scales/0000755000176200001440000000000013656356645011547 5ustar liggesusersscales/NAMESPACE0000644000176200001440000000676513655055770012776 0ustar liggesusers# Generated by roxygen2: do not edit by hand S3method(fullseq,Date) S3method(fullseq,POSIXt) S3method(fullseq,difftime) S3method(fullseq,numeric) S3method(lines,trans) S3method(plot,trans) S3method(print,trans) S3method(rescale,"NULL") S3method(rescale,Date) S3method(rescale,POSIXt) S3method(rescale,dist) S3method(rescale,integer64) S3method(rescale,logical) S3method(rescale,numeric) S3method(rescale_mid,"NULL") S3method(rescale_mid,Date) S3method(rescale_mid,POSIXt) S3method(rescale_mid,dist) S3method(rescale_mid,integer64) S3method(rescale_mid,logical) S3method(rescale_mid,numeric) S3method(round_any,POSIXct) S3method(round_any,numeric) export(ContinuousRange) export(DiscreteRange) export(Range) export(abs_area) export(alpha) export(area_pal) export(as.trans) export(asn_trans) export(atanh_trans) export(boxcox_trans) export(breaks_extended) export(breaks_log) export(breaks_pretty) export(breaks_width) export(brewer_pal) export(cbreaks) export(censor) export(col2hcl) export(col_bin) export(col_factor) export(col_numeric) export(col_quantile) export(colour_ramp) export(comma) export(comma_format) export(cscale) export(date_breaks) export(date_format) export(date_trans) export(demo_continuous) export(demo_datetime) export(demo_discrete) export(demo_log10) export(demo_time) export(dichromat_pal) export(discard) export(div_gradient_pal) export(dollar) export(dollar_format) export(dscale) export(exp_trans) export(expand_range) export(extended_breaks) export(format_format) export(fullseq) export(gradient_n_pal) export(grey_pal) export(hms_trans) export(hue_pal) export(identity_pal) export(identity_trans) export(is.trans) export(label_bytes) export(label_comma) export(label_date) export(label_date_short) export(label_dollar) export(label_math) export(label_number) export(label_number_auto) export(label_number_si) export(label_ordinal) export(label_parse) export(label_percent) export(label_pvalue) export(label_scientific) export(label_time) export(label_wrap) export(linetype_pal) export(log10_trans) export(log1p_trans) export(log2_trans) export(log_breaks) export(log_trans) export(logit_trans) export(manual_pal) export(math_format) export(minor_breaks_n) export(minor_breaks_width) export(modulus_trans) export(muted) export(number) export(number_bytes) export(number_bytes_format) export(number_format) export(oob_censor) export(oob_censor_any) export(oob_discard) export(oob_keep) export(oob_squish) export(oob_squish_any) export(oob_squish_infinite) export(ordinal) export(ordinal_english) export(ordinal_format) export(ordinal_french) export(ordinal_spanish) export(parse_format) export(percent) export(percent_format) export(pretty_breaks) export(probability_trans) export(probit_trans) export(pseudo_log_trans) export(pvalue) export(pvalue_format) export(reciprocal_trans) export(regular_minor_breaks) export(rescale) export(rescale_max) export(rescale_mid) export(rescale_none) export(rescale_pal) export(reverse_trans) export(scientific) export(scientific_format) export(seq_gradient_pal) export(shape_pal) export(show_col) export(sqrt_trans) export(squish) export(squish_infinite) export(time_format) export(time_trans) export(train_continuous) export(train_discrete) export(trans_breaks) export(trans_format) export(trans_new) export(trans_range) export(unit_format) export(viridis_pal) export(wrap_format) export(yj_trans) export(zero_range) importFrom(R6,R6Class) importFrom(graphics,par) importFrom(graphics,plot) importFrom(graphics,rect) importFrom(graphics,text) importFrom(lifecycle,deprecate_soft) importFrom(munsell,mnsl) scales/LICENSE0000644000176200001440000000006113010421256012520 0ustar liggesusersYEAR: 2010-2016 COPYRIGHT HOLDER: Hadley Wickham scales/README.md0000644000176200001440000000743213655055706013025 0ustar liggesusers # scales [![CRAN status](https://www.r-pkg.org/badges/version/scales)](https://CRAN.R-project.org/package=scales) [![R build status](https://github.com/r-lib/scales/workflows/R-CMD-check/badge.svg)](https://github.com/r-lib/scales/actions) [![Codecov test coverage](https://codecov.io/gh/r-lib/scales/branch/master/graph/badge.svg)](https://codecov.io/gh/r-lib/scales?branch=master) One of the most difficult parts of any graphics package is scaling, converting from data values to perceptual properties. The inverse of scaling, making guides (legends and axes) that can be used to read the graph, is often even harder\! The scales packages provides the internal scaling infrastructure used by [ggplot2](http://ggplot2.tidyverse.org/), and gives you tools to override the default breaks, labels, transformations and palettes. # Installation ``` r # Scales is installed when you install ggplot2 or the tidyverse. # But you can install just scales from CRAN: install.packages("scales") # Or the development version from Github: # install.packages("devtools") devtools::install_github("r-lib/scales") ``` # Usage ## Breaks and labels The most common use of the scales package is to customise to control the appearance of axis and legend labels. Use a `break_` function to control how breaks are generated from the limits, and a `label_` function to control how breaks are turned in to labels. ``` r library(ggplot2) library(dplyr, warn.conflicts = FALSE) library(lubridate, warn.conflicts = FALSE) txhousing %>% mutate(date = make_date(year, month, 1)) %>% group_by(city) %>% filter(min(sales) > 5e2) %>% ggplot(aes(date, sales, group = city)) + geom_line(na.rm = TRUE) + scale_x_date( NULL, breaks = scales::breaks_width("2 years"), labels = scales::label_date("'%y") ) + scale_y_log10( "Total sales", labels = scales::label_number_si() ) ``` ![](man/figures/README-labels-1.png) ``` r economics %>% filter(date < ymd("1970-01-01")) %>% ggplot(aes(date, pce)) + geom_line() + scale_x_date(NULL, breaks = scales::breaks_width("3 months"), labels = scales::label_date_short() ) + scale_y_continuous("Personal consumption expenditures", breaks = scales::breaks_extended(8), labels = scales::label_dollar() ) ``` ![](man/figures/README-labels-2.png) Generally, I don’t recommend running `library(scales)` because when you type (e.g.) `scales::label_` autocomplete will provide you with a list of labelling functions to job your memory. ## Advanced features Scales colour palettes are used to power the scales in ggplot2, but you can use them in any plotting system. The following example shows how you might apply them to a base plot. ``` r library(scales) # pull a list of colours from any palette viridis_pal()(4) #> [1] "#440154FF" "#31688EFF" "#35B779FF" "#FDE725FF" # use in combination with baseR `palette()` to set new defaults palette(brewer_pal(palette = "Set2")(4)) par(mar = c(5, 5, 1, 1)) plot(Sepal.Length ~ Sepal.Width, data = iris, col = Species, pch = 20) ``` ![](man/figures/README-palettes-1.png) scales also gives users the ability to define and apply their own custom transformation functions for repeated use. ``` r # use trans_new to build a new transformation logp3_trans <- trans_new( name = "logp", trans = function(x) log(x + 3), inverse = function(x) exp(x) - 3, breaks = log_breaks() ) dsamp <- sample_n(diamonds, 100) ggplot(dsamp, aes(carat, price, colour = color)) + geom_point() + scale_y_continuous(trans = logp3_trans) ``` ![](man/figures/README-transforms-1.png) scales/man/0000755000176200001440000000000013656262515012312 5ustar liggesusersscales/man/unit_format.Rd0000644000176200001440000000357213641652035015131 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/labels-retired.R \name{unit_format} \alias{unit_format} \title{Unit labels} \usage{ unit_format( accuracy = NULL, scale = 1, prefix = "", unit = "m", sep = " ", suffix = paste0(sep, unit), big.mark = " ", decimal.mark = ".", trim = TRUE, ... ) } \arguments{ \item{accuracy}{A number to round to. Use (e.g.) \code{0.01} to show 2 decimal places of precision. If \code{NULL}, the default, uses a heuristic that should ensure breaks have the minimum number of digits needed to show the difference between adjacent values. Applied to rescaled data.} \item{scale}{A scaling factor: \code{x} will be multiplied by \code{scale} before formating. This is useful if the underlying data is very small or very large.} \item{prefix}{Symbols to display before and after value.} \item{unit}{The units to append.} \item{sep}{The separator between the number and the unit label.} \item{suffix}{Symbols to display before and after value.} \item{big.mark}{Character used between every 3 digits to separate thousands.} \item{decimal.mark}{The character to be used to indicate the numeric decimal point.} \item{trim}{Logical, if \code{FALSE}, values are right-justified to a common width (see \code{\link[base:format]{base::format()}}).} \item{...}{Other arguments passed on to \code{\link[base:format]{base::format()}}.} } \description{ \Sexpr[results=rd, stage=render]{lifecycle::badge("retired")} This function is kept for backward compatiblity; you should either use \code{\link[=label_number]{label_number()}} or \code{\link[=label_number_si]{label_number_si()}} instead. } \examples{ # Label with units demo_continuous(c(0, 1), labels = unit_format(unit = "m")) # Labels in kg, but original data in g km <- unit_format(unit = "km", scale = 1e-3, digits = 2) demo_continuous(c(0, 2500), labels = km) } \keyword{internal} scales/man/seq_gradient_pal.Rd0000644000176200001440000000137013641652035016075 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pal-gradient.r \name{seq_gradient_pal} \alias{seq_gradient_pal} \title{Sequential colour gradient palette (continuous)} \usage{ seq_gradient_pal(low = mnsl("10B 4/6"), high = mnsl("10R 4/6"), space = "Lab") } \arguments{ \item{low}{colour for low end of gradient.} \item{high}{colour for high end of gradient.} \item{space}{colour space in which to calculate gradient. Must be "Lab" - other values are deprecated.} } \description{ Sequential colour gradient palette (continuous) } \examples{ x <- seq(0, 1, length.out = 25) show_col(seq_gradient_pal()(x)) show_col(seq_gradient_pal("white", "black")(x)) library(munsell) show_col(seq_gradient_pal("white", mnsl("10R 4/6"))(x)) } scales/man/fullseq.Rd0000644000176200001440000000074713320151564014251 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/full-seq.r \name{fullseq} \alias{fullseq} \title{Generate sequence of fixed size intervals covering range.} \usage{ fullseq(range, size, ...) } \arguments{ \item{range}{range} \item{size}{interval size} \item{...}{other arguments passed on to methods} } \description{ Generate sequence of fixed size intervals covering range. } \seealso{ \code{\link[plyr:round_any]{plyr::round_any()}} } \keyword{internal} scales/man/reciprocal_trans.Rd0000644000176200001440000000045613556361126016135 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/trans-numeric.r \name{reciprocal_trans} \alias{reciprocal_trans} \title{Reciprocal transformation} \usage{ reciprocal_trans() } \description{ Reciprocal transformation } \examples{ plot(reciprocal_trans(), xlim = c(0, 1)) } scales/man/area_pal.Rd0000644000176200001440000000066513556361126014351 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pal-area.r \name{area_pal} \alias{area_pal} \alias{abs_area} \title{Area palettes (continuous)} \usage{ area_pal(range = c(1, 6)) abs_area(max) } \arguments{ \item{range}{Numeric vector of length two, giving range of possible sizes. Should be greater than 0.} \item{max}{A number representing the maximum size.} } \description{ Area palettes (continuous) } scales/man/trans_breaks.Rd0000644000176200001440000000154713556361126015263 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/breaks-retired.R \name{trans_breaks} \alias{trans_breaks} \title{Pretty breaks on transformed scale} \usage{ trans_breaks(trans, inv, n = 5, ...) } \arguments{ \item{trans}{function of single variable, \code{x}, that given a numeric vector returns the transformed values} \item{inv}{inverse of the transformation function} \item{n}{desired number of ticks} \item{...}{other arguments passed on to pretty} } \description{ \Sexpr[results=rd, stage=render]{lifecycle::badge("retired")} These often do not produce very attractive breaks. } \examples{ trans_breaks("log10", function(x) 10 ^ x)(c(1, 1e6)) trans_breaks("sqrt", function(x) x ^ 2)(c(1, 100)) trans_breaks(function(x) 1 / x, function(x) 1 / x)(c(1, 100)) trans_breaks(function(x) -x, function(x) -x)(c(1, 100)) } \keyword{internal} scales/man/label_ordinal.Rd0000644000176200001440000000542213655052654015373 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/label-ordinal.R \name{label_ordinal} \alias{label_ordinal} \alias{ordinal_english} \alias{ordinal_french} \alias{ordinal_spanish} \alias{ordinal_format} \alias{ordinal} \title{Label ordinal numbers (1st, 2nd, 3rd, etc)} \usage{ label_ordinal( prefix = "", suffix = "", big.mark = " ", rules = ordinal_english(), ... ) ordinal_english() ordinal_french(gender = c("masculin", "feminin"), plural = FALSE) ordinal_spanish() ordinal_format( prefix = "", suffix = "", big.mark = " ", rules = ordinal_english(), ... ) ordinal( x, prefix = "", suffix = "", big.mark = " ", rules = ordinal_english(), ... ) } \arguments{ \item{prefix, suffix}{Symbols to display before and after value.} \item{big.mark}{Character used between every 3 digits to separate thousands.} \item{rules}{Named list of regular expressions, matched in order. Name gives suffix, and value specifies which numbers to match.} \item{...}{Other arguments passed on to \code{\link[base:format]{base::format()}}.} \item{gender}{Masculin or feminin gender for French ordinal.} \item{plural}{Plural or singular for French ordinal.} \item{x}{A numeric vector to format.} } \value{ All \code{label_()} functions return a "labelling" function, i.e. a function that takes a vector \code{x} and returns a character vector of \code{length(x)} giving a label for each input value. Labelling functions are designed to be used with the \code{labels} argument of ggplot2 scales. The examples demonstrate their use with x scales, but they work similarly for all scales, including those that generate legends rather than axes. } \description{ Round values to integers and then display as ordinal values (e.g. 1st, 2nd, 3rd). Built-in rules are provided for English, French, and Spanish. } \section{Old interface}{ \code{ordinal()} and \code{format_ordinal()} are retired; please use \code{label_ordinal()} instead. } \examples{ demo_continuous(c(1, 5)) demo_continuous(c(1, 5), labels = label_ordinal()) demo_continuous(c(1, 5), labels = label_ordinal(rules = ordinal_french())) # The rules are just a set of regular expressions that are applied in turn ordinal_french() ordinal_english() # Note that ordinal rounds values, so you may need to adjust the breaks too demo_continuous(c(1, 10)) demo_continuous(c(1, 10), labels = label_ordinal()) demo_continuous(c(1, 10), labels = label_ordinal(), breaks = breaks_width(2) ) } \seealso{ Other labels for continuous scales: \code{\link{label_bytes}()}, \code{\link{label_dollar}()}, \code{\link{label_number_auto}()}, \code{\link{label_number_si}()}, \code{\link{label_parse}()}, \code{\link{label_percent}()}, \code{\link{label_pvalue}()}, \code{\link{label_scientific}()} } \concept{labels for continuous scales} scales/man/hue_pal.Rd0000644000176200001440000000171213641652035014211 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pal-hue.r \name{hue_pal} \alias{hue_pal} \title{Hue palette (discrete)} \usage{ hue_pal(h = c(0, 360) + 15, c = 100, l = 65, h.start = 0, direction = 1) } \arguments{ \item{h}{range of hues to use, in [0, 360]} \item{c}{chroma (intensity of colour), maximum value varies depending on combination of hue and luminance.} \item{l}{luminance (lightness), in [0, 100]} \item{h.start}{hue to start at} \item{direction}{direction to travel around the colour wheel, 1 = clockwise, -1 = counter-clockwise} } \description{ Hue palette (discrete) } \examples{ show_col(hue_pal()(4)) show_col(hue_pal()(9)) show_col(hue_pal(l = 90)(9)) show_col(hue_pal(l = 30)(9)) show_col(hue_pal()(9)) show_col(hue_pal(direction = -1)(9)) show_col(hue_pal()(9)) show_col(hue_pal(h = c(0, 90))(9)) show_col(hue_pal(h = c(90, 180))(9)) show_col(hue_pal(h = c(180, 270))(9)) show_col(hue_pal(h = c(270, 360))(9)) } scales/man/minor_breaks_width.Rd0000644000176200001440000000154113655050777016460 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/minor_breaks.R \name{minor_breaks_width} \alias{minor_breaks_width} \alias{minor_breaks_n} \title{Minor breaks} \usage{ minor_breaks_width(width, offset) minor_breaks_n(n) } \arguments{ \item{width}{Distance between each break. Either a number, or for date/times, a single string of the form "{n} {unit}", e.g. "1 month", "5 days". Unit can be of one "sec", "min", "hour", "day", "week", "month", "year".} \item{offset}{Use if you don't want breaks to start at zero} \item{n}{number of breaks} } \description{ Generate minor breaks between major breaks either spaced with a fixed width, or having a fixed number. } \examples{ demo_log10(c(1, 1e6)) if (FALSE) { # Requires https://github.com/tidyverse/ggplot2/pull/3591 demo_log10(c(1, 1e6), minor_breaks = minor_breaks_n(10)) } } scales/man/label_scientific.Rd0000644000176200001440000000501113641652035016047 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/label-scientific.R \name{label_scientific} \alias{label_scientific} \alias{scientific_format} \alias{scientific} \title{Label numbers with scientific notation (e.g. 1e05, 1.5e-02)} \usage{ label_scientific( digits = 3, scale = 1, prefix = "", suffix = "", decimal.mark = ".", trim = TRUE, ... ) scientific_format( digits = 3, scale = 1, prefix = "", suffix = "", decimal.mark = ".", trim = TRUE, ... ) scientific( x, digits = 3, scale = 1, prefix = "", suffix = "", decimal.mark = ".", trim = TRUE, ... ) } \arguments{ \item{digits}{Number of digits to show before exponent.} \item{scale}{A scaling factor: \code{x} will be multiplied by \code{scale} before formating. This is useful if the underlying data is very small or very large.} \item{prefix, suffix}{Symbols to display before and after value.} \item{decimal.mark}{The character to be used to indicate the numeric decimal point.} \item{trim}{Logical, if \code{FALSE}, values are right-justified to a common width (see \code{\link[base:format]{base::format()}}).} \item{...}{Other arguments passed on to \code{\link[base:format]{base::format()}}.} \item{x}{A numeric vector to format.} } \value{ All \code{label_()} functions return a "labelling" function, i.e. a function that takes a vector \code{x} and returns a character vector of \code{length(x)} giving a label for each input value. Labelling functions are designed to be used with the \code{labels} argument of ggplot2 scales. The examples demonstrate their use with x scales, but they work similarly for all scales, including those that generate legends rather than axes. } \description{ Label numbers with scientific notation (e.g. 1e05, 1.5e-02) } \section{Old interface}{ \code{scientific_format()} and \code{scientific()} are retired; please use \code{label_scientific()}. } \examples{ demo_continuous(c(1, 10)) demo_continuous(c(1, 10), labels = label_scientific()) demo_continuous(c(1, 10), labels = label_scientific(digits = 3)) demo_log10(c(1, 1e9)) } \seealso{ Other labels for continuous scales: \code{\link{label_bytes}()}, \code{\link{label_dollar}()}, \code{\link{label_number_auto}()}, \code{\link{label_number_si}()}, \code{\link{label_ordinal}()}, \code{\link{label_parse}()}, \code{\link{label_percent}()}, \code{\link{label_pvalue}()} Other labels for log scales: \code{\link{label_bytes}()}, \code{\link{label_number_si}()} } \concept{labels for continuous scales} \concept{labels for log scales} scales/man/log_trans.Rd0000644000176200001440000000220013556361126014560 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/trans-numeric.r \name{log_trans} \alias{log_trans} \alias{log10_trans} \alias{log2_trans} \alias{log1p_trans} \alias{pseudo_log_trans} \title{Log transformations} \usage{ log_trans(base = exp(1)) log10_trans() log2_trans() log1p_trans() pseudo_log_trans(sigma = 1, base = exp(1)) } \arguments{ \item{base}{base of logarithm} \item{sigma}{Scaling factor for the linear part of pseudo-log transformation.} } \description{ \itemize{ \item \code{log_trans()}: \code{log(x)} \item \code{log1p()}: \code{log(x + 1)} \item \code{pseudo_log_trans()}: smoothly transition to linear scale around 0. } } \examples{ plot(log2_trans(), xlim = c(0, 5)) plot(log_trans(), xlim = c(0, 5)) plot(log10_trans(), xlim = c(0, 5)) plot(log_trans(), xlim = c(0, 2)) plot(log1p_trans(), xlim = c(-1, 1)) # The pseudo-log is defined for all real numbers plot(pseudo_log_trans(), xlim = c(-5, 5)) lines(log_trans(), xlim = c(0, 5), col = "red") # For large positives nubmers it's very close to log plot(pseudo_log_trans(), xlim = c(1, 20)) lines(log_trans(), xlim = c(1, 20), col = "red") } scales/man/identity_trans.Rd0000644000176200001440000000047513556361126015644 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/trans-numeric.r \name{identity_trans} \alias{identity_trans} \title{Identity transformation (do nothing)} \usage{ identity_trans() } \description{ Identity transformation (do nothing) } \examples{ plot(identity_trans(), xlim = c(-1, 1)) } scales/man/number.Rd0000644000176200001440000000305713641652035014070 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/label-number.r \name{number} \alias{number} \title{A low-level numeric formatter} \usage{ number( x, accuracy = NULL, scale = 1, prefix = "", suffix = "", big.mark = " ", decimal.mark = ".", trim = TRUE, ... ) } \arguments{ \item{x}{A numeric vector to format.} \item{accuracy}{A number to round to. Use (e.g.) \code{0.01} to show 2 decimal places of precision. If \code{NULL}, the default, uses a heuristic that should ensure breaks have the minimum number of digits needed to show the difference between adjacent values. Applied to rescaled data.} \item{scale}{A scaling factor: \code{x} will be multiplied by \code{scale} before formating. This is useful if the underlying data is very small or very large.} \item{prefix}{Symbols to display before and after value.} \item{suffix}{Symbols to display before and after value.} \item{big.mark}{Character used between every 3 digits to separate thousands.} \item{decimal.mark}{The character to be used to indicate the numeric decimal point.} \item{trim}{Logical, if \code{FALSE}, values are right-justified to a common width (see \code{\link[base:format]{base::format()}}).} \item{...}{Other arguments passed on to \code{\link[base:format]{base::format()}}.} } \value{ A character vector of \code{length(x)}. } \description{ This function is a low-level helper that powers many of the labelling functions. You should generally not need to call it directly unless you are creating your own labelling function. } \keyword{internal} scales/man/label_date.Rd0000644000176200001440000000550413641652035014653 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/label-date.R \name{label_date} \alias{label_date} \alias{label_date_short} \alias{label_time} \alias{date_format} \alias{time_format} \title{Label date/times} \usage{ label_date(format = "\%Y-\%m-\%d", tz = "UTC") label_date_short(format = c("\%Y", "\%b", "\%d", "\%H:\%M"), sep = "\\n") label_time(format = "\%H:\%M:\%S", tz = "UTC") date_format(format = "\%Y-\%m-\%d", tz = "UTC") time_format(format = "\%H:\%M:\%S", tz = "UTC") } \arguments{ \item{format}{For \code{date_format()} and \code{time_format()} a date/time format string using standard POSIX specification. See \code{\link[=strptime]{strptime()}} for details. For \code{date_short()} a character vector of length 4 giving the format components to use for year, month, day, and hour respectively.} \item{tz}{a time zone name, see \code{\link[=timezones]{timezones()}}. Defaults to UTC} \item{sep}{Separator to use when combining date formats into a single string.} } \value{ All \code{label_()} functions return a "labelling" function, i.e. a function that takes a vector \code{x} and returns a character vector of \code{length(x)} giving a label for each input value. Labelling functions are designed to be used with the \code{labels} argument of ggplot2 scales. The examples demonstrate their use with x scales, but they work similarly for all scales, including those that generate legends rather than axes. } \description{ \code{label_date()} and \code{label_time()} label date/times using date/time format strings. \code{label_date_short()} automatically constructs a short format string suffiicient to uniquely identify labels. It's inspired by matplotlib's \href{https://matplotlib.org/api/dates_api.html#matplotlib.dates.ConciseDateFormatter}{\code{ConciseDateFormatter}}, but uses a slightly different approach: \code{ConciseDateFormatter} formats "firsts" (e.g. first day of month, first day of day) specially; \code{date_short()} formats changes (e.g. new month, new year) specially. } \section{Old interface}{ \code{date_format()} and \code{time_format()} are retired; please use \code{label_date()} and \code{label_time()} instead. } \examples{ date_range <- function(start, days) { start <- as.POSIXct(start) c(start, start + days * 24 * 60 * 60) } two_months <- date_range("2020-05-01", 60) demo_datetime(two_months) demo_datetime(two_months, labels = date_format("\%m/\%d")) # ggplot2 provides a short-hand: demo_datetime(two_months, date_labels = "\%m/\%d") # An alternative labelling system is label_date_short() demo_datetime(two_months, date_breaks = "7 days", labels = label_date_short()) # This is particularly effective for dense labels one_year <- date_range("2020-05-01", 365) demo_datetime(one_year, date_breaks = "month") demo_datetime(one_year, date_breaks = "month", labels = label_date_short()) } scales/man/colour_ramp.Rd0000644000176200001440000000374713560021704015121 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/colour-ramp.R \name{colour_ramp} \alias{colour_ramp} \title{Fast colour interpolation} \usage{ colour_ramp(colors, na.color = NA, alpha = TRUE) } \arguments{ \item{colors}{Colours to interpolate; must be a valid argument to \code{\link[grDevices:col2rgb]{grDevices::col2rgb()}}. This can be a character vector of \code{"#RRGGBB"} or \code{"#RRGGBBAA"}, colour names from \code{\link[grDevices:colors]{grDevices::colors()}}, or a positive integer that indexes into \code{\link[grDevices:palette]{grDevices::palette()}}.} \item{na.color}{The colour to map to \code{NA} values (for example, \code{"#606060"} for dark grey, or \code{"#00000000"} for transparent) and values outside of [0,1]. Can itself by \code{NA}, which will simply cause an \code{NA} to be inserted into the output.} \item{alpha}{Whether to include alpha transparency channels in interpolation. If \code{TRUE} then the alpha information is included in the interpolation. The returned colours will be provided in \code{"#RRGGBBAA"} format when needed, i.e., in cases where the colour is not fully opaque, so that the \code{"AA"} part is not equal to \code{"FF"}. Fully opaque colours will be returned in \code{"#RRGGBB"} format. If \code{FALSE}, the alpha information is discarded before interpolation and colours are always returned as \code{"#RRGGBB"}.} } \value{ A function that takes a numeric vector and returns a character vector of the same length with RGB or RGBA hex colours. } \description{ Returns a function that maps the interval [0,1] to a set of colours. Interpolation is performed in the CIELAB colour space. Similar to \code{\link[grDevices]{colorRamp}(space = 'Lab')}, but hundreds of times faster, and provides results in \code{"#RRGGBB"} (or \code{"#RRGGBBAA"}) character form instead of RGB colour matrices. } \examples{ ramp <- colour_ramp(c("red", "green", "blue")) show_col(ramp(seq(0, 1, length = 12))) } \seealso{ \code{\link[grDevices]{colorRamp}} } scales/man/alpha.Rd0000644000176200001440000000105013556361126013657 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/colour-manip.r \name{alpha} \alias{alpha} \title{Modify colour transparency} \usage{ alpha(colour, alpha = NA) } \arguments{ \item{colour}{colour} \item{alpha}{new alpha level in [0,1]. If alpha is \code{NA}, existing alpha values are preserved.} } \description{ Vectorised in both colour and alpha. } \examples{ alpha("red", 0.1) alpha(colours(), 0.5) alpha("red", seq(0, 1, length.out = 10)) alpha(c("first" = "gold", "second" = "lightgray", "third" = "#cd7f32"), .5) } scales/man/col_numeric.Rd0000644000176200001440000001257313641652035015102 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/colour-mapping.r \name{col_numeric} \alias{col_numeric} \alias{col_bin} \alias{col_quantile} \alias{col_factor} \title{Colour mapping} \usage{ col_numeric( palette, domain, na.color = "#808080", alpha = FALSE, reverse = FALSE ) col_bin( palette, domain, bins = 7, pretty = TRUE, na.color = "#808080", alpha = FALSE, reverse = FALSE, right = FALSE ) col_quantile( palette, domain, n = 4, probs = seq(0, 1, length.out = n + 1), na.color = "#808080", alpha = FALSE, reverse = FALSE, right = FALSE ) col_factor( palette, domain, levels = NULL, ordered = FALSE, na.color = "#808080", alpha = FALSE, reverse = FALSE ) } \arguments{ \item{palette}{The colours or colour function that values will be mapped to} \item{domain}{The possible values that can be mapped. For \code{col_numeric} and \code{col_bin}, this can be a simple numeric range (e.g. \code{c(0, 100)}); \code{col_quantile} needs representative numeric data; and \code{col_factor} needs categorical data. If \code{NULL}, then whenever the resulting colour function is called, the \code{x} value will represent the domain. This implies that if the function is invoked multiple times, the encoding between values and colours may not be consistent; if consistency is needed, you must provide a non-\code{NULL} domain.} \item{na.color}{The colour to return for \code{NA} values. Note that \code{na.color = NA} is valid.} \item{alpha}{Whether alpha channels should be respected or ignored. If \code{TRUE} then colors without explicit alpha information will be treated as fully opaque.} \item{reverse}{Whether the colors (or color function) in \code{palette} should be used in reverse order. For example, if the default order of a palette goes from blue to green, then \code{reverse = TRUE} will result in the colors going from green to blue.} \item{bins}{Either a numeric vector of two or more unique cut points or a single number (greater than or equal to 2) giving the number of intervals into which the domain values are to be cut.} \item{pretty}{Whether to use the function \code{\link[=pretty]{pretty()}} to generate the bins when the argument \code{bins} is a single number. When \code{pretty = TRUE}, the actual number of bins may not be the number of bins you specified. When \code{pretty = FALSE}, \code{\link[=seq]{seq()}} is used to generate the bins and the breaks may not be "pretty".} \item{right}{parameter supplied to \code{\link[base:cut]{base::cut()}}. See Details} \item{n}{Number of equal-size quantiles desired. For more precise control, use the \code{probs} argument instead.} \item{probs}{See \code{\link[stats:quantile]{stats::quantile()}}. If provided, the \code{n} argument is ignored.} \item{levels}{An alternate way of specifying levels; if specified, domain is ignored} \item{ordered}{If \code{TRUE} and \code{domain} needs to be coerced to a factor, treat it as already in the correct order} } \value{ A function that takes a single parameter \code{x}; when called with a vector of numbers (except for \code{col_factor}, which expects factors/characters), #RRGGBB colour strings are returned (unless \code{alpha = TRUE} in which case #RRGGBBAA may also be possible). } \description{ Conveniently maps data values (numeric or factor/character) to colours according to a given palette, which can be provided in a variety of formats. } \details{ \code{col_numeric} is a simple linear mapping from continuous numeric data to an interpolated palette. \code{col_bin} also maps continuous numeric data, but performs binning based on value (see the \code{\link[base:cut]{base::cut()}} function). \code{col_bin} defaults for the \code{cut} function are \code{include.lowest = TRUE} and \code{right = FALSE}. \code{col_quantile} similarly bins numeric data, but via the \code{\link[stats:quantile]{stats::quantile()}} function. \code{col_factor} maps factors to colours. If the palette is discrete and has a different number of colours than the number of factors, interpolation is used. The \code{palette} argument can be any of the following: \enumerate{ \item{A character vector of RGB or named colours. Examples: \code{palette()}, \code{c("#000000", "#0000FF", "#FFFFFF")}, \code{topo.colors(10)}} \item{The name of an RColorBrewer palette, e.g. \code{"BuPu"} or \code{"Greens"}.