' } #' fn(data = NULL, mapping = NULL) # 2 #' #' # wrap function to change default value 'val' to 5 instead of 2 #' wrapped_fn1 <- wrap(fn, val = 5) #' wrapped_fn1(data = NULL, mapping = NULL) # 5 #' # you may still supply regular values #' wrapped_fn1(data = NULL, mapping = NULL, val = 3) # 3 #' #' # wrap function to change 'val' to 5 using the arg list #' wrapped_fn2 <- wrap_fn_with_param_arg(fn, params = list(val = 5)) #' wrapped_fn2(data = NULL, mapping = NULL) # 5 #' #' # change parameter settings in ggpairs for a particular function #' ## Goal output: #' regularPlot <- ggally_points( #' iris, #' ggplot2::aes(Sepal.Length, Sepal.Width), #' size = 5, color = "red" #' ) #' p_(regularPlot) #' #' # Wrap ggally_points to have parameter values size = 5 and color = 'red' #' w_ggally_points <- wrap(ggally_points, size = 5, color = "red") #' wrappedPlot <- w_ggally_points( #' iris, #' ggplot2::aes(Sepal.Length, Sepal.Width) #' ) #' p_(wrappedPlot) #' #' # Double check the aes parameters are the same for the geom_point layer #' identical(regularPlot$layers[[1]]$aes_params, wrappedPlot$layers[[1]]$aes_params) #' #' # Use a wrapped function in ggpairs #' pm <- ggpairs(iris, 1:3, lower = list(continuous = wrap(ggally_points, size = 5, color = "red"))) #' p_(pm) #' pm <- ggpairs(iris, 1:3, lower = list(continuous = w_ggally_points)) #' p_(pm) wrap_fn_with_param_arg <- function( funcVal, params = NULL, funcArgName = deparse(substitute(funcVal)) ) { if (missing(funcArgName)) { fnName <- attr(funcVal, "name") if (!is.null(fnName)) { funcArgName <- fnName } } if (!is.null(params)) { if (is.vector(params)) { params <- as.list(params) } if (length(params) > 0) { if (!is.list(params)) { stop("'params' must be a named list, named vector, or NULL") } if (is.null(names(params))) { stop("'params' must be a named list, named vector, or NULL") } if (any(nchar(names(params)) == 0)) { stop("'params' must be a named list, named vector, or NULL") } } } if (mode(funcVal) == "character") { if (missing(funcArgName)) { funcArgName <- str_c("ggally_", funcVal) } tryCatch({ funcVal <- get( str_c("ggally_", funcVal), mode = "function" ) }, error = function(e) { stop(str_c( "The following ggpairs plot functions are readily available: \n", "\tcontinuous: c('points', 'smooth', 'smooth_loess', 'density', 'cor', 'blank')\n", "\tcombo: c('box', 'box_no_facet', 'dot', 'dot_no_facet', 'facethist',", " 'facetdensity', 'denstrip', 'blank')\n", "\tdiscrete: c('ratio', 'facetbar', 'blank')\n", "\tna: c('na', 'blank')\n", "\n", "\tdiag continuous: c('densityDiag', 'barDiag', 'blankDiag')\n", "\tdiag discrete: c('barDiag', 'blankDiag')\n", "\tdiag na: c('naDiag', 'blankDiag')\n", "\n", "You may also provide your own function that follows the api of ", "function(data, mapping, ...){ . . . }\nand returns a ggplot2 plot object\n", "\tEx:\n", "\tmy_fn <- function(data, mapping, ...){\n", "\t p <- ggplot(data = data, mapping = mapping) + \n", "\t geom_point(...)\n", "\t p\n", "\t}\n", "\tggpairs(data, lower = list(continuous = my_fn))\n", "\n", "Function provided: ", funcVal )) } ) } allParams <- ifnull(attr(funcVal, "params"), list()) allParams[names(params)] <- params original_fn <- funcVal ret_fn <- function(data, mapping, ...) { allParams$data <- data allParams$mapping <- mapping argsList <- list(...) allParams[names(argsList)] <- argsList do.call(original_fn, allParams) } class(ret_fn) <- "ggmatrix_fn_with_params" attr(ret_fn, "name") <- as.character(funcArgName) attr(ret_fn, "params") <- allParams attr(ret_fn, "fn") <- original_fn ret_fn } #' @export #' @rdname wrap wrapp <- wrap_fn_with_param_arg #' @export #' @rdname wrap wrap <- function(funcVal, ..., funcArgName = deparse(substitute(funcVal))) { if (missing(funcArgName)) { fnName <- attr(funcVal, "name") if (!is.null(fnName)) { funcArgName <- fnName } else if (is.character(funcVal)) { funcArgName <- str_c("ggally_", funcVal) } } params <- list(...) if (length(params) > 0) { if (is.null(names(params))) { stop("all parameters must be named arguments") } if (any(nchar(names(params)) == 0)) { stop("all parameters must be named arguments") } } wrap_fn_with_param_arg(funcVal, params = params, funcArgName = funcArgName) } #' @export #' @rdname wrap wrap_fn_with_params <- wrap as.character.ggmatrix_fn_with_params <- function(x, ...) { params <- attr(x, "params") fnName <- attr(x, "name") if (length(params) == 0) { txt <- str_c("wrap: '", fnName, "'") } else { txt <- str_c("wrap: '", attr(x, "name"), "'; params: ", mapping_as_string(params)) } txt } make_ggmatrix_plot_obj <- function(fn, mapping = ggplot2::aes(), dataPos = 1, gg = NULL) { # nonCallVals <- which(lapply(mapping, mode) == "call") # if (length(nonCallVals) > 0) { # nonCallNames <- names(mapping)[nonCallVals] # browser() # stop( # paste( # "variables: ", # paste(shQuote(nonCallNames, type = "cmd"), sep = ", "), # " have non standard format: ", # paste(shQuote(unlist(mapping[nonCallVals]), type = "cmd"), collapse = ", "), # ". Please rename the columns or make a new column.", # sep = "" # ) # ) # } ret <- list( fn = fn, mapping = mapping, dataPos = dataPos, gg = gg ) class(ret) <- "ggmatrix_plot_obj" ret } blank_plot_string <- function() { "PM; (blank)" } mapping_as_string <- function(mapping) { str_c("c(", str_c(names(mapping), as.character(mapping), sep = " = ", collapse = ", "), ")") } as.character.ggmatrix_plot_obj <- function(x, ...) { hasGg <- (!is.null(x$gg)) mappingTxt <- mapping_as_string(x$mapping) fnTxt <- ifelse(inherits(x$fn, "ggmatrix_fn_with_params"), as.character(x$fn), "custom_function") if (inherits(x$fn, "ggmatrix_fn_with_params")) { if (attr(x$fn, "name") %in% c("ggally_blank", "ggally_blankDiag")) { return(blank_plot_string()) } } str_c( "PM", "; aes: ", mappingTxt, "; fn: {", fnTxt, "}", # "; dataPos: ", x$dataPos, "; gg: ", as.character(hasGg) ) } #' ggmatrix structure #' #' View the condensed version of the ggmatrix object. The attribute "class" is ALWAYS altered to "_class" to avoid recursion. #' #' @param object ggmatrix object to be viewed #' @param ... passed on to the default str method #' @param raw boolean to determine if the plots should be converted to text or kept as original objects #' @method str ggmatrix #' @importFrom utils str #' @export str.ggmatrix <- function(object, ..., raw = FALSE) { objName <- deparse(substitute(object)) obj <- object if (identical(raw, FALSE)) { cat(str_c( "\nCustom str.ggmatrix output: \nTo view original object use ", "'str(", objName, ", raw = TRUE)'\n\n" )) obj$plots <- lapply(obj$plots, function(plotObj) { if (ggplot2::is.ggplot(plotObj)) { str_c("PM; ggplot2 object; mapping: ", mapping_as_string(plotObj$mapping)) } else if (inherits(plotObj, "ggmatrix_plot_obj")) { as.character(plotObj) } else { plotObj } }) } attr(obj, "_class") <- attr(obj, "class") class(obj) <- NULL str(obj, ...) } GGally/R/utils.R0000644000176200001440000000211713276725426013134 0ustar liggesusers #' Print if not CRAN #' #' Small function to print a plot if the R session is interactive or in a travis build #' #' @param p plot to be displayed #' @export print_if_interactive <- function(p) { if (interactive() || nzchar(Sys.getenv("CAN_PRINT"))) { print(p) } } #' Loads package namespaces #' #' Loads package namespaces or yells at user... loudly #' #' @param pkgs vector of character values #' @keywords internal require_namespaces <- function(pkgs) { for (pkg in pkgs) { if (! requireNamespace(pkg, quietly = TRUE)) { stop(str_c("please install the package '", pkg, "'. install.packages('", pkg, "') ")) } } } str_c <- function (..., sep = "", collapse = NULL) { paste(..., sep = sep, collapse = collapse) } str_detect <- function(string, pattern, ...) { grepl(pattern, string, ...) } # str_replace <- function(string, pattern, replacement) { # sub(pattern, replacement, string) # } ifnull <- function(a, b) { if (!is.null(a)) { a } else { b } } hf <- function(field) { eval(parse(text = read.dcf(".helper_functions", fields = field))) } GGally/R/gg-plots.R0000644000176200001440000011312313277311162013515 0ustar liggesusers# add global variable if (getRversion() >= "2.15.1") { utils::globalVariables(unique(c( "labelp", # cor plot c("..density..", "..scaled..", "x"), # facetdensitystrip plot c("..scaled..", "x"), #density diagonal plot c("x", "y", "lab"), # internal axis plot c("x", "y", "result", "freq") # fluctuation plot ))) } # retrieve the evaulated data column given the aes (which could possibly do operations) #' Evaluate data column #' @param data data set to evaluate the data with #' @param aes_col Single value from an \code{ggplot2::\link[ggplot2]{aes}(...)} object #' @return Aes mapping with the x and y values switched #' @export #' @examples #' mapping <- ggplot2::aes(Petal.Length) #' eval_data_col(iris, mapping$x) eval_data_col <- function(data, aes_col) { rlang::eval_tidy(aes_col, data) } #' Aes name #' @param aes_col Single value from \code{ggplot2::\link[ggplot2]{aes}(...)} #' @return character string #' @export #' @examples #' mapping <- ggplot2::aes(Petal.Length) #' mapping_string(mapping$x) mapping_string <- function(aes_col) { gsub("^~", "", deparse(aes_col, 500L)) } # is categories on the left? #' Check if plot is horizontal #' #' @param data data used in ggplot2 plot #' @param mapping ggplot2 \code{aes()} mapping #' @param val key to retrieve from \code{mapping} #' @return Boolean determining if the data is a character-like data #' @export #' @rdname is_horizontal #' @examples #' is_horizontal(iris, ggplot2::aes(Sepal.Length, Species)) # TRUE #' is_horizontal(iris, ggplot2::aes(Sepal.Length, Species), "x") # FALSE #' is_horizontal(iris, ggplot2::aes(Sepal.Length, Sepal.Width)) # FALSE is_horizontal <- function(data, mapping, val = "y") { yData <- eval_data_col(data, mapping[[val]]) is.factor(yData) || is.character(yData) || is.logical(yData) } #' @export #' @rdname is_horizontal is_character_column <- is_horizontal #' Swap x and y mapping #' @param mapping output of \code{ggplot2::\link[ggplot2]{aes}(...)} #' @return Aes mapping with the x and y values switched #' @export #' @examples #' mapping <- ggplot2::aes(Petal.Length, Sepal.Width) #' mapping #' mapping_swap_x_y(mapping) mapping_swap_x_y <- function(mapping) { tmp <- mapping$x mapping$x <- mapping$y mapping$y <- tmp mapping } #' Plots the Scatter Plot #' #' Make a scatter plot with a given data set. #' #' @param data data set using #' @param mapping aesthetics being used #' @param ... other arguments are sent to geom_point #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @export #' @keywords hplot #' @examples #' data(mtcars) #' ggally_points(mtcars, mapping = ggplot2::aes(x = disp, y = hp)) #' ggally_points(mtcars, mapping = ggplot2::aes_string(x = "disp", y = "hp")) #' ggally_points( #' mtcars, #' mapping = ggplot2::aes_string( #' x = "disp", #' y = "hp", #' color = "as.factor(cyl)", #' size = "gear" #' ) #' ) ggally_points <- function(data, mapping, ...){ p <- ggplot(data = data, mapping = mapping) + geom_point(...) p } #' Plots the Scatter Plot with Smoothing #' #' Add a smoothed condition mean with a given scatter plot. #' #' Y limits are reduced to match original Y range with the goal of keeping the Y axis the same across plots. #' #' @param data data set using #' @param mapping aesthetics being used #' @param ... other arguments to add to geom_point #' @param method,se parameters supplied to \code{\link[ggplot2]{geom_smooth}} #' @param shrink boolean to determine if y range is reduced to range of points or points and error ribbon #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @export #' @keywords hplot #' @rdname ggally_smooth #' @examples #' data(tips, package = "reshape") #' ggally_smooth(tips, mapping = ggplot2::aes(x = total_bill, y = tip)) #' ggally_smooth(tips, mapping = ggplot2::aes_string(x = "total_bill", y = "tip")) #' ggally_smooth(tips, mapping = ggplot2::aes_string(x = "total_bill", y = "tip", color = "sex")) ggally_smooth <- function(data, mapping, ..., method = "lm", se = TRUE, shrink = TRUE) { p <- ggplot(data = data, mapping) p <- p + geom_point(...) if (! is.null(mapping$color) || ! is.null(mapping$colour)) { p <- p + geom_smooth(method = method, se = se) } else { p <- p + geom_smooth(method = method, se = se, colour = I("black")) } if (isTRUE(shrink)) { p <- p + coord_cartesian( ylim = range(eval_data_col(data, mapping$y), na.rm = TRUE) ) } p } #' @export #' @rdname ggally_smooth ggally_smooth_loess <- function(data, mapping, ...) { ggally_smooth(data = data, mapping = mapping, ..., method = "loess") } #' @export #' @rdname ggally_smooth ggally_smooth_lm <- function(data, mapping, ...) { ggally_smooth(data = data, mapping = mapping, ..., method = "lm") } #' Plots the Scatter Density Plot #' #' Make a scatter density plot from a given data. #' #' The aesthetic "fill" determines whether or not stat_density2d (filled) or geom_density2d (lines) is used. #' #' @param data data set using #' @param mapping aesthetics being used #' @param ... parameters sent to either stat_density2d or geom_density2d #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @export #' @keywords hplot #' @examples #' data(tips, package = "reshape") #' ggally_density(tips, mapping = ggplot2::aes(x = total_bill, y = tip)) #' ggally_density(tips, mapping = ggplot2::aes_string(x = "total_bill", y = "tip")) #' ggally_density( #' tips, #' mapping = ggplot2::aes_string(x = "total_bill", y = "tip", fill = "..level..") #' ) #' ggally_density( #' tips, #' mapping = ggplot2::aes_string(x = "total_bill", y = "tip", fill = "..level..") #' ) + ggplot2::scale_fill_gradient(breaks = c(0.05, 0.1, 0.15, 0.2)) ggally_density <- function(data, mapping, ...){ rangeX <- range(eval_data_col(data, mapping$x), na.rm = TRUE) rangeY <- range(eval_data_col(data, mapping$y), na.rm = TRUE) p <- ggplot(data = data) + geom_point( data = data.frame(rangeX = rangeX, rangeY = rangeY), mapping = aes(x = rangeX, y = rangeY), alpha = 0 ) if (!is.null(mapping$fill)) { p <- p + stat_density2d(mapping = mapping, geom = "polygon", ...) } else { p <- p + geom_density2d(mapping = mapping, ...) } p } #' Correlation from the Scatter Plot #' #' Estimate correlation from the given data. #' #' @param data data set using #' @param mapping aesthetics being used #' @param alignPercent right align position of numbers. Default is 60 percent across the horizontal #' @param method \code{method} supplied to cor function #' @param use \code{use} supplied to cor function #' @param corAlignPercent deprecated. Use parameter \code{alignPercent} #' @param corMethod deprecated. Use parameter \code{method} #' @param corUse deprecated. Use parameter \code{use} #' @param ... other arguments being supplied to geom_text #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @importFrom stats complete.cases cor #' @export #' @keywords hplot #' @examples #' data(tips, package = "reshape") #' ggally_cor(tips, mapping = ggplot2::aes_string(x = "total_bill", y = "tip")) #' ggally_cor( #' tips, #' mapping = ggplot2::aes(x = total_bill, y = tip), #' size = 15, #' colour = I("red") #' ) #' ggally_cor( #' tips, #' mapping = ggplot2::aes_string(x = "total_bill", y = "tip", color = "sex"), #' size = 5 #' ) ggally_cor <- function( data, mapping, alignPercent = 0.6, method = "pearson", use = "complete.obs", corAlignPercent = NULL, corMethod = NULL, corUse = NULL, ... ){ if (! is.null(corAlignPercent)) { stop("'corAlignPercent' is deprecated. Please use argument 'alignPercent'") } if (! is.null(corMethod)) { stop("'corMethod' is deprecated. Please use argument 'method'") } if (! is.null(corUse)) { stop("'corUse' is deprecated. Please use argument 'use'") } useOptions <- c( "all.obs", "complete.obs", "pairwise.complete.obs", "everything", "na.or.complete" ) use <- pmatch(use, useOptions) if (is.na(use)) { warning("correlation 'use' not found. Using default value of 'all.obs'") use <- useOptions[1] } else { use <- useOptions[use] } cor_fn <- function(x, y) { # also do ddply below if fn is altered cor(x, y, method = method, use = use) } # xVar <- data[[as.character(mapping$x)]] # yVar <- data[[as.character(mapping$y)]] # x_bad_rows <- is.na(xVar) # y_bad_rows <- is.na(yVar) # bad_rows <- x_bad_rows | y_bad_rows # if (any(bad_rows)) { # total <- sum(bad_rows) # if (total > 1) { # warning("Removed ", total, " rows containing missing values") # } else if (total == 1) { # warning("Removing 1 row that contained a missing value") # } # # xVar <- xVar[!bad_rows] # yVar <- yVar[!bad_rows] # } # mapping$x <- mapping$y <- NULL xData <- eval_data_col(data, mapping$x) yData <- eval_data_col(data, mapping$y) if (is_date(xData)) { xData <- as.numeric(xData) } if (is_date(yData)) { yData <- as.numeric(yData) } colorData <- eval_data_col(data, mapping$colour) if (is.numeric(colorData)) { stop("ggally_cor: mapping color column must be categorical, not numeric") } if (use %in% c("complete.obs", "pairwise.complete.obs", "na.or.complete")) { if (!is.null(colorData) && (length(colorData) == length(xData))) { rows <- complete.cases(xData, yData, colorData) } else { rows <- complete.cases(xData, yData) } if (any(!rows)) { total <- sum(!rows) if (total > 1) { warning("Removed ", total, " rows containing missing values") } else if (total == 1) { warning("Removing 1 row that contained a missing value") } } if (!is.null(colorData) && (length(colorData) == length(xData))) { colorData <- colorData[rows] } xData <- xData[rows] yData <- yData[rows] } xVal <- xData yVal <- yData # if the mapping has to deal with the data, remove it if (packageVersion("ggplot2") > "2.2.1") { for (mappingName in names(mapping)) { itemData <- eval_data_col(data, mapping[[mappingName]]) if (!inherits(itemData, "AsIs")) { mapping[[mappingName]] <- NULL } } } else { if (length(names(mapping)) > 0){ for (i in length(names(mapping)):1){ # find the last value of the aes, such as cyl of as.factor(cyl) tmp_map_val <- deparse(mapping[names(mapping)[i]][[1]]) if (tmp_map_val[length(tmp_map_val)] %in% colnames(data)) mapping[[names(mapping)[i]]] <- NULL if (length(names(mapping)) < 1){ mapping <- NULL break; } } } } if ( !is.null(colorData) && !inherits(colorData, "AsIs") ) { cord <- ddply( data.frame(x = xData, y = yData, color = colorData), "color", function(dt) { cor_fn(dt$x, dt$y) } ) colnames(cord)[2] <- "correlation" cord$correlation <- signif(as.numeric(cord$correlation), 3) # put in correct order lev <- levels(as.factor(colorData)) ord <- rep(-1, nrow(cord)) for (i in 1:nrow(cord)) { for (j in seq_along(lev)){ if (identical(as.character(cord$color[i]), as.character(lev[j]))) { ord[i] <- j } } } # print(order(ord[ord >= 0])) # print(lev) cord <- cord[order(ord[ord >= 0]), ] cord$label <- str_c(cord$color, ": ", cord$correlation) # calculate variable ranges so the gridlines line up xmin <- min(xVal, na.rm = TRUE) xmax <- max(xVal, na.rm = TRUE) xrange <- c(xmin - 0.01 * (xmax - xmin), xmax + 0.01 * (xmax - xmin)) ymin <- min(yVal, na.rm = TRUE) ymax <- max(yVal, na.rm = TRUE) yrange <- c(ymin - 0.01 * (ymax - ymin), ymax + 0.01 * (ymax - ymin)) # print(cord) p <- ggally_text( label = str_c("Cor : ", signif(cor_fn(xVal, yVal), 3)), mapping = mapping, xP = 0.5, yP = 0.9, xrange = xrange, yrange = yrange, color = "black", ... ) + #element_bw() + theme(legend.position = "none") xPos <- rep(alignPercent, nrow(cord)) * diff(xrange) + min(xrange, na.rm = TRUE) yPos <- seq( from = 0.9, to = 0.2, length.out = nrow(cord) + 1) yPos <- yPos * diff(yrange) + min(yrange, na.rm = TRUE) yPos <- yPos[-1] # print(range(yVal)) # print(yPos) cordf <- data.frame(xPos = xPos, yPos = yPos, labelp = cord$label) cordf$labelp <- factor(cordf$labelp, levels = cordf$labelp) # print(cordf) # print(str(cordf)) p <- p + geom_text( data = cordf, aes( x = xPos, y = yPos, label = labelp, color = labelp ), hjust = 1, ... ) p } else { # calculate variable ranges so the gridlines line up xmin <- min(xVal, na.rm = TRUE) xmax <- max(xVal, na.rm = TRUE) xrange <- c(xmin - 0.01 * (xmax - xmin), xmax + 0.01 * (xmax - xmin)) ymin <- min(yVal, na.rm = TRUE) ymax <- max(yVal, na.rm = TRUE) yrange <- c(ymin - 0.01 * (ymax - ymin), ymax + 0.01 * (ymax - ymin)) p <- ggally_text( label = paste( "Corr:\n", signif( cor_fn(xVal, yVal), 3 ), sep = "", collapse = "" ), mapping, xP = 0.5, yP = 0.5, xrange = xrange, yrange = yrange, ... ) + #element_bw() + theme(legend.position = "none") p } } #' Plots the Box Plot #' #' Make a box plot with a given data set. \code{ggally_box_no_facet} will be a single panel plot, while \code{ggally_box} will be a faceted plot #' #' @param data data set using #' @param mapping aesthetics being used #' @param ... other arguments being supplied to geom_boxplot #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @keywords hplot #' @export #' @examples #' data(tips, package = "reshape") #' ggally_box(tips, mapping = ggplot2::aes(x = total_bill, y = sex)) #' ggally_box(tips, mapping = ggplot2::aes_string(x = "total_bill", y = "sex")) #' ggally_box( #' tips, #' mapping = ggplot2::aes_string(y = "total_bill", x = "sex", color = "sex"), #' outlier.colour = "red", #' outlier.shape = 13, #' outlier.size = 8 #' ) ggally_box <- function(data, mapping, ...){ mapping <- mapping_color_to_fill(mapping) ggally_dot_and_box(data, mapping, ..., boxPlot = TRUE) } #' @export #' @rdname ggally_box ggally_box_no_facet <- function(data, mapping, ...) { mapping <- mapping_color_to_fill(mapping) ggally_dot_and_box_no_facet(data, mapping, ..., boxPlot = TRUE) } #' Plots the Box Plot with Dot #' #' Add jittering with the box plot. \code{ggally_dot_no_facet} will be a single panel plot, while \code{ggally_dot} will be a faceted plot #' #' @param data data set using #' @param mapping aesthetics being used #' @param ... other arguments being supplied to geom_jitter #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @keywords hplot #' @export #' @examples #' data(tips, package = "reshape") #' ggally_dot(tips, mapping = ggplot2::aes(x = total_bill, y = sex)) #' ggally_dot(tips, mapping = ggplot2::aes_string(x = "total_bill", y = "sex")) #' ggally_dot( #' tips, #' mapping = ggplot2::aes_string(y = "total_bill", x = "sex", color = "sex") #' ) #' ggally_dot( #' tips, #' mapping = ggplot2::aes_string(y = "total_bill", x = "sex", color = "sex", shape = "sex") #' ) + ggplot2::scale_shape(solid=FALSE) ggally_dot <- function(data, mapping, ...){ ggally_dot_and_box(data, mapping, ..., boxPlot = FALSE) } #' @export #' @rdname ggally_dot ggally_dot_no_facet <- function(data, mapping, ...) { ggally_dot_and_box_no_facet(data, mapping, ..., boxPlot = FALSE) } #' Plots either Box Plot or Dot Plots #' #' Place box plots or dot plots on the graph #' #' @param data data set using #' @param mapping aesthetics being used #' @param ... parameters passed to either geom_jitter or geom_boxplot #' @param boxPlot boolean to decide to plot either box plots (TRUE) or dot plots (FALSE) #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @keywords hplot #' @export #' @examples #' data(tips, package = "reshape") #' ggally_dot_and_box( #' tips, #' mapping = ggplot2::aes(x = total_bill, y = sex, color = sex), #' boxPlot = TRUE #' ) #' ggally_dot_and_box( #' tips, #' mapping = ggplot2::aes(x = total_bill, y = sex, color = sex), #' boxPlot = FALSE #' ) ggally_dot_and_box <- function(data, mapping, ..., boxPlot = TRUE){ horizontal <- is_horizontal(data, mapping) if (horizontal) { mapping <- mapping_swap_x_y(mapping) } xVal <- mapping_string(mapping$x) mapping$x <- 1 p <- ggplot(data = data) if (boxPlot) { p <- p + geom_boxplot(mapping, ...) } else { p <- p + geom_jitter(mapping, ...) } if (!horizontal) { p <- p + facet_grid(paste(". ~ ", xVal, sep = ""), scales = "free_x") + theme(panel.spacing = unit(0.1, "lines")) } else { p <- p + coord_flip() + theme( axis.text.y = element_text( angle = 90, vjust = 0, colour = "grey50" ) ) + facet_grid(paste(xVal, " ~ .", sep = "")) + theme(panel.spacing = unit(0.1, "lines")) } p <- p + scale_x_continuous(xVal, labels = "", breaks = 1) p } ggally_dot_and_box_no_facet <- function(data, mapping, ..., boxPlot = TRUE) { horizontal <- is_horizontal(data, mapping) if (horizontal) { mapping <- mapping_swap_x_y(mapping) } p <- ggplot(data = data) if (boxPlot) { p <- p + geom_boxplot(mapping, ...) } else { p <- p + geom_jitter(mapping, ...) } if (horizontal) { p <- p + scale_x_discrete( limits = rev(levels(eval_data_col(data, mapping$x))) ) + coord_flip() } p } #' Plots the Histograms by Faceting #' #' Make histograms by displaying subsets of the data in different panels. #' #' @param data data set using #' @param mapping aesthetics being used #' @param ... parameters sent to stat_bin() #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @keywords hplot #' @export #' @examples #' data(tips, package = "reshape") #' ggally_facethist(tips, mapping = ggplot2::aes(x = tip, y = sex)) #' ggally_facethist(tips, mapping = ggplot2::aes_string(x = "tip", y = "sex"), binwidth = 0.1) ggally_facethist <- function(data, mapping, ...){ mapping <- mapping_color_to_fill(mapping) horizontal <- is_horizontal(data, mapping) if (!horizontal) { mapping <- mapping_swap_x_y(mapping) } xVal <- mapping_string(mapping$x) yVal <- mapping_string(mapping$y) mapping$y <- NULL p <- ggplot(data = data, mapping) p <- p + stat_bin(...) if (horizontal) { p <- p + facet_grid(paste(yVal, " ~ .", sep = "")) + theme(panel.spacing = unit(0.1, "lines")) } else { p <- p + facet_grid(paste(". ~", yVal, sep = "")) + theme(panel.spacing = unit(0.1, "lines")) + coord_flip() } p <- p + labs(x = xVal, y = yVal) p } #' Plots the density plots by faceting #' #' Make density plots by displaying subsets of the data in different panels. #' #' @param data data set using #' @param mapping aesthetics being used #' @param ... other arguments being sent to stat_density #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @keywords hplot #' @export #' @examples #' data(tips, package = "reshape") #' ggally_facetdensity(tips, mapping = ggplot2::aes(x = total_bill, y = sex)) #' ggally_facetdensity( #' tips, #' mapping = ggplot2::aes_string(y = "total_bill", x = "sex", color = "sex") #' ) ggally_facetdensity <- function(data, mapping, ...){ ggally_facetdensitystrip(data, mapping, ..., den_strip = FALSE) } #' Plots a tile plot with facets #' #' Make Tile Plot as densely as possible. #' #' @param data data set using #' @param mapping aesthetics being used #' @param ... other arguments being sent to stat_bin #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @keywords hplot #' @export #' @examples #' data(tips, package = "reshape") #' ggally_denstrip(tips, mapping = ggplot2::aes(x = total_bill, y = sex)) #' ggally_denstrip(tips, mapping = ggplot2::aes_string(x = "total_bill", y = "sex")) #' ggally_denstrip( #' tips, #' mapping = ggplot2::aes_string(x = "sex", y = "tip", binwidth = "0.2") #' ) + ggplot2::scale_fill_gradient(low = "grey80", high = "black") ggally_denstrip <- function(data, mapping, ...){ mapping <- mapping_color_to_fill(mapping) ggally_facetdensitystrip(data, mapping, ..., den_strip = TRUE) } #' Plots a density plot with facets or a tile plot with facets #' #' Make Tile Plot as densely as possible. #' #' @param data data set using #' @param mapping aesthetics being used #' @param ... other arguments being sent to either geom_histogram or stat_density #' @param den_strip boolean to decide whether or not to plot a density strip(TRUE) or a facet density(FALSE) plot. #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @keywords hplot #' @export #' @examples #' example(ggally_facetdensity) #' example(ggally_denstrip) ggally_facetdensitystrip <- function(data, mapping, ..., den_strip = FALSE){ horizontal <- is_horizontal(data, mapping) if (!horizontal) { mapping <- mapping_swap_x_y(mapping) } xVal <- mapping_string(mapping$x) yVal <- mapping_string(mapping$y) mappingY <- mapping$y # nolint mapping$y <- NULL # will be faceted p <- ggplot(data = data, mapping) + labs(x = xVal, y = yVal) if (identical(den_strip, TRUE)) { p <- p + geom_histogram( mapping = aes(fill = ..density..), # nolint position = "fill", ... ) + scale_y_continuous( breaks = c(0.5), labels = "1" ) } else { p <- p + stat_density( aes( y = ..scaled.. * diff(range(x, na.rm = TRUE)) + min(x, na.rm = TRUE) # nolint ), position = "identity", geom = "line", ... ) } if (horizontal) { p <- p + facet_grid(paste(yVal, " ~ .", sep = "")) if (identical(den_strip, TRUE)) { p <- p + theme(axis.text.y = element_blank()) } } else { p <- p + coord_flip() p <- p + facet_grid(paste(". ~ ", yVal, sep = "")) if (identical(den_strip, TRUE)) { p <- p + theme(axis.text.x = element_blank()) } } p } #' Plots the Density Plots by Using Diagonal #' #' Plots the density plots by using Diagonal. #' #' @param data data set using #' @param mapping aesthetics being used. #' @param ... other arguments sent to stat_density #' @param rescale boolean to decide whether or not to rescale the count output #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @keywords hplot #' @export #' @examples #' data(tips, package = "reshape") #' ggally_densityDiag(tips, mapping = ggplot2::aes(x = total_bill)) #' ggally_densityDiag(tips, mapping = ggplot2::aes(x = total_bill, color = day)) ggally_densityDiag <- function(data, mapping, ..., rescale = FALSE){ mapping <- mapping_color_to_fill(mapping) p <- ggplot(data, mapping) + scale_y_continuous() if (identical(rescale, TRUE)) { p <- p + stat_density( aes( y = ..scaled.. * diff(range(x, na.rm = TRUE)) + min(x, na.rm = TRUE) # nolint ), position = "identity", geom = "line", ... ) } else { p <- p + geom_density(...) } p } #' Plots the Bar Plots by Using Diagonal #' #' Plots the bar plots by using Diagonal. #' #' @param data data set using #' @param mapping aesthetics being used #' @param ... other arguments are sent to geom_bar #' @param rescale boolean to decide whether or not to rescale the count output. Only applies to numeric data #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @keywords hplot #' @export #' @examples #' data(tips, package = "reshape") #' ggally_barDiag(tips, mapping = ggplot2::aes(x = day)) #' ggally_barDiag(tips, mapping = ggplot2::aes(x = tip), binwidth = 0.25) ggally_barDiag <- function(data, mapping, ..., rescale = FALSE){ mapping <- mapping_color_to_fill(mapping) mapping$y <- NULL x_data <- eval_data_col(data, mapping$x) numer <- ("continuous" == plotting_data_type(x_data)) p <- ggplot(data = data, mapping) if (is_date(x_data)) { p <- p + geom_histogram(...) #TODO make y axis lines match date positions # buildInfo <- ggplot_build(p + geom_bar(...)) # histBarPerc <- buildInfo$data[[1]]$ncount } else if (numer) { if (identical(rescale, TRUE)) { p <- p + geom_histogram( aes( y = ..density.. / max(..density..) * diff(range(x, na.rm = TRUE)) + min(x, na.rm = TRUE) # nolint ), ... ) + coord_cartesian(ylim = range(eval_data_col(data, mapping$x), na.rm = TRUE)) } else { p <- p + geom_histogram(...) } } else { p <- p + geom_bar(...) } p } #' Text Plot #' #' Plot text for a plot. #' #' @param label text that you want to appear #' @param mapping aesthetics that don't relate to position (such as color) #' @param xP horizontal position percentage #' @param yP vertical position percentage #' @param xrange range of the data around it. Only nice to have if plotting in a matrix #' @param yrange range of the data around it. Only nice to have if plotting in a matrix #' @param ... other arguments for geom_text #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @keywords hplot #' @export #' @examples #' ggally_text("Example 1") #' ggally_text("Example\nTwo", mapping = ggplot2::aes(size = 15), color = I("red")) ggally_text <- function( label, mapping = ggplot2::aes(color = "black"), xP = 0.5, yP = 0.5, xrange = c(0, 1), yrange = c(0, 1), ... ){ p <- ggplot() + xlim(xrange) + ylim(yrange) + theme( panel.background = element_blank(), panel.grid.minor = element_blank(), panel.grid.major = element_line(colour = "grey85") ) + labs(x = NULL, y = NULL) new_mapping <- aes_string( x = xP * diff(xrange) + min(xrange, na.rm = TRUE), y = yP * diff(yrange) + min(yrange, na.rm = TRUE) ) if (is.null(mapping)) { mapping <- new_mapping } else { mapping <- add_and_overwrite_aes(mapping, new_mapping) } # dont mess with color if it's already there if (!is.null(mapping$colour)) { p <- p + geom_text( label = label, mapping = mapping, ...) + guides(colour = FALSE) } else if ("colour" %in% names(aes(...))) { p <- p + geom_text( label = label, mapping = mapping, ...) } else { colour <- "grey50" p <- p + geom_text( label = label, mapping = mapping, colour = colour, ...) } p <- p + theme(legend.position = "none") p } #' Get x axis labels #' #' Retrieves x axis labels from the plot object directly. #' #' @importFrom gtable gtable_filter #' @param p plot object #' @param xRange range of x values #' @keywords internal get_x_axis_labels <- function(p, xRange) { pGrob <- ggplotGrob(p) axisTable <- gtable_filter(pGrob, "axis-b")$grobs[[1]]$children$axis # have to do a function as filter doesn't work get_raw_grob_by_name <- function(g, name) { for (item in g$grobs) { if (str_detect(item$name, name) ) { return(item$children[[1]]) } } NULL } xAxisGrob <- get_raw_grob_by_name(axisTable, "axis.text.x") axisBreaks <- as.numeric(xAxisGrob$label) axisLabs <- rbind( expand.grid(xPos = axisBreaks[1], yPos = axisBreaks), expand.grid(xPos = axisBreaks, yPos = axisBreaks[1]) )[-1, ] axisLabs <- as.data.frame(axisLabs) axisLabs$lab <- as.character(apply(axisLabs, 1, max)) axisLabs$hjust <- 0.5 axisLabs$vjust <- 0.5 minPos <- xRange[1] maxPos <- xRange[2] for (i in seq_len(nrow(axisLabs))) { xPos <- axisLabs[i, "xPos"] yPos <- axisLabs[i, "yPos"] if (yPos < minPos) { axisLabs[i, "yPos"] <- minPos axisLabs[i, "vjust"] <- 0 } else if (yPos > maxPos) { axisLabs[i, "yPos"] <- maxPos axisLabs[i, "vjust"] <- 1 } if (xPos < minPos) { axisLabs[i, "xPos"] <- minPos axisLabs[i, "hjust"] <- 0 } else if (xPos > maxPos) { axisLabs[i, "xPos"] <- maxPos axisLabs[i, "hjust"] <- 1 } } axisLabs } #' Internal Axis Labeling Plot for ggpairs #' #' This function is used when \code{axisLabels == "internal"}. #' #' @param data dataset being plotted #' @param mapping aesthetics being used (x is the variable the plot will be made for) #' @param label title to be displayed in the middle. Defaults to \code{mapping$x} #' @param labelSize size of variable label #' @param labelXPercent percent of horizontal range #' @param labelYPercent percent of vertical range #' @param labelHJust hjust supplied to label #' @param labelVJust vjust supplied to label #' @param gridLabelSize size of grid labels #' @param ... other arguments for geom_text #' @author Jason Crowley \email{crowley.jason.s@@gmail.com} and Barret Schloerke #' @export #' @examples #' data(tips, package = "reshape") #' ggally_diagAxis(tips, ggplot2::aes(x=tip)) #' ggally_diagAxis(tips, ggplot2::aes(x=sex)) ggally_diagAxis <- function( data, mapping, label = mapping$x, labelSize = 5, labelXPercent = 0.5, labelYPercent = 0.55, labelHJust = 0.5, labelVJust = 0.5, gridLabelSize = 4, ... ) { if (is.null(mapping$x)) { stop("mapping$x is null. There must be a column value in this location.") } mapping$y <- NULL numer <- ! is_horizontal(data, mapping, "x") if (! is.character(label)) { label <- mapping_string(mapping$x) } xData <- eval_data_col(data, mapping$x) if (numer) { xmin <- min(xData, na.rm = TRUE) xmax <- max(xData, na.rm = TRUE) # add a lil fluff... it looks better xrange <- c(xmin - .01 * (xmax - xmin), xmax + .01 * (xmax - xmin)) # xrange <- c(xmin, xmax) p <- ggally_text( label = label, mapping = aes(col = "grey50"), xrange = xrange, yrange = xrange, size = labelSize, xP = labelXPercent, yP = labelYPercent, hjust = labelHJust, vjust = labelVJust ) axisBreaks <- get_x_axis_labels(p, xrange) # print(axisBreaks) p <- p + geom_text( data = axisBreaks, mapping = aes_string( x = "xPos", y = "yPos", label = "lab", hjust = "hjust", vjust = "vjust" ), col = "grey50", size = gridLabelSize ) } else { breakLabels <- levels(as.factor(xData)) numLvls <- length(breakLabels) p <- ggally_text( label = label, mapping = aes(col = "grey50"), xrange = c(0, 1), yrange = c(0, 1), size = labelSize, yP = labelYPercent, xP = labelXPercent, hjust = labelHJust, vjust = labelVJust ) #axisBreaks <- (1+2*0:(numLvls-1))/(2*numLvls) axisBreaks <- 0:(numLvls - 1) * (0.125 + (1 - 0.125 * (numLvls - 1)) / numLvls) + (1 - 0.125 * (numLvls - 1)) / (2 * numLvls) axisLabs <- data.frame( x = axisBreaks[1:numLvls], y = axisBreaks[numLvls:1], lab = breakLabels ) p <- p + geom_text( data = axisLabs, mapping = aes( x = x, y = y, label = lab ), col = "grey50", size = gridLabelSize ) # hack to remove warning message... cuz it doesn't listen to suppress messages p$scales$scales[[1]]$breaks <- axisBreaks p$scales$scales[[2]]$breaks <- axisBreaks # pLabs <- pLabs + # scale_x_continuous(breaks=axisBreaks,limits=c(0,1)) + # scale_y_continuous(breaks=axisBreaks,limits=c(0,1)) } p } #' Plots the Bar Plots Faceted by Conditional Variable #' #' X variables are plotted using \code{geom_bar} and faceted by the Y variable. #' #' @param data data set using #' @param mapping aesthetics being used #' @param ... other arguments are sent to geom_bar #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @keywords hplot #' @export #' @examples #' data(tips, package = "reshape") #' ggally_facetbar(tips, ggplot2::aes(x = sex, y = smoker, fill = time)) #' ggally_facetbar(tips, ggplot2::aes(x = smoker, y = sex, fill = time)) ggally_facetbar <- function(data, mapping, ...){ mapping <- mapping_color_to_fill(mapping) # numer <- is.null(attributes(data[,as.character(mapping$x)])$class) # xVal <- mapping$x yVal <- mapping_string(mapping$y) mapping$y <- NULL p <- ggplot(data, mapping) + geom_bar(...) + facet_grid(paste(yVal, " ~ .", sep = "")) p } #' Plots a mosaic plot #' #' Plots the mosaic plot by using fluctuation. #' #' @param data data set using #' @param mapping aesthetics being used. Only x and y will used and both are required #' @param ... passed to \code{\link[ggplot2]{geom_tile}(...)} #' @param floor don't display cells smaller than this value #' @param ceiling max value to scale frequencies. If any frequency is larger than the ceiling, the fill color is displayed darker than other rectangles #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @keywords hplot #' @export #' @examples #' data(tips, package = "reshape") #' ggally_ratio(tips, ggplot2::aes(sex, day)) #' ggally_ratio(tips, ggplot2::aes(sex, day)) + ggplot2::coord_equal() #' # only plot tiles greater or equal to 20 and scale to a max of 50 #' ggally_ratio( #' tips, ggplot2::aes(sex, day), #' floor = 20, ceiling = 50 #' ) + ggplot2::theme(aspect.ratio = 4/2) ggally_ratio <- function( data, mapping = do.call(ggplot2::aes_string, as.list(colnames(data)[1:2])), ..., floor = 0, ceiling = NULL ) { # capture the original names xName <- mapping_string(mapping$x) yName <- mapping_string(mapping$y) countData <- plyr::count(data, vars = c(xName, yName)) # overwrite names so name clashes don't happen colnames(countData)[1:2] <- c("x", "y") xNames <- levels(countData[["x"]]) yNames <- levels(countData[["y"]]) countData <- subset(countData, freq >= floor) if (is.null(ceiling)) { ceiling <- max(countData$freq) } countData[["freqSize"]] <- sqrt(pmin(countData[["freq"]], ceiling) / ceiling) countData[["col"]] <- ifelse(countData[["freq"]] > ceiling, "grey30", "grey50") countData[["xPos"]] <- as.numeric(countData[["x"]]) + (1 / 2) * countData[["freqSize"]] countData[["yPos"]] <- as.numeric(countData[["y"]]) + (1 / 2) * countData[["freqSize"]] p <- ggplot( data = countData, mapping = aes_string( x = "xPos", y = "yPos", height = "freqSize", width = "freqSize", fill = "col" ) ) + geom_tile(...) + scale_fill_identity() + scale_x_continuous( name = xName, limits = c(0.9999, length(xNames) + 1), breaks = 1:(length(xNames) + 1), labels = c(xNames, ""), minor_breaks = FALSE ) + scale_y_continuous( name = yName, limits = c(0.9999, length(yNames) + 1), breaks = 1:(length(yNames) + 1), labels = c(yNames, ""), minor_breaks = FALSE ) + theme( axis.text.x = element_text( hjust = 0, vjust = 1, colour = "grey50" ), axis.text.y = element_text( hjust = 0, vjust = 0, angle = 90, colour = "grey50" ) ) p } #' Blank #' #' Draws nothing. #' #' Makes a "blank" ggplot object that will only draw white space #' #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @param ... other arguments ignored #' @export #' @keywords hplot ggally_blank <- function(...){ aes(...) # ignored a <- data.frame(X = 1:2, Y = 1:2) p <- ggplot(data = a, aes_string(x = "X", y = "Y")) + geom_point( colour = "transparent") + theme( axis.line = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank(), legend.background = element_blank(), legend.key = element_blank(), legend.text = element_blank(), legend.title = element_blank(), panel.background = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), plot.background = element_blank(), plot.title = element_blank(), strip.background = element_blank(), strip.text.x = element_blank(), strip.text.y = element_blank() ) class(p) <- c(class(p), "ggmatrix_blank") p } #' @rdname ggally_blank #' @export ggally_blankDiag <- function(...) { ggally_blank(...) } #' NA plot #' #' Draws a large \code{NA} in the middle of the plotting area. This plot is useful when all X or Y data is \code{NA} #' #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @param data ignored #' @param mapping ignored #' @param size size of the geom_text 'NA' #' @param color color of the geom_text 'NA' #' @param ... other arguments sent to geom_text #' @export #' @keywords hplot ggally_na <- function(data = NULL, mapping = NULL, size = 10, color = "grey20", ...) { a <- data.frame(x = 1, y = 1, label = "NA") p <- ggplot(data = a, aes_string(x = "x", y = "y", label = "label")) + geom_text(color = color, size = size, ...) + theme( axis.line = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank(), legend.background = element_blank(), legend.key = element_blank(), legend.text = element_blank(), legend.title = element_blank(), panel.background = element_blank(), panel.border = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), plot.background = element_blank(), plot.title = element_blank(), strip.background = element_blank(), strip.text.x = element_blank(), strip.text.y = element_blank() ) p } #' @rdname ggally_na #' @export ggally_naDiag <- function(...) { ggally_na(...) } GGally/R/ggmatrix_make_plot.R0000644000176200001440000000271313010131532015622 0ustar liggesusers make_label_plot <- function(types, sectionAes, label) { sectionAes$y <- NULL p <- make_ggmatrix_plot_obj( wrapp( "diagAxis", params = c("label" = label), funcArgName = "ggally_diagAxis" ), mapping = sectionAes ) return(p) } ggmatrix_plot_list <- (function(){ make_diag_plot_wrapper <- function(sub_type_val) { plot_fn <- make_plot_wrapper(sub_type_val) function(types, sectionAes) { sectionAes$y <- NULL plot_fn(types, sectionAes) } } make_plot_wrapper <- function(sub_type_val) { function(types, sectionAes) { sub_type <- types[[sub_type_val]] sub_type_name <- get_subtype_name(sub_type) p <- make_ggmatrix_plot_obj( wrapp(sub_type, funcArgName = sub_type_name), mapping = sectionAes ) return(p) } } na_fn <- make_plot_wrapper("na") na_diag_fn <- make_plot_wrapper("na") continuous_fn <- make_plot_wrapper("continuous") combo_fn <- make_plot_wrapper("combo") discrete_fn <- make_plot_wrapper("discrete") continuous_diag_fn <- make_diag_plot_wrapper("continuous") discrete_diag_fn <- make_diag_plot_wrapper("discrete") function(type) { switch(type, "na" = na_fn, "na-diag" = na_diag_fn, "continuous" = continuous_fn, "combo" = combo_fn, "discrete" = discrete_fn, "continuous-diag" = continuous_diag_fn, "discrete-diag" = discrete_diag_fn, "label" = make_label_plot ) } })() GGally/R/ggsurv.R0000644000176200001440000002642413276725426013320 0ustar liggesusersif(getRversion() >= "2.15.1") { utils::globalVariables(c("cens", "surv", "up", "low")) } #' Survival curves with ggplot2 #' #' This function produces Kaplan-Meier plots using \code{ggplot2}. #' As a first argument it needs a \code{survfit} object, created by the #' \code{survival} package. Default settings differ for single stratum and #' multiple strata objects. #' #' @export #' @param s an object of class \code{survfit} #' @param CI should a confidence interval be plotted? Defaults to \code{TRUE} #' for single stratum objects and \code{FALSE} for multiple strata objects. #' @param plot.cens mark the censored observations? #' @param surv.col colour of the survival estimate. Defaults to black for #' one stratum, and to the default \code{ggplot2} colours for multiple #' strata. Length of vector with colour names should be either 1 or equal #' to the number of strata. #' @param cens.col colour of the points that mark censored observations. #' @param lty.est linetype of the survival curve(s). Vector length should be #' either 1 or equal to the number of strata. #' @param lty.ci linetype of the bounds that mark the 95\% CI. #' @param size.est line width of the survival curve #' @param size.ci line width of the 95\% CI #' @param cens.size point size of the censoring points #' @param cens.shape shape of the points that mark censored observations. #' @param back.white if TRUE the background will not be the default #' grey of \code{ggplot2} but will be white with borders around the plot. #' @param xlab the label of the x-axis. #' @param ylab the label of the y-axis. #' @param main the plot label. #' @param order.legend boolean to determine if the legend display should be ordered by final survival time #' @return An object of class \code{ggplot} #' @author Edwin Thoen \email{edwinthoen@@gmail.com} #' @importFrom stats time #' @examples #' #' if (require(survival) && require(scales)) { #' data(lung, package = "survival") #' sf.lung <- survival::survfit(Surv(time, status) ~ 1, data = lung) #' ggsurv(sf.lung) #' #' # Multiple strata examples #' sf.sex <- survival::survfit(Surv(time, status) ~ sex, data = lung) #' pl.sex <- ggsurv(sf.sex) #' pl.sex #' #' # Adjusting the legend of the ggsurv fit #' pl.sex + #' ggplot2::guides(linetype = FALSE) + #' ggplot2::scale_colour_discrete( #' name = 'Sex', #' breaks = c(1,2), #' labels = c('Male', 'Female') #' ) #' #' # We can still adjust the plot after fitting #' data(kidney, package = "survival") #' sf.kid <- survival::survfit(Surv(time, status) ~ disease, data = kidney) #' pl.kid <- ggsurv(sf.kid, plot.cens = FALSE) #' pl.kid #' #' # Zoom in to first 80 days #' pl.kid + ggplot2::coord_cartesian(xlim = c(0, 80), ylim = c(0.45, 1)) #' #' # Add the diseases names to the plot and remove legend #' pl.kid + #' ggplot2::annotate( #' "text", #' label = c("PKD", "Other", "GN", "AN"), #' x = c(90, 125, 5, 60), #' y = c(0.8, 0.65, 0.55, 0.30), #' size = 5, #' colour = scales::hue_pal( #' h = c(0, 360) + 15, #' c = 100, #' l = 65, #' h.start = 0, #' direction = 1 #' )(4) #' ) + #' ggplot2::guides(color = FALSE, linetype = FALSE) #' } ggsurv <- function( s, CI = 'def', plot.cens = TRUE, surv.col = 'gg.def', cens.col = 'gg.def', lty.est = 1, lty.ci = 2, size.est = 0.5, size.ci = size.est, cens.size = 2, cens.shape = 3, back.white = FALSE, xlab = 'Time', ylab = 'Survival', main = '', order.legend = TRUE ){ require_namespaces(c("survival", "scales")) strata <- ifelse(is.null(s$strata) == TRUE, 1, length(s$strata)) stopifnot(length(surv.col) == 1 | length(surv.col) == strata) stopifnot(length(lty.est) == 1 | length(lty.est) == strata) if(strata == 1) { fn <- ggsurv_s } else { fn <- ggsurv_m } pl <- fn( s, CI , plot.cens, surv.col, cens.col, lty.est, lty.ci, size.est, size.ci, cens.size, cens.shape, back.white, xlab, ylab, main, strata, order.legend ) pl } # survival function for single survival ggsurv_s <- function( s, CI = 'def', plot.cens = TRUE, surv.col = 'gg.def', cens.col = 'gg.def', lty.est = 1, lty.ci = 2, size.est = 0.5, size.ci = size.est, cens.size = 2, cens.shape = 3, back.white = FALSE, xlab = 'Time', ylab = 'Survival', main = '', strata = 1, order.legend = TRUE ){ dat <- data.frame( time = c(0, s$time), surv = c(1, s$surv), up = c(1, s$upper), low = c(1, s$lower), cens = c(0, s$n.censor) ) dat.cens <- subset(dat, cens != 0) col <- ifelse(surv.col == 'gg.def', 'black', surv.col) pl <- ggplot(dat, aes(x = time, y = surv)) + geom_step(col = col, lty = lty.est, size = size.est) + xlab(xlab) + ylab(ylab) + ggtitle(main) if(identical(CI, TRUE) | identical(CI, 'def')) { pl <- pl + geom_step(aes(y = up), color = col, lty = lty.ci, size = size.ci) + geom_step(aes(y = low), color = col, lty = lty.ci, size = size.ci) } if (identical(plot.cens, TRUE) ) { if (nrow(dat.cens) == 0){ stop('There are no censored observations') } col <- ifelse(cens.col == 'gg.def', 'red', cens.col) pl <- pl + geom_point( data = dat.cens, mapping = aes(y = surv), shape = cens.shape, col = col, size = cens.size ) } if(back.white == TRUE) { pl <- pl + theme_bw() } pl } # survival function for multiple survivals ggsurv_m <- function( s, CI = 'def', plot.cens = TRUE, surv.col = 'gg.def', cens.col = 'gg.def', lty.est = 1, lty.ci = 2, size.est = 0.5, size.ci = size.est, cens.size = 2, cens.shape = 3, back.white = FALSE, xlab = 'Time', ylab = 'Survival', main = '', strata = length(s$strata), order.legend = TRUE ) { n <- s$strata strataEqualNames <- unlist(strsplit(names(s$strata), '=')) ugroups <- strataEqualNames[seq(2, 2*strata, by = 2)] getlast <- function(x) { res <- NULL maxTime <- max(x$time) for (mo in names(x$strata)) { sur <- x[mo]$surv n <- length(sur) # grab the last survival value surValue <- sur[n] if (isTRUE(all.equal(surValue, 0))) { # if they die, order by percent complete of max observation. # tie value of 0 if the last person dies at the last time surTime <- x[mo]$time[n] surValue <- (surTime / maxTime) - 1 } res <- append(res, surValue) } return(res) } if (isTRUE(order.legend)) { group_order <- order(getlast(s), decreasing = TRUE) lastv <- ugroups[group_order] if (length(surv.col) == length(n)) { surv.col <- surv.col[group_order] } if (length(cens.col) == length(n)) { cens.col <- cens.col[group_order] } } else { lastv <- ugroups } groups <- factor(ugroups, levels = lastv) gr.name <- strataEqualNames[1] gr.df <- vector('list', strata) n.ind <- cumsum(c(0, n)) for (i in 1:strata) { indI <- (n.ind[i]+1):n.ind[i+1] gr.df[[i]] <- data.frame( time = c(0, s$time[ indI ]), surv = c(1, s$surv[ indI ]), up = c(1, s$upper[ indI ]), low = c(1, s$lower[ indI ]), cens = c(0, s$n.censor[ indI ]), group = rep(groups[i], n[i] + 1) ) } dat <- do.call(rbind, gr.df) pl <- ggplot(dat, aes(x = time, y = surv, group = group)) + geom_step(aes(col = group, lty = group), size = size.est) + xlab(xlab) + ylab(ylab) + ggtitle(main) pl <- if(surv.col[1] != 'gg.def'){ scaleValues <- if (length(surv.col) == 1) { rep(surv.col, strata) } else{ surv.col } pl + scale_colour_manual(name = gr.name, values = scaleValues) } else { pl + scale_colour_discrete(name = gr.name) } lineScaleValues <- if (length(lty.est) == 1) { rep(lty.est, strata) } else { lty.est } pl <- pl + scale_linetype_manual(name = gr.name, values = lineScaleValues) if(identical(CI,TRUE)) { stepLty <- if ((length(surv.col) > 1 | surv.col == 'gg.def')[1]) { lty.ci } else { surv.col } pl <- pl + geom_step(aes(y = up, lty = group, col = group), lty = stepLty, size = size.ci) + geom_step(aes(y = low,lty = group, col = group), lty = stepLty, size = size.ci) } if (identical(plot.cens, TRUE) ){ dat.cens <- subset(dat, cens != 0) dat.cens <- subset(dat.cens, group != "PKD") if (nrow(dat.cens) == 0) { stop('There are no censored observations') } if (length(cens.col) == 1) { if (identical(cens.col, "gg.def")) { # match the colors of the lines pl <- pl + geom_point( data = dat.cens, mapping = aes(y = surv, col = group), shape = cens.shape, size = cens.size, show.legend = FALSE ) } else { # supply the raw color value pl <- pl + geom_point( data = dat.cens, mapping = aes(y = surv), shape = cens.shape, color = cens.col, size = cens.size ) } } else if (length(cens.col) > 0) { # if(!(identical(cens.col,surv.col) || is.null(cens.col))) { # warning ("Color scales for survival curves and censored points don't match.\nOnly one color scale can be used. Defaulting to surv.col") # } if (! identical(cens.col, "gg.def")) { if (length(cens.col) != strata) { warning("Color scales for censored points don't match the number of groups. Defaulting to ggplot2 default color scale") cens.col <- "gg.def" } } if (identical(cens.col, "gg.def")) { # match the group color value pl <- pl + geom_point( data = dat.cens, mapping = aes(y = surv, col = group), shape = cens.shape, show.legend = FALSE, size = cens.size ) } else { # custom colors and maybe custom shape uniqueGroupVals = levels(dat.cens$group) if (length(cens.shape) == 1) { cens.shape = rep(cens.shape, strata) } if (length(cens.shape) != strata) { warning("The length of the censored shapes does not match the number of groups (or 1). Defaulting shape = 3 (+)") cens.shape = rep(3, strata) } for (i in seq_along(uniqueGroupVals)) { groupVal = uniqueGroupVals[i] dtGroup <- subset(dat.cens, group == groupVal) if (nrow(dtGroup) == 0) { next } pl <- pl + geom_point( data = dtGroup, mapping = aes(y=surv), color = I(cens.col[i]), shape = cens.shape[i], show.legend = FALSE, size = cens.size ) } } } } if(identical(back.white, TRUE)) { pl <- pl + theme_bw() } pl } GGally/R/ggmatrix.R0000644000176200001440000001051113277311163013601 0ustar liggesusers #' ggmatrix - A ggplot2 Matrix #' #' Make a generic matrix of ggplot2 plots. #' #' @section Memory usage: #' Now that the print.ggmatrix method uses a large gtable object, rather than print each plot independently, memory usage may be of concern. From small tests, memory usage flutters around \code{object.size(data) * 0.3 * length(plots)}. So, for a 80Mb random noise dataset with 100 plots, about 2.4 Gb of memory needed to print. For the 3.46 Mb diamonds dataset with 100 plots, about 100 Mb of memory was needed to print. The benefits of using the ggplot2 format greatly outweigh the price of about 20% increase in memory usage from the prior ad-hoc print method. #' #' @param plots list of plots to be put into matrix #' @param nrow,ncol number of rows and columns #' @param xAxisLabels,yAxisLabels strip titles for the x and y axis respectively. Set to \code{NULL} to not be displayed #' @param title,xlab,ylab title, x label, and y label for the graph. Set to \code{NULL} to not be displayed #' @param byrow boolean that determines whether the plots should be ordered by row or by column #' @param showStrips boolean to determine if each plot's strips should be displayed. \code{NULL} will default to the top and right side plots only. \code{TRUE} or \code{FALSE} will turn all strips on or off respectively. #' @param showAxisPlotLabels,showXAxisPlotLabels,showYAxisPlotLabels booleans that determine if the plots axis labels are printed on the X (bottom) or Y (left) part of the plot matrix. If \code{showAxisPlotLabels} is set, both \code{showXAxisPlotLabels} and \code{showYAxisPlotLabels} will be set to the given value. #' @template ggmatrix-labeller-param #' @template ggmatrix-switch-param #' @param xProportions,yProportions Value to change how much area is given for each plot. Either \code{NULL} (default), numeric value matching respective length, or \code{grid::\link[grid]{unit}} object with matching respective length #' @template ggmatrix-progress #' @param data data set using. This is the data to be used in place of 'ggally_data' if the plot is a string to be evaluated at print time #' @param gg ggplot2 theme objects to be applied to every plot #' @template ggmatrix-legend-param #' @keywords hplot #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @importFrom rlang %||% #' @export #' @examples #' # small function to display plots only if it's interactive #' p_ <- GGally::print_if_interactive #' #' plotList <- list() #' for (i in 1:6) { #' plotList[[i]] <- ggally_text(paste("Plot #", i, sep = "")) #' } #' pm <- ggmatrix( #' plotList, #' 2, 3, #' c("A", "B", "C"), #' c("D", "E"), #' byrow = TRUE #' ) #' p_(pm) #' #' pm <- ggmatrix( #' plotList, #' 2, 3, #' xAxisLabels = c("A", "B", "C"), #' yAxisLabels = NULL, #' byrow = FALSE, #' showXAxisPlotLabels = FALSE #' ) #' p_(pm) ggmatrix <- function( plots, nrow, ncol, xAxisLabels = NULL, yAxisLabels = NULL, title = NULL, xlab = NULL, ylab = NULL, byrow = TRUE, showStrips = NULL, showAxisPlotLabels = TRUE, showXAxisPlotLabels = TRUE, showYAxisPlotLabels = TRUE, labeller = NULL, switch = NULL, xProportions = NULL, yProportions = NULL, progress = NULL, data = NULL, gg = NULL, legend = NULL ) { if (!is.list(plots)) { stop("'plots' must be a list()") } check_nrow_ncol(nrow, "nrow") check_nrow_ncol(ncol, "ncol") if (!missing(showAxisPlotLabels)) { showXAxisPlotLabels <- showAxisPlotLabels showYAxisPlotLabels <- showAxisPlotLabels } progress <- as_ggmatrix_progress(progress, nrow * ncol) plotMatrix <- list( data = data, plots = plots, title = title, xlab = xlab, ylab = ylab, showStrips = showStrips, xAxisLabels = xAxisLabels, yAxisLabels = yAxisLabels, showXAxisPlotLabels = showXAxisPlotLabels, showYAxisPlotLabels = showYAxisPlotLabels, labeller = labeller, switch = switch, xProportions = xProportions, yProportions = yProportions, progress = progress, legend = legend, gg = gg, nrow = nrow, ncol = ncol, byrow = byrow ) attributes(plotMatrix)$class <- c("gg", "ggmatrix") plotMatrix } check_nrow_ncol <- function(x, title) { if (!is.numeric(x)) { stop(paste("'", title, "' must be a numeric value", sep = "")) } if (length(x) != 