Color scales

class: center, middle, title-slide

.title[
# Color scales
]
.author[
### Claus O. Wilke, remixed by Joseph Elsherbini
]
.date[
### 26-08-2022
]

---

# 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[
![](color-scales_files/figure-html/popgrowth-vs-popsize-colored-by-state-1.svg)
]

Humans can only distinguish 7 or 8 colors.

???

Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

# Qualitative scale example

.center.nogap[
![](color-scales_files/figure-html/popgrowth-vs-popsize-colored-1.svg)
]

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[
![](color-scales_files/figure-html/popgrowth-vs-popsize-colored2-1.svg)
]

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[
![](color-scales_files/figure-html/popgrowth-vs-popsize-colored3-1.svg)
]

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[
![](color-scales_files/figure-html/four-locations-temps-by-month-1.svg)
]

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[
![](color-scales_files/figure-html/four-locations-temps-by-month2-1.svg)
]

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[
![](color-scales_files/figure-html/four-locations-temps-by-month3-1.svg)
]

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[

```
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
```

![](color-scales_files/figure-html/forensic-correlations1-1.svg)
]

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[
![](color-scales_files/figure-html/forensic-correlations2-1.svg)
]

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[
![](color-scales_files/figure-html/forensic-correlations3-1.svg)
]

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[
![](color-scales_files/figure-html/Aus-athletes-track-1.svg)
]

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[
![](color-scales_files/figure-html/Aus-athletes-track2-1.svg)
]

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[
![](color-scales_files/figure-html/Aus-athletes-track3-1.svg)
]

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**

---

## **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

.small-font.center[

... 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[
![](color-scales_files/figure-html/temps-tiles1-out-1.svg)
]

---

## 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[
![](color-scales_files/figure-html/temps-tiles2-out-1.svg)
]

---

## 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[
![](color-scales_files/figure-html/temps-tiles3-out-1.svg)
]

---

## 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[
![](color-scales_files/figure-html/temps-tiles4-out-1.svg)
]

---

## 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[
![](color-scales_files/figure-html/temps-tiles5-out-1.svg)
]

---

## 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[
![](color-scales_files/figure-html/temps-tiles6-out-1.svg)
]

---

## Examples

.tiny-font[

.center[
![](color-scales_files/figure-html/temps-tiles7-out-1.svg)
]

---

## Examples

.tiny-font[

.center[
![](color-scales_files/figure-html/temps-tiles8-out-1.svg)
]

---

.tiny-font.nogap[

```r
colorspace::hcl_palettes(type = "sequential", plot = TRUE) # all sequential palettes
```

.center[
![](color-scales_files/figure-html/colorspace-palettes-seq-out-1.svg)
]
]

---

.tiny-font.nogap[

```r
colorspace::hcl_palettes(type = "diverging", plot = TRUE, n = 9) # all diverging palettes
```

.center[
![](color-scales_files/figure-html/colorspace-palettes-div-out-1.svg)
]
]

---

.tiny-font.nogap[

```r
colorspace::divergingx_palettes(plot = TRUE, n = 9) # all divergingx palettes
```

.center[
![](color-scales_files/figure-html/colorspace-palettes-divx-out-1.svg)
]
]

[//]: # "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[
![](color-scales_files/figure-html/qual-scales-example1-out-1.svg)
]

---

## 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[
![](color-scales_files/figure-html/qual-scales-example2-out-1.svg)
]

---

## 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[
![](color-scales_files/figure-html/qual-scales-example3-out-1.svg)
]

---

## 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[
![](color-scales_files/figure-html/qual-scales-example4-out-1.svg)
]

---

## 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"

---

## Be aware of color-vision deficiency

5%–8% of men are color blind!

Red-green color-vision deficiency is the most common

---

## Be aware of color-vision deficiency

5%–8% of men are color blind!

Blue-green color-vision deficiency is rare but does occur

---

## Be aware of color-vision deficiency

Choose colors that can be distinguished with CVD

---

## Consider using the Okabe-Ito scale as your default

.center[
<img src = "https://clauswilke.com/dataviz/pitfalls_of_color_use_files/figure-html/palette-Okabe-Ito-1.png", width = 100%></img>
]

???

Figure from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## CVD is worse for thin lines and tiny dots

.move-up-1em.center[
<img src = "https://clauswilke.com/dataviz/pitfalls_of_color_use_files/figure-html/colors-thin-lines-1.png", width = 65%></img>
]

---

## When in doubt, run CVD simulations

<div class = "move-up-1em" style = "width: 50%; box-shadow: 3px 5px 3px 1px #00000080; padding: 8px;">
.small-font[Original]<br>
<img src = "https://clauswilke.com/dataviz/redundant_coding_files/figure-html/iris-scatter-three-shapes-1.png", width = 100%></img>
</div>

???

Figure from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## When in doubt, run CVD simulations

???

Figures from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

[//]: # "segment ends here"
--
the **colorspace**  
package can  
help
---

# Exercise 
Go to [https://elsherbini.github.io/AMNH_R_Workshop_2023/modules/data-wrangling-module/](https://elsherbini.github.io/AMNH_R_Workshop_2023/modules/data-wrangling-module/) and complete the color scales exercise.