Color scales

Claus O. Wilke

2025-01-13

Uses of color in data visualization

1. Distinguish categories (qualitative)

Qualitative scale example

Palette name: Okabe-Ito

Qualitative scale example

Palette name: ColorBrewer Set1

Qualitative scale example

Palette name: ColorBrewer Set3

Uses of color in data visualization

1. Distinguish categories (qualitative)
2. Represent numeric values (sequential)

Sequential scale example

Palette name: Viridis

Sequential scale example

Palette name: Inferno

Sequential scale example

Palette name: Cividis

Uses of color in data visualization

1. Distinguish categories (qualitative)
2. Represent numeric values (sequential)
3. Represent numeric values (diverging)

Diverging scale example

Palette name: ColorBrewer PiYG

Diverging scale example

Palette name: Carto Earth

Diverging scale example

Palette name: Blue-Red

Uses of color in data visualization

1. Distinguish categories (qualitative)
2. Represent numeric values (sequential)
3. Represent numeric values (diverging)
4. Highlight

Highlight example

Palette name: Grays with accents

Highlight example

Palette name: Okabe Ito accent

Highlight example

Palette name: ColorBrewer accent

Color scales in ggplot2

Getting the data

The temps_months dataset:

temps_months <- read_csv("https://wilkelab.org/SDS366/datasets/tempnormals.csv") |>
  group_by(location, month_name) |>
  summarize(mean = mean(temperature)) |>
  mutate(
    month = factor(
      month_name,
      levels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")
    ),
    location = factor(location, levels = c("Death Valley", "Houston", "San Diego", "Chicago"))
  ) |>
  select(-month_name)

temps_months

# A tibble: 48 × 3
# Groups:   location [4]
   location  mean month
   <fct>    <dbl> <fct>
 1 Chicago   50.4 Apr  
 2 Chicago   74.1 Aug  
 3 Chicago   29   Dec  
 4 Chicago   28.9 Feb  
 5 Chicago   24.8 Jan  
 6 Chicago   75.8 Jul  
 7 Chicago   71.0 Jun  
 8 Chicago   38.8 Mar  
 9 Chicago   60.9 May  
10 Chicago   41.6 Nov  
# ℹ 38 more rows

Getting the data

The popgrowth dataset:

US_census <- read_csv("https://wilkelab.org/SDS366/datasets/US_census.csv")
US_regions <- read_csv("https://wilkelab.org/SDS366/datasets/US_regions.csv")

popgrowth <- left_join(US_census, US_regions) |>
    group_by(region, division, state) |>
    summarize(
      pop2000 = sum(pop2000, na.rm = TRUE),
      pop2010 = sum(pop2010, na.rm = TRUE),
      popgrowth = (pop2010-pop2000)/pop2000,
      .groups = "drop"
    ) |>
    mutate(region = factor(region, levels = c("West", "South", "Midwest", "Northeast")))

popgrowth

# A tibble: 51 × 6
   region  division           state      pop2000  pop2010 popgrowth
   <fct>   <chr>              <chr>        <dbl>    <dbl>     <dbl>
 1 Midwest East North Central Illinois  12419293 12830632   0.0331 
 2 Midwest East North Central Indiana    6080485  6483802   0.0663 
 3 Midwest East North Central Michigan   9938444  9883640  -0.00551
 4 Midwest East North Central Ohio      11353140 11536504   0.0162 
 5 Midwest East North Central Wisconsin  5363675  5686986   0.0603 
 6 Midwest West North Central Iowa       2926324  3046355   0.0410 
 7 Midwest West North Central Kansas     2688418  2853118   0.0613 
 8 Midwest West North Central Minnesota  4919479  5303925   0.0781 
 9 Midwest West North Central Missouri   5595211  5988927   0.0704 
10 Midwest West North Central Nebraska   1711263  1826341   0.0672 
# ℹ 41 more rows

ggplot2 color scale functions are a bit of a mess

Scale function	Aesthetic	Data type	Palette type
`scale_color_hue()`	`color`	discrete	qualitative

ggplot2 color scale functions are a bit of a mess

Scale function	Aesthetic	Data type	Palette type
`scale_color_hue()`	`color`	discrete	qualitative
`scale_fill_hue()`	`fill`	discrete	qualitative

ggplot2 color scale functions are a bit of a mess

Scale function	Aesthetic	Data type	Palette type
`scale_color_hue()`	`color`	discrete	qualitative
`scale_fill_hue()`	`fill`	discrete	qualitative
`scale_color_gradient()`	`color`	continuous	sequential

ggplot2 color scale functions are a bit of a mess

Scale function	Aesthetic	Data type	Palette type
`scale_color_hue()`	`color`	discrete	qualitative
`scale_fill_hue()`	`fill`	discrete	qualitative
`scale_color_gradient()`	`color`	continuous	sequential
`scale_color_gradient2()`	`color`	continuous	diverging

ggplot2 color scale functions are a bit of a mess

Scale function	Aesthetic	Data type	Palette type
`scale_color_hue()`	`color`	discrete	qualitative
`scale_fill_hue()`	`fill`	discrete	qualitative
`scale_color_gradient()`	`color`	continuous	sequential
`scale_color_gradient2()`	`color`	continuous	diverging
`scale_fill_viridis_c()`	`color`	continuous	sequential
`scale_fill_viridis_d()`	`fill`	discrete	sequential
`scale_color_brewer()`	`color`	discrete	qualitative, diverging, sequential
`scale_fill_brewer()`	`fill`	discrete	qualitative, diverging, sequential
`scale_color_distiller()`	`color`	continuous	qualitative, diverging, sequential