1) { stop(paste("'", title, "' must be a single numeric value", sep = "")) } } GGally/R/ggmatrix_print.R0000644000176200001440000000317513277311163015025 0ustar liggesusers ggplot2_set_last_plot <- utils::getFromNamespace("set_last_plot", "ggplot2") #' Print ggmatrix object #' #' Print method taken from \code{ggplot2:::print.ggplot} and altered for a ggmatrix object #' #' @param x plot to display #' @param newpage draw new (empty) page first? #' @param vp viewport to draw plot in #' @param ... arguments passed onto \code{\link{ggmatrix_gtable}} #' @method print ggmatrix #' @author Barret Schloerke #' @import utils #' @importFrom grid grid.newpage grid.draw seekViewport pushViewport upViewport #' @export #' @examples #' data(tips, package = "reshape") #' pMat <- ggpairs(tips, c(1,3,2), mapping = ggplot2::aes_string(color = "sex")) #' pMat # calls print(pMat), which calls print.ggmatrix(pMat) print.ggmatrix <- function (x, newpage = is.null(vp), vp = NULL, ...) { if (newpage) { grid.newpage() } grDevices::recordGraphics(requireNamespace("GGally", quietly = TRUE), list(), getNamespace("GGally")) gtable <- ggmatrix_gtable(x, ...) # must be done after gtable, as gtable calls many ggplot2::print.ggplot methods ggplot2_set_last_plot(x) if (is.null(vp)) { grid.draw(gtable) } else { if (is.character(vp)) { seekViewport(vp) } else { pushViewport(vp) } grid.draw(gtable) upViewport() } invisible(data) } #' Is Blank Plot? #' Find out if the plot equals a blank plot #' #' @keywords internal #' @examples #' GGally:::is_blank_plot(ggally_blank()) #' GGally:::is_blank_plot(ggally_points(mtcars, ggplot2::aes_string(x = "disp", y = "hp"))) #' is_blank_plot <- function(p){ is.null(p) || identical(p, "blank") || inherits(p, "ggmatrix_blank") } GGally/R/ggcorr.R0000644000176200001440000003421213010131532013227 0ustar liggesusersif (getRversion() >= "2.15.1") { utils::globalVariables(c("x", "y", "coefficient", "breaks", "label")) } #' ggcorr - Plot a correlation matrix with ggplot2 #' #' Function for making a correlation matrix plot, using ggplot2. #' The function is directly inspired by Tian Zheng and Yu-Sung Su's #' \code{corrplot} function in the 'arm' package. #' Please visit \url{http://github.com/briatte/ggcorr} for the latest version #' of \code{ggcorr}, and see the vignette at #' \url{https://briatte.github.io/ggcorr/} for many examples of how to use it. #' #' @export #' @param data a data frame or matrix containing numeric (continuous) data. If #' any of the columns contain non-numeric data, they will be dropped with a #' warning. #' @param method a vector of two character strings. The first value gives the #' method for computing covariances in the presence of missing values, and must #' be (an abbreviation of) one of \code{"everything"}, \code{"all.obs"}, #' \code{"complete.obs"}, \code{"na.or.complete"} or #' \code{"pairwise.complete.obs"}. The second value gives the type of #' correlation coefficient to compute, and must be one of \code{"pearson"}, #' \code{"kendall"} or \code{"spearman"}. #' See \code{\link[stats]{cor}} for details. #' Defaults to \code{c("pairwise", "pearson")}. #' @param cor_matrix the named correlation matrix to use for calculations. #' Defaults to the correlation matrix of \code{data} when \code{data} is #' supplied. #' @param palette if \code{nbreaks} is used, a ColorBrewer palette to use #' instead of the colors specified by \code{low}, \code{mid} and \code{high}. #' Defaults to \code{NULL}. #' @param name a character string for the legend that shows the colors of the #' correlation coefficients. #' Defaults to \code{""} (no legend name). #' @param geom the geom object to use. Accepts either \code{"tile"}, #' \code{"circle"}, \code{"text"} or \code{"blank"}. #' @param min_size when \code{geom} has been set to \code{"circle"}, the minimum #' size of the circles. #' Defaults to \code{2}. #' @param max_size when \code{geom} has been set to \code{"circle"}, the maximum #' size of the circles. #' Defaults to \code{6}. #' @param label whether to add correlation coefficients to the plot. #' Defaults to \code{FALSE}. #' @param label_alpha whether to make the correlation coefficients increasingly #' transparent as they come close to 0. Also accepts any numeric value between #' \code{0} and \code{1}, in which case the level of transparency is set to that #' fixed value. #' Defaults to \code{FALSE} (no transparency). #' @param label_color the color of the correlation coefficients. #' Defaults to \code{"grey75"}. #' @param label_round the decimal rounding of the correlation coefficients. #' Defaults to \code{1}. #' @param label_size the size of the correlation coefficients. #' Defaults to \code{4}. #' @param nbreaks the number of breaks to apply to the correlation coefficients, #' which results in a categorical color scale. See 'Note'. #' Defaults to \code{NULL} (no breaks, continuous scaling). #' @param digits the number of digits to show in the breaks of the correlation #' coefficients: see \code{\link[base]{cut}} for details. #' Defaults to \code{2}. #' @param low the lower color of the gradient for continuous scaling of the #' correlation coefficients. #' Defaults to \code{"#3B9AB2"} (blue). #' @param mid the midpoint color of the gradient for continuous scaling of the #' correlation coefficients. #' Defaults to \code{"#EEEEEE"} (very light grey). #' @param high the upper color of the gradient for continuous scaling of the #' correlation coefficients. #' Defaults to \code{"#F21A00"} (red). #' @param midpoint the midpoint value for continuous scaling of the #' correlation coefficients. #' Defaults to \code{0}. #' @param limits bounding of color scaling for correlations, set \code{limits = NULL} or \code{FALSE} to remove #' @param drop if using \code{nbreaks}, whether to drop unused breaks from the #' color scale. #' Defaults to \code{FALSE} (recommended). #' @param layout.exp a multiplier to expand the horizontal axis to the left if #' variable names get clipped. #' Defaults to \code{0} (no expansion). #' @param legend.position where to put the legend of the correlation #' coefficients: see \code{\link[ggplot2]{theme}} for details. #' Defaults to \code{"bottom"}. #' @param legend.size the size of the legend title and labels, in points: see #' \code{\link[ggplot2]{theme}} for details. #' Defaults to \code{9}. #' @param ... other arguments supplied to \code{\link[ggplot2]{geom_text}} for #' the diagonal labels. #' @note Recommended values for the \code{nbreaks} argument are \code{3} to #' \code{11}, as values above 11 are visually difficult to separate and are not #' supported by diverging ColorBrewer palettes. #' #' @seealso \code{\link[stats]{cor}} and \code{corrplot} in the #' \code{arm} package. #' @author Francois Briatte, with contributions from Amos B. Elberg and #' Barret Schloerke #' @importFrom reshape melt melt.data.frame melt.default #' @importFrom stats cor #' @importFrom grDevices colorRampPalette #' @examples #' # Basketball statistics provided by Nathan Yau at Flowing Data. #' dt <- read.csv("http://datasets.flowingdata.com/ppg2008.csv") #' #' # Default output. #' ggcorr(dt[, -1]) #' #' # Labelled output, with coefficient transparency. #' ggcorr(dt[, -1], #' label = TRUE, #' label_alpha = TRUE) #' #' # Custom options. #' ggcorr( #' dt[, -1], #' name = expression(rho), #' geom = "circle", #' max_size = 10, #' min_size = 2, #' size = 3, #' hjust = 0.75, #' nbreaks = 6, #' angle = -45, #' palette = "PuOr" # colorblind safe, photocopy-able #' ) #' #' # Supply your own correlation matrix #' ggcorr( #' data = NULL, #' cor_matrix = cor(dt[, -1], use = "pairwise") #' ) ggcorr <- function( data, method = c("pairwise", "pearson"), cor_matrix = NULL, nbreaks = NULL, digits = 2, name = "", low = "#3B9AB2", mid = "#EEEEEE", high = "#F21A00", midpoint = 0, palette = NULL, geom = "tile", min_size = 2, max_size = 6, label = FALSE, label_alpha = FALSE, label_color = "black", label_round = 1, label_size = 4, limits = c(-1, 1), drop = is.null(limits) || identical(limits, FALSE), layout.exp = 0, legend.position = "right", legend.size = 9, ...) { if (is.numeric(limits)) { if (length(limits) != 2) { stop("'limits' must be of length 2 if numeric") } } if (is.logical(limits)) { if (limits) { limits <- c(-1, 1) } else { limits <- NULL } } # -- check geom argument ----------------------------------------------------- if (length(geom) > 1 || !geom %in% c("blank", "circle", "text", "tile")) { stop("incorrect geom value") } # -- correlation method ------------------------------------------------------ if (length(method) == 1) { method = c(method, "pearson") # for backwards compatibility } # -- check data columns ------------------------------------------------------ if (!is.null(data)) { if (!is.data.frame(data)) { data = as.data.frame(data) } x = which(!sapply(data, is.numeric)) if (length(x) > 0) { warning(paste("data in column(s)", paste0(paste0("'", names(data)[x], "'"), collapse = ", "), "are not numeric and were ignored")) data = data[, -x ] } } # -- correlation matrix ------------------------------------------------------ if (is.null(cor_matrix)) { cor_matrix = cor(data, use = method[1], method = method[2]) } m = cor_matrix colnames(m) = rownames(m) = gsub(" ", "_", colnames(m)) # protect spaces # -- correlation data.frame -------------------------------------------------- m = data.frame(m * lower.tri(m)) rownames(m) = names(m) m$.ggally_ggcorr_row_names = rownames(m) m = reshape::melt(m, id.vars = ".ggally_ggcorr_row_names") names(m) = c("x", "y", "coefficient") m$coefficient[ m$coefficient == 0 ] = NA # -- correlation quantiles --------------------------------------------------- if (!is.null(nbreaks)) { x = seq(-1, 1, length.out = nbreaks + 1) if (!nbreaks %% 2) { x = sort(c(x, 0)) } m$breaks = cut(m$coefficient, breaks = unique(x), include.lowest = TRUE, dig.lab = digits) } # -- gradient midpoint ------------------------------------------------------- if (is.null(midpoint)) { midpoint = median(m$coefficient, na.rm = TRUE) message(paste("Color gradient midpoint set at median correlation to", round(midpoint, 2))) } # -- plot structure ---------------------------------------------------------- m$label = round(m$coefficient, label_round) p = ggplot(na.omit(m), aes(x, y)) if (geom == "tile") { if (is.null(nbreaks)) { # -- tiles, continuous --------------------------------------------------- p = p + geom_tile(aes(fill = coefficient), color = "white") } else { # -- tiles, ordinal ------------------------------------------------------ p = p + geom_tile(aes(fill = breaks), color = "white") } # -- tiles, color scale ---------------------------------------------------- if (is.null(nbreaks) && !is.null(limits)) { p = p + scale_fill_gradient2(name, low = low, mid = mid, high = high, midpoint = midpoint, limits = limits) } else if (is.null(nbreaks)) { p = p + scale_fill_gradient2(name, low = low, mid = mid, high = high, midpoint = midpoint) } else if (is.null(palette)) { x = colorRampPalette(c(low, mid, high))(length(levels(m$breaks))) p = p + scale_fill_manual(name, values = x, drop = drop) } else { p = p + scale_fill_brewer(name, palette = palette, drop = drop) } } else if (geom == "circle") { p = p + geom_point(aes(size = abs(coefficient) * 1.25), color = "grey50") # border if (is.null(nbreaks)) { # -- circles, continuous ------------------------------------------------- p = p + geom_point(aes(size = abs(coefficient), color = coefficient)) } else { # -- circles, ordinal ---------------------------------------------------- p = p + geom_point(aes(size = abs(coefficient), color = breaks)) } p = p + scale_size_continuous(range = c(min_size, max_size)) + guides(size = FALSE) r = list(size = (min_size + max_size) / 2) # -- circles, color scale -------------------------------------------------- if (is.null(nbreaks) && !is.null(limits)) { p = p + scale_color_gradient2(name, low = low, mid = mid, high = high, midpoint = midpoint, limits = limits) } else if (is.null(nbreaks)) { p = p + scale_color_gradient2(name, low = low, mid = mid, high = high, midpoint = midpoint) } else if (is.null(palette)) { x = colorRampPalette(c(low, mid, high))(length(levels(m$breaks))) p = p + scale_color_manual(name, values = x, drop = drop) + guides(color = guide_legend(override.aes = r)) } else { p = p + scale_color_brewer(name, palette = palette, drop = drop) + guides(color = guide_legend(override.aes = r)) } } else if (geom == "text") { if (is.null(nbreaks)) { # -- text, continuous ---------------------------------------------------- p = p + geom_text(aes(label = label, color = coefficient), size = label_size) } else { # -- text, ordinal ------------------------------------------------------- p = p + geom_text(aes(label = label, color = breaks), size = label_size) } # -- text, color scale ---------------------------------------------------- if (is.null(nbreaks) && !is.null(limits)) { p = p + scale_color_gradient2(name, low = low, mid = mid, high = high, midpoint = midpoint, limits = limits) } else if (is.null(nbreaks)) { p = p + scale_color_gradient2(name, low = low, mid = mid, high = high, midpoint = midpoint) } else if (is.null(palette)) { x = colorRampPalette(c(low, mid, high))(length(levels(m$breaks))) p = p + scale_color_manual(name, values = x, drop = drop) } else { p = p + scale_color_brewer(name, palette = palette, drop = drop) } } # -- coefficient labels ------------------------------------------------------ if (label) { if (isTRUE(label_alpha)) { p = p + geom_text(aes(x, y, label = label, alpha = abs(coefficient)), color = label_color, size = label_size, show.legend = FALSE) } else if (label_alpha > 0) { p = p + geom_text( aes(x, y, label = label), show.legend = FALSE, alpha = label_alpha, color = label_color, size = label_size ) } else { p = p + geom_text(aes(x, y, label = label), color = label_color, size = label_size) } } # -- horizontal scale expansion ---------------------------------------------- textData <- m[ m$x == m$y & is.na(m$coefficient), ] xLimits <- levels(textData$y) textData$diagLabel <- textData$x if (!is.numeric(layout.exp) || layout.exp < 0) { stop("incorrect layout.exp value") } else if (layout.exp > 0) { layout.exp <- as.integer(layout.exp) # copy to fill in spacer info textData <- rbind(textData[1:layout.exp, ], textData) spacer <- paste(".ggally_ggcorr_spacer_value", 1:layout.exp, sep = "") textData$x[1:layout.exp] <- spacer textData$diagLabel[1:layout.exp] <- NA xLimits <- c(spacer, levels(m$y)) } p = p + geom_text(data = textData, aes_string(label = "diagLabel"), ..., na.rm = TRUE) + scale_x_discrete(breaks = NULL, limits = xLimits) + scale_y_discrete(breaks = NULL, limits = levels(m$y)) + labs(x = NULL, y = NULL) + coord_equal() + theme( panel.background = element_blank(), legend.key = element_blank(), legend.position = legend.position, legend.title = element_text(size = legend.size), legend.text = element_text(size = legend.size) ) return(p) } GGally/R/ggnetworkmap.R0000644000176200001440000003666013277311163014501 0ustar liggesusersif(getRversion() >= "2.15.1") { utils::globalVariables(c( "lon", "lat", "group", "id", "lon1", "lat1", "lon2", "lat2", ".label" )) } #' ggnetworkmap - Plot a network with ggplot2 suitable for overlay on a ggmap:: map ggplot, or other ggplot #' #' This is a descendent of the original \code{ggnet} function. \code{ggnet} added the innovation of plotting the network geographically. #' However, \code{ggnet} needed to be the first object in the ggplot chain. \code{ggnetworkmap} does not. If passed a \code{ggplot} object as its first argument, #' such as output from \code{ggmap}, \code{ggnetworkmap} will plot on top of that chart, looking for vertex attributes \code{lon} and \code{lat} as coordinates. #' Otherwise, \code{ggnetworkmap} will generate coordinates using the Fruchterman-Reingold algorithm. #' #' @export #' @param gg an object of class \code{ggplot}. #' @param net an object of class \code{\link[network]{network}}, or any object #' that can be coerced to this class, such as an adjacency or incidence matrix, #' or an edge list: see \link[network]{edgeset.constructors} and #' \link[network]{network} for details. If the object is of class #' \code{\link[igraph:igraph-package]{igraph}} and the #' \code{\link[intergraph:intergraph-package]{intergraph}} package is installed, #' it will be used to convert the object: see #' \code{\link[intergraph]{asNetwork}} for details. #' @param size size of the network nodes. Defaults to 3. If the nodes are weighted, their area is proportionally scaled up to the size set by \code{size}. #' @param alpha a level of transparency for nodes, vertices and arrows. Defaults to 0.75. #' @param weight if present, the unquoted name of a vertex attribute in \code{data}. Otherwise nodes are unweighted. #' @param node.group \code{NULL}, the default, or the unquoted name of a vertex attribute that will be used to determine the color of each node. #' @param ring.group if not \code{NULL}, the default, the unquoted name of a vertex attribute that will be used to determine the color of each node border. #' @param node.color If \code{node.group} is null, a character string specifying a color. #' @param node.alpha transparency of the nodes. Inherits from \code{alpha}. #' @param segment.alpha transparency of the vertex links. Inherits from \code{alpha} #' @param segment.color color of the vertex links. Defaults to \code{"grey"}. #' @param segment.size size of the vertex links, as a vector of values or as a single value. Defaults to 0.25. #' @param great.circles whether to draw edges as great circles using the \code{geosphere} package. Defaults to \code{FALSE} #' @param arrow.size size of the vertex arrows for directed network plotting, in centimeters. Defaults to 0. #' @param label.nodes label nodes with their vertex names attribute. If set to \code{TRUE}, all nodes are labelled. Also accepts a vector of character strings to match with vertex names. #' @param label.size size of the labels. Defaults to \code{size / 2}. #' @param ... other arguments supplied to geom_text for the node labels. Arguments pertaining to the title or other items can be achieved through ggplot2 methods. #' @author Amos Elberg \email{amos.elberg@@gmail.com}. Original by Moritz Marbach \email{mmarbach@@mail.uni-mannheim.de}, Francois Briatte \email{f.briatte@@gmail.com} #' @details This is a function for plotting graphs generated by \code{network} or \code{igraph} in a more flexible and elegant manner than permitted by ggnet. The function does not need to be the first plot in the ggplot chain, so the graph can be plotted on top of a map or other chart. Segments can be straight lines, or plotted as great circles. Note that the great circles feature can produce odd results with arrows and with vertices beyond the plot edges; this is a ggplot2 limitation and cannot yet be fixed. Nodes can have two color schemes, which are then plotted as the center and ring around the node. The color schemes are selected by adding scale_fill_ or scale_color_ just like any other ggplot2 plot. If there are no rings, scale_color sets the color of the nodes. If there are rings, scale_color sets the color of the rings, and scale_fill sets the color of the centers. Note that additional arguments in the ... are passed to geom_text for plotting labels. #' @importFrom utils installed.packages #' @examples #' # small function to display plots only if it's interactive #' p_ <- GGally::print_if_interactive #' #' invisible(lapply(c("ggplot2", "maps", "network", "sna"), base::library, character.only = TRUE)) #' #' ## Example showing great circles on a simple map of the USA #' ## http://flowingdata.com/2011/05/11/how-to-map-connections-with-great-circles/ #' \donttest{ #' airports <- read.csv("http://datasets.flowingdata.com/tuts/maparcs/airports.csv", header = TRUE) #' rownames(airports) <- airports$iata #' #' # select some random flights #' set.seed(1234) #' flights <- data.frame( #' origin = sample(airports[200:400, ]$iata, 200, replace = TRUE), #' destination = sample(airports[200:400, ]$iata, 200, replace = TRUE) #' ) #' #' # convert to network #' flights <- network(flights, directed = TRUE) #' #' # add geographic coordinates #' flights %v% "lat" <- airports[ network.vertex.names(flights), "lat" ] #' flights %v% "lon" <- airports[ network.vertex.names(flights), "long" ] #' #' # drop isolated airports #' delete.vertices(flights, which(degree(flights) < 2)) #' #' # compute degree centrality #' flights %v% "degree" <- degree(flights, gmode = "digraph") #' #' # add random groups #' flights %v% "mygroup" <- sample(letters[1:4], network.size(flights), replace = TRUE) #' #' # create a map of the USA #' usa <- ggplot(map_data("usa"), aes(x = long, y = lat)) + #' geom_polygon(aes(group = group), color = "grey65", #' fill = "#f9f9f9", size = 0.2) #' #' # overlay network data to map #' p <- ggnetworkmap( #' usa, flights, size = 4, great.circles = TRUE, #' node.group = mygroup, segment.color = "steelblue", #' ring.group = degree, weight = degree #' ) #' p_(p) #' #' ## Exploring a community of spambots found on Twitter #' ## Data by Amos Elberg: see ?twitter_spambots for details #' #' data(twitter_spambots) #' #' # create a world map #' world <- fortify(map("world", plot = FALSE, fill = TRUE)) #' world <- ggplot(world, aes(x = long, y = lat)) + #' geom_polygon(aes(group = group), color = "grey65", #' fill = "#f9f9f9", size = 0.2) #' #' # view global structure #' p <- ggnetworkmap(world, twitter_spambots) #' p_(p) #' #' # domestic distribution #' p <- ggnetworkmap(net = twitter_spambots) #' p_(p) #' #' # topology #' p <- ggnetworkmap(net = twitter_spambots, arrow.size = 0.5) #' p_(p) #' #' # compute indegree and outdegree centrality #' twitter_spambots %v% "indegree" <- degree(twitter_spambots, cmode = "indegree") #' twitter_spambots %v% "outdegree" <- degree(twitter_spambots, cmode = "outdegree") #' #' p <- ggnetworkmap( #' net = twitter_spambots, #' arrow.size = 0.5, #' node.group = indegree, #' ring.group = outdegree, size = 4 #' ) + #' scale_fill_continuous("Indegree", high = "red", low = "yellow") + #' labs(color = "Outdegree") #' p_(p) #' #' # show some vertex attributes associated with each account #' p <- ggnetworkmap( #' net = twitter_spambots, #' arrow.size = 0.5, #' node.group = followers, #' ring.group = friends, #' size = 4, #' weight = indegree, #' label.nodes = TRUE, vjust = -1.5 #' ) + #' scale_fill_continuous("Followers", high = "red", low = "yellow") + #' labs(color = "Friends") + #' scale_color_continuous(low = "lightgreen", high = "darkgreen") #' p_(p) #'} #' ggnetworkmap <- function ( gg, net, size = 3, alpha = 0.75, weight, node.group, node.color = NULL, node.alpha = NULL, ring.group, segment.alpha = NULL, segment.color = "grey", great.circles = FALSE, segment.size = 0.25, arrow.size = 0, label.nodes = FALSE, label.size = size/2, ...) { require_namespaces(c("network", "sna")) # sna # node placement if there is no ggplot object in function call # -- conversion to network class --------------------------------------------- if (class(net) == "igraph" && "intergraph" %in% rownames(installed.packages())) { net = intergraph::asNetwork(net) } else if (class(net) == "igraph") { stop("install the 'intergraph' package to use igraph objects with ggnet") } if (!network::is.network(net)) { net = try(network::network(net), silent = TRUE) } if (!network::is.network(net)) { stop("could not coerce net to a network object") } # -- network functions ------------------------------------------------------- get_v = get("%v%", envir = as.environment("package:network")) # -- network structure ------------------------------------------------------- vattr = network::list.vertex.attributes(net) is_dir = ifelse(network::is.directed(net), "digraph", "graph") if (!is.numeric(arrow.size) || arrow.size < 0) { stop("incorrect arrow.size value") } else if (arrow.size > 0 & is_dir == "graph") { warning("network is undirected; arrow.size ignored") arrow.size = 0 } if (network::is.hyper(net)) { stop("ggnetworkmap cannot plot hyper graphs") } if (network::is.multiplex(net)) { stop("ggnetworkmap cannot plot multiplex graphs") } if (network::has.loops(net)) { warning("ggnetworkmap does not know how to handle self-loops") } # -- ... ------------------------------------------------------- # get arguments labels = label.nodes # alpha default inherit <- function(x) ifelse(is.null(x), alpha, x) # get sociomatrix m <- network::as.matrix.network.adjacency(net) if (missing(gg)) { # mapproj doesn't need to be loaded, but # it needs to exist for ggplot2::coord_map() to work properly if (! ("mapproj" %in% installed.packages())) { require_namespaces("mapproj") } gg <- ggplot() + coord_map() plotcord <- sna::gplot.layout.fruchtermanreingold(net, list(m,layout.par = NULL)) plotcord <- data.frame(plotcord) colnames(plotcord) = c("lon", "lat") } else { plotcord = data.frame( lon = as.numeric(get_v(net, "lon")), lat = as.numeric(get_v(net, "lat")) ) } # Correct vertex labels if (! is.logical(labels)) { stopifnot(length(labels) == nrow(plotcord)) plotcord$.label <- labels } else if ("id" %in% vattr) { plotcord$.label <- as.character(get_v(net, "id")) } else if ("vertex.names" %in% vattr) { plotcord$.label <- network::network.vertex.names(net) } point_aes <- list( x = substitute(lon), y = substitute(lat) ) point_args <- list( alpha = substitute(inherit(node.alpha)) ) # get node groups if(!missing(node.group)) { plotcord$.ngroup <- get_v(net, as.character(substitute(node.group))) if (missing(ring.group)) { point_aes$color = substitute(.ngroup) } else { point_aes$fill = substitute(.ngroup) } } else if (! missing(node.color)) { point_args$color <- substitute(node.color) } else { point_args$color <- substitute( "black") } # rings if(!missing(ring.group)) { plotcord$.rgroup <- get_v(net, as.character(substitute(ring.group))) point_aes$color <- substitute(.rgroup) point_args$pch <- substitute(21) } # # # Plot edges # # # get edgelist edges <- network::as.matrix.network.edgelist(net) edges <- data.frame( lat1 = plotcord[ edges[, 1], "lat"], lon1 = plotcord[ edges[, 1], "lon"], lat2 = plotcord[ edges[, 2], "lat"], lon2 = plotcord[ edges[,2], "lon"]) edges <- subset(na.omit(edges), (! (lat1 == lat2 & lon2 == lon2))) edge_args <- list(size = substitute(segment.size), alpha = substitute(inherit(segment.alpha)), color = substitute(segment.color) ) edge_aes <- list() # -- edge arrows ------------------------------------------------------------- if (!missing(arrow.size) & arrow.size > 0) { edge_args$arrow <- substitute(arrow( type = "closed", length = unit(arrow.size, "cm") )) } # -- great circles ----------------------------------------------------------- if (great.circles) { # geosphere # great circles require_namespaces("geosphere") pts <- 25 # number of intermediate points for drawing great circles i <- 0 # used to keep track of groups when getting intermediate points for great circles edges <- ddply( .data = edges, .variables = c("lat1","lat2","lon1","lon2"), .parallel = FALSE, .fun = function(x) { p1Mat <- x[,c("lon1", "lat1")] colnames(p1Mat) <- NULL p2Mat <- x[,c("lon2", "lat2")] colnames(p2Mat) <- NULL inter <- geosphere::gcIntermediate( p1 = p1Mat, p2 = p2Mat, n = pts, addStartEnd = TRUE, breakAtDateLine = TRUE ) if (!is.list(inter)) { i <<- i + 1 inter <- data.frame(inter) inter$group <- i return(inter) } else { if (is.matrix(inter[[1]])) { i <<- i + 1 ret <- data.frame(inter[[1]]) ret$group <- i i <<- i + 1 ret2 <- data.frame(inter[[2]]) ret2$group <- i return(rbind(ret, ret2)) } else { ret <- data.frame(lon = numeric(0), lat = numeric(0), group = numeric(0)) for (j in 1: length(inter)) { i <<- i + 1 ret1 <- data.frame(inter[[j]][[1]]) ret1$group <- i i <<- i + 1 ret2 <- data.frame(inter[[j]][[2]]) ret2$group <- i ret <- rbind(ret, ret1, ret2) } return(ret) } } } ) edge_aes$x = substitute(lon) edge_aes$y = substitute(lat) edge_aes$group = substitute(group) edge_args$data = substitute(edges) edge_args$mapping <- do.call(aes, edge_aes) gg <- gg + do.call(geom_path, edge_args) } else { edge_aes$x = substitute(lon1) edge_aes$y = substitute(lat1) edge_aes$xend = substitute(lon2) edge_aes$yend = substitute(lat2) edge_args$data <- substitute(edges) edge_args$mapping = do.call(aes, edge_aes) gg <- gg + do.call(geom_segment, edge_args) } # # # Done drawing edges, time to draws nodes # # # custom weights: vertex attribute # null weighting sizer <- NULL if(missing(weight)) { point_args$size <- substitute(size) } else { # Setup weight-sizing plotcord$.weight = get_v(net, as.character(substitute(weight))) # proportional scaling if (is.factor(plotcord$.weight)) { sizer <- scale_size_discrete(name = substitute(weight), range = c(size/nlevels(plotcord$weight), size)) } else { sizer <- scale_size_area(name = substitute(weight), max_size = size) } point_aes$size <- substitute(.weight) } # Add points to plot point_args$data <- substitute(plotcord) point_args$mapping <- do.call(aes, point_aes) gg = gg + do.call(geom_point, point_args) if (!is.null(sizer)) { gg = gg + sizer } # -- node labels ------------------------------------------------------------- if (isTRUE(labels)) { gg <- gg + geom_text(data = plotcord, aes(x = lon, y = lat, label = .label), size = label.size, ...) } gg = gg + scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) + labs(color = "", fill = "", size = "", y = NULL, x = NULL) + theme(panel.background = element_blank(), legend.key = element_blank()) return(gg) } GGally/R/ggsave.R0000644000176200001440000000017013010131532013214 0ustar liggesusers #' @export # @examples # ggsave("test.pdf", ggpairs(iris, 1:2)) grid.draw.ggmatrix <- function(x, ...) { print(x) } GGally/R/gglyph.R0000644000176200001440000002164513010131532013244 0ustar liggesusers#' Create the data needed to generate a glyph plot. #' #' @param data A data frame containing variables named in \code{x_major}, #' \code{x_minor}, \code{y_major} and \code{y_minor}. #' @param x_major,x_minor,y_major,y_minor The name of the variable (as a #' string) for the major and minor x and y axes. Together, each unique # combination of \code{x_major} and \code{y_major} specifies a grid cell. #' @param polar A logical of length 1, specifying whether the glyphs should #' be drawn in polar coordinates. Defaults to \code{FALSE}. #' @param height,width The height and width of each glyph. Defaults to 95\% of #' the \code{\link[ggplot2]{resolution}} of the data. Specify the width #' absolutely by supplying a numeric vector of length 1, or relative to the # resolution of the data by using \code{\link[ggplot2]{rel}}. #' @param y_scale,x_scale The scaling function to be applied to each set of #' minor values within a grid cell. Defaults to \code{\link{identity}} so #' that no scaling is performed. #' @export #' @author Di Cook \email{dicook@@monash.edu}, Heike Hofmann, Hadley Wickham #' @examples #' data(nasa) #' nasaLate <- nasa[ #' nasa$date >= as.POSIXct("1998-01-01") & #' nasa$lat >= 20 & #' nasa$lat <= 40 & #' nasa$long >= -80 & #' nasa$long <= -60 #' , ] #' temp.gly <- glyphs(nasaLate, "long", "day", "lat", "surftemp", height=2.5) #' ggplot2::ggplot(temp.gly, ggplot2::aes(gx, gy, group = gid)) + #' add_ref_lines(temp.gly, color = "grey90") + #' add_ref_boxes(temp.gly, color = "grey90") + #' ggplot2::geom_path() + #' ggplot2::theme_bw() + #' ggplot2::labs(x = "", y = "") glyphs <- function( data, x_major, x_minor, y_major, y_minor, polar = FALSE, height = ggplot2::rel(0.95), width = ggplot2::rel(0.95), y_scale = identity, x_scale = identity ) { data$gid <- interaction(data[[x_major]], data[[y_major]], drop = TRUE) if (is.rel(width)) { width <- resolution(data[[x_major]], zero = FALSE) * unclass(width) message("Using width ", format(width, digits = 3)) } if (is.rel(height)) { height <- resolution(data[[y_major]], zero = FALSE) * unclass(height) message("Using height ", format(height, digits = 3)) } if (!identical(x_scale, identity) || !identical(y_scale, identity)) { data <- ddply(data, "gid", function(df) { df[[x_minor]] <- x_scale(df[[x_minor]]) df[[y_minor]] <- y_scale(df[[y_minor]]) df }) } if (polar) { theta <- 2 * pi * rescale01(data[[x_minor]]) r <- rescale01(data[[y_minor]]) data$gx <- data[[x_major]] + width / 2 * r * sin(theta) data$gy <- data[[y_major]] + height / 2 * r * cos(theta) data <- data[order(data[[x_major]], data[[x_minor]]), ] } else { data$gx <- data[[x_major]] + rescale11(data[[x_minor]]) * width / 2 data$gy <- data[[y_major]] + rescale11(data[[y_minor]]) * height / 2 } structure(data, width = width, height = height, polar = polar, x_major = x_major, y_major = y_major, class = c("glyphplot", "data.frame")) } # Create reference lines for a glyph plot ref_lines <- function(data) { stopifnot(is.glyphplot(data)) glyph <- attributes(data) cells <- unique(data[c(glyph$x_major, glyph$y_major, "gid")]) if (glyph$polar) { ref_line <- function(df) { theta <- seq(0, 2 * pi, length = 30) data.frame( gid = df$gid, gx = df[[glyph$x_major]] + glyph$width / 4 * sin(theta), gy = df[[glyph$y_major]] + glyph$height / 4 * cos(theta) ) } } else { ref_line <- function(df) { data.frame( gid = df$gid, gx = df[[glyph$x_major]] + c(-1, 1) * glyph$width / 2, gy = df[[glyph$y_major]] ) } } ddply(cells, "gid", ref_line) } # Create reference boxes for a glyph plot ref_boxes <- function(data, fill = NULL) { stopifnot(is.glyphplot(data)) glyph <- attributes(data) cells <- data.frame(unique(data[c(glyph$x_major, glyph$y_major, "gid", fill)])) df <- data.frame(xmin = cells[[glyph$x_major]] - glyph$width / 2, xmax = cells[[glyph$x_major]] + glyph$width / 2, ymin = cells[[glyph$y_major]] - glyph$height / 2, ymax = cells[[glyph$y_major]] + glyph$height / 2) if (!is.null(fill)){ df$fill <- cells[[fill]] } df } # Glyph plot class ----------------------------------------------------------- #' Glyph plot class #' #' @param data A data frame containing variables named in \code{x_major}, #' \code{x_minor}, \code{y_major} and \code{y_minor}. #' @param height,width The height and width of each glyph. Defaults to 95\% of #' the \code{\link[ggplot2]{resolution}} of the data. Specify the width #' absolutely by supplying a numeric vector of length 1, or relative to the # resolution of the data by using \code{\link[ggplot2]{rel}}. #' @param polar A logical of length 1, specifying whether the glyphs should #' be drawn in polar coordinates. Defaults to \code{FALSE}. #' @param x_major,y_major The name of the variable (as a #' string) for the major x and y axes. Together, the # combination of \code{x_major} and \code{y_major} specifies a grid cell. #' @export #' @author Di Cook \email{dicook@@monash.edu}, Heike Hofmann, Hadley Wickham glyphplot <- function(data, width, height, polar, x_major, y_major) { structure(data, width = width, height = height, polar = polar, x_major = x_major, y_major = y_major, class = c("glyphplot", "data.frame")) } #' @export #' @rdname glyphplot is.glyphplot <- function(x) { inherits(x, "glyphplot") } #' @export #' @rdname glyphplot "[.glyphplot" <- function(x, ...) { glyphplot(NextMethod(), width = attr(x, "width"), height = attr(x, "height"), x_major = attr(x, "x_major"), y_major = attr(x, "y_major"), polar = attr(x, "polar")) } #' @param x glyphplot to be printed #' @param ... ignored #' @export #' @rdname glyphplot #' @method print glyphplot print.glyphplot <- function(x, ...) { NextMethod() if (attr(x, "polar")) { cat("Polar ") } else { cat("Cartesian ") } width <- format(attr(x, "width"), digits = 3) height <- format(attr(x, "height"), digits = 3) cat("glyphplot: \n") cat(" Size: [", width, ", ", height, "]\n", sep = "") cat(" Major axes: ", attr(x, "x_major"), ", ", attr(x, "y_major"), "\n", sep = "") # cat("\n") } # Relative dimensions -------------------------------------------------------- # Relative dimensions # # @param x numeric value between 0 and 1 # rel <- function(x) { # structure(x, class = "rel") # } # @export # rel <- ggplot2::rel # @rdname rel # @param ... ignored # print.rel <- function(x, ...) { # print(noquote(paste(x, " *", sep = ""))) # } ## works even though it is not exported # @export # ggplot2::print.rel # @rdname rel # is.rel <- function(x) { # inherits(x, "rel") # } ## only used internally. and ggplot2 has this exported # @export # ggplot2:::is.rel is.rel <- ggplot2:::is.rel # Rescaling functions -------------------------------------------------------- #' Rescaling functions #' #' @param x numeric vector #' @param xlim value used in \code{range} #' @name rescale01 #' @export #' @rdname rescale01 range01 <- function(x) { rng <- range(x, na.rm = TRUE) (x - rng[1]) / (rng[2] - rng[1]) } #' @export #' @rdname rescale01 max1 <- function(x) { x / max(x, na.rm = TRUE) } #' @export #' @rdname rescale01 mean0 <- function(x) { x - mean(x, na.rm = TRUE) } #' @export #' @rdname rescale01 min0 <- function(x) { x - min(x, na.rm = TRUE) } #' @export #' @rdname rescale01 rescale01 <- function(x, xlim=NULL) { if (is.null(xlim)) { rng <- range(x, na.rm = TRUE) } else { rng <- xlim } (x - rng[1]) / (rng[2] - rng[1]) } #' @export #' @rdname rescale01 rescale11 <- function(x, xlim=NULL) { 2 * rescale01(x, xlim) - 1 } #' Add reference lines for each cell of the glyphmap. #' #' @param data A glyphmap structure. #' @param color Set the color to draw in, default is "white" #' @param size Set the line size, default is 1.5 #' @param ... other arguments passed onto \code{\link[ggplot2]{geom_line}} #' @export add_ref_lines <- function(data, color = "white", size = 1.5, ...){ rl <- ref_lines(data) geom_path(data = rl, color = color, size = size, ...) } #' Add reference boxes around each cell of the glyphmap. #' #' @param data A glyphmap structure. #' @param var_fill Variable name to use to set the fill color #' @param color Set the color to draw in, default is "white" #' @param size Set the line size, default is 0.5 #' @param fill fill value used if \code{var_fill} is \code{NULL} #' @param ... other arguments passed onto \code{\link[ggplot2]{geom_rect}} #' @export add_ref_boxes <- function(data, var_fill = NULL, color = "white", size = 0.5, fill = NA, ...){ rb <- ref_boxes(data, var_fill) if (!is.null(var_fill)){ geom_rect(aes_all(names(rb)), data = rb, color = color, size = size, inherit.aes = FALSE, ...) } else{ geom_rect(aes_all(names(rb)), data = rb, color = color, size = size, inherit.aes = FALSE, fill = fill, ...) } } GGally/R/data-flea.R0000644000176200001440000000165713010131532013571 0ustar liggesusers#' Historical data used for classification examples. #' #' This data contains physical measurements on three species of flea beetles. #' #' @details \itemize{ #' \item species Ch. concinna, Ch. heptapotamica, Ch. heikertingeri #' \item tars1 width of the first joint of the first tarsus in microns #' \item tars2 width of the second joint of the first tarsus in microns #' \item head the maximal width of the head between the external edges of the eyes in 0.01 mm #' \item aede1 the maximal width of the aedeagus in the fore-part in microns #' \item aede2 the front angle of the aedeagus (1 unit = 7.5 degrees) #' \item aede3 the aedeagus width from the side in microns #' } #' #' @docType data #' @keywords datasets #' @name flea #' @usage data(flea) #' @format A data frame with 74 rows and 7 variables #' @references #' Lubischew, A. A. (1962), On the Use of Discriminant Functions in #' Taxonomy, Biometrics 18:455-477. NULL GGally/R/data-twitter_spambots.R0000644000176200001440000000141613010131532016265 0ustar liggesusers#' Twitter spambots #' #' A network of spambots found on Twitter as part of a data mining project. #' #' Each node of the network is identified by the Twitter screen name of the #' account and further carries five vertex attributes: #' #' @details \itemize{ #' \item location user's location, as provided by the user #' \item lat latitude, based on the user's location #' \item lon longitude, based on the user's location #' \item followers number of Twitter accounts that follow this account #' \item friends number of Twitter accounts followed by the account #' } #' #' @docType data #' @author Amos Elberg #' @keywords datasets #' @name twitter_spambots #' @usage data(twitter_spambots) #' @format An object of class \code{network} with 120 edges and 94 vertices. NULL GGally/R/ggmatrix_legend.R0000644000176200001440000000626213114364223015122 0ustar liggesusers#' Grab the legend and print it as a plot #' #' @param p ggplot2 plot object #' @param x legend object that has been grabbed from a ggplot2 object #' @param ... ignored #' @param plotNew boolean to determine if the `grid.newpage()` command and a new blank rectangle should be printed #' @import ggplot2 #' @export #' @examples #' library(ggplot2) #' histPlot <- qplot( #' x = Sepal.Length, #' data = iris, #' fill = Species, #' geom = "histogram", #' binwidth = 1/4 #' ) #' (right <- histPlot) #' (bottom <- histPlot + theme(legend.position = "bottom")) #' (top <- histPlot + theme(legend.position = "top")) #' (left <- histPlot + theme(legend.position = "left")) #' #' grab_legend(right) #' grab_legend(bottom) #' grab_legend(top) #' grab_legend(left) grab_legend <- function(p) { builtP <- ggplot_build(p) pTable <- ggplot_gtable(builtP) ret <- get_legend_from_gtable(pTable) return(ret) } get_legend_from_gtable <- function(pTable) { ret <- ggplot2::zeroGrob() if (inherits(pTable, "gtable")) { if ("guide-box" %in% pTable$layout$name) { ret <- gtable_filter(pTable, "guide-box") } } class(ret) <- c("legend_guide_box", class(ret)) ret } #' @importFrom grid grid.newpage grid.draw gpar #' @importFrom gtable gtable_filter #' @rdname grab_legend #' @export print.legend_guide_box <- function(x, ..., plotNew = FALSE) { if (identical(plotNew, TRUE)) { grid.newpage() } grid::grid.rect(gp = grid::gpar(fill = "white", col = "white")) grid.draw(x) } #' Plot only legend of plot function #' #' @param fn this value is passed directly to an empty \code{\link{wrap}} call. Please see \code{?\link{wrap}} for more details. #' @return a function that when called with arguments will produce the legend of the plotting function supplied. #' @export #' @examples #' # display regular plot #' ggally_points(iris, ggplot2::aes(Sepal.Length, Sepal.Width, color = Species)) #' #' # Make a function that will only print the legend #' points_legend <- gglegend(ggally_points) #' points_legend(iris, ggplot2::aes(Sepal.Length, Sepal.Width, color = Species)) #' #' # produce the sample legend plot, but supply a string that 'wrap' understands #' same_points_legend <- gglegend("points") #' identical( #' attr(attr(points_legend, "fn"), "original_fn"), #' attr(attr(same_points_legend, "fn"), "original_fn") #' ) #' #' # Complicated examples #' custom_legend <- wrap(gglegend("points"), size = 6) #' custom_legend(iris, ggplot2::aes(Sepal.Length, Sepal.Width, color = Species)) #' #' # Use within ggpairs #' pm <- ggpairs( #' iris, 1:2, #' mapping = ggplot2::aes(color = Species), #' upper = list(continuous = gglegend("points")) #' ) #' # pm #' #' # Place a legend in a specific location #' pm <- ggpairs(iris, 1:2, mapping = ggplot2::aes(color = Species)) #' # Make the legend #' pm[1,2] <- points_legend(iris, ggplot2::aes(Sepal.Width, Sepal.Length, color = Species)) #' pm gglegend <- function(fn) { # allows users to supply a character just like in ggpairs fn <- wrapp(fn, list()) fn <- attr(fn, "fn") ret <- function(...) { p <- fn(...) grab_legend(p) } # attach function so people can see what it is attr(ret, "fn") <- fn attr(ret, "name") <- "gglegend" ret } GGally/R/ggmatrix_progress.R0000644000176200001440000000277613277311163015543 0ustar liggesusers#' ggmatrix default progress bar #' #' @param format,clear,show_after,... parameters supplied directly to \code{progress::\link[progress]{progress_bar}$new()} #' @return function that accepts a plot matrix as the first argument and \code{...} for future expansion. Internally, the plot matrix is used to determine the total number of plots for the progress bar. #' @export #' @examples #' p_ <- GGally::print_if_interactive #' #' pm <- ggpairs(iris, 1:2, progress = ggmatrix_progress()) #' p_(pm) #' #' # does not clear after finishing #' pm <- ggpairs(iris, 1:2, progress = ggmatrix_progress(clear = FALSE)) #' p_(pm) ggmatrix_progress <- function( format = " plot: [:plot_i,:plot_j] [:bar]:percent est::eta ", clear = TRUE, show_after = 0, ... ) { ret <- function(pm, ...) { progress::progress_bar$new( format = format, clear = clear, show_after = show_after, total = pm$ncol * pm$nrow, ... ) } ret } as_ggmatrix_progress <- function(x, total, ...) { if (isFALSE(x)) { return(FALSE) } if (isTRUE(x)) { return(ggmatrix_progress(...)) } if (is.null(x)) { shouldDisplay <- interactive() && total > 15 if (!shouldDisplay) { return(FALSE) } else { return(ggmatrix_progress(...)) } } if (is.function(x)) { return(x) } stop( "as_ggmatrix_progress only knows how to handle TRUE, FALSE, NULL, or a function.", " If a function, it must return a new progress_bar" ) } isFALSE <- function(x) { identical(FALSE, x) } GGally/R/data-nasa.R0000644000176200001440000000156213010131532013577 0ustar liggesusers#' Data from the Data Expo JSM 2006. #' #' This data was provided by NASA for the competition. #' #' The data shows 6 years of monthly measurements of a 24x24 spatial grid #' from Central America: #' #' @details \itemize{ #' \item time integer specifying temporal order of measurements #' \item x, y, lat, long spatial location of measurements. #' \item cloudhigh, cloudlow, cloudmid, ozone, pressure, surftemp, temperature #' are the various satellite measurements. #' \item date, day, month, year specifying the time of measurements. #' \item id unique ide for each spatial position. #' } #' #' @docType data #' @keywords datasets #' @name nasa #' @usage data(nasa) #' @format A data frame with 41472 rows and 17 variables #' @references #' Murrell, P. (2010) The 2006 Data Expo of the American Statistical Association. #' Computational Statistics, 25:551-554. NULL GGally/R/data-psychademic.R0000644000176200001440000000162313114364223015156 0ustar liggesusers#' UCLA canonical correlation analysis data #' #' This data contains 600 observations on eight variables #' #' @details \itemize{ #' \item locus_of_control - psychological #' \item self_concept - psychological #' \item motivation - psychological. Converted to four character groups #' \item read - academic #' \item write - academic #' \item math - academic #' \item science - academic #' \item female - academic. Dropped from original source #' \item sex - academic. Added as a character version of female column #' } #' #' @docType data #' @keywords datasets #' @name psychademic #' @usage data(psychademic) #' @format A data frame with 600 rows and 8 variables #' @references #' R Data Analysis Examples | Canonical Correlation Analysis. UCLA: Institute for Digital Research and Education. from http://www.stats.idre.ucla.edu/r/dae/canonical-correlation-analysis (accessed May 22, 2017). NULL GGally/R/data-pigs.R0000644000176200001440000000214613010131532013616 0ustar liggesusers#' United Kingdom Pig Production #' #' This data contains about the United Kingdom Pig Production from the book 'Data' by Andrews and Herzberg. The original data can be on Statlib: http://lib.stat.cmu.edu/datasets/Andrews/T62.1 #' #' The time variable has been added from a combination of year and quarter #' #' @details \itemize{ #' \item time year + (quarter - 1) / 4 #' \item year year of production #' \item quarter quarter of the year of production #' \item gilts number of sows giving birth for the first time #' \item profit ratio of price to an index of feed price #' \item s_per_herdsz ratio of the number of breeding pigs slaughtered to the total breeding herd size #' \item production number of pigs slaughtered that were reared for meat #' \item herdsz breeding herd size #' } #' #' @docType data #' @keywords datasets #' @name pigs #' @usage data(pigs) #' @format A data frame with 48 rows and 8 variables #' @references #' Andrews, David F., and Agnes M. Herzberg. Data: a collection of problems from many fields for the student and research worker. Springer Science & Business Media, 2012. NULL GGally/R/ggparcoord.R0000644000176200001440000006204413277315152014120 0ustar liggesusersif (getRversion() >= "2.15.1") { utils::globalVariables(c("variable", "value", "ggally_splineFactor")) } #' ggparcoord - A ggplot2 Parallel Coordinate Plot #' #' A function for plotting static parallel coordinate plots, utilizing #' the \code{ggplot2} graphics package. #' #' \code{scale} is a character string that denotes how to scale the variables #' in the parallel coordinate plot. Options: #' \itemize{ #' \item{\code{std}}{: univariately, subtract mean and divide by standard deviation} #' \item{\code{robust}}{: univariately, subtract median and divide by median absolute deviation} #' \item{\code{uniminmax}}{: univariately, scale so the minimum of the variable is zero, and the maximum is one} #' \item{\code{globalminmax}}{: no scaling is done; the range of the graphs is defined #' by the global minimum and the global maximum} #' \item{\code{center}}{: use \code{uniminmax} to standardize vertical height, then #' center each variable at a value specified by the \code{scaleSummary} param} #' \item{\code{centerObs}}{: use \code{uniminmax} to standardize vertical height, then #' center each variable at the value of the observation specified by the \code{centerObsID} param} #' } #' #' \code{missing} is a character string that denotes how to handle missing #' missing values. Options: #' \itemize{ #' \item{\code{exclude}}{: remove all cases with missing values} #' \item{\code{mean}}{: set missing values to the mean of the variable} #' \item{\code{median}}{: set missing values to the median of the variable} #' \item{\code{min10}}{: set missing values to 10\% below the minimum of the variable} #' \item{\code{random}}{: set missing values to value of randomly chosen observation #' on that variable} #' } #' #' \code{order} is either a vector of indices or a character string that denotes how to #' order the axes (variables) of the parallel coordinate plot. Options: #' \itemize{ #' \item{\code{(default)}}{: order by the vector denoted by \code{columns}} #' \item{\code{(given vector)}}{: order by the vector specified} #' \item{\code{anyClass}}{: order variables by their separation between any one class and #' the rest (as opposed to their overall variation between classes). This is accomplished #' by calculating the F-statistic for each class vs. the rest, for each axis variable. #' The axis variables are then ordered (decreasing) by their maximum of k F-statistics, #' where k is the number of classes.} #' \item{\code{allClass}}{: order variables by their overall F statistic (decreasing) from #' an ANOVA with \code{groupColumn} as the explanatory variable (note: it is required #' to specify a \code{groupColumn} with this ordering method). Basically, this method #' orders the variables by their variation between classes (most to least).} #' \item{\code{skewness}}{: order variables by their sample skewness (most skewed to #' least skewed)} #' \item{\code{Outlying}}{: order by the scagnostic measure, Outlying, as calculated #' by the package \code{scagnostics}. Other scagnostic measures available to order #' by are \code{Skewed}, \code{Clumpy}, \code{Sparse}, \code{Striated}, \code{Convex}, \code{Skinny}, \code{Stringy}, and #' \code{Monotonic}. Note: To use these methods of ordering, you must have the \code{scagnostics} #' package loaded.} #' } #' #' @param data the dataset to plot #' @param columns a vector of variables (either names or indices) to be axes in the plot #' @param groupColumn a single variable to group (color) by #' @param scale method used to scale the variables (see Details) #' @param scaleSummary if scale=="center", summary statistic to univariately #' center each variable by #' @param centerObsID if scale=="centerObs", row number of case plot should #' univariately be centered on #' @param missing method used to handle missing values (see Details) #' @param order method used to order the axes (see Details) #' @param showPoints logical operator indicating whether points should be #' plotted or not #' @param splineFactor logical or numeric operator indicating whether spline interpolation should be used. Numeric values will multiplied by the number of columns, \code{TRUE} will default to cubic interpolation, \code{\link[base]{AsIs}} to set the knot count directly and \code{0}, \code{FALSE}, or non-numeric values will not use spline interpolation. #' @param alphaLines value of alpha scaler for the lines of the parcoord plot or a column name of the data #' @param boxplot logical operator indicating whether or not boxplots should #' underlay the distribution of each variable #' @param shadeBox color of underlaying box which extends from the min to the #' max for each variable (no box is plotted if shadeBox == NULL) #' @param mapping aes string to pass to ggplot object #' @param title character string denoting the title of the plot #' @author Jason Crowley \email{crowley.jason.s@@gmail.com}, Barret Schloerke \email{schloerke@@gmail.com}, Di Cook \email{dicook@@iastate.edu}, Heike Hofmann \email{hofmann@@iastate.edu}, Hadley Wickham \email{h.wickham@@gmail.com} #' @return ggplot object that if called, will print #' @import plyr #' @importFrom reshape melt melt.data.frame #' @importFrom stats complete.cases sd median mad lm spline #' @export #' @examples #' # small function to display plots only if it's interactive #' p_ <- GGally::print_if_interactive #' #' # use sample of the diamonds data for illustrative purposes #' data(diamonds, package="ggplot2") #' diamonds.samp <- diamonds[sample(1:dim(diamonds)[1], 100), ] #' #' # basic parallel coordinate plot, using default settings #' p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10)) #' p_(p) #' #' # this time, color by diamond cut #' p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2) #' p_(p) #' #' # underlay univariate boxplots, add title, use uniminmax scaling #' p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, #' scale = "uniminmax", boxplot = TRUE, title = "Parallel Coord. Plot of Diamonds Data") #' p_(p) #' #' # utilize ggplot2 aes to switch to thicker lines #' p <- ggparcoord(data = diamonds.samp, columns = c(1, 5:10), groupColumn = 2, #' title ="Parallel Coord. Plot of Diamonds Data", mapping = ggplot2::aes(size = 1)) + #' ggplot2::scale_size_identity() #' p_(p) #' #' # basic parallel coord plot of the msleep data, using 'random' imputation and #' # coloring by diet (can also use variable names in the columns and groupColumn #' # arguments) #' data(msleep, package="ggplot2") #' p <- ggparcoord(data = msleep, columns = 6:11, groupColumn = "vore", missing = #' "random", scale = "uniminmax") #' p_(p) #' #' # center each variable by its median, using the default missing value handler, #' # 'exclude' #' p <- ggparcoord(data = msleep, columns = 6:11, groupColumn = "vore", scale = #' "center", scaleSummary = "median") #' p_(p) #' #' # with the iris data, order the axes by overall class (Species) separation using #' # the anyClass option #' p <- ggparcoord(data = iris, columns = 1:4, groupColumn = 5, order = "anyClass") #' p_(p) #' #' # add points to the plot, add a title, and use an alpha scalar to make the lines #' # transparent #' p <- ggparcoord(data = iris, columns = 1:4, groupColumn = 5, order = "anyClass", #' showPoints = TRUE, title = "Parallel Coordinate Plot for the Iris Data", #' alphaLines = 0.3) #' p_(p) #' #' # color according to a column #' iris2 <- iris #' iris2$alphaLevel <- c("setosa" = 0.2, "versicolor" = 0.3, "virginica" = 0)[iris2$Species] #' p <- ggparcoord(data = iris2, columns = 1:4, groupColumn = 5, order = "anyClass", #' showPoints = TRUE, title = "Parallel Coordinate Plot for the Iris Data", #' alphaLines = "alphaLevel") #' p_(p) #' #' ## Use splines on values, rather than lines (all produce the same result) #' columns <- c(1, 5:10) #' p <- ggparcoord(diamonds.samp, columns, groupColumn = 2, splineFactor = TRUE) #' p_(p) #' p <- ggparcoord(diamonds.samp, columns, groupColumn = 2, splineFactor = 3) #' p_(p) #' splineFactor <- length(columns) * 3 #' p <- ggparcoord(diamonds.samp, columns, groupColumn = 2, splineFactor = I(splineFactor)) #' p_(p) ggparcoord <- function( data, columns = 1:ncol(data), groupColumn = NULL, scale = "std", scaleSummary = "mean", centerObsID = 1, missing = "exclude", order = columns, showPoints = FALSE, splineFactor = FALSE, alphaLines = 1, boxplot = FALSE, shadeBox = NULL, mapping = NULL, title = "" ) { if (! identical(class(data), "data.frame")) { data <- as.data.frame(data) } saveData <- data ### Error Checking ### if (is.null(groupColumn)) { if (any(tolower(order) %in% c("anyclass", "allclass"))) { stop("can't use the 'order' methods anyClass or allClass without specifying groupColumn") } } else if ( !( (length(groupColumn) == 1) && (is.numeric(groupColumn) || is.character(groupColumn))) ) { stop("invalid value for 'groupColumn'; must be a single numeric or character index") } if (!(tolower(scale) %in% c( "std", "robust", "uniminmax", "globalminmax", "center", "centerobs" ))) { stop(str_c( "invalid value for 'scale'; must be one of ", "'std', 'robust', 'uniminmax', 'globalminmax', 'center', or 'centerObs'" )) } if (!(centerObsID %in% 1:dim(data)[1])) { stop("invalid value for 'centerObsID'; must be a single numeric row index") } if (!(tolower(missing) %in% c("exclude", "mean", "median", "min10", "random"))) { stop( "invalid value for 'missing'; must be one of 'exclude', 'mean', 'median', 'min10', 'random'" ) } if (!