KEY - colors

Colors

Author

Your Name

Published

February 4, 2026

Note

This template follows the colors lecture. Please be sure to cross-reference the slides, which contain important information and additional context!

Setup

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                    setup                                 ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

#..........................load packages.........................
library(palmerpenguins)
library(tidyverse)

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                              create base plots                           ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

#................requires categorical color scale................
cat_color_plot <- ggplot(na.omit(penguins), 
                         aes(x = bill_length_mm, y = bill_depth_mm, 
                             color = species, shape = species)) +
  geom_point(size = 4, alpha = 0.8)

cat_color_plot
#................requires continuous color scale.................
con_color_plot <- ggplot(na.omit(penguins), 
                         aes(x = bill_length_mm, y = bill_depth_mm, 
                             color = body_mass_g)) +
  geom_point(size = 4, alpha = 0.8) 

con_color_plot

Colors for inclusive & accessible design

Viridis scales

#....................discrete viridis scales.....................
cat_color_plot +
  scale_color_viridis_d(option = "viridis") 
#....................continuous viridis scales...................
con_color_plot +
  scale_color_viridis_c(option = "magma")

RColorBrewer scales

  • Exploring available palettes and generating HEX codes (or check out the web-based interface):
#.....display only colorblind-friendly RColorBrewer palettes.....
RColorBrewer::display.brewer.all(colorblindFriendly = TRUE)

#.......preview palette with your number of desired colors.......
RColorBrewer::display.brewer.pal(n = 3, name = "Dark2")

#..............print the HEX codes of your palette...............
RColorBrewer::brewer.pal(n = 3, name = "Dark2")
[1] "#1B9E77" "#D95F02" "#7570B3"
#..................or save hex codes as a vector.................
my_brewer_pal <- RColorBrewer::brewer.pal(n = 3, name = "Dark2")
  • Using RColorBrewer palettes
#....................for qualitative palettes....................
# Accessibility tip: use redundant mapping (e.g. colors + shapes) whenever possible
cat_color_plot +
  scale_color_brewer(palette = "Dark2") 
#..............for unclassed continuous color scales.............
con_color_plot +
  scale_color_distiller(palette = "BuPu")
#..............for classed continuous color scales...............

con_color_plot +
  scale_color_fermenter(palette = "YlGnBu")
# Accessibility tip: outline points to make light colors more visible
ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, fill = body_mass_g)) + # notice switch from `color` > `fill`
  geom_point(shape = 21, size = 4, alpha = 0.8) + # notice point `shape`
  scale_fill_fermenter(palette = "YlGnBu")

{paletteer}

A few different ways to use {paletteer} palettes, but my preferred approach takes two steps:

  1. create vector of colors using paletteer_*()
  2. apply your palette using the appropriate ggplot::scale_*() function

For discrete data:

#........................1. create palette.......................
pal_d <- paletteer::paletteer_d("wesanderson::GrandBudapest1", n = 3)
pal_d
<colors>
#F1BB7BFF #FD6467FF #5B1A18FF 
#.........2. apply to scatter plot (use `color` variant).........
cat_color_plot + 
  scale_color_manual(values = pal_d)

#...........2. apply to histogram (use `fill` variant)...........
ggplot(penguins, aes(x = body_mass_g, fill = species)) +
  geom_histogram() +
  scale_fill_manual(values = pal_d)

For continuous data:

#........................1. create palette.......................
# reverse order of colors using the `direction` arg
pal_c <- paletteer::paletteer_c("scico::batlow", n = 5, direction = -1)
pal_c
<colors>
#F9CCF9FF #F19D6BFF #818231FF #215F61FF #001959FF 
#...2. apply as an unclassed palette (use `gradientn` variant)...
con_color_plot + 
  scale_color_gradientn(colors = pal_c)

#..2. apply as a classed (binned) palette (use `stepsn` variant).
con_color_plot + 
  scale_color_stepsn(colors = pal_c)

Some final palette tips

Save palette outside of plot

#.........................create palette.........................
my_palette <- c("#32DE8A", "#E36414", "#0F4C5C")

#..........................apply to plot.........................
cat_color_plot +
  scale_color_manual(values = my_palette)

Set color names

#.........................create palette.........................
my_palette_named <- c("Adelie" = "#32DE8A","Chinstrap" = "#E36414", "Gentoo" = "#0F4C5C")

#..................apply to plot (all penguins)..................
ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) +
  geom_point(size = 4, alpha = 0.8) +
  scale_color_manual(values = my_palette_named)

#..............apply to plot (just adelie & gentoo)..............
penguins |> 
  filter(species != "Chinstrap") |> 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species)) +
  geom_point(size = 4, alpha = 0.8) +
  scale_color_manual(values = my_palette_named)