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Escaping RGBland: Selecting colors for statistical graphics

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  • Zeileis, Achim
  • Hornik, Kurt
  • Murrell, Paul

Abstract

Statistical graphics are often augmented by the use of color coding information contained in some variable. When this involves the shading of areas (and not only points or lines)--e.g., as in bar plots, pie charts, mosaic displays or heatmaps--it is important that the colors are perceptually based and do not introduce optical illusions or systematic bias. Based on the perceptually-based Hue-Chroma-Luminance (HCL) color space suitable color palettes are derived for coding categorical data (qualitative palettes) and numerical variables (sequential and diverging palettes).

Suggested Citation

  • Zeileis, Achim & Hornik, Kurt & Murrell, Paul, 2009. "Escaping RGBland: Selecting colors for statistical graphics," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3259-3270, July.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:9:p:3259-3270
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    2. Ben Jann, 2022. "ColrSpace: A Mata class for color management," University of Bern Social Sciences Working Papers 42, University of Bern, Department of Social Sciences, revised 03 Jun 2022.
    3. Ben Jann, 2023. "Color palettes for Stata graphics: An update," Stata Journal, StataCorp LP, vol. 23(2), pages 336-385, June.
    4. Meyer, Sebastian & Held, Leonhard & Höhle, Michael, 2017. "Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i11).
    5. Emura, Takeshi & Lin, Yi-Shuan, 2013. "A comparison of normal approximation rules for attribute control charts," MPRA Paper 51029, University Library of Munich, Germany.
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    9. Matthias Templ & Andreas Alfons & Peter Filzmoser, 2012. "Exploring incomplete data using visualization techniques," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(1), pages 29-47, April.
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    13. Pilhöfer, Alexander & Unwin, Antony, 2013. "New Approaches in Visualization of Categorical Data: R Package extracat," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i07).
    14. Anette Fasang & Marcel Raab, 2014. "Beyond Transmission: Intergenerational Patterns of Family Formation Among Middle-Class American Families," Demography, Springer;Population Association of America (PAA), vol. 51(5), pages 1703-1728, October.
    15. Jonas Schöley, 2021. "The centered ternary balance scheme: A technique to visualize surfaces of unbalanced three-part compositions," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 44(19), pages 443-458.
    16. Reto Stauffer & Georg J. Mayr & Markus Dabernig & Achim Zeileis, 2013. "Somewhere over the Rainbow: How to Make Effective Use of Colors in Meteorological Visualizations," Working Papers 2013-37, Faculty of Economics and Statistics, Universität Innsbruck.
    17. MacDonald, Blake & Ranjan, Pritam & Chipman, Hugh, 2015. "GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i12).

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