Visualizing Information

# .fancy[Visualizing Information]

### John Paul Helveston, Ph.D.
### The George Washington University 
### Dept. of Engineering Management & Systems Engineering

]]

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# Download [this cheatsheet](https://jhelvy.github.io/stories-with-data/content/cheatsheet-dataviz.pdf) for today's content

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# .fancy[Visualizing Information]

## 1. The Psychology of Data Viz
## 2. 5 Data Viz Don'ts

## BREAK

## 3. 5 Data Viz Do's

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# .fancy[Visualizing Information]

## 1. .orange[The Psychology of Data Viz]
## 2. 5 Data Viz Don'ts

## BREAK

## 3. 5 Data Viz Do's

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# Check out John Rauser's [talk](https://www.youtube.com/watch?v=fSgEeI2Xpdc) on YouTube

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# Good visualizations optimize for our<br> .orange[Visual-Memory System]

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## A (very) simplified model of the visual-memory system

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## A (very) simplified model of the visual-memory system

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## A (very) simplified model of the visual-memory system

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## A (very) simplified model of the visual-memory system

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# Two objectives of effective charts:

## 1. Grab & direct attention (iconic memory)
## 2. Reduce processing demands (working memory)

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# The power of pre-attentive processing

## Count all the "5"s

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# The power of pre-attentive processing

## Count all the "5s

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## Pre-attentive attributes

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## Pre-attentive attributes

### .red[Numerical (ratio) data]

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## Pre-attentive attributes

### .red[Numerical (ratio) data]<br>.blue[Categorical (ordinal) data]

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# Not all pre-attentive attributes are equal

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# Where is the red dot?

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# .center[For categorical data:]

### 1. Hue (color) > shape
### 2. Less is more (stay in working memory!)

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# What about _quantitative_ data?

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Cleveland, W. S., & McGill, R. (1985). Graphical perception and graphical methods for analyzing scientific data. Science, New Series, 229(4716), 828-833.

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# Hierarchy for _numerical_ data

## .left[More Accurate]

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## .right[Less Accurate]

]

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# Example: Life expectancy in countries in Asia

```
#>               country lifeExp
#> 1         Afghanistan  43.828
#> 2                Iraq  59.545
#> 3            Cambodia  59.723
#> 4             Myanmar  62.069
#> 5         Yemen, Rep.  62.698
#> 6               Nepal  63.785
#> 7          Bangladesh  64.062
#> 8               India  64.698
#> 9            Pakistan  65.483
#> 10           Mongolia  66.803
#> 11   Korea, Dem. Rep.  67.297
#> 12           Thailand  70.616
#> 13          Indonesia  70.650
#> 14               Iran  70.964
#> 15        Philippines  71.688
#> 16            Lebanon  71.993
#> 17             Jordan  72.535
#> 18       Saudi Arabia  72.777
#> 19              China  72.961
#> 20 West Bank and Gaza  73.422
#> 21              Syria  74.143
#> 22           Malaysia  74.241
#> 23            Vietnam  74.249
#> 24            Bahrain  75.635
#> 25               Oman  75.640
#> 26             Kuwait  77.588
#> 27             Taiwan  78.400
#> 28              Korea  78.623
#> 29          Singapore  79.972
#> 30             Israel  80.745
#> 31   Hong Kong, China  82.208
#> 32              Japan  82.603
```

]

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1. Position on a common scale
2. Position on<br>non-aligned scales
3. Length
4. Angle
5. Area
6. Color saturation
7. **.red[Color hue]**

<br>

- <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M342.6 150.6c12.5-12.5 12.5-32.8 0-45.3s-32.8-12.5-45.3 0L192 210.7 86.6 105.4c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L146.7 256 41.4 361.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0L192 301.3 297.4 406.6c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L237.3 256 342.6 150.6z"/></svg> **Discriminate**
- <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M342.6 150.6c12.5-12.5 12.5-32.8 0-45.3s-32.8-12.5-45.3 0L192 210.7 86.6 105.4c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L146.7 256 41.4 361.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0L192 301.3 297.4 406.6c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L237.3 256 342.6 150.6z"/></svg> **Rank**
- <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M342.6 150.6c12.5-12.5 12.5-32.8 0-45.3s-32.8-12.5-45.3 0L192 210.7 86.6 105.4c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L146.7 256 41.4 361.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0L192 301.3 297.4 406.6c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L237.3 256 342.6 150.6z"/></svg> **Ratio**

]

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1. Position on a common scale
2. Position on<br>non-aligned scales
3. Length
4. Angle
5. Area
6. Color saturation
7. **.red[Color hue]**

Sorting helps a bit...