( is.numeric(order) || ( is.character(order) && (order %in% c( "skewness", "allClass", "anyClass", "Outlying", "Skewed", "Clumpy", "Sparse", "Striated", "Convex", "Skinny", "Stringy", "Monotonic" )) )) ) { stop(str_c( "invalid value for 'order'; must either be a vector of column indices or one of ", "'skewness', 'allClass', 'anyClass', 'Outlying', 'Skewed', 'Clumpy', 'Sparse', 'Striated', ", "'Convex', 'Skinny', 'Stringy', 'Monotonic'" )) } if (!(is.logical(showPoints))) { stop("invalid value for 'showPoints'; must be a logical operator") } alphaLinesIsCharacter <- is.character(alphaLines) if (alphaLinesIsCharacter) { if (!(alphaLines %in% names(data))) { stop("'alphaLines' column is missing in data") } alphaVar <- data[[alphaLines]] alphaRange <- range(alphaVar) if (any(is.na(alphaRange))) { stop("missing data in 'alphaLines' column") } if (alphaRange[1] < 0 || alphaRange[2] > 1) { stop("invalid value for 'alphaLines' column; max range must be from 0 to 1") } } else if ((alphaLines < 0) || (alphaLines > 1)) { # nolint stop("invalid value for 'alphaLines'; must be a scalar value between 0 and 1") } if (!(is.logical(boxplot))) { stop("invalid value for 'boxplot'; must be a logical operator") } if (!is.null(shadeBox) && length(shadeBox) != 1) { stop("invalid value for 'shadeBox'; must be a single color") } else { valid_color <- tryCatch(is.matrix(grDevices::col2rgb(shadeBox)), error = function(e) FALSE) if (!valid_color) { stop("invalid value for 'shadeBox'; must be a valid R color") } } if (is.logical(splineFactor)) { if (splineFactor) { splineFactor <- 3 } else { splineFactor <- 0 } } else if (! is.numeric(splineFactor)) { stop("invalid value for 'splineFactor'; must be a logical or numeric value") } ### Setup ### if (is.numeric(groupColumn)) { groupColumn <- names(data)[groupColumn] } if (!is.null(groupColumn)) { groupVar <- data[[groupColumn]] } if (is.character(columns)) { columns_ <- c() for (colPos in seq_along(columns)) { columns_[colPos] <- which(colnames(data) == columns[colPos]) } columns <- columns_ } # data <- data[columns] # Change character vars to factors char.vars <- column_is_character(data) if (length(char.vars) >= 1) { for (char.var in char.vars) { data[[char.var]] <- factor(data[[char.var]]) } } # Change factors to numeric fact.vars <- column_is_factor(data) fact.vars <- setdiff(fact.vars, groupColumn) if (length(fact.vars) >= 1) { for (fact.var in fact.vars) { data[[fact.var]] <- as.numeric(data[[fact.var]]) } } # Save this form of the data for order calculations (don't want imputed # missing values affecting order, but do want any factor/character vars # being plotted as numeric) saveData2 <- data if (!is.null(groupColumn)) { saveData2[[groupColumn]] <- as.numeric(saveData2[[groupColumn]]) } p <- c(ncol(data) + 1, ncol(data) + 2) data$.ID <- as.factor(1:nrow(data)) data$anyMissing <- apply(is.na(data[, columns]), 1, any) columnsPlusTwo <- c(columns, p) inner_rescaler_default <- function (x, type = "sd", ...) { # copied directly from reshape because of import difficulties :-( # rescaler.default switch(type, rank = rank(x, ...), var = , # nolint sd = (x - mean(x, na.rm = TRUE)) / sd(x, na.rm = TRUE), robust = (x - median(x, na.rm = TRUE)) / mad(x, na.rm = TRUE), I = x, range = (x - min(x, na.rm = TRUE)) / diff(range(x, na.rm = TRUE)) ) } inner_rescaler <- function(x, type = "sd", ...) { # copied directly from reshape because of import difficulties :-( # rescaler.data.frame continuous <- sapply(x, is.numeric) if (any(continuous)) { if (type %in% c("sd", "robust", "range")) { # indicating columns containing only one single value singleVal <- sapply(x, function(col){ if (length(unique(col)) == 1) { TRUE } else { FALSE } }) ind <- continuous & !singleVal x[ind] <- lapply(x[ind], inner_rescaler_default, type = type, ...) x[singleVal] <- 1 } else { x[continuous] <- lapply(x[continuous], inner_rescaler_default, type = type, ...) } } x } ### Scaling ### if (tolower(scale) %in% c("std", "robust", "uniminmax", "center")) { rescalerType <- c( "std" = "sd", "robust" = "robust", "uniminmax" = "range", "center" = "range" )[tolower(scale)] data[columnsPlusTwo] <- inner_rescaler(data[columnsPlusTwo], type = rescalerType) if (tolower(scale) == "center") { data[columns] <- apply(data[columns], 2, function(x) { x <- x - eval( parse(text = paste( scaleSummary, "(x, na.rm=TRUE)", sep = "" )) ) }) } } ### Imputation ### if (tolower(missing) == "exclude") { dataCompleteCases <- complete.cases(data[columnsPlusTwo]) if (!is.null(groupColumn)) { groupVar <- groupVar[dataCompleteCases] } if (alphaLinesIsCharacter) { alphaVar <- alphaVar[dataCompleteCases] } data <- data[dataCompleteCases, ] } else if (tolower(missing) %in% c("mean", "median", "min10", "random")) { missingFns <- list( mean = function(x) { mean(x, na.rm = TRUE) }, median = function(x) { median(x, na.rm = TRUE) }, min10 = function(x){ 0.9 * min(x, na.rm = TRUE) }, random = function(x) { num <- sum(is.na(x)) idx <- sample(which(!is.na(x)), num, replace = TRUE) x[idx] } ) missing_fn <- missingFns[[tolower(missing)]] data[columns] <- apply(data[columns], 2, function(x) { if (any(is.na(x))){ x[is.na(x)] <- missing_fn(x) } return(x) }) } ### Scaling (round 2) ### # Centering by observation needs to be done after handling missing values # in case the observation to be centered on has missing values if (tolower(scale) == "centerobs") { data[columnsPlusTwo] <- inner_rescaler(data[columnsPlusTwo], type = "range") data[columns] <- apply(data[columns], 2, function(x){ x <- x - x[centerObsID] }) } # meltIDVars <- c(".ID", "anyMissing") meltIDVars <- colnames(data)[-columns] if (!is.null(groupColumn)) { # data <- cbind(data, groupVar) # names(data)[dim(data)[2]] <- groupCol meltIDVars <- union(groupColumn, meltIDVars) } if (alphaLinesIsCharacter) { data <- cbind(data, alphaVar) names(data)[dim(data)[2]] <- alphaLines meltIDVars <- union(meltIDVars, alphaLines) } # if(is.list(mapping)) { # mappingNames <- names(mapping) # } data.m <- melt(data, id.vars = meltIDVars, measure.vars = columns) ### Ordering ### if (length(order) > 1 & is.numeric(order)) { data.m$variable <- factor(data.m$variable, levels = names(saveData)[order]) } else if (order %in% c("Outlying", "Skewed", "Clumpy", "Sparse", "Striated", "Convex", "Skinny", "Stringy", "Monotonic")) { require_namespaces("scagnostics") scag <- scagnostics::scagnostics(saveData2) data.m$variable <- factor(data.m$variable, levels = scag_order(scag, names(saveData2), order)) } else if (tolower(order) == "skewness") { abs.skew <- abs(apply(saveData2, 2, skewness)) data.m$variable <- factor( data.m$variable, levels = names(abs.skew)[order(abs.skew, decreasing = TRUE)] ) } else if (tolower(order) == "allclass") { f.stats <- rep(NA, length(columns)) names(f.stats) <- names(saveData2[columns]) for (i in 1:length(columns)) { f.stats[i] <- summary(lm(saveData2[, i] ~ groupVar))$fstatistic[1] } data.m$variable <- factor( data.m$variable, levels = names(f.stats)[order(f.stats, decreasing = TRUE)] ) } else if (tolower(order) == "anyclass") { axis.order <- singleClassOrder(groupVar, saveData2) data.m$variable <- factor(data.m$variable, levels = axis.order) } if (!is.null(groupColumn)) { mapping2 <- aes_string( x = "variable", y = "value", group = ".ID", colour = groupColumn ) } else { mapping2 <- aes_string( x = "variable", y = "value", group = ".ID" ) } mapping2 <- add_and_overwrite_aes(mapping2, mapping) # mapping2 <- add_and_overwrite_aes(aes_string(size = I(0.5)), mapping2) p <- ggplot(data = data.m, mapping = mapping2) if (!is.null(shadeBox)) { # Fix so that if missing = "min10", the box only goes down to the true min d.sum <- ddply(data.m, c("variable"), summarize, min = min(value), max = max(value)) p <- p + geom_linerange( data = d.sum, size = I(10), col = shadeBox, inherit.aes = FALSE, mapping = aes_string( x = "variable", ymin = "min", ymax = "max", group = "variable" ) ) } if (boxplot) { p <- p + geom_boxplot(mapping = aes_string(group = "variable"), alpha = 0.8) } if (!is.null(mapping2$size)) { lineSize <- mapping2$size } else { lineSize <- 0.5 } if (splineFactor > 0) { data.m$ggally_splineFactor <- splineFactor if (class(splineFactor) == "AsIs") { data.m <- ddply( data.m, ".ID", transform, spline = spline(variable, value, n = ggally_splineFactor[1]) ) } else { data.m <- ddply( data.m, ".ID", transform, spline = spline(variable, value, n = length(variable) * ggally_splineFactor[1]) ) } linexvar <- "spline.x" lineyvar <- "spline.y" if (alphaLinesIsCharacter) { p <- p + geom_line( aes_string(x = linexvar, y = lineyvar, alpha = alphaLines), size = lineSize, data = data.m ) + scale_alpha(range = alphaRange) } else { p <- p + geom_line( aes_string(x = linexvar, y = lineyvar), alpha = alphaLines, size = lineSize, data = data.m ) } if (showPoints) { p <- p + geom_point(aes(x = as.numeric(variable), y = value)) } xAxisLabels <- levels(data.m$variable) # while continuous data, this makes it present like it's discrete p <- p + scale_x_continuous( breaks = seq_along(xAxisLabels), labels = xAxisLabels, minor_breaks = FALSE ) } else { if (alphaLinesIsCharacter) { p <- p + geom_line(aes_string(alpha = alphaLines), size = lineSize, data = data.m) + scale_alpha(range = alphaRange) } else { # p <- p + geom_line(alpha = alphaLines, size = lineSize) p <- p + geom_line(alpha = alphaLines) } if (showPoints) { p <- p + geom_point() } } if (title != "") { p <- p + labs(title = title) } p } #' Get vector of variable types from data frame #' #' @keywords internal #' @param df data frame to extract variable types from #' @author Jason Crowley \email{crowley.jason.s@@gmail.com} #' @return character vector with variable types, with names corresponding to #' the variable names from df column_is_character <- function(df) { x <- unlist(lapply(unclass(df), is.character)) names(x)[x] } #' @rdname column_is_character column_is_factor <- function(df) { x <- unlist(lapply(unclass(df), is.factor)) names(x)[x] } #' Find order of variables #' #' Find order of variables based on a specified scagnostic measure #' by maximizing the index values of that measure along the path. #' #' @param scag \code{scagnostics} object #' @param vars character vector of the variables to be ordered #' @param measure scagnostics measure to order according to #' @author Barret Schloerke #' @return character vector of variable ordered according to the given #' scagnostic measure scag_order <- function(scag, vars, measure) { scag <- sort(scag[measure, ], decreasing = TRUE) scagNames <- names(scag) # retrieve all names. assume name doesn't contain a space nameLocs <- regexec("^([^ ]+) \\* ([^ ]+)$", scagNames) colNames <- lapply(seq_along(nameLocs), function(i) { nameLoc <- nameLocs[[i]] scagName <- scagNames[[i]] # retrieve the column name from "FIRSTNAME * SECONDNAME" substr(rep(scagName, 2), nameLoc[-1], nameLoc[-1] + attr(nameLoc, "match.length")[-1] - 1) }) ret <- c() colNamesLength <- length(colNames) colNameValues <- unlist(colNames) for (i in seq_along(colNames)) { cols <- colNames[[i]] colsUsed <- cols %in% ret # if none of the columns have been added... if (colsUsed[1] == FALSE && colsUsed[2] == FALSE) { # find out which column comes next in the set, append that one first if (i < colNamesLength) { remainingColumns <- colNameValues[(2 * (i + 1)):(2 * colNamesLength)] col1Pos <- which.min(cols[1] == remainingColumns) col2Pos <- which.min(cols[2] == remainingColumns) if (col2Pos < col1Pos) { cols <- rev(cols) } ret <- append(ret, cols) } else { # nothing left in set, append both ret <- append(ret, cols) } # if only the first hasn't been added... } else if (colsUsed[1] == FALSE) { ret <- append(ret, cols[1]) # if only the second hasn't been added... } else if (colsUsed[2] == FALSE) { ret <- append(ret, cols[2]) } } if (length(ret) != length(vars)) { stop(str_c( "Could not compute a correct ordering: ", length(vars) - length(ret), " values are missing. ", "Missing: ", paste0(vars[! (vars %in% ret)], collapse = ", ") )) } return(ret) } #' Order axis variables #' #' Order axis variables by separation between one class and the rest #' (most separation to least). #' #' @param classVar class variable (vector from original dataset) #' @param axisVars variables to be plotted as axes (data frame) #' @param specClass character string matching to level of \code{classVar}; instead #' of looking for separation between any class and the rest, will only look for #' separation between this class and the rest #' @author Jason Crowley \email{crowley.jason.s@@gmail.com} #' @importFrom stats lm #' @return character vector of names of axisVars ordered such that the first #' variable has the most separation between one of the classes and the rest, and #' the last variable has the least (as measured by F-statistics from an ANOVA) singleClassOrder <- function(classVar, axisVars, specClass=NULL) { if (!is.null(specClass)) { # for when user is interested in ordering by variation between one class and # the rest...will add this later } else { var.names <- colnames(axisVars) class.names <- levels(classVar) f.stats <- matrix(NA, nrow = length(class.names), ncol = length(var.names), dimnames = list(class.names, var.names)) for (i in 1:length(class.names)) { f.stats[i, ] <- apply(axisVars, 2, function(x) { return(summary(lm(x ~ as.factor(classVar == class.names[i])))$fstatistic[1]) }) } var.maxF <- apply(f.stats, 2, max) return(names(var.maxF)[order(var.maxF, decreasing = TRUE)]) } } #' Sample skewness #' #' Calculate the sample skewness of a vector #' while ignoring missing values. #' #' @param x numeric vector #' @author Jason Crowley \email{crowley.jason.s@@gmail.com} #' @return sample skewness of \code{x} skewness <- function(x) { x <- x[!is.na(x)] xbar <- mean(x) n <- length(x) skewness <- (1 / n) * sum( (x - xbar) ^ 3) / ( (1 / n) * sum( (x - xbar) ^ 2)) ^ (3 / 2) return(skewness) } GGally/R/data-happy.R0000644000176200001440000000333613010131532013777 0ustar liggesusers#' Data related to happiness from the General Social Survey, 1972-2006. #' #' This data extract is taken from Hadley Wickham's \code{productplots} package. #' The original description follows, with minor edits. #' #' The data is a small sample of variables related to #' happiness from the General Social Survey (GSS). The GSS #' is a yearly cross-sectional survey of Americans, run from #' 1972. We combine data for 25 years to yield 51,020 #' observations, and of the over 5,000 variables, we select #' nine related to happiness: #' #' @details \itemize{ #' \item age. age in years: 18--89. #' \item degree. highest education: lt high school, high school, junior college, bachelor, graduate. #' \item finrela. relative financial status: far above, above average, average, below average, far below. #' \item happy. happiness: very happy, pretty happy, not too happy. #' \item health. health: excellent, good, fair, poor. #' \item marital. marital status: married, never married, divorced, widowed, separated. #' \item sex. sex: female, male. #' \item wtsall. probability weight. 0.43--6.43. #' } #' #' @docType data #' @keywords datasets #' @name happy #' @usage data(happy) #' @format A data frame with 51020 rows and 10 variables #' @references #' Smith, Tom W., Peter V. Marsden, Michael Hout, Jibum Kim. \emph{General Social Surveys, 1972-2006}. #' [machine-readable data file]. Principal Investigator, Tom W. Smith; Co-Principal Investigators, #' Peter V. Marsden and Michael Hout, NORC ed. #' Chicago: National Opinion Research Center, producer, 2005; #' Storrs, CT: The Roper Center for Public Opinion Research, University of Connecticut, distributor. #' 1 data file (57,061 logical records) and 1 codebook (3,422 pp). NULL GGally/R/ggpairs_add.R0000644000176200001440000000713313277311163014231 0ustar liggesusers #' Modify a ggmatrix object by adding an ggplot2 object to all plots #' #' This operator allows you to add ggplot2 objects to a ggmatrix object. #' #' If the first object is an object of class \code{ggmatrix}, you can add #' the following types of objects, and it will return a modified ggplot #' object. #' #' \itemize{ ###### \item \code{data.frame}: replace current data.frame ###### (must use \code{\%+\%}) ###### \item \code{uneval}: replace current aesthetics ###### \item \code{layer}: add new layer #' \item \code{theme}: update plot theme ###### \item \code{scale}: replace current scale ###### \item \code{coord}: override current coordinate system ###### \item \code{facet}: override current coordinate faceting #' } #' #' The \code{+} operator completely replaces elements #' with elements from e2. #' #' @param e1 An object of class \code{ggplot} or \code{theme} #' @param e2 A component to add to \code{e1} #' #' @export #' @seealso \code{\link[ggplot2]{+.gg}} and \code{\link[ggplot2]{theme}} #' @method + gg #' @rdname gg-add #' @examples #' data(tips, package = "reshape") #' pm <- ggpairs(tips[, 2:3]) #' ## change to black and white theme #' pm + ggplot2::theme_bw() #' ## change to linedraw theme #' # pm + ggplot2::theme_linedraw() #' ## change to custom theme #' # pm + ggplot2::theme(panel.background = ggplot2::element_rect(fill = "lightblue")) #' ## add a list of information #' extra <- list(ggplot2::theme_bw(), ggplot2::labs(caption = "My caption!")) #' pm + extra "+.gg" <- function(e1, e2) { if (!is.ggmatrix(e1)) { return(e1 %+% e2) } if (is.null(e1$gg)) { e1$gg <- list() } if (inherits(e2, "labels")) { add_labels_to_ggmatrix(e1, e2) } else if (is.theme(e2)) { add_theme_to_ggmatrix(e1, e2) } else if (is.list(e2)) { add_list_to_ggmatrix(e1, e2) } else { stop( "'ggmatrix' does not know how to add objects that do not have class 'theme' or 'labels'.", " Received object with class: '", paste(class(e2), collapse = ", "), "'" ) } } add_gg_info <- function(p, gg) { if (!is.null(gg)) { if (!is.null(gg$theme)) { p <- p + gg$theme } if (!is.null(gg$labs)) { p <- p + gg$labs } } p } add_labels_to_ggmatrix <- function(e1, e2) { label_names <- names(e2) if ("x" %in% label_names) { e1$xlab <- e2$x } if ("y" %in% label_names) { e1$ylab <- e2$y } if ("title" %in% label_names) { e1$title <- e2$title } non_ggmatrix_labels <- label_names[!label_names %in% c("x", "y", "title")] if (length(non_ggmatrix_labels) > 0) { if (is.null(e1$gg$labs)) { e1$gg$labs <- structure(list(), class = "labels") } e1$gg$labs[non_ggmatrix_labels] <- e2[non_ggmatrix_labels] } e1 } add_theme_to_ggmatrix <- function(e1, e2) { # Get the name of what was passed in as e2, and pass along so that it # can be displayed in error messages # e2name <- deparse(substitute(e2)) if (is.null(e1$gg$theme)) { e1$gg$theme <- e2 } else { # calls ggplot2 add method and stores the result in gg e1$gg$theme <- e1$gg$theme %+% e2 } e1 } add_list_to_ggmatrix <- function(e1, e2) { for (item in e2) { e1 <- e1 + item } e1 } is.ggmatrix <- function(x) { inherits(x, "ggmatrix") } #' Modify a ggmatrix object by adding an ggplot2 object to all plots #' #' @export #' @examples #' #' ggpairs(iris, 1:2) + v1_ggmatrix_theme() #' # move the column names to the left and bottom #' ggpairs(iris, 1:2, switch = "both") + v1_ggmatrix_theme() v1_ggmatrix_theme <- function() { theme( strip.background = element_rect(fill = "white"), strip.placement = "outside" ) } GGally/R/ggnet2.R0000644000176200001440000010510213276725426013160 0ustar liggesusersif (getRversion() >= "2.15.1") { utils::globalVariables(c("X1", "X2", "Y1", "Y2", "midX", "midY")) } #' ggnet2 - Plot a network with ggplot2 #' #' Function for plotting network objects using ggplot2, with additional control #' over graphical parameters that are not supported by the \code{\link{ggnet}} #' function. Please visit \url{http://github.com/briatte/ggnet} for the latest #' version of ggnet2, and \url{https://briatte.github.io/ggnet} for a vignette #' that contains many examples and explanations. #' #' @export #' @param net an object of class \code{\link[network]{network}}, or any object #' that can be coerced to this class, such as an adjacency or incidence matrix, #' or an edge list: see \link[network]{edgeset.constructors} and #' \link[network]{network} for details. If the object is of class #' \code{\link[igraph:igraph-package]{igraph}} and the #' \code{\link[intergraph:intergraph-package]{intergraph}} package is installed, #' it will be used to convert the object: see #' \code{\link[intergraph]{asNetwork}} for details. #' @param mode a placement method from those provided in the #' \code{\link[sna]{sna}} package: see \link[sna:gplot.layout]{gplot.layout} for #' details. Also accepts the names of two numeric vertex attributes of #' \code{net}, or a matrix of numeric coordinates, in which case the first two #' columns of the matrix are used. #' Defaults to the Fruchterman-Reingold force-directed algorithm. #' @param layout.par options to be passed to the placement method, as listed in #' \link[sna]{gplot.layout}. #' Defaults to \code{NULL}. #' @param layout.exp a multiplier to expand the horizontal axis if node labels #' get clipped: see \link[scales]{expand_range} for details. #' Defaults to \code{0} (no expansion). #' @param alpha the level of transparency of the edges and nodes, which might be #' a single value, a vertex attribute, or a vector of values. #' Also accepts \code{"mode"} on bipartite networks (see 'Details'). #' Defaults to \code{1} (no transparency). #' @param color the color of the nodes, which might be a single value, a vertex #' attribute, or a vector of values. #' Also accepts \code{"mode"} on bipartite networks (see 'Details'). #' Defaults to \code{grey75}. #' @param shape the shape of the nodes, which might be a single value, a vertex #' attribute, or a vector of values. #' Also accepts \code{"mode"} on bipartite networks (see 'Details'). #' Defaults to \code{19} (solid circle). #' @param size the size of the nodes, in points, which might be a single value, #' a vertex attribute, or a vector of values. Also accepts \code{"indegree"}, #' \code{"outdegree"}, \code{"degree"} or \code{"freeman"} to size the nodes by #' their unweighted degree centrality (\code{"degree"} and \code{"freeman"} are #' equivalent): see \code{\link[sna]{degree}} for details. All node sizes must #' be strictly positive. #' Also accepts \code{"mode"} on bipartite networks (see 'Details'). #' Defaults to \code{9}. #' @param max_size the \emph{maximum} size of the node when \code{size} produces #' nodes of different sizes, in points. #' Defaults to \code{9}. #' @param na.rm whether to subset the network to nodes that are \emph{not} #' missing a given vertex attribute. If set to any vertex attribute of #' \code{net}, the nodes for which this attribute is \code{NA} will be removed. #' Defaults to \code{NA} (does nothing). #' @param palette the palette to color the nodes, when \code{color} is not a #' color value or a vector of color values. Accepts named vectors of color #' values, or if \code{\link[RColorBrewer]{RColorBrewer}} is installed, any #' ColorBrewer palette name: see \code{\link[RColorBrewer]{brewer.pal}} and #' \url{http://colorbrewer2.org/} for details. #' Defaults to \code{NULL}, which will create an array of grayscale color values #' if \code{color} is not a color value or a vector of color values. #' @param alpha.palette the palette to control the transparency levels of the #' nodes set by \code{alpha} when the levels are not numeric values. #' Defaults to \code{NULL}, which will create an array of alpha transparency #' values if \code{alpha} is not a numeric value or a vector of numeric values. #' @param alpha.legend the name to assign to the legend created by #' \code{alpha} when its levels are not numeric values. #' Defaults to \code{NA} (no name). #' @param color.palette see \code{palette} #' @param color.legend the name to assign to the legend created by #' \code{palette}. #' Defaults to \code{NA} (no name). #' @param shape.palette the palette to control the shapes of the nodes set by #' \code{shape} when the shapes are not numeric values. #' Defaults to \code{NULL}, which will create an array of shape values if #' \code{shape} is not a numeric value or a vector of numeric values. #' @param shape.legend the name to assign to the legend created by #' \code{shape} when its levels are not numeric values. #' Defaults to \code{NA} (no name). #' @param size.palette the palette to control the sizes of the nodes set by #' \code{size} when the sizes are not numeric values. #' @param size.legend the name to assign to the legend created by #' \code{size}. #' Defaults to \code{NA} (no name). #' @param size.zero whether to accept zero-sized nodes based on the value(s) of #' \code{size}. #' Defaults to \code{FALSE}, which ensures that zero-sized nodes are still #' shown in the plot and its size legend. #' @param size.cut whether to cut the size of the nodes into a certain number of #' quantiles. Accepts \code{TRUE}, which tries to cut the sizes into quartiles, #' or any positive numeric value, which tries to cut the sizes into that many #' quantiles. If the size of the nodes do not contain the specified number of #' distinct quantiles, the largest possible number is used. #' See \code{\link[stats]{quantile}} and \code{\link[base]{cut}} for details. #' Defaults to \code{FALSE} (does nothing). #' @param size.min whether to subset the network to nodes with a minimum size, #' based on the values of \code{size}. #' Defaults to \code{NA} (preserves all nodes). #' @param size.max whether to subset the network to nodes with a maximum size, #' based on the values of \code{size}. #' Defaults to \code{NA} (preserves all nodes). #' @param label whether to label the nodes. If set to \code{TRUE}, nodes are #' labeled with their vertex names. If set to a vector that contains as many #' elements as there are nodes in \code{net}, nodes are labeled with these. If #' set to any other vector of values, the nodes are labeled only when their #' vertex name matches one of these values. #' Defaults to \code{FALSE} (no labels). #' @param label.alpha the level of transparency of the node labels, as a #' numeric value, a vector of numeric values, or as a vertex attribute #' containing numeric values. #' Defaults to \code{1} (no transparency). #' @param label.color the color of the node labels, as a color value, a vector #' of color values, or as a vertex attribute containing color values. #' Defaults to \code{"black"}. #' @param label.size the size of the node labels, in points, as a numeric value, #' a vector of numeric values, or as a vertex attribute containing numeric #' values. #' Defaults to \code{max_size / 2} (half the maximum node size), which defaults #' to \code{4.5}. #' @param label.trim whether to apply some trimming to the node labels. Accepts #' any function that can process a character vector, or a strictly positive #' numeric value, in which case the labels are trimmed to a fixed-length #' substring of that length: see \code{\link[base]{substr}} for details. #' Defaults to \code{FALSE} (does nothing). #' @param node.alpha see \code{alpha} #' @param node.color see \code{color} #' @param node.label see \code{label} #' @param node.shape see \code{shape} #' @param node.size see \code{size} #' @param edge.alpha the level of transparency of the edges. #' Defaults to the value of \code{alpha}, which defaults to \code{1}. #' @param edge.color the color of the edges, as a color value, a vector of color #' values, or as an edge attribute containing color values. #' Defaults to \code{"grey50"}. #' @param edge.lty the linetype of the edges, as a linetype value, a vector of #' linetype values, or as an edge attribute containing linetype values. #' Defaults to \code{"solid"}. #' @param edge.size the size of the edges, in points, as a numeric value, a #' vector of numeric values, or as an edge attribute containing numeric values. #' All edge sizes must be strictly positive. #' Defaults to \code{0.25}. #' @param edge.label the labels to plot at the middle of the edges, as a single #' value, a vector of values, or as an edge attribute. #' Defaults to \code{NULL} (no edge labels). #' @param edge.label.alpha the level of transparency of the edge labels, as a #' numeric value, a vector of numeric values, or as an edge attribute #' containing numeric values. #' Defaults to \code{1} (no transparency). #' @param edge.label.color the color of the edge labels, as a color value, a #' vector of color values, or as an edge attribute containing color values. #' Defaults to \code{label.color}, which defaults to \code{"black"}. #' @param edge.label.fill the background color of the edge labels. #' Defaults to \code{"white"}. #' @param edge.label.size the size of the edge labels, in points, as a numeric #' value, a vector of numeric values, or as an edge attribute containing numeric #' values. All