ggpubfigs is a ggplot2 extension that helps create publication ready figures for the life sciences.
Importantly, ggpubfigs implements themes and color palettes that are both aesthetically pleasing and colorblind friendly.

If you found ggpubfigs helpful, please cite: ggpubfigs: Colorblind-Friendly Color Palettes and ggplot2 Graphic System Extensions for Publication-Quality Scientific Figures. DOI: 10.1128/MRA.00871-21

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Guide

Install
Quick Start
Color palettes
Palette Swatches and Hex Codes
Themes
FAQ
Change Log
Versioning
Acknowledgements

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Install

# execute this command only the first time you use ggpubfigs
devtools::install_github("JLSteenwyk/ggpubfigs")
# load ggpubfigs
library(ggpubfigs)

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Quick Start

These quick start examples demonstrate how to implement two aesthetically pleasing themes and color palettes.

# using the "ito_seven" color palette and theme_big_simple()
ggplot(mtcars, aes(factor(carb), fill=factor(cyl))) + geom_bar() + scale_fill_manual(values = friendly_pal("ito_seven")) + theme_big_simple()

# using the "bright_seven" color palette and theme_big_grid()
ggplot(mtcars, aes(factor(carb), fill=factor(cyl))) + geom_bar() + scale_fill_manual(values = friendly_pal("bright_seven")) + theme_big_grid()

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Color palettes

Color palettes are all colorblind friendly. Thus, your figures will be accessible to more people, which is inarguably better for your audience and you.

Color palettes are named using the following scheme: identifier (underscore) number of colors in color palette.
Palette swatches and hex codes are shown below for quick visual comparison.

To use these colorblind friendly color palettes, use the following command as an example for both discrete and continuous purposes:

ggplot(mtcars, aes(factor(carb), fill=factor(cyl))) + geom_bar() + scale_fill_manual(values = friendly_pal("contrast_three"))

pal <- friendly_pal("contrast_three", 50, type = "continuous")
image(volcano, col = pal)

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Palette Swatches and Hex Codes

Palette	Swatches	Colors used
bright_seven		#4477AA, #228833, #AA3377, #BBBBBB, #66CCEE, #CCBB44, #EE6677
contrast_three		#004488, #BB5566, #DDAA33
vibrant_seven		#0077BB, #EE7733, #33BBEE, #CC3311, #009988, #EE3377, #BBBBBB
muted_nine		#332288, #117733, #CC6677, #88CCEE, #999933, #882255, #44AA99, #DDCC77, #AA4499
nickel_five		#648FFF, #FE6100, #785EF0, #FFB000, #DC267F
ito_seven		#0072B2, #D55E00, #009E73, #CC79A7, #56B4E9, #E69F00, #F0E442
ibm_five		#648FFF, #785EF0, #DC267F, #FE6100, #FFB000
wong_eight		#E69F00, #56B4E9, #009E73, #F0E442, #0072B2, #D55E00, #CC79A7, #000000
tol_eight		#332288, #117733, #44AA99, #88CCEE, #DDCC77, #CC6677, #AA4499, #882255
zesty_four		#F5793A, #A95AA1, #85C0F9, #0F2080
retro_four		#601A4A, #EE442F, #63ACBE, #F9F4EC
tableau_ten		#4E79A7, #F28E2B, #E15759, #76B7B2, #59A14F, #EDC948, #B07AA1, #FF9DA7, #9C755F, #BAB0AC
glasbey_twelve		#0000FF, #FF0000, #00FF00, #000033, #FF00B6, #005300, #FFD300, #009FFF, #9A4D42, #00FFBE, #783FC1, #1F9698
viridis_eight		#440154, #46337E, #365C8D, #277F8E, #1FA187, #4AC16D, #9FDA3A, #FDE725
cividis_eight		#00204D, #16396D, #4B546C, #6C6E72, #8E8A79, #B3A772, #DBC761, #FFEA46

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Themes

Publication-ready figures

theme_grid()

ggplot(iris, aes(Sepal.Length, Petal.Width, color = Species)) + geom_point() + theme_grid()

ggplot(iris, aes(Sepal.Length, fill = Species)) + geom_histogram() + theme_grid()

theme_big_grid()

ggplot(iris, aes(Sepal.Length, Petal.Width, color = Species)) + geom_point() + theme_big_grid()

ggplot(iris, aes(Sepal.Length, fill = Species)) + geom_histogram() + theme_big_grid()

theme_simple()

ggplot(iris, aes(Sepal.Length, Petal.Width, color = Species)) + geom_point() + theme_simple()

ggplot(iris, aes(Sepal.Length, fill = Species)) + geom_histogram() + theme_simple()

theme_big_simple()

ggplot(iris, aes(Sepal.Length, Petal.Width, color = Species)) + geom_point() + theme_big_simple()

ggplot(iris, aes(Sepal.Length, fill = Species)) + geom_histogram() + theme_big_simple()

theme_grey()

ggplot(iris, aes(Sepal.Length, Petal.Width, color = Species)) + geom_point() + theme_grey()

ggplot(iris, aes(Sepal.Length, fill = Species)) + geom_histogram() + theme_grey()

Presentation-ready figures

theme_black()

ggplot(iris, aes(Sepal.Length, Petal.Width, color = Species)) + geom_point() + theme_black()

ggplot(iris, aes(Sepal.Length, fill = Species)) + geom_histogram() + theme_black()

theme_blue()

ggplot(iris, aes(Sepal.Length, Petal.Width, color = Species)) + geom_point() + theme_blue()

ggplot(iris, aes(Sepal.Length, fill = Species)) + geom_histogram() + theme_blue()

theme_red()

ggplot(iris, aes(Sepal.Length, Petal.Width, color = Species)) + geom_point() + theme_red()

ggplot(iris, aes(Sepal.Length, fill = Species)) + geom_histogram() + theme_red()

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FAQ

Can I submit color palettes or themes to be incorporated into ggpubfigs?
Yes! Submissions are encouraged, please feel free to contact me via twitter or from my contact information.

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Acknowledgements

I would like to thank the blooming R community for all the very helpful online forums, discussions, and open source books that have helped me learn R.

I would also like to acknowledge that some of the example usage and color palette code was developed based on open source code from the wes anderson color palette package.

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Change Log

For full release notes, see NEWS.md.

1.1.3 (2026-02-24)

• Added tableau_ten, glasbey_twelve, viridis_eight, and cividis_eight.
• Added test coverage for new palette availability and expected sizes.
• Updated palette documentation/man pages and README palette table.

1.1.2 (2026-02-24)

• Added testthat coverage for palette and theme behavior.
• Migrated theme internals to linewidth in element_line() and element_rect().
• Fixed friendly_pal() documentation to reference ito_seven.

1.1.1 (2026-02-17)

• Added CI-based R compatibility checks for R 4.4, R 4.5, and devel.
• Fixed CI dependency setup and package metadata/import issues for R CMD check.
• Updated build ignore settings and README R-version testing badge.