- <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M342.6 150.6c12.5-12.5 12.5-32.8 0-45.3s-32.8-12.5-45.3 0L192 210.7 86.6 105.4c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L146.7 256 41.4 361.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0L192 301.3 297.4 406.6c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L237.3 256 342.6 150.6z"/></svg> **Discriminate**
- <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:green;overflow:visible;position:relative;"><path d="M438.6 105.4c12.5 12.5 12.5 32.8 0 45.3l-256 256c-12.5 12.5-32.8 12.5-45.3 0l-128-128c-12.5-12.5-12.5-32.8 0-45.3s32.8-12.5 45.3 0L160 338.7 393.4 105.4c12.5-12.5 32.8-12.5 45.3 0z"/></svg> **Rank**
- <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M342.6 150.6c12.5-12.5 12.5-32.8 0-45.3s-32.8-12.5-45.3 0L192 210.7 86.6 105.4c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L146.7 256 41.4 361.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0L192 301.3 297.4 406.6c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L237.3 256 342.6 150.6z"/></svg> **Ratio**

]

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1. Position on a common scale
2. Position on<br>non-aligned scales
3. **.red[Length]**
4. Angle
5. Area
6. Color saturation
7. Color hue

<br>

- <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:green;overflow:visible;position:relative;"><path d="M438.6 105.4c12.5 12.5 12.5 32.8 0 45.3l-256 256c-12.5 12.5-32.8 12.5-45.3 0l-128-128c-12.5-12.5-12.5-32.8 0-45.3s32.8-12.5 45.3 0L160 338.7 393.4 105.4c12.5-12.5 32.8-12.5 45.3 0z"/></svg> / <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M342.6 150.6c12.5-12.5 12.5-32.8 0-45.3s-32.8-12.5-45.3 0L192 210.7 86.6 105.4c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L146.7 256 41.4 361.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0L192 301.3 297.4 406.6c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L237.3 256 342.6 150.6z"/></svg> **Discriminate**
- <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:green;overflow:visible;position:relative;"><path d="M438.6 105.4c12.5 12.5 12.5 32.8 0 45.3l-256 256c-12.5 12.5-32.8 12.5-45.3 0l-128-128c-12.5-12.5-12.5-32.8 0-45.3s32.8-12.5 45.3 0L160 338.7 393.4 105.4c12.5-12.5 32.8-12.5 45.3 0z"/></svg> / <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:red;overflow:visible;position:relative;"><path d="M342.6 150.6c12.5-12.5 12.5-32.8 0-45.3s-32.8-12.5-45.3 0L192 210.7 86.6 105.4c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L146.7 256 41.4 361.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0L192 301.3 297.4 406.6c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L237.3 256 342.6 150.6z"/></svg> **Rank**
- <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:green;overflow:visible;position:relative;"><path d="M438.6 105.4c12.5 12.5 12.5 32.8 0 45.3l-256 256c-12.5 12.5-32.8 12.5-45.3 0l-128-128c-12.5-12.5-12.5-32.8 0-45.3s32.8-12.5 45.3 0L160 338.7 393.4 105.4c12.5-12.5 32.8-12.5 45.3 0z"/></svg> **Ratio**

]

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1. Position on a common scale
2. **.red[Position on<br>non-aligned scales]**
3. Length
4. Angle
5. Area
6. Color saturation
7. Color hue

<br>

]

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1. **.red[Position on a common scale]**
2. Position on<br>non-aligned scales
3. Length
4. Angle
5. Area
6. Color saturation
7. Color hue

<br>

]

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1. **.red[Position on a common scale]**
2. Position on<br>non-aligned scales
3. Length
4. Angle
5. Area
6. Color saturation
7. Color hue

**.red[Sorting still matters!]**

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# .font130[Gestalt Psychology]

<br>

### The whole has a reality that is entirely separate from the parts

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# Reification

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# Emergence

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background-color: #FFFFFF

# Law of Closure

Our minds fill in the missing information

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# .font120[Prägnanz]

<br>

### We strongly prefer to interpret stimuli as regular, simple, and orderly

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# .font120[Prägnanz]

It's the graphical equivalent of this:
<center>
<img src="images/unsatisfying-pie.gif">
</center>

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# .font120[Prägnanz]

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# .fancy[Visualizing Information]

## 1. The Psychology of Data Viz
## 2. .orange[5 Data Viz Don'ts]

## BREAK

## 3. 5 Data Viz Do's

---

# .center[Data Viz Don'ts]

## 1. Don't use chart chunk
## 2. Don't make 3D plots
## 3. Don't use pattern fills
## 4. Don't use red & green together
## 5. Don't lie

