Tufte: The Visual Display of Quantitative Information

Last year I read a bunch of Edward Tufte books: The Visual Display of Quantitative Information, Envisioning Information, Visual Explanations: Images and Quantities, Evidence and Narrative, Beautiful Evidence, and The Cognitive Style of PowerPoint. I found them compelling and ended up writing You've Got To See This for the Gurock Blog.

In the intervening year I've found ways to incorporate aspects of what I learned into my work: I've tried hard to remove the junk from my figures and charts; I've noted that when we're talking about how to talk about our data, something like small multiples can help us to visualise more of it more easily; I've encouraged members of my team to think about the difference between exploring data in a tool such as Excel, and presenting data in a chart produced by Excel.

After that experience, I thought it might be interesting to review the notes I took as I went through the books (which I did, and it was). Then I thought it might also be useful to share them (which I'm doing, and you can judge).

This short set of posts contain the quotes I took from each book, presented in the order that I happened to read them. Themes recur across the series, but the quotes don't necessarily reflect that; instead they show something of what I felt was interesting to me in the context of what I'd already read, what I already knew, and what I was working on at the time.

Beautiful Evidence
Envisioning Information
The Visual Display of Quantitative Information
The Cognitive Style of PowerPoint
Visual Explanations

All of the books are published by Graphics Press and available direct from the author at edwardtufte.com. Particular thanks go to Šime for the loans and the comments.

--00--

For Playfair, graphics were preferable to tables because graphics showed the shape of the data in a comparative perspective. (p. 32)

... small non-comparative, highly labeled data sets usually belong in tables. (p. 33)

... the relational graphic — in its barest form, the scatterplot and its variants — is the greatest of all graphical designs ... It confronts causal theories that X causes Y with empirical evidence as the actual relationship between X and Y (p. 47)

Graphical excellence consists of complex ideas communicated with clarity, precision, and efficiency. (p. 51)

Particularly disheartening is the securely established finding that the reported perception of something as clear and simple as line length depends on the context and what other people have already said about the lines. (p. 56)

... given the perceptual difficulties, the best we can hope for is some uniformity in graphics (if not in the perceivers) and some assurance that two perceivers have a fair chance of getting the numbers right. (p. 56)

Deception results from the incorrect extrapolation of visual expectations generated at one place on the graphic to other places. (p. 60)

Show data variation, not design variation. (p. 61)

Graphics must not quote data out of context. (p. 74)

If the statistics are boring then you've got the wrong numbers. Finding the right numbers requires as much specialized skill — statistical skill — and hard work as creating a beautiful design or covering a complex news story. (p.80)

Occasionally artfulness of design makes a graphic worthy of the Museum of Modern Art, but essentially statistical graphs are instruments to help people reason about quantitative information. (p. 91)

Above all else show the data. (p. 92)

The best designs ... are intriguing and curiosity-provoking, drawing the viewer into the wonder of the data, sometimes by narrative power, sometimes by immense details, and sometimes by elegant presentation of simple but interesting data. (p. 121)

John Tukey wrote: "If we are going to make a mark, it may as well be a meaningful one. The simplest — and most useful — meaningful mark is a digit" (p. 140)

Small multiples resemble the frames of a movie: a series of graphics showing the same combination of variables, indexed by changes in another variable. (p. 168)

Small multiples are inherently multivariate, like nearly all interesting problems and solutions in data analysis. (p. 169)

Tables are clearly the best way to show exact numerical values, although the entries can be arranged in semi-graphical form. (p. 178)

Given their low data-density and failure to order numbers along a visual dimension, pie charts should never be used. (p. 178)

Explanations that give access to the richness of the data make graphics more attractive to the viewer. (p. 180)

Words and pictures belong together. Viewers need the help that words can provide. (p. 180)

Thus, for graphics in exploratory data analysis, words should tell the viewer how to read the design ... and not what to read in terms of content. (p. 182)

What is to be sought in designs for the display of information is the clear portrayal of complexity. Not the complication of the simple; rather the task of the designer is to give visual access to the subtle and difficult — that is, the revelation of the complex (p. 191)

Image: Tufte, Andrew Gelman

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