chroma is a R package for parsing and
formating colors in various specifications, manipulating colors, and
creating nice color scales and palettes. Much of the functionality is
based on the excellent chroma.js
javascript library by Gregor Aish.
chroma is not on CRAN yet. To get it from this page, install the
package devtools and
devtools::install_github("jiho/chroma")
library("chroma")NB: some functions in the chroma package mask functions in basic R
packages (e.g. grDevices) or often used ones (e.g. ggplot2). Most of
the time, they are argument-compatible with the function they mask and,
if their result is different, I would argue that the one in chroma is
more correct. If you want to use chroma but would rather not mask the
other functions, just install the package but do not load it with
library(); instead, just call the functions with the chroma:: prefix
(e.g. chroma::rgb() instead of rgb(), to avoid masking rgb() in
package grDevices).
Parse colors in various specifications
rgb(r=0.5, g=0.5, b=0.5)# [1] "#808080"
ryb(r=0.5, y=0.5, b=0.5)# [1] "#957149"
hsv(h=120, s=0.5, v=0.5)# [1] "#408040"
hsl(h=120, s=0.5, l=0.5)# [1] "#40BF40"
hsi(h=120, s=0.5, i=0.5)# [1] "#40FF40"
hcl(h=120, c=0.5, l=0.5)# [1] "#638127"
lab(l=0.5, a=-0.5, b=0.2)# [1] "#008A53"
hex("#393939")# [1] "#393939"
hex("F39")# [1] "#FF3399"
css("rgb(100,100,100)")# [1] "#646464"
temperature(5600)# [1] "#FFEDE2"
wavelength(500)# [1] "#178672"
Parse directly from a matrix
x <- matrix(c(0.2, 0.5, 0.5, 0.5, 0.6, 0.4), ncol=3)
print(x)# [,1] [,2] [,3]
# [1,] 0.2 0.5 0.6
# [2,] 0.5 0.5 0.4
rgb(x)# [1] "#338099" "#808066"
Convert back in various formats
as.rgb("coral1")# r g b
# [1,] 1 0.4470588 0.3372549
as.ryb("coral1")# r y b
# [1,] 0.424082 0.296286 0.08746012
as.hsv("coral1")# h s v
# [1,] 9.940828 0.6627451 1
as.hsl("coral1")# h s l
# [1,] 9.940828 1 0.6686275
as.hsi("coral1")# h s i
# [1,] 8.89147 0.432967 0.5947712
as.hcl("coral1")# h c l
# [1,] 38.74251 0.6651031 0.6494095
as.lab("coral1")# l a b
# [1,] 0.6494095 0.518758 0.4162358
as.hex("coral1")# [1] "#FF7256"
as.css("coral1")# [1] "RGB(255,114,86)"
as.temperature("coral1")# [1] 2686
as.wavelength("coral1")# [1] 616
Slightly modify a base color
col <- "#7BBBFE"
show_col(c(col, brighten(col), darken(col)))
show_col(c(col, desaturate(col), saturate(col)))
Make a color semi-transparent
col <- "#7BBBFE"
show_col(c(col, alpha(col), alpha(col, 0.2)))
Mix or blend two colors
show_col(c("#7BBBFE", "#FDFF68", mix("#7BBBFE", "#FDFF68")))
show_col(c("#7BBBFE", "#FDFF68", blend("#7BBBFE", "#FDFF68")))
show_col(c("#7BBBFE", "#FDFF68", blend("#7BBBFE", "#FDFF68", mode="screen")))
Compute the contrast between two colors
contrast("darkblue", "darkgreen")# [1] 2.056261
contrast("yellow", "darkgreen")# [1] 6.927705
Compute the euclidean or perceptual distance between two colors (following the CIE Delta E 2000 formula)
# pick three colors; lightgreen is closer to darkgreen that darkblue is
show_col(c("darkblue", "darkgreen", "lightgreen"))
color_distance("darkblue", "darkgreen")# [1] 146.5474
color_distance("lightgreen", "darkgreen")# [1] 50.67107
CMClc("darkblue", "darkgreen")# [1] 81.42556
CMClc("lightgreen", "darkgreen")# [1] 35.98541
deltaE("darkblue", "darkgreen")# [1] 57.91349
deltaE("lightgreen", "darkgreen")# [1] 43.52127
which allows to find the closest perceived color in a array of possibilities
target <- "limegreen"
choices <- rainbow(10)
closest_color <- choices[which.min(deltaE(target, choices))]
show_col(target, choices, closest_color)
Extract or set a color channel
col <- "#7BBBFE"
channel(col, model="hcl", "h")# h
# 266.7734
channel(col, model="hsv", "s")# s
# 0.515748
channel(col, model="hsi", "i")# i
# 0.7372549
channel(col, model="rgb", "r")# r
# 0.4823529
col1 <- col2 <- col
channel(col1, model="hcl", "h") <- 120
channel(col2, model="hcl", "l") <- 1
show_col(c(col, col1, col2))
Compute or set the perceived luminance of a color
luminance(c("red", "yellow", "darkblue"))# [1] 0.2126000 0.9278000 0.0186408
col1 <- col2 <- col <- "#7BBBFE"
luminance(col)# [1] 0.4690736
luminance(col1) <- 0.6
luminance(col2) <- 0.2
show_col(c(col, col1, col2))
All scales and palettes are organised the same way:
- functions ending in
*_scalereturn a function that takes a numeric vectorxas argument and returns the corresponding colors in the scale. - functions ending in
*_mapare shortcuts that build the scale, map the values, and return the colors. - functions ending in
*_palettereturn a function that takes an integernas argument and returnsnequally spaced colors along the scale. - functions ending in
*_colorsare shortcut that create the palette and return thencolors.