… and there are many many more

Examples

ggplot(temps_months, aes(x = month, y = location, fill = mean)) + 
  geom_tile(width = 0.95, height = 0.95) + 
  coord_fixed(expand = FALSE) +
  theme_classic()
  # no fill scale defined, default is scale_fill_gradient()

Examples

ggplot(temps_months, aes(x = month, y = location, fill = mean)) + 
  geom_tile(width = 0.95, height = 0.95) + 
  coord_fixed(expand = FALSE) +
  theme_classic() +
  scale_fill_gradient()

Examples

ggplot(temps_months, aes(x = month, y = location, fill = mean)) + 
  geom_tile(width = 0.95, height = 0.95) + 
  coord_fixed(expand = FALSE) +
  theme_classic() +
  scale_fill_viridis_c() # the _c stands for continuous

Examples

ggplot(temps_months, aes(x = month, y = location, fill = mean)) + 
  geom_tile(width = 0.95, height = 0.95) + 
  coord_fixed(expand = FALSE) +
  theme_classic() +
  scale_fill_viridis_c(option = "B", begin = 0.15)

Examples

ggplot(temps_months, aes(x = month, y = location, fill = mean)) + 
  geom_tile(width = 0.95, height = 0.95) + 
  coord_fixed(expand = FALSE) +
  theme_classic() +
  scale_fill_distiller(palette = "YlGnBu")

The colorspace package creates some order

Scale name: scale_<aesthetic>_<datatype>_<colorscale>()

<aesthetic>: name of the aesthetic (fill, color, colour)

<datatype>: type of variable plotted (discrete, continuous, binned)

<colorscale>: type of the color scale (qualitative, sequential, diverging, divergingx)

Scale function	Aesthetic	Data type	Palette type
`scale_color_discrete_qualitative()`	`color`	discrete	qualitative
`scale_fill_continuous_sequential()`	`fill`	continuous	sequential
`scale_colour_continous_divergingx()`	`colour`	continuous	diverging

Examples

ggplot(temps_months, aes(x = month, y = location, fill = mean)) + 
  geom_tile(width = 0.95, height = 0.95) + 
  coord_fixed(expand = FALSE) +
  theme_classic() +
  scale_fill_continuous_sequential(palette = "YlGnBu", rev = FALSE)

Examples

ggplot(temps_months, aes(x = month, y = location, fill = mean)) + 
  geom_tile(width = 0.95, height = 0.95) + 
  coord_fixed(expand = FALSE) +
  theme_classic() +
  scale_fill_continuous_sequential(palette = "Viridis", rev = FALSE)

Examples

ggplot(temps_months, aes(x = month, y = location, fill = mean)) + 
  geom_tile(width = 0.95, height = 0.95) + 
  coord_fixed(expand = FALSE) +
  theme_classic() +
  scale_fill_continuous_sequential(
    palette = "Inferno", begin = 0.15, rev = FALSE
  )

# all sequential palettes provided by the colorspace package
colorspace::hcl_palettes(type = "sequential", plot = TRUE)

# all diverging palettes provided by the colorspace package
colorspace::hcl_palettes(type = "diverging", plot = TRUE, n = 9)

# all divergingx palettes provided by the colorspace package
colorspace::divergingx_palettes(plot = TRUE, n = 9)

Setting colors manually for discrete, qualitative scales

Discrete, qualitative scales are best set manually

ggplot(popgrowth, aes(x = pop2000, y = popgrowth, color = region)) +
  geom_point() +
  scale_x_log10()
  # no color scale defined, default is scale_color_hue()

Discrete, qualitative scales are best set manually

ggplot(popgrowth, aes(x = pop2000, y = popgrowth, color = region)) +
  geom_point() +
  scale_x_log10() +
  scale_color_hue()

Discrete, qualitative scales are best set manually

library(ggthemes)  # for scale_color_colorblind()

ggplot(popgrowth, aes(x = pop2000, y = popgrowth, color = region)) +
  geom_point() +
  scale_x_log10() +
  scale_color_colorblind()  # uses Okabe-Ito colors

Discrete, qualitative scales are best set manually

ggplot(popgrowth, aes(x = pop2000, y = popgrowth, color = region)) +
  geom_point() +
  scale_x_log10() +
  scale_color_manual(
    values = c(West = "#E69F00", South = "#56B4E9",
               Midwest = "#009E73", Northeast = "#F0E442")
  )

Okabe-Ito RGB codes

Name	Hex code	R, G, B (0-255)
orange	#E69F00	230, 159, 0
sky blue	#56B4E9	86, 180, 233
bluish green	#009E73	0, 158, 115
yellow	#F0E442	240, 228, 66
blue	#0072B2	0, 114, 178
vermilion	#D55E00	213, 94, 0
reddish purple	#CC79A7	204, 121, 167
black	#000000	0, 0, 0

Color scales

Uses of color in data visualization

Qualitative scale example

Qualitative scale example

Qualitative scale example

Uses of color in data visualization

Sequential scale example

Sequential scale example

Sequential scale example

Uses of color in data visualization

Diverging scale example

Diverging scale example

Diverging scale example

Uses of color in data visualization

Highlight example

Highlight example

Highlight example

Color scales in ggplot2

Getting the data

Getting the data

ggplot2 color scale functions are a bit of a mess

ggplot2 color scale functions are a bit of a mess

ggplot2 color scale functions are a bit of a mess

ggplot2 color scale functions are a bit of a mess

ggplot2 color scale functions are a bit of a mess

Examples

Examples

Examples

Examples

Examples

The colorspace package creates some order

Examples

Examples

Examples

Setting colors manually for discrete, qualitative scales

Discrete, qualitative scales are best set manually

Discrete, qualitative scales are best set manually

Discrete, qualitative scales are best set manually

Discrete, qualitative scales are best set manually

Okabe-Ito RGB codes

Further reading