D.7 Resources

This page provides a (necessarily incomplete) collection of color-related resources.

D.7.1 Books

The landmark publications by Jacques Bertin (e.g., Bertin, 2011) and Edward R. Tufte (Tufte, 2001, 2006; Tufte et al., 1990) provide solid advice and many inspiring examples.

More recent publications that are geared to the needs of aspiring data scientists include:

• Data Visualization. A practical introduction (by Kieran Healy) is beautiful, informative, and elegant.

• Fundamentals of Data Visualization (by Claus O. Wilke) provides many instructive examples and helps distinguishing good from ugly graphs.

• R Graphics Cookbook (by Winston Chang) provides hands-on advice on using ggplot2 and many useful recipes for data transformation.

• Data visualization principles (by Rafael A. Irizarry)

• Data visualization: Basic principles (by Peter Aldhous)

See the Wikipedia articles on color model, color vision, and color theory for a primer on the theoretical underpinnings of color perception and color systems.

D.7.2 Color resources in R

Base R colors

The grDevices component of R comes with many options and tools for selecting and modifying colors:

• Call colors() or demo("colors") in the Console to view the in-built colors of R.

• Search for colors in R cheatsheet to find many collections of these colors.

See the CRAN task view: Graphic displays, dynamic graphics, graphic devices & visualization for background information and many pointers to additional resources.

Recent color developments in R

For recent color developments in R (as of 2020), see the following posts on the R developer blog:

• A new palette() for R (by Achim Zeileis, Paul Murrell, Martin Maechler & Deepayan Sarkar, 2019-11-21)

• HCL-based color palettes in grDevices (by Achim Zeileis & Paul Murrell, 2019-04-01)

A sound introduction to the ideas behind colorspace package is provided by the article colorspace: A toolbox for manipulating and assessing colors and palettes (Zeileis et al., 2020). The colorspace documentation also provides links to additional resources.

D.7.3 Color packages

This chapter mentions, uses, or recommends the following R packages:

• Popular and powerful color packages include: colorspace, RColorBrewer, and viridis/viridisLite.

• The ggsci and scico packages provide ordered and perceptually-uniform palettes for scientific visualizations. (See Fabio Crameri et al., 2020 for an excellent overview of using and misusing color in science communication.)

• For more personal color choices, check out the colourlovers, rijkspalette, wesanderson, and yarrr packages.

• When dealing with maps and geographic data, consider using the color palettes from the RColorBrewer, viridis, or cartography packages.

• The dichromat package allows simulating the effects of different types of color-blindness.

• The unikn package provides the color palettes for the University of Konstanz, but also many useful functions for easily defining, modifying, and viewing color palettes, and for finding similar colors or color names.

• The paletteer package is a meta-color package that provides a uniform interface for palettes from many other packages.

D.7.4 Miscellaneous online links

There is an abundance of sites providing support for individual colors and color palettes. Examples include:

• HTML color picker provides HTML/HEX color codes.
• MyColor.space generates color palettes based on a single color.
• ColorBrewer 2.0 (Brewer, 2019) provides color support for cartography.
• Pantone color finder allows finding the color codes of many “official” colors.

Many online sites collect color palettes and provide their definitions so that they can easily be implemented in various systems. Examples include:

• Color Lisa: Color palette masterpieces from the world’s greatest artists
• ColourLovers.com (see the R package colourlovers)
• ColorHunt.co
• Schemecolor.com (though many names seem dubious)
• Scientific colour maps

Beware

As the wealth of color options are often overwhelming, please be aware of two final notes of caution:

• When defining or looking up colors, always double-check their specifications and names — and acknowledge your sources.

• While playing with colors can be fun, using color palettes designed by experts typically yields superior results over home-brew solutions.

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[65_colors.Rmd updated on 2024-10-16 22:07:21.439293 by hn.]

References

Bertin, J. (2011). Semiology of graphics: Diagrams, networks, maps (Vol. 1). ESRI Press.

Brewer, C. (2019). ColorBrewer 2.0: Color advice for cartography. Retrieved from http://www.colorbrewer2.org

Crameri, Fabio, Shephard, G. E., & Heron, P. J. (2020). The misuse of colour in science communication. Nature Communications, 11(1), 1–10. https://doi.org/10.1038/s41467-020-19160-7

Tufte, E. R. (2001). The visual display of quantitative information (2nd ed.). Cheshire, CT: Graphics Press.

Tufte, E. R. (2006). Beautiful evidence (Vol. 1). Cheshire, CT: Graphics Press.

Tufte, E. R., Goeler, N. H., & Benson, R. (1990). Envisioning information (Vol. 126). Cheshire, CT: Graphics Press.

Zeileis, A., Fisher, J. C., Hornik, K., Ihaka, R., McWhite, C. D., Murrell, P., … Wilke, C. O. (2020). colorspace: A toolbox for manipulating and assessing colors and palettes. Journal of Statistical Software, 96(1), 1–49. https://doi.org/10.18637/jss.v096.i01