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Data Visualization

Chapter 1. Fundamentals of Graphical Practice

Iñaki Úcar

Department of Statistics | uc3m-Santander Big Data Institute

Master in Computational Social Science

Licensed under Creative Commons Attribution CC BY 4.0 Last generated: 2023-09-27

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Graphical Integrity

How to Lie with Graphs

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Bad Charts

  • We live surrounded by bad charts

    • For many people, statistical charts = lie
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Bad Charts

  • We live surrounded by bad charts

    • For many people, statistical charts = lie
  • Deception vs. ineptitude
    • Lack of quantitative skills, anumerism
    • Lack of knowledge about visual perception
    • Lack of visual skills
    • Intention to deceive
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Sources of Bad Charts

  • For Tufte:

    • Distortion
    • Data variation
    • Visual area and numerical measure
    • Context
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Sources of Bad Charts

  • For Tufte:

    • Distortion
    • Data variation
    • Visual area and numerical measure
    • Context

  • But there are many more common patterns:

    • Bad representation (deception, sometimes)
    • Bad data
    • Too many data (optimism)
    • Too little data (optimism)
    • Hiding relevant data (deception)
    • Wrong representation (deception)
    • Creativity (without skill)
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Prologue: Pie Charts

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Bad Representation: Areas

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Bad Representation: Longitudes

  • Cutting the origin is the most common deception technique with bar plots

  • Some well-intentioned axis cuts may have nefarious consequences
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Bad Data

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Too Many Data

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Too Little Data

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Hiding Relevant Data

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Wrong Representation

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Creativity

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Bonus: Scientific Horrors

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12 Rules to Display Data Badly

Wainer, H. (1984) How to Display Data Badly. The American Statistician 38(2), pp. 137-147.

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12 Rules to Display Data Badly

Wainer, H. (1984) How to Display Data Badly. The American Statistician 38(2), pp. 137-147.

Showing data

  • Rule 1. Show as few data as possible (minimize the data density)
  • Rule 2. Hide what data you do show (minimize the data-ink ratio)
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12 Rules to Display Data Badly

Wainer, H. (1984) How to Display Data Badly. The American Statistician 38(2), pp. 137-147.

Showing data

  • Rule 1. Show as few data as possible (minimize the data density)
  • Rule 2. Hide what data you do show (minimize the data-ink ratio)

Showing data accurately

  • Rule 3. Ignore the visual metaphor altogether
  • Rule 4. Only order matters
  • Rule 5. Graph data out of context
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12 Rules to Display Data Badly

Wainer, H. (1984) How to Display Data Badly. The American Statistician 38(2), pp. 137-147.

Showing data

  • Rule 1. Show as few data as possible (minimize the data density)
  • Rule 2. Hide what data you do show (minimize the data-ink ratio)

Showing data accurately

  • Rule 3. Ignore the visual metaphor altogether
  • Rule 4. Only order matters
  • Rule 5. Graph data out of context

Showing data clearly

  • Rule 6. Change scales in mid-axis
  • Rule 7. Emphasize the trivial (ignore the important)
  • Rule 8. Jiggle the baseline
  • Rule 9. Austria first!
  • Rule 10. Label illegibly, incompletely, incorrectly, and ambiguously
  • Rule 11. More is murkier: more decimal places and more dimensions
  • Rule 12. If it has been done well in the past, think of another way to do it
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Chart Rethinking: Exercise 1

https://junkcharts.com

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Chart Rethinking: Exercise 2

https://junkcharts.com

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Chart Rethinking: Exercise 3

https://www.datanalytics.com/

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Chart Rethinking: Exercise 4

https://junkcharts.com

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Chart Rethinking: Exercise 5

https://junkcharts.com

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Chart Rethinking: Exercise 6

Schwabish, J. A. (2014). An Economist’s Guide to Visualizing Data

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Chart Rethinking: Exercise 7

Schwabish, J. A. (2014). An Economist’s Guide to Visualizing Data

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Chart Rethinking: Exercise 8

Schwabish, J. A. (2014). An Economist’s Guide to Visualizing Data

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Summary

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Summary

  • We live surrounded by bad charts, either by deception or ineptitude.
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Summary

  • We live surrounded by bad charts, either by deception or ineptitude.

  • There are some common patterns to be aware of.
    From those, we can distill a series of basic rules:

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Summary

  • We live surrounded by bad charts, either by deception or ineptitude.

  • There are some common patterns to be aware of.
    From those, we can distill a series of basic rules:

    • Pie charts are very rarely justified.
    • Areas are tricky.
    • 3D is rarely justified in 2D mediums.
    • Axes (cuts, scales...) should be clear.
    • Proper context should be shown when relevant.
    • Do not go beyond what data tells.
    • There are well-established visual metaphors.
      • E.g. up-down for positive-negative.
      • E.g. areas are perceived as whole quantities,
        so cutting the axis is deceiving.
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