What’s new in {PrettyCols} 1.0.1?

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Back in September 2022 I submitted {PrettyCols}, an R package containing aesthetically pleasing colour palettes, to CRAN. If you missed it, you can read the blog post introducing the package!

After a few months, it was time for an update and this blog post will give you a brief overview of some of the new features and palettes! You can install {PrettyCols} using:


You can also install the development version from GitHub using:


More palettes!

The new release contains twelve additional palettes, including two new sequential palettes (Yellows and Reds), two new diverging palettes (PurpleYellows and RedBlues), and eight new qualitative palettes. You can see the complete list of available palettes by running view_all_palettes():

Grid showing 32 different colour palettes

or browsing the package vignette on available palettes.

Colourblind friendly palettes

PrettyCols::view_all_palettes(colourblind_friendly = TRUE)

Grid showing colourblind friendly colour palettes

You can combine filtering by palette types with filtering by colourblind friendliness, for example to view only colourblind friendly diverging palettes:

PrettyCols::view_all_palettes(type = "div", colourblind_friendly = TRUE)

Grid showing colourblind friendly diverging colour palettes

The view_all_palettes() function now also supports filtering by multiple types, e.g. to view all sequential and diverging palettes:

PrettyCols::view_all_palettes(type = c("seq", "div"))

Grid showing diverging and sequential colour palettes

The colourblind-friendliness has been checked using colorblindcheck::palette_check(), and a judgement made based on the number of distinct colour pairs with deuteranopia, protanopia, tritanopia in comparison to full colour vision. This judgement may not always be correct, and if you think a colour palette has been listed as colourblind friendly in error, please raise a GitHub issue and I’ll re-classify the palette.

Python support

This colour palette package was originally developed for use with R, particularly with {ggplot2}. However, many people make charts and generative art with Python instead. So, now you can use PrettyCols with Python. The Python code is adapted from the {MetBrewer} package from Blake Robert Mills which also provides support for R and Python.

You can install from GitHub and read more about how to use these palettes with Python in the Using with Python package vignette.

Here’s a small example of using the Bright colour palette with matplotlib to make a bar chart:

import prettycols
import matplotlib.pyplot as plt

colors = pretty_cols(name="Bright", n=3, palette_type="discrete")
x = ['A', 'B', 'C']
value = [1, 2, 3]
plt.bar(x, value, color=colors)

Bar chart with A, B, and C on the x-axis and 1, 2, and 3 on the y-axis. Bars are three different colours.

To end this post, here’s another couple of examples of these palettes being used for generative art!

Generative art piece composed of squares and rectangles inside squares and rectangles.

Generative art piece composed of a 4x4 grid with cutouts in the middle of each squares, with shadows shown.

Show code
aRt::puzzles(n = 200,
             num_groups = 30,
             col_palette = PrettyCols::prettycols("Lively"),
             bg_col = "white",
             s = 1234)
aRt::stacked(n_x = 4,
             n_y = 4,
             col_palette = PrettyCols::prettycols("Lucent"),
             shadow_intensity = 0.1,
             sunangle = 315,
             s = 124)

There are plans for a few more updates in the future including even more palettes, and support for diverging colour scales in {ggplot2} which allows you to specify a midpoint for the scale. If you find that something in {PrettyCols} isn’t working, or if you’ve got an idea for more features, please raise a GitHub issue!

To leave a comment for the author, please follow the link and comment on their blog: R on Nicola Rennie.

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