Color can make or break a figure. The right palette draws attention to the story in your data; the wrong one buries it under visual noise. In this post we will walk through the major palette ecosystems available in R, learn when to reach for each one, and pick up practical rules that keep figures readable, accessible, and honest.

Setup

We will use several packages throughout this tutorial. Install any you are missing, then load them all at once.

Code

# Uncomment to install any missing packages
# install.packages(c(
#   "ggplot2", "dplyr", "scales", "viridis",
#   "RColorBrewer", "colorspace", "paletteer",
#   "scico", "MetBrewer", "wesanderson", "ggsci",
#   "rcartocolor", "palmerpenguins", "khroma"
# ))

library(ggplot2)
library(dplyr)
library(scales)
library(viridis)
library(RColorBrewer)
library(colorspace)
library(paletteer)
library(scico)
library(MetBrewer)
library(wesanderson)
library(ggsci)
library(rcartocolor)
library(palmerpenguins)
library(khroma)

We will rely on the palmerpenguins dataset for most examples. It gives us both categorical variables (species, island) and continuous ones (bill_length_mm, body_mass_g).

Code

penguins <- palmerpenguins::penguins
glimpse(penguins)

Rows: 344
Columns: 8
$ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
$ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
$ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
$ sex               <fct> male, female, female, NA, female, male, female, male…
$ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

Continuous vs Discrete Color Scales

Before choosing a palette, decide whether the variable you are mapping to color is continuous (numeric) or discrete (categorical). In ggplot2:

Continuous variables use scale_color_*() / scale_fill_*() functions that interpolate between colors.
Discrete variables use functions that assign one distinct color per level.

Many palette packages offer both variants. Here is a quick comparison.

Code

# Continuous: bill length mapped to a gradient
p_cont <- ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g,
                                color = bill_length_mm)) +
  geom_point(size = 2, na.rm = TRUE) +
  scale_color_viridis_c() +
  labs(title = "Continuous scale", color = "Bill length (mm)") +
  theme_classic()

# Discrete: species mapped to distinct colors
p_disc <- ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g,
                                color = species)) +
  geom_point(size = 2, na.rm = TRUE) +
  scale_color_viridis_d() +
  labs(title = "Discrete scale", color = "Species") +
  theme_classic()

p_cont

Code

p_disc

Rule of thumb: continuous scales for numbers, discrete scales for categories.

Viridis (viridis / viridisLite)

The viridis family is the gold standard for perceptually uniform, colorblind-safe palettes. The colors change smoothly in lightness and chroma, so a plot printed in grayscale still makes sense.

Sub-palettes: viridis, magma, inferno, plasma, cividis, rocket, mako, turbo.

When to use: any time you need a safe default for continuous data, heatmaps, or ordered categories.

Code

ggplot(faithfuld, aes(waiting, eruptions, fill = density)) +
  geom_tile() +
  scale_fill_viridis_c(option = "magma") +
  labs(title = "Old Faithful eruptions — viridis magma") +
  theme_classic()

Code

ggplot(penguins, aes(x = species, y = flipper_length_mm,
                      fill = species)) +
  geom_boxplot(na.rm = TRUE, show.legend = FALSE) +
  scale_fill_viridis_d(option = "cividis") +
  labs(title = "Flipper length by species — viridis cividis",
       x = "Species", y = "Flipper length (mm)") +
  theme_classic()

RColorBrewer

RColorBrewer provides three palette families designed by cartographer Cynthia Brewer:

Type	Purpose	Example palettes
Sequential	Low-to-high values	Blues, YlOrRd, Greens
Diverging	Deviation from a midpoint	RdBu, BrBG, PiYG
Qualitative	Unordered categories	Set2, Paired, Dark2

When to use: you need a well-tested, publication-ready palette and want to choose the family explicitly.

Code

# Display available palettes
display.brewer.all(n = 8)

Code

ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm,
                      color = body_mass_g)) +
  geom_point(size = 2, na.rm = TRUE) +
  scale_color_distiller(palette = "YlOrRd", direction = 1) +
  labs(title = "Body mass — Brewer YlOrRd (sequential)",
       x = "Bill length (mm)", y = "Bill depth (mm)",
       color = "Mass (g)") +
  theme_classic()

Code

ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm,
                      color = species)) +
  geom_point(size = 2, na.rm = TRUE) +
  scale_color_brewer(palette = "Set2") +
  labs(title = "Species — Brewer Set2 (qualitative)",
       x = "Bill length (mm)", y = "Bill depth (mm)") +
  theme_classic()

colorspace

The colorspace package lets you build palettes from scratch using perceptual color spaces (HCL). It ships ready-made palettes and diagnostic tools.

When to use: you want fine control over hue, chroma, and luminance or need to evaluate existing palettes.

Code

ggplot(penguins, aes(x = species, y = body_mass_g, fill = species)) +
  geom_violin(na.rm = TRUE, show.legend = FALSE) +
  scale_fill_discrete_qualitative(palette = "Dark 3") +
  labs(title = "Body mass — colorspace Dark 3",
       x = "Species", y = "Body mass (g)") +
  theme_classic()

Code

ggplot(faithfuld, aes(waiting, eruptions, fill = density)) +
  geom_tile() +
  scale_fill_continuous_sequential(palette = "ag_Sunset") +
  labs(title = "Old Faithful — colorspace ag_Sunset") +
  theme_classic()

A powerful diagnostic: colorspace::specplot() shows how lightness, chroma, and hue vary across a palette.

Code

colorspace::specplot(colorspace::sequential_hcl(256, palette = "ag_Sunset"))

paletteer

paletteer is a meta-package that gives you a unified interface to hundreds of palettes from dozens of packages.

When to use: you want to browse or switch between palettes without loading each individual package.

Code

ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g,
                      color = species)) +
  geom_point(size = 2, na.rm = TRUE) +
  paletteer::scale_color_paletteer_d("ggsci::default_jco") +
  labs(title = "Species — paletteer (ggsci JCO)",
       x = "Bill length (mm)", y = "Body mass (g)") +
  theme_classic()

Code

ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm,
                      color = body_mass_g)) +
  geom_point(size = 2, na.rm = TRUE) +
  paletteer::scale_color_paletteer_c("scico::batlow", direction = -1) +
  labs(title = "Body mass — paletteer (scico batlow)",
       x = "Bill length (mm)", y = "Bill depth (mm)",
       color = "Mass (g)") +
  theme_classic()

scico

scico provides perceptually uniform, colorblind-safe scientific colour maps developed by Fabio Crameri.

When to use: earth science, oceanography, or any continuous data where perceptual uniformity is critical.

Code

ggplot(faithfuld, aes(waiting, eruptions, fill = density)) +
  geom_tile() +
  scico::scale_fill_scico(palette = "roma") +
  labs(title = "Old Faithful — scico roma") +
  theme_classic()

Code

ggplot(penguins, aes(x = species, y = flipper_length_mm,
                      fill = species)) +
  geom_boxplot(na.rm = TRUE, show.legend = FALSE) +
  scico::scale_fill_scico_d(palette = "batlow") +
  labs(title = "Flipper length — scico batlow (discrete)",
       x = "Species", y = "Flipper length (mm)") +
  theme_classic()

MetBrewer

MetBrewer offers palettes inspired by works at the Metropolitan Museum of Art. They are visually striking and still colorblind-friendly.

When to use: presentations, posters, or publications where you want an artistic aesthetic without sacrificing readability.

Code

ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm,
                      color = species)) +
  geom_point(size = 2, na.rm = TRUE) +
  MetBrewer::scale_color_met_d("Lakota") +
  labs(title = "Species — MetBrewer Lakota",
       x = "Bill length (mm)", y = "Bill depth (mm)") +
  theme_classic()

Code

ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g,
                      color = bill_depth_mm)) +
  geom_point(size = 2, na.rm = TRUE) +
  MetBrewer::scale_color_met_c("Hiroshige") +
  labs(title = "Bill depth — MetBrewer Hiroshige",
       x = "Bill length (mm)", y = "Body mass (g)",
       color = "Bill depth (mm)") +
  theme_classic()

wesanderson

wesanderson provides palettes inspired by the films of Wes Anderson — charming pastels and earthy tones.

When to use: informal presentations, blog graphics, or any context where personality is a plus. These are primarily discrete palettes.

Code

ggplot(penguins, aes(x = species, y = body_mass_g, fill = species)) +
  geom_boxplot(na.rm = TRUE, show.legend = FALSE) +
  scale_fill_manual(values = wes_palette("Darjeeling1", 3)) +
  labs(title = "Body mass — wesanderson Darjeeling1",
       x = "Species", y = "Body mass (g)") +
  theme_classic()

Code

ggplot(penguins, aes(x = bill_length_mm, y = flipper_length_mm,
                      color = island)) +
  geom_point(size = 2, na.rm = TRUE) +
  scale_color_manual(values = wes_palette("GrandBudapest1", 3)) +
  labs(title = "Islands — wesanderson GrandBudapest1",
       x = "Bill length (mm)", y = "Flipper length (mm)") +
  theme_classic()

ggsci

ggsci provides color palettes from scientific journals and sci-fi franchises (JAMA, Lancet, NEJM, Nature, Star Trek, and more).

When to use: journal submissions where you want colors that match the publication’s own figures, or when you need a conservative, professional palette.

Code

ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g,
                      color = species)) +
  geom_point(size = 2, na.rm = TRUE) +
  ggsci::scale_color_jco() +
  labs(title = "Species — ggsci JCO",
       x = "Bill length (mm)", y = "Body mass (g)") +
  theme_classic()

Code

ggplot(penguins, aes(x = species, fill = island)) +
  geom_bar(position = "dodge") +
  ggsci::scale_fill_nejm() +
  labs(title = "Island counts by species — ggsci NEJM",
       x = "Species", y = "Count") +
  theme_classic()

rcartocolor

rcartocolor provides the CARTOColors palettes, designed for cartography. They include sequential, diverging, and qualitative schemes that work well in maps and standard plots alike.

When to use: maps, geographic data, or when you want palettes designed for spatial visualization.

Code

ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm,
                      color = species)) +
  geom_point(size = 2, na.rm = TRUE) +
  rcartocolor::scale_color_carto_d(palette = "Vivid") +
  labs(title = "Species — rcartocolor Vivid",
       x = "Bill length (mm)", y = "Bill depth (mm)") +
  theme_classic()

Code

ggplot(faithfuld, aes(waiting, eruptions, fill = density)) +
  geom_tile() +
  rcartocolor::scale_fill_carto_c(palette = "SunsetDark") +
  labs(title = "Old Faithful — rcartocolor SunsetDark") +
  theme_classic()

khroma

khroma provides colour schemes for scientific data visualization, with a strong focus on accessibility. All palettes are designed to be distinct for people with colour vision deficiencies.

When to use: scientific figures where accessibility is non-negotiable and you want assurance the palette has been tested for CVD safety.

