Colorblind-Friendly Color Maps (Lite Version)

Color maps 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. This is the 'lite' version of the 'viridis' package that also contains 'ggplot2' bindings for discrete and continuous color and fill scales and can be found at <>.

Matplotlib recently [introduced new color maps] ( for their graphs. They are called viridis, magma, inferno, and plasma. viridis was made the new default color map of Matplotlib.

These four color maps are designed in such a way that they will analytically be perfectly perceptually-uniform, both in regular form and also when converted to black-and-white. They are also designed to be perceived by readers with the most common form of color blindness.

AND... they are pretty!

AND... they are now available for R!

viridis package. viridisLite contains only the core functions of viridis that generate the color vectors for each of the aforementioned color maps. It does not have any of the other features of the full viridis package (e.g. scale functions for ggplot2). This was requested by users of viridis who did not want to have to import the dependencies of viridis but still wanted to be able to use the color maps it provides.

viridisLite 0.1.2 is now available on CRAN.
You can install it using RStudio package manager or by typing the following line in your R terminal:


If you prefer to install the development version from this GitHub repository, simply copy the following lines of code in your R terminal and it should install everything you need to use viridisLite on your computer:

if (!require("devtools")) install.packages("devtools")

Simon Garnier - @sjmgarnier - [email protected]

The color maps in the viridis package 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.


Reference manual

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