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How can you create colorblind-friendly visualizations with the viridis package?

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Colorblindness is a common condition that affects how people perceive colors, especially in the red-green spectrum. This can make it difficult for them to interpret some data visualizations that rely on color coding or gradients. Fortunately, there are ways to create colorblind-friendly visualizations with the viridis package in R, a popular tool for data analysis and visualization. In this article, you will learn how to use the viridis package to choose and apply color palettes that are accessible, aesthetic, and informative for your data.

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Henny Speelman

Founder of the A.R.T. model. Follow me to learn everything about data, -visualizations, -storytelling, and AI. Keynote…

1 What is the viridis package?

The viridis package is a collection of color palettes that are designed to be perceptually uniform, meaning that the changes in color intensity correspond to the changes in data values. The package also ensures that the colors are distinguishable by most forms of colorblindness, as well as by black-and-white printing or screen display. The package offers four main palettes: viridis, magma, inferno, and plasma, each with a different hue and contrast range. You can also use the option "cividis" to get a palette that is optimized for the most severe form of colorblindness, deuteranopia.

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2 How to install and load the viridis package?

To use the viridis package, you need to install it from CRAN, the official repository of R packages. You can do this by running the following code in your R console: install.packages("viridis") After installing the package, you need to load it into your R session with the library function: library(viridis) This will make the package functions and palettes available for your use.

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Esther Muthui

Founder School of Statisticians| Master AI b4 it masters U
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1. Install the `viridis` package Open your R console or RStudio and execute the following command to install the package from CRAN (Comprehensive R Archive Network). ```R install.packages("viridis") ``` This will download and install the `viridis` package along with its dependencies. 2. Load the `viridis` package: Once the package is installed, you need to load it into your R session to use its functions and features. ```R library(viridis) ``` The `library()` function is used to load the package. After running this command, you can start using the functions and color palettes provided by the `viridis` package in your R scripts or console.

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3 How to display and choose a viridis palette?

To see the available palettes in the viridis package, you can use the viridis_pal function with the option plot = TRUE. This will show you a plot of each palette with its name and the number of colors it contains. For example, the following code will display the four main palettes with 11 colors each: viridis_pal(plot = TRUE, option = c("viridis", "magma", "inferno", "plasma"), n = 11) You can choose a palette based on your preference and the type of data you want to visualize. For example, if you want to emphasize the low values in your data, you might choose the viridis palette, which has a dark blue background and a yellow-green foreground. If you want to emphasize the high values, you might choose the inferno palette, which has a black background and a yellow-red foreground.

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4 How to apply a viridis palette to your data?

To apply a viridis palette to your data, you need to use the scale_color_viridis or scale_fill_viridis functions, depending on whether you want to color the points or the areas in your plot. These functions are compatible with ggplot2, a popular package for creating data visualizations in R. You can specify the name of the palette you want to use with the option discrete = TRUE for categorical data or discrete = FALSE for continuous data. For example, the following code will create a scatter plot of the iris dataset with the viridis palette for the species variable:

library(ggplot2)
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() +
  scale_color_viridis(discrete = TRUE, option = "viridis")

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Zita Demeter
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Python code that replicates a similar diagram as in the example created by R. import seaborn as sns import matplotlib.pyplot as plt # Create a scatter plot with viridis color map sns.scatterplot(data=iris, x='sepal_length', y='sepal_width', hue='species', palette='viridis') # Show the plot plt.show()

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5 How to test your visualization for colorblindness?

To test how your visualization looks to different types of colorblindness, you can use the viridisLite package, which is a lighter version of the viridis package that only contains the color palettes. The viridisLite package has a function called viridisTest, which allows you to simulate various forms of colorblindness on your plot. You can specify the type of colorblindness with the option cvd, which can be one of the following: "deutan" (red-green), "protan" (red-green), or "tritan" (blue-yellow). For example, the following code will show how the previous plot looks to a deutan person:

library(viridisLite)
viridisTest(last_plot(), cvd = "deutan")

You can compare the original plot and the simulated plot to see if the colors are still distinguishable and meaningful. If not, you might want to change the palette or use other visual cues, such as shapes or labels, to enhance your visualization.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Henny Speelman

Founder of the A.R.T. model. Follow me to learn everything about data, -visualizations, -storytelling, and AI. Keynote speaker & Trainer.
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If you want to test your data visualizations for colour blindness before using the R package, you can use the site Coolors.co It is a colour palette generator that can test for several colour blind modes and the tool can give you impressions of what merchandise, visualizations, logos can look like.

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How can you create colorblind-friendly visualizations with the viridis package?

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5

6

1 What is the viridis package?

2 How to install and load the viridis package?

3 How to display and choose a viridis palette?

4 How to apply a viridis palette to your data?

5 How to test your visualization for colorblindness?

6 Here’s what else to consider

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How can you create colorblind-friendly visualizations with the viridis package?

1

2

3

4

5

6

1 What is the viridis package?

2 How to install and load the viridis package?

3 How to display and choose a viridis palette?

4 How to apply a viridis palette to your data?

5 How to test your visualization for colorblindness?

6 Here’s what else to consider

Data Visualization

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