Introduction to the viridis color maps (original) (raw)

2024-01-28

tl;dr

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 gray scale.

Install viridis like any R package:

install.packages("viridis")
library(viridis)

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 scale_color_viridis() andscale_fill_viridis():

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()

Introduction

viridis, and its companion package viridisLiteprovide a series of color maps that are designed to improve graph readability for readers with common forms of color blindness and/or color vision deficiency. The color maps are also perceptually-uniform, both in regular form and also when converted to black-and-white for printing.

These color maps are designed to be:

viridisLite provides the base functions for generating the color maps in base R. The package is meant to be as lightweight and dependency-free as possible for maximum compatibility with all the R ecosystem. viridisprovides additional functionalities, in particular bindings forggplot2.

The Color Scales

The package contains eight color scales: “viridis”, the primary choice, and five alternatives with similar properties - “magma”, “plasma”, “inferno”, “civids”, “mako”, and “rocket” -, and a rainbow color map - “turbo”.

The color maps viridis, magma,inferno, and plasma were created by Stéfan van der Walt (@stefanv) and Nathaniel Smith (@njsmith). If you want to know more about the science behind the creation of these color maps, you can watch thispresentation ofviridis by their authors at SciPy 2015.

The color map cividis is a corrected version of ‘viridis’, developed by Jamie R. Nuñez, Christopher R. Anderton, and Ryan S. Renslow, and originally ported to R by Marco Sciaini (@msciain). More info aboutcividis can be found in this paper.

The color maps mako and rocket were originally created for the Seaborn statistical data visualization package for Python. More info about mako androcket can be found on the Seabornwebsite.

The color map turbo was developed by Anton Mikhailov to address the shortcomings of the Jet rainbow color map such as false detail, banding and color blindness ambiguity. More infor aboutturbo can be found here.

Comparison

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:

Green-Blind (Deuteranopia)

Red-Blind (Protanopia)

Blue-Blind (Tritanopia)

Desaturated

We can see that in these cases, “rainbow” is quite problematic - it is not perceptually consistent across its range. “Heat” washes out at bright colors, as do the brewer scales to a lesser extent. The ggplot scale does not wash out, but it has a low perceptual range - there’s not much contrast between low and high values. The “viridis” and “magma” scales do better - they cover a wide perceptual range in brightness in brightness and blue-yellow, and do not rely as much on red-green contrast. They do less well under tritanopia (blue-blindness), but this is an extrememly rare form of colorblindness.

Usage

The viridis() function produces the viridiscolor scale. You can choose the other color scale options using theoption parameter or the convenience functionsmagma(), plasma(), inferno(),cividis(), mako(),rocket(), andturbo()`.

Here the inferno() scale is used for a raster of U.S. max temperature:

library(terra)
library(httr)
par(mfrow=c(1,1), mar=rep(0.5, 4))
temp_raster <- "http://ftp.cpc.ncep.noaa.gov/GIS/GRADS_GIS/GeoTIFF/TEMP/us_tmax/us.tmax_nohads_ll_20150219_float.tif"
try(GET(temp_raster,
        write_disk("us.tmax_nohads_ll_20150219_float.tif")), silent=TRUE)
us <- rast("us.tmax_nohads_ll_20150219_float.tif")
us <- project(us, y="+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=37.5 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs")
image(us, col=inferno(256), asp=1, axes=FALSE, xaxs="i", xaxt='n', yaxt='n', ann=FALSE)

The package also contains color scale functions forggplot plots: scale_color_viridis() andscale_fill_viridis(). As with viridis(), you can use the other scales with the option argument in theggplot scales.
Here the “magma” scale is used for a cloropleth map of U.S. unemployment:

## 
## Attaching package: 'maps'
## The following object is masked from 'package:viridis':
## 
##     unemp
library(mapproj)

data(unemp, package = "viridis")

county_df <- map_data("county", projection = "albers", parameters = c(39, 45))
names(county_df) <- c("long", "lat", "group", "order", "state_name", "county")
county_df$state <- state.abb[match(county_df$state_name, tolower(state.name))]
county_df$state_name <- NULL

state_df <- map_data("state", projection = "albers", parameters = c(39, 45))

choropleth <- merge(county_df, unemp, by = c("state", "county"))
choropleth <- choropleth[order(choropleth$order), ]

ggplot(choropleth, aes(long, lat, group = group)) +
  geom_polygon(aes(fill = rate), colour = alpha("white", 1 / 2), linewidth = 0.2) +
  geom_polygon(data = state_df, colour = "white", fill = NA) +
  coord_fixed() +
  theme_minimal() +
  ggtitle("US unemployment rate by county") +
  theme(axis.line = element_blank(), axis.text = element_blank(),
        axis.ticks = element_blank(), axis.title = element_blank()) +
  scale_fill_viridis(option="magma")

The ggplot functions also can be used for discrete scales with the argument discrete=TRUE.

p <- ggplot(mtcars, aes(wt, mpg))
p + geom_point(size=4, aes(colour = factor(cyl))) +
    scale_color_viridis(discrete=TRUE) +
    theme_bw()

Here are some examples of viridis being used in the wild:

James Curley uses viridis for matrix plots (Code):

Christopher Moore created these contour plots of potential in a dynamic plankton-consumer model: