Design of Graphics
Good and Bad

Data 304

Two books (of many)

Tufte: The Visual Display of Quantitative Information

Healy: Data Visualization: A Practical Introduction

From Healy’s Preface

My main goal is to introduce you to both the ideas and the methods of data visualization in a sensible, comprehensible, reproducible way.

When teaching people how to make graphics with data, however, I have repeatedly found the need for an introduction that motivates and explains why you are doing something but that does not skip the necessary details of how to produce the images you see on the page. And so this book has two main aims. First, I want you get to the point where you can reproduce almost every figure in the text for yourself. Second, I want you to understand why the code is written the way it is, such that when you look at data of your own you can feel confident about your ability to get from a rough picture in your head to a high-quality graphic on your screen or page.

This book is a hands-on introduction to the principles and practice of looking at and presenting data using R and ggplot.

Designing Good Graphics

Graphical excellence accoring to Edward R.Tufte

Graphical excellence is the well-designed presentation of interesting data-—a matter of substance, of statistics, and of design.

Graphical excellence consists of complex ideas communicated with clarity, precision, and efficiency.

Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.

Graphcial excellence is nearly always multivariate.

And graphical excellence requires telling the truth about the data.

(Tufte 2001, 51, italics original, bold mine)

Tufte’s table of contents

Part 1: Graphical Practice

  1. Graphical Excellence
  2. Graphical Integrity
  3. Sources of Graphical Integrity and Sophistication

Tufte’s table of contents

Part 2: Theory of Data Graphics

  1. Data-Ink and Graphical Redesign
  2. Chartjunk: Vibrations, Grids, and Ducks
  3. Data-Ink Maximization and Graphical Design
  4. Multifuntioning Graphical Elements
  5. Data Density and Small Multiples
  6. Aesthetics and Technique in Data Graphical Design

Tufte: Elements of good design

Good design has two key elements:

Graphical elegance is often found in simplicity of design and complexity of data.

Visually attractive graphics also gather power from content and interpretations beyond the immediate display of some numbers. The best graphics are about the useful and important, about life and death, about the universe. Beautiful graphics do not traffic with the trivial.

(Tufte 2001, 177)

A Tufte favorite

  • Charles Minard, 1869 (Click on image to view larger.)

(Tufte 2001, 51)

More about Minard’s graphic

How do you pass the Tufte test?

On rare occasions graphical architecture combines with the data content to yield a uniquely spectacular graphic. Such performances can be described and admired but there are no easy compositional principles on how to create that one wonderful graphic in millions.

What can be suggested, though, are some guides for enhancing the visual quality of routine, workaday designs.

(Tufte 2001, 177)

Unicorns vs workaday designs

  • Most of our graphics do not look like Minard’s graphic.

    • Most of his did not either!

  • We need principles and guidelines to help us effectively build and customize “workaday graphics”.

  • Healy: Layer, Highlight, Repeat

    • The grammar of graphics sets us up to use these elements flexibly.

    • We also need to use them effectively and honestly.

Tufte’s guides

Attractive displays of statistical information

  • have a properly chosen format and design
  • use words, numbers, and drawing together
  • reflect a balance, a proportion, a sense of relevant scale
  • display an accessible complexity of detail
  • often have a narrative quality, a story to tell about the data
  • are drawn in a professional manner, with the technical details of production done with care
  • avoid content-free decoration, including chartjunk.

Healy: Graphical Virtues

  • Clarity
  • Honesty
  • Truth

Healy: Graphical Virtues

  • Clarity
  • Honesty
  • Truth

 

within

  • Context
  • Convention
  • Meaning

Audience matters.

Bad Grapahics

What makes a graphic bad?

Healy (2019) enumerates three categories of badness.

  1. Bad taste

  2. Bad data

  3. Bad perception

Bad taste

Figure 1.4 from Healy (2019)

Data-ink ratio

Tufte’s advice has often been summarized as a desire to increase the data-to-ink ratio.

This is practical advice. It is not hard to jettison tasteless junk, and if we look a little harder we may find that the chart can do without other visual scaffolding as well. We can often clean up the typeface, remove extraneous colors and backgrounds, and simplify, mute, or delete gridlines, superfluous axis marks, or needless keys and legends. Given all that, we might think that a solid rule of “simpify, simplify” is almost all of what we need to make sure that our charts remain junk-free, and thus effective. But …

(Healy 2019, 1.2.1)

The flip side

Figure 1.6 from Healy (2019)

Somewhat annoyingly…

Somewhat annoyingly, there is evidence that highly embellished charts like Nigel Holmes’s “Monstrous Costs” are often more easily recalled than their plainer alternatives (Bateman et al., 2010). Viewers do not find them more easily interpretable, but they do remember them more easily and also seem to find them more enjoyable to look at. They also associate them more directly with value judgments, as opposed to just trying to get information across. Borkin et al. (2013) also found that visually unique, “Infographic” style graphs were more memorable than more standard statistical visualizations.

(“It appears that novel and unexpected visualizations can be better remembered than the visualizations with limited variability that we are exposed to since elementary school”, they remark.))

(Healy 2019, 1.2.1)

Even worse…

Even worse, it may be the case that graphics that really do maximize the data-to-ink ratio are harder to interpret than those that are a little more relaxed about it.

Figure 1.7 from Healy (2019)

Relax

Cues like labels and gridlines, together with some strictly superfluous embellishment of data points or other design elements, may often be an aid rather than an impediment to interpretation.

(Healy 2019, 1.2.1)

Bad Data

From (Healy 2019, 1.2.2):

{.width=90%}

the survey question asked respondents to rate the importance of living in a democracy on a ten point scale, with 1 being “Not at all Important” and 10 being “Absolutely Important”. The graph showed the difference across ages of people who had given a score of “10” only

Another graphic

Showing average scores this time.

{.width=70%}

Bad Perception

Our eyes and brains are designed to view the real world, not data graphics.

This mismatch can cause us to misinterpret data graphics that don’t take human perception into account.

Perception is not a simple matter of direct visual inputs producing straightforward mental representations of their content. Rather, our visual system is tuned to accomplish some tasks very well, and this comes at a cost in other ways. (Healy 2019, 1.3.1)

Example: How tall are these bars?

Figure 1.10 from Healy (2019)

What’s next?

Where do we go from here?

Technical

  • Continue to learn how to add and refine features in Vega-Lite
  • Data wrangling – both inside an outside of Vega-Lite
  • Vega-Lite via R, Python, etc.

Where do we go from here?

Design

  • Guidelines for designing good data graphics
  • Key principles of visual perception
  • Gallery of examples to illustrate and inspire

Where do we go from here?

Story

  • How do we make sure our graphic tells (the intended) data story?

Where do we go from here?

Story

  • How do we make sure our graphic tells (the intended) data story?

Practice, practice, practice

  • It takes practice to combine technical and design elements

Your turn: Return to our genetics data

With a partner,

  1. Look at the gallery and identify good and bad features of the examples there.

  2. Design a better graphic.

  3. If time, create that graphic.

References

Healy, K. 2019. Data Visualization: A Practical Introduction. Princeton University Press. https://socviz.co/.
Tufte, Edward R. 2001. The Visual Display of Quantitative Information. 2nd ed. Cheshire, CT: Graphics Press. https://kyl.neocities.org/books/%5BTEC%20TUF%5D%20the%20visual%20display%20of%20quantitative%20information.pdf.