Author: Bob Rudis, Noam Ross and Simon Garnier
Date: 2024-01-28

# Introduction to the viridis color maps

#### Bob Rudis, Noam Ross and Simon Garnier

#### 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()` and `scale_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 `viridisLite` provide 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:

- Colorful, spanning as wide a palette as possible so as to make differences easy to see,
- Perceptually uniform, meaning that values close to each other have similar-appearing colors and values far away from each other have more different-appearing colors, consistently across the range of values,
- Robust to colorblindness, so that the above properties hold true for people with common forms of colorblindness, as well as in grey scale printing, and
- Pretty, oh so pretty

`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. `viridis` provides additional functionalities, in particular bindings for `ggplot2`.

# 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 this presentation of `viridis` 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 about `cividis` 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` and `rocket` can be found on the `Seaborn` website.

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 about `turbo` can be found here.

# Comparison

Let’s compare the viridis and magma scales against these other commonly used sequential color palettes in R:

- Base R palettes: `rainbow.colors`, `heat.colors`, `cm.colors`
- The default ggplot2 palette
- Sequential colorbrewer palettes, both default blues and the more viridis-like yellow-green-blue

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 `viridis` color scale. You can choose the other color scale options using the `option` parameter or the convenience functions `magma()`, `plasma()`, `inferno()`, `cividis()`, `mako()`, `rocket`()`, and`turbo()`.

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 for ggplot plots: `scale_color_viridis()` and `scale_fill_viridis()`. As with `viridis()`, you can use the other scales with the `option` argument in the `ggplot` scales.
Here the “magma” scale is used for a cloropleth map of U.S. unemployment:

    library(maps)

    ##
    ## 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()

# Gallery

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: