Principles of Figure Design

class: center, middle, title-slide

.title[
# Principles of Figure Design
]
.author[
### Claus O. Wilke
]
.date[
### last updated: 2022-06-26
]

---

## Topics covered

1. Uses of color in data visualization

2. Compound figures

3. Balance data and context

---

## 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 = "figure-design_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 = "figure-design_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 = "figure-design_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 = "figure-design_files/highlight.png" width = 100% style = "text-align: right; vertical-align: middle"></img>
</td>
</tr>
</table>

---

## 1. Distinguish categories (qualitative)

---

## Qualitative scale example

.center.nogap[
![](figure-design_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[
![](figure-design_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[
![](figure-design_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)

---

## 2. Represent numeric values (sequential)

---

## Sequential scale example

<br>

.center[
![](figure-design_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[
![](figure-design_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[
![](figure-design_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)

---

## 3. Represent numeric values (diverging)

---

## Diverging scale example

.center.nogap[
![](figure-design_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[
![](figure-design_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[
![](figure-design_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)

---

## 4. Highlight

---

## Highlight example

.center[
![](figure-design_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[
![](figure-design_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[
![](figure-design_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

---
class: center middle

## Compound figures

---

## Compound figures

Two common scenarios:

1\. The same type of plot is replicated many times (small multiples)

2\. Several disparate plots are combined into one display

---

background-image: url("https://clauswilke.com/dataviz/multi-panel_figures_files/figure-html/titanic-passenger-breakdown-1.png")
background-position: left 50% top 60%
background-size: 45%

## 1. Small multiples (facets)

???

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

---

background-image: url("https://clauswilke.com/dataviz/balance_data_context_files/figure-html/titanic-survival-by-gender-class-bad-1.png")
background-position: left 50% top 60%
background-size: 45%

## 1. Small multiples (facets)

.absolute-bottom-left[
Avoid bars or other elements that are floating in space
]

???

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

---

background-image: url("https://clauswilke.com/dataviz/balance_data_context_files/figure-html/titanic-survival-by-gender-class-1.png")
background-position: left 50% top 60%
background-size: 45%

## 1. Small multiples (facets)

???

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

---
class: center middle

## Small multiples work even for massive amounts of data

---
background-image: url("https://clauswilke.com/dataviz/multi-panel_figures_files/figure-html/movie-rankings-1.png")
background-position: left 50% top 60%
background-size: 45%

???

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

---

background-image: url("https://clauswilke.com/dataviz/multi-panel_figures_files/figure-html/BA-degrees-variable-y-lims-1.png")
background-position: left 50% top 70%
background-size: 50%

## y-axis ranges should be consistent among panels

???

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

---

background-image: url("https://clauswilke.com/dataviz/multi-panel_figures_files/figure-html/BA-degrees-fixed-y-lims-1.png")
background-position: left 50% top 70%
background-size: 50%

## y-axis ranges should be consistent among panels

???

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

---

background-image: url("https://clauswilke.com/dataviz/multi-panel_figures_files/figure-html/BA-degrees-compound-1.png")
background-position: left 50% top 55%
background-size: 75%

## 2. Combining disparate figures into one display

???

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

.absolute-bottom-left[
Note: We now need plot labels (a), (b) to identify sub-plots
]

---

background-image: url("https://clauswilke.com/dataviz/multi-panel_figures_files/figure-html/BA-degrees-compound-bad-1.png")
background-position: left 50% top 55%
background-size: 75%

## Don't use overly large or otherwise prominent labels

.absolute-bottom-left[
Note: Also don't use serif fonts (such as Times) for labels
]

???

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

---

background-image: url("https://clauswilke.com/dataviz/multi-panel_figures_files/figure-html/athletes-composite-good-1.png")
background-position: left 50% top 65%
background-size: 50%

## Use a consistent color language among sub-plots

???

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

---

background-image: url("https://clauswilke.com/dataviz/multi-panel_figures_files/figure-html/athletes-composite-inconsistent-1.png")
background-position: left 50% top 65%
background-size: 50%

## Use a consistent color language among sub-plots

???

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

---

background-image: url("https://clauswilke.com/dataviz/multi-panel_figures_files/figure-html/athletes-composite-good-1.png")
background-position: left 50% top 65%
background-size: 50%

## Pay attention to sub-plot alignment

???

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

---

background-image: url("https://clauswilke.com/dataviz/multi-panel_figures_files/figure-html/athletes-composite-misaligned-1.png")
background-position: left 50% top 65%
background-size: 50%

## Pay attention to sub-plot alignment

???

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

---

background-image: url("https://clauswilke.com/dataviz/multi-panel_figures_files/figure-html/athletes-composite-good-1.png")
background-position: left 50% top 65%
background-size: 50%

## Combine plots of different types

.absolute-bottom-left[
This helps your readers to distinguish different parts of the analysis 
]

???

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

---

background-image: url("https://clauswilke.com/dataviz/telling_a_story_files/figure-html/athletes-composite-repetitive-1.png")
background-position: left 50% top 65%
background-size: 50%

## Combine plots of different types

.absolute-bottom-left[
This helps your readers to distinguish different parts of the analysis 
]

???

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

---

background-image: url("https://clauswilke.com/dataviz/multi-panel_figures_files/figure-html/athletes-composite-good-1.png")
background-position: left 50% top 65%
background-size: 50%

## Combine plots of different types

.absolute-bottom-left[
This helps your readers to distinguish different parts of the analysis 
]

???

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

---

## Distinguish infographics from figures in article/book

There are two distinct use cases:

**Infographic:** Standalone, has title/subtitle/caption

**Figure in article/book:** Caption contains title, not part of figure

---

background-image: url("https://clauswilke.com/dataviz/figure_titles_captions_files/figure-html/corruption-development-infographic-1.png")
background-position: left 50% top 45%
background-size: 50%

.absolute-bottom-left[
Example of infographic
]

???

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

---

background-image: url("https://clauswilke.com/dataviz/figure_titles_captions_files/figure-html/corruption-development-1.png")
background-position: left 15% top 40%
background-size: 50%

.pull-right.width-35.move-down-2em.small-font[
Figure 1. **Corruption and human development.** The most developed countries experience the least corruption. Inspired by a posting in [The Economist online (2011)](https://www.economist.com/graphic-detail/2011/12/02/corrosive-corruption). Data sources: Transparency International & UN Human Development Report.
]

.absolute-bottom-left[
Example of figure in article or book
]

???

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

---
class: center middle

## Balance data and context

---

background-image: url("https://clauswilke.com/dataviz/figure_titles_captions_files/figure-html/blue-jays-scatter-bubbles2-1.png")
background-position: left 50% top 60%
background-size: 45%

## Label all axes and guides

.absolute-bottom-left[
Add units where appropriate or necessary
]

---

background-image: url("https://clauswilke.com/dataviz/figure_titles_captions_files/figure-html/tech-stocks-minimal-labeling-1.png")
background-position: left 50% top 60%
background-size: 55%

## Self-explanatory labels can be omitted

---

background-image: url("https://clauswilke.com/dataviz/balance_data_context_files/figure-html/Aus-athletes-grid-bad-1.png")
background-position: left 50% top 60%
background-size: 55%

## Don't overload your figure with boxes and grid-lines

---

background-image: url("https://clauswilke.com/dataviz/balance_data_context_files/figure-html/Aus-athletes-min-bad-1.png")
background-position: left 50% top 60%
background-size: 55%

## But also don't hide useful information

---

background-image: url("https://clauswilke.com/dataviz/balance_data_context_files/figure-html/Aus-athletes-grid-good-1.png")
background-position: left 50% top 60%
background-size: 55%

## Axes and grid should be visible but not dominate
---

background-image: url("https://clauswilke.com/dataviz/balance_data_context_files/figure-html/Aus-athletes-grid-good-frame-1.png")
background-position: left 50% top 60%
background-size: 55%

## A box around the figure can help separate the legend

---

## Further reading

Relevant chapters from Fundamentals of Data Visualization:
- [Chapter 4: Color scales](https://clauswilke.com/dataviz/color-basics.html)
- [Chapter 21: Multi-panel figures](https://clauswilke.com/dataviz/multi-panel-figures.html)
- [Chapter 22: Titles, captions, and tables](https://clauswilke.com/dataviz/figure-titles-captions.html)
- [Chapter 23: Balance the data and the context](https://clauswilke.com/dataviz/balance-data-context.html)