Code

ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g,
                      color = species)) +
  geom_point(size = 2, na.rm = TRUE) +
  khroma::scale_color_bright() +
  labs(title = "Species — khroma bright",
       x = "Bill length (mm)", y = "Body mass (g)") +
  theme_classic()

Code

ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm,
                      color = body_mass_g)) +
  geom_point(size = 2, na.rm = TRUE) +
  khroma::scale_color_batlow() +
  labs(title = "Body mass — khroma batlow",
       x = "Bill length (mm)", y = "Bill depth (mm)",
       color = "Mass (g)") +
  theme_classic()

Handling NA Colors

Missing values appear in almost every real dataset. By default ggplot2 maps NA to grey, but you can control this.

Code

# Create some NAs for demonstration
penguins_na <- penguins |>
  mutate(species_na = if_else(row_number() %% 10 == 0, NA_character_, as.character(species)))

ggplot(penguins_na, aes(x = bill_length_mm, y = body_mass_g,
                         color = species_na)) +
  geom_point(size = 2, na.rm = TRUE) +
  scale_color_viridis_d(na.value = "red") +
  labs(title = "NA values shown in red",
       x = "Bill length (mm)", y = "Body mass (g)",
       color = "Species") +
  theme_classic()

Use na.value = inside any scale_color_*() or scale_fill_*() call to set the NA color. Common choices:

"grey50" — subtle, does not draw attention
"red" or "black" — highlights missing data for QC
NA — removes NA points from the legend entirely

Practical Rules for Number of Colors

The 6–8 Rule

Humans can reliably distinguish roughly 6 to 8 hues at a glance. Beyond that, colors start to blur together, especially in scatterplots where points overlap. Here is what happens when you push it.

Code

# mtcars has many cylinder + gear combos — let's create a busy variable
mtcars_busy <- mtcars |>
  mutate(car_name = rownames(mtcars))

ggplot(mtcars_busy, aes(x = wt, y = mpg, color = car_name)) +
  geom_point(size = 3) +
  labs(title = "Too many categories — the legend is unreadable",
       x = "Weight (1000 lbs)", y = "Miles per gallon") +
  theme_classic() +
  theme(legend.text = element_text(size = 5))

That is a mess. Here are your escape hatches:

Fix 1: Lump rare categories

Code

penguins_islands <- penguins |>
  count(island, species) |>
  mutate(label = paste(island, species, sep = " — "))

# Works fine with a manageable number of groups
ggplot(penguins_islands, aes(x = reorder(label, n), y = n, fill = island)) +
  geom_col(show.legend = TRUE) +
  coord_flip() +
  scale_fill_brewer(palette = "Set2") +
  labs(title = "Lumped into a few groups",
       x = NULL, y = "Count", fill = "Island") +
  theme_classic()

Fix 2: Facet instead of coloring

Code

ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g)) +
  geom_point(size = 1.5, na.rm = TRUE, color = "steelblue") +
  facet_wrap(~ species) +
  labs(title = "Faceting removes the need for color",
       x = "Bill length (mm)", y = "Body mass (g)") +
  theme_classic()

Fix 3: Use shape or linetype in addition to color

Code

ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g,
                      color = species, shape = species)) +
  geom_point(size = 2, na.rm = TRUE) +
  scale_color_brewer(palette = "Dark2") +
  labs(title = "Shape + color = double encoding",
       x = "Bill length (mm)", y = "Body mass (g)") +
  theme_classic()

Fix 4: Highlight one group, grey the rest

Code

penguins_hl <- penguins |>
  mutate(highlight = if_else(species == "Chinstrap", "Chinstrap", "Other"))

ggplot(penguins_hl, aes(x = bill_length_mm, y = body_mass_g,
                         color = highlight)) +
  geom_point(size = 2, na.rm = TRUE) +
  scale_color_manual(values = c("Chinstrap" = "#E63946", "Other" = "grey70")) +
  labs(title = "Highlighting one group against the rest",
       x = "Bill length (mm)", y = "Body mass (g)",
       color = NULL) +
  theme_classic()

Accessibility: Contrast & Colorblind-Friendly Design

Approximately 8% of men and 0.5% of women have some form of color vision deficiency (CVD). Designing for accessibility is not optional — it is good science.

Key Principles

Never rely on color alone. Pair color with shape, size, or labels.
Use perceptually uniform palettes. Viridis, scico, and khroma are safe bets.
Avoid red-green contrasts. The most common CVD type (deuteranopia) confuses red and green.
Test your palette. The colorspace package can simulate what your plot looks like under different CVD types.

Simulating Color Vision Deficiency

Code

# Create a base plot
p_base <- ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g,
                                color = species)) +
  geom_point(size = 2, na.rm = TRUE) +
  scale_color_manual(values = c("Adelie" = "#E63946",
                                 "Chinstrap" = "#2A9D8F",
                                 "Gentoo" = "#264653")) +
  labs(x = "Bill length (mm)", y = "Body mass (g)") +
  theme_classic()

# Original
p_base + labs(title = "Original")

Code

# Simulate deuteranopia (red-green deficiency)
# cvd_emulator() expects a file path, so save the plot to a temp file first
tmp_plot <- tempfile(fileext = ".png")
ggsave(tmp_plot, p_base + labs(title = "Simulated deuteranopia"),
       width = 7, height = 5, dpi = 150)
colorspace::cvd_emulator(tmp_plot)

Convert deutan          -> /var/folders/03/d3dmjds91_vckyhkgdt677300000gn/T//RtmpSVvF9F/deutan_file7e027e5356fa.png
Convert protan          -> /var/folders/03/d3dmjds91_vckyhkgdt677300000gn/T//RtmpSVvF9F/protan_file7e027e5356fa.png
Convert tritan          -> /var/folders/03/d3dmjds91_vckyhkgdt677300000gn/T//RtmpSVvF9F/tritan_file7e027e5356fa.png
Convert desaturate      -> /var/folders/03/d3dmjds91_vckyhkgdt677300000gn/T//RtmpSVvF9F/desaturate_file7e027e5356fa.png

If the simulated plot makes it hard to distinguish groups, switch to a CVD-safe palette such as viridis, "Okabe-Ito" (khroma), or Brewer’s qualitative palettes.