} \item{The full name of a viridis palette: \code{"viridis"}, \code{"magma"}, \code{"inferno"}, or \code{"plasma"}.} \item{A function that receives a single value between 0 and 1 and returns a colour. Examples: \code{colorRamp(c("#000000", "#FFFFFF"), interpolate="spline")}.} } } \examples{ pal <- col_bin("Greens", domain = 0:100) show_col(pal(sort(runif(10, 60, 100)))) # Exponential distribution, mapped continuously show_col(col_numeric("Blues", domain = NULL)(sort(rexp(16)))) # Exponential distribution, mapped by interval show_col(col_bin("Blues", domain = NULL, bins = 4)(sort(rexp(16)))) # Exponential distribution, mapped by quantile show_col(col_quantile("Blues", domain = NULL)(sort(rexp(16)))) # Categorical data; by default, the values being coloured span the gamut... show_col(col_factor("RdYlBu", domain = NULL)(LETTERS[1:5])) # ...unless the data is a factor, without droplevels... show_col(col_factor("RdYlBu", domain = NULL)(factor(LETTERS[1:5], levels=LETTERS))) # ...or the domain is stated explicitly. show_col(col_factor("RdYlBu", levels = LETTERS)(LETTERS[1:5])) } scales/man/rescale.Rd0000644000176200001440000000250413641652035014212 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/bounds.r \name{rescale} \alias{rescale} \alias{rescale.numeric} \alias{rescale.dist} \alias{rescale.logical} \alias{rescale.POSIXt} \alias{rescale.Date} \alias{rescale.integer64} \title{Rescale continuous vector to have specified minimum and maximum} \usage{ rescale(x, to, from, ...) \method{rescale}{numeric}(x, to = c(0, 1), from = range(x, na.rm = TRUE, finite = TRUE), ...) \method{rescale}{dist}(x, to = c(0, 1), from = range(x, na.rm = TRUE, finite = TRUE), ...) \method{rescale}{logical}(x, to = c(0, 1), from = range(x, na.rm = TRUE, finite = TRUE), ...) \method{rescale}{POSIXt}(x, to = c(0, 1), from = range(x, na.rm = TRUE, finite = TRUE), ...) \method{rescale}{Date}(x, to = c(0, 1), from = range(x, na.rm = TRUE, finite = TRUE), ...) \method{rescale}{integer64}(x, to = c(0, 1), from = range(x, na.rm = TRUE), ...) } \arguments{ \item{x}{continuous vector of values to manipulate.} \item{to}{output range (numeric vector of length two)} \item{from}{input range (vector of length two). If not given, is calculated from the range of \code{x}} \item{...}{other arguments passed on to methods} } \description{ Rescale continuous vector to have specified minimum and maximum } \examples{ rescale(1:100) rescale(runif(50)) rescale(1) } \keyword{manip} scales/man/label_number_auto.Rd0000644000176200001440000000263513641652035016260 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/label-number-auto.R \name{label_number_auto} \alias{label_number_auto} \title{Label numbers, avoiding scientific notation where possible} \usage{ label_number_auto() } \description{ Switches between \code{\link[=number_format]{number_format()}} and \code{\link[=scientific_format]{scientific_format()}} based on a set of heuristics designed to automatically generate useful labels across a wide range of inputs } \examples{ # Very small and very large numbers get scientific notation demo_continuous(c(0, 1e-6), labels = label_number_auto()) demo_continuous(c(0, 1e9), labels = label_number_auto()) # Other ranges get the numbers printed in full demo_continuous(c(0, 1e-3), labels = label_number_auto()) demo_continuous(c(0, 1), labels = label_number_auto()) demo_continuous(c(0, 1e3), labels = label_number_auto()) demo_continuous(c(0, 1e6), labels = label_number_auto()) # Transformation is applied individually so you get as little # scientific notation as possible demo_log10(c(1, 1e7), labels = label_number_auto()) } \seealso{ Other labels for continuous scales: \code{\link{label_bytes}()}, \code{\link{label_dollar}()}, \code{\link{label_number_si}()}, \code{\link{label_ordinal}()}, \code{\link{label_parse}()}, \code{\link{label_percent}()}, \code{\link{label_pvalue}()}, \code{\link{label_scientific}()} } \concept{labels for continuous scales} scales/man/reverse_trans.Rd0000644000176200001440000000043513556361126015462 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/trans-numeric.r \name{reverse_trans} \alias{reverse_trans} \title{Reverse transformation} \usage{ reverse_trans() } \description{ Reverse transformation } \examples{ plot(reverse_trans(), xlim = c(-1, 1)) } scales/man/brewer_pal.Rd0000644000176200001440000000200113556361126014711 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pal-brewer.r \name{brewer_pal} \alias{brewer_pal} \title{Colour Brewer palette (discrete)} \usage{ brewer_pal(type = "seq", palette = 1, direction = 1) } \arguments{ \item{type}{One of seq (sequential), div (diverging) or qual (qualitative)} \item{palette}{If a string, will use that named palette. If a number, will index into the list of palettes of appropriate \code{type}} \item{direction}{Sets the order of colours in the scale. If 1, the default, colours are as output by \code{\link[RColorBrewer:brewer.pal]{RColorBrewer::brewer.pal()}}. If -1, the order of colours is reversed.} } \description{ Colour Brewer palette (discrete) } \examples{ show_col(brewer_pal()(10)) show_col(brewer_pal("div")(5)) show_col(brewer_pal(palette = "Greens")(5)) # Can use with gradient_n to create a continous gradient cols <- brewer_pal("div")(5) show_col(gradient_n_pal(cols)(seq(0, 1, length.out = 30))) } \references{ \url{http://colorbrewer2.org} } scales/man/oob.Rd0000644000176200001440000000747013655052654013370 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/bounds.r \name{oob} \alias{oob} \alias{oob_censor} \alias{oob_censor_any} \alias{oob_discard} \alias{oob_squish} \alias{oob_squish_any} \alias{oob_squish_infinite} \alias{oob_keep} \alias{censor} \alias{discard} \alias{squish} \alias{squish_infinite} \title{Out of bounds handling} \usage{ oob_censor(x, range = c(0, 1), only.finite = TRUE) oob_censor_any(x, range = c(0, 1)) oob_discard(x, range = c(0, 1)) oob_squish(x, range = c(0, 1), only.finite = TRUE) oob_squish_any(x, range = c(0, 1)) oob_squish_infinite(x, range = c(0, 1)) oob_keep(x, range = c(0, 1)) censor(x, range = c(0, 1), only.finite = TRUE) discard(x, range = c(0, 1)) squish(x, range = c(0, 1), only.finite = TRUE) squish_infinite(x, range = c(0, 1)) } \arguments{ \item{x}{A numeric vector of values to modify.} \item{range}{A numeric vector of length two giving the minimum and maximum limit of the desired output range respectively.} \item{only.finite}{A logical of length one. When \code{TRUE}, only finite values are altered. When \code{FALSE}, also infinite values are altered.} } \value{ Most \code{oob_()} functions return a vector of numerical values of the same length as the \code{x} argument, wherein out of bounds values have been modified. Only \code{oob_discard()} returns a vector of less than or of equal length to the \code{x} argument. } \description{ This set of functions modify data values outside a given range. The \verb{oob_*()} functions are designed to be passed as the \code{oob} argument of ggplot2 continuous and binned scales, with \code{oob_discard} being an exception. These functions affect out of bounds values in the following ways: \itemize{ \item \code{oob_censor()} replaces out of bounds values with \code{NA}s. This is the default \code{oob} argument for continuous scales. \item \code{oob_censor_any()} acts like \code{oob_censor()}, but also replaces infinite values with \code{NA}s. \item \code{oob_squish()} replaces out of bounds values with the nearest limit. This is the default \code{oob} argument for binned scales. \item \code{oob_squish_any()} acts like \code{oob_squish()}, but also replaces infinite values with the nearest limit. \item \code{oob_squish_infinite()} only replaces infinite values by the nearest limit. \item \code{oob_keep()} does not adjust out of bounds values. In position scales, behaves as zooming limits without data removal. \item \code{oob_discard()} removes out of bounds values from the input. Not suitable for ggplot2 scales. } } \details{ The \code{oob_censor_any()} and \code{oob_squish_any()} functions are the same as \code{oob_censor()} and \code{oob_squish()} with the \code{only.finite} argument set to \code{FALSE}. Replacing position values with \code{NA}s, as \code{oob_censor()} does, will typically lead to removal of those datapoints in ggplot. Setting ggplot coordinate limits is equivalent to using \code{oob_keep()} in position scales. } \section{Old interface}{ \code{censor()}, \code{squish()}, \code{squish_infinite()} and \code{discard()} are no longer recommended; please use \code{oob_censor()}, \code{oob_squish()}, \code{oob_squish_infinite()} and \code{oob_discard()} instead. } \examples{ # Censoring replaces out of bounds values with NAs oob_censor(c(-Inf, -1, 0.5, 1, 2, NA, Inf)) oob_censor_any(c(-Inf, -1, 0.5, 1, 2, NA, Inf)) # Squishing replaces out of bounds values with the nearest range limit oob_squish(c(-Inf, -1, 0.5, 1, 2, NA, Inf)) oob_squish_any(c(-Inf, -1, 0.5, 1, 2, NA, Inf)) oob_squish_infinite(c(-Inf, -1, 0.5, 1, 2, NA, Inf)) # Keeping does not alter values oob_keep(c(-Inf, -1, 0.5, 1, 2, NA, Inf)) # Discarding will remove out of bounds values oob_discard(c(-Inf, -1, 0.5, 1, 2, NA, Inf)) } \author{ \code{oob_squish()}: Homer Strong \href{mailto:homer.strong@gmail.com}{homer.strong@gmail.com} } scales/man/label_parse.Rd0000644000176200001440000000414513641652035015050 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/label-expression.R \name{label_parse} \alias{label_parse} \alias{label_math} \alias{parse_format} \alias{math_format} \title{Label with mathematical annotations} \usage{ label_parse() label_math(expr = 10^.x, format = force) parse_format() math_format(expr = 10^.x, format = force) } \arguments{ \item{expr}{expression to use} \item{format}{another format function to apply prior to mathematical transformation - this makes it easier to use floating point numbers in mathematical expressions.} } \value{ All \code{label_()} functions return a "labelling" function, i.e. a function that takes a vector \code{x} and returns a character vector of \code{length(x)} giving a label for each input value. Labelling functions are designed to be used with the \code{labels} argument of ggplot2 scales. The examples demonstrate their use with x scales, but they work similarly for all scales, including those that generate legends rather than axes. } \description{ \code{label_parse()} produces expression from strings by parsing them; \code{label_math()} constructs expressions by replacing the pronoun \code{.x} with each string. } \section{Old interface}{ \code{parse_format()} and \code{math_format()} was retired; please use \code{label_parse()} and \code{label_math()} instead. } \examples{ # Use label_parse() with discrete scales greek <- c("alpha", "beta", "gamma") demo_discrete(greek) demo_discrete(greek, labels = label_parse()) # Use label_math() with continuous scales demo_continuous(c(1, 5)) demo_continuous(c(1, 5), labels = label_math(alpha[.x])) } \seealso{ \link{plotmath} for the details of mathematical formatting in R. Other labels for continuous scales: \code{\link{label_bytes}()}, \code{\link{label_dollar}()}, \code{\link{label_number_auto}()}, \code{\link{label_number_si}()}, \code{\link{label_ordinal}()}, \code{\link{label_percent}()}, \code{\link{label_pvalue}()}, \code{\link{label_scientific}()} Other labels for discrete scales: \code{\link{label_wrap}()} } \concept{labels for continuous scales} \concept{labels for discrete scales} scales/man/probability_trans.Rd0000644000176200001440000000132313556361126016324 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/trans-numeric.r \name{probability_trans} \alias{probability_trans} \alias{logit_trans} \alias{probit_trans} \title{Probability transformation} \usage{ probability_trans(distribution, ...) logit_trans() probit_trans() } \arguments{ \item{distribution}{probability distribution. Should be standard R abbreviation so that "p" + distribution is a valid probability density function, and "q" + distribution is a valid quantile function.} \item{...}{other arguments passed on to distribution and quantile functions} } \description{ Probability transformation } \examples{ plot(logit_trans(), xlim = c(0, 1)) plot(probit_trans(), xlim = c(0, 1)) } scales/man/label_dollar.Rd0000644000176200001440000000644513641652035015220 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/label-dollar.R \name{label_dollar} \alias{label_dollar} \alias{dollar_format} \alias{dollar} \title{Label currencies ($100, $2.50, etc)} \usage{ label_dollar( accuracy = NULL, scale = 1, prefix = "$", suffix = "", big.mark = ",", decimal.mark = ".", trim = TRUE, largest_with_cents = 1e+05, negative_parens = FALSE, ... ) dollar_format( accuracy = NULL, scale = 1, prefix = "$", suffix = "", big.mark = ",", decimal.mark = ".", trim = TRUE, largest_with_cents = 1e+05, negative_parens = FALSE, ... ) dollar( x, accuracy = NULL, scale = 1, prefix = "$", suffix = "", big.mark = ",", decimal.mark = ".", trim = TRUE, largest_with_cents = 1e+05, negative_parens = FALSE, ... ) } \arguments{ \item{accuracy, largest_with_cents}{Number to round to. If \code{NULL}, the default, values will be rounded to the nearest integer, unless any of the values has non-zero fractional component (e.g. cents) and the largest value is less than \code{largest_with_cents} which by default is 100,000.} \item{scale}{A scaling factor: \code{x} will be multiplied by \code{scale} before formating. This is useful if the underlying data is very small or very large.} \item{prefix, suffix}{Symbols to display before and after value.} \item{big.mark}{Character used between every 3 digits to separate thousands.} \item{decimal.mark}{The character to be used to indicate the numeric decimal point.} \item{trim}{Logical, if \code{FALSE}, values are right-justified to a common width (see \code{\link[base:format]{base::format()}}).} \item{negative_parens}{Display negative using parentheses?} \item{...}{Other arguments passed on to \code{\link[base:format]{base::format()}}.} \item{x}{A numeric vector} } \value{ All \code{label_()} functions return a "labelling" function, i.e. a function that takes a vector \code{x} and returns a character vector of \code{length(x)} giving a label for each input value. Labelling functions are designed to be used with the \code{labels} argument of ggplot2 scales. The examples demonstrate their use with x scales, but they work similarly for all scales, including those that generate legends rather than axes. } \description{ Format numbers as currency, rounding values to dollars or cents using a convenient heuristic. } \section{Old interface}{ \code{dollar()} and \code{format_dollar()} are retired; please use \code{label_dollar()} instead. } \examples{ demo_continuous(c(0, 1), labels = label_dollar()) demo_continuous(c(1, 100), labels = label_dollar()) # Customise currency display with prefix and suffix demo_continuous(c(1, 100), labels = label_dollar(prefix = "USD ")) euro <- dollar_format( prefix = "", suffix = "\u20ac", big.mark = ".", decimal.mark = "," ) demo_continuous(c(1000, 1100), labels = euro) # Use negative_parens = TRUE for finance style display demo_continuous(c(-100, 100), labels = label_dollar(negative_parens = TRUE)) } \seealso{ Other labels for continuous scales: \code{\link{label_bytes}()}, \code{\link{label_number_auto}()}, \code{\link{label_number_si}()}, \code{\link{label_ordinal}()}, \code{\link{label_parse}()}, \code{\link{label_percent}()}, \code{\link{label_pvalue}()}, \code{\link{label_scientific}()} } \concept{labels for continuous scales} scales/man/show_col.Rd0000644000176200001440000000152513655054675014426 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/colour-manip.r \name{show_col} \alias{show_col} \title{Show colours} \usage{ show_col(colours, labels = TRUE, borders = NULL, cex_label = 1, ncol = NULL) } \arguments{ \item{colours}{A character vector of colours} \item{labels}{Label each colour with its hex name?} \item{borders}{Border colour for each tile. Default uses \code{par("fg")}. Use \code{border = NA} to omit borders.} \item{cex_label}{Size of printed labels, as multiplier of default size.} \item{ncol}{Number of columns. If not supplied, tries to be as square as possible.} } \description{ A quick and dirty way to show colours in a plot. } \examples{ show_col(hue_pal()(9)) show_col(hue_pal()(9), borders = NA) show_col(viridis_pal()(16)) show_col(viridis_pal()(16), labels = FALSE) } \keyword{internal} scales/man/rescale_mid.Rd0000644000176200001440000000262613641652035015050 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/bounds.r \name{rescale_mid} \alias{rescale_mid} \alias{rescale_mid.numeric} \alias{rescale_mid.logical} \alias{rescale_mid.dist} \alias{rescale_mid.POSIXt} \alias{rescale_mid.Date} \alias{rescale_mid.integer64} \title{Rescale vector to have specified minimum, midpoint, and maximum} \usage{ rescale_mid(x, to, from, mid, ...) \method{rescale_mid}{numeric}(x, to = c(0, 1), from = range(x, na.rm = TRUE), mid = 0, ...) \method{rescale_mid}{logical}(x, to = c(0, 1), from = range(x, na.rm = TRUE), mid = 0, ...) \method{rescale_mid}{dist}(x, to = c(0, 1), from = range(x, na.rm = TRUE), mid = 0, ...) \method{rescale_mid}{POSIXt}(x, to = c(0, 1), from = range(x, na.rm = TRUE), mid, ...) \method{rescale_mid}{Date}(x, to = c(0, 1), from = range(x, na.rm = TRUE), mid, ...) \method{rescale_mid}{integer64}(x, to = c(0, 1), from = range(x, na.rm = TRUE), mid = 0, ...) } \arguments{ \item{x}{vector of values to manipulate.} \item{to}{output range (numeric vector of length two)} \item{from}{input range (vector of length two). If not given, is calculated from the range of \code{x}} \item{mid}{mid-point of input range} \item{...}{other arguments passed on to methods} } \description{ Rescale vector to have specified minimum, midpoint, and maximum } \examples{ rescale_mid(1:100, mid = 50.5) rescale_mid(runif(50), mid = 0.5) rescale_mid(1) } scales/man/trans_new.Rd0000644000176200001440000000275713641652035014606 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/trans.r \name{trans_new} \alias{trans_new} \alias{trans} \alias{is.trans} \alias{as.trans} \title{Create a new transformation object} \usage{ trans_new( name, transform, inverse, breaks = extended_breaks(), minor_breaks = regular_minor_breaks(), format = format_format(), domain = c(-Inf, Inf) ) is.trans(x) as.trans(x) } \arguments{ \item{name}{transformation name} \item{transform}{function, or name of function, that performs the transformation} \item{inverse}{function, or name of function, that performs the inverse of the transformation} \item{breaks}{default breaks function for this transformation. The breaks function is applied to the raw data.} \item{minor_breaks}{default minor breaks function for this transformation.} \item{format}{default format for this transformation. The format is applied to breaks generated to the raw data.} \item{domain}{domain, as numeric vector of length 2, over which transformation is valued} } \description{ A transformation encapsulates a transformation and its inverse, as well as the information needed to create pleasing breaks and labels. The breaks function is applied on the transformed range of the range, and it's expected that the labels function will perform some kind of inverse transformation on these breaks to give them labels that are meaningful on the original scale. } \seealso{ \Sexpr[results=rd,stage=build]{scales:::seealso_trans()} } \keyword{internal} scales/man/grey_pal.Rd0000644000176200001440000000077713556361126014413 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pal-grey.r \name{grey_pal} \alias{grey_pal} \title{Grey scale palette (discrete)} \usage{ grey_pal(start = 0.2, end = 0.8) } \arguments{ \item{start}{grey value at low end of palette} \item{end}{grey value at high end of palette} } \description{ Grey scale palette (discrete) } \examples{ show_col(grey_pal()(25)) show_col(grey_pal(0, 1)(25)) } \seealso{ \code{\link[=seq_gradient_pal]{seq_gradient_pal()}} for continuous version } scales/man/dichromat_pal.Rd0000644000176200001440000000132413556361126015404 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pal-dichromat.r \name{dichromat_pal} \alias{dichromat_pal} \title{Dichromat (colour-blind) palette (discrete)} \usage{ dichromat_pal(name) } \arguments{ \item{name}{Name of colour palette. One of: \Sexpr[results=rd,stage=build]{scales:::dichromat_schemes()}} } \description{ Dichromat (colour-blind) palette (discrete) } \examples{ if (requireNamespace("dichromat", quietly = TRUE)) { show_col(dichromat_pal("BluetoOrange.10")(10)) show_col(dichromat_pal("BluetoOrange.10")(5)) # Can use with gradient_n to create a continous gradient cols <- dichromat_pal("DarkRedtoBlue.12")(12) show_col(gradient_n_pal(cols)(seq(0, 1, length.out = 30))) } } scales/man/muted.Rd0000644000176200001440000000066413556361126013722 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/colour-manip.r \name{muted} \alias{muted} \title{Mute standard colour} \usage{ muted(colour, l = 30, c = 70) } \arguments{ \item{colour}{character vector of colours to modify} \item{l}{new luminance} \item{c}{new chroma} } \description{ Mute standard colour } \examples{ muted("red") muted("blue") show_col(c("red", "blue", muted("red"), muted("blue"))) } scales/man/format_format.Rd0000644000176200001440000000104513556361126015436 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/labels-retired.R \name{format_format} \alias{format_format} \title{Label using \code{format()}} \usage{ format_format(...) } \arguments{ \item{...}{Arguments passed on to \code{\link[=format]{format()}}.} } \description{ \Sexpr[results=rd, stage=render]{lifecycle::badge("retired")} This function is kept for backward compatiblity; you should either use \code{\link[=label_number]{label_number()}} or \code{\link[=label_date]{label_date()}} instead. } \keyword{internal} scales/man/breaks_extended.Rd0000644000176200001440000000161213556361126015725 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/breaks.r \name{breaks_extended} \alias{breaks_extended} \alias{extended_breaks} \title{Automatic breaks for numeric axes} \usage{ breaks_extended(n = 5, ...) } \arguments{ \item{n}{Desired number of breaks. You may get slightly more or fewer breaks that requested.} \item{...}{other arguments passed on to \code{\link[labeling:extended]{labeling::extended()}}} } \description{ Uses Wilkinson's extended breaks algorithm as implemented in the \pkg{labeling} package. } \examples{ demo_continuous(c(0, 10)) demo_continuous(c(0, 10), breaks = breaks_extended(3)) demo_continuous(c(0, 10), breaks = breaks_extended(10)) } \references{ Talbot, J., Lin, S., Hanrahan, P. (2010) An Extension of Wilkinson's Algorithm for Positioning Tick Labels on Axes, InfoVis 2010 \url{http://vis.stanford.edu/files/2010-TickLabels-InfoVis.pdf}. } scales/man/col2hcl.Rd0000644000176200001440000000145013641652035014121 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/colour-manip.r \name{col2hcl} \alias{col2hcl} \title{Modify standard R colour in hcl colour space.} \usage{ col2hcl(colour, h = NULL, c = NULL, l = NULL, alpha = NULL) } \arguments{ \item{colour}{character vector of colours to be modified} \item{h}{Hue, \verb{[0, 360]}} \item{c}{Chroma, \verb{[0, 100]}} \item{l}{Luminance, \verb{[0, 100]}} \item{alpha}{Alpha, \verb{[0, 1]}.} } \description{ Transforms rgb to hcl, sets non-missing arguments and then backtransforms to rgb. } \examples{ reds <- rep("red", 6) show_col(col2hcl(reds, h = seq(0, 180, length = 6))) show_col(col2hcl(reds, c = seq(0, 80, length = 6))) show_col(col2hcl(reds, l = seq(0, 100, length = 6))) show_col(col2hcl(reds, alpha = seq(0, 1, length = 6))) } scales/man/train_continuous.Rd0000644000176200001440000000063213560275211016174 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/scale-continuous.r \name{train_continuous} \alias{train_continuous} \title{Train (update) a continuous scale} \usage{ train_continuous(new, existing = NULL) } \arguments{ \item{new}{New data to add to scale} \item{existing}{Optional existing scale to update} } \description{ Strips attributes and always returns a numeric vector } scales/man/shape_pal.Rd0000644000176200001440000000044413556361126014534 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pal-shape.r \name{shape_pal} \alias{shape_pal} \title{Shape palette (discrete)} \usage{ shape_pal(solid = TRUE) } \arguments{ \item{solid}{should shapes be solid or not?} } \description{ Shape palette (discrete) } scales/man/hms_trans.Rd0000644000176200001440000000060713556361126014577 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/trans-date.r \name{hms_trans} \alias{hms_trans} \title{Transformation for times (class hms)} \usage{ hms_trans() } \description{ Transformation for times (class hms) } \examples{ if (require("hms")) { hms <- round(runif(10) * 86400) t <- hms_trans() t$transform(hms) t$inverse(t$transform(hms)) t$breaks(hms) } } scales/man/asn_trans.Rd0000644000176200001440000000052013556361126014563 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/trans-numeric.r \name{asn_trans} \alias{asn_trans} \title{Arc-sin square root transformation} \usage{ asn_trans() } \description{ This is the variance stabilising transformation for the binomial distribution. } \examples{ plot(asn_trans(), xlim = c(0, 1)) } scales/man/expand_range.Rd0000644000176200001440000000102213556361126015224 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/bounds.r \name{expand_range} \alias{expand_range} \title{Expand a range with a multiplicative or additive constant} \usage{ expand_range(range, mul = 0, add = 0, zero_width = 1) } \arguments{ \item{range}{range of data, numeric vector of length 2} \item{mul}{multiplicative constant} \item{add}{additive constant} \item{zero_width}{distance to use if range has zero width} } \description{ Expand a range with a multiplicative or additive constant } scales/man/figures/0000755000176200001440000000000013656262515013756 5ustar liggesusersscales/man/figures/README-labels-1.png0000644000176200001440000015577413655055705017042 0ustar liggesusers‰PNG  IHDRŕ l< !iCCPkCGColorSpaceGenericRGB8ŤŤU]hU>»sg#$ÎSl4…t¨? % “V4ˇ´şÝÝ6n–I6Ú"čdöîÎÉÎ83»ýˇOEP|1ę›Äż·€ (őŰ>´/• %ÚÔ (>´řP苦ë™;3™iş±Ţeî|óťďž{îągď蹪X–‘š®-2âs‡Ź=+„‡ ˇWQ+]©L6O wµ[ßCÂ{_ŮŐÝţź­·F qbłć¨ Źđ§UËvzú‘?ęZöbč·1@Ä/z¸ác×Ăs>~Ťifä,âÓUSj—ŹĚĹřF 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*x_*:ä}‚¨%}>~üŐŞU+š7ož×ď—/_’:@qśÚOM,˙ŚŔřńă —,±ÓąsçÖďĘ•+=»jIÎ"K—1Ö/2‘¸ůqćĚdÇëvŕŔIÖ2Ç÷ďßww/ S—.]˛ ʄЂ Ç—ř^ŁF #–»ˇYwăĹBĹUĚz)$“Ĺmy˝uë–»›±răRąreëßż|šď{ť:u¬E‹I;qsÜůÚĆ• ŃA $Ŕ)@×”"đo ”.]Úă§«Wݶ;wî[“îÝ»gÁ:ôˇqMă>f{PŹ=’Bś¦sýúőł–CvňÇ“şµk×ZÓ¦M]Đq ďßżßcĐÁZHúvŔu.\đř5kČYމC#čÄź3Ű2Ç%6L zfµgJgUüŕ„lęĚR¦L?͵¬ńĚ5ęř×' ţőżc}Â_„qT,ÚŕĘýË'b0[~° )$‘…•G’Rć+ďžZ„:ł˙eź1eË–-ÜÂ[elöÇó˙]&öKŚ—µôęŐ+™Á˝~ýş 4•™ŃI§p@¦7%ŽűIře®"?# đĎř­iÍż%öĆV©RĹ6nÜh!v™Ĺ€‡Y`!âb¦ŕňĹŇ%‹¤&^&‰ZXÍ™%v˙R‡8ž:uĘÚ·oď]B ŮZµjĺ×)Ť»±d,Ä˝¨Á%Á‹dtîÜ9k_o÷îÝ}Ğ䲊+ţpŘ:xý‰'’vÖ»¶ăJ>ëßY[|ťŢE ¸ H€‹›¸ćA`É’%ľmk!F,5\Ílá!>LáaXÁÓ¦Ms—1[n.\h»vír×µwúßźůóç'™ÉłfÍň e¶óP°Fqm#zí M\FrŇŃŁG}¶ç=vě­_żŢbK2ž¤&’’Iú0~Ľť‰-LC‡µÁ[ůňĺ}?0[ ¸Śăqâ÷7nxL:o|•80–{^ć:ęňk‹Çĺť­W5ýńWfŤX˝qáÉYđ!ŚŘ˛Šä*?‚ §č™ąú)´.ř X¸™y4Vd~KńÓ§OnAföÁBD¤x.3YÂ= n-2}, are returned or all candidates have been used. } \examples{ demo_log10(c(1, 1e5)) demo_log10(c(1, 1e6)) # Request more breaks by setting n demo_log10(c(1, 1e6), breaks = breaks_log(6)) # Some tricky ranges demo_log10(c(2000, 9000)) demo_log10(c(2000, 14000)) demo_log10(c(2000, 85000), expand = c(0, 0)) # An even smaller range that requires falling back to linear breaks demo_log10(c(1800, 2000)) } scales/man/trans_range.Rd0000644000176200001440000000072013556361126015100 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/trans.r \name{trans_range} \alias{trans_range} \title{Compute range of transformed values} \usage{ trans_range(trans, x) } \arguments{ \item{trans}{a transformation object, or the name of a transformation object given as a string.} \item{x}{a numeric vector to compute the range of} } \description{ Silently drops any ranges outside of the domain of \code{trans}. } \keyword{internal} scales/man/identity_pal.Rd0000644000176200001440000000041213556361126015260 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pal-identity.r \name{identity_pal} \alias{identity_pal} \title{Identity palette} \usage{ identity_pal() } \description{ Leaves values unchanged - useful when the data is already scaled. } scales/man/date_trans.Rd0000644000176200001440000000065413556361126014727 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/trans-date.r \name{date_trans} \alias{date_trans} \title{Transformation for dates (class Date)} \usage{ date_trans() } \description{ Transformation for dates (class Date) } \examples{ years <- seq(as.Date("1910/1/1"), as.Date("1999/1/1"), "years") t <- date_trans() t$transform(years) t$inverse(t$transform(years)) t$format(t$breaks(range(years))) } scales/man/demo_continuous.Rd0000644000176200001440000000104413655052655016013 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.r \name{demo_continuous} \alias{demo_continuous} \alias{demo_log10} \alias{demo_discrete} \alias{demo_datetime} \alias{demo_time} \title{Demonstrate scales functions with ggplot2 code} \usage{ demo_continuous(x, ...) demo_log10(x, ...) demo_discrete(x, ...) demo_datetime(x, ...) demo_time(x, ...) } \arguments{ \item{x}{A vector of data} } \description{ These functions generate ggplot2 code needed to use scales functions for real code. } \keyword{internal} scales/man/breaks_width.Rd0000644000176200001440000000241413655052655015251 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/breaks.r \name{breaks_width} \alias{breaks_width} \title{Equally spaced breaks} \usage{ breaks_width(width, offset = 0) } \arguments{ \item{width}{Distance between each break. Either a number, or for date/times, a single string of the form "{n} {unit}", e.g. "1 month", "5 days". Unit can be of one "sec", "min", "hour", "day", "week", "month", "year".} \item{offset}{Use if you don't want breaks to start at zero} } \description{ Useful for numeric, date, and date-time scales. } \examples{ demo_continuous(c(0, 100)) demo_continuous(c(0, 100), breaks = breaks_width(10)) demo_continuous(c(0, 100), breaks = breaks_width(20, -4)) demo_continuous(c(0, 100), breaks = breaks_width(20, 4)) # This is also useful for dates one_month <- as.POSIXct(c("2020-05-01", "2020-06-01")) demo_datetime(one_month) demo_datetime(one_month, breaks = breaks_width("1 week")) demo_datetime(one_month, breaks = breaks_width("5 days")) # This is so useful that scale_x_datetime() has a shorthand: demo_datetime(one_month, date_breaks = "5 days") # hms times also work one_hour <- hms::hms(hours = 0:1) demo_time(one_hour) demo_time(one_hour, breaks = breaks_width("15 min")) demo_time(one_hour, breaks = breaks_width("600 sec")) } scales/man/sqrt_trans.Rd0000644000176200001440000000051313556361126014775 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/trans-numeric.r \name{sqrt_trans} \alias{sqrt_trans} \title{Square-root transformation} \usage{ sqrt_trans() } \description{ This is the variance stabilising transformation for the Poisson distribution. } \examples{ plot(sqrt_trans(), xlim = c(0, 5)) } scales/man/yj_trans.Rd0000644000176200001440000000271613556361126014435 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/trans-numeric.r \name{yj_trans} \alias{yj_trans} \title{Yeo-Johnson transformation} \usage{ yj_trans(p) } \arguments{ \item{p}{Transformation exponent, \eqn{\lambda}.} } \description{ The Yeo-Johnson transformation is a flexible transformation that is similiar to Box-Cox, \code{\link[=boxcox_trans]{boxcox_trans()}}, but does not require input values to be greater than zero. } \details{ The transformation takes one of four forms depending on the values of \code{y} and \eqn{\lambda}. \itemize{ \item \eqn{y \ge 0} and \eqn{\lambda \neq 0}{\lambda != 0} : \eqn{y^{(\lambda)} = \frac{(y + 1)^\lambda - 1}{\lambda}}{y^(\lambda) = ((y + 1)^\lambda - 1)/\lambda} \item \eqn{y \ge 0} and \eqn{\lambda = 0}: \eqn{y^{(\lambda)} = \ln(y + 1)}{y^(\lambda) = ln(y + 1)} \item \eqn{y < 0} and \eqn{\lambda \neq 2}{\lambda != 2}: \eqn{y^{(\lambda)} = -\frac{(-y + 1)^{(2 - \lambda)} - 1}{2 - \lambda}}{y^(\lambda) = -((-y + 1)^(2 - \lambda) - 1)/(2 - \lambda)} \item \eqn{y < 0} and \eqn{\lambda = 2}: \eqn{y^{(\lambda)} = -\ln(-y + 1)}{y^(\lambda) = -ln(-y + 1)} } } \examples{ plot(yj_trans(-1), xlim = c(-10, 10)) plot(yj_trans(0), xlim = c(-10, 10)) plot(yj_trans(1), xlim = c(-10, 10)) plot(yj_trans(2), xlim = c(-10, 10)) } \references{ Yeo, I., & Johnson, R. (2000). A New Family of Power Transformations to Improve Normality or Symmetry. Biometrika, 87(4), 954-959. \url{http://www.jstor.org/stable/2673623} } scales/man/date_breaks.Rd0000644000176200001440000000103613556361126015042 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/breaks-retired.R \name{date_breaks} \alias{date_breaks} \title{Regularly spaced dates} \usage{ date_breaks(width = "1 month") } \arguments{ \item{width}{an interval specification, one of "sec", "min", "hour", "day", "week", "month", "year". Can be by an integer and a space, or followed by "s". Fractional seconds are supported.