x <- 0:10/10
s <- interp_scale()
s(x)# [1] "#FFFFFF" "#E2E2E2" "#C6C6C6" "#ABABAB" "#919191" "#777777" "#5E5E5E"
# [8] "#474747" "#303030" "#1B1B1B" "#000000"
# or
interp_map(x)# [1] "#FFFFFF" "#E2E2E2" "#C6C6C6" "#ABABAB" "#919191" "#777777" "#5E5E5E"
# [8] "#474747" "#303030" "#1B1B1B" "#000000"
n <- 11
p <- interp_palette()
p(n)# [1] "#FFFFFF" "#E2E2E2" "#C6C6C6" "#ABABAB" "#919191" "#777777" "#5E5E5E"
# [8] "#474747" "#303030" "#1B1B1B" "#000000"
# or
interp_colors(n)# [1] "#FFFFFF" "#E2E2E2" "#C6C6C6" "#ABABAB" "#919191" "#777777" "#5E5E5E"
# [8] "#474747" "#303030" "#1B1B1B" "#000000"
Palettes can be built by interpolating between colors
show_col(interp_colors(10))
show_col(interp_colors(10, colors=c("#2D2B63", "#F7FF84")))
show_col(interp_colors(10, colors=c("#2D2B63", "#FB3C44", "#F7FF84")))
For the perception of “change” along the scale to be more linear, colors can be interpolated using bezier curves and their lightness can be corrected.
show_col(interp_colors(10, colors=c("#2D2B63", "#FB3C44", "#F7FF84")))
show_col(interp_colors(10, colors=c("#2D2B63", "#FB3C44", "#F7FF84"), interp="bezier"))
show_col(interp_colors(10, colors=c("#2D2B63", "#FB3C44", "#F7FF84"), interp="bezier", correct.lightness=TRUE))
Preset palettes are available, from colorbrewer
show_col(lapply(brewer_info$name, function(x) {brewer_colors(n=7, name=x)}))
Or viridis
show_col(
viridis_colors(10),
magma_colors(10),
plasma_colors(10),
inferno_colors(10)
)
Or cubehelix
show_col(
cubehelix_colors(10),
cubehelix_colors(10, h=300, rot=-0.75),
cubehelix_colors(10, h=120, rot=0.5),
cubehelix_colors(10, h=300, rot=0.5)
)
Or ETOPO1 and Wikipedia for topographical maps
show_col(
etopo_colors(100),
wikitopo_colors(100)
)
Custom, perceptually appropriate, palettes can be built in HCL space, for either discrete
show_col(hue_colors(10))
or continuous variables
show_col(chroma_colors(10, h=140))
show_col(light_colors(10, h=140))
Additional functions are included to facilitate the use of the color scales in base and ggplot2 plots.
sidemargin() and sidelegend() allow to place legends on the side of
base plots
attach(iris)
# make larger right margin
pars <- sidemargin()
# plot the data with a nice color scale
plot(Petal.Length, Petal.Width, col=hue_map(Species), pch=19)
# add a legend for the scale on the side
sidelegend(legend=levels(Species), col=hue_colors(nlevels(Species)), pch=19)
# set graphical parameters back to default
par(pars)
detach(iris)persp_facets() allows to compute the colors for each facet of a
persp() plot
persp(maunga, theta=50, phi=25, scale=FALSE, expand=2,
border=alpha("black", 0.4),
col=magma_map(persp_facets(maunga$z)))
Various scale_color_*() and scale_fill_*() functions allow to use
the color scales defined in chroma directly with ggplots and
scale_xy_map() draws nice x and y scales for maps.
ggplot(iris) +
geom_point(aes(Petal.Length, Sepal.Length, color=Species)) +
scale_color_cubehelix_d(h=0, rot=0.75, c=1, l=c(0.6, 0.6))
ggplot(thaixyz) + coord_quickmap() +
geom_raster(aes(x, y, fill=z)) +
scale_fill_wikitopo() + scale_xy_map()
Happy coloring!