Functions for easily manipulating colours, creating colour scales and calculating colour distances.
shades package allows colours to be manipulated easily in R. Properties such as brightness and saturation can be quickly queried, changed or varied, and perceptually uniform colour gradients can be constructed. It plays nicely with the pipe operator from the popular
magrittr package, and fits naturally into that paradigm.
Feedback on the package or suggestions are welcome, either by filing an issue or by email.
Colours are represented in R using CSS-style hex strings, but there is also a dictionary of predefined named colours such as
"blue". Either of these may be passed to most graphics functions, but creating variations on a particular colour can be awkward.
shades package defines a simple class,
shade, which uses exactly this same convention and is entirely compatible with built-in colours, but it also stores information about the coordinates of the colours in a particular colour space.
library(shades)red <- shade("red")print(unclass(red))##  "red"## attr(,"space")##  "sRGB"## attr(,"coords")## R G B## [1,] 1 0 0
From here, the package switches between colour spaces as required, allowing various kinds of colour manipulation to be performed straightforwardly. For example, let's find the saturation level of a few built-in colours.
saturation(c("papayawhip","lavenderblush","olivedrab"))##  0.1647100 0.0588200 0.7535287
Now let's consider a colour gradient stepping through two different colour spaces, which we might want to use as a palette or colour scale.
swatch(gradient(c("red","blue"), 5, space="Lab"))
Here, we are using the
swatch function to visualise a set of colours as a series of squares. Notice the more uniform appearance of the gradient when it traverses through the Lab colour space.
Similarly, we can create a set of new colours by changing the brightness and saturation levels of some base colours, and make the code more readable by using the
magrittr pipe operator.
library(shades); library(magrittr)c("red","blue") %>% brightness(0.6) %>% saturation(seq(0,1,0.25)) %>% swatch
This operation takes the original two colours, reduces their brightness to 60%, assigns a whole series of saturation levels to the result, and then passes it to
swatch for visualisation. Notice that the pipeline is combinative (like the base function
outer), returning each combination of parameters in a multidimensional array. The final shades are arranged in two rows by
swatch, for convenience.
Any of these gradients can be directly passed to a standard graphical function, to be used as a colour scale. However, when choosing a colour scale, it is helpful to bear in mind that some viewers may have a colour vision deficiency (colour blindness), making it harder for them to distinguish certain colours and therefore to see a continuous scale. The
dichromat function can be used to simulate this.
rev(grDevices::rainbow(9)) %>% dichromat %>% swatch
gradient("viridis",9) %>% dichromat %>% swatch
Here we are using the built-in "viridis" colour map, developed for Python's
matplotlib, which was specifically designed to appear continuous under as many conditions as possible. When shown with simulated red-blindness, the default for
dichromat, it is clearly much more interpretable than a typical rainbow palette generated by R's built-in graphics functions.
The package also supports colour mixing, either additively (as with light) or subtractively (as with paint). For example, consider additive mixtures of the three primary RGB colours.
c("red", addmix("red","green"), "green", addmix("green","blue"), "blue") %>% swatch
Similarly, we can subtractively combine the three secondary colours.
c("cyan", submix("cyan","magenta"), "magenta", submix("magenta","yellow"), "yellow") %>% swatch
A "light mixture" infix operator,
%.)%, and a "paint mixture" infix operator,
%_/%, are also available.
("red" %.)% "green") == "yellow"##  TRUE("cyan" %_/% "magenta") == "blue"##  TRUE
Finally, you can calculate perceptual distances to a reference colour, as in
distance(c("red","green","blue"), "red")##  0.00000 86.52385 53.07649
Gradients from this package can be used as
ggplot2 colour scales through the manual scale functions; for example,
library(shades); library(ggplot2)mtcars$cyl<- factor(mtcars$cyl)ggplot(mtcars, aes(mpg,qsec,col=cyl)) + geom_point() + scale_color_manual(values=gradient("viridis",3))
ggplot(mtcars, aes(cyl,mpg,fill=cyl)) + geom_boxplot() + scale_fill_manual(values=gradient("viridis",3))
shades package aims to bring together a range of colour manipulation tools and make them easy to use. However, there are several other packages available that can do similar things, sometimes in slightly different ways. These include
grDevicespackage, which is shipped with R and used as the basis for
colorspacepackage, which provides formal colour classes and transformations between spaces;
munsell, which interprets colours in Munsell notation and does some colour manipulation;
RColorBrewer, which provide the colour scales from
dichromat, which provides another implementation of the
dichromatfunction (a duplication which I didn't discover until after writing this package's version!); and
colorblindr, which provides alternative tools for simulating colour blindness in figures.
This package was also partly influenced by Colors.jl, a colour manipulation package for Julia.
Significant changes to the shades package are laid out below for each release.