} } \description{ \Sexpr[results=rd, stage=render]{lifecycle::badge("retired")} Use \code{breaks_width()} instead. } \keyword{internal} scales/man/Range.Rd0000644000176200001440000000052013641652035013624 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/range.r \name{Range} \alias{Range} \alias{DiscreteRange} \alias{ContinuousRange} \title{Mutable ranges} \description{ Mutable ranges have a two methods (\code{train} and \code{reset}), and make it possible to build up complete ranges with multiple passes. } scales/man/atanh_trans.Rd0000644000176200001440000000043513556361126015102 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/trans-numeric.r \name{atanh_trans} \alias{atanh_trans} \title{Arc-tangent transformation} \usage{ atanh_trans() } \description{ Arc-tangent transformation } \examples{ plot(atanh_trans(), xlim = c(-1, 1)) } scales/man/div_gradient_pal.Rd0000644000176200001440000000175513641652035016076 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pal-gradient.r \name{div_gradient_pal} \alias{div_gradient_pal} \title{Diverging colour gradient (continuous).} \usage{ div_gradient_pal( low = mnsl("10B 4/6"), mid = mnsl("N 8/0"), high = mnsl("10R 4/6"), space = "Lab" ) } \arguments{ \item{low}{colour for low end of gradient.} \item{mid}{colour for mid point} \item{high}{colour for high end of gradient.} \item{space}{colour space in which to calculate gradient. Must be "Lab" - other values are deprecated.} } \description{ Diverging colour gradient (continuous). } \examples{ x <- seq(-1, 1, length.out = 100) r <- sqrt(outer(x^2, x^2, "+")) image(r, col = div_gradient_pal()(seq(0, 1, length.out = 12))) image(r, col = div_gradient_pal()(seq(0, 1, length.out = 30))) image(r, col = div_gradient_pal()(seq(0, 1, length.out = 100))) library(munsell) image(r, col = div_gradient_pal(low = mnsl(complement("10R 4/6"), fix = TRUE))(seq(0, 1, length = 100))) } scales/man/label_number.Rd0000644000176200001440000000671513641652035015233 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/label-number.r \name{label_number} \alias{label_number} \alias{label_comma} \alias{comma} \alias{number_format} \alias{comma_format} \title{Label numbers in decimal format (e.g. 0.12, 1,234)} \usage{ label_number( accuracy = NULL, scale = 1, prefix = "", suffix = "", big.mark = " ", decimal.mark = ".", trim = TRUE, ... ) label_comma( accuracy = NULL, scale = 1, prefix = "", suffix = "", big.mark = ",", decimal.mark = ".", trim = TRUE, digits, ... ) comma( x, accuracy = NULL, scale = 1, prefix = "", suffix = "", big.mark = ",", decimal.mark = ".", trim = TRUE, digits, ... ) number_format( accuracy = NULL, scale = 1, prefix = "", suffix = "", big.mark = " ", decimal.mark = ".", trim = TRUE, ... ) comma_format( accuracy = NULL, scale = 1, prefix = "", suffix = "", big.mark = ",", decimal.mark = ".", trim = TRUE, digits, ... ) } \arguments{ \item{accuracy}{A number to round to. Use (e.g.) \code{0.01} to show 2 decimal places of precision. If \code{NULL}, the default, uses a heuristic that should ensure breaks have the minimum number of digits needed to show the difference between adjacent values. Applied to rescaled data.} \item{scale}{A scaling factor: \code{x} will be multiplied by \code{scale} before formating. This is useful if the underlying data is very small or very large.} \item{prefix, suffix}{Symbols to display before and after value.} \item{big.mark}{Character used between every 3 digits to separate thousands.} \item{decimal.mark}{The character to be used to indicate the numeric decimal point.} \item{trim}{Logical, if \code{FALSE}, values are right-justified to a common width (see \code{\link[base:format]{base::format()}}).} \item{...}{Other arguments passed on to \code{\link[base:format]{base::format()}}.} \item{digits}{Deprecated, use \code{accuracy} instead.} \item{x}{A numeric vector to format.} } \value{ All \code{label_()} functions return a "labelling" function, i.e. a function that takes a vector \code{x} and returns a character vector of \code{length(x)} giving a label for each input value. Labelling functions are designed to be used with the \code{labels} argument of ggplot2 scales. The examples demonstrate their use with x scales, but they work similarly for all scales, including those that generate legends rather than axes. } \description{ Use \code{label_number()} force decimal display of numbers (i.e. don't use \link[=label_scientific]{scientific} notation). \code{label_comma()} is a special case that inserts a comma every three digits. } \section{Old interface}{ \code{number_format()}, \code{comma_format()}, and \code{comma()} are retired; please use \code{label_number()} and \code{label_comma()} instead. } \examples{ demo_continuous(c(-1e6, 1e6)) demo_continuous(c(-1e6, 1e6), labels = label_number()) demo_continuous(c(-1e6, 1e6), labels = label_comma()) # Use scale to rescale very small or large numbers to generate # more readable labels demo_continuous(c(0, 1e6), labels = label_number()) demo_continuous(c(0, 1e6), labels = label_number(scale = 1 / 1e3)) demo_continuous(c(0, 1e-6), labels = label_number()) demo_continuous(c(0, 1e-6), labels = label_number(scale = 1e6)) # You can use prefix and suffix for other types of display demo_continuous(c(32, 212), label = label_number(suffix = "\u00b0F")) demo_continuous(c(0, 100), label = label_number(suffix = "\u00b0C")) } scales/man/gradient_n_pal.Rd0000644000176200001440000000136513556361126015551 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pal-gradient.r \name{gradient_n_pal} \alias{gradient_n_pal} \title{Arbitrary colour gradient palette (continuous)} \usage{ gradient_n_pal(colours, values = NULL, space = "Lab") } \arguments{ \item{colours}{vector of colours} \item{values}{if colours should not be evenly positioned along the gradient this vector gives the position (between 0 and 1) for each colour in the \code{colours} vector. See \code{\link[=rescale]{rescale()}} for a convenience function to map an arbitrary range to between 0 and 1.} \item{space}{colour space in which to calculate gradient. Must be "Lab" - other values are deprecated.} } \description{ Arbitrary colour gradient palette (continuous) } scales/man/dscale.Rd0000644000176200001440000000074213556361126014034 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/scale-discrete.r \name{dscale} \alias{dscale} \title{Discrete scale} \usage{ dscale(x, palette, na.value = NA) } \arguments{ \item{x}{vector of discrete values to scale} \item{palette}{aesthetic palette to use} \item{na.value}{aesthetic to use for missing values} } \description{ Discrete scale } \examples{ with(mtcars, plot(disp, mpg, pch = 20, cex = 3, col = dscale(factor(cyl), brewer_pal()))) } scales/man/breaks_pretty.Rd0000644000176200001440000000215213556361126015454 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/breaks.r \name{breaks_pretty} \alias{breaks_pretty} \alias{pretty_breaks} \title{Pretty breaks for date/times} \usage{ breaks_pretty(n = 5, ...) } \arguments{ \item{n}{Desired number of breaks. You may get slightly more or fewer breaks that requested.} \item{...}{other arguments passed on to \code{\link[=pretty]{pretty()}}} } \description{ Uses default R break algorithm as implemented in \code{\link[=pretty]{pretty()}}. This is primarily useful for date/times, as \code{\link[=extended_breaks]{extended_breaks()}} should do a slightly better job for numeric scales. } \details{ \code{pretty_breaks()} is retired; use \code{breaks_pretty()} instead. } \examples{ one_month <- as.POSIXct(c("2020-05-01", "2020-06-01")) demo_datetime(one_month) demo_datetime(one_month, breaks = breaks_pretty(2)) demo_datetime(one_month, breaks = breaks_pretty(4)) # Tightly spaced date breaks often need custom labels too demo_datetime(one_month, breaks = breaks_pretty(12)) demo_datetime(one_month, breaks = breaks_pretty(12), labels = label_date_short() ) } scales/man/linetype_pal.Rd0000644000176200001440000000043113556361126015261 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pal-linetype.r \name{linetype_pal} \alias{linetype_pal} \title{Line type palette (discrete)} \usage{ linetype_pal() } \description{ Based on a set supplied by Richard Pearson, University of Manchester } scales/man/train_discrete.Rd0000644000176200001440000000103513137732040015564 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/scale-discrete.r \name{train_discrete} \alias{train_discrete} \title{Train (update) a discrete scale} \usage{ train_discrete(new, existing = NULL, drop = FALSE, na.rm = FALSE) } \arguments{ \item{new}{New data to add to scale} \item{existing}{Optional existing scale to update} \item{drop}{\code{TRUE}, will drop factor levels not associated with data} \item{na.rm}{If \code{TRUE}, will remove missing values} } \description{ Train (update) a discrete scale } scales/man/cbreaks.Rd0000644000176200001440000000321213641652035014203 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/breaks-retired.R \name{cbreaks} \alias{cbreaks} \title{Compute breaks for continuous scale} \usage{ cbreaks(range, breaks = extended_breaks(), labels = scientific_format()) } \arguments{ \item{range}{numeric vector of length 2 giving the range of the underlying data} \item{breaks}{either a vector of break values, or a break function that will make a vector of breaks when given the range of the data} \item{labels}{either a vector of labels (character vector or list of expression) or a format function that will make a vector of labels when called with a vector of breaks. Labels can only be specified manually if breaks are - it is extremely dangerous to supply labels if you don't know what the breaks will be.} } \description{ \Sexpr[results=rd, stage=render]{lifecycle::badge("retired")} This function wraps up the components needed to go from a continuous range to a set of breaks and labels suitable for display on axes or legends. } \examples{ cbreaks(c(0, 100)) cbreaks(c(0, 100), breaks_pretty(3)) cbreaks(c(0, 100), breaks_pretty(10)) cbreaks(c(1, 100), log_breaks()) cbreaks(c(1, 1e4), log_breaks()) cbreaks(c(0, 100), labels = math_format()) cbreaks(c(0, 1), labels = percent_format()) cbreaks(c(0, 1e6), labels = comma_format()) cbreaks(c(0, 1e6), labels = dollar_format()) cbreaks(c(0, 30), labels = dollar_format()) # You can also specify them manually: cbreaks(c(0, 100), breaks = c(15, 20, 80)) cbreaks(c(0, 100), breaks = c(15, 20, 80), labels = c(1.5, 2.0, 8.0)) cbreaks(c(0, 100), breaks = c(15, 20, 80), labels = expression(alpha, beta, gamma)) } \keyword{internal} scales/man/rescale_none.Rd0000644000176200001440000000056113320151564015225 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/bounds.r \name{rescale_none} \alias{rescale_none} \title{Don't perform rescaling} \usage{ rescale_none(x, ...) } \arguments{ \item{x}{numeric vector of values to manipulate.} \item{...}{all other arguments ignored} } \description{ Don't perform rescaling } \examples{ rescale_none(1:100) } scales/man/label_pvalue.Rd0000644000176200001440000000475013641652035015234 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/label-pvalue.R \name{label_pvalue} \alias{label_pvalue} \alias{pvalue_format} \alias{pvalue} \title{Label p-values (e.g. <0.001, 0.25, p >= 0.99)} \usage{ label_pvalue( accuracy = 0.001, decimal.mark = ".", prefix = NULL, add_p = FALSE ) pvalue_format( accuracy = 0.001, decimal.mark = ".", prefix = NULL, add_p = FALSE ) pvalue(x, accuracy = 0.001, decimal.mark = ".", prefix = NULL, add_p = FALSE) } \arguments{ \item{accuracy}{A number to round to. Use (e.g.) \code{0.01} to show 2 decimal places of precision. If \code{NULL}, the default, uses a heuristic that should ensure breaks have the minimum number of digits needed to show the difference between adjacent values. Applied to rescaled data.} \item{decimal.mark}{The character to be used to indicate the numeric decimal point.} \item{prefix}{A character vector of length 3 giving the prefixes to put in front of numbers. The default values are \code{c("<", "", ">")} if \code{add_p} is \code{TRUE} and \code{c("p<", "p=", "p>")} if \code{FALSE}.} \item{add_p}{Add "p=" before the value?} \item{x}{A numeric vector to format.} } \value{ All \code{label_()} functions return a "labelling" function, i.e. a function that takes a vector \code{x} and returns a character vector of \code{length(x)} giving a label for each input value. Labelling functions are designed to be used with the \code{labels} argument of ggplot2 scales. The examples demonstrate their use with x scales, but they work similarly for all scales, including those that generate legends rather than axes. } \description{ Formatter for p-values, using "<" and ">" for p-values close to 0 and 1. } \section{Old interface}{ \code{pvalue()} and \code{pvalue_dollar()} are retired; please use \code{label_pvalue()} instead. } \examples{ demo_continuous(c(0, 1)) demo_continuous(c(0, 1), labels = label_pvalue()) demo_continuous(c(0, 1), labels = label_pvalue(accuracy = 0.1)) demo_continuous(c(0, 1), labels = label_pvalue(add_p = TRUE)) # Or provide your own prefixes prefix <- c("p < ", "p = ", "p > ") demo_continuous(c(0, 1), labels = label_pvalue(prefix = prefix)) } \seealso{ Other labels for continuous scales: \code{\link{label_bytes}()}, \code{\link{label_dollar}()}, \code{\link{label_number_auto}()}, \code{\link{label_number_si}()}, \code{\link{label_ordinal}()}, \code{\link{label_parse}()}, \code{\link{label_percent}()}, \code{\link{label_scientific}()} } \concept{labels for continuous scales} scales/man/trans_format.Rd0000644000176200001440000000121113556361126015270 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/labels-retired.R \name{trans_format} \alias{trans_format} \title{Format labels after transformation} \usage{ trans_format(trans, format = scientific_format()) } \arguments{ \item{trans}{transformation to apply} \item{format}{additional formatter to apply after transformation} } \value{ a function with single parameter x, a numeric vector, that returns a character vector of list of expressions } \description{ \Sexpr[results=rd, stage=render]{lifecycle::badge("retired")} } \examples{ tf <- trans_format("log10", scientific_format()) tf(10 ^ 1:6) } \keyword{internal} scales/man/regular_minor_breaks.Rd0000644000176200001440000000106413556361126016773 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/minor_breaks.R \name{regular_minor_breaks} \alias{regular_minor_breaks} \title{Minor breaks} \usage{ regular_minor_breaks(reverse = FALSE) } \arguments{ \item{reverse}{if TRUE, calculates the minor breaks for a reversed scale} } \description{ Places minor breaks between major breaks. } \examples{ m <- extended_breaks()(c(1, 10)) regular_minor_breaks()(m, c(1, 10), n = 2) n <- extended_breaks()(c(0, -9)) regular_minor_breaks(reverse = TRUE)(n, c(0, -9), n = 2) } \keyword{internal} scales/man/exp_trans.Rd0000644000176200001440000000077513556361126014612 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/trans-numeric.r \name{exp_trans} \alias{exp_trans} \title{Exponential transformation (inverse of log transformation)} \usage{ exp_trans(base = exp(1)) } \arguments{ \item{base}{Base of logarithm} } \description{ Exponential transformation (inverse of log transformation) } \examples{ plot(exp_trans(0.5), xlim = c(-2, 2)) plot(exp_trans(1), xlim = c(-2, 2)) plot(exp_trans(2), xlim = c(-2, 2)) plot(exp_trans(), xlim = c(-2, 2)) } scales/man/number_bytes_format.Rd0000644000176200001440000000165513641652035016650 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/labels-retired.R \name{number_bytes_format} \alias{number_bytes_format} \alias{number_bytes} \title{Older interface to \code{label_bytes()}} \usage{ number_bytes_format(symbol = "auto", units = "binary", ...) number_bytes(x, symbol = "auto", units = c("binary", "si"), accuracy = 1, ...) } \arguments{ \item{symbol}{byte symbol to use. If "auto" the symbol used will be determined separately for each value of \code{x}. Valid symbols are "B", "kB", "MB", "GB", "TB", "PB", "EB", "ZB", and "YB" for SI units, and the "iB" variants for binary units.} \item{units}{which unit base to use, "binary" (1024 base) or "si" (1000 base)} } \description{ \Sexpr[results=rd, stage=render]{lifecycle::badge("retired")} These functions are kept for backward compatibility, but you should switch to \code{\link[=label_bytes]{label_bytes()}} for new code. } \keyword{internal} scales/man/boxcox_trans.Rd0000644000176200001440000000414613556361126015314 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/trans-numeric.r \name{boxcox_trans} \alias{boxcox_trans} \alias{modulus_trans} \title{Box-Cox & modulus transformations} \usage{ boxcox_trans(p, offset = 0) modulus_trans(p, offset = 1) } \arguments{ \item{p}{Transformation exponent, \eqn{\lambda}.} \item{offset}{Constant offset. 0 for Box-Cox type 1, otherwise any non-negative constant (Box-Cox type 2). \code{modulus_trans()} sets the default to 1.} } \description{ The Box-Cox transformation is a flexible transformation, often used to transform data towards normality. The modulus transformation generalises Box-Cox to also work with negative values. } \details{ The Box-Cox power transformation (type 1) requires strictly positive values and takes the following form for \code{y > 0}: \deqn{y^{(\lambda)} = \frac{y^\lambda - 1}{\lambda}}{y^(\lambda) = (y^\lambda - 1)/\lambda} When \code{y = 0}, the natural log transform is used. The modulus transformation implements a generalisation of the Box-Cox transformation that works for data with both positive and negative values. The equation takes the following forms, when \code{y != 0} : \deqn{y^{(\lambda)} = sign(y) * \frac{(|y| + 1)^\lambda - 1}{\lambda}}{ y^(\lambda) = sign(y)*((|y|+1)^\lambda - 1)/\lambda} and when \code{y = 0}: \deqn{y^{(\lambda)} = sign(y) * \ln(|y| + 1)}{ y^(\lambda) = sign(y) * ln(|y| + 1)} } \examples{ plot(boxcox_trans(-1), xlim = c(0, 10)) plot(boxcox_trans(0), xlim = c(0, 10)) plot(boxcox_trans(1), xlim = c(0, 10)) plot(boxcox_trans(2), xlim = c(0, 10)) plot(modulus_trans(-1), xlim = c(-10, 10)) plot(modulus_trans(0), xlim = c(-10, 10)) plot(modulus_trans(1), xlim = c(-10, 10)) plot(modulus_trans(2), xlim = c(-10, 10)) } \references{ Box, G. E., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society. Series B (Methodological), 211-252. \url{https://www.jstor.org/stable/2984418} John, J. A., & Draper, N. R. (1980). An alternative family of transformations. Applied Statistics, 190-197. \url{http://www.jstor.org/stable/2986305} } \seealso{ \code{\link[=yj_trans]{yj_trans()}} } scales/man/rescale_max.Rd0000644000176200001440000000115613556361126015064 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/bounds.r \name{rescale_max} \alias{rescale_max} \title{Rescale numeric vector to have specified maximum} \usage{ rescale_max(x, to = c(0, 1), from = range(x, na.rm = TRUE)) } \arguments{ \item{x}{numeric vector of values to manipulate.} \item{to}{output range (numeric vector of length two)} \item{from}{input range (numeric vector of length two). If not given, is calculated from the range of \code{x}} } \description{ Rescale numeric vector to have specified maximum } \examples{ rescale_max(1:100) rescale_max(runif(50)) rescale_max(1) } scales/man/label_bytes.Rd0000644000176200001440000000460313641652035015063 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/label-bytes.R \name{label_bytes} \alias{label_bytes} \title{Label bytes (1 kb, 2 MB, etc)} \usage{ label_bytes(units = "auto_si", accuracy = 1, ...) } \arguments{ \item{units}{Unit to use. Should either one of: \itemize{ \item "kB", "MB", "GB", "TB", "PB", "EB", "ZB", and "YB" for SI units (base 1000). \item "kiB", "MiB", "GiB", "TiB", "PiB", "EiB", "ZiB", and "YiB" for binary units (base 1024). \item \code{auto_si} or \code{auto_binary} to automatically pick the most approrpiate unit for each value. }} \item{accuracy}{A number to round to. Use (e.g.) \code{0.01} to show 2 decimal places of precision. If \code{NULL}, the default, uses a heuristic that should ensure breaks have the minimum number of digits needed to show the difference between adjacent values. Applied to rescaled data.} \item{...}{Other arguments passed on to \code{\link[=number]{number()}}} } \value{ A labeller function that takes a numeric vector of breaks and returns a character vector of labels. } \description{ Scale bytes into human friendly units. Can use either SI units (e.g. kB = 1000 bytes) or binary units (e.g. kiB = 1024 bytes). See \href{http://en.wikipedia.org/wiki/Units_of_information}{Units of Information} on Wikipedia for more details. } \examples{ demo_continuous(c(1, 1e6)) demo_continuous(c(1, 1e6), label = label_bytes()) # Force all to use same units demo_continuous(c(1, 1e6), label = label_bytes("kB")) # Auto units are particularly nice on log scales demo_log10(c(1, 1e6)) demo_log10(c(1, 1e7), label = label_bytes()) # You can also use binary units where a megabyte is defined as # (1024) ^ 2 bytes rather than (1000) ^ 2. You'll need to override # the default breaks to make this more informative. demo_continuous(c(1, 1024^2), label = label_bytes("auto_binary")) demo_continuous(c(1, 1024^2), breaks = breaks_width(250 * 1024), label = label_bytes("auto_binary") ) } \seealso{ Other labels for continuous scales: \code{\link{label_dollar}()}, \code{\link{label_number_auto}()}, \code{\link{label_number_si}()}, \code{\link{label_ordinal}()}, \code{\link{label_parse}()}, \code{\link{label_percent}()}, \code{\link{label_pvalue}()}, \code{\link{label_scientific}()} Other labels for log scales: \code{\link{label_number_si}()}, \code{\link{label_scientific}()} } \concept{labels for continuous scales} \concept{labels for log scales} scales/man/scales-package.Rd0000644000176200001440000000152313641652035015437 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/scales-package.R \docType{package} \name{scales-package} \alias{scales} \alias{scales-package} \title{scales: Scale Functions for Visualization} \description{ \if{html}{\figure{logo.png}{options: align='right' alt='logo' width='120'}} Graphical scales map data to aesthetics, and provide methods for automatically determining breaks and labels for axes and legends. } \seealso{ Useful links: \itemize{ \item \url{https://scales.r-lib.org} \item \url{https://github.com/r-lib/scales} \item Report bugs at \url{https://github.com/r-lib/scales/issues} } } \author{ \strong{Maintainer}: Hadley Wickham \email{hadley@rstudio.com} Authors: \itemize{ \item Dana Seidel } Other contributors: \itemize{ \item RStudio [copyright holder] } } \keyword{internal} scales/man/cscale.Rd0000644000176200001440000000205313556361126014030 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/scale-continuous.r \name{cscale} \alias{cscale} \title{Continuous scale} \usage{ cscale(x, palette, na.value = NA_real_, trans = identity_trans()) } \arguments{ \item{x}{vector of continuous values to scale} \item{palette}{palette to use. Built in palettes: \Sexpr[results=rd,stage=build]{scales:::seealso_pal()}} \item{na.value}{value to use for missing values} \item{trans}{transformation object describing the how to transform the raw data prior to scaling. Defaults to the identity transformation which leaves the data unchanged. Built in transformations: \Sexpr[results=rd,stage=build]{scales:::seealso_trans()}.} } \description{ Continuous scale } \examples{ with(mtcars, plot(disp, mpg, cex = cscale(hp, rescale_pal()))) with(mtcars, plot(disp, mpg, cex = cscale(hp, rescale_pal(), trans = sqrt_trans()))) with(mtcars, plot(disp, mpg, cex = cscale(hp, area_pal()))) with(mtcars, plot(disp, mpg, pch = 20, cex = 5, col = cscale(hp, seq_gradient_pal("grey80", "black")))) } scales/man/label_number_si.Rd0000644000176200001440000000423213641652035015716 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/label-number-si.R \name{label_number_si} \alias{label_number_si} \title{Label numbers with SI prefixes (2k, 1M, 5T etc)} \usage{ label_number_si(accuracy = 1, unit = NULL, sep = NULL, ...) } \arguments{ \item{accuracy}{A number to round to. Use (e.g.) \code{0.01} to show 2 decimal places of precision. If \code{NULL}, the default, uses a heuristic that should ensure breaks have the minimum number of digits needed to show the difference between adjacent values. Applied to rescaled data.} \item{unit}{Optional units specifier.} \item{sep}{Separator between number and SI unit. Defaults to \code{" "} if \code{units} is supplied, and \code{""} if not.} \item{...}{Other arguments passed on to \code{\link[base:format]{base::format()}}.} } \value{ All \code{label_()} functions return a "labelling" function, i.e. a function that takes a vector \code{x} and returns a character vector of \code{length(x)} giving a label for each input value. Labelling functions are designed to be used with the \code{labels} argument of ggplot2 scales. The examples demonstrate their use with x scales, but they work similarly for all scales, including those that generate legends rather than axes. } \description{ \code{number_si()} automatically scales and labels with the best SI prefix, "K" for values \eqn{\ge} 10e3, "M" for \eqn{\ge} 10e6, "B" for \eqn{\ge} 10e9, and "T" for \eqn{\ge} 10e12. } \examples{ demo_continuous(c(1, 1e9), label = label_number_si()) demo_continuous(c(1, 5000), label = label_number_si(unit = "g")) demo_continuous(c(1, 1000), label = label_number_si(unit = "m")) demo_log10(c(1, 1e9), breaks = log_breaks(10), labels = label_number_si()) } \seealso{ Other labels for continuous scales: \code{\link{label_bytes}()}, \code{\link{label_dollar}()}, \code{\link{label_number_auto}()}, \code{\link{label_ordinal}()}, \code{\link{label_parse}()}, \code{\link{label_percent}()}, \code{\link{label_pvalue}()}, \code{\link{label_scientific}()} Other labels for log scales: \code{\link{label_bytes}()}, \code{\link{label_scientific}()} } \concept{labels for continuous scales} \concept{labels for log scales} scales/DESCRIPTION0000644000176200001440000000222713656356645013260 0ustar liggesusersPackage: scales Title: Scale Functions for Visualization Version: 1.1.1 Authors@R: c(person(given = "Hadley", family = "Wickham", role = c("aut", "cre"), email = "hadley@rstudio.com"), person(given = "Dana", family = "Seidel", role = "aut"), person(given = "RStudio", role = "cph")) Description: Graphical scales map data to aesthetics, and provide methods for automatically determining breaks and labels for axes and legends. 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ĘĚ̵ŐÓ߸ňsŚ>|sAö_pCBsÍĄžs›D,ľ mőL¨–éŽ#˛‘˙ň $÷F0%˘!ŕĆż®)Éq(%śYŇ­Lń1ů/Đ@f×IO6đqďŮŚŹC‘ˇŃÎReíĎ+¸đ„8ÔP©şă}’äR–9ň<ĆϧąJHS3Lk§Ő˘u8’v‡˙8 ŢyÄrăÉFpü·â5á ©ŮńD ąNÇÇź—âG„ ČáH%¸ĎdĺęCcwŤ°˙âO ó/‘¸ŮŮ”đňéŢ ĺ1ž?şŢg W=‚yâ„8f|zĺ ľjř·>\Ă6tŐđoo2TŤ’B5ü»™Tމđ•˘;ÄĽü7+٨"¶Żt+‹/ó‰+Í’g×ô÷%Îp2tŘAL8P¬;8“óü‚ĂG&D´rűśEyă2#rä:¬3Č]ădř<†ě/rðap׼Ą¬Őýř;ŮaKLxç¸ çs)&†s˘%X»mĂ˝ÝdŤ–Ka8ľŁűˇÓ`Q‹ZÜf%şsĺ=ŚßKmäą`Ý‹‰‰óČxç÷¨«Ý9.[·6ß?ÎuýŇ…n6ŚOPľ<šj7C·ŕÎcE9‹ńD#É÷SéÎc:w0~'µ‰?A]XФŞĐä‚T8ę:xžË6»±nlň_‡aĂ ąNç„upÜęÇÄv«ČŚ'r+…Uđ[•î*mÝń'RüĘCFnͱ»H^2Ć{‰ăjJ0ŹňĆĄ6öĺdViµtŤŞ°rj¦ĄěUq”bL¤ËČ—1~ůč+ĺE.[î4ča×ČţSNb6 ţŮŁ«”3ü,\L„—±:‚\ÄřbfăŞ~Őt\A”áC”Ź0ž¨RĆŁ:W«r?aü§ás ˛ĚI ÆÁý~„ýŹ)e,lź"±Afß˙|ç“çśűv;¦ŰÎÇŐ3”aĺcڧ7÷eÖŰ`í¬w–:¦ě>C|–jô)—í{ě>C(ľ|O=ÉVKď]FLl«XWʤZnzÖb÷ZR­FöhÍaµ“ zĽEůd“ ŰŘ@ě7´Z#®nWsÝWw0~'µ‰? ÍE˛¦Â˝ř8jeq:n@şCńŞß*—-799lŢCög9‰!ŁŇ÷ßękű‹Ôu.• ľđ*EGbHXő'ŇÂĄísśÄR+?