README.md

Colors for all!

cols4all is an R package for selecting color palettes. “Color for all” refers to our mission that colors should be usable for not just people with normal color vision, but also for people with color vision deficiency. Currently, this package contains palettes from several popular and lesser known color palette series. Users can also add their own palette series.

Color palettes are well organized and made consistent with each other. Moreover, they are scored on several aspects: color-blind-friendliness, the presence of intense colors (which should be avoided), the overall aesthetic harmony, and how many different hues are used. Finally, for each color palette a color for missing values is assigned, which is especially important for spatial data visualization. Currently we support several types: categorical (qualitative) palettes, sequential palettes, diverging palettes, cycling palettes and bivariate palettes (divided into four subtypes).

Installation

cols4all is available on CRAN:

install.packages("cols4all", dependencies = TRUE)

The development version can be installed as follows:

install.packages("remotes")
remotes::install_github("mtennekes/cols4all", dependencies = TRUE)

Getting started

Load the package:

library(cols4all)

The main tool is a dashboard, which is started with:

c4a_gui()

What palettes are available? That is, by default; other palettes can be added!

c4a_series()
#>        series                                         description
#> 1      brewer                                ColorBrewer palettes
#> 2       carto                          Palettes designed by CARTO
#> 3    cols4all                  cols4all palettes (in development)
#> 4         hcl  Palettes from the Hue Chroma Luminance color space
#> 5      kovesi                   Palettes designed by Peter Kovesi
#> 6  matplotlib         Palettes from the Python library matplotlib
#> 7         met Palettes inspired by The Metropolitan Museum of Art
#> 8        misc                              Miscellaneous palettes
#> 9       parks                 Palettes inspired by National Parks
#> 10       poly               Qualitative palettes with many colors
#> 11    powerbi                    Palettes from Microsoft Power BI
#> 12      scico             Scientific colour maps by Fabio Crameri
#> 13    seaborn            Palettes from the Python library Seaborn
#> 14    stevens                Bivariate palettes by Joshua Stevens
#> 15    tableau                        Palettes designed by Tableau
#> 16        tol                       Palettes designed by Paul Tol
#> 17        wes                   Palettes from Wes Anderson movies

Using the tool

Use the tool to compare palettes and if needed analyse a palette in depth (via the other tabs).

Find a trade-off you like among the following properties (the columns in the main table):

• Colorblind friendly: Is the palette color blind friendly?
• Fair: Is the palette fair? In a fair palette, colors stand out about equally.
• Hues: What hue ranges are used? All across the (rainbow) color spectrum, or a limited range?
• Vivid: Are there any colors that are very saturated? Perhaps a little too much?
• Contrast: Is there sufficient contrast against white, black (using WACG criteria) and between colors?
• 3D Blues: If blue is a palette color, a 3D visual illusion could appear.
• Naming (in development): How well can palette colors be named?

Example 1

When we are looking for a fair categorical palette of seven colors that is as color blind friendly as possible, then filter on “Fair”, and sort by “Colorblind-friendly”:

This inspired us to develop our own palettes: see these cols4all palettes below.

Example 2

Say we need a diverging palette that is color blind friendly, and what to choose one by eye. Then filter by “Colorblind-friendly” and sort by “Hue Middle L” (the hue of the left wing):

Reverse sorting is also applied.

Preliminary set of new cols4all palettes

We applied a basic heuristic to explore palettes that score well on a mix of the properties named above

area7, area8 and area9 are fair, contain low pastel colors, and are color-blind friendly (up to 7 colors). So ideal for maps and other space-filling visualizations! These are used in tmap4.

area7d, area8d and area9d similar but for dark mode:.

line7, line8 and line9 are colors with good contrast against both black and white, and are also colorblind-friendly to some extent. So ideal for line graphs and scatter plots:

Finally friendly7 … friendly13 are colorblind-friendly palettes (disregarding the other properties):

ggplot2 integration

library(ggplot2)
data("diamonds")
diam_exp = diamonds[diamonds$price >= 15000, ]

# discrete categorical scale
ggplot(diam_exp, aes(x = carat, y = price, color = color)) +
    geom_point(size = 2) +
    scale_color_discrete_c4a_cat("carto.safe") +
    theme_light()

# continuous diverging scale
ggplot(diam_exp, aes(x = carat, y = depth, color = price)) +
    geom_point(size = 2) +
    scale_color_continuous_c4a_div("wes.zissou1", mid = mean(diam_exp$price)) +
    theme_light()

Overview of functions

Main functions:

• c4a_gui Dashboard for analyzing the palettes
• c4a Get the colors from a palette (c4a_na for the associated color for missing values)
• c4a_plot Plot a color palette

Palette names and properties:

• c4a_palettes Get available palette names
• c4a_series Get available series names
• c4a_types Get implemented types
• c4a_overview Get an overview of palettes per series x type.
• c4a_citation Show how to cite palettes (with bibtex code).
• c4a_info Get information from a palette, such as type and maximum number of colors
• .P Environment via which palette names can be browsed with auto-completion (using $)

Importing and exporting palettes:

• c4a_data Build color palette data
• c4a_load Load color palette data
• c4a_sysdata_import Import system data
• c4a_sysdata_export Export system data

Edit color palette data

• c4a_duplicate Duplicates a color palette
• c4a_modify Modifies palette colors

ggplot2

• scale_<aesthetic>_<mapping>_c4a_<type> e.g. scale_color_continuous_c4a_div Add scale to ggplot2.

Other R functions

What palettes are available, e.g diverging from the hcl series?

# Diverging palettes from the 'hcl' series
c4a_palettes(type = "div", series = "hcl")
#>  [1] "hcl.blue_red"     "hcl.blue_red2"    "hcl.blue_red3"    "hcl.red_green"   
#>  [5] "hcl.purple_green" "hcl.purple_brown" "hcl.green_brown"  "hcl.blue_yellow2"
#>  [9] "hcl.blue_yellow3" "hcl.green_orange" "hcl.cyan_magenta"

Give me the colors!

# select purple green palette from the hcl series:
c4a("hcl.purple_green", 11)
#>  [1] "#492050" "#82498C" "#B574C2" "#D2A9DB" "#E8D4ED" "#F1F1F1" "#C8E1C9"
#>  [8] "#91C392" "#4E9D4F" "#256C26" "#023903"

# get the associated color for missing values
c4a_na("hcl.purple_green")
#> [1] "#BABABA"

Plot these colors:

c4a_plot_cvd("hcl.purple_green", 11, include.na = TRUE)

Related R packages

The foundation of this package is another R package: colorspace. We use this package to analyse colors. For this purpose and specifically for color blind friendliness checks, we also use colorblindcheck.

There are a few other packages with a large collection of color palettes, in particular pals and paletteer. There are a few features that distinguish cols4all from those packages:

• Color palettes are characterized and analysed. Properties such as color blindness, fairness (whether colors stand out about equally), and contrast are determined for each palette.

• Bivariate color palettes are available.

• Own color palettes can be loaded and analysed.

• Colors for missing values are made explicit.

• There is native support for ggplot2 and tmap (as of the upcoming version 4).

• There are a couple of exporting options, including (bibtex) citation.

Feedback welcome!

• Is everything working as expected?

• Do you miss certain palettes?

• Do you have ideas for improvement how to measure palette properties?

Let us know! (via github issues)