---

# .center[Data Viz Don'ts]

## 1. .orange[Don't use chart chunk]
## 2. Don't make 3D plots
## 3. Don't use pattern fills
## 4. Don't use red & green together
## 5. Don't lie

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# "_Erase non-data ink._"
## — Ed Tufte

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Figure 1.6: `Monstrous Costs’ by Nigel Holmes, in Healy, 2018

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# .center[Data Viz Don'ts]

## 1. Don't use chart chunk
## 2. .orange[Don't make 3D plots]
## 3. Don't use pattern fills
## 4. Don't use red & green together
## 5. Don't lie

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## Humans aren't good at distinguishing 3D space

[Penrose Stairs](https://en.wikipedia.org/wiki/Penrose_stairs
), made famous by<br>M.C. Escher (1898-1972)

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## Ink proportions !=<br>true proportions

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# Occlusion: geoms are obscured

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# Multiple interpretations

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<center>
<img src="images/check-good.png" width=100>
.border[<img src="images/facetedBars.png" width=600>]
</center>

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# The third dimension distracts from the data

## (this is what Tufte calls "chart junk")

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<center>
<img src="images/check-good.png" width=100>
.border[<img src="images/simpleBars.png">]
</center>

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# .center[Data Viz Don'ts]

## 1. Don't use chart chunk
## 2. Don't make 3D plots
## 3. .orange[Don't use pattern fills]
## 4. Don't use red & green together
## 5. Don't lie

---

---

<center>
<img src="images/check-bad.png" width=75>
.border[<img src="images/pattern_fills1.png">]
</center>

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<center>
<img src="images/check-good.png" width=100>
.border[<img src="images/pattern_fills2.png">]
</center>

]

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# .center[Data Viz Don'ts]

## 1. Don't use chart chunk
## 2. Don't make 3D plots
## 3. Don't use pattern fills
## 4. .orange[Don't use red & green together]
## 5. Don't lie

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# 10% of males and 1% of females are color blind

<center>
.border[<img src="images/colorBlindBad1.png">]
</center>

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<center>
.border[<img src="images/colorBlindBad2.png">]
</center>

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## Facets can be used to avoid color altogether

<center>
<img src="images/check-bad.png" width=75>
.border[<img src="images/colorBlindBad1.png">]
</center>

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<center>
<img src="images/check-good.png" width=100>
.border[<img src="images/colorBlindBad3.png">]
</center>

]

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# .center[Data Viz Don'ts]

## 1. Don't use chart chunk
## 2. Don't make 3D plots
## 3. Don't use pattern fills
## 4. Don't use red & green together
## 5. .orange[Don't lie]

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# Bar charts should always start at 0

Image from http://livingqlikview.com/the-9-worst-data-visualizations-ever-created/

]]]

<center>
<img src="images/check-good.png" width=100>
.border[<img src="images/barchart_zero.png" width=330px>]
</center>

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# Don't cherry pick your data

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https://x.com/PEWilliams_/status/1760688839193231550?s=20

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### .center[Don't cherry pick your data]

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## Use common conventions

- "Up" on Y axis should mean larger
- Time moves left to right

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# Make sure your chart makes sense

Image from Image from http://livingqlikview.com/the-9-worst-data-visualizations-ever-created/

]]

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# Intermission!

## Stand up, Move around, Stretch!

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# .fancy[Visualizing Information]

## 1. The Psychology of Data Viz
## 2. 5 Data Viz Don'ts

## BREAK

## 3. .orange[5 Data Viz Do's]

---

# .center[Data Viz Do's]

## 1. Annotate your charts
## 2. Eliminate legends
## 3. Show amounts with bars, dots, and lollipops
## 4. Show proportions with bars and waffles (not pies)
## 5. Show trends with lines, bars, and heatmaps

---

# .center[Data Viz Do's]

## 1. .orange[Annotate your charts]
## 2. Eliminate legends
## 3. Show amounts with bars, dots, and lollipops
## 4. Show proportions with bars and waffles (not pies)
## 5. Show trends with lines, bars, and heatmaps

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# Text is usually the single most important component on your chart

---

.font80[Michelle Borkin, et al. (2015) [Beyond Memorability: Visualization Recognition and Recall](https://ieeexplore.ieee.org/document/7192646)]

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.font80[https://www.reddit.com/r/dataisugly/comments/odk65x/this_was_not_the_right_headline_to_go_along_with]

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## Good annotations should tell a story

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## Use reference lines to provide context

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# .center[Data Viz Do's]

## 1. Annotate your charts
## 2. .orange[Eliminate legends]
## 3. Show amounts with bars, dots, and lollipops
## 4. Show proportions with bars and waffles (not pies)
## 5. Show trends with lines, bars, and heatmaps

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# Legends suck

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# Legends suck

### Legends require look-up task

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### Direct labeling is much better

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## Directly label things to remove legends!