Ţ /١FMiŮm¸áŚu˛áoC%šcw–@ë°Ou=niřĺÚ?büÇÔĄůIqç:î]ĄŞ×ăF®,ęÖŚşßaC‘tĄJu;Íȶ…™ť7ű@4˛ećśŘGîö>2Ęýc MőYě’”=Čľ$ô Ĺz°LéNOéÝáĚľ¦ëţ\±Í&%Ż4VV*d[Ü’¬#úőLKň(ĽĆÍ5‡íV×ö(|ŘôËč–Y±(iRĹ`ÜŰmëěwqËłeůăégO>[–V× E'ŢJq‘m"‚3÷SvďÚŰíďúńďgxOdźçdş#őÉűpEÄ'1ćănęZÍu— _ę)¶±ÓwqnLnç6őif~–w˝řĐłŤ8Ŕ$łlSž0“Ž~¤fĂ^Ł!i!iśp­š5ó@ňBň"€kJeţąd…dEĐł`#I IŠ )M[’B’"¸NSá ordinal(c(1:4, 11:21, 101, NA)) [1] "1st" "2nd" "3rd" "4th" "11th" "12th" "13th" "14th" "15th" [10] "16th" "17th" "18th" "19th" "20th" "21st" "101st" NA > ordinal(c(1, 2, 10, 11, NA), rules = ordinal_french()) [1] "1er" "2e" "10e" "11e" NA > ordinal(c(1, 2, 10, 11, NA), rules = ordinal_french("f", TRUE)) [1] "1res" "2es" "10es" "11es" NA scales/tests/testthat/test-colour-manip.r0000644000176200001440000000243713562606553020314 0ustar liggesusers# hcl --------------------------------------------------------------------- test_that("can modify each hcl component", { expect_equal(col2hcl("red", h = 180), "#00B793") expect_equal(col2hcl("red", l = 50), "#F40000") expect_equal(col2hcl("red", c = 50), "#B36B6B") expect_equal(col2hcl("red", alpha = 0.5), "#FF000080") }) # alpha ------------------------------------------------------------------- test_that("missing alpha preserves existing", { rgb <- farver::decode_colour(rep("red", 5), to = "rgb") alpha <- seq(0, 1, length.out = nrow(rgb)) reds <- farver::encode_colour(rgb, alpha) expect_equal(reds, alpha(reds, NA)) expect_equal(reds, alpha(reds, rep(NA, 5))) }) test_that("alpha values recycled to match colour", { cols <- farver::encode_colour(farver::decode_colour(c("red", "green", "blue", "pink"))) expect_equal(cols, alpha(cols, NA)) expect_equal(cols, alpha(cols, 1)) }) test_that("col values recycled to match alpha", { alphas <- round(seq(0, 1, length.out = 3)) reds <- alpha("red", alphas) reds_alpha <- farver::decode_colour(reds, TRUE)[, 4] expect_equal(alphas, reds_alpha) }) test_that("preserves names", { x <- c("deeppink", "hotpink", "lightpink") expect_named(alpha(x, 0.5), NULL) names(x) <- x expect_named(alpha(x, 0.5), names(x)) }) scales/tests/testthat/test-breaks-extended.R0000644000176200001440000000036412775473604020715 0ustar liggesuserscontext("breaks - extended") test_that("extended breaks returns no breaks for bad inputs", { breaks <- extended_breaks() expect_equal(breaks(NA), numeric()) expect_equal(breaks(Inf), numeric()) expect_equal(breaks(NaN), numeric()) }) scales/tests/testthat/test-label-expression.R0000644000176200001440000000072613556361126021117 0ustar liggesuserstest_that("parse_format() returns an expression object", { expect_equal( parse_format()(c("alpha", "beta", "gamma")), expression(alpha, beta, gamma) ) expect_equal( parse_format()(1:5), expression(1, 2, 3, 4, 5) ) expect_identical(label_math()(character()), expression()) expect_identical(label_parse()(character()), expression()) }) test_that("math_format() returns expression", { expect_equal(label_math()(1), expression(10 ^ 1)) }) scales/tests/testthat/test-scale-discrete.r0000644000176200001440000000051713655052654020573 0ustar liggesuserstest_that("NA.value works for discrete", { x <- c(NA, "a", "b", "c", NA) pal <- brewer_pal() expect_that(dscale(x, pal)[1], equals(NA_character_)) expect_that(dscale(x, pal)[5], equals(NA_character_)) expect_that(dscale(x, pal, "grey50")[1], equals("grey50")) expect_that(dscale(x, pal, "grey50")[5], equals("grey50")) }) scales/tests/testthat/test-label-scientific.R0000644000176200001440000000213513556361126021034 0ustar liggesusers test_that("scientific format shows specific sig figs", { expect_equal(label_scientific(digits = 1)(123456), "1e+05") expect_equal(label_scientific(digits = 2)(123456), "1.2e+05") expect_equal(label_scientific(digits = 3)(123456), "1.23e+05") expect_equal(label_scientific(digits = 1)(0.123456), "1e-01") expect_equal(label_scientific(digits = 2)(0.123456), "1.2e-01") expect_equal(label_scientific(digits = 3)(0.123456), "1.23e-01") }) test_that("prefix and suffix works with scientific format", { expect_equal(scientific(123456, digits = 2, prefix = "V="), "V=1.2e+05") expect_equal(scientific(123456, digits = 2, suffix = " km"), "1.2e+05 km") }) test_that("scale works with scientific format", { expect_equal(scientific(123456, digits = 2, scale = 1000), "1.2e+08") }) test_that("decimal.mark works with scientific format", { expect_equal(scientific(123456, digits = 2, decimal.mark = ","), "1,2e+05") }) test_that("scientific format respects NAs", { expect_equal(scientific(NA), NA_character_) }) test_that("scientific preserves names", { expect_named(scientific(c(a = 1)), "a") }) scales/tests/testthat/test-label-percent.R0000644000176200001440000000130513556361126020352 0ustar liggesusers test_that("negative percents work", { expect_equal(percent(-0.6, accuracy = 1), "-60%") }) test_that("Single 0 gives 0%", { expect_equal(percent(0), "0%") }) test_that("preserves NAs", { expect_equal(percent(c(NA, 1, 2, 3)), c(NA, "100%", "200%", "300%")) expect_equal(percent(NA_real_), NA_character_) }) test_that("preserves names", { expect_named(percent(c(a = 1)), "a") }) test_that("default accuracy works for range of inputs", { x <- c(0.1, 0.2, 0.5) expect_equal(percent(x / 100), c("0.10%", "0.20%", "0.50%")) expect_equal(percent(x / 10), c("1.0%", "2.0%", "5.0%")) expect_equal(percent(x), c("10%", "20%", "50%")) expect_equal(percent(x * 10), c("100%", "200%", "500%")) }) scales/tests/testthat/test-label-number-si.R0000644000176200001440000000137713556361126020624 0ustar liggesuserstest_that("rescales values independently", { number_si <- label_number_si() expect_equal(number_si(c(1e3, 1e6, 1e9)), c("1K", "1M", "1B")) expect_equal(number_si(c(-1e3, 1e6, 1e9)), c("-1K", "1M", "1B")) expect_equal(number_si(c(.50, 1e6, 1e15)), c("0", "1M", "1 000T")) }) test_that("handles bad inputs gracefully", { number_si <- label_number_si() expect_equal(number_si(c(1, NA)), c("1", NA)) expect_equal(number_si(c(1, Inf)), c("1", "Inf")) }) test_that("arguments passed on to number()", { number_si <- label_number_si(accuracy = .1, prefix = "$") expect_equal(number_si(c(.50, 1e6, 1e9)), c("$0.5", "$1.0M", "$1.0B")) }) test_that("number_si preserves names", { number_si <- label_number_si() expect_named(number_si(c(a = 1)), "a") }) scales/tests/testthat/test-pal-hue.r0000644000176200001440000000103513641652035017225 0ustar liggesuserscontext("Hue pal") test_that("hue_pal arguments are forcely evaluated on each call #81", { col1 <- hue_pal(h.start = 0) col2 <- hue_pal(h.start = 90) colours <- list() hues <- c(0, 90) for (i in 1:2) { colours[[i]] <- hue_pal(h.start = hues[i]) } expect_equal(col1(1), colours[[1]](1)) expect_equal(col2(1), colours[[2]](1)) }) test_that("hue_pal respects direction argument #252", { col1 <- hue_pal() col2 <- hue_pal(direction = -1) expect_equal(col1(3), rev(col2(3))) expect_equal(col1(9), rev(col2(9))) }) scales/tests/testthat/test-label-date-short.txt0000644000176200001440000000220013655051665021401 0ustar liggesusers> dformat <- label_date_short() > # dates > jan1 <- as.Date("2010-01-01") > dformat(seq(jan1, length = 8, by = "7 day")) [1] "01\nJan\n2010" "08" "15" "22" [5] "29" "05\nFeb" "12" "19" > dformat(seq(jan1, length = 8, by = "3 month")) [1] "Jan\n2010" "Apr" "Jul" "Oct" "Jan\n2011" "Apr" [7] "Jul" "Oct" > dformat(seq(jan1, length = 8, by = "1 year")) [1] "2010" "2011" "2012" "2013" "2014" "2015" "2016" "2017" > # date-times > jan1 <- as.POSIXct("2010-01-01", tz = "UTC") > dformat(seq(jan1, length = 6, by = "3 hours")) [1] "00:00\n01\nJan\n2010" "03:00" "06:00" [4] "09:00" "12:00" "15:00" > dformat(seq(jan1, length = 6, by = "7 day")) [1] "01\nJan\n2010" "08" "15" "22" [5] "29" "05\nFeb" > dformat(seq(jan1, length = 6, by = "3 month")) [1] "Jan\n2010" "Apr" "Jul" "Oct" "Jan\n2011" "Apr" > dformat(seq(jan1, length = 6, by = "1 year")) [1] "2010" "2011" "2012" "2013" "2014" "2015" scales/tests/testthat/test-label-number-auto.txt0000644000176200001440000000170513655051665021576 0ustar liggesusers> number_auto <- label_number_auto() > number_auto(c(0, 1e-06)) [1] "0e+00" "1e-06" > number_auto(c(9e-04, 0.001, 0.0011)) [1] "0.0009" "0.0010" "0.0011" > number_auto(c(9e-05, 1e-04, 0.00011)) [1] "0.00009" "0.00010" "0.00011" > number_auto(c(9e-06, 1e-05, 1.1e-05)) [1] "9.0e-06" "1.0e-05" "1.1e-05" > number_auto(c(999, 1000, 1001)) [1] "999" "1 000" "1 001" > number_auto(c(999999, 1e+06, 1000001)) [1] "999 999" "1 000 000" "1 000 001" > number_auto(c(9999999, 1e+07, 10000001)) [1] "9 999 999" "10 000 000" "10 000 001" > number_auto(c(99999999, 1e+08, 100000001)) [1] "1e+08" "1e+08" "1e+08" > number_auto(c(9e-07, 1e-06, 1.1e-06)) [1] "9.0e-07" "1.0e-06" "1.1e-06" > # Years shouldn't get commas > number_auto(c(2010, 2013, 2020)) [1] "2010" "2013" "2020" > number_auto(c(-2010, -2013, -2020)) [1] "-2 010" "-2 013" "-2 020" > # Pick shortest individually > number_auto(10^(1:7)) [1] "10" "100" "1 000" "1e+04" "1e+05" "1e+06" "1e+07" scales/tests/testthat/test-label-bytes.R0000644000176200001440000000524313556361126020045 0ustar liggesuserstest_that("auto units always rounds down", { expect_equal(label_bytes()(1000^(1:3)), c("1 kB", "1 MB", "1 GB")) }) test_that("auto units handles 0 and other special values", { expect_equal(label_bytes()(NA), NA_character_) expect_equal(label_bytes()(0), "0 B") expect_equal(label_bytes()(-1), "-1 B") expect_equal(label_bytes()(Inf), "Inf") }) test_that("can use either binary or si units", { expect_equal(label_bytes("kB")(1000), "1 kB") expect_equal(label_bytes("kiB")(1024), "1 kiB") }) test_that("errors if unknown unit", { expect_error(label_bytes("unit")(0), "valid unit") }) # deprecated interface ---------------------------------------------------- test_that("Byte formatter can take a symbol designator", { expect_equal( number_bytes(c(50, 400, 502, NA), symbol = "B"), c("50 B", "400 B", "502 B", NA) ) expect_equal( number_bytes(3:5 * 1024^2, symbol = "MiB"), c("3 MiB", "4 MiB", "5 MiB") ) expect_equal( number_bytes(1000^(1:3), symbol = "kB", units = "si"), c("1 kB", "1 000 kB", "1 000 000 kB") ) # informative warning for incorrect spelling expect_warning(number_bytes(c(50, 400, 502, NA), symbol = "k"), "must be") # respects unit designation expect_equal(number_bytes(1024, accuracy = .01), c("1.00 KiB")) expect_equal(number_bytes(1024, units = "si", accuracy = .01), c("1.02 kB")) expect_equal(number_bytes(1000, units = "si", accuracy = .01), c("1.00 kB")) # takes parameters from number() expect_equal( number_bytes(c(3e6, 4e6, 5e6), accuracy = .001), c("2.861 MiB", "3.815 MiB", "4.768 MiB") ) expect_equal( number_bytes(c(3e6, 4e6, 5e6), units = "si", accuracy = .1), c("3.0 MB", "4.0 MB", "5.0 MB") ) # unit system is enforced expect_warning(number_bytes(1024^(1:2), "kB", units = "binary"), "KiB") expect_warning(number_bytes(1024^(1:2), "KiB", units = "si"), "kB") }) test_that("Byte formatter handles zero values", { expect_equal(number_bytes(0), "0 B") }) test_that("Byte formatter handles large values", { expect_equal(number_bytes(1024^11), "1 073 741 824 YiB") expect_equal(number_bytes(1000^9, units = "si"), "1 000 YB") }) test_that("Byte formatter handles negative values", { expect_equal(number_bytes(-1024^2), "-1 MiB") }) test_that('Byte formatter symbol = "auto" can show variable multiples', { expect_equal(number_bytes(1024^(1:3)), c("1 KiB", "1 MiB", "1 GiB")) }) test_that("Byte formatter throws informative error for wrong length symbol", { expect_error(number_bytes(symbol = character()), "not length 0") expect_error(number_bytes(symbol = c("kB", "MB")), "not length 2") }) test_that("preserves names", { expect_named(number_bytes(c(a = 1)), "a") }) scales/tests/testthat/test-colour-mapping.r0000644000176200001440000002203513562561114020630 0ustar liggesuserscontext("Colors") bw <- c("black", "white") test_that("Edgy col_bin scenarios", { # Do these cases make sense? expect_equal(col_bin(bw, NULL)(1), "#777777") expect_equal(col_bin(bw, 1)(1), "#FFFFFF") }) test_that("Outside of domain returns na.color", { suppressWarnings({ expect_identical("#808080", col_factor(bw, letters)("foo")) expect_identical("#808080", col_quantile(bw, 0:1)(-1)) expect_identical("#808080", col_quantile(bw, 0:1)(2)) expect_identical("#808080", col_numeric(bw, c(0, 1))(-1)) expect_identical("#808080", col_numeric(bw, c(0, 1))(2)) expect_true(is.na(col_factor(bw, letters, na.color = NA)("foo"))) expect_true(is.na(col_quantile(bw, 0:1, na.color = NA)(-1))) expect_true(is.na(col_quantile(bw, 0:1, na.color = NA)(2))) expect_true(is.na(col_numeric(bw, c(0, 1), na.color = NA)(-1))) expect_true(is.na(col_numeric(bw, c(0, 1), na.color = NA)(2))) }) expect_warning(col_factor(bw, letters, na.color = NA)("foo")) expect_warning(col_quantile(bw, 0:1, na.color = NA)(-1)) expect_warning(col_quantile(bw, 0:1, na.color = NA)(2)) expect_warning(col_numeric(bw, c(0, 1), na.color = NA)(-1)) expect_warning(col_numeric(bw, c(0, 1), na.color = NA)(2)) }) test_that("Basic color accuracy", { expect_identical(c("#000000", "#808080", "#FFFFFF"), col_numeric(colorRamp(bw), NULL)(c(0, 0.5, 1))) expect_identical(c("#000000", "#FFFFFF"), col_bin(bw, NULL)(c(1, 2))) expect_identical(c("#000000", "#FFFFFF"), col_bin(bw, c(1, 2))(c(1, 2))) expect_identical(c("#000000", "#FFFFFF"), col_bin(bw, c(1, 2), 2)(c(1, 2))) expect_identical(c("#000000", "#FFFFFF"), col_bin(bw, NULL, bins = c(1, 1.5, 2))(c(1, 2))) expect_identical(c("#000000", "#FFFFFF"), col_bin(bw, c(1, 2), bins = c(1, 1.5, 2))(c(1, 2))) expect_identical(c("#000000", "#777777", "#FFFFFF"), col_numeric(bw, NULL)(1:3)) expect_identical(c("#000000", "#777777", "#FFFFFF"), col_numeric(bw, c(1:3))(1:3)) expect_identical(rev(c("#000000", "#777777", "#FFFFFF")), col_numeric(rev(bw), c(1:3))(1:3)) # domain != unique(x) expect_identical(c("#000000", "#0E0E0E", "#181818"), col_factor(bw, LETTERS)(LETTERS[1:3])) # domain == unique(x) expect_identical(c("#000000", "#777777", "#FFFFFF"), col_factor(bw, LETTERS[1:3])(LETTERS[1:3])) # no domain expect_identical(c("#000000", "#777777", "#FFFFFF"), col_factor(bw, NULL)(LETTERS[1:3])) # Non-factor domains are sorted unless instructed otherwise expect_identical(c("#000000", "#777777", "#FFFFFF"), col_factor(bw, rev(LETTERS[1:3]))(LETTERS[1:3])) expect_identical(rev(c("#000000", "#777777", "#FFFFFF")), col_factor(bw, rev(LETTERS[1:3]), ordered = TRUE)(LETTERS[1:3])) }) test_that("col_numeric respects alpha", { expect_equal( col_numeric(c("#FF000000", "#FF0000FF"), c(0, 1), alpha = TRUE)(0.5), "#FF000080" ) }) test_that("CIELab overflow", { expect_identical(c("#FFFFFF", "#CFB1FF", "#9265FF", "#0000FF"), colour_ramp(c("white", "blue"))(0:3 / 3)) }) test_that("factors match by name, not position", { full <- factor(letters[1:5]) pal <- col_factor("magma", na.color = NA, levels = full) partial <- full[2:4] expect_identical(pal(partial), pal(droplevels(partial))) # Sending in values outside of the color scale should result in a warning and na.color col <- expect_warning(pal(letters[10:20])) expect_true(all(is.na(col))) }) test_that("qualitative palettes don't interpolate", { pal <- col_factor("Accent", na.color = NA, levels = letters[1:5]) allColors <- RColorBrewer::brewer.pal( n = RColorBrewer::brewer.pal.info["Accent", "maxcolors"], name = "Accent") # If we're not interpolating, then the colors for each level should match # exactly with the color in the corresponding position in the palette. expect_identical(pal(letters[1:5]), allColors[1:5]) # Same behavior when domain is provided initially expect_identical( col_factor("Accent", domain = rep(letters[1:5], 2))(letters[1:5]), allColors[1:5] ) # Same behavior when domain is provided initially, and is a factor expect_identical( col_factor("Accent", domain = factor(rep(letters[5:1], 2)))(letters[1:5]), allColors[1:5] ) # Same behavior when domain is provided initially, and is not a factor expect_identical( col_factor("Accent", domain = rep(letters[5:1], 2), ordered = TRUE)(letters[5:1]), allColors[1:5] ) # Same behavior when no domain or level is provided initially expect_identical( col_factor("Accent", NULL)(letters[1:5]), allColors[1:5] ) # Values outside of the originally provided levels should be NA with warning expect_warning(pal(letters[6])) expect_true(suppressWarnings(is.na(pal(letters[6])))) }) test_that("OK, qualitative palettes sometimes interpolate", { pal <- col_factor("Accent", na.color = NA, levels = letters[1:20]) allColors <- RColorBrewer::brewer.pal( n = RColorBrewer::brewer.pal.info["Accent", "maxcolors"], name = "Accent") result <- expect_warning(pal(letters[1:20])) # The first and last levels are the first and last palette colors expect_true(all(result[c(1, 20)] %in% allColors)) # All the rest are interpolated though expect_true(!any(result[-c(1, 20)] %in% allColors)) }) verifyReversal <- function(colorFunc, values, ..., filter = identity) { f1 <- filter(colorFunc("Blues", domain = values, ...)(values)) f2 <- filter(colorFunc("Blues", domain = NULL, ...)(values)) f3 <- filter(colorFunc("Blues", domain = values, reverse = FALSE, ...)(values)) f4 <- filter(colorFunc("Blues", domain = NULL, reverse = FALSE, ...)(values)) r1 <- filter(colorFunc("Blues", domain = values, reverse = TRUE, ...)(values)) r2 <- filter(colorFunc("Blues", domain = NULL, reverse = TRUE, ...)(values)) expect_identical(f1, f2) expect_identical(f1, f3) expect_identical(f1, f4) expect_identical(r1, r2) expect_identical(f1, rev(r1)) } test_that("col_numeric can be reversed", { verifyReversal(col_numeric, 1:10) }) test_that("col_bin can be reversed", { # col_bin needs to filter because with 10 values and 7 bins, there is some # repetition that occurs in the results. Hard to explain but easy to see: # scales::show_col(col_bin("Blues", NULL)(1:8)) # scales::show_col(col_bin("Blues", NULL, reverse = TRUE)(1:8)) verifyReversal(col_bin, 1:10, filter = unique) }) test_that("col_quantile can be reversed", { verifyReversal(col_quantile, 1:10, n = 7) }) test_that("col_factor can be reversed", { # With interpolation verifyReversal(col_factor, letters, filter = expect_warning) # Without interpolation accent <- suppressWarnings(RColorBrewer::brewer.pal(Inf, "Accent")) result1 <- col_factor("Accent", NULL)(letters[1:5]) expect_identical(result1, head(accent, 5)) # Reversing a qualitative palette means we should pull the same colors, but # apply them in reverse order result2 <- col_factor("Accent", NULL, reverse = TRUE)(letters[1:5]) expect_identical(result2, rev(head(accent, 5))) }) test_that("Palettes with ncolor < 3 work properly", { test_palette <- function(palette) { colors <- col_factor(palette, letters[1:2])(letters[1:2]) # brewer.pal returns minimum 3 colors, and warns if you request less than 3. expected_colors <- suppressWarnings(RColorBrewer::brewer.pal(2, palette))[1:2] # The expected behavior is that the first two colors in the palette are returned. # This is different than the behavior in Leaflet color* functions; in those # functions, when 2 colors are needed from an RColorBrewer palette, the first and # third colors are used. Using the first and third is arguably a better choice # for sequential and diverging palettes, and very arguably worse for qualitative. # The scales' col_* functions use the first 2 colors for consistency with # scales::brewer_pal. expect_identical(colors, expected_colors) colors <- col_bin(palette, 1:2, bins = 2)(1:2) expect_identical(colors, expected_colors) } # Qualitative palette test_palette("Accent") # Sequential palette test_palette("Blues") # Diverging palette test_palette("Spectral") }) test_that("Arguments to `cut` are respected", { colors1 <- col_bin("Greens", 1:3, 1:3)(1:3) # Intervals are [1,2) and [2,3], so 2 and 3 are the same expect_identical(colors1, c("#E5F5E0", "#A1D99B", "#A1D99B")) colors2 <- col_bin("Blues", 1:3, 1:3, right = TRUE)(1:3) # Intervals are [1,2] and (2,3], so 1 and 2 are the same expect_identical(colors2, c("#DEEBF7", "#DEEBF7", "#9ECAE1")) # Shows that you can use cut + col_factor to achieve finer grained # control than with col_bin pal <- col_factor("Reds", domain = NULL, na.color = NA) colorsTT <- pal(cut(1:3, 1:3, include.lowest = TRUE, right = TRUE)) expect_identical(colorsTT, c("#FEE0D2", "#FEE0D2", "#FC9272")) colorsTF <- pal(cut(1:3, 1:3, include.lowest = TRUE, right = FALSE)) expect_identical(colorsTF, c("#FEE0D2", "#FC9272", "#FC9272")) colorsFT <- pal(cut(1:3, 1:3, include.lowest = FALSE, right = TRUE)) expect_identical(colorsFT, c(NA, "#FEE0D2", "#FC9272")) colorsFF <- pal(cut(1:3, 1:3, include.lowest = FALSE, right = FALSE)) expect_identical(colorsFF, c("#FEE0D2", "#FC9272", NA)) }) scales/tests/testthat/test-trans-date.r0000644000176200001440000000305013326174377017743 0ustar liggesuserscontext("Trans - dates and times") a_time <- ISOdatetime(2012, 1, 1, 11, 30, 0, tz = "UTC") a_date <- as.Date(a_time) tz <- function(x) attr(as.POSIXlt(x), "tzone")[1] tz2 <- function(x) format(x, "%Z") with_tz <- function(x, value) { as.POSIXct(format(x, tz = value, usetz = TRUE), tz = value) } test_that("date/time scales raise error on incorrect inputs", { time <- time_trans() expect_error(time$trans(a_date), "Invalid input") date <- date_trans() expect_error(date$trans(a_time), "Invalid input") }) test_that("time scales learn timezones", { skip_if_not(getRversion() > "3.3.3") time <- time_trans() x <- time$inv(time$trans(a_time)) expect_equal(tz(x), "UTC") expect_equal(tz2(x), "UTC") time <- time_trans() x <- time$inv(time$trans(with_tz(a_time, "GMT"))) expect_equal(tz(x), "GMT") expect_equal(tz2(x), "GMT") }) test_that("tz arugment overrules default time zone", { time <- time_trans("GMT") x <- time$inv(time$trans(a_time)) expect_equal(tz(x), "GMT") expect_equal(tz2(x), "GMT") }) test_that("date_breaks() works", { times <- as.POSIXct(c("2000-01-01 08:29:58", "2000-01-01 08:30:10"), tz = "UTC") expect_equal( date_breaks("1 hour")(times), as.POSIXct(c("2000-01-01 8:00:00 UTC", "2000-01-01 9:00:00 UTC"), tz = "UTC") ) expect_equal( date_breaks(".5 secs")(times)[1:2], as.POSIXct(c("2000-01-01 08:29:58.0 UTC", "2000-01-01 08:29:58.5 UTC"), tz = "UTC") ) dates <- a_date + 1:30 expect_equal( date_breaks("1 month")(dates), as.Date(c("2012-01-01", "2012-02-01")) ) }) scales/tests/testthat/test-rescale.R0000644000176200001440000000020512554266001017242 0ustar liggesuserscontext("rescale") test_that("rescale preserves NAs even when x has zero range", { expect_equal(rescale(c(1, NA)), c(0.5, NA)) }) scales/tests/testthat/test-labels-retired.R0000644000176200001440000000065313556361126020540 0ustar liggesuserstest_that("unit format", { expect_equal( unit_format(unit = "km", scale = 1e-3)(c(1e3, NA, 2e3)), c("1.0 km", NA, "2.0 km") ) expect_equal( unit_format(unit = "ha", scale = 1e-4, accuracy = .1)(c(1e3, 2e3)), c("0.1 ha", "0.2 ha") ) expect_equal( unit_format()(c(1e2, 2e2)), c("100 m", "200 m") ) }) test_that("unit_format preserves names", { expect_named(unit_format()(c(a = 1)), "a") }) scales/tests/testthat/test-label-date.R0000644000176200001440000000263713655052654017643 0ustar liggesuserstest_that("date_format works correctly", { a_date <- ISOdate(2012, 1, 1, 11, tz = "UTC") na_date <- ISOdate(NA, 1, 1) # date of value NA expect_equal(date_format()(a_date), "2012-01-01") expect_equal(date_format(format = "%m/%d/%Y")(a_date), "01/01/2012") expect_equal(date_format(format = "%m/%d/%Y", tz = "Etc/GMT+12")(a_date), "12/31/2011") expect_equal(date_format()(na_date), NA_character_) }) test_that("time_format works correctly", { a_time <- ISOdatetime(2012, 1, 1, 11, 30, 0, tz = "UTC") na_time <- ISOdatetime(NA, 1, 1, 1, 1, 0) # time of value NA expect_equal(time_format()(a_time), "11:30:00") expect_equal(time_format()(hms::as_hms(a_time)), "11:30:00") expect_equal(time_format(format = "%H")(hms::as_hms(a_time)), "11") expect_equal(time_format()(na_time), NA_character_) }) test_that("date_short doesn't change unexpectedly", { verify_output(test_path("test-label-date-short.txt"), { dformat <- label_date_short() "dates" jan1 <- as.Date("2010-01-01") dformat(seq(jan1, length = 8, by = "7 day")) dformat(seq(jan1, length = 8, by = "3 month")) dformat(seq(jan1, length = 8, by = "1 year")) "date-times" jan1 <- as.POSIXct("2010-01-01", tz = "UTC") dformat(seq(jan1, length = 6, by = "3 hours")) dformat(seq(jan1, length = 6, by = "7 day")) dformat(seq(jan1, length = 6, by = "3 month")) dformat(seq(jan1, length = 6, by = "1 year")) }) }) scales/tests/testthat/test-full-seq.r0000644000176200001440000000172413655052655017436 0ustar liggesuserstest_that("works with numeric", { x <- c(0, 100) expect_equal(fullseq(x, 50), c(0, 50, 100)) }) test_that("works with POSIXct", { x <- as.POSIXct(c("2000-01-01 08:29:58", "2000-01-01 08:30:10"), tz = "UTC") expect_equal( fullseq(x, "1 hour"), as.POSIXct(c("2000-01-01 8:00:00 UTC", "2000-01-01 9:00:00 UTC"), tz = "UTC") ) expect_equal( fullseq(x, ".5 secs")[1:2], as.POSIXct(c("2000-01-01 08:29:58.0 UTC", "2000-01-01 08:29:58.5 UTC"), tz = "UTC") ) }) test_that("works with Date", { x <- as.Date("2012-01-01") + 1:30 expect_equal(fullseq(x, "1 month"), as.Date(c("2012-01-01", "2012-02-01"))) }) test_that("works with hms/difftime", { x <- hms::hms(hours = 0:1) y <- as.difftime(c(0, 1800, 3600), units = "secs") expect_equal(fullseq(x, 1800), y) expect_equal(fullseq(x, "30 mins"), y) # Preserves units x <- as.difftime(c(0, 1), units = "hours") expect_equal(fullseq(x, 1800), as.difftime(c(0, 0.5, 1), units = "hours")) }) scales/tests/testthat/test-breaks-log.r0000644000176200001440000000463113556361126017730 0ustar liggesuserscontext("Breaks - log") test_that("Five ticks over 10^4 range work", { expect_equal(breaks_log()(10^(1:5)), 10^(1:5)) }) test_that("behaves nicely when inputs are wacky", { expect_equal(breaks_log()(c(0, NA)), 0) expect_equal(breaks_log()(c(0, Inf)), numeric()) expect_equal(breaks_log()(c(NA, NA)), numeric()) expect_equal(breaks_log()(c(-Inf, Inf)), numeric()) expect_equal(breaks_log()(numeric(0)), numeric()) }) test_that("use integer base powers when at least 3 breaks are within range", { base <- 10 expect_equal( breaks_log(base = base)(base^c(0, 2)), base^(0:2) ) base <- 4 expect_equal( breaks_log(base = base)(base^c(0, 2)), base^(0:2) ) base <- 2 expect_equal( breaks_log(base = base)(base^c(0, 2)), base^(0:2) ) }) test_that("add intermediate breaks when more breaks are needed", { base <- 10 expect_equal( breaks_log(base = base)(base^c(2, 4) + c(1, -1)), c(100, 300, 1000, 3000, 10000) ) expect_equal( breaks_log(base = base)(base^c(2, 3) + c(1, -1)), c(100, 200, 300, 500, 1000) ) base <- 4 expect_equal( breaks_log(base = base)(base^c(2, 4) + c(1, -1)), c(16, 32, 64, 128, 256) ) base <- 3 expect_equal( breaks_log(base = base)(base^c(2, 4) + c(1, -1)), c(9, 18, 27, 54, 81) ) base <- 2 expect_equal( breaks_log(n = 5, base = 2)(c(3, 100)), c(2, 4, 8, 16, 32, 64, 128) ) }) test_that("breaks_log arguments are forcely evaluated on each call #81", { subfun1 <- breaks_log(n = 5, base = 10) subfun2 <- breaks_log(n = 20, base = 5) subfuns <- list() cases_n <- c(5, 20) cases_base <- c(10, 5) for (i in 1:2) { subfuns[[i]] <- breaks_log(n = cases_n[i], base = cases_base[i]) } expect_equal(subfun1(1:1000), subfuns[[1]](1:1000)) expect_equal(subfun2(1:1000), subfuns[[2]](1:1000)) }) test_that("breaks_log with very small ranges fall back to extended_breaks", { expect_equal( breaks_log(n = 5, base = 10)(c(2.001, 2.002)), extended_breaks(n = 5)(c(2.001, 2.002)) ) expect_equal( breaks_log(n = 5, base = 10)(c(0.95, 1.1)), extended_breaks(n = 5)(c(0.95, 1.1)) ) # The switch to extended_breaks occurs at approximately the half-log point expect_equal( breaks_log(n = 5, base = 10)(c(0.95, 2.9)), extended_breaks(n = 5)(c(0.95, 2.9)) ) expect_false(identical( breaks_log(n = 5, base = 10)(c(0.95, 3)), extended_breaks(n = 5)(c(0.95, 3)) )) }) scales/tests/testthat/test-scale-continuous.r0000644000176200001440000000145713655052654021203 0ustar liggesuserstest_that("NA.value works for continuous scales", { x <- c(NA, seq(0, 1, length.out = 10), NA) pal <- rescale_pal() expect_that(cscale(x, pal)[1], equals(NA_real_)) expect_that(cscale(x, pal)[12], equals(NA_real_)) expect_that(cscale(x, pal, 5)[1], equals(5)) expect_that(cscale(x, pal, 5)[12], equals(5)) }) test_that("train_continuous stops on discrete values", { expect_error(train_continuous(LETTERS[1:5]), regexp = "Discrete value supplied" ) }) test_that("train_continuous strips attributes", { expect_equal(train_continuous(1:5), c(1, 5)) x <- as.Date("1970-01-01") + c(1, 5) expect_equal(train_continuous(x), c(1, 5)) }) test_that("train_continuous with new=NULL maintains existing range.", { expect_equal( train_continuous(NULL, existing = c(1, 5)), c(1, 5) ) }) scales/tests/testthat/test-breaks.r0000644000176200001440000000127213655051260017142 0ustar liggesuserscontext("Breaks") test_that("breaks_pretty() arguments are forcely evaluated on each call #81", { subfun1 <- breaks_pretty(n = 5) subfun2 <- breaks_pretty(n = 10) subfuns <- list() cases <- c(5, 10) for (i in 1:2) { subfuns[[i]] <- breaks_pretty(n = cases[i]) } expect_equal(subfun1(1), subfuns[[1]](1)) expect_equal(subfun2(1), subfuns[[2]](1)) # A ... argument: subfun1 <- breaks_pretty(n = 10, min.n = 2) subfun2 <- breaks_pretty(n = 10, min.n = 5) subfuns <- list() cases <- c(2, 5) for (i in 1:2) { subfuns[[i]] <- breaks_pretty(n = 10, min.n = cases[i]) } expect_equal(subfun1(1), subfuns[[1]](1)) expect_equal(subfun2(1), subfuns[[2]](1)) }) scales/tests/testthat/test-zero-range.r0000644000176200001440000000423713320151564017745 0ustar liggesuserscontext("Zero range") test_that("large numbers with small differences", { expect_false(zero_range(c(1330020857.8787, 1330020866.8787))) expect_true(zero_range(c(1330020857.8787, 1330020857.8787))) }) test_that("small numbers with differences on order of values", { expect_false(zero_range(c(5.63e-147, 5.93e-123))) expect_false(zero_range(c(-7.254574e-11, 6.035387e-11))) expect_false(zero_range(c(-7.254574e-11, -6.035387e-11))) }) test_that("ranges with 0 endpoint(s)", { expect_false(zero_range(c(0, 10))) expect_true(zero_range(c(0, 0))) expect_false(zero_range(c(-10, 0))) expect_false(zero_range(c(0, 1) * 1e-100)) expect_false(zero_range(c(0, 1) * 1e+100)) }) test_that("symmetric ranges", { expect_false(zero_range(c(-1, 1))) expect_false(zero_range(c(-1, 1 * (1 + 1e-20)))) expect_false(zero_range(c(-1, 1) * 1e-100)) }) test_that("length 1 ranges", { expect_true(zero_range(c(1))) expect_true(zero_range(c(0))) expect_true(zero_range(c(1e100))) expect_true(zero_range(c(1e-100))) }) test_that("NA and Inf", { # Should return NA expect_true(is.na(zero_range(c(NA, NA)))) expect_true(is.na(zero_range(c(1, NA)))) expect_true(is.na(zero_range(c(1, NaN)))) # Not zero range expect_false(zero_range(c(1, Inf))) expect_false(zero_range(c(-Inf, Inf))) # Can't know if these are truly zero range expect_true(zero_range(c(Inf, Inf))) expect_true(zero_range(c(-Inf, -Inf))) }) test_that("Tolerance", { # By default, tolerance is 1000 times this eps <- .Machine$double.eps expect_true(zero_range(c(1, 1 + eps))) expect_true(zero_range(c(1, 1 + 99 * eps))) # Cross the threshold expect_false(zero_range(c(1, 1 + 1001 * eps))) expect_false(zero_range(c(1, 1 + 2 * eps), tol = eps)) # Scaling up or down all the values has no effect since the values # are rescaled to 1 before checking against tol expect_true(zero_range(100000 * c(1, 1 + eps))) expect_true(zero_range(.00001 * c(1, 1 + eps))) expect_true(zero_range(100000 * c(1, 1 + 99 * eps))) expect_true(zero_range(.00001 * c(1, 1 + 99 * eps))) expect_false(zero_range(100000 * c(1, 1 + 1001 * eps))) expect_false(zero_range(.00001 * c(1, 1 + 1001 * eps))) }) scales/tests/testthat/test-breaks-minor.r0000644000176200001440000000073713137732040020266 0ustar liggesuserscontext("minor breaks") l1 <- c(0, 9) l2 <- -l1 b1 <- extended_breaks()(l1) b2 <- extended_breaks()(l2) m1 <- regular_minor_breaks()(b1, l1, n = 2) m2 <- regular_minor_breaks()(b2, l2, n = 2) test_that("minor breaks are calculated correctly", { expect_equal(m1, seq(b1[1], b1[length(b1)], by = 1.25)) expect_equal(m2, seq(b2[1], b2[length(b2)], by = 1.25)) }) test_that("minor breaks for reversed scales are comparable to non-reversed", { expect_equal(m1, sort(-m2)) }) scales/tests/testthat/test-colour-ramp.R0000644000176200001440000000217113560656254020103 0ustar liggesuserscontext("Colour ramp") test_that("Special values yield NAs", { pal <- seq_gradient_pal() expect_equal(pal(NA), NA_character_) expect_equal(pal(NaN), NA_character_) expect_equal(pal(Inf), NA_character_) expect_equal(pal(-Inf), NA_character_) }) test_that("can make ramp with single color", { expect_equal(colour_ramp("black")(0.5), "black") expect_equal(colour_ramp("black", na.color = "red")(NA), "red") }) test_that("Fully opaque colors are returned without alpha", { expect_equal( colour_ramp(c("#1234AB", "#BA4321"))(0:1), c("#1234AB", "#BA4321") ) }) test_that("Partially transparent colors are returned with alpha", { expect_equal( colour_ramp(c("#1234AB20", "#BA43218F"))(0:1), c("#1234AB20", "#BA43218F") ) }) test_that("Partially transparent colors are returned without alpha when `alpha = FALSE`", { expect_equal( colour_ramp(c("#1234AB20", "#BA43218F"), alpha = FALSE)(0:1), c("#1234AB", "#BA4321") ) }) test_that("Interpolation works from color without to color with alpha channel", { expect_identical( colour_ramp(c("#1234AB", "#1234AB00"))(0.5), "#1234AB80" ) }) scales/tests/testthat/test-range.r0000644000176200001440000000314213554557357017005 0ustar liggesuserscontext("Ranges") test_that("R6 inheritance works", { expect_error(ContinuousRange$new(), NA) expect_error(DiscreteRange$new(), NA) expect_true(R6::is.R6(ContinuousRange$new())) expect_true(R6::is.R6(DiscreteRange$new())) }) test_that("Mutable ranges work", { x <- ContinuousRange$new() x$train(c(-1, 45, 10)) expect_equal(x$range, c(-1, 45)) x$train(c(1000)) expect_equal(x$range, c(-1, 1000)) x$reset() expect_equal(x$range, NULL) x <- DiscreteRange$new() x$train(factor(letters[1:3])) expect_equal(x$range, c("a", "b", "c")) x$train(factor("a", "h")) expect_equal(x$range, c("a", "b", "c", "h")) x$reset() expect_equal(x$range, NULL) }) test_that("starting with NULL always returns new", { expect_equal(discrete_range(NULL, 1:3), 1:3) expect_equal(discrete_range(NULL, 3:1), 1:3) expect_equal(discrete_range(NULL, c("a", "b", "c")), c("a", "b", "c")) expect_equal(discrete_range(NULL, c("c", "b", "a")), c("a", "b", "c")) f1 <- factor(letters[1:3], levels = letters[1:4]) expect_equal(discrete_range(NULL, f1, drop = FALSE), letters[1:4]) expect_equal(discrete_range(NULL, f1, drop = TRUE), letters[1:3]) f2 <- factor(letters[1:3], levels = letters[4:1]) expect_equal(discrete_range(NULL, f2, drop = FALSE), letters[4:1]) expect_equal(discrete_range(NULL, f2, drop = TRUE), letters[3:1]) }) test_that("factor discrete ranges stay in order", { f <- factor(letters[1:3], levels = letters[3:1]) expect_equal(discrete_range(f, f), letters[3:1]) expect_equal(discrete_range(f, "c"), letters[3:1]) expect_equal(discrete_range(f, c("a", "b", "c")), letters[3:1]) }) scales/tests/testthat/test-label-pvalue.R0000644000176200001440000000137013556361126020210 0ustar liggesusers test_that("arguments passed onto number()", { expect_equal(label_pvalue()(c(.5, NA)), c("0.500", NA)) expect_equal(label_pvalue(0.1)(c(.5, NA)), c("0.5", NA)) expect_equal(label_pvalue(decimal.mark = ",")(c(.5, NA)), c("0,500", NA)) }) test_that("preserves names", { expect_named(label_pvalue()(c(a = 1)), "a") }) test_that("values close to 0 and 1 get special treamtent", { expect_equal(label_pvalue(0.1)(0.001), "<0.1") expect_equal(label_pvalue(0.1)(0.999), ">0.9") }) test_that("can control prefixes", { x <- c(0.001, 0.5, 0.999) expect_equal( label_pvalue(0.01, add_p = TRUE)(x), c("p<0.01", "p=0.50", "p>0.99") ) expect_equal( label_pvalue(0.01, prefix = c("a", "b", "c"))(x), c("a0.01", "b0.50", "c0.99") ) }) scales/tests/testthat/test-label-number.r0000644000176200001440000000577613655052654020265 0ustar liggesusers# Number formatter -------------------------------------------------------- test_that("number format works correctly", { expect_equal(number(123.45, accuracy = 1), "123") expect_equal(number(123.45, accuracy = 10), "120") expect_equal(number(123.45, accuracy = .25), "123.5") expect_equal( number(12345, big.mark = ","), "12,345" ) expect_equal( number(12.3, decimal.mark = ",", accuracy = .1), "12,3" ) expect_equal( number(1.234, scale = 100), "123" ) expect_equal( number(123, prefix = "pre", suffix = "post"), "pre123post" ) expect_equal(number(c(1, 23)), c("1", "23")) expect_equal(number(c(1, 23), trim = FALSE), c(" 1", "23")) }) test_that("number_format works with Inf", { cust <- number_format(suffix = "suff", accuracy = NULL) expect_equal(cust(c(Inf, -Inf)), c("Inf", "-Inf")) }) test_that("number preserves names", { expect_named(number(c(a = 1)), "a") }) # Comma formatter -------------------------------------------------------- test_that("comma format always adds commas", { expect_equal(comma(1e3), "1,000") expect_equal(comma(1e6), "1,000,000") expect_equal(comma(1e9), "1,000,000,000") }) test_that("comma preserves names", { expect_named(comma(c(a = 1)), "a") }) # Common tests -------------------------------------------------------- test_that("formatters don't add extra spaces", { has_space <- function(x) any(grepl("\\s", x)) x <- 10^c(-1, 0, 1, 3, 6, 9) expect_false(has_space(number(x, big.mark = ","))) expect_false(has_space(comma(x))) expect_false(has_space(dollar(x))) expect_false(has_space(percent(x, big.mark = ","))) expect_false(has_space(scientific(x))) }) test_that("formats work with 0 length input", { x <- numeric() expected <- character() expect_identical(number(x), expected) expect_identical(comma(x), expected) expect_identical(dollar(x), expected) expect_identical(percent(x), expected) expect_identical(scientific(x), expected) expect_identical(comma_format()(x), expected) expect_identical(date_format()(as.Date(character(0))), expected) expect_identical(dollar_format()(x), expected) expect_identical(parse_format()(x), expression()) expect_identical(parse_format()(character()), expression()) expect_identical(percent_format()(x), expected) expect_identical(scientific_format()(x), expected) expect_identical(trans_format(identity)(x), expected) }) # precision --------------------------------------------------------------- test_that("precision rounds large numbers appropriately", { x <- c(0, 0.025) expect_equal(precision(x), 0.001) expect_equal(precision(x * 10), 0.01) expect_equal(precision(x * 100), 0.1) expect_equal(precision(x * 1000), 1) expect_equal(precision(x * 10000), 1) }) test_that("precision handles duplicate values", { expect_equal(precision(c(0, 0, 0.025)), 0.001) expect_equal(precision(c(Inf, 0.1, 0.2, Inf)), 0.01) }) test_that("precision ignores Inf and NA", { expect_equal(precision(c(NA, Inf, -Inf)), 1) expect_equal(precision(c(1, NA)), 1) }) scales/tests/testthat/test-bounds.r0000644000176200001440000000656613655052654017210 0ustar liggesuserscontext("Bounds") test_that("rescale_mid returns correct results", { x <- c(-1, 0, 1) expect_equal(rescale_mid(x), c(0, 0.5, 1)) expect_equal(rescale_mid(x, mid = -1), c(0.5, 0.75, 1)) expect_equal(rescale_mid(x, mid = 1), c(0, 0.25, 0.5)) expect_equal(rescale_mid(x, mid = 1, to = c(0, 10)), c(0, 2.5, 5)) expect_equal(rescale_mid(x, mid = 1, to = c(8, 10)), c(8, 8.5, 9)) expect_equal(rescale_mid(c(1, NA, 1)), c(0.5, NA, 0.5)) }) test_that("rescale_max returns correct results", { expect_equal(rescale_max(0), NaN) expect_equal(rescale_max(1), 1) expect_equal(rescale_max(.3), 1) expect_equal(rescale_max(c(4, 5)), c(0.8, 1.0)) expect_equal(rescale_max(c(-3, 0, -1, 2)), c(-1.5, 0, -0.5, 1)) expect_equal(rescale_max(c(-3, 0, -1, 2)), c(-1.5, 0, -0.5, 1)) }) test_that("rescale functions handle NAs consistently", { expect_equal(rescale(c(2, NA, 0, -2)), c(1, NA, 0.5, 0)) expect_equal(rescale(c(-2, NA, -2)), c(.5, NA, .5)) expect_equal(rescale_mid(c(NA, 1, 2)), c(NA, 0.75, 1)) expect_equal(rescale_mid(c(2, NA, 0, -2), mid = .5), c(0.8, NA, 0.4, 0)) expect_equal(rescale_mid(c(-2, NA, -2)), c(.5, NA, .5)) expect_equal(rescale_max(c(1, NA)), c(1, NA)) expect_equal(rescale_max(c(2, NA, 0, -2)), c(1, NA, 0, -1)) expect_equal(rescale_max(c(-2, NA, -2)), c(1, NA, 1)) }) test_that("zero range inputs return mid range", { expect_that(rescale(0), equals(0.5)) expect_that(rescale(c(0, 0)), equals(c(0.5, 0.5))) }) test_that("scaling is possible with dates and times", { dates <- as.Date(c("2010-01-01", "2010-01-03", "2010-01-05", "2010-01-07")) expect_equal(rescale(dates, from = c(dates[1], dates[4])), seq(0, 1, 1 / 3)) expect_equal(rescale_mid(dates, mid = dates[3])[3], 0.5) dates <- as.POSIXct(c( "2010-01-01 01:40:40", "2010-01-01 03:40:40", "2010-01-01 05:40:40", "2010-01-01 07:40:40" )) expect_equal(rescale(dates, from = c(dates[1], dates[4])), seq(0, 1, 1 / 3)) expect_equal(rescale_mid(dates, mid = dates[3])[3], 0.5) }) test_that("scaling is possible with integer64 data", { skip_if_not_installed("bit64") x <- bit64::as.integer64(2^60) + c(0:3) expect_equal( rescale_mid(x, mid = bit64::as.integer64(2^60) + 1), c(0.25, 0.5, 0.75, 1) ) }) test_that("scaling is possible with NULL values", { expect_null(rescale(NULL)) expect_null(rescale_mid(NULL)) }) test_that("scaling is possible with logical values", { expect_equal(rescale(c(FALSE, TRUE)), c(0, 1)) expect_equal(rescale_mid(c(FALSE, TRUE), mid = 0.5), c(0, 1)) }) test_that("expand_range respects mul and add values", { expect_equal(expand_range(c(1,1), mul = 0, add = 0.6), c(0.4, 1.6)) expect_equal(expand_range(c(1,1), mul = 1, add = 0.6), c(-0.6, 2.6)) expect_equal(expand_range(c(1,9), mul = 0, add = 2), c(-1, 11)) }) test_that("out of bounds functions return correct values", { x <- c(-Inf, -1, 0.5, 1, 2, NA, Inf) expect_equal(oob_censor(x), c(-Inf, NA, 0.5, 1, NA, NA, Inf)) expect_equal(oob_censor_any(x), c(NA, NA, 0.5, 1, NA, NA, NA)) expect_equal(oob_censor(x), censor(x)) expect_equal(oob_squish(x), c(-Inf, 0, 0.5, 1, 1, NA, Inf)) expect_equal(oob_squish_any(x), c(0, 0, 0.5, 1, 1, NA, 1)) expect_equal(oob_squish_infinite(x), c(0, -1, 0.5, 1, 2, NA, 1)) expect_equal(oob_squish(x), squish(x)) expect_equal(oob_discard(x), c(0.5, 1, NA)) expect_equal(oob_discard(x), discard(x)) expect_equal(oob_keep(x), x) }) scales/tests/testthat/test-round-any.R0000644000176200001440000000166613320151564017553 0ustar liggesuserscontext("round_any") test_that("round_any function rounds numeric", { expect_equal(round_any(135, 10), 140) expect_equal(round_any(135, 100), 100) expect_equal(round_any(135, 25), 125) expect_equal(round_any(135, 10, floor), 130) expect_equal(round_any(135, 100, floor), 100) expect_equal(round_any(135, 25, floor), 125) expect_equal(round_any(135, 10, ceiling), 140) expect_equal(round_any(135, 100, ceiling), 200) expect_equal(round_any(135, 25, ceiling), 150) }) test_that("round_any() function rounds POSIXct", { expect_equal( round_any(as.POSIXct("2000-01-01 11:00:00", tz = "UTC"), 86400), as.POSIXct("2000-01-01", tz = "UTC") ) expect_equal( round_any(as.POSIXct("2000-01-01 11:11:11", tz = "UTC"), 3600), as.POSIXct("2000-01-01 11:00", tz = "UTC") ) expect_equal( round_any(as.POSIXct("2000-01-01 11:11:11", tz = "UTC"), 10, ceiling), as.POSIXct("2000-01-01 11:11:20", tz = "UTC") ) }) scales/tests/testthat/test-label-number-auto.R0000644000176200001440000000175313556361126021157 0ustar liggesuserstest_that("gracefully handles bad input works", { number_auto <- label_number_auto() expect_equal(number_auto(NULL), character()) expect_equal(number_auto(numeric()), character()) expect_equal(number_auto(NA), "NA") expect_equal(number_auto(Inf), "Inf") }) test_that("tricky breaks don't change unexpectedly", { verify_output(test_path("test-label-number-auto.txt"), { number_auto <- label_number_auto() number_auto(c(0, 0.000001)) number_auto(c(0.0009, 0.0010, 0.0011)) number_auto(c(0.00009, 0.00010, 0.00011)) number_auto(c(0.000009, 0.000010, 0.000011)) number_auto(c(999, 1000, 1001)) number_auto(c(999999, 1000000, 1000001)) number_auto(c(9999999, 10000000, 10000001)) number_auto(c(99999999, 100000000, 100000001)) number_auto(c(0.0000009, 0.0000010, 0.0000011)) "Years shouldn't get commas" number_auto(c(2010, 2013, 2020)) number_auto(c(-2010, -2013, -2020)) "Pick shortest individually" number_auto(10^(1:7)) }) }) scales/tests/testthat.R0000644000176200001440000000007012325313150014641 0ustar liggesuserslibrary(testthat) library(scales) test_check("scales") scales/R/0000755000176200001440000000000013656262515011740 5ustar liggesusersscales/R/pal-hue.r0000644000176200001440000000255213641652035013454 0ustar liggesusers#' Hue palette (discrete) #' #' @param h range of hues to use, in \[0, 360] #' @param l luminance (lightness), in \[0, 100] #' @param c chroma (intensity of colour), maximum value varies depending on #' combination of hue and luminance. #' @param h.start hue to start at #' @param direction direction to travel around the colour wheel, #' 1 = clockwise, -1 = counter-clockwise #' @export #' @examples #' show_col(hue_pal()(4)) #' show_col(hue_pal()(9)) #' show_col(hue_pal(l = 90)(9)) #' show_col(hue_pal(l = 30)(9)) #' #' show_col(hue_pal()(9)) #' show_col(hue_pal(direction = -1)(9)) #' #' show_col(hue_pal()(9)) #' show_col(hue_pal(h = c(0, 90))(9)) #' show_col(hue_pal(h = c(90, 180))(9)) #' show_col(hue_pal(h = c(180, 270))(9)) #' show_col(hue_pal(h = c(270, 360))(9)) hue_pal <- function(h = c(0, 360) + 15, c = 100, l = 65, h.start = 0, direction = 1) { stopifnot(length(h) == 2) stopifnot(length(c) == 1) stopifnot(length(l) == 1) force_all(h, c, l, h.start, direction) function(n) { if (n == 0) { stop("Must request at least one colour from a hue palette.", call. = FALSE) } if ((diff(h) %% 360) < 1) { h[2] <- h[2] - 360 / n } hues <- seq(h[1], h[2], length.out = n) hcl <- cbind(hues, c, l) pal <- farver::encode_colour(hcl, from = "hcl") if (direction == -1) { rev(pal) } else { pal } } } scales/R/pal-identity.r0000644000176200001440000000023113556361126014517 0ustar liggesusers#' Identity palette #' #' Leaves values unchanged - useful when the data is already scaled. #' #' @export identity_pal <- function() { function(x) x } scales/R/range.r0000644000176200001440000000137313556361126013220 0ustar liggesusers#' Mutable ranges #' #' Mutable ranges have a two methods (`train` and `reset`), and #' make it possible to build up complete ranges with multiple passes. #' #' @export Range <- R6::R6Class("Range", list( range = NULL, initialize = function() { self$range <- NULL } )) #' @export #' @rdname Range DiscreteRange <- R6::R6Class( "DiscreteRange", inherit = Range, list( train = function(x, drop = FALSE) { self$range <- train_discrete(x, self$range, drop) }, reset = function() self$range <- NULL ) ) #' @export #' @rdname Range ContinuousRange <- R6::R6Class( "ContinuousRange", inherit = Range, list( train = function(x) self$range <- train_continuous(x, self$range), reset = function() self$range <- NULL ) ) scales/R/pal-shape.r0000644000176200001440000000121013556361126013764 0ustar liggesusers#' Shape palette (discrete) #' #' @param solid should shapes be solid or not? #' @export shape_pal <- function(solid = TRUE) { force(solid) function(n) { if (n > 6) { msg <- paste("The shape palette can deal with a maximum of 6 discrete ", "values because more than 6 becomes difficult to discriminate; ", "you have ", n, ". Consider specifying shapes manually if you ", "must have them.", sep = "" ) warning(paste(strwrap(msg), collapse = "\n"), call. = FALSE) } if (solid) { c(16, 17, 15, 3, 7, 8)[seq_len(n)] } else { c(1, 2, 0, 3, 7, 8)[seq_len(n)] } } } scales/R/bounds.r0000644000176200001440000002552013655052654013421 0ustar liggesusers#' Rescale continuous vector to have specified minimum and maximum #' #' @param x continuous vector of values to manipulate. #' @param to output range (numeric vector of length two) #' @param from input range (vector of length two). If not given, is #' calculated from the range of `x` #' @param ... other arguments passed on to methods #' @keywords manip #' @export #' @examples #' rescale(1:100) #' rescale(runif(50)) #' rescale(1) rescale <- function(x, to, from, ...) { UseMethod("rescale") } #' @rdname rescale #' @export rescale.numeric <- function(x, to = c(0, 1), from = range(x, na.rm = TRUE, finite = TRUE), ...) { if (zero_range(from) || zero_range(to)) { return(ifelse(is.na(x), NA, mean(to))) } (x - from[1]) / diff(from) * diff(to) + to[1] } #' @export rescale.NULL <- function(...) NULL #' @rdname rescale #' @export rescale.dist <- rescale.numeric #' @rdname rescale #' @export rescale.logical <- rescale.numeric #' @rdname rescale #' @export rescale.POSIXt <- function(x, to = c(0, 1), from = range(x, na.rm = TRUE, finite = TRUE), ...) { x <- as.numeric(x) from <- as.numeric(from) rescale.numeric(x = x, to = to, from = from) } #' @rdname rescale #' @export rescale.Date <- rescale.POSIXt #' @rdname rescale #' @export rescale.integer64 <- function(x, to = c(0, 1), from = range(x, na.rm = TRUE), ...) { if (zero_range(from, tol = 0) || zero_range(to)) { return(ifelse(is.na(x), NA, mean(to))) } (x - from[1]) / diff(from) * diff(to) + to[1] } #' Rescale vector to have specified minimum, midpoint, and maximum #' #' @export #' @param x vector of values to manipulate. #' @param to output range (numeric vector of length two) #' @param from input range (vector of length two). If not given, is #' calculated from the range of `x` #' @param mid mid-point of input range #' @param ... other arguments passed on to methods #' @examples #' rescale_mid(1:100, mid = 50.5) #' rescale_mid(runif(50), mid = 0.5) #' rescale_mid(1) rescale_mid <- function(x, to, from, mid, ...) { UseMethod("rescale_mid") } #' @rdname rescale_mid #' @export rescale_mid.numeric <- function(x, to = c(0, 1), from = range(x, na.rm = TRUE), mid = 0, ...) { if (zero_range(from) || zero_range(to)) { return(ifelse(is.na(x), NA, mean(to))) } extent <- 2 * max(abs(from - mid)) (x - mid) / extent * diff(to) + mean(to) } #' @export rescale_mid.NULL <- function(...) NULL #' @rdname rescale_mid #' @export rescale_mid.logical <- rescale_mid.numeric #' @rdname rescale_mid #' @export rescale_mid.dist <- rescale_mid.numeric #' @rdname rescale_mid #' @export rescale_mid.POSIXt <- function(x, to = c(0, 1), from = range(x, na.rm = TRUE), mid, ...) { x <- as.numeric(as.POSIXct(x)) if (!is.numeric(from)) { from <- as.numeric(as.POSIXct(from)) } if (!is.numeric(mid)) { mid <- as.numeric(as.POSIXct(mid)) } rescale_mid.numeric(x = x, to = to, from = from, mid = mid) } #' @rdname rescale_mid #' @export rescale_mid.Date <- rescale_mid.POSIXt #' @rdname rescale_mid #' @export rescale_mid.integer64 <- function(x, to = c(0, 1), from = range(x, na.rm = TRUE), mid = 0, ...) { if (zero_range(from, tol = 0) || zero_range(to)) { return(ifelse(is.na(x), NA, mean(to))) } extent <- 2 * max(abs(from - mid)) (x - mid) / extent * diff(to) + mean(to) } #' Rescale numeric vector to have specified maximum #' #' @export #' @param x numeric vector of values to manipulate. #' @param to output range (numeric vector of length two) #' @param from input range (numeric vector of length two). If not given, is #' calculated from the range of `x` #' @examples #' rescale_max(1:100) #' rescale_max(runif(50)) #' rescale_max(1) rescale_max <- function(x, to = c(0, 1), from = range(x, na.rm = TRUE)) { x / from[2] * to[2] } #' Don't perform rescaling #' #' @param x numeric vector of values to manipulate. #' @param ... all other arguments ignored #' @export #' @examples #' rescale_none(1:100) rescale_none <- function(x, ...) { x } #' Out of bounds handling #' #' @description #' This set of functions modify data values outside a given range. #' The `oob_*()` functions are designed to be passed as the `oob` argument of #' ggplot2 continuous and binned scales, with `oob_discard` being an exception. #' #' These functions affect out of bounds values in the following ways: #' #' * `oob_censor()` replaces out of bounds values with `NA`s. This is the #' default `oob` argument for continuous scales. #' * `oob_censor_any()` acts like `oob_censor()`, but also replaces infinite #' values with `NA`s. #' * `oob_squish()` replaces out of bounds values with the nearest limit. This #' is the default `oob` argument for binned scales. #' * `oob_squish_any()` acts like `oob_squish()`, but also replaces infinite #' values with the nearest limit. #' * `oob_squish_infinite()` only replaces infinite values by the nearest limit. #' * `oob_keep()` does not adjust out of bounds values. In position scales, #' behaves as zooming limits without data removal. #' * `oob_discard()` removes out of bounds values from the input. Not suitable #' for ggplot2 scales. #' #' @param x A numeric vector of values to modify. #' @param range A numeric vector of length two giving the minimum and maximum #' limit of the desired output range respectively. #' @param only.finite A logical of length one. When `TRUE`, only finite values #' are altered. When `FALSE`, also infinite values are altered. #' #' @return Most `oob_()` functions return a vector of numerical values of the #' same length as the `x` argument, wherein out of bounds values have been #' modified. Only `oob_discard()` returns a vector of less than or of equal #' length to the `x` argument. #' #' @details The `oob_censor_any()` and `oob_squish_any()` functions are the same #' as `oob_censor()` and `oob_squish()` with the `only.finite` argument set to #' `FALSE`. #' #' Replacing position values with `NA`s, as `oob_censor()` does, will typically #' lead to removal of those datapoints in ggplot. #' #' Setting ggplot coordinate limits is equivalent to using `oob_keep()` in #' position scales. #' #' @section Old interface: `censor()`, `squish()`, `squish_infinite()` and #' `discard()` are no longer recommended; please use `oob_censor()`, #' `oob_squish()`, `oob_squish_infinite()` and `oob_discard()` instead. #' #' @name oob #' #' @examples #' # Censoring replaces out of bounds values with NAs #' oob_censor(c(-Inf, -1, 0.5, 1, 2, NA, Inf)) #' oob_censor_any(c(-Inf, -1, 0.5, 1, 2, NA, Inf)) #' #' # Squishing replaces out of bounds values with the nearest range limit #' oob_squish(c(-Inf, -1, 0.5, 1, 2, NA, Inf)) #' oob_squish_any(c(-Inf, -1, 0.5, 1, 2, NA, Inf)) #' oob_squish_infinite(c(-Inf, -1, 0.5, 1, 2, NA, Inf)) #' #' # Keeping does not alter values #' oob_keep(c(-Inf, -1, 0.5, 1, 2, NA, Inf)) #' #' # Discarding will remove out of bounds values #' oob_discard(c(-Inf, -1, 0.5, 1, 2, NA, Inf)) NULL #' @rdname oob #' @export oob_censor <- function(x, range = c(0, 1), only.finite = TRUE) { force(range) finite <- if (only.finite) is.finite(x) else TRUE x[finite & x < range[1]] <- NA_real_ x[finite & x > range[2]] <- NA_real_ x } #' @rdname oob #' @export oob_censor_any <- function(x, range = c(0, 1)) { oob_censor(x, range = range, only.finite = FALSE) } #' @rdname oob #' @export oob_discard <- function(x, range = c(0, 1)) { force(range) x[x >= range[1] & x <= range[2]] } #' @rdname oob #' @author `oob_squish()`: Homer Strong #' @export oob_squish <- function(x, range = c(0, 1), only.finite = TRUE) { force(range) finite <- if (only.finite) is.finite(x) else TRUE x[finite & x < range[1]] <- range[1] x[finite & x > range[2]] <- range[2] x } #' @rdname oob #' @export oob_squish_any <- function(x, range = c(0, 1)) { oob_squish(x, range, only.finite = FALSE) } #' @rdname oob #' @export oob_squish_infinite <- function(x, range = c(0, 1)) { force(range) x[x == -Inf] <- range[1] x[x == Inf] <- range[2] x } #' @rdname oob #' @export oob_keep <- function(x, range = c(0, 1)) { x } #' @rdname oob #' @export censor <- oob_censor #' @rdname oob #' @export discard <- oob_discard #' @rdname oob #' @export squish <- oob_squish #' @rdname oob #' @export squish_infinite <- oob_squish_infinite #' Expand a range with a multiplicative or additive constant #' #' @param range range of data, numeric vector of length 2 #' @param mul multiplicative constant #' @param add additive constant #' @param zero_width distance to use if range has zero width #' @export expand_range <- function(range, mul = 0, add = 0, zero_width = 1) { if (is.null(range)) return() width <- if (zero_range(range)) zero_width else diff(range) range + c(-1, 1) * (width * mul + add) } #' Determine if range of vector is close to zero, with a specified tolerance #' #' The machine epsilon is the difference between 1.0 and the next number #' that can be represented by the machine. By default, this function #' uses epsilon * 1000 as the tolerance. First it scales the values so that #' they have a mean of 1, and then it checks if the difference between #' them is larger than the tolerance. #' #' @examples #' eps <- .Machine$double.eps #' zero_range(c(1, 1 + eps)) # TRUE #' zero_range(c(1, 1 + 99 * eps)) # TRUE #' zero_range(c(1, 1 + 1001 * eps)) # FALSE - Crossed the tol threshold #' zero_range(c(1, 1 + 2 * eps), tol = eps) # FALSE - Changed tol #' #' # Scaling up or down all the values has no effect since the values #' # are rescaled to 1 before checking against tol #' zero_range(100000 * c(1, 1 + eps)) # TRUE #' zero_range(100000 * c(1, 1 + 1001 * eps)) # FALSE #' zero_range(.00001 * c(1, 1 + eps)) # TRUE #' zero_range(.00001 * c(1, 1 + 1001 * eps)) # FALSE #' #' # NA values #' zero_range(c(1, NA)) # NA #' zero_range(c(1, NaN)) # NA #' #' # Infinite values #' zero_range(c(1, Inf)) # FALSE #' zero_range(c(-Inf, Inf)) # FALSE #' zero_range(c(Inf, Inf)) # TRUE #' #' @export #' @param x numeric range: vector of length 2 #' @param tol A value specifying the tolerance. #' @return logical `TRUE` if the relative difference of the endpoints of #' the range are not distinguishable from 0. zero_range <- function(x, tol = 1000 * .Machine$double.eps) { if (length(x) == 1) return(TRUE) if (length(x) != 2) stop("x must be length 1 or 2") if (any(is.na(x))) return(NA) # Special case: if they are equal as determined by ==, then there # is zero range. Also handles (Inf, Inf) and (-Inf, -Inf) if (x[1] == x[2]) return(TRUE) # If we reach this, then x must be (-Inf, Inf) or (Inf, -Inf) if (all(is.infinite(x))) return(FALSE) # Take the smaller (in magnitude) value of x, and use it as the scaling # factor. m <- min(abs(x)) # If we get here, then exactly one of the x's is 0. Return FALSE if (m == 0) return(FALSE) # If x[1] - x[2] (scaled to 1) is smaller than tol, then return # TRUE; otherwise return FALSE abs((x[1] - x[2]) / m) < tol } scales/R/scales-package.R0000644000176200001440000000041413560022206014706 0ustar liggesusers#' @keywords internal #' @importFrom R6 R6Class "_PACKAGE" # The following block is used by usethis to automatically manage # roxygen namespace tags. Modify with care! ## usethis namespace: start #' @importFrom lifecycle deprecate_soft ## usethis namespace: end NULL scales/R/breaks.r0000644000176200001440000000654513655052655013405 0ustar liggesusers#' Equally spaced breaks #' #' Useful for numeric, date, and date-time scales. #' #' @param width Distance between each break. Either a number, or for #' date/times, a single string of the form "{n} {unit}", e.g. "1 month", #' "5 days". Unit can be of one "sec", "min", "hour", "day", "week", #' "month", "year". #' @param offset Use if you don't want breaks to start at zero #' @export #' @examples #' demo_continuous(c(0, 100)) #' demo_continuous(c(0, 100), breaks = breaks_width(10)) #' demo_continuous(c(0, 100), breaks = breaks_width(20, -4)) #' demo_continuous(c(0, 100), breaks = breaks_width(20, 4)) #' #' # This is also useful for dates #' one_month <- as.POSIXct(c("2020-05-01", "2020-06-01")) #' demo_datetime(one_month) #' demo_datetime(one_month, breaks = breaks_width("1 week")) #' demo_datetime(one_month, breaks = breaks_width("5 days")) #' # This is so useful that scale_x_datetime() has a shorthand: #' demo_datetime(one_month, date_breaks = "5 days") #' #' # hms times also work #' one_hour <- hms::hms(hours = 0:1) #' demo_time(one_hour) #' demo_time(one_hour, breaks = breaks_width("15 min")) #' demo_time(one_hour, breaks = breaks_width("600 sec")) breaks_width <- function(width, offset = 0) { force_all(width, offset) function(x) { fullseq(x, width) + offset } } #' Automatic breaks for numeric axes #' #' Uses Wilkinson's extended breaks algorithm as implemented in the #' \pkg{labeling} package. #' #' @param n Desired number of breaks. You may get slightly more or fewer #' breaks that requested. #' @param ... other arguments passed on to [labeling::extended()] #' @references Talbot, J., Lin, S., Hanrahan, P. (2010) An Extension of #' Wilkinson's Algorithm for Positioning Tick Labels on Axes, InfoVis #' 2010 . #' @export #' @examples #' demo_continuous(c(0, 10)) #' demo_continuous(c(0, 10), breaks = breaks_extended(3)) #' demo_continuous(c(0, 10), breaks = breaks_extended(10)) breaks_extended <- function(n = 5, ...) { n_default <- n function(x, n = n_default) { x <- x[is.finite(x)] if (length(x) == 0) { return(numeric()) } rng <- range(x) labeling::extended(rng[1], rng[2], n, ...) } } #' @export #' @usage NULL #' @rdname breaks_extended extended_breaks <- breaks_extended #' Pretty breaks for date/times #' #' Uses default R break algorithm as implemented in [pretty()]. This is #' primarily useful for date/times, as [extended_breaks()] should do a slightly #' better job for numeric scales. #' #' `pretty_breaks()` is retired; use `breaks_pretty()` instead. #' #' @inheritParams breaks_extended #' @param ... other arguments passed on to [pretty()] #' @export #' @examples #' one_month <- as.POSIXct(c("2020-05-01", "2020-06-01")) #' demo_datetime(one_month) #' demo_datetime(one_month, breaks = breaks_pretty(2)) #' demo_datetime(one_month, breaks = breaks_pretty(4)) #' #' # Tightly spaced date breaks often need custom labels too #' demo_datetime(one_month, breaks = breaks_pretty(12)) #' demo_datetime(one_month, #' breaks = breaks_pretty(12), #' labels = label_date_short() #') breaks_pretty <- function(n = 5, ...) { force_all(n, ...) n_default <- n function(x, n = n_default) { breaks <- pretty(x, n, ...) names(breaks) <- attr(breaks, "labels") breaks } } #' @export #' @usage NULL #' @rdname breaks_pretty pretty_breaks <- breaks_pretty scales/R/full-seq.r0000644000176200001440000000326613655052655013663 0ustar liggesusers#' Generate sequence of fixed size intervals covering range. #' #' @param range range #' @param size interval size #' @param ... other arguments passed on to methods #' @keywords internal #' @export #' @seealso [plyr::round_any()] fullseq <- function(range, size, ...) UseMethod("fullseq") #' @export fullseq.numeric <- function(range, size, ..., pad = FALSE) { if (zero_range(range)) return(range + size * c(-1, 1) / 2) x <- seq( round_any(range[1], size, floor), round_any(range[2], size, ceiling), by = size ) if (pad) { # Add extra bin on bottom and on top, to guarantee that we cover complete # range of data, whether right = T or F c(min(x) - size, x, max(x) + size) } else { x } } #' @export fullseq.Date <- function(range, size, ...) { seq(floor_date(range[1], size), ceiling_date(range[2], size), by = size) } #' @export fullseq.POSIXt <- function(range, size, ...) { # for subsecond interval support # seq() does not support partial secs in character strings parsed <- parse_unit_spec(size) if (parsed$unit == "sec") { seq(floor_time(range[1], size), ceiling_time(range[2], size), by = parsed$mult) } else { seq(floor_time(range[1], size), ceiling_time(range[2], size), by = size) } } #' @export fullseq.difftime <- function(range, size, ...) { if (is.numeric(size)) { size_seconds <- size } else { size_seconds <- unit_seconds(size) } input_units <- units(range) x <- seq( round_any(as.numeric(range[1], units = "secs"), size_seconds, floor), round_any(as.numeric(range[2], units = "secs"), size_seconds, ceiling), by = size_seconds ) x <- as.difftime(x, units = "secs") units(x) <- input_units x } scales/R/pal-gradient.r0000644000176200001440000000441013560656247014474 0ustar liggesusers#' Arbitrary colour gradient palette (continuous) #' #' @param colours vector of colours #' @param values if colours should not be evenly positioned along the gradient #' this vector gives the position (between 0 and 1) for each colour in the #' `colours` vector. See [rescale()] for a convenience function #' to map an arbitrary range to between 0 and 1. #' @param space colour space in which to calculate gradient. Must be "Lab" - #' other values are deprecated. #' @export gradient_n_pal <- function(colours, values = NULL, space = "Lab") { if (!identical(space, "Lab")) { warning("Non Lab interpolation is deprecated", call. = FALSE) } ramp <- colour_ramp(colours) force(values) function(x) { if (length(x) == 0) return(character()) if (!is.null(values)) { xs <- seq(0, 1, length.out = length(values)) f <- stats::approxfun(values, xs) x <- f(x) } ramp(x) } } #' Diverging colour gradient (continuous). #' #' @param low colour for low end of gradient. #' @param mid colour for mid point #' @param high colour for high end of gradient. #' @inheritParams gradient_n_pal #' @export #' @examples #' x <- seq(-1, 1, length.out = 100) #' r <- sqrt(outer(x^2, x^2, "+")) #' image(r, col = div_gradient_pal()(seq(0, 1, length.out = 12))) #' image(r, col = div_gradient_pal()(seq(0, 1, length.out = 30))) #' image(r, col = div_gradient_pal()(seq(0, 1, length.out = 100))) #' #' library(munsell) #' image(r, col = div_gradient_pal(low = #' mnsl(complement("10R 4/6"), fix = TRUE))(seq(0, 1, length = 100))) #' @importFrom munsell mnsl div_gradient_pal <- function(low = mnsl("10B 4/6"), mid = mnsl("N 8/0"), high = mnsl("10R 4/6"), space = "Lab") { gradient_n_pal(c(low, mid, high), space = space) } #' Sequential colour gradient palette (continuous) #' #' @param low colour for low end of gradient. #' @param high colour for high end of gradient. #' @inheritParams gradient_n_pal #' @export #' @examples #' x <- seq(0, 1, length.out = 25) #' show_col(seq_gradient_pal()(x)) #' show_col(seq_gradient_pal("white", "black")(x)) #' #' library(munsell) #' show_col(seq_gradient_pal("white", mnsl("10R 4/6"))(x)) seq_gradient_pal <- function(low = mnsl("10B 4/6"), high = mnsl("10R 4/6"), space = "Lab") { gradient_n_pal(c(low, high), space = space) } scales/R/pal-rescale.r0000644000176200001440000000060013556361126014304 0ustar liggesusers#' Rescale palette (continuous) #' #' Just rescales the input to the specific output range. Useful for #' alpha, size, and continuous position. #' #' @param range Numeric vector of length two, giving range of possible #' values. Should be between 0 and 1. #' @export rescale_pal <- function(range = c(0.1, 1)) { force(range) function(x) { rescale(x, range, c(0, 1)) } } scales/R/pal-grey.r0000644000176200001440000000063413556361126013643 0ustar liggesusers#' Grey scale palette (discrete) #' #' @param start grey value at low end of palette #' @param end grey value at high end of palette #' @seealso [seq_gradient_pal()] for continuous version #' @export #' @examples #' show_col(grey_pal()(25)) #' show_col(grey_pal(0, 1)(25)) grey_pal <- function(start = 0.2, end = 0.8) { force_all(start, end) function(n) grDevices::grey.colors(n, start = start, end = end) } scales/R/pal-area.r0000644000176200001440000000067213556361126013607 0ustar liggesusers#' Area palettes (continuous) #' #' @param range Numeric vector of length two, giving range of possible sizes. #' Should be greater than 0. #' @export area_pal <- function(range = c(1, 6)) { force(range) function(x) rescale(sqrt(x), range, c(0, 1)) } #' @param max A number representing the maximum size. #' @export #' @rdname area_pal abs_area <- function(max) { force(max) function(x) rescale(sqrt(abs(x)), c(0, max), c(0, 1)) } scales/R/documentation.r0000644000176200001440000000052012325312655014762 0ustar liggesusers# Functions used for producing Rd chunks to reduce duplication in # documentation seealso <- function(pattern) { require("scales") names <- ls("package:scales", pattern = pattern) paste0("\\code{\\link{", names, "}}", collapse = ", ") } seealso_trans <- function() seealso("_trans$") seealso_pal <- function() seealso("_pal$") scales/R/trans-numeric.r0000644000176200001440000002231513560272220014701 0ustar liggesusers#' Arc-sin square root transformation #' #' This is the variance stabilising transformation for the binomial #' distribution. #' #' @export #' @examples #' plot(asn_trans(), xlim = c(0, 1)) asn_trans <- function() { trans_new( "asn", function(x) 2 * asin(sqrt(x)), function(x) sin(x / 2)^2 ) } #' Arc-tangent transformation #' #' @export #' @examples #' plot(atanh_trans(), xlim = c(-1, 1)) atanh_trans <- function() { trans_new("atanh", "atanh", "tanh") } #' Box-Cox & modulus transformations #' #' The Box-Cox transformation is a flexible transformation, often used to #' transform data towards normality. The modulus transformation generalises #' Box-Cox to also work with negative values. #' #' The Box-Cox power transformation (type 1) requires strictly positive values and #' takes the following form for `y > 0`: #' \deqn{y^{(\lambda)} = \frac{y^\lambda - 1}{\lambda}}{y^(\lambda) = (y^\lambda - 1)/\lambda} #' When `y = 0`, the natural log transform is used. #' #' The modulus transformation implements a generalisation of the Box-Cox #' transformation that works for data with both positive and negative values. #' The equation takes the following forms, when `y != 0` : #' \deqn{y^{(\lambda)} = sign(y) * \frac{(|y| + 1)^\lambda - 1}{\lambda}}{ #' y^(\lambda) = sign(y)*((|y|+1)^\lambda - 1)/\lambda} #' and when `y = 0`: \deqn{y^{(\lambda)} = sign(y) * \ln(|y| + 1)}{ #' y^(\lambda) = sign(y) * ln(|y| + 1)} #' #' @param p Transformation exponent, \eqn{\lambda}. #' @param offset Constant offset. 