A Safer Version

Code

ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g,
                      color = species, shape = species)) +
  geom_point(size = 2, na.rm = TRUE) +
  scale_color_viridis_d(option = "cividis") +
  labs(title = "CVD-safe: viridis cividis + shape encoding",
       x = "Bill length (mm)", y = "Body mass (g)") +
  theme_classic()

Common Pitfalls

Pitfall 1: The Rainbow Palette

Rainbow (jet) palettes are not perceptually uniform — yellow appears brighter than blue, creating false visual emphasis. They also fail under grayscale printing and for colorblind viewers.

Code

# Don't do this
ggplot(faithfuld, aes(waiting, eruptions, fill = density)) +
  geom_tile() +
  scale_fill_gradientn(colours = rainbow(256)) +
  labs(title = "Rainbow palette — avoid this") +
  theme_classic()

Code

# Do this instead
ggplot(faithfuld, aes(waiting, eruptions, fill = density)) +
  geom_tile() +
  scale_fill_viridis_c() +
  labs(title = "Viridis — perceptually uniform") +
  theme_classic()

Pitfall 2: Low-Contrast Palettes

Pastel-on-white palettes look washed out on screens and vanish when printed.

Code

# Low contrast
ggplot(penguins, aes(x = species, y = body_mass_g, fill = species)) +
  geom_boxplot(na.rm = TRUE, show.legend = FALSE) +
  scale_fill_manual(values = c("#FFF5F5", "#F5FFF5", "#F5F5FF")) +
  labs(title = "Low contrast — hard to read") +
  theme_classic()

Code

# Better contrast
ggplot(penguins, aes(x = species, y = body_mass_g, fill = species)) +
  geom_boxplot(na.rm = TRUE, show.legend = FALSE) +
  scale_fill_brewer(palette = "Set2") +
  labs(title = "Brewer Set2 — clear contrast") +
  theme_classic()

Pitfall 3: Diverging Scales on Non-Diverging Data

Diverging palettes imply a meaningful midpoint (zero, average, threshold). Using them for data without a natural center misleads the reader.

Code

# Misleading: diverging scale on body mass (no natural midpoint)
ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm,
                      color = body_mass_g)) +
  geom_point(size = 2, na.rm = TRUE) +
  scale_color_distiller(palette = "RdBu") +
  labs(title = "Diverging scale on non-diverging data — misleading",
       x = "Bill length (mm)", y = "Bill depth (mm)",
       color = "Mass (g)") +
  theme_classic()

Code

# Correct: sequential scale
ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm,
                      color = body_mass_g)) +
  geom_point(size = 2, na.rm = TRUE) +
  scale_color_viridis_c() +
  labs(title = "Sequential scale — appropriate for body mass",
       x = "Bill length (mm)", y = "Bill depth (mm)",
       color = "Mass (g)") +
  theme_classic()

Pitfall 4: Too Many Categories

We covered this above, but it bears repeating: if your legend has more than 8 entries, rethink your encoding strategy. Lump, facet, highlight, or use a different visual channel.

Palette Checklist

Before you finalize a figure, run through this checklist:

#	Question	Action if “No”
1	Is the variable continuous or discrete?	Switch scale type
2	Does the palette type match the data? (sequential / diverging / qualitative)	Change palette family
3	Are there 8 or fewer color categories?	Lump, facet, or highlight
4	Is the palette colorblind-safe?	Use viridis, scico, or khroma
5	Does the plot read in grayscale?	Use a perceptually uniform palette
6	Is there enough contrast against the background?	Darken colors or switch palette
7	Are NAs handled intentionally?	Set `na.value =` explicitly
8	Is color the only encoding?	Add shape, size, or labels as backup

Quick Reference: Which Package When?

Package	Best for	Continuous	Discrete
viridis	Safe default, heatmaps	Yes	Yes
RColorBrewer	Publication-ready, classic	Yes	Yes
colorspace	Custom HCL palettes, diagnostics	Yes	Yes
paletteer	Browsing many packages at once	Yes	Yes
scico	Scientific maps, earth science	Yes	Yes
MetBrewer	Artistic, striking figures	Yes	Yes
wesanderson	Fun, informal graphics	No	Yes
ggsci	Journal-style figures	No	Yes
rcartocolor	Cartography, spatial data	Yes	Yes
khroma	Accessibility-first science	Yes	Yes

Wrapping Up

There is no single “best” palette — but there are wrong ones. Start with viridis as a safe default, reach for RColorBrewer when you need classic publication palettes, and explore MetBrewer, scico, or khroma when you want something that is both beautiful and accessible. Always test for colorblind safety, keep your categories under 8, and never rely on color alone.

Happy plotting!

