cleanplots for R

Full documentation and more examples: tdmize.github.io/cleanplots

Publication-ready defaults for ggplot2: the cleanplots graphing scheme. cleanplots provides professional-looking figures with strong data visualization and accessibility defaults – colors are chosen to be colorblind-friendly and to remain distinguishable when printed in black & white, with matching marker shapes and line patterns so that groups stay distinct across multiple visual channels. A companion scheme is available for Stata, so figures made in R and Stata share the same design.

Installation

# install.packages("remotes")
remotes::install_github("tdmize/cleanplots")

Usage

The quickest way to use cleanplots is one setup call: it sets the theme, makes markers and lines larger and more visible, and applies the cleanplots colors to all plots by default (main palette for color, softer bar palette for fill):

library(ggplot2)
library(cleanplots)
cleanplots_defaults()

ggplot(mpg, aes(displ, hwy, color = class, shape = class)) +
  geom_point() +
  scale_shape_cleanplots()

cleanplots_save("my-figure.png")   # saves at a fixed 7 x 5 in, 300 dpi

Or apply the pieces individually per plot. (If you are not using the full cleanplots setup, we recommend adding theme_minimal() or theme_cleanplots() – the colors and markers are much easier to see against a white background.)

library(ggplot2)
library(cleanplots)

# Scatterplot with the main palette and theme
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point(size = 2) +
  theme_cleanplots() +
  scale_color_cleanplots()

# Bar charts use the softer bar/area palette
titanic <- aggregate(Freq ~ Class + Sex, data = as.data.frame(Titanic), sum)
ggplot(titanic, aes(Sex, Freq, fill = Class)) +
  geom_col(position = "dodge") +
  theme_cleanplots() +
  scale_fill_cleanplots(palette = "bars")

# Reorder or reverse the colors
scale_color_cleanplots(order = c(7, 1, 2))
scale_color_cleanplots(reverse = TRUE)

# Extract hex codes directly
cleanplots_colors()
cleanplots_colors("red", "navy")
cleanplots_colors(bars = TRUE)

The palettes

Main colors (palette = "default") – used for markers, lines, and confidence intervals:

#	Name	Hex	Definition
1	red	#D50000	red*1.2
2	ltblue	#8FC6EB	eltblue*.9
3	black	#000000	black
4	gray	#909090	gs9
5	purple	#740074	purple*1.1
6	pink	#FFB3D9	pink*.3
7	navy	#143755	navy*1.3
8	ltgray	#C0C0C0	gs12
9	dkgray	#404040	gs4
10	lavender	#D9D7F0	lavender*.35

Bar/area colors (palette = "bars") – softer versions used for bar charts, area plots, and pie charts, which use far more ink than points and lines:

#	Name	Hex	Definition
1	red	#FF4D4D	red @ 70%
2	ltblue	#C2E0F5	eltblue*.7 @ 70%
3	black	#5F5F5F	black*.9 @ 70%
4	gray	#B1B1B1	gs9 @ 70%
5	purple	#AF5FAF	purple*.9 @ 70%
6	pink	#FFDBEE	pink*.2 @ 70%
7	navy	#5D7A93	navy*1.1 @ 70%
8	ltgray	#D3D3D3	gs12 @ 70%
9	dkgray	#909090	gs6 @ 70%
10	lavender	#E8E7F6	lavender*.3 @ 70%

Shapes and line patterns

Color alone reliably distinguishes about 4-5 groups. Beyond that – and for black & white printing and colorblind readers – cleanplots varies several visual channels at once: scale_shape_cleanplots() assigns hollow marker shapes to the dark colors and solid shapes to the light colors, and scale_linetype_cleanplots() assigns line patterns (solid, solid, dashed, dashed, shortdash, shortdash, longdash, longdash) so that any two groups sharing a shape or pattern always differ strongly in lightness. Groups remain distinguishable by color, by lightness, by marker shape and fill, and by line pattern.

ggplot(mpg, aes(displ, hwy, color = class, shape = class)) +
  geom_point(size = 2) +
  theme_cleanplots() +
  scale_color_cleanplots() +
  scale_shape_cleanplots()

Design goals

The palette alternates dark and light colors so the first several groups are distinguishable by lightness alone when printed in black & white, contains no red-green pair, and passes deuteranopia, protanopia, and tritanopia simulation checks for the first five colors (minimum Delta-E of about 21). Check the palette yourself at Coloring for Colorblindness.

The theme

theme_cleanplots(): white background, no plot border, light gray axis lines and ticks, dotted light gray gridlines, a frameless, untitled legend at the right, and black-outlined facet strips with bold labels. To restore a legend title:

theme_cleanplots() + theme(legend.title = element_text())

cleanplots for Stata

The original Stata scheme is available at https://www.trentonmize.com/software/cleanplots or via net install cleanplots, from("https://tdmize.github.io/data/cleanplots") replace. Colors, marker symbols, line patterns, and layout match this package, so figures made in R and Stata look like siblings.