0 for Box-Cox type 1, #' otherwise any non-negative constant (Box-Cox type 2). `modulus_trans()` #' sets the default to 1. #' @seealso [yj_trans()] #' @references Box, G. E., & Cox, D. R. (1964). An analysis of transformations. #' Journal of the Royal Statistical Society. Series B (Methodological), 211-252. #' \url{https://www.jstor.org/stable/2984418} #' #' John, J. A., & Draper, N. R. (1980). #' An alternative family of transformations. Applied Statistics, 190-197. #' \url{http://www.jstor.org/stable/2986305} #' @export #' @examples #' plot(boxcox_trans(-1), xlim = c(0, 10)) #' plot(boxcox_trans(0), xlim = c(0, 10)) #' plot(boxcox_trans(1), xlim = c(0, 10)) #' plot(boxcox_trans(2), xlim = c(0, 10)) #' #' plot(modulus_trans(-1), xlim = c(-10, 10)) #' plot(modulus_trans(0), xlim = c(-10, 10)) #' plot(modulus_trans(1), xlim = c(-10, 10)) #' plot(modulus_trans(2), xlim = c(-10, 10)) boxcox_trans <- function(p, offset = 0) { trans <- function(x) { if (any((x + offset) < 0, na.rm = TRUE)) { stop("boxcox_trans must be given only positive values. Consider using modulus_trans instead?", call. = F ) } if (abs(p) < 1e-07) { log(x + offset) } else { ((x + offset)^p - 1) / p } } inv <- function(x) { if (abs(p) < 1e-07) { exp(x) - offset } else { (x * p + 1)^(1 / p) - offset } } trans_new( paste0("pow-", format(p)), trans, inv ) } #' @rdname boxcox_trans #' @export modulus_trans <- function(p, offset = 1) { if (abs(p) < 1e-07) { trans <- function(x) sign(x) * log(abs(x) + offset) inv <- function(x) sign(x) * (exp(abs(x)) - offset) } else { trans <- function(x) sign(x) * ((abs(x) + offset)^p - 1) / p inv <- function(x) sign(x) * ((abs(x) * p + 1)^(1 / p) - offset) } trans_new( paste0("mt-pow-", format(p)), trans, inv ) } #' Yeo-Johnson transformation #' #' The Yeo-Johnson transformation is a flexible transformation that is similiar #' to Box-Cox, [boxcox_trans()], but does not require input values to be greater #' than zero. #' #' The transformation takes one of four forms depending on the values of `y` and \eqn{\lambda}. #' #' * \eqn{y \ge 0} and \eqn{\lambda \neq 0}{\lambda != 0} : #' \eqn{y^{(\lambda)} = \frac{(y + 1)^\lambda - 1}{\lambda}}{y^(\lambda) = ((y + 1)^\lambda - 1)/\lambda} #' * \eqn{y \ge 0} and \eqn{\lambda = 0}: #' \eqn{y^{(\lambda)} = \ln(y + 1)}{y^(\lambda) = ln(y + 1)} #' * \eqn{y < 0} and \eqn{\lambda \neq 2}{\lambda != 2}: #' \eqn{y^{(\lambda)} = -\frac{(-y + 1)^{(2 - \lambda)} - 1}{2 - \lambda}}{y^(\lambda) = -((-y + 1)^(2 - \lambda) - 1)/(2 - \lambda)} #' * \eqn{y < 0} and \eqn{\lambda = 2}: #' \eqn{y^{(\lambda)} = -\ln(-y + 1)}{y^(\lambda) = -ln(-y + 1)} #' #' @param p Transformation exponent, \eqn{\lambda}. #' @references Yeo, I., & Johnson, R. (2000). #' A New Family of Power Transformations to Improve Normality or Symmetry. Biometrika, 87(4), 954-959. #' \url{http://www.jstor.org/stable/2673623} #' @export #' @examples #' plot(yj_trans(-1), xlim = c(-10, 10)) #' plot(yj_trans(0), xlim = c(-10, 10)) #' plot(yj_trans(1), xlim = c(-10, 10)) #' plot(yj_trans(2), xlim = c(-10, 10)) yj_trans <- function(p) { eps <- 1e-7 if (abs(p) < eps) { trans_pos <- function(x) log(x + 1) inv_pos <- function(x) exp(x) - 1 } else { trans_pos <- function(x) ((x + 1)^p - 1) / p inv_pos <- function(x) (p*x + 1)^(1/p) - 1 } if (abs(2 - p) < eps) { trans_neg <- function(x) -log(-x + 1) inv_neg <- function(x) 1 - exp(-x) } else { trans_neg <- function(x) -((-x + 1)^(2 - p) - 1)/(2 - p) inv_neg <- function(x) 1 - (-(2 - p)*x + 1)^(1/(2 - p)) } trans_new( paste0("yeo-johnson-", format(p)), function(x) trans_two_sided(x, trans_pos, trans_neg), function(x) trans_two_sided(x, inv_pos, inv_neg) ) } trans_two_sided <- function(x, pos, neg) { out <- rep(NA_real_, length(x)) present <- !is.na(x) out[present & x > 0] <- pos(x[present & x > 0]) out[present & x < 0] <- neg(x[present & x < 0]) out[present & x == 0] <- 0 out } #' Exponential transformation (inverse of log transformation) #' #' @param base Base of logarithm #' @export #' @examples #' plot(exp_trans(0.5), xlim = c(-2, 2)) #' plot(exp_trans(1), xlim = c(-2, 2)) #' plot(exp_trans(2), xlim = c(-2, 2)) #' plot(exp_trans(), xlim = c(-2, 2)) exp_trans <- function(base = exp(1)) { force(base) trans_new( paste0("power-", format(base)), function(x) base^x, function(x) log(x, base = base) ) } #' Identity transformation (do nothing) #' #' @export #' @examples #' plot(identity_trans(), xlim = c(-1, 1)) identity_trans <- function() { trans_new("identity", "force", "force") } #' Log transformations #' #' * `log_trans()`: `log(x)` #' * `log1p()`: `log(x + 1)` #' * `pseudo_log_trans()`: smoothly transition to linear scale around 0. #' #' @param base base of logarithm #' @export #' @examples #' plot(log2_trans(), xlim = c(0, 5)) #' plot(log_trans(), xlim = c(0, 5)) #' plot(log10_trans(), xlim = c(0, 5)) #' #' plot(log_trans(), xlim = c(0, 2)) #' plot(log1p_trans(), xlim = c(-1, 1)) #' #' # The pseudo-log is defined for all real numbers #' plot(pseudo_log_trans(), xlim = c(-5, 5)) #' lines(log_trans(), xlim = c(0, 5), col = "red") #' #' # For large positives nubmers it's very close to log #' plot(pseudo_log_trans(), xlim = c(1, 20)) #' lines(log_trans(), xlim = c(1, 20), col = "red") log_trans <- function(base = exp(1)) { force(base) trans <- function(x) log(x, base) inv <- function(x) base^x trans_new(paste0("log-", format(base)), trans, inv, log_breaks(base = base), domain = c(1e-100, Inf) ) } #' @export #' @rdname log_trans log10_trans <- function() { log_trans(10) } #' @export #' @rdname log_trans log2_trans <- function() { log_trans(2) } #' @rdname log_trans #' @export log1p_trans <- function() { trans_new("log1p", "log1p", "expm1") } #' @rdname log_trans #' @param sigma Scaling factor for the linear part of pseudo-log transformation. #' @export pseudo_log_trans <- function(sigma = 1, base = exp(1)) { trans_new( "pseudo_log", function(x) asinh(x / (2 * sigma)) / log(base), function(x) 2 * sigma * sinh(x * log(base)) ) } #' Probability transformation #' #' @param distribution probability distribution. Should be standard R #' abbreviation so that "p" + distribution is a valid probability density #' function, and "q" + distribution is a valid quantile function. #' @param ... other arguments passed on to distribution and quantile functions #' @export #' @examples #' plot(logit_trans(), xlim = c(0, 1)) #' plot(probit_trans(), xlim = c(0, 1)) probability_trans <- function(distribution, ...) { qfun <- match.fun(paste0("q", distribution)) pfun <- match.fun(paste0("p", distribution)) trans_new( paste0("prob-", distribution), function(x) qfun(x, ...), function(x) pfun(x, ...) ) } #' @export #' @rdname probability_trans logit_trans <- function() probability_trans("logis") #' @export #' @rdname probability_trans probit_trans <- function() probability_trans("norm") #' Reciprocal transformation #' #' @export #' @examples #' plot(reciprocal_trans(), xlim = c(0, 1)) reciprocal_trans <- function() { trans_new( "reciprocal", function(x) 1 / x, function(x) 1 / x ) } #' Reverse transformation #' #' @export #' @examples #' plot(reverse_trans(), xlim = c(-1, 1)) reverse_trans <- function() { trans_new( "reverse", function(x) -x, function(x) -x, minor_breaks = regular_minor_breaks(reverse = TRUE) ) } #' Square-root transformation #' #' This is the variance stabilising transformation for the Poisson #' distribution. #' #' @export #' @examples #' plot(sqrt_trans(), xlim = c(0, 5)) sqrt_trans <- function() { trans_new( "sqrt", "sqrt", function(x) x ^ 2, domain = c(0, Inf) ) } scales/R/pal-linetype.r0000644000176200001440000000050513556361126014523 0ustar liggesusers#' Line type palette (discrete) #' #' Based on a set supplied by Richard Pearson, University of Manchester #' #' @export linetype_pal <- function() { types <- c( "solid", "22", "42", "44", "13", "1343", "73", "2262", "12223242", "F282", "F4448444", "224282F2", "F1" ) function(n) { types[seq_len(n)] } } scales/R/utils.r0000644000176200001440000000310213655052655013260 0ustar liggesusers# Evaluates all arguments (see #81) force_all <- function(...) list(...) range_finite <- function(x) { suppressWarnings(range(x, na.rm = TRUE, finite = TRUE)) } seq2 <- function(from, to) { if (from > to) { numeric() } else { from:to } } demo_ggplot <- function(x, scale_name, ...) { call <- substitute(list(...)) call[[1]] <- as.name(scale_name) cat(paste0(deparse(call), "\n", collapse = "")) if (!requireNamespace("ggplot2", quietly = TRUE)) { message("Skipping; ggplot2 not installed") return(invisible()) } scale <- getExportedValue("ggplot2", scale_name) df <- data.frame(x = x, stringsAsFactors = FALSE) ggplot2::ggplot(df, ggplot2::aes(x, 1)) + ggplot2::geom_blank() + scale(NULL, ...) + ggplot2::scale_y_continuous(NULL, breaks = NULL) + ggplot2::theme(aspect.ratio = 1 / 5) } #' Demonstrate scales functions with ggplot2 code #' #' These functions generate ggplot2 code needed to use scales functions for #' real code. #' #' @param x A vector of data #' @keywords internal #' @export demo_continuous <- function(x, ...) { demo_ggplot(x, "scale_x_continuous", ...) } #' @rdname demo_continuous #' @export demo_log10 <- function(x, ...) { demo_ggplot(x, "scale_x_log10", ...) } #' @rdname demo_continuous #' @export demo_discrete <- function(x, ...) { demo_ggplot(x, "scale_x_discrete", ...) } #' @rdname demo_continuous #' @export demo_datetime <- function(x, ...) { demo_ggplot(x, "scale_x_datetime", ...) } #' @rdname demo_continuous #' @export demo_time <- function(x, ...) { demo_ggplot(x, "scale_x_time", ...) } scales/R/label-expression.R0000644000176200001440000000365613641652035015343 0ustar liggesusers#' Label with mathematical annotations #' #' `label_parse()` produces expression from strings by parsing them; #' `label_math()` constructs expressions by replacing the pronoun `.x` #' with each string. #' #' @section Old interface: #' `parse_format()` and `math_format()` was retired; please use #' `label_parse()` and `label_math()` instead. #' @inherit number_format return #' @seealso [plotmath] for the details of mathematical formatting in R. #' @export #' @family labels for continuous scales #' @family labels for discrete scales #' @examples #' # Use label_parse() with discrete scales #' greek <- c("alpha", "beta", "gamma") #' demo_discrete(greek) #' demo_discrete(greek, labels = label_parse()) #' #' # Use label_math() with continuous scales #' demo_continuous(c(1, 5)) #' demo_continuous(c(1, 5), labels = label_math(alpha[.x])) label_parse <- function() { # From ggplot2:::parse_safe # See https://github.com/tidyverse/ggplot2/issues/2864 for discussion. function(text) { text <- as.character(text) out <- vector("expression", length(text)) for (i in seq_along(text)) { expr <- parse(text = text[[i]]) out[[i]] <- if (length(expr) == 0) NA else expr[[1]] } out } } #' @rdname label_parse #' @export #' @param expr expression to use #' @param format another format function to apply prior to mathematical #' transformation - this makes it easier to use floating point numbers in #' mathematical expressions. label_math <- function(expr = 10^.x, format = force) { .x <- NULL quoted <- substitute(expr) subs <- function(x) { do.call("substitute", list(quoted, list(.x = x))) } function(x) { x <- format(x) ret <- lapply(x, subs) ret <- as.expression(ret) # restore NAs from input vector ret[is.na(x)] <- NA names(ret) <- names(x) ret } } #' @rdname label_parse #' @export parse_format <- label_parse #' @rdname label_parse #' @export math_format <- label_math scales/R/date-time.r0000644000176200001440000000353213655052655014000 0ustar liggesusers# Minimal date time code so no external dependencies needed, and # we can do the date operations we need. Need to look at this again once we # switch to S4 for lubridate. "%||%" <- function(a, b) if (!is.null(a)) a else b floor_date <- function(date, time) { prec <- parse_unit_spec(time) if (prec$unit == "day") { structure(round_any(as.numeric(date), prec$mult), class = "Date") } else { as.Date(cut(date, time, right = TRUE, include.lowest = TRUE)) } } floor_time <- function(date, time) { to_time <- function(x) { force(x) structure(x, class = c("POSIXt", "POSIXct"), tzone = attr(date, "tzone", exact = TRUE) %||% "" ) } prec <- parse_unit_spec(time) if (prec$unit == "sec") { to_time(round_any(as.numeric(date), prec$mult)) } else if (prec$unit == "min") { to_time(round_any(as.numeric(date), prec$mult * 60)) } else { as.POSIXct( cut(date, time, right = TRUE, include.lowest = TRUE), tz = attr(date, "tzone", exact = TRUE) %||% "" ) } } ceiling_date <- function(date, time) { prec <- parse_unit_spec(time) up <- c("day" = 1, "week" = 7, "month" = 31, "year" = 365) date <- date + prec$mult * up[prec$unit] floor_date(date, time) } ceiling_time <- function(date, time) { date <- date + unit_seconds(time) floor_time(date, time) } unit_seconds <- function(unitspec) { prec <- parse_unit_spec(unitspec) unit_in_seconds <- c( "sec" = 1, "min" = 60, "hour" = 3600, c("day" = 1, "week" = 7, "month" = 31, "year" = 365) * 3600 * 24 ) prec$mult * unit_in_seconds[prec$unit] } parse_unit_spec <- function(unitspec) { parts <- strsplit(unitspec, " ")[[1]] if (length(parts) == 1) { mult <- 1 unit <- unitspec } else { mult <- as.numeric(parts[[1]]) unit <- parts[[2]] } unit <- gsub("s$", "", unit) list(unit = unit, mult = mult) } scales/R/label-number-si.R0000644000176200001440000000267313556361126015046 0ustar liggesusers#' Label numbers with SI prefixes (2k, 1M, 5T etc) #' #' `number_si()` automatically scales and labels with the best SI prefix, #' "K" for values \eqn{\ge} 10e3, "M" for \eqn{\ge} 10e6, #' "B" for \eqn{\ge} 10e9, and "T" for \eqn{\ge} 10e12. #' #' @inherit number_format return params #' @param unit Optional units specifier. #' @param sep Separator between number and SI unit. Defaults to `" "` if #' `units` is supplied, and `""` if not. #' @export #' @family labels for continuous scales #' @family labels for log scales #' @examples #' demo_continuous(c(1, 1e9), label = label_number_si()) #' demo_continuous(c(1, 5000), label = label_number_si(unit = "g")) #' demo_continuous(c(1, 1000), label = label_number_si(unit = "m")) #' #' demo_log10(c(1, 1e9), breaks = log_breaks(10), labels = label_number_si()) label_number_si <- function(accuracy = 1, unit = NULL, sep = NULL, ...) { sep <- if (is.null(unit)) "" else " " force_all(accuracy, ...) function(x) { breaks <- c(0, 10^c(K = 3, M = 6, B = 9, T = 12)) n_suffix <- cut(abs(x), breaks = c(unname(breaks), Inf), labels = c(names(breaks)), right = FALSE ) n_suffix[is.na(n_suffix)] <- "" suffix <- paste0(sep, n_suffix, unit) scale <- 1 / breaks[n_suffix] # for handling Inf and 0-1 correctly scale[which(scale %in% c(Inf, NA))] <- 1 number(x, accuracy = accuracy, scale = unname(scale), suffix = suffix, ... ) } } scales/R/breaks-retired.R0000644000176200001440000000707213556361126014771 0ustar liggesusers#' Regularly spaced dates #' #' \Sexpr[results=rd, stage=render]{lifecycle::badge("retired")} #' Use `breaks_width()` instead. #' #' @param width an interval specification, one of "sec", "min", "hour", #' "day", "week", "month", "year". Can be by an integer and a space, or #' followed by "s". Fractional seconds are supported. #' @keywords internal #' @export date_breaks <- function(width = "1 month") { force(width) function(x) fullseq(x, width) } #' Pretty breaks on transformed scale #' #' \Sexpr[results=rd, stage=render]{lifecycle::badge("retired")} #' These often do not produce very attractive breaks. #' #' @param trans function of single variable, `x`, that given a numeric #' vector returns the transformed values #' @param inv inverse of the transformation function #' @param n desired number of ticks #' @param ... other arguments passed on to pretty #' @keywords internal #' @export #' @examples #' trans_breaks("log10", function(x) 10 ^ x)(c(1, 1e6)) #' trans_breaks("sqrt", function(x) x ^ 2)(c(1, 100)) #' trans_breaks(function(x) 1 / x, function(x) 1 / x)(c(1, 100)) #' trans_breaks(function(x) -x, function(x) -x)(c(1, 100)) trans_breaks <- function(trans, inv, n = 5, ...) { trans <- match.fun(trans) inv <- match.fun(inv) force_all(n, ...) n_default <- n function(x, n = n_default) { inv(pretty(trans(x), n, ...)) } } #' Compute breaks for continuous scale #' #' \Sexpr[results=rd, stage=render]{lifecycle::badge("retired")} #' This function wraps up the components needed to go from a continuous range #' to a set of breaks and labels suitable for display on axes or legends. #' #' @param range numeric vector of length 2 giving the range of the underlying #' data #' @param breaks either a vector of break values, or a break function that #' will make a vector of breaks when given the range of the data #' @param labels either a vector of labels (character vector or list of #' expression) or a format function that will make a vector of labels when #' called with a vector of breaks. Labels can only be specified manually if #' breaks are - it is extremely dangerous to supply labels if you don't know #' what the breaks will be. #' @keywords internal #' @export #' @examples #' cbreaks(c(0, 100)) #' cbreaks(c(0, 100), breaks_pretty(3)) #' cbreaks(c(0, 100), breaks_pretty(10)) #' cbreaks(c(1, 100), log_breaks()) #' cbreaks(c(1, 1e4), log_breaks()) #' #' cbreaks(c(0, 100), labels = math_format()) #' cbreaks(c(0, 1), labels = percent_format()) #' cbreaks(c(0, 1e6), labels = comma_format()) #' cbreaks(c(0, 1e6), labels = dollar_format()) #' cbreaks(c(0, 30), labels = dollar_format()) #' #' # You can also specify them manually: #' cbreaks(c(0, 100), breaks = c(15, 20, 80)) #' cbreaks(c(0, 100), breaks = c(15, 20, 80), labels = c(1.5, 2.0, 8.0)) #' cbreaks(c(0, 100), breaks = c(15, 20, 80), #' labels = expression(alpha, beta, gamma)) cbreaks <- function(range, breaks = extended_breaks(), labels = scientific_format()) { if (zero_range(range)) { return(list(breaks = range[1], labels = format(range[1]))) } if (is.function(breaks)) { breaks <- breaks(range) if (!is.function(labels)) { stop("Labels can only be manually specified in conjunction with breaks", call. = FALSE ) } } if (is.function(labels)) { labels <- labels(breaks) } else { if (length(labels) != length(breaks)) { stop("Labels and breaks must be same length") } if (is.expression(labels)) { labels <- as.list(labels) } else { labels <- as.character(labels) } } list(breaks = breaks, labels = labels) } scales/R/label-number.r0000644000176200001440000001324313655052654014473 0ustar liggesusers#' Label numbers in decimal format (e.g. 0.12, 1,234) #' #' Use `label_number()` force decimal display of numbers (i.e. don't use #' [scientific][label_scientific] notation). `label_comma()` is a special case #' that inserts a comma every three digits. #' #' @return #' All `label_()` functions return a "labelling" function, i.e. a function that #' takes a vector `x` and returns a character vector of `length(x)` giving a #' label for each input value. #' #' Labelling functions are designed to be used with the `labels` argument of #' ggplot2 scales. The examples demonstrate their use with x scales, but #' they work similarly for all scales, including those that generate legends #' rather than axes. #' @section Old interface: #' `number_format()`, `comma_format()`, and `comma()` are retired; please use #' `label_number()` and `label_comma()` instead. #' @param x A numeric vector to format. #' @param accuracy A number to round to. Use (e.g.) `0.01` to show 2 decimal #' places of precision. If `NULL`, the default, uses a heuristic that should #' ensure breaks have the minimum number of digits needed to show the #' difference between adjacent values. #' #' Applied to rescaled data. #' @param scale A scaling factor: `x` will be multiplied by `scale` before #' formating. This is useful if the underlying data is very small or very #' large. #' @param prefix,suffix Symbols to display before and after value. #' @param big.mark Character used between every 3 digits to separate thousands. #' @param decimal.mark The character to be used to indicate the numeric #' decimal point. #' @param trim Logical, if `FALSE`, values are right-justified to a common #' width (see [base::format()]). #' @param ... Other arguments passed on to [base::format()]. #' @export #' @examples #' demo_continuous(c(-1e6, 1e6)) #' demo_continuous(c(-1e6, 1e6), labels = label_number()) #' demo_continuous(c(-1e6, 1e6), labels = label_comma()) #' #' # Use scale to rescale very small or large numbers to generate #' # more readable labels #' demo_continuous(c(0, 1e6), labels = label_number()) #' demo_continuous(c(0, 1e6), labels = label_number(scale = 1 / 1e3)) #' demo_continuous(c(0, 1e-6), labels = label_number()) #' demo_continuous(c(0, 1e-6), labels = label_number(scale = 1e6)) #' #' # You can use prefix and suffix for other types of display #' demo_continuous(c(32, 212), label = label_number(suffix = "\u00b0F")) #' demo_continuous(c(0, 100), label = label_number(suffix = "\u00b0C")) label_number <- function(accuracy = NULL, scale = 1, prefix = "", suffix = "", big.mark = " ", decimal.mark = ".", trim = TRUE, ...) { force_all( accuracy, scale, prefix, suffix, big.mark, decimal.mark, trim, ... ) function(x) number( x, accuracy = accuracy, scale = scale, prefix = prefix, suffix = suffix, big.mark = big.mark, decimal.mark = decimal.mark, trim = trim, ... ) } #' @export #' @rdname label_number #' @param digits Deprecated, use `accuracy` instead. label_comma <- function(accuracy = NULL, scale = 1, prefix = "", suffix = "", big.mark = ",", decimal.mark = ".", trim = TRUE, digits, ...) { if (!missing(digits)) { warning( "`digits` argument is deprecated, use `accuracy` instead.", .call = FALSE ) } number_format( accuracy = accuracy, scale = scale, prefix = prefix, suffix = suffix, big.mark = big.mark, decimal.mark = decimal.mark, trim = trim, ... ) } #' @export #' @rdname label_number comma <- function(x, accuracy = NULL, scale = 1, prefix = "", suffix = "", big.mark = ",", decimal.mark = ".", trim = TRUE, digits, ...) { if (!missing(digits)) { warning( "`digits` argument is deprecated, use `accuracy` instead.", .call = FALSE ) } number( x = x, accuracy = accuracy, scale = scale, prefix = prefix, suffix = suffix, big.mark = big.mark, decimal.mark = decimal.mark, trim = trim, ... ) } #' @export #' @rdname label_number number_format <- label_number #' @export #' @rdname label_number comma_format <- label_comma #' A low-level numeric formatter #' #' This function is a low-level helper that powers many of the labelling #' functions. You should generally not need to call it directly unless you #' are creating your own labelling function. #' #' @keywords internal #' @export #' @inheritParams label_number #' @return A character vector of `length(x)`. number <- function(x, accuracy = NULL, scale = 1, prefix = "", suffix = "", big.mark = " ", decimal.mark = ".", trim = TRUE, ...) { if (length(x) == 0) return(character()) accuracy <- accuracy %||% precision(x * scale) x <- round_any(x, accuracy / scale) nsmall <- -floor(log10(accuracy)) nsmall <- min(max(nsmall, 0), 20) ret <- format( scale * x, big.mark = big.mark, decimal.mark = decimal.mark, trim = trim, nsmall = nsmall, scientific = FALSE, ... ) ret <- paste0(prefix, ret, suffix) ret[is.infinite(x)] <- as.character(x[is.infinite(x)]) # restore NAs from input vector ret[is.na(x)] <- NA names(ret) <- names(x) ret } # Helpers ----------------------------------------------------------------- precision <- function(x) { x <- unique(x) # ignore NA and Inf/-Inf x <- x[is.finite(x)] if (length(x) <= 1) { return(1) } smallest_diff <- min(diff(sort(x))) if (smallest_diff < sqrt(.Machine$double.eps)) { 1 } else { # Never return precision bigger than 1 pmin(10^(floor(log10(smallest_diff)) - 1), 1) } } scales/R/pal-viridis.r0000644000176200001440000000102413320151564014330 0ustar liggesusers#' Viridis palette #' #' @inheritParams viridisLite::viridis #' @references #' @export #' @examples #' show_col(viridis_pal()(10)) #' show_col(viridis_pal(direction = -1)(6)) #' show_col(viridis_pal(begin = 0.2, end = 0.8)(4)) #' show_col(viridis_pal(option = "plasma")(6)) viridis_pal <- function(alpha = 1, begin = 0, end = 1, direction = 1, option= "D") { force_all(alpha, begin, end, direction, option) function(n) { viridisLite::viridis(n, alpha, begin, end, direction, option) } } scales/R/label-pvalue.R0000644000176200001440000000420413556361126014431 0ustar liggesusers#' Label p-values (e.g. <0.001, 0.25, p >= 0.99) #' #' Formatter for p-values, using "<" and ">" for p-values close to 0 and 1. #' #' @section Old interface: #' `pvalue()` and `pvalue_dollar()` are retired; please use `label_pvalue()` #' instead. #' @inherit number_format return params #' @param prefix A character vector of length 3 giving the prefixes to #' put in front of numbers. The default values are `c("<", "", ">")` #' if `add_p` is `TRUE` and `c("p<", "p=", "p>")` if `FALSE`. #' @param add_p Add "p=" before the value? #' @export #' @family labels for continuous scales #' @examples #' demo_continuous(c(0, 1)) #' demo_continuous(c(0, 1), labels = label_pvalue()) #' demo_continuous(c(0, 1), labels = label_pvalue(accuracy = 0.1)) #' demo_continuous(c(0, 1), labels = label_pvalue(add_p = TRUE)) #' #' # Or provide your own prefixes #' prefix <- c("p < ", "p = ", "p > ") #' demo_continuous(c(0, 1), labels = label_pvalue(prefix = prefix)) label_pvalue <- function(accuracy = .001, decimal.mark = ".", prefix = NULL, add_p = FALSE) { force_all(accuracy, decimal.mark, add_p) function(x) pvalue( x, accuracy = accuracy, decimal.mark = decimal.mark, prefix = prefix, add_p = add_p ) } #' @rdname label_pvalue #' @export pvalue_format <- label_pvalue #' @rdname label_pvalue #' @export pvalue <- function(x, accuracy = .001, decimal.mark = ".", prefix = NULL, add_p = FALSE) { out <- number(x, accuracy, decimal.mark = decimal.mark) below <- number(accuracy, accuracy, decimal.mark = decimal.mark) above <- number(1 - accuracy, accuracy, decimal.mark = decimal.mark) if (is.null(prefix)) { if (add_p) { prefix <- c("p<", "p=", "p>") } else { prefix <- c("<", "", ">") } } else { if (!is.character(prefix) || length(prefix) != 3) { stop("`prefix` must be a length 3 character vector", call. = FALSE) } } out <- paste0(prefix[[2]], out) out[x < accuracy] <- paste0(prefix[[1]], below) out[x > 1 - accuracy] <- paste0(prefix[[3]], above) out[is.na(x)] <- NA names(out) <- names(x) out } scales/R/pal-dichromat.r0000644000176200001440000000222113556361126014641 0ustar liggesusers#' Dichromat (colour-blind) palette (discrete) #' #' @param name Name of colour palette. One of: #' \Sexpr[results=rd,stage=build]{scales:::dichromat_schemes()} #' @export #' @examples #' if (requireNamespace("dichromat", quietly = TRUE)) { #' show_col(dichromat_pal("BluetoOrange.10")(10)) #' show_col(dichromat_pal("BluetoOrange.10")(5)) #' #' # Can use with gradient_n to create a continous gradient #' cols <- dichromat_pal("DarkRedtoBlue.12")(12) #' show_col(gradient_n_pal(cols)(seq(0, 1, length.out = 30))) #' } dichromat_pal <- function(name) { if (!requireNamespace("dichromat", quietly = TRUE)) { stop("Package dichromat must be installed for this function to work. Please install it.", call. = FALSE ) } if (!any(name == names(dichromat::colorschemes))) { stop("Palette name must be one of ", paste0(names(dichromat::colorschemes), collapse = ", "), call. = FALSE ) } pal <- dichromat::colorschemes[[name]] function(n) pal[seq_len(n)] } dichromat_schemes <- function() { if (requireNamespace("dichromat", quietly = TRUE)){ paste0("\\code{", names(dichromat::colorschemes), "}", collapse = ", ") } } scales/R/trans.r0000644000176200001440000000603013560275235013246 0ustar liggesusers#' Create a new transformation object #' #' A transformation encapsulates a transformation and its inverse, as well #' as the information needed to create pleasing breaks and labels. The breaks #' function is applied on the transformed range of the range, and it's #' expected that the labels function will perform some kind of inverse #' transformation on these breaks to give them labels that are meaningful on #' the original scale. #' #' @param name transformation name #' @param transform function, or name of function, that performs the #' transformation #' @param inverse function, or name of function, that performs the #' inverse of the transformation #' @param breaks default breaks function for this transformation. The breaks #' function is applied to the raw data. #' @param minor_breaks default minor breaks function for this transformation. #' @param format default format for this transformation. The format is applied #' to breaks generated to the raw data. #' @param domain domain, as numeric vector of length 2, over which #' transformation is valued #' @seealso \Sexpr[results=rd,stage=build]{scales:::seealso_trans()} #' @export #' @keywords internal #' @aliases trans trans_new <- function(name, transform, inverse, breaks = extended_breaks(), minor_breaks = regular_minor_breaks(), format = format_format(), domain = c(-Inf, Inf)) { if (is.character(transform)) transform <- match.fun(transform) if (is.character(inverse)) inverse <- match.fun(inverse) structure( list( name = name, transform = transform, inverse = inverse, breaks = breaks, minor_breaks = minor_breaks, format = format, domain = domain ), class = "trans" ) } #' @rdname trans_new #' @export is.trans <- function(x) inherits(x, "trans") #' @export print.trans <- function(x, ...) cat("Transformer: ", x$name, "\n") #' @export plot.trans <- function(x, y, ..., xlim, ylim = NULL) { if (is.null(ylim)) { ylim <- range(x$transform(seq(xlim[1], xlim[2], length = 100)), finite = TRUE) } plot( xlim, ylim, xlab = "", ylab = "", type = "n", main = paste0("Transformer: ", x$name), ) graphics::grid(lty = "solid") graphics::abline(h = 0, v = 0, col = "grey90", lwd = 5) graphics::lines(x, xlim = xlim) } #' @export lines.trans <- function(x, ..., xlim) { xgrid <- seq(xlim[1], xlim[2], length = 100) y <- suppressWarnings(x$transform(xgrid)) graphics::lines(xgrid, y, ...) } #' @rdname trans_new #' @export as.trans <- function(x) { if (is.trans(x)) return(x) f <- paste0(x, "_trans") match.fun(f)() } #' Compute range of transformed values #' #' Silently drops any ranges outside of the domain of `trans`. #' #' @param trans a transformation object, or the name of a transformation object #' given as a string. #' @param x a numeric vector to compute the range of #' @export #' @keywords internal trans_range <- function(trans, x) { trans <- as.trans(trans) range(trans$transform(range(squish(x, trans$domain), na.rm = TRUE))) } scales/R/breaks-log.R0000644000176200001440000001010313556361126014101 0ustar liggesusers#' Breaks for log axes #' #' This algorithm starts by looking for integer powers of `base`. If that #' doesn't provide enough breaks, it then looks for additional intermediate #' breaks which are integer multiples of integer powers of base. If that fails #' (which it can for very small ranges), we fall back to [extended_breaks()] #' #' @details #' The algorithm starts by looking for a set of integer powers of `base` that #' cover the range of the data. If that does not generate at least `n - 2` #' breaks, we look for an integer between 1 and `base` that splits the interval #' approximately in half. For example, in the case of `base = 10`, this integer #' is 3 because `log10(3) = 0.477`. This leaves 2 intervals: `c(1, 3)` and #' `c(3, 10)`. If we still need more breaks, we look for another integer #' that splits the largest remaining interval (on the log-scale) approximately #' in half. For `base = 10`, this is 5 because `log10(5) = 0.699`. #' #' The generic algorithm starts with a set of integers `steps` containing #' only 1 and a set of candidate integers containing all integers larger than 1 #' and smaller than `base`. Then for each remaining candidate integer #' `x`, the smallest interval (on the log-scale) in the vector #' `sort(c(x, steps, base))` is calculated. The candidate `x` which #' yields the largest minimal interval is added to `steps` and removed from #' the candidate set. This is repeated until either a sufficient number of #' breaks, `>= n-2`, are returned or all candidates have been used. #' @param n desired number of breaks #' @param base base of logarithm to use #' @export #' @examples #' demo_log10(c(1, 1e5)) #' demo_log10(c(1, 1e6)) #' #' # Request more breaks by setting n #' demo_log10(c(1, 1e6), breaks = breaks_log(6)) #' #' # Some tricky ranges #' demo_log10(c(2000, 9000)) #' demo_log10(c(2000, 14000)) #' demo_log10(c(2000, 85000), expand = c(0, 0)) #' #' # An even smaller range that requires falling back to linear breaks #' demo_log10(c(1800, 2000)) breaks_log <- function(n = 5, base = 10) { force_all(n, base) n_default = n function(x, n = n_default) { raw_rng <- suppressWarnings(range(x, na.rm = TRUE)) if (any(!is.finite(raw_rng))) { return(numeric()) } rng <- log(raw_rng, base = base) min <- floor(rng[1]) max <- ceiling(rng[2]) if (max == min) return(base^min) by <- floor((max - min) / n) + 1 breaks <- base^seq(min, max, by = by) relevant_breaks <- base^rng[1] <= breaks & breaks <= base^rng[2] if (sum(relevant_breaks) >= (n - 2)) return(breaks) # the easy solution to get more breaks is to decrease 'by' while (by > 1) { by <- by - 1 breaks <- base^seq(min, max, by = by) relevant_breaks <- base^rng[1] <= breaks & breaks <= base^rng[2] if (sum(relevant_breaks) >= (n - 2)) return(breaks) } log_sub_breaks(rng, n = n, base = base) } } #' @export #' @usage NULL #' @rdname breaks_log log_breaks <- breaks_log #' @author Thierry Onkelinx, \email{thierry.onkelinx@inbo.be} #' @noRd log_sub_breaks <- function(rng, n = 5, base = 10) { min <- floor(rng[1]) max <- ceiling(rng[2]) if (base <= 2) { return(base^(min:max)) } steps <- 1 # 'delta()' calculates the smallest distance in the log scale between the # currectly selected breaks and a new candidate 'x' delta <- function(x) { min(diff(log(sort(c(x, steps, base)), base = base))) } candidate <- seq_len(base) candidate <- candidate[1 < candidate & candidate < base] while (length(candidate)) { best <- which.max(vapply(candidate, delta, 0)) steps <- c(steps, candidate[best]) candidate <- candidate[-best] breaks <- as.vector(outer(base^seq(min, max), steps)) relevant_breaks <- base^rng[1] <= breaks & breaks <= base^rng[2] if (sum(relevant_breaks) >= (n - 2)) { break } } if (sum(relevant_breaks) >= (n - 2)) { breaks <- sort(breaks) lower_end <- pmax(min(which(base^rng[1] <= breaks)) - 1, 1) upper_end <- pmin(max(which(breaks <= base^rng[2])) + 1, length(breaks)) breaks[lower_end:upper_end] } else { extended_breaks(n = n)(base^rng) } } scales/R/trans-date.r0000644000176200001440000000464013655052654014171 0ustar liggesusers#' Transformation for dates (class Date) #' #' @export #' @examples #' years <- seq(as.Date("1910/1/1"), as.Date("1999/1/1"), "years") #' t <- date_trans() #' t$transform(years) #' t$inverse(t$transform(years)) #' t$format(t$breaks(range(years))) date_trans <- function() { trans_new("date", "from_date", "to_date", breaks = breaks_pretty()) } to_date <- function(x) structure(x, class = "Date") from_date <- function(x) { if (!inherits(x, "Date")) { stop("Invalid input: date_trans works with objects of class Date only", call. = FALSE ) } structure(as.numeric(x), names = names(x)) } #' Transformation for date-times (class POSIXt) #' #' @param tz Optionally supply the time zone. If `NULL`, the default, #' the time zone will be extracted from first input with a non-null tz. #' @export #' @examples #' hours <- seq(ISOdate(2000,3,20, tz = ""), by = "hour", length.out = 10) #' t <- time_trans() #' t$transform(hours) #' t$inverse(t$transform(hours)) #' t$format(t$breaks(range(hours))) time_trans <- function(tz = NULL) { force(tz) to_time <- function(x) { structure(x, class = c("POSIXt", "POSIXct"), tzone = tz) } from_time <- function(x) { if (!inherits(x, "POSIXct")) { stop("Invalid input: time_trans works with objects of class ", "POSIXct only", call. = FALSE ) } if (is.null(tz)) { tz <<- attr(as.POSIXlt(x), "tzone")[[1]] } structure(as.numeric(x), names = names(x)) } trans_new("time", "from_time", "to_time", breaks = breaks_pretty()) } #' Transformation for times (class hms) #' #' @export #' @examples #' if (require("hms")) { #' hms <- round(runif(10) * 86400) #' t <- hms_trans() #' t$transform(hms) #' t$inverse(t$transform(hms)) #' t$breaks(hms) #' } hms_trans <- function() { trans_new( "hms", transform = function(x) { structure(as.numeric(x), names = names(x)) }, inverse = hms::as_hms, breaks = time_breaks() ) } time_breaks <- function(n = 5) { force(n) function(x) { rng <- as.numeric(range(x)) diff <- rng[2] - rng[1] if (diff <= 2 * 60) { scale <- 1 } else if (diff <= 2 * 3600) { scale <- 60 } else if (diff <= 2 * 86400) { scale <- 3600 } else { scale <- 86400 } rng <- rng / scale breaks <- labeling::extended( rng[1], rng[2], n, Q = c(1, 2, 1.5, 4, 3), only.loose = FALSE ) hms::as_hms(breaks * scale) } } scales/R/pal-manual.r0000644000176200001440000000062613556361126014153 0ustar liggesusers#' Manual palette (discrete) #' #' @param values vector of values to be used as a palette. #' @export manual_pal <- function(values) { force(values) function(n) { n_values <- length(values) if (n > n_values) { warning("This manual palette can handle a maximum of ", n_values, " values. You have supplied ", n, ".", call. = FALSE ) } values[seq_len(n)] } } scales/R/label-date.R0000644000176200001440000001003413641652035014045 0ustar liggesusers#' Label date/times #' #' `label_date()` and `label_time()` label date/times using date/time format #' strings. `label_date_short()` automatically constructs a short format string #' suffiicient to uniquely identify labels. It's inspired by matplotlib's #' [`ConciseDateFormatter`](https://matplotlib.org/api/dates_api.html#matplotlib.dates.ConciseDateFormatter), #' but uses a slightly different approach: `ConciseDateFormatter` formats #' "firsts" (e.g. first day of month, first day of day) specially; #' `date_short()` formats changes (e.g. new month, new year) specially. #' #' @section Old interface: #' `date_format()` and `time_format()` are retired; please use `label_date()` #' and `label_time()` instead. #' @inherit number_format return #' @param format For `date_format()` and `time_format()` a date/time format #' string using standard POSIX specification. See [strptime()] for details. #' #' For `date_short()` a character vector of length 4 giving the format #' components to use for year, month, day, and hour respectively. #' @param tz a time zone name, see [timezones()]. Defaults #' to UTC #' @export #' @examples #' date_range <- function(start, days) { #' start <- as.POSIXct(start) #' c(start, start + days * 24 * 60 * 60) #' } #' #' two_months <- date_range("2020-05-01", 60) #' demo_datetime(two_months) #' demo_datetime(two_months, labels = date_format("%m/%d")) #' # ggplot2 provides a short-hand: #' demo_datetime(two_months, date_labels = "%m/%d") #' #' # An alternative labelling system is label_date_short() #' demo_datetime(two_months, date_breaks = "7 days", labels = label_date_short()) #' # This is particularly effective for dense labels #' one_year <- date_range("2020-05-01", 365) #' demo_datetime(one_year, date_breaks = "month") #' demo_datetime(one_year, date_breaks = "month", labels = label_date_short()) label_date <- function(format = "%Y-%m-%d", tz = "UTC") { force_all(format, tz) function(x) format(x, format, tz = tz) # format handles NAs correctly when dealing with dates } #' @export #' @rdname label_date #' @param sep Separator to use when combining date formats into a single string. label_date_short <- function(format = c("%Y", "%b", "%d", "%H:%M"), sep = "\n") { force_all(format, sep) function(x) { dt <- unclass(as.POSIXlt(x)) changes <- cbind( year = changed(dt$year), month = changed(dt$mon), day = changed(dt$mday) ) # Ensure large unit changes implies that small units change too # Would be more elegant with cumany() but cumsum() does the job changes <- t(apply(changes, 1, cumsum)) >= 1 # Trim out "firsts" from smallest to largest - only want to trim (e.g.) # January if all dates are the first of the month. if (inherits(x, "Date") || all(dt$hour == 0 & dt$min == 0, na.rm = TRUE)) { format[[4]] <- NA if (all(dt$mday == 1, na.rm = TRUE)) { format[[3]] <- NA if (all(dt$mon == 0, na.rm = TRUE)) { format[[2]] <- NA } } } for_mat <- cbind( ifelse(changes[, 1], format[[1]], NA), ifelse(changes[, 2], format[[2]], NA), ifelse(changes[, 3], format[[3]], NA), format[[4]] ) format <- apply(for_mat, 1, function(x) paste(rev(x[!is.na(x)]), collapse = sep)) format(x, format) } } changed <- function(x) c(TRUE, is.na(x[-length(x)]) | x[-1] != x[-length(x)]) append_if <- function(x, cond, value) { x[cond] <- lapply(x[cond], c, value) x } #' @export #' @rdname label_date label_time <- function(format = "%H:%M:%S", tz = "UTC") { force_all(format, tz) function(x) { if (inherits(x, "POSIXt")) { format(x, format = format, tz = tz) # format handles NAs correctly for times } else if (inherits(x, "difftime")) { format(as.POSIXct(x), format = format, tz = tz) } else { stop( "time_format can't be used with objects of class ", paste(class(x), collapse = "/"), ".", call. = FALSE ) } } } #' @export #' @rdname label_date date_format <- label_date #' @export #' @rdname label_date time_format <- label_time scales/R/label-ordinal.R0000644000176200001440000000577313655052654014604 0ustar liggesusers#' Label ordinal numbers (1st, 2nd, 3rd, etc) #' #' Round values to integers and then display as ordinal values (e.g. 1st, 2nd, #' 3rd). Built-in rules are provided for English, French, and Spanish. #' #' @section Old interface: #' `ordinal()` and `format_ordinal()` are retired; please use `label_ordinal()` #' instead. #' @inherit number_format return params #' @param prefix,suffix Symbols to display before and after value. #' @param rules Named list of regular expressions, matched in order. #' Name gives suffix, and value specifies which numbers to match. #' @param gender Masculin or feminin gender for French ordinal. #' @param plural Plural or singular for French ordinal. #' @param ... Other arguments passed on to [base::format()]. #' @export #' @family labels for continuous scales #' @examples #' demo_continuous(c(1, 5)) #' demo_continuous(c(1, 5), labels = label_ordinal()) #' demo_continuous(c(1, 5), labels = label_ordinal(rules = ordinal_french())) #' #' # The rules are just a set of regular expressions that are applied in turn #' ordinal_french() #' ordinal_english() #' #' # Note that ordinal rounds values, so you may need to adjust the breaks too #' demo_continuous(c(1, 10)) #' demo_continuous(c(1, 10), labels = label_ordinal()) #' demo_continuous(c(1, 10), #' labels = label_ordinal(), #' breaks = breaks_width(2) #' ) label_ordinal <- function(prefix = "", suffix = "", big.mark = " ", rules = ordinal_english(), ...) { force_all(prefix, suffix, big.mark, rules, ...) function(x) ordinal( x, prefix = prefix, suffix = suffix, big.mark = big.mark, rules = rules, ... ) } #' @export #' @rdname label_ordinal ordinal_english <- function() { list( st = "(? max(b)) b <- c(b, b[length(b)] + bd) } else { if (max(limits) > max(b)) b <- c(b[1] - bd, b) if (min(limits) < min(b)) b <- c(b, b[length(b)] + bd) } # Find minor breaks between major breaks seq_between <- function(a, b) { seq(a, b, length.out = n + 1)[-(n + 1)] } breaks <- unlist(Map(seq_between, b[-length(b)], b[-1])) # Add the final break back breaks <- c(breaks, b[length(b)]) breaks } } scales/R/colour-mapping.r0000644000176200001440000003154213562561114015055 0ustar liggesusers#' Colour mapping #' #' Conveniently maps data values (numeric or factor/character) to colours #' according to a given palette, which can be provided in a variety of formats. #' #' `col_numeric` is a simple linear mapping from continuous numeric data #' to an interpolated palette. #' #' @param palette The colours or colour function that values will be mapped to #' @param domain The possible values that can be mapped. #' #' For `col_numeric` and `col_bin`, this can be a simple numeric #' range (e.g. `c(0, 100)`); `col_quantile` needs representative #' numeric data; and `col_factor` needs categorical data. #' #' If `NULL`, then whenever the resulting colour function is called, the #' `x` value will represent the domain. This implies that if the function #' is invoked multiple times, the encoding between values and colours may not #' be consistent; if consistency is needed, you must provide a non-`NULL` #' domain. #' @param na.color The colour to return for `NA` values. Note that #' `na.color = NA` is valid. #' @param alpha Whether alpha channels should be respected or ignored. If `TRUE` #' then colors without explicit alpha information will be treated as fully #' opaque. #' @param reverse Whether the colors (or color function) in `palette` should be #' used in reverse order. For example, if the default order of a palette goes #' from blue to green, then `reverse = TRUE` will result in the colors going #' from green to blue. #' @return A function that takes a single parameter `x`; when called with a #' vector of numbers (except for `col_factor`, which expects #' factors/characters), #RRGGBB colour strings are returned (unless #' `alpha = TRUE` in which case #RRGGBBAA may also be possible). #' #' @export col_numeric <- function(palette, domain, na.color = "#808080", alpha = FALSE, reverse = FALSE) { rng <- NULL if (length(domain) > 0) { rng <- range(domain, na.rm = TRUE) if (!all(is.finite(rng))) { stop("Wasn't able to determine range of domain") } } pf <- safePaletteFunc(palette, na.color, alpha) withColorAttr("numeric", list(na.color = na.color), function(x) { if (length(x) == 0 || all(is.na(x))) { return(pf(x)) } if (is.null(rng)) rng <- range(x, na.rm = TRUE) rescaled <- rescale(x, from = rng) if (any(rescaled < 0 | rescaled > 1, na.rm = TRUE)) warning("Some values were outside the color scale and will be treated as NA", call. = FALSE) if (reverse) { rescaled <- 1 - rescaled } pf(rescaled) }) } # Attach an attribute colorType to a color function f so we can derive legend # items from it withColorAttr <- function(type, args = list(), fun) { structure(fun, colorType = type, colorArgs = args) } # domain may or may not be NULL. # Iff domain is non-NULL, x may be NULL. # bins is non-NULL. It may be a scalar value (# of breaks) or a set of breaks. getBins <- function(domain, x, bins, pretty) { if (is.null(domain) && is.null(x)) { stop("Assertion failed: domain and x can't both be NULL") } # Hard-coded bins if (length(bins) > 1) { return(bins) } if (bins < 2) { stop("Invalid bins value of ", bins, "; bin count must be at least 2") } if (pretty) { base::pretty(domain %||% x, n = bins) } else { rng <- range(domain %||% x, na.rm = TRUE) seq(rng[1], rng[2], length.out = bins + 1) } } #' @details `col_bin` also maps continuous numeric data, but performs #' binning based on value (see the [base::cut()] function). `col_bin` #' defaults for the `cut` function are `include.lowest = TRUE` and #' `right = FALSE`. #' @param bins Either a numeric vector of two or more unique cut points or a #' single number (greater than or equal to 2) giving the number of intervals #' into which the domain values are to be cut. #' @param pretty Whether to use the function [pretty()] to generate #' the bins when the argument `bins` is a single number. When #' `pretty = TRUE`, the actual number of bins may not be the number of #' bins you specified. When `pretty = FALSE`, [seq()] is used #' to generate the bins and the breaks may not be "pretty". #' @param right parameter supplied to [base::cut()]. See Details #' @rdname col_numeric #' @export col_bin <- function(palette, domain, bins = 7, pretty = TRUE, na.color = "#808080", alpha = FALSE, reverse = FALSE, right = FALSE) { # domain usually needs to be explicitly provided (even if NULL) but not if # breaks are specified if (missing(domain) && length(bins) > 1) { domain <- NULL } autobin <- is.null(domain) && length(bins) == 1 if (!is.null(domain)) bins <- getBins(domain, NULL, bins, pretty) numColors <- if (length(bins) == 1) bins else length(bins) - 1 colorFunc <- col_factor(palette, domain = if (!autobin) 1:numColors, na.color = na.color, alpha = alpha, reverse = reverse) pf <- safePaletteFunc(palette, na.color, alpha) withColorAttr("bin", list(bins = bins, na.color = na.color, right = right), function(x) { if (length(x) == 0 || all(is.na(x))) { return(pf(x)) } binsToUse <- getBins(domain, x, bins, pretty) ints <- cut(x, binsToUse, labels = FALSE, include.lowest = TRUE, right = right) if (any(is.na(x) != is.na(ints))) warning("Some values were outside the color scale and will be treated as NA", call. = FALSE) colorFunc(ints) }) } #' @details `col_quantile` similarly bins numeric data, but via the #' [stats::quantile()] function. #' @param n Number of equal-size quantiles desired. For more precise control, #' use the `probs` argument instead. #' @param probs See [stats::quantile()]. If provided, the `n` #' argument is ignored. #' @rdname col_numeric #' @export col_quantile <- function(palette, domain, n = 4, probs = seq(0, 1, length.out = n + 1), na.color = "#808080", alpha = FALSE, reverse = FALSE, right = FALSE) { if (!is.null(domain)) { bins <- stats::quantile(domain, probs, na.rm = TRUE, names = FALSE) return(withColorAttr( "quantile", list(probs = probs, na.color = na.color, right = right), col_bin(palette, domain = NULL, bins = bins, na.color = na.color, alpha = alpha, reverse = reverse) )) } # I don't have a precise understanding of how quantiles are meant to map to colors. # If you say probs = seq(0, 1, 0.25), which has length 5, does that map to 4 colors # or 5? 4, right? colorFunc <- col_factor(palette, domain = 1:(length(probs) - 1), na.color = na.color, alpha = alpha, reverse = reverse) withColorAttr("quantile", list(probs = probs, na.color = na.color, right = right), function(x) { binsToUse <- stats::quantile(x, probs, na.rm = TRUE, names = FALSE) ints <- cut(x, binsToUse, labels = FALSE, include.lowest = TRUE, right = right) if (any(is.na(x) != is.na(ints))) warning("Some values were outside the color scale and will be treated as NA", call. = FALSE) colorFunc(ints) }) } # If already a factor, return the levels. Otherwise, convert to factor then # return the levels. calcLevels <- function(x, ordered) { if (is.null(x)) { NULL } else if (is.factor(x)) { levels(x) } else if (ordered) { unique(x) } else { sort(unique(x)) } } getLevels <- function(domain, x, lvls, ordered) { if (!is.null(lvls)) { return(lvls) } if (!is.null(domain)) { return(calcLevels(domain, ordered)) } if (!is.null(x)) { return(calcLevels(x, ordered)) } } #' @details `col_factor` maps factors to colours. If the palette is #' discrete and has a different number of colours than the number of factors, #' interpolation is used. #' @param levels An alternate way of specifying levels; if specified, domain is #' ignored #' @param ordered If `TRUE` and `domain` needs to be coerced to a #' factor, treat it as already in the correct order #' @rdname col_numeric #' @export col_factor <- function(palette, domain, levels = NULL, ordered = FALSE, na.color = "#808080", alpha = FALSE, reverse = FALSE) { # domain usually needs to be explicitly provided (even if NULL) but not if # levels are specified if (missing(domain) && !is.null(levels)) { domain <- NULL } if (!is.null(levels) && anyDuplicated(levels)) { warning("Duplicate levels detected", call. = FALSE) levels <- unique(levels) } lvls <- getLevels(domain, NULL, levels, ordered) force(palette) # palette loses scope withColorAttr("factor", list(na.color = na.color), function(x) { if (length(x) == 0 || all(is.na(x))) { return(rep.int(na.color, length(x))) } lvls <- getLevels(domain, x, lvls, ordered) pf <- safePaletteFunc(palette, na.color, alpha, nlevels = length(lvls) * ifelse(reverse, -1, 1)) origNa <- is.na(x) x <- match(as.character(x), lvls) if (any(is.na(x) != origNa)) { warning("Some values were outside the color scale and will be treated as NA", call. = FALSE) } scaled <- rescale(as.integer(x), from = c(1, length(lvls))) if (any(scaled < 0 | scaled > 1, na.rm = TRUE)) { warning("Some values were outside the color scale and will be treated as NA", call. = FALSE) } if (reverse) { scaled <- 1 - scaled } pf(scaled) }) } #' @details The `palette` argument can be any of the following: #' \enumerate{ #' \item{A character vector of RGB or named colours. Examples: `palette()`, `c("#000000", "#0000FF", "#FFFFFF")`, `topo.colors(10)`} #' \item{The name of an RColorBrewer palette, e.g. `"BuPu"` or `"Greens"`.} #' \item{The full name of a viridis palette: `"viridis"`, `"magma"`, `"inferno"`, or `"plasma"`.} #' \item{A function that receives a single value between 0 and 1 and returns a colour. Examples: `colorRamp(c("#000000", "#FFFFFF"), interpolate="spline")`.} #' } #' @examples #' pal <- col_bin("Greens", domain = 0:100) #' show_col(pal(sort(runif(10, 60, 100)))) #' #' # Exponential distribution, mapped continuously #' show_col(col_numeric("Blues", domain = NULL)(sort(rexp(16)))) #' # Exponential distribution, mapped by interval #' show_col(col_bin("Blues", domain = NULL, bins = 4)(sort(rexp(16)))) #' # Exponential distribution, mapped by quantile #' show_col(col_quantile("Blues", domain = NULL)(sort(rexp(16)))) #' #' # Categorical data; by default, the values being coloured span the gamut... #' show_col(col_factor("RdYlBu", domain = NULL)(LETTERS[1:5])) #' # ...unless the data is a factor, without droplevels... #' show_col(col_factor("RdYlBu", domain = NULL)(factor(LETTERS[1:5], levels=LETTERS))) #' # ...or the domain is stated explicitly. #' show_col(col_factor("RdYlBu", levels = LETTERS)(LETTERS[1:5])) #' @rdname col_numeric #' @name col_numeric NULL safePaletteFunc <- function(pal, na.color, alpha, nlevels = NULL) { filterRange( filterNA( na.color = na.color, filterZeroLength( filterRGB( toPaletteFunc(pal, alpha = alpha, nlevels = nlevels) ) ) ) ) } toPaletteFunc <- function(pal, alpha, nlevels) { UseMethod("toPaletteFunc") } # Strings are interpreted as color names, unless length is 1 and it's the name # of an RColorBrewer palette that is marked as qualitative toPaletteFunc.character <- function(pal, alpha, nlevels) { if (length(pal) == 1 && pal %in% row.names(RColorBrewer::brewer.pal.info)) { paletteInfo <- RColorBrewer::brewer.pal.info[pal, ] if (!is.null(nlevels)) { # brewer_pal will return NAs if you ask for more colors than the palette has colors <- brewer_pal(palette = pal)(abs(nlevels)) colors <- colors[!is.na(colors)] } else { colors <- brewer_pal(palette = pal)(RColorBrewer::brewer.pal.info[pal, "maxcolors"]) # Get all colors } } else if (length(pal) == 1 && pal %in% c("viridis", "magma", "inferno", "plasma")) { colors <- viridis_pal(option = pal)(256) } else { colors <- pal } colour_ramp(colors, alpha = alpha) } # Accept colorRamp style matrix toPaletteFunc.matrix <- function(pal, alpha, nlevels) { toPaletteFunc(farver::decode_colour(pal), alpha = alpha) } # If a function, just assume it's already a function over [0-1] toPaletteFunc.function <- function(pal, alpha, nlevels) { pal } # colorRamp(space = 'Lab') throws error when called with # zero-length input filterZeroLength <- function(f) { force(f) function(x) { if (length(x) == 0) { character(0) } else { f(x) } } } # Wraps an underlying non-NA-safe function (like colorRamp). filterNA <- function(f, na.color) { force(f) function(x) { results <- character(length(x)) nas <- is.na(x) results[nas] <- na.color results[!nas] <- f(x[!nas]) results } } # Wraps a function that may return RGB color matrix instead of rgb string. filterRGB <- function(f) { force(f) function(x) { results <- f(x) if (is.character(results)) { results } else if (is.matrix(results)) { farver::encode_colour(results, from = "rgb") } else { stop("Unexpected result type ", class(x)[[1]]) } } } filterRange <- function(f) { force(f) function(x) { x[x < 0 | x > 1] <- NA f(x) } } scales/R/colour-manip.r0000644000176200001440000000711413655054675014540 0ustar liggesusers#' Modify standard R colour in hcl colour space. #' #' Transforms rgb to hcl, sets non-missing arguments and then backtransforms #' to rgb. #' #' @param colour character vector of colours to be modified #' @param h Hue, `[0, 360]` #' @param l Luminance, `[0, 100]` #' @param c Chroma, `[0, 100]` #' @param alpha Alpha, `[0, 1]`. #' @export #' @examples #' reds <- rep("red", 6) #' show_col(col2hcl(reds, h = seq(0, 180, length = 6))) #' show_col(col2hcl(reds, c = seq(0, 80, length = 6))) #' show_col(col2hcl(reds, l = seq(0, 100, length = 6))) #' show_col(col2hcl(reds, alpha = seq(0, 1, length = 6))) col2hcl <- function(colour, h = NULL, c = NULL, l = NULL, alpha = NULL) { hcl <- farver::decode_colour(colour, to = "hcl") if (!is.null(h)) hcl[, "h"] <- h if (!is.null(c)) hcl[, "c"] <- c if (!is.null(l)) hcl[, "l"] <- l farver::encode_colour(hcl, alpha, from = "hcl") } #' Mute standard colour #' #' @param colour character vector of colours to modify #' @param l new luminance #' @param c new chroma #' @export #' @examples #' muted("red") #' muted("blue") #' show_col(c("red", "blue", muted("red"), muted("blue"))) muted <- function(colour, l=30, c=70) col2hcl(colour, l = l, c = c) #' Modify colour transparency #' #' Vectorised in both colour and alpha. #' #' @param colour colour #' @param alpha new alpha level in \[0,1]. If alpha is `NA`, #' existing alpha values are preserved. #' @export #' @examples #' alpha("red", 0.1) #' alpha(colours(), 0.5) #' alpha("red", seq(0, 1, length.out = 10)) #' alpha(c("first" = "gold", "second" = "lightgray", "third" = "#cd7f32"), .5) alpha <- function(colour, alpha = NA) { if (length(colour) != length(alpha)) { if (length(colour) > 1 && length(alpha) > 1) { stop("Only one of colour and alpha can be vectorised") } if (length(colour) > 1) { alpha <- rep(alpha, length.out = length(colour)) } else { colour <- rep(colour, length.out = length(alpha)) } } rgb <- farver::decode_colour(colour, alpha = TRUE) rgb[!is.na(alpha), 4] <- alpha[!is.na(alpha)] farver::encode_colour(rgb, rgb[, 4]) } #' Show colours #' #' A quick and dirty way to show colours in a plot. #' #' @param colours A character vector of colours #' @param labels Label each colour with its hex name? #' @param borders Border colour for each tile. Default uses `par("fg")`. #' Use `border = NA` to omit borders. #' @param cex_label Size of printed labels, as multiplier of default size. #' @param ncol Number of columns. If not supplied, tries to be as square as #' possible. #' @export #' @importFrom graphics par plot rect text #' @keywords internal #' @examples #' show_col(hue_pal()(9)) #' show_col(hue_pal()(9), borders = NA) #' #' show_col(viridis_pal()(16)) #' show_col(viridis_pal()(16), labels = FALSE) show_col <- function(colours, labels = TRUE, borders = NULL, cex_label = 1, ncol = NULL) { n <- length(colours) ncol <- ncol %||% ceiling(sqrt(length(colours))) nrow <- ceiling(n / ncol) colours <- c(colours, rep(NA, nrow * ncol - length(colours))) colours <- matrix(colours, ncol = ncol, byrow = TRUE) old <- par(pty = "s", mar = c(0, 0, 0, 0)) on.exit(par(old)) size <- max(dim(colours)) plot(c(0, size), c(0, -size), type = "n", xlab = "", ylab = "", axes = FALSE) rect(col(colours) - 1, -row(colours) + 1, col(colours), -row(colours), col = colours, border = borders ) if (labels) { hcl <- farver::decode_colour(colours, "rgb", "hcl") label_col <- ifelse(hcl[, "l"] > 50, "black", "white") text(col(colours) - 0.5, -row(colours) + 0.5, colours, cex = cex_label, col = label_col) } } scales/R/label-dollar.R0000644000176200001440000000665513556361126014426 0ustar liggesusers#' Label currencies ($100, $2.50, etc) #' #' Format numbers as currency, rounding values to dollars or cents using #' a convenient heuristic. #' #' @section Old interface: #' `dollar()` and `format_dollar()` are retired; please use `label_dollar()` #' instead. #' #' @inherit number_format return params #' @param accuracy,largest_with_cents Number to round to. If `NULL`, the default, #' values will be rounded to the nearest integer, unless any of the #' values has non-zero fractional component (e.g. cents) and the largest #' value is less than `largest_with_cents` which by default is 100,000. #' @param prefix,suffix Symbols to display before and after value. #' @param negative_parens Display negative using parentheses? #' @param ... Other arguments passed on to [base::format()]. #' @export #' @family labels for continuous scales #' @examples #' demo_continuous(c(0, 1), labels = label_dollar()) #' demo_continuous(c(1, 100), labels = label_dollar()) #' #' # Customise currency display with prefix and suffix #' demo_continuous(c(1, 100), labels = label_dollar(prefix = "USD ")) #' euro <- dollar_format( #' prefix = "", #' suffix = "\u20ac", #' big.mark = ".", #' decimal.mark = "," #' ) #' demo_continuous(c(1000, 1100), labels = euro) #' #' # Use negative_parens = TRUE for finance style display #' demo_continuous(c(-100, 100), labels = label_dollar(negative_parens = TRUE)) label_dollar <- function(accuracy = NULL, scale = 1, prefix = "$", suffix = "", big.mark = ",", decimal.mark = ".", trim = TRUE, largest_with_cents = 100000, negative_parens = FALSE, ...) { force_all( accuracy, scale, prefix, suffix, big.mark, decimal.mark, trim, largest_with_cents, negative_parens, ... ) function(x) dollar( x, accuracy = accuracy, scale = scale, prefix = prefix, suffix = suffix, big.mark = big.mark, decimal.mark = decimal.mark, trim = trim, largest_with_cents = largest_with_cents, negative_parens, ... ) } needs_cents <- function(x, threshold) { if (all(is.na(x))) { return(FALSE) } if (max(abs(x), na.rm = TRUE) > threshold) { return(FALSE) } !all(x == floor(x), na.rm = TRUE) } #' @export #' @rdname label_dollar dollar_format <- label_dollar #' @export #' @rdname label_dollar #' @param x A numeric vector dollar <- function(x, accuracy = NULL, scale = 1, prefix = "$", suffix = "", big.mark = ",", decimal.mark = ".", trim = TRUE, largest_with_cents = 100000, negative_parens = FALSE, ...) { if (length(x) == 0) return(character()) if (is.null(accuracy)) { if (needs_cents(x * scale, largest_with_cents)) { accuracy <- .01 } else { accuracy <- 1 } } if (identical(big.mark, ",") & identical(decimal.mark, ",")) { big.mark <- " " } negative <- !is.na(x) & x < 0 x <- abs(x) amount <- number( x, accuracy = accuracy, scale = scale, prefix = prefix, suffix = suffix, big.mark = big.mark, decimal.mark = decimal.mark, trim = trim, ... ) if (negative_parens) { amount <- paste0(ifelse(negative, "(", ""), amount, ifelse(negative, ")", "")) } else { amount <- paste0(ifelse(negative, "-", ""), amount) } # restore NAs from input vector amount[is.na(x)] <- NA names(amount) <- names(x) amount } scales/R/label-scientific.R0000644000176200001440000000314113556361126015254 0ustar liggesusers#' Label numbers with scientific notation (e.g. 1e05, 1.5e-02) #' #' @section Old interface: #' `scientific_format()` and `scientific()` are retired; please use #' `label_scientific()`. #' @inherit number_format return params #' @param digits Number of digits to show before exponent. #' @param prefix,suffix Symbols to display before and after value. #' @param ... Other arguments passed on to [base::format()]. #' @family labels for continuous scales #' @family labels for log scales #' @export #' @examples #' demo_continuous(c(1, 10)) #' demo_continuous(c(1, 10), labels = label_scientific()) #' demo_continuous(c(1, 10), labels = label_scientific(digits = 3)) #' #' demo_log10(c(1, 1e9)) label_scientific <- function(digits = 3, scale = 1, prefix = "", suffix = "", decimal.mark = ".", trim = TRUE, ...) { force_all(digits, scale, prefix, suffix, decimal.mark, trim, ...) function(x) scientific( x, digits = digits, scale = scale, prefix = prefix, suffix = suffix, decimal.mark = decimal.mark, ... ) } #' @export #' @rdname label_scientific scientific_format <- label_scientific #' @export #' @rdname label_scientific scientific <- function(x, digits = 3, scale = 1, prefix = "", suffix = "", decimal.mark = ".", trim = TRUE, ...) { if (length(x) == 0) return(character()) x <- signif(x * scale, digits) ret <- paste0( prefix, format(x, decimal.mark = decimal.mark, trim = trim, scientific = TRUE, ...), suffix ) # restore NAs from input vector ret[is.na(x)] <- NA names(ret) <- names(x) ret } scales/R/label-percent.R0000644000176200001440000000257713641652035014605 0ustar liggesusers#' Label percentages (2.5%, 50%, etc) #' #' @section Old interface: #' `percent()` and `percent_format()` are retired; please use `label_percent()` #' instead. #' @inherit number_format return params #' @export #' @family labels for continuous scales #' @examples #' demo_continuous(c(0, 1)) #' demo_continuous(c(0, 1), labels = label_percent()) #' #' # Use prefix and suffix to create your own variants #' french_percent <- label_percent( #' decimal.mark = ",", #' suffix = " %" #' ) #' demo_continuous(c(0, .01), labels = french_percent) label_percent <- function(accuracy = NULL, scale = 100, prefix = "", suffix = "%", big.mark = " ", decimal.mark = ".", trim = TRUE, ...) { number_format( accuracy = accuracy, scale = scale, prefix = prefix, suffix = suffix, big.mark = big.mark, decimal.mark = decimal.mark, trim = trim, ... ) } #' @export #' @rdname label_percent percent_format <- label_percent #' @export #' @rdname label_percent percent <- function(x, accuracy = NULL, scale = 100, prefix = "", suffix = "%", big.mark = " ", decimal.mark = ".", trim = TRUE, ...) { number( x = x, accuracy = accuracy, scale = scale, prefix = prefix, suffix = suffix, big.mark = big.mark, decimal.mark = decimal.mark, trim = trim, ... ) } scales/R/round-any.r0000644000176200001440000000070013320151564014021 0ustar liggesusers# Methods used to round to multiple of any number. Added from plyr. round_any <- function(x, accuracy, f = round) { UseMethod("round_any") } #' @export round_any.numeric <- function(x, accuracy, f = round) { f(x / accuracy) * accuracy } #' @export round_any.POSIXct <- function(x, accuracy, f = round) { tz <- format(x[1], "%Z") xr <- round_any(as.numeric(x), accuracy, f) as.POSIXct(xr, origin = "1970-01-01 00:00.00 UTC", tz = tz) } scales/R/labels-retired.R0000644000176200001440000001025713556361126014763 0ustar liggesusers#' Older interface to `label_bytes()` #' #' \Sexpr[results=rd, stage=render]{lifecycle::badge("retired")} #' These functions are kept for backward compatibility, but you should switch #' to [label_bytes()] for new code. #' #' @keywords internal #' @param symbol byte symbol to use. If "auto" the symbol used will be #' determined separately for each value of `x`. Valid symbols are "B", "kB", #' "MB", "GB", "TB", "PB", "EB", "ZB", and "YB" for SI units, and the "iB" #' variants for binary units. #' @param units which unit base to use, "binary" (1024 base) or "si" (1000 base) #' @export number_bytes_format <- function(symbol = "auto", units = "binary", ...) { force_all(symbol, units, ...) function(x) { number_bytes(x, symbol, units, ...) } } #' @export #' @rdname number_bytes_format number_bytes <- function(x, symbol = "auto", units = c("binary", "si"), accuracy = 1, ...) { units <- match.arg(units, c("binary", "si")) powers <- si_powers[si_powers >= 3] / 3 # powers of 1000 prefix <- names(powers) symbols <- c("B", switch(units, si = paste0(prefix, "B"), binary = paste0(toupper(prefix), "iB") )) symbol <- validate_byte_symbol(symbol, symbols) base <- switch(units, binary = 1024, si = 1000) if (symbol == "auto") { power <- findInterval(abs(x), base^powers) symbol <- symbols[1L + power] } else { power <- match(symbol, symbols) - 1L } number(x / base^power, accuracy = accuracy, suffix = paste0(" ", symbol), ...) } validate_byte_symbol <- function(symbol, symbols, default = "auto") { if (length(symbol) != 1) { n <- length(symbol) stop("`symbol` must have length 1, not length ", n, ".", call. = FALSE) } valid_symbols <- c(default, symbols) if (!