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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Color scales</title>
<meta charset="utf-8" />
<meta name="author" content="Claus O. Wilke" />
<script src="libs/header-attrs/header-attrs.js"></script>
<link href="libs/remark-css/default.css" rel="stylesheet" />
<link rel="stylesheet" href="Wilke-slides-theme.css" type="text/css" />
</head>
<body>
<textarea id="source">
class: center, middle, title-slide
.title[
# Color scales
]
.author[
### Claus O. Wilke
]
.date[
### last updated: 2024-02-05
]
---
# Uses of color in data visualization
--
<table style = "border: none; line-height: 2.5;">
<tr style = "background: white;">
<td style = "text-align: left; width: 50%;">
1. Distinguish categories (qualitative)
</td>
<td>
<img src = "color-scales_files/qualitative.png" width = 100% style = "text-align: right; vertical-align: middle"></img>
</td>
</tr>
</table>
---
# Qualitative scale example
.center.nogap[
<img src="color-scales_files/figure-html/popgrowth-vs-popsize-colored-1.svg" width="65%" />
]
Palette name: Okabe-Ito
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
# Qualitative scale example
.center.nogap[
<img src="color-scales_files/figure-html/popgrowth-vs-popsize-colored2-1.svg" width="65%" />
]
Palette name: ColorBrewer Set1
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
# Qualitative scale example
.center.nogap[
<img src="color-scales_files/figure-html/popgrowth-vs-popsize-colored3-1.svg" width="65%" />
]
Palette name: ColorBrewer Set3
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
# Uses of color in data visualization
<table style = "border: none; line-height: 2.5;">
<tr style = "background: white;">
<td style = "text-align: left; width: 50%;">
1. Distinguish categories (qualitative)
</td>
<td>
<img src = "color-scales_files/qualitative.png" width = 100% style = "text-align: right; vertical-align: middle"></img>
</td>
</tr>
<tr style = "background: white;">
<td style = "text-align: left;">
2. Represent numeric values (sequential)
</td>
<td>
<img src = "color-scales_files/sequential.png" width = 100% style = "text-align: right; vertical-align: middle"></img>
</td>
</tr>
</table>
---
# Sequential scale example
<br>
.center[
<img src="color-scales_files/figure-html/four-locations-temps-by-month-1.svg" width="90%" />
]
Palette name: Viridis
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
# Sequential scale example
<br>
.center[
<img src="color-scales_files/figure-html/four-locations-temps-by-month2-1.svg" width="90%" />
]
Palette name: Inferno
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
# Sequential scale example
<br>
.center[
<img src="color-scales_files/figure-html/four-locations-temps-by-month3-1.svg" width="90%" />
]
Palette name: Cividis
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
# Uses of color in data visualization
<table style = "border: none; line-height: 2.5;">
<tr style = "background: white;">
<td style = "text-align: left; width: 50%;">
1. Distinguish categories (qualitative)
</td>
<td>
<img src = "color-scales_files/qualitative.png" width = 100% style = "text-align: right; vertical-align: middle"></img>
</td>
</tr>
<tr style = "background: white;">
<td style = "text-align: left;">
2. Represent numeric values (sequential)
</td>
<td>
<img src = "color-scales_files/sequential.png" width = 100% style = "text-align: right; vertical-align: middle"></img>
</td>
</tr>
<tr style = "background: white;">
<td style = "text-align: left;">
3. Represent numeric values (diverging)
</td>
<td>
<img src = "color-scales_files/diverging.png" width = 100% style = "text-align: right; vertical-align: middle"></img>
</td>
</tr>
</table>
---
# Diverging scale example
.center.nogap[
<img src="color-scales_files/figure-html/forensic-correlations1-1.svg" width="35%" />
]
Palette name: ColorBrewer PiYG
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
# Diverging scale example
.center.nogap[
<img src="color-scales_files/figure-html/forensic-correlations2-1.svg" width="35%" />
]
Palette name: Carto Earth
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
# Diverging scale example
.center.nogap[
<img src="color-scales_files/figure-html/forensic-correlations3-1.svg" width="35%" />
]
Palette name: Blue-Red
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
# Uses of color in data visualization
<table style = "border: none; line-height: 2.5;">
<tr style = "background: white;">
<td style = "text-align: left; width: 50%;">
1. Distinguish categories (qualitative)
</td>
<td>
<img src = "color-scales_files/qualitative.png" width = 100% style = "text-align: right; vertical-align: middle;"></img>
</td>
</tr>
<tr style = "background: white;">
<td style = "text-align: left;">
2. Represent numeric values (sequential)
</td>
<td>
<img src = "color-scales_files/sequential.png" width = 100% style = "text-align: right; vertical-align: middle"></img>
</td>
</tr>
<tr style = "background: white;">
<td style = "text-align: left;">