edge label sizes must be strictly positive. #' Defaults to \code{max_size / 2} (half the maximum node size), which defaults #' to \code{4.5}. #' @param arrow.size the size of the arrows for directed network edges, in #' points. See \code{\link[grid]{arrow}} for details. #' Defaults to \code{0} (no arrows). #' @param arrow.gap a setting aimed at improving the display of edge arrows by #' plotting slightly shorter edges. Accepts any value between \code{0} and #' \code{1}, where a value of \code{0.05} will generally achieve good results #' when the size of the nodes is reasonably small. #' Defaults to \code{0} (no shortening). #' @param arrow.type the type of the arrows for directed network edges. See #' \code{\link[grid]{arrow}} for details. #' Defaults to \code{"closed"}. #' @param legend.size the size of the legend symbols and text, in points. #' Defaults to \code{9}. #' @param legend.position the location of the plot legend(s). Accepts all #' \code{legend.position} values supported by \code{\link[ggplot2]{theme}}. #' Defaults to \code{"right"}. #' @param ... other arguments passed to the \code{geom_text} object that sets #' the node labels: see \code{\link[ggplot2]{geom_text}} for details. #' @seealso \code{\link{ggnet}} in this package, #' \code{\link[sna]{gplot}} in the \code{\link[sna]{sna}} package, and #' \code{\link[network]{plot.network}} in the \code{\link[network]{network}} #' package #' @author Moritz Marbach and Francois Briatte, with help from Heike Hoffmann, #' Pedro Jordano and Ming-Yu Liu #' @details The degree centrality measures that can be produced through the #' \code{size} argument will take the directedness of the network into account, #' but will be unweighted. To compute weighted network measures, see the #' \code{tnet} package by Tore Opsahl (\code{help("tnet", package = "tnet")}). #' #' The nodes of bipartite networks can be mapped to their mode by passing the #' \code{"mode"} argument to any of \code{alpha}, \code{color}, \code{shape} and #' \code{size}, in which case the nodes of the primary mode will be mapped as #' \code{"actor"}, and the nodes of the secondary mode will be mapped as #' \code{"event"}. #' @importFrom utils installed.packages #' @importFrom grDevices gray.colors #' @examples #' library(network) #' #' # random adjacency matrix #' x <- 10 #' ndyads <- x * (x - 1) #' density <- x / ndyads #' m <- matrix(0, nrow = x, ncol = x) #' dimnames(m) <- list(letters[ 1:x ], letters[ 1:x ]) #' m[ row(m) != col(m) ] <- runif(ndyads) < density #' m #' #' # random undirected network #' n <- network::network(m, directed = FALSE) #' n #' #' ggnet2(n, label = TRUE) #' ggnet2(n, label = TRUE, shape = 15) #' ggnet2(n, label = TRUE, shape = 15, color = "black", label.color = "white") #' #' # add vertex attribute #' x = network.vertex.names(n) #' x = ifelse(x %in% c("a", "e", "i"), "vowel", "consonant") #' n %v% "phono" = x #' #' ggnet2(n, color = "phono") #' ggnet2(n, color = "phono", palette = c("vowel" = "gold", "consonant" = "grey")) #' ggnet2(n, shape = "phono", color = "phono") #' #' if (require(RColorBrewer)) { #' #' # random groups #' n %v% "group" <- sample(LETTERS[1:3], 10, replace = TRUE) #' #' ggnet2(n, color = "group", palette = "Set2") #' #' } #' #' # random weights #' n %e% "weight" <- sample(1:3, network.edgecount(n), replace = TRUE) #' ggnet2(n, edge.size = "weight", edge.label = "weight") #' #' # edge arrows on a directed network #' ggnet2(network(m, directed = TRUE), arrow.gap = 0.05, arrow.size = 10) #' #' # Padgett's Florentine wedding data #' data(flo, package = "network") #' flo #' #' ggnet2(flo, label = TRUE) #' ggnet2(flo, label = TRUE, label.trim = 4, vjust = -1, size = 3, color = 1) #' ggnet2(flo, label = TRUE, size = 12, color = "white") ggnet2 <- function( net, mode = "fruchtermanreingold", layout.par = NULL, layout.exp = 0, alpha = 1, color = "grey75", shape = 19, size = 9, max_size = 9, na.rm = NA, palette = NULL, alpha.palette = NULL, alpha.legend = NA, color.palette = palette, color.legend = NA, shape.palette = NULL, shape.legend = NA, size.palette = NULL, size.legend = NA, size.zero = FALSE, size.cut = FALSE, size.min = NA, size.max = NA, label = FALSE, label.alpha = 1, label.color = "black", label.size = max_size / 2, label.trim = FALSE, node.alpha = alpha, node.color = color, node.label = label, node.shape = shape, node.size = size, edge.alpha = 1, edge.color = "grey50", edge.lty = "solid", edge.size = .25, edge.label = NULL, edge.label.alpha = 1, edge.label.color = label.color, edge.label.fill = "white", edge.label.size = max_size / 2, arrow.size = 0, arrow.gap = 0, arrow.type = "closed", legend.size = 9, legend.position = "right", ... ){ # -- packages ---------------------------------------------------------------- require_namespaces(c("network", "sna", "scales")) # -- conversion to network class --------------------------------------------- if (class(net) == "igraph" && "intergraph" %in% rownames(installed.packages())) { net = intergraph::asNetwork(net) } else if (class("net") == "igraph") { stop("install the 'intergraph' package to use igraph objects with ggnet2") } if (!network::is.network(net)) { net = try(network::network(net), silent = TRUE) } if (!network::is.network(net)) { stop("could not coerce net to a network object") } # -- network functions ------------------------------------------------------- get_v = get("%v%", envir = getNamespace("network")) get_e = get("%e%", envir = getNamespace("network")) set_mode = function(x, mode = network::get.network.attribute(x, "bipartite")) { c(rep("actor", mode), rep("event", n_nodes - mode)) } set_node = function(x, value, mode = TRUE) { if (is.null(x) || is.na(x) || is.infinite(x) || is.nan(x)) { stop(paste("incorrect", value, "value")) } else if (is.numeric(x) && any(x < 0)) { stop(paste("incorrect", value, "value")) } else if (length(x) == n_nodes) { x } else if (length(x) > 1) { stop(paste("incorrect", value, "length")) } else if (x %in% v_attr) { get_v(net, x) } else if (mode && x == "mode" & is_bip) { set_mode(net) } else { x } } set_edge = function(x, value) { if (is.null(x) || is.na(x) || is.infinite(x) || is.nan(x)) { stop(paste("incorrect", value, "value")) } else if (is.numeric(x) && any(x < 0)) { stop(paste("incorrect", value, "value")) } else if (length(x) == n_edges) { x } else if (length(x) > 1) { stop(paste("incorrect", value, "length")) } else if (x %in% e_attr) { get_e(net, x) } else { x } } set_attr = function(x) { if (length(x) == n_nodes) { x } else if (length(x) > 1) { stop(paste("incorrect coordinates length")) } else if (!x %in% v_attr) { stop(paste("vertex attribute", x, "was not found")) } else if (!is.numeric(get_v(net, x))) { stop(paste("vertex attribute", x, "is not numeric")) } else { get_v(net, x) } } set_name = function(x, y) { z = length(x) == 1 && x %in% v_attr z = ifelse(is.na(y), z, y) z = ifelse(isTRUE(z), x, z) ifelse(is.logical(z), "", z) } set_size = function(x) { y = x + (0 %in% x) * !size.zero y = scales::rescale_max(y) y = scales::abs_area(max_size)(y) if (is.null(names(x))) names(y) = x else names(y) = names(x) y } is_one = function(x) length(unique(x)) == 1 is_col = function(x) all(is.numeric(x)) | all(network::is.color(x)) # -- network structure ------------------------------------------------------- n_nodes = network::network.size(net) n_edges = network::network.edgecount(net) v_attr = network::list.vertex.attributes(net) e_attr = network::list.edge.attributes(net) is_bip = network::is.bipartite(net) is_dir = ifelse(network::is.directed(net), "digraph", "graph") if (!is.numeric(arrow.size) || arrow.size < 0) { stop("incorrect arrow.size value") } else if (arrow.size > 0 & is_dir == "graph") { warning("network is undirected; arrow.size ignored") arrow.size = 0 } if (!is.numeric(arrow.gap) || arrow.gap < 0 || arrow.gap > 1) { stop("incorrect arrow.gap value") } else if (arrow.gap > 0 & is_dir == "graph") { warning("network is undirected; arrow.gap ignored") arrow.gap = 0 } if (network::is.hyper(net)) { stop("ggnet2 cannot plot hyper graphs") } if (network::is.multiplex(net)) { stop("ggnet2 cannot plot multiplex graphs") } if (network::has.loops(net)) { warning("ggnet2 does not know how to handle self-loops") } # -- check max_size ---------------------------------------------------------- x = max_size if (!is.numeric(x) || is.infinite(x) || is.nan(x) || x < 0) { stop("incorrect max_size value") } # -- initialize dataset ------------------------------------------------------ data = data.frame(label = get_v(net, "vertex.names"), stringsAsFactors = FALSE) data$alpha = set_node(node.alpha , "node.alpha") data$color = set_node(node.color , "node.color") data$shape = set_node(node.shape , "node.shape") data$size = set_node(node.size , "node.size") # -- node removal ------------------------------------------------------------ if (length(na.rm) > 1) { stop("incorrect na.rm value") } else if (!is.na(na.rm)) { if (!na.rm %in% v_attr) { stop(paste("vertex attribute", na.rm, "was not found")) } x = which(is.na(get_v(net, na.rm))) message(paste("na.rm removed", length(x), "nodes out of", nrow(data))) if (length(x) > 0) { data = data[ -x, ] network::delete.vertices(net, x) if (!nrow(data)) { warning("na.rm removed all nodes; nothing left to plot") return(invisible(NULL)) } } } # -- weight methods ---------------------------------------------------------- x = size if (length(x) == 1 && x %in% c("indegree", "outdegree", "degree", "freeman")) { # prevent namespace conflict with igraph if ("package:igraph" %in% search()) { y = ifelse(is_dir == "digraph", "directed", "undirected") z = c("indegree" = "in", "outdegree" = "out", "degree" = "all", "freeman" = "all")[ x ] data$size = igraph::degree(igraph::graph.adjacency(as.matrix(net), mode = y), mode = z) } else { data$size = sna::degree(net, gmode = is_dir, cmode = ifelse(x == "degree", "freeman", x)) } size.legend = ifelse(is.na(size.legend), x, size.legend) } # -- weight thresholds ------------------------------------------------------- x = ifelse(is.na(size.min), 0, size.min) if (length(x) > 1 || !is.numeric(x) || is.infinite(x) || is.nan(x) || x < 0) { stop("incorrect size.min value") } else if (x > 0 && !is.numeric(data$size)) { warning("node.size is not numeric; size.min ignored") } else if (x > 0) { x = which(data$size < x) message(paste("size.min removed", length(x), "nodes out of", nrow(data))) if (length(x) > 0) { data = data[ -x, ] network::delete.vertices(net, x) if (!nrow(data)) { warning("size.min removed all nodes; nothing left to plot") return(invisible(NULL)) } } } x = ifelse(is.na(size.max), 0, size.max) if (length(x) > 1 || !is.numeric(x) || is.infinite(x) || is.nan(x) || x < 0) { stop("incorrect size.max value") } else if (x > 0 && !is.numeric(data$size)) { warning("node.size is not numeric; size.max ignored") } else if (x > 0) { x = which(data$size > x) message(paste("size.max removed", length(x), "nodes out of", nrow(data))) if (length(x) > 0) { data = data[ -x, ] network::delete.vertices(net, x) if (!nrow(data)) { warning("size.max removed all nodes; nothing left to plot") return(invisible(NULL)) } } } # -- weight quantiles -------------------------------------------------------- x = size.cut if (length(x) > 1 || is.null(x) || is.na(x) || is.infinite(x) || is.nan(x)) { stop("incorrect size.cut value") } else if (isTRUE(x)) { x = 4 } else if (is.logical(x) && !x) { x = 0 } else if (!is.numeric(x)) { stop("incorrect size.cut value") } if (x >= 1 && !is.numeric(data$size)) { warning("node.size is not numeric; size.cut ignored") } else if (x >= 1) { x = unique(quantile(data$size, probs = seq(0, 1, by = 1 / as.integer(x)))) if (length(x) > 1) { data$size = cut(data$size, unique(x), include.lowest = TRUE) } else { warning("node.size is invariant; size.cut ignored") } } # -- alpha palette ----------------------------------------------------------- if (!is.null(alpha.palette)) { x = alpha.palette } else if (is.factor(data$alpha)) { x = levels(data$alpha) } else { x = unique(data$alpha) } if (!is.null(names(x))) { y = unique(na.omit(data$alpha[ !data$alpha %in% names(x) ])) if (length(y) > 0) { stop(paste("no alpha.palette value for", paste0(y, collapse = ", "))) } } else if (is.factor(data$alpha) || !is.numeric(x)) { data$alpha = factor(data$alpha) x = scales::rescale_max(1:length(levels(data$alpha))) names(x) = levels(data$alpha) } alpha.palette = x # -- color palette ----------------------------------------------------------- if (!is.null(color.palette)) { x = color.palette } else if (is.factor(data$color)) { x = levels(data$color) } else { x = unique(data$color) } if (length(x) == 1 && "RColorBrewer" %in% rownames(installed.packages()) && x %in% rownames(RColorBrewer::brewer.pal.info)) { data$color = factor(data$color) n_groups = length(levels(data$color)) n_colors = RColorBrewer::brewer.pal.info[ x, "maxcolors" ] if (n_groups > n_colors) { stop(paste0("too many node groups (", n_groups, ") for ", "ColorBrewer palette ", x, " (max: ", n_colors, ")")) } else if (n_groups < 3) { n_groups = 3 } x = RColorBrewer::brewer.pal(n_groups, x)[ 1:length(levels(data$color)) ] names(x) = levels(data$color) } if (!is.null(names(x))) { y = unique(na.omit(data$color[ !data$color %in% names(x) ])) if (length(y) > 0) { stop(paste("no color.palette value for", paste0(y, collapse = ", "))) } } else if (is.factor(data$color) || !is_col(x)) { data$color = factor(data$color) x = gray.colors(length(x)) names(x) = levels(data$color) } color.palette = x # -- shape palette ----------------------------------------------------------- if (!is.null(shape.palette)) { x = shape.palette } else if (is.factor(data$shape)) { x = levels(data$shape) } else { x = unique(data$shape) } if (!is.null(names(x))) { y = unique(na.omit(data$shape[ !data$shape %in% names(x) ])) if (length(y) > 0) { stop(paste("no shape.palette value for", paste0(y, collapse = ", "))) } } else if (is.factor(data$shape) || !is.numeric(x)) { data$shape = factor(data$shape) x = scales::shape_pal()(length(levels(data$shape))) names(x) = levels(data$shape) } shape.palette = x # -- size palette ------------------------------------------------------------ if (!is.null(size.palette)) { x = size.palette } else if (is.factor(data$size)) { x = levels(data$size) } else { x = unique(data$size) } if (!is.null(names(x))) { y = unique(na.omit(data$size[ !data$size %in% names(x) ])) if (length(y) > 0) { stop(paste("no size.palette value for", paste0(y, collapse = ", "))) } } else if (is.factor(data$size) || !is.numeric(x)) { data$size = factor(data$size) x = 1:length(levels(data$size)) names(x) = levels(data$size) } size.palette = x # -- node labels ------------------------------------------------------------- l = node.label if (isTRUE(l)) { l = data$label } else if (length(l) > 1 & length(l) == n_nodes) { data$label = l } else if (length(l) == 1 && l %in% v_attr) { l = get_v(net, l) } else { l = ifelse(data$label %in% l, data$label, "") } # -- node placement ---------------------------------------------------------- if (is.character(mode) && length(mode) == 1) { mode = paste0("gplot.layout.", mode) if (!exists(mode, where = getNamespace("sna"))) { stop(paste("unsupported placement method:", mode)) } else { mode <- get(mode, getNamespace("sna")) } # sna placement algorithm xy = network::as.matrix.network.adjacency(net) xy = do.call(mode, list(xy, layout.par)) xy = data.frame(x = xy[, 1], y = xy[, 2]) } else if (is.character(mode) && length(mode) == 2) { # fixed coordinates from vertex attributes xy = data.frame(x = set_attr(mode[1]), y = set_attr(mode[2])) } else if (is.numeric(mode) && is.matrix(mode)) { # fixed coordinates from matrix xy = data.frame(x = set_attr(mode[, 1]), y = set_attr(mode[, 2])) } else { stop("incorrect mode value") } xy$x = scale(xy$x, min(xy$x), diff(range(xy$x)))[,1] xy$y = scale(xy$y, min(xy$y), diff(range(xy$y)))[,1] data = cbind(data, xy) # -- edge colors ------------------------------------------------------------- edges = network::as.matrix.network.edgelist(net) if (edge.color[1] == "color" && length(edge.color) == 2) { # edge colors from node source and target edge.color = ifelse(data$color[ edges[, 1]] == data$color[ edges[, 2]], as.character(data$color[ edges[, 1]]), edge.color[2]) if (!is.null(names(color.palette))) { x = which(edge.color %in% names(color.palette)) edge.color[x] = color.palette[ edge.color[x] ] } edge.color[ is.na(edge.color) ] = edge.color[2] } edge.color = set_edge(edge.color, "edge.color") if (!is_col(edge.color)) { stop("incorrect edge.color value") } # -- edge list --------------------------------------------------------------- edges = data.frame(xy[ edges[, 1], ], xy[ edges[, 2], ]) names(edges) = c("X1", "Y1", "X2", "Y2") # -- edge labels, colors and sizes ------------------------------------------- if (!is.null(edge.label)) { edges$midX = (edges$X1 + edges$X2) / 2 edges$midY = (edges$Y1 + edges$Y2) / 2 edges$label = set_edge(edge.label, "edge.label") edge.label.alpha = set_edge(edge.label.alpha, "edge.label.alpha") if (!is.numeric(edge.label.alpha)) { stop("incorrect edge.label.alpha value") } edge.label.color = set_edge(edge.label.color, "edge.label.color") if (!is_col(edge.label.color)) { stop("incorrect edge.label.color value") } edge.label.size = set_edge(edge.label.size, "edge.label.size") if (!is.numeric(edge.label.size)) { stop("incorrect edge.label.size value") } } # -- edge linetype ----------------------------------------------------------- edge.lty = set_edge(edge.lty, "edge.lty") # -- edge size --------------------------------------------------------------- edge.size = set_edge(edge.size, "edge.size") if (!is.numeric(edge.size) || any(edge.size <= 0)) { stop("incorrect edge.size value") } # -- plot edges -------------------------------------------------------------- p = ggplot(data, aes(x = x, y = y)) if (nrow(edges) > 0) { if (arrow.gap > 0) { x.dir = with(edges, (X2 - X1)) # do not use absolute value y.dir = with(edges, (Y2 - Y1)) arrow.gap = with(edges, arrow.gap / sqrt(x.dir ^ 2 + y.dir ^ 2)) edges = transform(edges, X1 = X1 + arrow.gap * x.dir, Y1 = Y1 + arrow.gap * y.dir, X2 = X1 + (1 - arrow.gap) * x.dir, Y2 = Y1 + (1 - arrow.gap) * y.dir) } p = p + geom_segment( data = edges, aes(x = X1, y = Y1, xend = X2, yend = Y2), size = edge.size, color = edge.color, alpha = edge.alpha, lty = edge.lty, arrow = arrow( type = arrow.type, length = unit(arrow.size, "pt") ) ) } if (nrow(edges) > 0 && !is.null(edge.label)) { p = p + geom_point( data = edges, aes(x = midX, y = midY), alpha = edge.alpha, color = edge.label.fill, size = edge.label.size * 1.5 ) + geom_text( data = edges, aes(x = midX, y = midY, label = label), alpha = edge.label.alpha, color = edge.label.color, size = edge.label.size ) } # -- plot nodes -------------------------------------------------------------- x = list() if (is.numeric(data$alpha) && is_one(data$alpha)) { x = c(x, alpha = unique(data$alpha)) } if (!is.factor(data$color) && is_one(data$color)) { x = c(x, colour = unique(data$color)) # must be English spelling } if (is.numeric(data$shape) && is_one(data$shape)) { x = c(x, shape = unique(data$shape)) } if (is.numeric(data$size) && is_one(data$size)) { x = c(x, size = unique(data$size)) } else { x = c(x, size = max_size) } p = p + geom_point(aes(alpha = factor(alpha), color = factor(color), shape = factor(shape), size = factor(size))) # -- legend: alpha ----------------------------------------------------------- if (is.numeric(data$alpha)) { v_alpha = unique(data$alpha) names(v_alpha) = unique(data$alpha) p = p + scale_alpha_manual("", values = v_alpha) + guides(alpha = FALSE) } else { p = p + scale_alpha_manual(set_name(node.alpha, alpha.legend), values = alpha.palette, breaks = names(alpha.palette), guide = guide_legend(override.aes = x)) } # -- legend: color ----------------------------------------------------------- if (!is.null(names(color.palette))) { p = p + scale_color_manual(set_name(node.color, color.legend), values = color.palette, breaks = names(color.palette), guide = guide_legend(override.aes = x)) } else { v_color = unique(data$color) names(v_color) = unique(data$color) p = p + scale_color_manual("", values = v_color) + guides(color = FALSE) } # -- legend: shape ----------------------------------------------------------- if (is.numeric(data$shape)) { v_shape = unique(data$shape) names(v_shape) = unique(data$shape) p = p + scale_shape_manual("", values = v_shape) + guides(shape = FALSE) } else { p = p + scale_shape_manual(set_name(node.shape, shape.legend), values = shape.palette, breaks = names(shape.palette), guide = guide_legend(override.aes = x)) } # -- legend: size ------------------------------------------------------------ x = x[ names(x) != "size" ] if (is.numeric(data$size)) { v_size = set_size(unique(data$size)) if (length(v_size) == 1) { v_size = as.numeric(names(v_size)) p = p + scale_size_manual("", values = v_size) + guides(size = FALSE) } else { p = p + scale_size_manual(set_name(node.size, size.legend), values = v_size, guide = guide_legend(override.aes = x)) } } else { p = p + scale_size_manual(set_name(node.size, size.legend), values = set_size(size.palette), guide = guide_legend(override.aes = x)) } # -- plot node labels -------------------------------------------------------- if (!is_one(l) || unique(l) != "") { label.alpha = set_node(label.alpha, "label.alpha", mode = FALSE) if (!is.numeric(label.alpha)) { stop("incorrect label.alpha value") } label.color = set_node(label.color, "label.color", mode = FALSE) if (!is_col(label.color)) { stop("incorrect label.color value") } label.size = set_node(label.size, "label.size", mode = FALSE) if (!is.numeric(label.size)) { stop("incorrect label.size value") } x = label.trim if (length(x) > 1 || (!is.logical(x) & !is.numeric(x) & !is.function(x))) { stop("incorrect label.trim value") } else if (is.numeric(x) && x > 0) { l = substr(l, 1, x) } else if (is.function(x)) { l = x(l) } p = p + geom_text( label = l, alpha = label.alpha, color = label.color, size = label.size, ... ) } # -- horizontal scale expansion ---------------------------------------------- x = range(data$x) if (!is.numeric(layout.exp) || layout.exp < 0) { stop("incorrect layout.exp value") } else if (layout.exp > 0) { x = scales::expand_range(x, layout.exp / 2) } # -- finalize ---------------------------------------------------------------- p = p + scale_x_continuous(breaks = NULL, limits = x) + scale_y_continuous(breaks = NULL) + theme( panel.background = element_blank(), panel.grid = element_blank(), axis.title = element_blank(), legend.key = element_blank(), legend.position = legend.position, legend.text = element_text(size = legend.size), legend.title = element_text(size = legend.size) ) return(p) } GGally/R/data-australia-pisa-2012.R0000644000176200001440000000502113010131532016150 0ustar liggesusers#' Programme for International Student Assesment (PISA) 2012 Data for Australia #' #' About PISA #' #' The Programme for International Student Assessment (PISA) is a triennial international survey which aims to evaluate education systems worldwide by testing the skills and knowledge of 15-year-old students. To date, students representing more than 70 economies have participated in the assessment. #' #' While 65 economies took part in the 2012 study, this data set only contains information from the country of Australia. #' #' @details \itemize{ #' \item gender : Factor w/ 2 levels "female","male": 1 1 2 2 2 1 1 1 2 1 ... #' \item age : Factor w/ 4 levels "4","5","6","7": 2 2 2 4 3 1 2 2 2 2 ... #' \item homework : num 5 5 9 3 2 3 4 3 5 1 ... #' \item desk : num 1 0 1 1 1 1 1 1 1 1 ... #' \item room : num 1 1 1 1 1 1 1 1 1 1 ... #' \item study : num 1 1 1 1 1 1 1 1 1 1 ... #' \item computer : num 1 1 1 1 1 1 1 1 1 1 ... #' \item software : num 1 1 1 1 1 1 1 1 1 1 ... #' \item internet : num 1 1 1 1 1 1 1 1 1 1 ... #' \item literature : num 0 0 1 0 1 1 1 1 1 0 ... #' \item poetry : num 0 0 1 0 1 1 0 1 1 1 ... #' \item art : num 1 0 1 0 1 1 0 1 1 1 ... #' \item textbook : num 1 1 1 1 1 0 1 1 1 1 ... #' \item dictionary : num 1 1 1 1 1 1 1 1 1 1 ... #' \item dishwasher : num 1 1 1 1 0 1 1 1 1 1 ... #' \item PV1MATH : num 562 565 602 520 613 ... #' \item PV2MATH : num 569 557 594 507 567 ... #' \item PV3MATH : num 555 553 552 501 585 ... #' \item PV4MATH : num 579 538 526 521 596 ... #' \item PV5MATH : num 548 573 619 547 603 ... #' \item PV1READ : num 582 617 650 554 605 ... #' \item PV2READ : num 571 572 608 560 557 ... #' \item PV3READ : num 602 560 594 517 627 ... #' \item PV4READ : num 572 564 575 564 597 ... #' \item PV5READ : num 585 565 620 572 598 ... #' \item PV1SCIE : num 583 627 668 574 639 ... #' \item PV2SCIE : num 579 600 665 612 635 ... #' \item PV3SCIE : num 593 574 620 571 666 ... #' \item PV4SCIE : num 567 582 592 598 700 ... #' \item PV5SCIE : num 587 625 656 662 670 ... #' \item SENWGT_STU : num 0.133 0.133 0.141 0.141 0.141 ... #' \item possessions: num 10 8 12 9 11 11 10 12 12 11 ... #' } #' #' @docType data #' @keywords datasets #' @name australia_PISA2012 #' @usage data(australia_PISA2012) #' @format A data frame with 8247 rows and 32 variables #' @source \url{http://www.oecd.org/pisa/pisaproducts/database-cbapisa2012.htm} NULL GGally/R/ggcoef.R0000644000176200001440000001000613277311163013210 0ustar liggesusers#' ggcoef - Plot Model Coefficients with broom and ggplot2 #' #' Plot the coefficients of a model with \pkg{broom} and \pkg{ggplot2}. #' #' @param x a model object to be tidied with \code{\link[broom]{tidy}} or a data frame (see Details) #' @param mapping default aesthetic mapping #' @param conf.int display confidence intervals as error bars? #' @param conf.level level of confidence intervals (passed to \code{\link[broom]{tidy}} #' if \code{x} is not a data frame) #' @param exponentiate if \code{TRUE}, x-axis will be logarithmic (also passed to \code{\link[broom]{tidy}} #' if \code{x} is not a data frame) #' @param exclude_intercept should the intercept be excluded from the plot? #' @param vline print a vertical line? #' @param vline_intercept \code{xintercept} for the vertical line. #' \code{"auto"} for \code{x = 0} (or \code{x = 1} if {exponentiate} is \code{TRUE}) #' @param vline_color color of the vertical line #' @param vline_linetype line type of the vertical line #' @param vline_size size of the vertical line #' @param errorbar_color color of the error bars #' @param errorbar_height height of the error bars #' @param errorbar_linetype line type of the error bars #' @param errorbar_size size of the error bars #' @param sort \code{"none"} (default) do not sort, \code{"ascending"} sort by increasing coefficient value, or \code{"decending"} sort by decreasing coefficient value #' @param ... additional arguments sent to \code{\link[ggplot2]{geom_point}} #' @examples #' library(broom) #' reg <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width, data = iris) #' ggcoef(reg) #' \donttest{d <- as.data.frame(Titanic) #' reg2 <- glm(Survived ~ Sex + Age + Class, family = binomial, data = d, weights = d$Freq) #' ggcoef(reg2, exponentiate = TRUE) #' ggcoef( #' reg2, exponentiate = TRUE, exclude_intercept = TRUE, #' errorbar_height = .2, color = "blue", sort = "ascending" #' )} #' @export ggcoef <- function( x, mapping = aes_string(y = "term", x = "estimate"), conf.int = TRUE, conf.level = 0.95, exponentiate = FALSE, exclude_intercept = FALSE, vline = TRUE, vline_intercept = "auto", vline_color = "gray50", vline_linetype = "dotted", vline_size = 1, errorbar_color = "gray25", errorbar_height = 0, errorbar_linetype = "solid", errorbar_size = .5, sort = c("none", "ascending", "decending"), ... ) { if (!is.data.frame(x)) { require_namespaces("broom") x <- broom::tidy( x, conf.int = conf.int, conf.level = conf.level, exponentiate = exponentiate ) } if (!("term" %in% names(x))) { stop("x doesn't contain a column names 'term'.") } if (!