(symbol %in% valid_symbols)) { warning( "`symbol` must be one of: '", paste0(valid_symbols, collapse = "', '"), "'; not '", symbol, "'.\n", "Defaulting to '", default, "'.", call. = FALSE ) symbol <- default } symbol } #' Format labels after transformation #' #' \Sexpr[results=rd, stage=render]{lifecycle::badge("retired")} #' #' @param trans transformation to apply #' @param format additional formatter to apply after transformation #' @return a function with single parameter x, a numeric vector, that #' returns a character vector of list of expressions #' @export #' @keywords internal #' @examples #' tf <- trans_format("log10", scientific_format()) #' tf(10 ^ 1:6) trans_format <- function(trans, format = scientific_format()) { if (is.character(trans)) trans <- match.fun(trans) force(format) function(x) { x <- trans(x) format(x) } } #' Unit labels #' #' \Sexpr[results=rd, stage=render]{lifecycle::badge("retired")} #' This function is kept for backward compatiblity; you should either use #' [label_number()] or [label_number_si()] instead. #' #' @inheritParams number_format #' @keywords internal #' @param unit The units to append. #' @param sep The separator between the number and the unit label. #' @export #' @examples #' # Label with units #' demo_continuous(c(0, 1), labels = unit_format(unit = "m")) #' # Labels in kg, but original data in g #' km <- unit_format(unit = "km", scale = 1e-3, digits = 2) #' demo_continuous(c(0, 2500), labels = km) unit_format <- function(accuracy = NULL, scale = 1, prefix = "", unit = "m", sep = " ", suffix = paste0(sep, unit), big.mark = " ", decimal.mark = ".", trim = TRUE, ...) { number_format( accuracy = accuracy, scale = scale, prefix = prefix, suffix = suffix, big.mark = big.mark, decimal.mark = decimal.mark, trim = trim, ... ) } #' Label using `format()` #' #' \Sexpr[results=rd, stage=render]{lifecycle::badge("retired")} #' This function is kept for backward compatiblity; you should either use #' [label_number()] or [label_date()] instead. #' #' @param ... Arguments passed on to [format()]. #' @export #' @keywords internal format_format <- function(...) { force_all(...) function(x) { if (!is.null(names(x))) return(names(x)) ret <- format(x, ..., trim = TRUE, justify = "left") # format.character() renders NA as "NA" ret[is.na(x)] <- NA ret } } scales/R/label-bytes.R0000644000176200001440000000556113560312023014255 0ustar liggesusers#' Label bytes (1 kb, 2 MB, etc) #' #' Scale bytes into human friendly units. Can use either SI units (e.g. #' kB = 1000 bytes) or binary units (e.g. kiB = 1024 bytes). See #' [Units of Information](http://en.wikipedia.org/wiki/Units_of_information) #' on Wikipedia for more details. #' #' @param units Unit to use. Should either one of: #' * "kB", "MB", "GB", "TB", "PB", "EB", "ZB", and "YB" for #' SI units (base 1000). #' * "kiB", "MiB", "GiB", "TiB", "PiB", "EiB", "ZiB", and "YiB" for #' binary units (base 1024). #' * `auto_si` or `auto_binary` to automatically pick the most approrpiate #' unit for each value. #' @inheritParams number_format #' @param ... Other arguments passed on to [number()] #' @return A labeller function that takes a numeric vector of breaks and #' returns a character vector of labels. #' @export #' @family labels for continuous scales #' @family labels for log scales #' @examples #' demo_continuous(c(1, 1e6)) #' demo_continuous(c(1, 1e6), label = label_bytes()) #' # Force all to use same units #' demo_continuous(c(1, 1e6), label = label_bytes("kB")) #' #' # Auto units are particularly nice on log scales #' demo_log10(c(1, 1e6)) #' demo_log10(c(1, 1e7), label = label_bytes()) #' #' # You can also use binary units where a megabyte is defined as #' # (1024) ^ 2 bytes rather than (1000) ^ 2. You'll need to override #' # the default breaks to make this more informative. #' demo_continuous(c(1, 1024^2), label = label_bytes("auto_binary")) #' demo_continuous(c(1, 1024^2), #' breaks = breaks_width(250 * 1024), #' label = label_bytes("auto_binary") #' ) label_bytes <- function(units = "auto_si", accuracy = 1, ...) { stopifnot(is.character(units), length(units) == 1) force_all(accuracy, ...) function(x) { powers <- si_powers[si_powers >= 3] / 3 # powers of 1000 if (units %in% c("auto_si", "auto_binary")) { base <- switch(units, auto_binary = 1024, auto_si = 1000) suffix <- switch(units, auto_binary = "iB", auto_si = "B") power <- findInterval(abs(x), c(0, base^powers)) - 1L units <- paste0(c("", names(powers))[power + 1L], suffix) } else { si_units <- paste0(names(powers), "B") bin_units <- paste0(names(powers), "iB") if (units %in% si_units) { base <- 1000 power <- powers[[match(units, si_units)]] } else if (units %in% bin_units) { base <- 1024 power <- powers[[match(units, bin_units)]] } else { stop("'", units, "' is not a valid unit", call. = FALSE) } } number( x / base^power, accuracy = accuracy, suffix = paste0(" ", units), ... ) } } # Helpers ----------------------------------------------------------------- si_powers <- (-8:8) * 3 names(si_powers) <- c( rev(c("m", "\u00b5", "n", "p", "f", "a", "z", "y")), "", "k", "M", "G", "T", "P", "E", "Z", "Y" ) si_powers scales/R/scale-discrete.r0000644000176200001440000000372013556361126015011 0ustar liggesusers#' Discrete scale #' #' @param x vector of discrete values to scale #' @param palette aesthetic palette to use #' @param na.value aesthetic to use for missing values #' @export #' @examples #' with(mtcars, plot(disp, mpg, pch = 20, cex = 3, #' col = dscale(factor(cyl), brewer_pal()))) dscale <- function(x, palette, na.value = NA) { limits <- train_discrete(x) map_discrete(palette, x, limits, na.value) } is.discrete <- function(x) { is.factor(x) || is.character(x) || is.logical(x) } #' Train (update) a discrete scale #' #' @param new New data to add to scale #' @param existing Optional existing scale to update #' @param drop `TRUE`, will drop factor levels not associated with data #' @param na.rm If `TRUE`, will remove missing values #' @export train_discrete <- function(new, existing = NULL, drop = FALSE, na.rm = FALSE) { if (is.null(new)) return(existing) if (!is.discrete(new)) { stop("Continuous value supplied to discrete scale", call. = FALSE) } discrete_range(existing, new, drop = drop, na.rm = na.rm) } discrete_range <- function(old, new, drop = FALSE, na.rm = FALSE) { new <- clevels(new, drop = drop, na.rm = na.rm) if (is.null(old)) return(new) if (!is.character(old)) old <- clevels(old, na.rm = na.rm) new_levels <- setdiff(new, as.character(old)) # Keep as a factor if we don't have any new levels if (length(new_levels) == 0) { return(old) } sort(c(old, new_levels)) } clevels <- function(x, drop = FALSE, na.rm = FALSE) { if (is.null(x)) { character() } else if (is.factor(x)) { if (drop) x <- factor(x) values <- levels(x) if (na.rm) { values <- values[!is.na(values)] } else if (any(is.na(x))) { values <- c(values, NA) } values } else { sort(unique(x), na.last = if (na.rm) NA else TRUE) } } map_discrete <- function(palette, x, limits, na.value = NA) { n <- length(limits) pal <- palette(n)[match(as.character(x), limits)] ifelse(!is.na(x), pal, na.value) } scales/R/pal-brewer.r0000644000176200001440000000425313556361126014164 0ustar liggesusers#' Colour Brewer palette (discrete) #' #' @param type One of seq (sequential), div (diverging) or qual (qualitative) #' @param palette If a string, will use that named palette. If a number, will #' index into the list of palettes of appropriate `type` #' @param direction Sets the order of colours in the scale. If 1, the default, #' colours are as output by [RColorBrewer::brewer.pal()]. If -1, the #' order of colours is reversed. #' @references #' @export #' @examples #' show_col(brewer_pal()(10)) #' show_col(brewer_pal("div")(5)) #' show_col(brewer_pal(palette = "Greens")(5)) #' #' # Can use with gradient_n to create a continous gradient #' cols <- brewer_pal("div")(5) #' show_col(gradient_n_pal(cols)(seq(0, 1, length.out = 30))) brewer_pal <- function(type = "seq", palette = 1, direction = 1) { pal <- pal_name(palette, type) force(direction) function(n) { # If <3 colors are requested, brewer.pal will return a 3-color palette and # give a warning. This warning isn't useful, so suppress it. # If the palette has k colors and >k colors are requested, brewer.pal will # return a k-color palette and give a warning. This warning is useful, so # don't suppress it. if (n < 3) { pal <- suppressWarnings(RColorBrewer::brewer.pal(n, pal)) } else { pal <- RColorBrewer::brewer.pal(n, pal) } # In both cases ensure we have n items pal <- pal[seq_len(n)] if (direction == -1) { pal <- rev(pal) } pal } } pal_name <- function(palette, type) { if (is.character(palette)) { if (!palette %in% unlist(brewer)) { warning("Unknown palette ", palette) palette <- "Greens" } return(palette) } type <- match.arg(type, c("div", "qual", "seq")) brewer[[type]][palette] } brewer <- list( div = c( "BrBG", "PiYG", "PRGn", "PuOr", "RdBu", "RdGy", "RdYlBu", "RdYlGn", "Spectral" ), qual = c( "Accent", "Dark2", "Paired", "Pastel1", "Pastel2", "Set1", "Set2", "Set3" ), seq = c( "Blues", "BuGn", "BuPu", "GnBu", "Greens", "Greys", "Oranges", "OrRd", "PuBu", "PuBuGn", "PuRd", "Purples", "RdPu", "Reds", "YlGn", "YlGnBu", "YlOrBr", "YlOrRd" ) ) scales/R/scale-continuous.r0000644000176200001440000000354113560275204015412 0ustar liggesusers#' Continuous scale #' #' @param x vector of continuous values to scale #' @param palette palette to use. #' #' Built in palettes: #' \Sexpr[results=rd,stage=build]{scales:::seealso_pal()} #' @param na.value value to use for missing values #' @param trans transformation object describing the how to transform the #' raw data prior to scaling. Defaults to the identity transformation which #' leaves the data unchanged. #' #' Built in transformations: #' \Sexpr[results=rd,stage=build]{scales:::seealso_trans()}. #' @export #' @examples #' with(mtcars, plot(disp, mpg, cex = cscale(hp, rescale_pal()))) #' with(mtcars, plot(disp, mpg, cex = cscale(hp, rescale_pal(), #' trans = sqrt_trans()))) #' with(mtcars, plot(disp, mpg, cex = cscale(hp, area_pal()))) #' with(mtcars, plot(disp, mpg, pch = 20, cex = 5, #' col = cscale(hp, seq_gradient_pal("grey80", "black")))) cscale <- function(x, palette, na.value = NA_real_, trans = identity_trans()) { stopifnot(is.trans(trans)) x <- trans$transform(x) limits <- train_continuous(x) map_continuous(palette, x, limits, na.value) } #' Train (update) a continuous scale #' #' Strips attributes and always returns a numeric vector #' #' @inheritParams train_discrete #' @export train_continuous <- function(new, existing = NULL) { if (is.null(new)) return(existing) if (is.factor(new) || !typeof(new) %in% c("integer", "double")) { stop("Discrete value supplied to continuous scale", call. = FALSE) } suppressWarnings(range(existing, new, na.rm = TRUE, finite = TRUE)) } # Map values for a continuous palette. # # @param oob out of bounds behaviour. Defaults to \code{\link{censor}} # which turns oob values into missing values. map_continuous <- function(palette, x, limits, na.value = NA_real_, oob = censor) { x <- oob(rescale(x, from = limits)) pal <- palette(x) ifelse(!is.na(x), pal, na.value) } scales/R/colour-ramp.R0000644000176200001440000000554213655075010014320 0ustar liggesusers#' Fast colour interpolation #' #' Returns a function that maps the interval \[0,1] to a set of colours. #' Interpolation is performed in the CIELAB colour space. Similar to #' \code{\link[grDevices]{colorRamp}(space = 'Lab')}, but hundreds of #' times faster, and provides results in `"#RRGGBB"` (or #' `"#RRGGBBAA"`) character form instead of RGB colour matrices. #' #' @param colors Colours to interpolate; must be a valid argument to #' [grDevices::col2rgb()]. This can be a character vector of #' `"#RRGGBB"` or `"#RRGGBBAA"`, colour names from #' [grDevices::colors()], or a positive integer that indexes into #' [grDevices::palette()]. #' @param na.color The colour to map to `NA` values (for example, #' `"#606060"` for dark grey, or `"#00000000"` for transparent) and #' values outside of \[0,1]. Can itself by `NA`, which will simply cause #' an `NA` to be inserted into the output. #' @param alpha Whether to include alpha transparency channels in interpolation. #' If `TRUE` then the alpha information is included in the interpolation. #' The returned colours will be provided in `"#RRGGBBAA"` format when needed, #' i.e., in cases where the colour is not fully opaque, so that the `"AA"` #' part is not equal to `"FF"`. Fully opaque colours will be returned in #' `"#RRGGBB"` format. If `FALSE`, the alpha information is discarded #' before interpolation and colours are always returned as `"#RRGGBB"`. #' #' @return A function that takes a numeric vector and returns a character vector #' of the same length with RGB or RGBA hex colours. #' #' @seealso \code{\link[grDevices]{colorRamp}} #' #' @export #' @examples #' ramp <- colour_ramp(c("red", "green", "blue")) #' show_col(ramp(seq(0, 1, length = 12))) colour_ramp <- function(colors, na.color = NA, alpha = TRUE) { if (length(colors) == 0) { stop("Must provide at least one colour to create a colour ramp") } if (length(colors) == 1) { return(structure( function(x) { ifelse(is.na(x), na.color, colors) }, safe_palette_func = TRUE )) } # farver is not currently case insensitive, but col2rgb() is colors <- tolower(colors) lab_in <- farver::decode_colour(colors, alpha = TRUE, to = "lab", na_value = "transparent") x_in <- seq(0, 1, length.out = length(colors)) l_interp <- stats::approxfun(x_in, lab_in[, 1]) u_interp <- stats::approxfun(x_in, lab_in[, 2]) v_interp <- stats::approxfun(x_in, lab_in[, 3]) if (!alpha || all(lab_in[, 4] == 1)) { alpha_interp <- function(x) NULL } else { alpha_interp <- stats::approxfun(x_in, lab_in[, 4]) } structure( function(x) { lab_out <- cbind(l_interp(x), u_interp(x), v_interp(x)) out <- farver::encode_colour(lab_out, alpha = alpha_interp(x), from = "lab") out[is.na(out)] <- na.color out }, safe_palette_func = TRUE ) } scales/R/label-number-auto.R0000644000176200001440000000373513641652035015400 0ustar liggesusers#' Label numbers, avoiding scientific notation where possible #' #' Switches between [number_format()] and [scientific_format()] based on a set of #' heuristics designed to automatically generate useful labels across a wide #' range of inputs #' #' @export #' @family labels for continuous scales #' @examples #' # Very small and very large numbers get scientific notation #' demo_continuous(c(0, 1e-6), labels = label_number_auto()) #' demo_continuous(c(0, 1e9), labels = label_number_auto()) #' #' # Other ranges get the numbers printed in full #' demo_continuous(c(0, 1e-3), labels = label_number_auto()) #' demo_continuous(c(0, 1), labels = label_number_auto()) #' demo_continuous(c(0, 1e3), labels = label_number_auto()) #' demo_continuous(c(0, 1e6), labels = label_number_auto()) #' #' # Transformation is applied individually so you get as little #' # scientific notation as possible #' demo_log10(c(1, 1e7), labels = label_number_auto()) label_number_auto <- function() { function(x) { if (length(x) == 0) return(character(0)) if (sum(is.finite(x)) == 0) return(format(x, trim = TRUE)) max_magnitude <- max(abs(x[x != 0 & is.finite(x)])) min_magnitude <- min(abs(x[x != 0 & is.finite(x)])) if (max_magnitude > 1e6) { format_shortest(x, number_format(1), format_format(scientific = TRUE) ) } else if (min_magnitude < 1e-3) { format_shortest(x, format_format(scientific = FALSE), format_format(scientific = TRUE) ) } else if (all(x > 0) && min_magnitude >= 1000 && max_magnitude <= 2200) { # Probably a year so don't use commas format(x, trim = TRUE) } else if (max_magnitude > 1e3) { number(x, 1) } else { format(x, trim = TRUE) } } } format_shortest <- function(breaks, ...) { options <- list(...) labels <- vapply(options, function(labeller) labeller(breaks), character(length(breaks))) apply(labels, 1, shortest) } shortest <- function(x) { x[which.min(nchar(x))] } scales/R/label-wrap.R0000644000176200001440000000144613641652035014110 0ustar liggesusers#' Label strings by wrapping across multiple lines #' #' Uses [strwrap()] to split long labels across multiple lines. #' #' @section Old interface: #' `wrap_format()` is retired; please use `label_format()` instead. #' @inherit number_format return #' @param width Number of characters per line. #' @export #' @family labels for discrete scales #' @examples #' x <- c( #' "this is a long label", #' "this is another long label", #' "this a label this is even longer" #' ) #' demo_discrete(x) #' demo_discrete(x, labels = label_wrap(10)) #' demo_discrete(x, labels = label_wrap(20)) label_wrap <- function(width) { force(width) function(x) { unlist(lapply(strwrap(x, width = width, simplify = FALSE), paste0, collapse = "\n")) } } #' @export #' @rdname label_wrap wrap_format <- label_wrap scales/NEWS.md0000644000176200001440000003470213656262445012645 0ustar liggesusers# scales 1.1.1 * `breaks_width()` now handles `difftime`/`hms` objects (@bhogan-mitre, #244). * `hue_pal()` now correctly inverts color palettes when `direction = -1` (@dpseidel, #252). * Internal `precision()`, used when `accuracy = NULL`, now does a better job when duplicate values are present (@teunbrand, #251). It also does a better job when there's a mix of finite and non-finite values (#257). * New `oob_keep()` to keep data outside range, allowing for zoom-limits when `oob_keep` is used as `oob` argument in scales. Existing out of bounds functions have been renamed with the `oob_`-prefix to indicate their role (@teunbrand, #255). * `ordinal_french()` gains `plural` and `gender` arguments (@stephLH, #256). # scales 1.1.0 * Axis breaks and labels have a new naming scheme: functions that generate breaks from limits are called `breaks_`; functions that generate labels from breaks are called `labels_` (#226). * All breaks and labels examples have been overhauled to use new `demo_continuous()`, `demo_discrete()`, and `demo_log10()`, so you can see how to use scales functions with ggplot2. ## Labels * All label functions preserve names (#202) and keep `NA`s as `NA`s instead of trying to convert to `"NA"` (@clauswilke, #187). * New `label_bytes()` replaces `number_bytes_format()` with a more convenient interface. It takes a single `unit` argument which can either be an SI unit (e.g. "kB"), a binary unit (e.g. "kIB"), or an automatic unit (either "auto_si" or "auto_binary"). It always uses "B" as the symbol for bytes (#174), and checks that `units` are valid. Additionally, auto units are now used to determine the symbol separately for each value (@mikmart): ```R label_bytes("auto_binary")(1024^(1:3)) #> [1] "1 kiB" "1 MiB" "1 GiB" ``` * New `label_date_short()` creates labels for a date axis that only show the components of the date that have changed since the previous label. For example, if you have Jan 10, Jan 20, Jan 30, and Feb 1, `label_date_short()` will use labels Jan 10, 20, 30, Feb 1 (#209). * `label_dollar()` now correctly formats negative numbers as (e.g.) -$200 (#216). * `label_math()` now returns an expression vector, and doesn't coerce inputs to names. * `label_number()` takes `scale` into account when computing `accuracy`, if not supplied. This means that `label_percent()` should have better default accuracy in many cases (#192). * `label_number()` now picks the accuracy automatically by default. The underlying heuristic has been improved to use the distance between adjacent breaks (rather than the total range of the break). * New `label_number_auto()` automatically picks between `number_format()` and `scientific_format()` based on the range of the input. It should produce nice output over a very wide range of inputs (@paleolimbot, #208). * New `label_number_si()` formats numeric vectors with limited SI units. Individual values are scaled and labelled with abbreviations "K", "M", "B", or "T" dependent on magnitude (@dpseidel, #83). * `label_parse()` now generates an expression object that can be used to display formatted labels in ggplot2 (@agila5, #203). * `label_pvalue()` now reports values close to 1 (as determined by `accuracy`) as (e.g.) ">0.99". You can control the prefixes used with the new `prefix` argument (#213). ## Breaks * The built in breaks functions now returns a function that takes both a range and a desired number of breaks, making it possible to overwrite the defaults number of desired breaks given in the constructor call (@thomasp85). * `breaks_log()` has nicer behaviour when there are no finite inputs (#210). It also provides usable breaks even with very small ranges (@billdenney, #168) * New `breaks_width()` which allows you to specify a fixed distance between breaks (along with optional offset). ## Transformations * New `yj_trans()` implements the Yeo-Johnson transformation (@zamorarr, #196) * `trans` objects gets methods for `plot()` and `lines()`, and all numeric transformations get an example showing the transformation. * `boxcox_trans()` no longer throws an error when given NA values (@sflippl, #181). ## Other bug fixes and minor improvements * scales now uses the farver package for colour manipulation instead of a combination of grDevices and hand-rolled C++ code (#223). * `alpha()` now preserves element names (@wibeasley, #195) * `ContinuousRange` and `DiscreteRange` methods now properly inherit and are fully mutable (@dpseidel). * `col_numeric()`, `col_bin()`, `col_quantile()`, and `col_factor()` now support viridis colors. Just pass a palette name (`"magma"`, `"inferno"`, `"plasma"`, or `"viridis"`) as the `palette` argument (@jcheng5, #191). * `col_numeric()`, `col_bin()`, `col_quantile()`, and `col_factor()` now have a `reverse` parameter, to apply color palettes in the opposite of their usual order (i.e. high-to-low instead of low-to-high) (@jcheng5, #191). * `col_bin()` and `col_quantile()` now take a `right` argument, which is passed to `base::cut()`; it indicates whether the bin/quantile intervals should be closed on the right (and open on the left), or vice versa (@jcheng5, #191). * `col_factor()` now tries to avoid interpolating qualitative RColorBrewer palettes. Instead, it attempts to assign a palette color to each factor level. Interpolation will still be used if there are more factor levels than available colors, and a warning will be emitted in that case (@jcheng5, #191). * `dichromat_pal()` documentation now builds without requiring suggested `dichromat` package to be installed (@dpseidel, #172). * `date_breaks()` now supports subsecond intervals (@dpseidel, #85). # scales 1.0.0 ## New Features ### Formatters * `comma_format()`, `percent_format()` and `unit_format()` gain new arguments: `accuracy`, `scale`, `prefix`, `suffix`, `decimal.mark`, `big.mark` (@larmarange, #146). * `dollar_format()` gains new arguments: `accuracy`, `scale`, `decimal.mark`, `trim` (@larmarange, #148). * New `number_bytes_format()` and `number_bytes()` format numeric vectors into byte measurements (@hrbrmstr, @dpseidel). * New `number_format()` provides a generic formatter for numbers (@larmarange, #142). * New `pvalue_format()` formats p-values (@larmarange, #145). * `ordinal_format()` gains new arguments: `prefix`, `suffix`, `big.mark`, `rules`; rules for French and Spanish are also provided (@larmarange, #149). * `scientific_format()` gains new arguments: `scale`, `prefix`, `suffix`, `decimal.mark`, `trim` (@larmarange, #147). * New `time_format()` formats `POSIXt` and `hms` objects (@dpseidel, #88). ### Transformations & breaks * `boxcox_trans()` is now invertible for `x >= 0` and requires positive values. A new argument `offset` allows specification of both type-1 and type-2 Box-Cox transformations (@dpseidel, #103). * `log_breaks()` returns integer multiples of integer powers of base when finer breaks are needed (@ThierryO, #117). * New function `modulus_trans()` implements the modulus transformation for positive and negative values (@dpseidel). * New `pseudo_log_trans()` for transforming numerics into a signed logarithmic scale with a smooth transition to a linear scale around 0 (@lepennec, #106). ## Minor bug fixes and improvements * scales functions now work as expected when it is used inside a for loop. In previous package versions if a scales function was used with variable custom parameters inside a for loop, some of the parameters were not evaluated until the end of the loop, due to how R lazy evaluation works (@zeehio, #81). * `colour_ramp()` now uses `alpha = TRUE` by default (@clauswilke, #108). * `date_breaks()` now supports subsecond intervals (@dpseidel, #85). * Removes `dichromat` and `plyr` dependencies. `dichromat` is now suggested (@dpseidel, #118). * `expand_range()` arguments `mul` and `add` now affect scales with a range of 0 (@dpseidel, [ggplot2-2281](https://www.github.com/tidyverse/ggplot2/issues/2281)). * `extended_breaks()` now allows user specification of the `labeling::extended()` argument `only.loose` to permit more flexible breaks specification (@dpseidel, #99). * New `rescale()` and `rescale_mid()` methods support `dist` objects (@zeehio, #105). * `rescale_mid()` now properly handles NAs (@foo-bar-baz-qux, #104). # scales 0.5.0 * New function `regular_minor_breaks()` calculates minor breaks as a property of the transformation (@karawoo). * Adds `viridis_pal()` for creating palettes with color maps from the viridisLite package (@karawoo). * Switched from reference classes to R6 (#96). * `rescale()` and `rescale_mid()` are now S3 generics, and work with `numeric`, `Date`, `POSIXct`, `POSIXlt` and `bit64::integer64` objects (@zeehio, #74). # scales 0.4.1 * `extended_breaks()` no longer fails on pathological inputs. * New `hms_trans()` for transforming hms time vectors. * `train_discrete()` gets a new `na.rm` argument which controls whether `NA`s are preserved or dropped. # scales 0.4.0 * Switched from `NEWS` to `NEWS.md`. * `manual_pal()` produces a warning if n is greater than the number of values in the palette (@jrnold, #68). * `precision(0)` now returns 1, which means `percent(0)` now returns 0% (#50). * `scale_continuous()` uses a more correct check for numeric values. * NaN is correctly recognised as a missing value by the gradient palettes ([ggplot2-1482](https://www.github.com/tidyverse/ggplot2/issues/1482)). # scales 0.3.0 * `rescale()` preserves missing values in input when the range of `x` is (effectively) 0 ([ggplot2-985](https://www.github.com/tidyverse/ggplot2/issues/985)). * Continuous colour palettes now use `colour_ramp()` instead of `colorRamp()`. This only supports interpolation in Lab colour space, but is hundreds of times faster. # scales 0.2.5 ## Improved formatting functions * `date_format()` gains an option to specify time zone (#51). * `dollar_format()` is now more flexible and can add either prefixes or suffixes for different currencies (#53). It gains a `negative_parens` argument to show negative values as `($100)` and now passes missing values through unchanged (@dougmitarotonda, #40). * New `ordinal_format()` generates ordinal numbers (1st, 2nd, etc) (@aaronwolen, #55). * New `unit_format()` makes it easier to add units to labels, optionally scaling (@ThierryO, #46). * New `wrap_format()` function to wrap character vectors to a desired width. (@jimhester, #37). ## New colour scaling functions * New color scaling functions `col_numeric()`, `col_bin()`, `col_quantile()`, and `col_factor()`. These functions provide concise ways to map continuous or categorical values to color spectra. * New `colour_ramp()` function for performing color interpolation in the CIELAB color space (like `grDevices::colorRamp(space = 'Lab')`, but much faster). ## Other bug fixes and minor improvements * `boxcox_trans()` returns correct value when p is close to zero (#31). * `dollar()` and `percent()` both correctly return a zero length string for zero length input (@BrianDiggs, #35). * `brewer_pal()` gains a `direction` argument to easily invert the order of colours (@jiho, #36). * `show_col()` has additional options to showcase colors better (@jiho, #52). * Relaxed tolerance in `zero_range()` to `.Machine$double.eps * 1000` (#33). # scales 0.2.4 * Eliminate stringr dependency. * Fix outstanding errors in R CMD check. # scales 0.2.3 * `floor_time()` calls `to_time()`, but that function was moved into a function so it was no longer available in the scales namespace. Now `floor_time()` has its own copy of that function (Thanks to Stefan Novak). * Color palettes generated by `brewer_pal()` no longer give warnings when fewer than 3 colors are requested (@wch). * `abs_area()` and `rescale_max()` functions have been added, for scaling the area of points to be proportional to their value. These are used by `scale_size_area()` in ggplot2. # scales 0.2.2 * `zero_range()` has improved behaviour thanks to Brian Diggs. * `brewer_pal()` complains if you give it an incorrect palette type. (Fixes #15, thanks to Jean-Olivier Irisson). * `shape_pal()` warns if asked for more than 6 values. (Fixes #16, thanks to Jean-Olivier Irisson). * `time_trans()` gains an optional argument `tz` to specify the time zone to use for the times. If not specified, it will be guess from the first input with a non-null time zone. * `date_trans()` and `time_trans()` now check that their inputs are of the correct type. This prevents ggplot2 scales from silently giving incorrect outputs when given incorrect inputs. * Change the default breaks algorithm for `cbreaks()` and `trans_new()`. Previously it was `pretty_breaks()`, and now it's `extended_breaks()`, which uses the `extended()` algorithm from the labeling package. * fixed namespace problem with `fullseq()`. # scales 0.2.1 * `suppressWarnings` from `train_continuous()` so zero-row or all infinite data frames don't potentially cause problems. * check for zero-length colour in `gradient_n_pal()`. * added `extended_breaks()` which implements an extension to Wilkinson's labelling approach, as implemented in the `labeling` package. This should generally produce nicer breaks than `pretty_breaks()`. * `alpha()` can now preserve existing alpha values if `alpha()` is missing. * `log_breaks()` always gives breaks evenly spaced on the log scale, never evenly spaced on the data scale. 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