Use the color scales in this package to make plots that are pretty, better represent your data, easier to read by those with colorblindness, and print well in grey scale.
Install viridis like any R package:
For base plots, use the
viridis() function to generate a palette:
x <- y <- seq(-8*pi, 8*pi, len = 40) r <- sqrt(outer(x^2, y^2, "+")) filled.contour(cos(r^2)*exp(-r/(2*pi)), axes=FALSE, color.palette=viridis, asp=1)
For ggplot, use
library(ggplot2) ggplot(data.frame(x = rnorm(10000), y = rnorm(10000)), aes(x = x, y = y)) + geom_hex() + coord_fixed() + scale_fill_viridis() + theme_bw()
These color scales are designed to be:
If you want to know more about the science behind creating these color scales, van der Walt and Smith’s talk at SciPy 2015 (YouTube) is quite interesting. On the project website you will find more details and a Python tool for creating other scales with similar properties.
The package contains four color scales: “Viridis”, the primary choice, and three alternatives with similar properties, “magma”, “plasma”, and “inferno.”
Let’s compare the viridis and magma scales against these other commonly used sequential color palettes in R:
It is immediately clear that the “rainbow” palette is not perceptually uniform; there are several “kinks” where the apparent color changes quickly over a short range of values. This is also true, though less so, for the “heat” colors. The other scales are more perceptually uniform, but “viridis” stands out for its large perceptual range. It makes as much use of the available color space as possible while maintaining uniformity.
Now, let’s compare these as they might appear under various forms of colorblindness, which can be simulated using the dichromat package: