Color scales

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

<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

<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

<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

[//]: # "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[

---

## **ggplot2** color scale functions are a bit of a mess

.small-font.center[

---

## **ggplot2** color scale functions are a bit of a mess

.small-font.center[

---

## **ggplot2** color scale functions are a bit of a mess

.small-font.center[

---

## **ggplot2** color scale functions are a bit of a mess

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... 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[

---

## 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

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.center[
<img src="color-scales_files/figure-html/temps-tiles7-out-1.svg" width="90%" />
]

---

## Examples

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.center[
<img src="color-scales_files/figure-html/temps-tiles8-out-1.svg" width="90%" />
]

---

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```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)