<center>
<img src="images/check-bad.png" width=75>
.border[<img src="images/milk_region.png">]
</center>

]

<center>
<img src="images/check-good.png" width=100>
.border[<img src="images/milk_region_label.png">]
</center>

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## Exception: When you have repeated categories

<center>
.border[<img src="images/pattern_fills2.png" width=600>]
</center>

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## If you use a legend, use **few categories as possible**

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## .center[Or use faceting to eliminate the legend!]

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# .center[Data Viz Do's]

## 1. Annotate your charts
## 2. Eliminate legends

## .center[.orange[Match your chart type to your message]]

## 3. Show amounts with bars, dots, and lollipops
## 4. Show proportions with bars and waffles (not pies)
## 5. Show trends with lines, bars, and heatmaps

---

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# Norman door (n.):

1. A door where the design tells you to do the opposite of what you're actually supposed to do.

2. A door that gives the wrong signal and needs a sign to correct it.

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# Norman door

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# Non-Norman door

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# Match your chart type to your message

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# Show amounts with:

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## Bar chart

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## Dot chart

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## Lollipop chart

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## Bars are good for highlighting specific categories

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## Rotate axes if you can't read them

<center>
<img src="images/check-bad.png" width=80>
.border[<img src="images/mpg_bars_bad.png" width=400>]
</center>

]

<center>
<img src="images/check-good.png" width=100>
.border[<img src="images/mpg_bars_flipped.png" width=750>]
</center>

]

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## Default order is almost always wrong

<center>
<img src="images/check-bad.png" width=80>
Ordered by alphabet (default)
.border[<img src="images/mpg_bars_flipped.png">]
</center>

]

<center>
<img src="images/check-good.png" width=100>
Ordered by count
.border[<img src="images/mpg_bars_flipped_sorted.png">]
</center>

]

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## Exception: When the order matters

<center>
.border[<img src="images/wildlife-months-bar.png" width=900>]
</center>

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## Use lollipops when:

### - The bars are overwhelming<br> - You're not highlighting categories

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## Or use dots

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# .center[Data Viz Do's]

## 1. Annotate your charts
## 2. Eliminate legends
## 3. Show amounts with bars, dots, and lollipops
## 4. .orange[Show proportions with bars and waffles (not pies)]
## 5. Show trends with lines, bars, and heatmaps

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# Show proportions with:

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# Bar charts

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# Waffle charts

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# Stacked bars are rarely a good idea

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# Stacked bars are rarely a good idea

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## "Parallel coordinates" plot is a good alternative

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## .center[Where stacking is useful]

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### **With 2 to 3 groups**
### Proportions over time

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## .center[Where stacking is useful]

.font70[https://fivethirtyeight.com/features/the-dollar-and-cents-case-against-hollywoods-exclusion-of-women/]

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### With 2 to 3 groups
### **Proportions over time**

]

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## .center[Where stacking is useful]

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### With 2 to 3 groups
### **Proportions over time**

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# Pies are rarely a good idea

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# Exceptions:

## - Small data
## - Simple fractions
## - If sum of parts matters

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# Best pie chart of all time

---

background-color: #fff 
class: center

## The best pies are **square pies**

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# Waffles make rough comparisons easy

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# .center[Data Viz Do's]

## 1. Annotate your charts
## 2. Eliminate legends
## 3. Show amounts with bars, dots, and lollipops
## 4. Show proportions with bars and waffles (not pies)
## 5. .orange[Show trends with lines, bars, and heatmaps]

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# For small number of categories, **lines work**

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# For small number of categories, **bars work**

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## With many categories, lines don't work

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## With many categories, **summary lines work**

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## With many categories, **heatmaps work**

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# Bonus suggestions!

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## With **more than 2** things, dodged bars can get confusing

Still comparing 2 time periods, but across **10** categories

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Dumbbell charts highlight:

- Compare **magnitudes** across two periods / groups

]

Slope charts highlight:

- _Change_ in **rankings**
- Highlight individual categories

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# Consider using tables for small data

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---

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## Who do you think did a better job in tonight’s debate?

| | Clinton | Trump |
|-|-|-|
| Among Democrats | 99% | 1% |
| Among Republicans | 53% | 47% |

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# Your turn - go [here](https://docs.google.com/presentation/d/1sL3k4S2e7shShlu_crNxunEH8qMHtuOH_vl_a_Mc1wI/edit?usp=sharing)

For your "bad" visualization:

1) Identify where the graphic falls on Cleveland's pattern recognition hierarchy

2) Any design rules that are broken

3) Suggest at least two improvements

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