3. Represent numeric values (diverging)
</td>
<td>
<img src = "color-scales_files/diverging.png" width = 100% style = "text-align: right; vertical-align: middle"></img>
</td>
</tr>
<tr style = "background: white;">
<td style = "text-align: left;">
4. Highlight
</td>
<td>
<img src = "color-scales_files/highlight.png" width = 100% style = "text-align: right; vertical-align: middle"></img>
</td>
</tr>
</table>
---
# Highlight example
.center[
<img src="color-scales_files/figure-html/Aus-athletes-track-1.svg" width="60%" />
]
Palette name: Grays with accents
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
# Highlight example
.center[
<img src="color-scales_files/figure-html/Aus-athletes-track2-1.svg" width="60%" />
]
Palette name: Okabe-Ito accent
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
# Highlight example
.center[
<img src="color-scales_files/figure-html/Aus-athletes-track3-1.svg" width="60%" />
]
Palette name: ColorBrewer accent
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
# Uses of color in data visualization
<table style = "border: none; line-height: 2.5;">
<tr style = "background: white;">
<td style = "text-align: left; width: 50%;">
1. Distinguish categories (qualitative)
</td>
<td>
<img src = "color-scales_files/qualitative.png" width = 100% style = "text-align: right; vertical-align: middle;"></img>
</td>
</tr>
<tr style = "background: white;">
<td style = "text-align: left;">
2. Represent numeric values (sequential)
</td>
<td>
<img src = "color-scales_files/sequential.png" width = 100% style = "text-align: right; vertical-align: middle"></img>
</td>
</tr>
<tr style = "background: white;">
<td style = "text-align: left;">
3. Represent numeric values (diverging)
</td>
<td>
<img src = "color-scales_files/diverging.png" width = 100% style = "text-align: right; vertical-align: middle"></img>
</td>
</tr>
<tr style = "background: white;">
<td style = "text-align: left;">
4. Highlight
</td>
<td>
<img src = "color-scales_files/highlight.png" width = 100% style = "text-align: right; vertical-align: middle"></img>
</td>
</tr>
</table>
[//]: # "segment ends here"
---
class: center middle
## Color scales in **ggplot2**
---
## Getting the data
The `temps_months` dataset:
.tiny-font[
```r
temps_months <- read_csv("https://wilkelab.org/SDS375/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)
```
]
---
## Getting the data
The `popgrowth` dataset:
.tiny-font[
```r
US_census <- read_csv("https://wilkelab.org/SDS375/datasets/US_census.csv")
US_regions <- read_csv("https://wilkelab.org/SDS375/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")))
```
]
---
## **ggplot2** color scale functions are a bit of a mess
--
.small-font.center[
Scale function | Aesthetic &nbsp;&nbsp;&nbsp; | Data type | Palette type
:----------- | :---------- | :------------ | :------------
`scale_color_hue()` &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | `color` | discrete &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | qualitative &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
]
---
## **ggplot2** color scale functions are a bit of a mess
.small-font.center[
Scale function | Aesthetic &nbsp;&nbsp;&nbsp; | Data type | Palette type
:----------- | :---------- | :------------ | :------------
`scale_color_hue()` &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | `color` | discrete &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | qualitative &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
`scale_fill_hue()` | `fill ` | discrete | qualitative
]
---
## **ggplot2** color scale functions are a bit of a mess
.small-font.center[
Scale function | Aesthetic &nbsp;&nbsp;&nbsp; | Data type | Palette type
:----------- | :---------- | :------------ | :------------
`scale_color_hue()` &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | `color` | discrete &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | qualitative &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
`scale_fill_hue()` | `fill ` | discrete | qualitative
`scale_color_gradient()` | `color` | continuous | sequential
]
---
## **ggplot2** color scale functions are a bit of a mess
.small-font.center[
Scale function | Aesthetic &nbsp;&nbsp;&nbsp; | Data type | Palette type
:----------- | :---------- | :------------ | :------------
`scale_color_hue()` &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | `color` | discrete &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | qualitative &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
`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
.small-font.center[
Scale function | Aesthetic &nbsp;&nbsp;&nbsp; | Data type | Palette type
:----------- | :---------- | :------------ | :------------
`scale_color_hue()` &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | `color` | discrete &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | qualitative &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
`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
