README

Color blindness affects a large number of individuals. When communicating scientific results colour palettes must therefore be carefully chosen to be accessible to all readers.

This R package provides an implementation of Paul Tol[1] and Okabe and Ito[2] colour schemes. These schemes are ready for each type of data (qualitative, diverging or sequential), with colours that are distinct for all people, including colour-blind readers. This package also provides tools to simulate colour-blindness and to test how well the colours of any palette are identifiable. To simulate colour-blindness in production-ready R figures you may also be interested in the colorblindr package.

For specific uses, several scientific thematic schemes (geologic timescale, land cover, FAO soils, etc.) are implemented, but these colour schemes may not be colour-blind safe.

Installation

install.packages("khroma")

# install.packages("remotes")
remotes::install_github("nfrerebeau/khroma")

Usage

# Load packages
library(khroma)
library(ggplot2)

Colour palettes and scales

colour() returns a palette function that when called with a single integer argument returns a vector of colours.

# Paul Tol's bright colour scheme
bright <- colour("bright")

If crayon is installed on your machine and if the crayon.enabled option is set to TRUE with options(), colours will be nicely printed in the console.

options(crayon.enabled = FALSE)
bright(7)
#>      blue       red     green    yellow      cyan    purple      grey 
#> "#4477AA" "#EE6677" "#228833" "#CCBB44" "#66CCEE" "#AA3377" "#BBBBBB" 
#> attr(,"missing")
#> [1] NA

# Show the colour palette
plot_scheme(bright(7), colours = TRUE)

# Use with ggplot2
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, colour = class)) +
  geom_point() +
  scale_colour_bright()

Diagnostic tools

Test how well the colours are identifiable

# Okabe & Ito's colour scheme
okabe <- colour("okabe ito")

set.seed(12345)
plot_map(okabe(8))

Compute CIELAB distance metric

DeltaE <- compare(okabe(8))
round(DeltaE, 2)
#>                black orange sky blue bluish green yellow  blue vermilion
#> orange         64.74                                                    
#> sky blue       60.95  53.61                                             
#> bluish green   50.51  42.87    34.69                                    
#> yellow         88.42  21.72    57.53        38.04                       
#> blue           39.23  55.35    22.31        38.40  70.37                
#> vermilion      49.36  22.24    52.27        54.36  43.71 49.62          
#> reddish purple 53.11  49.01    45.51        63.45  62.54 41.11     37.02

Simulate colour-blindness

# convert() returns a modified palette function
deuteranopia <- convert(okabe, mode = "deuteranopia")
plot_scheme(deuteranopia(8), colours = TRUE)


protanopia <- convert(okabe, mode = "protanopia")
plot_scheme(protanopia(8), colours = TRUE)


tritanopia <- convert(okabe, mode = "tritanopia")
plot_scheme(tritanopia(8), colours = TRUE)


achromatopsia <- convert(okabe, mode = "achromatopsia")
plot_scheme(achromatopsia(8), colours = TRUE)

plot_scheme_colourblind(okabe(8))


# ggplot2 default colour scheme
# (equally spaced hues around the colour wheel)
x <- scales::hue_pal()(8)
plot_scheme_colourblind(x)

Colour Schemes

Paul Tol’s Colour Schemes

Paul Tol offers carefully chosen schemes, ready for each type of data, with colours that are:

All the scales presented in Paul Tol’s technical note are implemented here, for use with base R or ggplot2. Refer to the original document for details about the recommended uses.

Scientific colour schemes

Contributing

Please note that the khroma project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

khroma

Overview