("estimate" %in% names(x))) { stop("x doesn't contain a column names 'estimate'.") } if (exclude_intercept) { x <- x[x$term != "(Intercept)", ] } sort <- match.arg(sort) if (sort != "none") { x$term <- as.factor(x$term) if (sort == "ascending") { new_order <- order(x$estimate, decreasing = FALSE) } else { new_order <- order(x$estimate, decreasing = TRUE) } x$term <- as.character(x$term) x$term <- factor(x$term, levels = x$term[new_order]) } p <- ggplot(x, mapping = mapping) if (vline) { if (exponentiate) { if (vline_intercept == "auto") { vline_intercept <- 1 } p <- p + geom_vline( xintercept = vline_intercept, color = vline_color, linetype = vline_linetype, size = vline_size ) + scale_x_log10() } else { if (vline_intercept == "auto") { vline_intercept <- 0 } p <- p + geom_vline( xintercept = vline_intercept, color = vline_color, linetype = vline_linetype, size = vline_size ) } } if (conf.int & "conf.low" %in% names(x) & "conf.high" %in% names(x)) p <- p + geom_errorbarh( aes_string(xmin = "conf.low", xmax = "conf.high"), color = errorbar_color, height = errorbar_height, linetype = errorbar_linetype, size = errorbar_size ) p + geom_point(...) } GGally/R/find-combo.R0000644000176200001440000000617713277311162014010 0ustar liggesusers#' Plot Types #' #' Retrieves the type of plot that should be used for all combinations #' #' @param data data set to be used #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @keywords internal plot_types <- function(data, columnsX, columnsY, allowDiag = TRUE) { plotTypesX <- lapply(data[columnsX], plotting_data_type) plotTypesY <- lapply(data[columnsY], plotting_data_type) columnNamesX <- names(data)[columnsX] columnNamesY <- names(data)[columnsY] isNaData <- as.data.frame(is.na(data)) lenX <- length(plotTypesX) lenY <- length(plotTypesY) n <- lenX * lenY plotType <- character(n) xVar <- character(n) yVar <- character(n) posX <- integer(n) posY <- integer(n) #horizontal then vertical for (yI in seq_len(lenY)) { yColName <- columnNamesY[yI] for (xI in seq_len(lenX)) { xColName <- columnNamesX[xI] yVarVal <- ifelse(xColName == yColName && allowDiag, NA, yColName) pos <- (yI - 1) * lenX + xI plotType[pos] <- find_plot_type( xColName, yColName, plotTypesX[xI], plotTypesY[yI], isAllNa = all(isNaData[[xColName]] | isNaData[[yColName]]), allowDiag = allowDiag ) xVar[pos] <- xColName yVar[pos] <- yVarVal posX[pos] <- xI posY[pos] <- yI } } dataInfo <- data.frame( plotType = plotType, xVar = xVar, yVar = yVar, posX = posX, posY = posY, isVertical = NA, stringsAsFactors = FALSE ) isCombo <- dataInfo$plotType == "combo" if (any(isCombo)) { dataInfo$isVertical[isCombo] <- unlist(plotTypesX[xVar[isCombo]]) == "discrete" } dataInfo } #' Find Plot Types #' #' Retrieves the type of plot for the specific columns #' #' @param col1Name x column name #' @param col2Name y column name #' @param type1 x column type #' @param type2 y column type #' @param isAllNa is.na(data) #' @param allowDiag allow for diag values to be returned #' @author Barret Schloerke \email{schloerke@@gmail.com} find_plot_type <- function(col1Name, col2Name, type1, type2, isAllNa, allowDiag) { # diag calculations if (col1Name == col2Name && allowDiag) { if (type1 == "na") { return("na-diag") } else if (type1 == "continuous") { return("continuous-diag") } else { return("discrete-diag") } } if (type1 == "na" | type2 == "na") { return("na") } #cat(names(data)[col2Name],": ", type2,"\t",names(data)[col1Name],": ",type1,"\n") isCats <- c(type1, type2) %in% "discrete" if (any(isCats)) { if (all(isCats)) { return("discrete") } return("combo") } # check if any combo of the two columns is all na if (isAllNa) { return("na") } return("continuous") } #' Check if object is a date #' #' @keywords internal #' @param x vector is_date <- function(x) { inherits(x, c("POSIXt", "POSIXct", "POSIXlt", "Date")) } #' Get plotting data type #' #' @keywords internal #' @param x vector plotting_data_type <- function(x) { if (all(is.na(x))) { return("na") } if (is_date(x)) { "continuous" } else if (!is.null(attributes(x)) || all(is.character(x)) || is.logical(x)) { "discrete" } else { "continuous" } } GGally/R/ggmatrix_gtable_helpers.R0000644000176200001440000000773513223454553016661 0ustar liggesusers plot_gtable <- function(p) { ggplot_gtable(ggplot_build(p)) } # axis_size_left(p) # axis_size_bottom(p) # axis_size_left(g) # axis_size_bottom(g) axis_list <- (function(){ axis_label_size_wrapper <- function(fn, filter_val, select_val, unitTo, valueOnly) { function(pg) { pg_axis <- gtable::gtable_filter(pg, filter_val) items <- pg_axis[[select_val]] if (!inherits(items, "unit.list")) { ret <- fn(items, unitTo = unitTo, valueOnly = valueOnly) } else { ret <- vapply(items, fn, numeric(1), unitTo = unitTo, valueOnly = valueOnly) } max(ret) } } axis_size_left <- axis_label_size_wrapper( grid::convertWidth, "axis-l", "widths", unitTo = "cm", valueOnly = TRUE ) axis_size_bottom <- axis_label_size_wrapper( grid::convertHeight, "axis-b", "heights", unitTo = "cm", valueOnly = TRUE ) list(axis_size_left, axis_size_bottom) })() axis_size_left <- axis_list[[1]] axis_size_bottom <- axis_list[[2]] # add_correct_label <- function(pmg, pm, plot_panel <- function( pg, row_pos, col_pos, matrix_show_strips, matrix_ncol, plot_show_axis_labels ) { # ask about strips layout_names <- c("panel") strip_right_name <- "strip-r|strip-l" strip_top_name <- "strip-t|strip-b" legend_name <- "guide-box" all_layout_names <- c(layout_names, strip_right_name, strip_top_name, legend_name) if (is.null(matrix_show_strips)) { # make sure it's on the outer right and top edge if (col_pos == (matrix_ncol)) { layout_names <- c(layout_names, strip_right_name) } if (row_pos == 1) { layout_names <- c(layout_names, strip_top_name) } } else if (matrix_show_strips) { layout_names <- c(layout_names, strip_right_name, strip_top_name) } # if they have a custom plot, make sure it shows up if (! is.null(plot_show_axis_labels)) { # pShowStrips <- ! identical(p$axisLabels, FALSE) # copied from old code. want to replace it to something like above if (plot_show_axis_labels %in% c("internal", "none")) { layout_names <- all_layout_names } } # get correct panel (and strips) layout_rows <- str_detect(pg$layout$name, paste(layout_names, collapse = "|")) layout_info <- pg$layout[layout_rows, ] top_bottom <- layout_info[, c("t", "b")] left_right <- layout_info[, c("l", "r")] plot_panel <- pg[ min(top_bottom):max(top_bottom), min(left_right):max(left_right) ] plot_panel } add_left_axis <- function(pmg, pg, show_strips, grob_pos) { layout <- pg$layout layout_name <- layout$name # axis layout info al <- layout[str_detect(layout_name, "axis-l"), ] if (show_strips) { alx <- layout[str_detect(layout_name, "axis-l|strip-t|strip-b"), ] } else { alx <- al } # get only the axis left objects (and maybe strip top spacer) axis_panel <- pg[min(alx$b):max(alx$t), min(al$l)] # force to align left axis_panel <- gtable::gtable_add_cols(axis_panel, grid::unit(1, "null"), 0) pmg$grobs[[grob_pos]] <- axis_panel pmg } add_bottom_axis <- function(pmg, pg, show_strips, grob_pos) { layout <- pg$layout layout_name <- layout$name # axis layout info al <- layout[str_detect(layout_name, "axis-b"), ] if (show_strips) { alx <- layout[str_detect(layout_name, "axis-b|strip-r|strip-l"), ] } else { alx <- al } # get only the axis left objects (and maybe strip top spacer) axis_panel <- pg[min(al$t), min(alx$l):max(alx$r)] # force to align top axis_panel <- gtable::gtable_add_rows(axis_panel, grid::unit(1, "null"), 1) pmg$grobs[[grob_pos]] <- axis_panel pmg } set_max_axis_size <- function(pmg, axis_sizes, layout_name, layout_cols, pmg_key) { m_axis_size <- max(axis_sizes, na.rm = TRUE) grob_pos_vals <- which(str_detect(pmg$layout$name, layout_name)) val_pos <- pmg$layout[grob_pos_vals, layout_cols] val_pos <- unique(unlist(val_pos)) # if (length(val_pos) > 1) { # stop(stop_msg) # } pmg[[pmg_key]][[val_pos]] <- unit(m_axis_size, "cm") pmg } GGally/R/ggnet.R0000644000176200001440000005705613276725426013114 0ustar liggesusersif (getRversion() >= "2.15.1") { utils::globalVariables(c("X1", "X2", "Y1", "Y2", "midX", "midY")) } #' ggnet - Plot a network with ggplot2 #' #' Function for plotting network objects using ggplot2, now replaced by the #' \code{\link{ggnet2}} function, which provides additional control over #' plotting parameters. Please visit \url{http://github.com/briatte/ggnet} for #' the latest version of ggnet2, and \url{https://briatte.github.io/ggnet} for a #' vignette that contains many examples and explanations. #' #' @export #' @param net an object of class \code{\link[network]{network}}, or any object #' that can be coerced to this class, such as an adjacency or incidence matrix, #' or an edge list: see \link[network]{edgeset.constructors} and #' \link[network]{network} for details. If the object is of class #' \code{\link[igraph:igraph-package]{igraph}} and the #' \code{\link[intergraph:intergraph-package]{intergraph}} package is installed, #' it will be used to convert the object: see #' \code{\link[intergraph]{asNetwork}} for details. #' @param mode a placement method from those provided in the #' \code{\link[sna]{sna}} package: see \link[sna:gplot.layout]{gplot.layout} for #' details. Also accepts the names of two numeric vertex attributes of #' \code{net}, or a matrix of numeric coordinates, in which case the first two #' columns of the matrix are used. #' Defaults to the Fruchterman-Reingold force-directed algorithm. #' @param layout.par options to be passed to the placement method, as listed in #' \link[sna]{gplot.layout}. #' Defaults to \code{NULL}. #' @param layout.exp a multiplier to expand the horizontal axis if node labels #' get clipped: see \link[scales]{expand_range} for details. #' Defaults to \code{0} (no expansion). #' @param size size of the network nodes. If the nodes are weighted, their area is proportionally scaled up to the size set by \code{size}. #' Defaults to \code{9}. #' @param alpha a level of transparency for nodes, vertices and arrows. #' Defaults to \code{1}. #' @param weight the weighting method for the nodes, which might be a vertex #' attribute or a vector of size values. Also accepts \code{"indegree"}, #' \code{"outdegree"}, \code{"degree"} or \code{"freeman"} to size the nodes by #' their unweighted degree centrality (\code{"degree"} and \code{"freeman"} are #' equivalent): see \code{\link[sna]{degree}} for details. All node weights must #' be positive. #' Defaults to \code{"none"} (no weighting). #' @param weight.method see \code{weight} #' @param weight.legend the name to assign to the legend created by #' \code{weight}. #' Defaults to \code{NA} (no name). #' @param weight.min whether to subset the network to nodes with a minimum size, #' based on the values of \code{weight}. #' Defaults to \code{NA} (preserves all nodes). #' @param weight.max whether to subset the network to nodes with a maximum size, #' based on the values of \code{weight}. #' Defaults to \code{NA} (preserves all nodes). #' @param weight.cut whether to cut the size of the nodes into a certain number #' of quantiles. Accepts \code{TRUE}, which tries to cut the sizes into #' quartiles, or any positive numeric value, which tries to cut the sizes into #' that many quantiles. If the size of the nodes do not contain the specified #' number of distinct quantiles, the largest possible number is used. #' See \code{\link[stats]{quantile}} and \code{\link[base]{cut}} for details. #' Defaults to \code{FALSE} (does nothing). #' @param group the groups of the nodes, either as a vector of values or as a #' vertex attribute. If set to \code{mode} on a bipartite network, the nodes #' will be grouped as \code{"actor"} if they belong to the primary mode and #' \code{"event"} if they belong to the secondary mode. #' @param group.legend the name to assign to the legend created by #' \code{group}. #' @param node.group see \code{group} #' @param node.color a vector of character strings to color the nodes with, #' holding as many colors as there are levels in \code{node.group}. #' Defaults to \code{NULL}, which will assign grayscale colors to each group. #' @param node.alpha transparency of the nodes. Inherits from \code{alpha}. #' @param segment.alpha the level of transparency of the edges. #' Defaults to \code{alpha}, which defaults to \code{1}. #' @param segment.color the color of the edges, as a color value, a vector of #' color values, or as an edge attribute containing color values. #' Defaults to \code{"grey50"}. #' @param segment.size the size of the edges, in points, as a single numeric #' value, a vector of values, or as an edge attribute. #' Defaults to \code{0.25}. #' @param segment.label the labels to plot at the middle of the edges, as a #' single value, a vector of values, or as an edge attribute. #' Defaults to \code{NULL} (no edge labels). #' @param arrow.size the size of the arrows for directed network edges, in #' points. See \code{\link[grid]{arrow}} for details. #' Defaults to \code{0} (no arrows). #' @param arrow.gap a setting aimed at improving the display of edge arrows by #' plotting slightly shorter edges. Accepts any value between \code{0} and #' \code{1}, where a value of \code{0.05} will generally achieve good results #' when the size of the nodes is reasonably small. #' Defaults to \code{0} (no shortening). #' @param arrow.type the type of the arrows for directed network edges. See #' \code{\link[grid]{arrow}} for details. #' Defaults to \code{"closed"}. #' @param label whether to label the nodes. If set to \code{TRUE}, nodes are #' labeled with their vertex names. If set to a vector that contains as many #' elements as there are nodes in \code{net}, nodes are labeled with these. If #' set to any other vector of values, the nodes are labeled only when their #' vertex name matches one of these values. #' Defaults to \code{FALSE} (no labels). #' @param label.nodes see \code{label} #' @param label.size the size of the node labels, in points, as a numeric value, #' a vector of numeric values, or as a vertex attribute containing numeric #' values. #' Defaults to \code{size / 2} (half the maximum node size), which defaults to #' \code{6}. #' @param label.trim whether to apply some trimming to the node labels. Accepts #' any function that can process a character vector, or a strictly positive #' numeric value, in which case the labels are trimmed to a fixed-length #' substring of that length: see \code{\link[base]{substr}} for details. #' Defaults to \code{FALSE} (does nothing). #' @param legend.size the size of the legend symbols and text, in points. #' Defaults to \code{9}. #' @param legend.position the location of the plot legend(s). Accepts all #' \code{legend.position} values supported by \code{\link[ggplot2]{theme}}. #' Defaults to \code{"right"}. #' @param names deprecated: see \code{group.legend} and \code{size.legend} #' @param quantize.weights deprecated: see \code{weight.cut} #' @param subset.threshold deprecated: see \code{weight.min} #' @param top8.nodes deprecated: this functionality was experimental and has #' been removed entirely from \code{ggnet} #' @param trim.labels deprecated: see \code{label.trim} #' @param ... other arguments passed to the \code{geom_text} object that sets #' the node labels: see \code{\link[ggplot2]{geom_text}} for details. #' @seealso \code{\link{ggnet2}} in this package, #' \code{\link[sna]{gplot}} in the \code{\link[sna]{sna}} package, and #' \code{\link[network]{plot.network}} in the \code{\link[network]{network}} #' package #' @author Moritz Marbach and Francois Briatte, with help from Heike Hoffmann, #' Pedro Jordano and Ming-Yu Liu #' @details The degree centrality measures that can be produced through the #' \code{weight} argument will take the directedness of the network into account, #' but will be unweighted. To compute weighted network measures, see the #' \code{tnet} package by Tore Opsahl (\code{help("tnet", package = "tnet")}). #' @importFrom stats quantile na.omit #' @importFrom utils head installed.packages #' @importFrom grDevices gray.colors #' @examples #' library(network) #' #' # random adjacency matrix #' x <- 10 #' ndyads <- x * (x - 1) #' density <- x / ndyads #' m <- matrix(0, nrow = x, ncol = x) #' dimnames(m) <- list(letters[ 1:x ], letters[ 1:x ]) #' m[ row(m) != col(m) ] <- runif(ndyads) < density #' m #' #' # random undirected network #' n <- network::network(m, directed = FALSE) #' n #' #' ggnet(n, label = TRUE, alpha = 1, color = "white", segment.color = "black") #' #' # random groups #' g <- sample(letters[ 1:3 ], 10, replace = TRUE) #' #' # color palette #' p <- c("a" = "steelblue", "b" = "forestgreen", "c" = "tomato") #' #' ggnet(n, node.group = g, node.color = p, label = TRUE, color = "white") #' #' # edge arrows on a directed network #' ggnet(network(m, directed = TRUE), arrow.gap = 0.05, arrow.size = 10) ggnet <- function( net, mode = "fruchtermanreingold", layout.par = NULL, layout.exp = 0, size = 9, alpha = 1, weight = "none", weight.legend = NA, weight.method = weight, weight.min = NA, weight.max = NA, weight.cut = FALSE, group = NULL, group.legend = NA, node.group = group, node.color = NULL, node.alpha = alpha, segment.alpha = alpha, segment.color = "grey50", segment.label = NULL, segment.size = 0.25, arrow.size = 0, arrow.gap = 0, arrow.type = "closed", label = FALSE, label.nodes = label, label.size = size / 2, label.trim = FALSE, legend.size = 9, legend.position = "right", # -- deprecated arguments ---------------------------------------------------- names = c("", ""), quantize.weights = FALSE, subset.threshold = 0, top8.nodes = FALSE, trim.labels = FALSE, ... ){ # -- packages ---------------------------------------------------------------- require_namespaces(c("network", "sna", "scales")) # -- deprecations ------------------------------------------------------------ if (length(mode) == 1 && mode == "geo") { warning("mode = 'geo' is deprecated; please use mode = c('lon', 'lat') instead") mode = c("lon", "lat") } if (!identical(names, c("", ""))) { warning("names is deprecated; please use group.legend and size.legend instead") group.legend = names[1] size.legend = names[2] } if (isTRUE(quantize.weights)) { warning("quantize.weights is deprecated; please use weight.cut instead") weight.cut = TRUE } if (subset.threshold > 0) { warning("subset.threshold is deprecated; please use weight.min instead") weight.min = subset.threshold } if (isTRUE(top8.nodes)) { warning("top8.nodes is deprecated") } if (isTRUE(trim.labels)) { warning("trim.labels is deprecated; please use label.trim instead") label.trim = function(x) gsub("^@|^http://(www\\.)?|/$", "", x) } # -- conversion to network class --------------------------------------------- if (class(net) == "igraph" && "intergraph" %in% rownames(installed.packages())) { net = intergraph::asNetwork(net) } else if (class("net") == "igraph") { stop("install the 'intergraph' package to use igraph objects with ggnet") } if (!network::is.network(net)) { net = try(network::network(net), silent = TRUE) } if (!network::is.network(net)) { stop("could not coerce net to a network object") } # -- network functions ------------------------------------------------------- get_v = get("%v%", envir = as.environment("package:network")) get_e = get("%e%", envir = as.environment("package:network")) set_mode = function(x, mode = network::get.network.attribute(x, "bipartite")) { c(rep("actor", mode), rep("event", n_nodes - mode)) } set_node = function(x, value, mode = TRUE) { if (is.null(x) || is.na(x) || is.infinite(x) || is.nan(x)) { stop(paste("incorrect", value, "value")) } else if (is.numeric(x) && any(x < 0)) { stop(paste("incorrect", value, "value")) } else if (length(x) == n_nodes) { x } else if (length(x) > 1) { stop(paste("incorrect", value, "length")) } else if (x %in% v_attr) { get_v(net, x) } else if (mode && x == "mode" & is_bip) { set_mode(net) } else { x } } set_edge = function(x, value) { if (is.null(x) || is.na(x) || is.infinite(x) || is.nan(x)) { stop(paste("incorrect", value, "value")) } else if (is.numeric(x) && any(x < 0)) { stop(paste("incorrect", value, "value")) } else if (length(x) == n_edges) { x } else if (length(x) > 1) { stop(paste("incorrect", value, "length")) } else if (x %in% e_attr) { get_e(net, x) } else { x } } set_attr = function(x) { if (length(x) == n_nodes) { x } else if (length(x) > 1) { stop(paste("incorrect coordinates length")) } else if (!x %in% v_attr) { stop(paste("vertex attribute", x, "was not found")) } else if (!is.numeric(get_v(net, x))) { stop(paste("vertex attribute", x, "is not numeric")) } else { get_v(net, x) } } set_name = function(x, y) ifelse(length(x) == 1, x, ifelse(is.na(y), "", y)) is_one = function(x) length(unique(x)) == 1 is_col = function(x) all(is.numeric(x)) | all(network::is.color(x)) # -- network structure ------------------------------------------------------- n_nodes = network::network.size(net) n_edges = network::network.edgecount(net) v_attr = network::list.vertex.attributes(net) e_attr = network::list.edge.attributes(net) is_bip = network::is.bipartite(net) is_dir = ifelse(network::is.directed(net), "digraph", "graph") if (!is.numeric(arrow.size) || arrow.size < 0) { stop("incorrect arrow.size value") } else if (arrow.size > 0 & is_dir == "graph") { warning("network is undirected; arrow.size ignored") arrow.size = 0 } if (!is.numeric(arrow.gap) || arrow.gap < 0 || arrow.gap > 1) { stop("incorrect arrow.gap value") } else if (arrow.gap > 0 & is_dir == "graph") { warning("network is undirected; arrow.gap ignored") arrow.gap = 0 } if (network::is.hyper(net)) { stop("ggnet cannot plot hyper graphs") } if (network::is.multiplex(net)) { stop("ggnet cannot plot multiplex graphs") } if (network::has.loops(net)) { warning("ggnet does not know how to handle self-loops") } # -- check size -------------------------------------------------------------- x = size if (!is.numeric(x) || is.infinite(x) || is.nan(x) || x < 0 || length(x) > 1) { stop("incorrect size value") } # -- initialize dataset ------------------------------------------------------ data = data.frame(label = get_v(net, "vertex.names"), stringsAsFactors = FALSE) # -- weight methods ---------------------------------------------------------- x = weight.method if (length(x) == 1 && x %in% c("indegree", "outdegree", "degree", "freeman")) { # prevent namespace conflict with igraph if ("package:igraph" %in% search()) { y = ifelse(is_dir == "digraph", "directed", "undirected") z = c("indegree" = "in", "outdegree" = "out", "degree" = "all", "freeman" = "all")[ x ] data$weight = igraph::degree(igraph::graph.adjacency(as.matrix(net), mode = y), mode = z) } else { data$weight = sna::degree(net, gmode = is_dir, cmode = ifelse(x == "degree", "freeman", x)) } } else if (length(x) > 1 && length(x) == n_nodes) { data$weight = x } else if (length(x) == 1 && x %in% v_attr) { data$weight = get_v(net, x) } if (!is.null(data$weight) && !is.numeric(data$weight)) { stop("incorrect weight.method value") } # -- weight thresholds ------------------------------------------------------- x = ifelse(is.na(weight.min), 0, weight.min) if (length(x) > 1 || !is.numeric(x) || is.infinite(x) || is.nan(x) || x < 0) { stop("incorrect weight.min value") } else if (x > 0) { x = which(data$weight < x) message(paste("weight.min removed", length(x), "nodes out of", nrow(data))) if (length(x) > 0) { data = data[ -x, ] network::delete.vertices(net, x) if (!nrow(data)) { warning("weight.min removed all nodes; nothing left to plot") return(invisible(NULL)) } } } x = ifelse(is.na(weight.max), 0, weight.max) if (length(x) > 1 || !is.numeric(x) || is.infinite(x) || is.nan(x) || x < 0) { stop("incorrect weight.max value") } else if (x > 0) { x = which(data$weight > x) message(paste("weight.max removed", length(x), "nodes out of", nrow(data))) if (length(x) > 0) { data = data[ -x, ] network::delete.vertices(net, x) if (!nrow(data)) { warning("weight.max removed all nodes; nothing left to plot") return(invisible(NULL)) } } } # -- weight quantiles -------------------------------------------------------- x = weight.cut if (length(x) > 1 || is.null(x) || is.na(x) || is.infinite(x) || is.nan(x)) { stop("incorrect weight.cut value") } else if (isTRUE(x)) { x = 4 } else if (is.logical(x) && !x) { x = 0 } else if (!is.numeric(x)) { stop("incorrect weight.cut value") } if (x >= 1) { x = unique(quantile(data$weight, probs = seq(0, 1, by = 1 / as.integer(x)))) if (length(x) > 1) { data$weight = cut(data$weight, unique(x), include.lowest = TRUE) } else { warning("node weight is invariant; weight.cut ignored") } } # -- node sizing ------------------------------------------------------------- if (is.factor(data$weight)) { sizer = scale_size_area( set_name(weight.method, weight.legend), max_size = size, breaks = sort(unique(as.integer(data$weight))), labels = levels(data$weight)[ sort(unique(as.integer(data$weight))) ] ) data$weight = as.integer(data$weight) } else { sizer = scale_size_area( set_name(weight.method, weight.legend), max_size = size ) } # -- node grouping ----------------------------------------------------------- if (!is.null(node.group)) { data$group = factor(set_node(node.group, "node.group")) x = length(unique(na.omit(data$group))) if (length(node.color) != x) { if (!is.null(node.color)) { warning("node groups and colors are of unequal length; using grayscale colors") } node.color = gray.colors(x) names(node.color) = unique(na.omit(data$group)) } } # -- node labels ------------------------------------------------------------- l = label.nodes if (isTRUE(l)) { l = data$label } else if (length(l) > 1 & length(l) == n_nodes) { data$label = l } else if (length(l) == 1 && l %in% v_attr) { l = get_v(net, l) } else { l = ifelse(data$label %in% l, data$label, "") } # -- node placement ---------------------------------------------------------- if (is.character(mode) && length(mode) == 1) { mode = paste0("gplot.layout.", mode) snaNamespace = asNamespace("sna") if (!exists(mode, envir = snaNamespace)) { stop(paste("unsupported placement method:", mode)) } mode = get(mode, envir = snaNamespace) # sna placement algorithm xy = network::as.matrix.network.adjacency(net) xy = do.call(mode, list(xy, layout.par)) xy = data.frame(x = xy[, 1], y = xy[, 2]) } else if (is.character(mode) && length(mode) == 2) { # fixed coordinates from vertex attributes xy = data.frame(x = set_attr(mode[1]), y = set_attr(mode[2])) } else if (is.numeric(mode) && is.matrix(mode)) { # fixed coordinates from matrix xy = data.frame(x = set_attr(mode[, 1]), y = set_attr(mode[, 2])) } else { stop("incorrect mode value") } xy$x = scale(xy$x, min(xy$x), diff(range(xy$x)))[,1] xy$y = scale(xy$y, min(xy$y), diff(range(xy$y)))[,1] data = cbind(data, xy) # -- edge list --------------------------------------------------------------- edges = network::as.matrix.network.edgelist(net) edges = data.frame(xy[ edges[, 1], ], xy[ edges[, 2], ]) names(edges) = c("X1", "Y1", "X2", "Y2") # -- edge labels ------------------------------------------------------------- if (!is.null(segment.label)) { edges$midX = (edges$X1 + edges$X2) / 2 edges$midY = (edges$Y1 + edges$Y2) / 2 edges$label = set_edge(segment.label, "segment.label") } # -- plot edges -------------------------------------------------------------- p = ggplot(data, aes(x = x, y = y)) if (nrow(edges) > 0) { if (arrow.gap > 0) { x.length = with(edges, abs(X2 - X1)) y.length = with(edges, abs(Y2 - Y1)) arrow.gap = with(edges, arrow.gap / sqrt(x.length ^ 2 + y.length ^ 2)) edges = transform(edges, X1 = X1 + arrow.gap * x.length, Y1 = Y1 + arrow.gap * y.length, X2 = X1 + (1 - arrow.gap) * x.length, Y2 = Y1 + (1 - arrow.gap) * y.length) } p = p + geom_segment( data = edges, aes(x = X1, y = Y1, xend = X2, yend = Y2), alpha = segment.alpha, size = segment.size, color = segment.color, arrow = arrow( type = arrow.type, length = unit(arrow.size, "pt") ) ) } if (nrow(edges) > 0 && !is.null(segment.label)) { p = p + geom_point( data = edges, aes(x = midX, y = midY), color = "white", size = size ) + geom_text( data = edges, aes(x = midX, y = midY, label = label), alpha = segment.alpha, color = segment.color, size = size / 2 ) } # -- plot nodes -------------------------------------------------------------- if (length(weight.method) == 1 && weight.method == "none") { p = p + geom_point( alpha = node.alpha, size = size ) } else { p = p + geom_point( aes(size = weight), alpha = node.alpha ) + sizer } # -- plot node colors -------------------------------------------------------- if (!is.null(node.group)) { p = p + aes(color = group) + scale_color_manual( set_name(node.group, group.legend), values = node.color, guide = guide_legend(override.aes = list(size = legend.size)) ) } # -- plot node labels -------------------------------------------------------- if (!is_one(l) || unique(l) != "") { label.size = set_node(label.size, "label.size", mode = FALSE) if (!is.numeric(label.size)) { stop("incorrect label.size value") } x = label.trim if (length(x) > 1 || (!is.logical(x) & !is.numeric(x) & !is.function(x))) { stop("incorrect label.trim value") } else if (is.numeric(x) && x > 0) { l = substr(l, 1, x) } else if (is.function(x)) { l = x(l) } p = p + geom_text( label = l, size = label.size, show.legend = FALSE, # required by ggplot2 >= 1.0.1.9003 ... ) } # -- horizontal scale expansion ---------------------------------------------- x = range(data$x) if (!is.numeric(layout.exp) || layout.exp < 0) { stop("incorrect layout.exp value") } else if (layout.exp > 0) { x = scales::expand_range(x, layout.exp / 2) } # -- finalize ---------------------------------------------------------------- p = p + scale_x_continuous(breaks = NULL, limits = x) + scale_y_continuous(breaks = NULL) + theme( panel.background = element_blank(), panel.grid = element_blank(), axis.title = element_blank(), legend.key = element_blank(), legend.position = legend.position, legend.text = element_text(size = legend.size), legend.title = element_text(size = legend.size) ) return(p) } GGally/R/ggmatrix_gtable.R0000644000176200001440000002110713277311163015122 0ustar liggesusers #' Print ggmatrix object #' #' Specialized method to print the ggmatrix object- #' #' @param pm ggmatrix object to be plotted #' @param ... ignored #' @param progress,progress_format Please use the 'progress' parameter in your ggmatrix-like function. See \code{\link{ggmatrix_progress}} for a few examples. These parameters will soon be deprecated. #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @importFrom grid gpar grid.layout grid.newpage grid.text grid.rect popViewport pushViewport viewport grid.draw #' @export #' @examples #' data(tips, package = "reshape") #' pm <- ggpairs(tips, c(1,3,2), mapping = ggplot2::aes_string(color = "sex")) #' ggmatrix_gtable(pm) ggmatrix_gtable <- function( pm, ..., progress = NULL, progress_format = formals(ggmatrix_progress)$format ) { # pm is for "plot matrix" # init progress bar handle if (missing(progress) && missing(progress_format)) { # only look at plot matrix for progress bar hasProgressBar <- !isFALSE(pm$progress) progress_fn <- pm$progress } else { warning("Please use the 'progress' parameter in your ggmatrix-like function call. See ?ggmatrix_progress for a few examples. ggmatrix_gtable 'progress' and 'progress_format' will soon be deprecated.", immediate = TRUE) # has progress variable defined # overrides pm$progress if (missing(progress_format)) { progress_fn <- as_ggmatrix_progress(progress) } else { progress_fn <- as_ggmatrix_progress( progress, pm$ncol * pm$nrow, format = progress_format ) } hasProgressBar <- !isFALSE(progress_fn) ggmatrix_progress } if (hasProgressBar) { pb <- progress_fn(pm) # pb$tick(tokens = list(plot_i = 1, plot_j = 1)) } # make a fake facet grid to fill in with proper plot panels get_labels <- function(labels, length_out, name) { if (is.expression(labels)) { stop("'", name, "' can only be a character vector or NULL.", " Character values can be parsed using the 'labeller' parameter.") } ifnull(labels, as.character(seq_len(length_out))) } fake_data <- expand.grid( Var1 = get_labels(pm$xAxisLabels, pm$ncol, "xAxisLabels"), Var2 = get_labels(pm$yAxisLabels, pm$nrow, "yAxisLabels") ) fake_data$x <- 1 fake_data$y <- 1 # make the smallest plot possible so the guts may be replaced pm_fake <- ggplot(fake_data, mapping = aes_("x", "y")) + geom_point() + # make the 'fake' strips for x and y titles facet_grid(Var2 ~ Var1, labeller = ifnull(pm$labeller, "label_value"), switch = pm$switch) + # remove both x and y titles labs(x = pm$xlab, y = pm$ylab) # add all custom ggplot2 things pm_fake <- add_gg_info(pm_fake, pm$gg) # add the title or remove the location completely if (is.null(pm$title)) { pm_fake <- pm_fake + theme(plot.title = element_blank()) } else { pm_fake <- pm_fake + labs(title = pm$title) } # if there are no labels, then there should be no strips if (is.null(pm$xAxisLabels)) { pm_fake <- pm_fake + theme(strip.text.x = element_blank()) } if (is.null(pm$yAxisLabels)) { pm_fake <- pm_fake + theme(strip.text.y = element_blank()) } # if there is a legend, make a fake legend that will be replaced later if (!is.null(pm$legend)) { pm_fake <- pm_fake + geom_point(mapping = aes_(color = "Var1")) } # make a gtable of the plot matrix (to be filled in) pmg <- plot_gtable(pm_fake) ############### ## Everything beyond this point is only to fill in the correct information. ## No grobs should be appended or removed. It should be done with themes or geoms above ############### # help with grob positions pmg$layout$grob_pos <- seq_along(pmg$grobs) pmg_layout <- pmg$layout pmg_layout_name <- pmg_layout$name pmg_layout_grob_pos <- pmg_layout$grob_pos # zero out rest of the plotting area (just in case it is not replaced) zero_pos_vals <- pmg_layout_grob_pos[ str_detect( pmg_layout_name, paste(c("panel", "axis-l", "axis-b", "guide-box"), collapse = "|") ) ] for (zero_pos in zero_pos_vals) { pmg$grobs[[zero_pos]] <- ggplot2::zeroGrob() } pmg # insert legend if (!is.null(pm$legend)) { legend <- pm$legend if (is.numeric(legend)) { if (length(legend) == 1) { legend <- get_pos_rev(pm, legend) } else if (length(legend) > 2) { stop("'legend' must be a single or double numberic value. Or 'legend' must be an object produced from 'grab_legend()'") # nolint } legend_obj <- grab_legend(pm[legend[1], legend[2]]) } else if (inherits(legend, "legend_guide_box")) { legend_obj <- legend } legend_layout <- (pmg_layout[pmg_layout_name == "guide-box", ])[1, ] class(legend_obj) <- setdiff(class(legend_obj), "legend_guide_box") pmg$grobs[[legend_layout$grob_pos]] <- legend_obj legend_position <- ifnull(pm_fake$theme$legend.position, "right") if (legend_position %in% c("right", "left")) { pmg$widths[[legend_layout$l]] <- legend_obj$widths[1] } else if (legend_position %in% c("top", "bottom")) { pmg$heights[[legend_layout$t]] <- legend_obj$heights[1] } else { stop(paste("ggmatrix does not know how display a legend when legend.position with value: '", legend_position, "'. Valid values: c('right', 'left', 'bottom', 'top')", sep = "")) # nolint } } # Get all 'panel' grob_pos in the pmg panel_layout <- pmg_layout[str_detect(pmg_layout_name, "panel"), ] panel_locations_order <- order(panel_layout$t, panel_layout$l, decreasing = FALSE) panel_locations <- panel_layout[panel_locations_order, "grob_pos"] # init the axis sizes left_axis_sizes <- numeric(pm$nrow + 1) bottom_axis_sizes <- numeric(pm$ncol + 1) axis_l_grob_pos <- pmg_layout_grob_pos[str_detect(pmg_layout_name, "axis-l")] axis_b_grob_pos <- pmg_layout_grob_pos[str_detect(pmg_layout_name, "axis-b")] # change the plot size ratios x_proportions <- pm$xProportions if (!is.null(x_proportions)) { panel_width_pos <- sort(unique(panel_layout$l)) if (!inherits(x_proportions, "unit")) { x_proportions <- grid::unit(x_proportions, "null") } pmg$widths[panel_width_pos] <- x_proportions } y_proportions <- pm$yProportions if (!is.null(y_proportions)) { panel_height_pos <- sort(unique(panel_layout$t)) if (!inherits(y_proportions, "unit")) { y_proportions <- grid::unit(y_proportions, "null") } pmg$heights[panel_height_pos] <- y_proportions } # build and insert all plots and axis labels plot_number <- 0 for (i in seq_len(pm$nrow)) { for (j in seq_len(pm$ncol)) { plot_number <- plot_number + 1 grob_pos_panel <- panel_locations[plot_number] # update the progress bar is possible if (hasProgressBar) { pb$tick(tokens = list(plot_i = i, plot_j = j)) } # retrieve plot p <- pm[i, j] # ignore all blank plots. all blank plots do not draw anything else if (is_blank_plot(p)) { next } # if it's not a ggplot2 obj, insert it and pray it works if (!is.ggplot(p)) { pmg$grobs[[grob_pos_panel]] <- p next } # get the plot's gtable to slice and dice pg <- plot_gtable(p) # if the left axis should be added if (j == 1 && pm$showYAxisPlotLabels) { left_axis_sizes[i] <- axis_size_left(pg) pmg <- add_left_axis( pmg, pg, show_strips = ( (i == 1) && is.null(pm$showStrips) ) || isTRUE(pm$showStrips), grob_pos = axis_l_grob_pos[i] ) } # if the bottom axis should be added if (i == pm$nrow && pm$showXAxisPlotLabels) { bottom_axis_sizes[j] <- axis_size_bottom(pg) pmg <- add_bottom_axis( pmg, pg, show_strips = ( (j == pm$ncol) && is.null(pm$showStrips) ) || isTRUE(pm$showStrips), grob_pos = axis_b_grob_pos[j] ) } # grab plot panel and insert pmg$grobs[[grob_pos_panel]] <- plot_panel( pg = pg, row_pos = i, col_pos = j, matrix_show_strips = pm$showStrips, matrix_ncol = pm$ncol, plot_show_axis_labels = p$showLabels ) } } # make sure the axes have enough room pmg <- set_max_axis_size( pmg, axis_sizes = left_axis_sizes, layout_name = "axis-l", layout_cols = c("l", "r"), pmg_key = "widths" #stop_msg = "left axis width issue!! Fix!" ) pmg <- set_max_axis_size( pmg, axis_sizes = bottom_axis_sizes, layout_name = "axis-b", layout_cols = c("t", "b"), pmg_key = "heights" #stop_msg = "bottom axis height issue!! Fix!" ) pmg } GGally/R/ggpairs_getput.R0000644000176200001440000001007213267373630015013 0ustar liggesusers#' Put Plot #' #' Function to place your own plot in the layout. #' #' @param pm ggally object to be altered #' @param value ggplot object to be placed #' @param i row from the top #' @param j column from the left #' @keywords hplot #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @export #' @examples #' custom_car <- ggpairs(mtcars[, c("mpg", "wt", "cyl")], upper = "blank", title = "Custom Example") #' # ggplot example taken from example(geom_text) #' plot <- ggplot2::ggplot(mtcars, ggplot2::aes(x=wt, y=mpg, label=rownames(mtcars))) #' plot <- plot + #' ggplot2::geom_text(ggplot2::aes(colour=factor(cyl)), size = 3) + #' ggplot2::scale_colour_discrete(l=40) #' custom_car[1, 2] <- plot #' personal_plot <- ggally_text( #' "ggpairs allows you\nto put in your\nown plot.\nLike that one.\n <---" #' ) #' custom_car[1, 3] <- personal_plot #' # custom_car #' #' # remove plots after creating a plot matrix #' custom_car[2,1] <- NULL #' custom_car[3,1] <- "blank" # the same as storing null #' custom_car[3,2] <- NULL #' custom_car putPlot <- function(pm, value, i, j){ pos <- get_pos(pm, i, j) if (is.null(value)) { pm$plots[[pos]] <- make_ggmatrix_plot_obj(wrap("blank", funcArgName = "ggally_blank")) } else if (mode(value) == "character") { if (value == "blank") { pm$plots[[pos]] <- make_ggmatrix_plot_obj(wrap("blank", funcArgName = "ggally_blank")) } else { stop("character values (besides 'blank') are not allowed to be stored as plot values.") } } else { pm$plots[[pos]] <- value } pm } #' getPlot #' #' Retrieves the ggplot object at the desired location. #' #' @param pm ggmatrix object to select from #' @param i row from the top #' @param j column from the left #' @keywords hplot #' @author Barret Schloerke \email{schloerke@@gmail.com} #' @importFrom utils capture.output #' @export #' @examples #' data(tips, package = "reshape") #' plotMatrix2 <- ggpairs(tips[, 3:2], upper = list(combo = "denstrip")) #' plotMatrix2[1, 2] getPlot <- function(pm, i, j){ if (FALSE) { cat("i: ", i, " j: ", j, "\n") } pos <- get_pos(pm, i, j) if (pos > length(pm$plots)) { plotObj <- NULL } else { plotObj <- pm$plots[[pos]] } if (is.null(plotObj)) { p <- ggally_blank() } else { if (ggplot2::is.ggplot(plotObj)) { p <- plotObj } else if (inherits(plotObj, "ggmatrix_plot_obj")) { fn <- plotObj$fn p <- fn(pm$data, plotObj$mapping) } else if (inherits(plotObj, "legend_guide_box")) { p <- plotObj } else { firstNote <- str_c("Position: i = ", i, ", j = ", j, "\nstr(plotObj):\n", sep = "") strObj <- capture.output({ str(plotObj) }) stop(str_c("unknown plot object type.\n", firstNote, strObj)) } p <- add_gg_info(p, pm$gg) } p } get_pos <- function(pm, i, j) { if (isTRUE(pm$byrow)) { pos <- j + (pm$ncol * (i - 1)) } else { pos <- i + (pm$nrow * (j - 1)) } pos } get_pos_rev <- function(pm, pos) { if (isTRUE(pm$byrow)) { i <- ceiling(pos / pm$ncol) j <- (pos - 1) %% pm$ncol + 1 } else { i <- (pos - 1) %% pm$nrow + 1 j <- ceiling(pos / pm$nrow) } c(i, j) } check_i_j <- function(pm, i, j) { if ( (length(i) > 1) || (mode(i) != "numeric")) { stop("'i' may only be a single numeric value") } if ( (length(j) > 1) || (mode(j) != "numeric")) { stop("'j' may only be a single numeric value") } if (i > pm$nrow || i < 1) { stop("'i' may only be in the range from 1:", pm$nrow) } if (j > pm$ncol || j < 1) { stop("'j' may only be in the range from 1:", pm$ncol) } invisible() } #' @rdname getPlot #' @usage \method{[}{ggmatrix}(pm, i, j, ...) #' @param ... ignored #' @export `[.ggmatrix` <- function(pm, i, j, ...) { # print(list(x = i, y = j)) check_i_j(pm, i, j) getPlot(pm, i, j) } #' @rdname putPlot #' @usage \method{[}{ggmatrix}(pm, i, j, ...) <- value #' @param ... ignored #' @export `[<-.ggmatrix` <- function(pm, i, j, ..., value) { # x = matrix # i = first subset # j = second subset # y = value check_i_j(pm, i, j) putPlot(pm, value, i, j) } GGally/R/ggscatmat.R0000644000176200001440000002574113276725426013756 0ustar liggesusersif (getRversion() >= "2.15.1") { utils::globalVariables(c("xvalue", "yvalue")) } #' lowertriangle - rearrange dataset as the preparation of ggscatmat function #' #' function for making the melted dataset used to plot the lowertriangle scatterplots. #' #' @export #' @param data a data matrix. Should contain numerical (continuous) data. #' @param columns an option to choose the column to be used in the raw dataset. Defaults to \code{1:ncol(data)} #' @param color an option to choose a factor variable to be grouped with. Defaults to \code{(NULL)} #' @author Mengjia Ni, Di Cook \email{dicook@@monash.edu} #' @examples #' data(flea) #' head(lowertriangle(flea, columns= 2:4)) #' head(lowertriangle(flea)) #' head(lowertriangle(flea, color="species")) lowertriangle <- function(data, columns=1:ncol(data), color=NULL) { data <- upgrade_scatmat_data(data) data.choose <- data[columns] dn <- data.choose[sapply(data.choose, is.numeric)] factor <- data[sapply(data, is.factor)] p <- ncol(dn) newdata <- NULL for (i in 1:p) { for (j in 1:p) { newdata <- rbind(newdata, cbind(dn[[i]], dn[[j]], i, j, colnames(dn)[i], colnames(dn)[j], factor) ) } } colnames(newdata) <- c("xvalue", "yvalue", "xslot", "yslot", "xlab", "ylab", colnames(factor)) rp <- data.frame(newdata) rp[[2]][rp[[3]] >= rp[[4]]] <- "NA" rp[[1]][rp[[3]] > rp[[4]]] <- "NA" rp$xvalue <- suppressWarnings(as.numeric(as.character(rp$xvalue))) rp$yvalue <- suppressWarnings(as.numeric(as.character(rp$yvalue))) if (is.null(color)){ rp.new <- rp[1:6] } else { colorcolumn <- rp[[which(colnames(rp) == color)]] rp.new <- cbind(rp[1:6], colorcolumn) } return(rp.new) } #' uppertriangle - rearrange dataset as the preparation of ggscatmat function #' #' function for making the dataset used to plot the uppertriangle plots. #' #' @export #' @param data a data matrix. Should contain numerical (continuous) data. #' @param columns an option to choose the column to be used in the raw dataset. Defaults to \code{1:ncol(data)} #' @param color an option to choose a factor variable to be grouped with. Defaults to \code{(NULL)} #' @param corMethod method argument supplied to \code{\link[stats]{cor}} #' @author Mengjia Ni, Di Cook \email{dicook@@monash.edu} #' @importFrom stats cor #' @examples #' data(flea) #' head(uppertriangle(flea, columns=2:4)) #' head(uppertriangle(flea)) #' head(uppertriangle(flea, color="species")) uppertriangle <- function(data, columns=1:ncol(data), color=NULL, corMethod = "pearson") { data <- upgrade_scatmat_data(data) data.choose <- data[columns] dn <- data.choose[sapply(data.choose, is.numeric)] factor <- data[sapply(data, is.factor)] p <- ncol(dn) newdata <- NULL for (i in 1:p) { for (j in 1:p) { newdata <- rbind(newdata, cbind(dn[, i], dn[, j], i, j, colnames(dn)[i], colnames(dn)[j], min(dn[, i]) + 0.5 * (max(dn[, i]) - min(dn[, i])), min(dn[, j]) + 0.5 * (max(dn[, j]) - min(dn[, j])), factor) ) } } colnames(newdata) <- c( "xvalue", "yvalue", "xslot", "yslot", "xlab", "ylab", "xcenter", "ycenter", colnames(factor) ) rp <- data.frame(newdata) rp[[2]][rp[[3]] <= rp[[4]]] <- "NA" rp[[1]][rp[[3]] < rp[[4]]] <- "NA" rp$xvalue <- suppressWarnings(as.numeric(as.character(rp$xvalue))) rp$yvalue <- suppressWarnings(as.numeric(as.character(rp$yvalue))) if (is.null(color)){ rp.new <- rp[1:8] }else{ colorcolumn <- rp[[which(colnames(rp) == color)]] rp.new <- cbind(rp[1:8], colorcolumn) } a <- rp.new b <- subset(a, (a$yvalue != "NA") & (a$xvalue != "NA")) if (is.null(color)){ data.cor <- ddply( b, .(ylab, xlab), function(subsetDt) { xlab <- subsetDt$xlab ylab <- subsetDt$ylab xvalue <- subsetDt$xvalue yvalue <- subsetDt$yvalue if (identical(corMethod, "rsquare")) { r <- cor( xvalue, yvalue, use = "pairwise.complete.obs", method = "pearson" ) r <- r ^ 2 } else { r <- cor( xvalue, yvalue, use = "pairwise.complete.obs", method = corMethod ) } r <- paste(round(r, digits = 2)) data.frame( xlab = unique(xlab), ylab = unique(ylab), r = r, xvalue = min(xvalue) + 0.5 * (max(xvalue) - min(xvalue)), yvalue = min(yvalue) + 0.5 * (max(yvalue) - min(yvalue)) ) } ) return(data.cor) }else{ c <- b data.cor1 <- ddply( c, .(ylab, xlab, colorcolumn), function(subsetDt) { xlab <- subsetDt$xlab ylab <- subsetDt$ylab colorcolumn <- subsetDt$colorcolumn xvalue <- subsetDt$xvalue yvalue <- subsetDt$yvalue if (identical(corMethod, "rsquare")) { r <- cor( xvalue, yvalue, use = "pairwise.complete.obs", method = "pearson" ) r <- r ^ 2 } else { r <- cor( xvalue, yvalue, use = "pairwise.complete.obs", method = corMethod ) } r <- paste(round(r, digits = 2)) data.frame( ylab = unique(ylab), xlab = unique(xlab), colorcolumn = unique(colorcolumn), r = r ) } ) n <- nrow(data.frame(unique(b$colorcolumn))) position <- ddply(b, .(ylab, xlab), summarise, xvalue = min(xvalue) + 0.5 * (max(xvalue) - min(xvalue)), ymin = min(yvalue), ymax = max(yvalue), range = max(yvalue) - min(yvalue)) df <- data.frame() for (i in 1:nrow(position)) { for (j in 1:n){ row <- position[i, ] df <- rbind(df, cbind(row[, 3], (row[, 4] + row[, 6] * j / (n + 1)))) } } data.cor <- cbind(data.cor1, df) colnames(data.cor) <- c("ylab", "xlab", "colorcolumn", "r", "xvalue", "yvalue") return(data.cor) } } #' scatmat - plot the lowertriangle plots and density plots of the scatter plot matrix. #' #' function for making scatterplots in the lower triangle and diagonal density plots. #' #' @export #' @param data a data matrix. Should contain numerical (continuous) data. #' @param columns an option to choose the column to be used in the raw dataset. Defaults to \code{1:ncol(data)} #' @param color an option to group the dataset by the factor variable and color them by different colors. Defaults to \code{NULL} #' @param alpha an option to set the transparency in scatterplots for large data. Defaults to \code{1}. #' @author Mengjia Ni, Di Cook \email{dicook@@monash.edu} #' @examples #' data(flea) #' scatmat(flea, columns=2:4) #' scatmat(flea, columns= 2:4, color="species") scatmat <- function(data, columns=1:ncol(data), color=NULL, alpha=1) { data <- upgrade_scatmat_data(data) data.choose <- data[columns] dn <- data.choose[sapply(data.choose, is.numeric)] if (ncol(dn) == 0) { stop("All of your variables are factors. Need numeric variables to make scatterplot matrix.") } else { ltdata.new <- lowertriangle(data, columns = columns, color = color) r <- ggplot(ltdata.new, mapping = aes_string(x = "xvalue", y = "yvalue")) + theme(axis.title.x = element_blank(), axis.title.y = element_blank()) + facet_grid(ylab ~ xlab, scales = "free") + theme(aspect.ratio = 1) if (is.null(color)) { densities <- do.call("rbind", lapply(1:ncol(dn), function(i) { data.frame(xlab = names(dn)[i], ylab = names(dn)[i], x = dn[, i]) })) for (m in 1:ncol(dn)) { j <- subset(densities, xlab == names(dn)[m]) r <- r + stat_density( aes( x = x, y = ..scaled.. * diff(range(x)) + min(x) # nolint ), data = j, position = "identity", geom = "line", color = "black") } r <- r + geom_point(alpha = alpha, na.rm = TRUE) return(r) } else { densities <- do.call("rbind", lapply(1:ncol(dn), function(i) { data.frame(xlab = names(dn)[i], ylab = names(dn)[i], x = dn[, i], colorcolumn = data[, which(colnames(data) == color)]) })) for (m in 1:ncol(dn)) { j <- subset(densities, xlab == names(dn)[m]) r <- r + stat_density( aes_string( x = "x", y = "..scaled.. * diff(range(x)) + min(x)", colour = "colorcolumn" ), data = j, position = "identity", geom = "line" ) } r <- r + geom_point( data = ltdata.new, aes_string(colour = "colorcolumn"), alpha = alpha, na.rm = TRUE ) return(r) } } } #' ggscatmat - a traditional scatterplot matrix for purely quantitative variables #' #' This function makes a scatterplot matrix for quantitative variables with density plots on the diagonal #' and correlation printed in the upper triangle. #' #' @export #' @param data a data matrix. Should contain numerical (continuous) data. #' @param columns an option to choose the column to be used in the raw dataset. Defaults to \code{1:ncol(data)}. #' @param color an option to group the dataset by the factor variable and color them by different colors. Defaults to \code{NULL}. #' @param alpha an option to set the transparency in scatterplots for large data. Defaults to \code{1}. #' @param corMethod method argument supplied to \code{\link[stats]{cor}} #' @author Mengjia Ni, Di Cook \email{dicook@@monash.edu} #' @examples #' data(flea) #' ggscatmat(flea, columns = 2:4) #' ggscatmat(flea, columns = 2:4, color = "species") ggscatmat <- function(data, columns = 1:ncol(data), color = NULL, alpha = 1, corMethod = "pearson"){ data <- upgrade_scatmat_data(data) data.choose <- data[columns] dn <- data.choose[sapply(data.choose, is.numeric)] if (ncol(dn) == 0) { stop("All of your variables are factors. Need numeric variables to make scatterplot matrix.") } if (ncol(dn) < 2){ stop ("Not enough numeric variables to make a scatter plot matrix") } a <- uppertriangle(data, columns = columns, color = color, corMethod = corMethod) if (is.null(color)){ plot <- scatmat(data, columns = columns, alpha = alpha) + geom_text(data = a, aes_string(label = "r"), colour = "black") } else { plot <- scatmat(data, columns = columns, color = color, alpha = alpha) + geom_text(data = a, aes_string(label = "r", color = "colorcolumn")) + labs(color = color) } factor <- data.choose[sapply(data.choose, is.factor)] if (ncol(factor) == 0){ return(plot) } else { warning("Factor variables are omitted in plot") return(plot) } } upgrade_scatmat_data <- function(data) { data <- as.data.frame(data) dataIsCharacter <- sapply(data, is.character) if (any(dataIsCharacter)) { dataCharacterColumns <- names(dataIsCharacter[dataIsCharacter]) for (dataCol in dataCharacterColumns) { data[[dataCol]] <- as.factor(data[[dataCol]]) } } data } GGally/R/ggfacet.R0000644000176200001440000001046613277311163013370 0ustar liggesusers#' ggfacet - single ggplot2 plot matrix with facet_grid #' #' #' @param data data.frame that contains all columns to be displayed. This data will be melted before being passed into the function \code{fn} #' @param mapping aesthetic mapping (besides \code{x} and \code{y}). See \code{\link[ggplot2]{aes}()} #' @param fn function to be executed. Similar to \code{\link{ggpairs}} and \code{\link{ggduo}}, the function may either be a string identifier or a real function that \code{\link{wrap}} understands. #' @param ... extra arguments passed directly to \code{fn} #' @param columnsX columns to be displayed in the plot matrix #' @param columnsY rows to be displayed in the plot matrix #' @param columnLabelsX,columnLabelsY column and row labels to display in the plot matrix #' @param xlab,ylab,title plot matrix labels #' @param scales parameter supplied to \code{ggplot2::\link[ggplot2]{facet_grid}}. Default behavior is \code{"free"} #' @export #' @examples #' # Small function to display plots only if it's interactive #' p_ <- GGally::print_if_interactive #' if (requireNamespace("chemometrics", quietly = TRUE)) { #' data(NIR, package = "chemometrics") #' NIR_sub <- data.frame(NIR$yGlcEtOH, NIR$xNIR[,1:3]) #' str(NIR_sub) #' x_cols <- c("X1115.0", "X1120.0", "X1125.0") #' y_cols <- c("Glucose", "Ethanol") #' #' # using ggduo directly #' p <- ggduo(NIR_sub, x_cols, y_cols, types = list(continuous = "points")) #' p_(p) #' #' # using ggfacet #' p <- ggfacet(NIR_sub, x_cols, y_cols) #' p_(p) #' #' # add a smoother #' p <- ggfacet(NIR_sub, x_cols, y_cols, fn = 'smooth_loess') #' p_(p) #' # same output #' p <- ggfacet(NIR_sub, x_cols, y_cols, fn = ggally_smooth_loess) #' p_(p) #' #' # Change scales to be the same in for every row and for every column #' p <- ggfacet(NIR_sub, x_cols, y_cols, scales = "fixed") #' p_(p) #' } ggfacet <- function( data, mapping = NULL, columnsX = 1:ncol(data), columnsY = 1:ncol(data), fn = ggally_points, ..., columnLabelsX = names(data[columnsX]), columnLabelsY = names(data[columnsY]), xlab = NULL, ylab = NULL, title = NULL, scales = "free" ) { data <- fix_data(data) fn <- wrap(fn) # fix args if ( !missing(mapping) & !is.list(mapping) & !missing(columnsX) & missing(columnsY) ) { columnsY <- columnsX columnsX <- mapping mapping <- NULL } stop_if_bad_mapping(mapping) columnsX <- fix_column_values(data, columnsX, columnLabelsX, "columnsX", "columnLabelsX") columnsY <- fix_column_values(data, columnsY, columnLabelsY, "columnsY", "columnLabelsY") # could theoretically work like # mtc <- mtcars # mtc$am <- as.factor(mtc$am) # mtc$cyl <- as.factor(mtc$cyl) # ggfacet( # mtc, # columnsY = c(1,3,4,5), columnsX = c("am", "cyl"), # fn = function(data, mapping){ggplot(data, mapping) + geom_boxplot()} # ) is_factor_x <- sapply(data[columnsX], is.factor) if (sum(is_factor_x) != 0) { warning(paste(sum(is_factor_x), " factor variables are being removed from X columns", sep = "")) columnsX <- columnsX[!is_factor_x] columnLabelsX <- columnLabelsX[!is_factor_x] } is_factor_y <- sapply(data[columnsY], is.factor) if (sum(is_factor_y) != 0) { warning(paste(sum(is_factor_y), " factor variables are being removed from Y columns", sep = "")) columnsY <- columnsY[!is_factor_y] columnLabelsY <- columnLabelsY[!is_factor_y] } tall_data <- ddply( expand.grid(.x_col = columnsX, .y_col = columnsY), c(".x_col", ".y_col"), function(row) { x_var <- row$.x_col[1] y_var <- row$.y_col[1] ret <- data ret[[".x_val"]] <- data[[x_var]] ret[[".y_val"]] <- data[[y_var]] ret } ) if (is.null(mapping)) { mapping <- aes() } mapping[c("x", "y")] <- aes_string(x = ".x_val", y = ".y_val") names(columnLabelsX) <- as.character(columnsX) names(columnLabelsY) <- as.character(columnsY) labeller <- function(vals) { val_names <- names(vals) if (".x_col" %in% val_names) { vals[[".x_col"]] <- columnLabelsX[as.character(vals[[".x_col"]])] } if (".y_col" %in% val_names) { vals[[".y_col"]] <- columnLabelsY[as.character(vals[[".y_col"]])] } vals } p <- fn(tall_data, mapping, ...) + facet_grid(.y_col ~ .x_col, labeller = labeller, scales = scales) + labs(title = title, x = xlab, y = ylab) p } GGally/vignettes/0000755000176200001440000000000013277320370013445 5ustar liggesusersGGally/vignettes/rd_index.yaml0000644000176200001440000000525213114357267016136 0ustar liggesusers## layout the order in which topics are presented in the rd docs # 1. get a list of topics with the following: # db <- tools::Rd_db("GGally") # topics <- gsub("\\.Rd", "", names(db)) # cat(paste(topics, collapse = "\n")) # 2. arrange the topic names into sections as desired in the format below: ## If you need to see which topics are missing, do the following in addition: # cur <- yaml::yaml.load_file("rd_index.yaml") # cur <- unlist(lapply(cur, function(x) x$topics)) # cat(paste(setdiff(topics, cur), collapse = "\n")) knitr: eval: true cache: true fig.height: 8 fig.width: 10 sections: - section_name: Plot Matrix topics: - ggmatrix - ggpairs - ggduo - ggscatmat - ggfacet - ggts - section_name: ggmatrix helpers topics: - file: wrap.Rd title: "wrap" - print.ggmatrix - ggmatrix_gtable - file: grab_legend.Rd title: "grab_legend" - gglegend - file: putPlot.Rd title: "[<-.ggmatrix" - file: getPlot.Rd title: "[.ggmatrix" - gg-add - mapping_color_to_fill - fn_switch - v1_ggmatrix_theme - str.ggmatrix - print_if_interactive - section_name: Model Diagnostics topics: - ggnostic - broomify - ggally_nostic_cooksd - ggally_nostic_hat - ggally_nostic_line - ggally_nostic_resid - ggally_nostic_se_fit - ggally_nostic_sigma - ggally_nostic_std_resid - section_name: Major Plotting Functions topics: - ggcoef - ggcorr - ggparcoord - ggsurv - section_name: Glyph Plot topics: - glyphs - add_ref_boxes - add_ref_lines - file: glyphplot.Rd title: "glyphplot" # - # file: rescale01.Rd # title: "rescale01: max1, mean0, min0, range01, rescale01, rescale11" - section_name: Networks topics: - ggnet - ggnet2 - ggnetworkmap - section_name: High-Level Plots topics: - ggally_barDiag - file: ggally_box.Rd title: "ggally_box, ggally_box_no_facet" - ggally_cor - ggally_density - ggally_densityDiag - ggally_denstrip - file: ggally_dot.Rd title: "ggally_dot, ggally_dot_no_facet" - ggally_facetbar - ggally_facetdensity - ggally_facethist - ggally_points - ggally_ratio - file: ggally_smooth.Rd title: "ggally_smooth, ggally_smooth_lm, ggally_smooth_loess" - ggally_text - file: ggally_blank.Rd title: "ggally_blank, ggally_blankDiag" - file: ggally_na.Rd title: "ggally_na, ggally_naDiag" - ggally_diagAxis - section_name: Datasets topics: - australia_PISA2012 - flea - happy - nasa - twitter_spambots GGally/vignettes/ggscatmat.html0000644000176200001440000000057013001231535016274 0ustar liggesusers