--- title: "Everything Palettes in R: Viridis, Brewer, and Beyond" subtitle: "A comprehensive guide to color palettes in ggplot2" execute: warning: false message: false author: "Noah Weidig" date: "2026-02-24" categories: [code, ggplot2, dataviz, color] description: "Master color palettes in ggplot2 — from viridis and RColorBrewer to MetBrewer, wesanderson, and more. Learn when to use continuous vs discrete scales, how to design for colorblind accessibility, and how to avoid common pitfalls." toc: true toc-depth: 2 code-fold: show --- Color can make or break a figure. The right palette draws attention to the story in your data; the wrong one buries it under visual noise. In this post we will walk through the major palette ecosystems available in R, learn when to reach for each one, and pick up practical rules that keep figures readable, accessible, and honest. # Setup We will use several packages throughout this tutorial. Install any you are missing, then load them all at once. ```{r} #| label: setup # Uncomment to install any missing packages # install.packages(c( # "ggplot2", "dplyr", "scales", "viridis", # "RColorBrewer", "colorspace", "paletteer", # "scico", "MetBrewer", "wesanderson", "ggsci", # "rcartocolor", "palmerpenguins", "khroma" # )) library(ggplot2) library(dplyr) library(scales) library(viridis) library(RColorBrewer) library(colorspace) library(paletteer) library(scico) library(MetBrewer) library(wesanderson) library(ggsci) library(rcartocolor) library(palmerpenguins) library(khroma) ``` We will rely on the **palmerpenguins** dataset for most examples. It gives us both categorical variables (`species`, `island`) and continuous ones (`bill_length_mm`, `body_mass_g`). ```{r} penguins <- palmerpenguins::penguins glimpse(penguins) ``` # Continuous vs Discrete Color Scales Before choosing a palette, decide whether the variable you are mapping to color is **continuous** (numeric) or **discrete** (categorical). In ggplot2: - **Continuous** variables use `scale_color_*()` / `scale_fill_*()` functions that interpolate between colors. - **Discrete** variables use functions that assign one distinct color per level. Many palette packages offer both variants. Here is a quick comparison. ```{r} #| label: continuous-vs-discrete # Continuous: bill length mapped to a gradient p_cont <- ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g, color = bill_length_mm)) + geom_point(size = 2, na.rm = TRUE) + scale_color_viridis_c() + labs(title = "Continuous scale", color = "Bill length (mm)") + theme_classic() # Discrete: species mapped to distinct colors p_disc <- ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g, color = species)) + geom_point(size = 2, na.rm = TRUE) + scale_color_viridis_d() + labs(title = "Discrete scale", color = "Species") + theme_classic() p_cont p_disc ``` **Rule of thumb:** continuous scales for numbers, discrete scales for categories. # Viridis (viridis / viridisLite) The **viridis** family is the gold standard for perceptually uniform, colorblind-safe palettes. The colors change smoothly in lightness and chroma, so a plot printed in grayscale still makes sense. **Sub-palettes:** `viridis`, `magma`, `inferno`, `plasma`, `cividis`, `rocket`, `mako`, `turbo`. **When to use:** any time you need a safe default for continuous data, heatmaps, or ordered categories. ```{r} #| label: viridis-continuous ggplot(faithfuld, aes(waiting, eruptions, fill = density)) + geom_tile() + scale_fill_viridis_c(option = "magma") + labs(title = "Old Faithful eruptions — viridis magma") + theme_classic() ``` ```{r} #| label: viridis-discrete ggplot(penguins, aes(x = species, y = flipper_length_mm, fill = species)) + geom_boxplot(na.rm = TRUE, show.legend = FALSE) + scale_fill_viridis_d(option = "cividis") + labs(title = "Flipper length by species — viridis cividis", x = "Species", y = "Flipper length (mm)") + theme_classic() ``` # RColorBrewer **RColorBrewer** provides three palette families designed by cartographer Cynthia Brewer: | Type | Purpose | Example palettes | |------|---------|-----------------| | Sequential | Low-to-high values | Blues, YlOrRd, Greens | | Diverging | Deviation from a midpoint | RdBu, BrBG, PiYG | | Qualitative | Unordered categories | Set2, Paired, Dark2 | **When to use:** you need a well-tested, publication-ready palette and want to choose the family explicitly. ```{r} #| label: brewer-overview # Display available palettes display.brewer.all(n = 8) ``` ```{r} #| label: brewer-sequential ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = body_mass_g)) + geom_point(size = 2, na.rm = TRUE) + scale_color_distiller(palette = "YlOrRd", direction = 1) + labs(title = "Body mass — Brewer YlOrRd (sequential)", x = "Bill length (mm)", y = "Bill depth (mm)", color = "Mass (g)") + theme_classic() ``` ```{r} #| label: brewer-qualitative ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + geom_point(size = 2, na.rm = TRUE) + scale_color_brewer(palette = "Set2") + labs(title = "Species — Brewer Set2 (qualitative)", x = "Bill length (mm)", y = "Bill depth (mm)") + theme_classic() ``` # colorspace The **colorspace** package lets you build palettes from scratch using perceptual color spaces (HCL). It ships ready-made palettes and diagnostic tools. **When to use:** you want fine control over hue, chroma, and luminance or need to evaluate existing palettes. ```{r} #| label: colorspace-qualitative ggplot(penguins, aes(x = species, y = body_mass_g, fill = species)) + geom_violin(na.rm = TRUE, show.legend = FALSE) + scale_fill_discrete_qualitative(palette = "Dark 3") + labs(title = "Body mass — colorspace Dark 3", x = "Species", y = "Body mass (g)") + theme_classic() ``` ```{r} #| label: colorspace-sequential ggplot(faithfuld, aes(waiting, eruptions, fill = density)) + geom_tile() + scale_fill_continuous_sequential(palette = "ag_Sunset") + labs(title = "Old Faithful — colorspace ag_Sunset") + theme_classic() ``` A powerful diagnostic: `colorspace::specplot()` shows how lightness, chroma, and hue vary across a palette. ```{r} #| label: colorspace-specplot colorspace::specplot(colorspace::sequential_hcl(256, palette = "ag_Sunset")) ``` # paletteer **paletteer** is a meta-package that gives you a unified interface to hundreds of palettes from dozens of packages. **When to use:** you want to browse or switch between palettes without loading each individual package. ```{r} #| label: paletteer-discrete ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g, color = species)) + geom_point(size = 2, na.rm = TRUE) + paletteer::scale_color_paletteer_d("ggsci::default_jco") + labs(title = "Species — paletteer (ggsci JCO)", x = "Bill length (mm)", y = "Body mass (g)") + theme_classic() ``` ```{r} #| label: paletteer-continuous ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = body_mass_g)) + geom_point(size = 2, na.rm = TRUE) + paletteer::scale_color_paletteer_c("scico::batlow", direction = -1) + labs(title = "Body mass — paletteer (scico batlow)", x = "Bill length (mm)", y = "Bill depth (mm)", color = "Mass (g)") + theme_classic() ``` # scico **scico** provides perceptually uniform, colorblind-safe scientific colour maps developed by Fabio Crameri. **When to use:** earth science, oceanography, or any continuous data where perceptual uniformity is critical. ```{r} #| label: scico-continuous ggplot(faithfuld, aes(waiting, eruptions, fill = density)) + geom_tile() + scico::scale_fill_scico(palette = "roma") + labs(title = "Old Faithful — scico roma") + theme_classic() ``` ```{r} #| label: scico-discrete ggplot(penguins, aes(x = species, y = flipper_length_mm, fill = species)) + geom_boxplot(na.rm = TRUE, show.legend = FALSE) + scico::scale_fill_scico_d(palette = "batlow") + labs(title = "Flipper length — scico batlow (discrete)", x = "Species", y = "Flipper length (mm)") + theme_classic() ``` # MetBrewer **MetBrewer** offers palettes inspired by works at the Metropolitan Museum of Art. They are visually striking and still colorblind-friendly. **When to use:** presentations, posters, or publications where you want an artistic aesthetic without sacrificing readability. ```{r} #| label: metbrewer-discrete ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + geom_point(size = 2, na.rm = TRUE) + MetBrewer::scale_color_met_d("Lakota") + labs(title = "Species — MetBrewer Lakota", x = "Bill length (mm)", y = "Bill depth (mm)") + theme_classic() ``` ```{r} #| label: metbrewer-continuous ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g, color = bill_depth_mm)) + geom_point(size = 2, na.rm = TRUE) + MetBrewer::scale_color_met_c("Hiroshige") + labs(title = "Bill depth — MetBrewer Hiroshige", x = "Bill length (mm)", y = "Body mass (g)", color = "Bill depth (mm)") + theme_classic() ``` # wesanderson **wesanderson** provides palettes inspired by the films of Wes Anderson — charming pastels and earthy tones. **When to use:** informal presentations, blog graphics, or any context where personality is a plus. These are primarily discrete palettes. ```{r} #| label: wesanderson-plot ggplot(penguins, aes(x = species, y = body_mass_g, fill = species)) + geom_boxplot(na.rm = TRUE, show.legend = FALSE) + scale_fill_manual(values = wes_palette("Darjeeling1", 3)) + labs(title = "Body mass — wesanderson Darjeeling1", x = "Species", y = "Body mass (g)") + theme_classic() ``` ```{r} #| label: wesanderson-scatter ggplot(penguins, aes(x = bill_length_mm, y = flipper_length_mm, color = island)) + geom_point(size = 2, na.rm = TRUE) + scale_color_manual(values = wes_palette("GrandBudapest1", 3)) + labs(title = "Islands — wesanderson GrandBudapest1", x = "Bill length (mm)", y = "Flipper length (mm)") + theme_classic() ``` # ggsci **ggsci** provides color palettes from scientific journals and sci-fi franchises (JAMA, Lancet, NEJM, Nature, Star Trek, and more). **When to use:** journal submissions where you want colors that match the publication's own figures, or when you need a conservative, professional palette. ```{r} #| label: ggsci-jco ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g, color = species)) + geom_point(size = 2, na.rm = TRUE) + ggsci::scale_color_jco() + labs(title = "Species — ggsci JCO", x = "Bill length (mm)", y = "Body mass (g)") + theme_classic() ``` ```{r} #| label: ggsci-nejm ggplot(penguins, aes(x = species, fill = island)) + geom_bar(position = "dodge") + ggsci::scale_fill_nejm() + labs(title = "Island counts by species — ggsci NEJM", x = "Species", y = "Count") + theme_classic() ``` # rcartocolor **rcartocolor** provides the CARTOColors palettes, designed for cartography. They include sequential, diverging, and qualitative schemes that work well in maps and standard plots alike. **When to use:** maps, geographic data, or when you want palettes designed for spatial visualization. ```{r} #| label: rcartocolor-qualitative ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + geom_point(size = 2, na.rm = TRUE) + rcartocolor::scale_color_carto_d(palette = "Vivid") + labs(title = "Species — rcartocolor Vivid", x = "Bill length (mm)", y = "Bill depth (mm)") + theme_classic() ``` ```{r} #| label: rcartocolor-continuous ggplot(faithfuld, aes(waiting, eruptions, fill = density)) + geom_tile() + rcartocolor::scale_fill_carto_c(palette = "SunsetDark") + labs(title = "Old Faithful — rcartocolor SunsetDark") + theme_classic() ``` # khroma **khroma** provides colour schemes for scientific data visualization, with a strong focus on accessibility. All palettes are designed to be distinct for people with colour vision deficiencies. **When to use:** scientific figures where accessibility is non-negotiable and you want assurance the palette has been tested for CVD safety. ```{r} #| label: khroma-discrete ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g, color = species)) + geom_point(size = 2, na.rm = TRUE) + khroma::scale_color_bright() + labs(title = "Species — khroma bright", x = "Bill length (mm)", y = "Body mass (g)") + theme_classic() ``` ```{r} #| label: khroma-continuous ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = body_mass_g)) + geom_point(size = 2, na.rm = TRUE) + khroma::scale_color_batlow() + labs(title = "Body mass — khroma batlow", x = "Bill length (mm)", y = "Bill depth (mm)", color = "Mass (g)") + theme_classic() ``` # Handling NA Colors Missing values appear in almost every real dataset. By default ggplot2 maps `NA` to grey, but you can control this. ```{r} #| label: na-colors # Create some NAs for demonstration penguins_na <- penguins |> mutate(species_na = if_else(row_number() %% 10 == 0, NA_character_, as.character(species))) ggplot(penguins_na, aes(x = bill_length_mm, y = body_mass_g, color = species_na)) + geom_point(size = 2, na.rm = TRUE) + scale_color_viridis_d(na.value = "red") + labs(title = "NA values shown in red", x = "Bill length (mm)", y = "Body mass (g)", color = "Species") + theme_classic() ``` Use `na.value =` inside any `scale_color_*()` or `scale_fill_*()` call to set the NA color. Common choices: - `"grey50"` — subtle, does not draw attention - `"red"` or `"black"` — highlights missing data for QC - `NA` — removes NA points from the legend entirely # Practical Rules for Number of Colors ## The 6–8 Rule Humans can reliably distinguish roughly **6 to 8** hues at a glance. Beyond that, colors start to blur together, especially in scatterplots where points overlap. Here is what happens when you push it. ```{r} #| label: too-many-colors # mtcars has many cylinder + gear combos — let's create a busy variable mtcars_busy <- mtcars |> mutate(car_name = rownames(mtcars)) ggplot(mtcars_busy, aes(x = wt, y = mpg, color = car_name)) + geom_point(size = 3) + labs(title = "Too many categories — the legend is unreadable", x = "Weight (1000 lbs)", y = "Miles per gallon") + theme_classic() + theme(legend.text = element_text(size = 5)) ``` That is a mess. Here are your escape hatches: ### Fix 1: Lump rare categories ```{r} #| label: fix-lump penguins_islands <- penguins |> count(island, species) |> mutate(label = paste(island, species, sep = " — ")) # Works fine with a manageable number of groups ggplot(penguins_islands, aes(x = reorder(label, n), y = n, fill = island)) + geom_col(show.legend = TRUE) + coord_flip() + scale_fill_brewer(palette = "Set2") + labs(title = "Lumped into a few groups", x = NULL, y = "Count", fill = "Island") + theme_classic() ``` ### Fix 2: Facet instead of coloring ```{r} #| label: fix-facet ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g)) + geom_point(size = 1.5, na.rm = TRUE, color = "steelblue") + facet_wrap(~ species) + labs(title = "Faceting removes the need for color", x = "Bill length (mm)", y = "Body mass (g)") + theme_classic() ``` ### Fix 3: Use shape or linetype in addition to color ```{r} #| label: fix-shape ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g, color = species, shape = species)) + geom_point(size = 2, na.rm = TRUE) + scale_color_brewer(palette = "Dark2") + labs(title = "Shape + color = double encoding", x = "Bill length (mm)", y = "Body mass (g)") + theme_classic() ``` ### Fix 4: Highlight one group, grey the rest ```{r} #| label: fix-highlight penguins_hl <- penguins |> mutate(highlight = if_else(species == "Chinstrap", "Chinstrap", "Other")) ggplot(penguins_hl, aes(x = bill_length_mm, y = body_mass_g, color = highlight)) + geom_point(size = 2, na.rm = TRUE) + scale_color_manual(values = c("Chinstrap" = "#E63946", "Other" = "grey70")) + labs(title = "Highlighting one group against the rest", x = "Bill length (mm)", y = "Body mass (g)", color = NULL) + theme_classic() ``` # Accessibility: Contrast & Colorblind-Friendly Design Approximately 8% of men and 0.5% of women have some form of color vision deficiency (CVD). Designing for accessibility is not optional — it is good science. ## Key Principles 1. **Never rely on color alone.** Pair color with shape, size, or labels. 2. **Use perceptually uniform palettes.** Viridis, scico, and khroma are safe bets. 3. **Avoid red-green contrasts.** The most common CVD type (deuteranopia) confuses red and green. 4. **Test your palette.** The `colorspace` package can simulate what your plot looks like under different CVD types. ## Simulating Color Vision Deficiency ```{r} #| label: cvd-simulation # Create a base plot p_base <- ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g, color = species)) + geom_point(size = 2, na.rm = TRUE) + scale_color_manual(values = c("Adelie" = "#E63946", "Chinstrap" = "#2A9D8F", "Gentoo" = "#264653")) + labs(x = "Bill length (mm)", y = "Body mass (g)") + theme_classic() # Original p_base + labs(title = "Original") # Simulate deuteranopia (red-green deficiency) # cvd_emulator() expects a file path, so save the plot to a temp file first tmp_plot <- tempfile(fileext = ".png") ggsave(tmp_plot, p_base + labs(title = "Simulated deuteranopia"), width = 7, height = 5, dpi = 150) colorspace::cvd_emulator(tmp_plot) ``` If the simulated plot makes it hard to distinguish groups, switch to a CVD-safe palette such as viridis, `"Okabe-Ito"` (khroma), or Brewer's qualitative palettes. ## A Safer Version ```{r} #| label: cvd-safe ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g, color = species, shape = species)) + geom_point(size = 2, na.rm = TRUE) + scale_color_viridis_d(option = "cividis") + labs(title = "CVD-safe: viridis cividis + shape encoding", x = "Bill length (mm)", y = "Body mass (g)") + theme_classic() ``` # Common Pitfalls ## Pitfall 1: The Rainbow Palette Rainbow (jet) palettes are not perceptually uniform — yellow appears brighter than blue, creating false visual emphasis. They also fail under grayscale printing and for colorblind viewers. ```{r} #| label: pitfall-rainbow # Don't do this ggplot(faithfuld, aes(waiting, eruptions, fill = density)) + geom_tile() + scale_fill_gradientn(colours = rainbow(256)) + labs(title = "Rainbow palette — avoid this") + theme_classic() # Do this instead ggplot(faithfuld, aes(waiting, eruptions, fill = density)) + geom_tile() + scale_fill_viridis_c() + labs(title = "Viridis — perceptually uniform") + theme_classic() ``` ## Pitfall 2: Low-Contrast Palettes Pastel-on-white palettes look washed out on screens and vanish when printed. ```{r} #| label: pitfall-low-contrast # Low contrast ggplot(penguins, aes(x = species, y = body_mass_g, fill = species)) + geom_boxplot(na.rm = TRUE, show.legend = FALSE) + scale_fill_manual(values = c("#FFF5F5", "#F5FFF5", "#F5F5FF")) + labs(title = "Low contrast — hard to read") + theme_classic() # Better contrast ggplot(penguins, aes(x = species, y = body_mass_g, fill = species)) + geom_boxplot(na.rm = TRUE, show.legend = FALSE) + scale_fill_brewer(palette = "Set2") + labs(title = "Brewer Set2 — clear contrast") + theme_classic() ``` ## Pitfall 3: Diverging Scales on Non-Diverging Data Diverging palettes imply a meaningful midpoint (zero, average, threshold). Using them for data without a natural center misleads the reader. ```{r} #| label: pitfall-diverging # Misleading: diverging scale on body mass (no natural midpoint) ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = body_mass_g)) + geom_point(size = 2, na.rm = TRUE) + scale_color_distiller(palette = "RdBu") + labs(title = "Diverging scale on non-diverging data — misleading", x = "Bill length (mm)", y = "Bill depth (mm)", color = "Mass (g)") + theme_classic() # Correct: sequential scale ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = body_mass_g)) + geom_point(size = 2, na.rm = TRUE) + scale_color_viridis_c() + labs(title = "Sequential scale — appropriate for body mass", x = "Bill length (mm)", y = "Bill depth (mm)", color = "Mass (g)") + theme_classic() ``` ## Pitfall 4: Too Many Categories We covered this above, but it bears repeating: if your legend has more than 8 entries, rethink your encoding strategy. Lump, facet, highlight, or use a different visual channel. # Palette Checklist Before you finalize a figure, run through this checklist: | # | Question | Action if "No" | |---|----------|----------------| | 1 | Is the variable continuous or discrete? | Switch scale type | | 2 | Does the palette type match the data? (sequential / diverging / qualitative) | Change palette family | | 3 | Are there 8 or fewer color categories? | Lump, facet, or highlight | | 4 | Is the palette colorblind-safe? | Use viridis, scico, or khroma | | 5 | Does the plot read in grayscale? | Use a perceptually uniform palette | | 6 | Is there enough contrast against the background? | Darken colors or switch palette | | 7 | Are NAs handled intentionally? | Set `na.value =` explicitly | | 8 | Is color the only encoding? | Add shape, size, or labels as backup | # Quick Reference: Which Package When? | Package | Best for | Continuous | Discrete | |---------|----------|:----------:|:--------:| | **viridis** | Safe default, heatmaps | Yes | Yes | | **RColorBrewer** | Publication-ready, classic | Yes | Yes | | **colorspace** | Custom HCL palettes, diagnostics | Yes | Yes | | **paletteer** | Browsing many packages at once | Yes | Yes | | **scico** | Scientific maps, earth science | Yes | Yes | | **MetBrewer** | Artistic, striking figures | Yes | Yes | | **wesanderson** | Fun, informal graphics | No | Yes | | **ggsci** | Journal-style figures | No | Yes | | **rcartocolor** | Cartography, spatial data | Yes | Yes | | **khroma** | Accessibility-first science | Yes | Yes | # Wrapping Up There is no single "best" palette — but there are wrong ones. Start with **viridis** as a safe default, reach for **RColorBrewer** when you need classic publication palettes, and explore **MetBrewer**, **scico**, or **khroma** when you want something that is both beautiful and accessible. Always test for colorblind safety, keep your categories under 8, and never rely on color alone. Happy plotting!

Stay in the loop

Setup

Continuous vs Discrete Color Scales

Viridis (viridis / viridisLite)

RColorBrewer

colorspace

paletteer

scico

MetBrewer

wesanderson

ggsci

rcartocolor

khroma

Handling NA Colors

Practical Rules for Number of Colors

The 6–8 Rule

Fix 1: Lump rare categories

Fix 2: Facet instead of coloring

Fix 3: Use shape or linetype in addition to color

Fix 4: Highlight one group, grey the rest

Accessibility: Contrast & Colorblind-Friendly Design

Key Principles

Simulating Color Vision Deficiency

A Safer Version

Common Pitfalls

Pitfall 1: The Rainbow Palette

Pitfall 2: Low-Contrast Palettes

Pitfall 3: Diverging Scales on Non-Diverging Data

Pitfall 4: Too Many Categories

Palette Checklist

Quick Reference: Which Package When?

Wrapping Up