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Thecetcolor package is designed to bring to R the colour maps created byPeter Kovesi that avoid points of locally high colour contrast leading to the perception of false anomalies in your data when there is none. The colour maps have been designed to avoid this phenomenon by having uniform perceptual contrast over their whole range.

# install.packages("devtools")# Install the packagedevtools::install_github("coatless/cetcolor")

# Load the Librarylibrary("cetcolor")# Get RGB Hexadecimals for graphingcet_pal(5,name="blues")# Sample use with ggplot2library("ggplot2")ggplot(faithfuld, aes(waiting,eruptions))+  geom_raster(aes(fill=density))+  theme_bw()+ theme(panel.grid=element_blank())->glibrary("gridExtra")# Frequently used colour maps have "nicknames" see ?cet_color_mapsgrid.arrange(g+ scale_fill_gradientn(colours= cet_pal(5,name="fire")),g+ scale_fill_gradientn(colours= cet_pal(5,name="inferno")),g+ scale_fill_gradientn(colours= cet_pal(5,name="blues")),g+ scale_fill_gradientn(colours= cet_pal(5,name="kgy")),ncol=2,nrow=2)

# Show a panel of possible values (without nicknames)display_cet_all()

• CET Perceptually Uniform Colour Maps: Download Page
  • Location of CSV data, references, and data.
• colorcet for Python
  • Shorthand naming of colour schemes and presentation of palettes
• PerceptualColourMaps.jl by Peter Kovesi
  • Referenced documentation and possible reimplementation of generation functions to avoid using CSV data.

• viridis (Source)
  • Provides MATLAB perceptually uniform colour maps in a manner that is similar to this package.
• RColorBrewer (Source)
  • Function interface naming, e.g.cet_pal(n, name), and colour map displays.
• scales andggplot2
  • Proper ways to interface colour palettes withggplot2 as shown forRColorBrewer.

• Allow for n > 256.
• Provide hooks for ggplot2 gradient and discrete scales
• Port over the Peter's generation code from Julia / MATLAB to R.