.small-font[
```r
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()
```
]
.center[
<img src="color-scales_files/figure-html/temps-tiles1-out-1.svg" width="90%" />
]
---
## Examples
.small-font[
```r
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()
```
]
.center[
<img src="color-scales_files/figure-html/temps-tiles2-out-1.svg" width="90%" />
]
---
## Examples
.small-font[
```r
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()
```
]
.center[
<img src="color-scales_files/figure-html/temps-tiles3-out-1.svg" width="90%" />
]
---
## Examples
.small-font[
```r
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)
```
]
.center[
<img src="color-scales_files/figure-html/temps-tiles4-out-1.svg" width="90%" />
]
---
## Examples
.small-font[
```r
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")
```
]
.center[
<img src="color-scales_files/figure-html/temps-tiles5-out-1.svg" width="90%" />
]
---
## The **colorspace** package creates some order
Scale name: `scale_<aesthetic>_<datatype>_<colorscale>()`
--
.small-font[
- `<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`)
]
--
.small-font.center[
Scale function | Aesthetic &nbsp;&nbsp;&nbsp; | Data type | Palette type &nbsp;&nbsp;&nbsp;
:----------- | :-------- | :--------- | :------------
`scale_color_discrete_qualitative()` | `color` | discrete | qualitative
`scale_fill_continuous_sequential()` | `fill` | continuous | sequential
`scale_colour_continous_divergingx()` | `colour` | continuous | diverging
]
---
## Examples
.tiny-font[
```r
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)
```
]
.center[
<img src="color-scales_files/figure-html/temps-tiles6-out-1.svg" width="90%" />
]
---
## Examples
.tiny-font[
```r
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)
```
]
.center[
<img src="color-scales_files/figure-html/temps-tiles7-out-1.svg" width="90%" />
]
---
## Examples
.tiny-font[
```r
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)
```
]
.center[
<img src="color-scales_files/figure-html/temps-tiles8-out-1.svg" width="90%" />
]
---
.tiny-font.nogap[
```r
colorspace::hcl_palettes(type = "sequential", plot = TRUE) # all sequential palettes
```
.center[
<img src="color-scales_files/figure-html/colorspace-palettes-seq-out-1.svg" width="95%" />
]
]
---
.tiny-font.nogap[
```r
colorspace::hcl_palettes(type = "diverging", plot = TRUE, n = 9) # all diverging palettes
```
.center[
<img src="color-scales_files/figure-html/colorspace-palettes-div-out-1.svg" width="55%" />
]
]
---
.tiny-font.nogap[
```r
colorspace::divergingx_palettes(plot = TRUE, n = 9) # all divergingx palettes
```
.center[
<img src="color-scales_files/figure-html/colorspace-palettes-divx-out-1.svg" width="75%" />
]
]
[//]: # "segment ends here"
---
class: center middle
## Setting colors manually for discrete, qualitative scales
---
## Discrete, qualitative scales are best set manually
.tiny-font[
```r
ggplot(popgrowth, aes(x = pop2000, y = popgrowth, color = region)) +
geom_point() +
scale_x_log10()
# no color scale defined, default is scale_color_hue()
```
]
.center[
<img src="color-scales_files/figure-html/qual-scales-example1-out-1.svg" width="55%" />
]
---
## Discrete, qualitative scales are best set manually
.tiny-font[
```r
ggplot(popgrowth, aes(x = pop2000, y = popgrowth, color = region)) +
geom_point() +
scale_x_log10() +
scale_color_hue()
```
]
.center[
<img src="color-scales_files/figure-html/qual-scales-example2-out-1.svg" width="55%" />
]
---
## Discrete, qualitative scales are best set manually
.tiny-font[
```r
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
```
]
.center[
<img src="color-scales_files/figure-html/qual-scales-example3-out-1.svg" width="55%" />
]
---
## Discrete, qualitative scales are best set manually
.tiny-font[
```r
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")
)
```
]
.center[
<img src="color-scales_files/figure-html/qual-scales-example4-out-1.svg" width="55%" />
]
---
## Okabe-Ito RGB codes
.center[
<img src = "https://clauswilke.com/dataviz/pitfalls_of_color_use_files/figure-html/palette-Okabe-Ito-1.png", width = 100%></img>
]
.tiny-font[
Name | Hex code &nbsp;&nbsp; | 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
]
???
Figure from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
[//]: # "segment ends here"
---
## Further reading
- Fundamentals of Data Visualization: [Chapter 4: Color scales](https://clauswilke.com/dataviz/color-basics.html)
- Fundamentals of Data Visualization: [Figure 19.10: Okabe-Ito color palette](https://clauswilke.com/dataviz/color-pitfalls.html#fig:palette-Okabe-Ito)
- **ggplot2** book: [Colour scales and legends](https://ggplot2-book.org/scale-colour.html)
- **ggplot2** reference documentation: [Scales](https://ggplot2.tidyverse.org/reference/index.html#section-scales)
- **colorspace** package: [HCL-Based Color Scales for ggplot2](https://colorspace.r-forge.r-project.org/articles/ggplot2_color_scales.html)
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