The Visual Display of Quantitative Information

The Visual Display of Quantitative Information is an absolute classic on the creation and use of graphs. Done correctly, a good graph can make complex information instantly comprehensible, reveal relationships and patterns, and both delight and inform. Done poorly, a bad graph causes eyestrain, confusion, and the deliberate obfuscation of the truth. And in a world where graphs are ordinary, Tufte provides a quick history of how they came to be, and the cognitive leaps required.

Tufte rails against the sins of bad graphics: scaling and axes that lie about trends in the data; the use of unnecessary ink to convey redundant information; visual clutter and bad aesthetics. He advocates for a kind of elegant minimalism, conveying the most show more information with a few well-chosen lines of varying weights, and cleverly using edges and white space to mark boundaries, while supporting information with text. The advice is for a pre-computer graphics era (at least in my signed 1983 edition), but the aesthetics still hold, even if we aren't drawing graphs with a marker and straight-edge.

The problem is that Tufte turned out to be a voice crying in the wilderness. There are the majors flaws, like the use of flashy cluttered "infographics" that combine the worst features of text-heavy articles and data graphics. But then there is the minor things. I have at my fingertips about a half-dozen data visualizations packages, from Excel (boo!) to ggplot and bokeh. And not a single one, by default, does everything that Tufte says. They get close, but the defaults are not quite minimalist enough. And truly great graphs, like Minard's plot of Napoleon's invasion of Russia, with his army vanishing into the snows, still require an artist's touch. show less

“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 the difficult — that is, the revelation of the complex” (191).

Edward Tufte, the curmudgeonly statistician and information designer, wrote this book back in the 1980’s and self-published it in order to maintain artistic control over the layout and presentation of the material. As a result, this book was expensive and difficult to find. This version is the second edition, published in 2000. It was no easier to find. It is worth putting your hands on a copy, however, to view some of the truly artistic data visualizations show more included.

These include the Marey graphic train schedule (31) where the X-axis is time, the Y-axis is cities, and the line segments are train routes (source: E.J. Marey, La Méthode Graphique (Paris, 1885), p.20)

And the Minard graphic depicting the diminishment of Napoleon’s forces during the 1812 campaign against Russia (40), where the tan shape shows the dwindling size of the army en route to Moscow, and the black shape the reduction of the army on the retreat (source: Charles Joseph Minard, Tableaux Graphiques et Cartes Figuratives de M. Minard, 1845-1869, Bibliothéque de l'École Nationale des Ponts et Chaussées, Paris.)

Most of the examples are more pedestrian, drawn from newspapers and news magazines, anywhere people attempt to communicate statistical information visually. Tufte’s main points are that graphics ought to communicate clearly, efficiently, and aesthetically. Graphics should support readers in seeing the data points, understanding relationships among data points, and making decisions on the basis of that data. Graphics that fail in this work tend to either obscure the data, unintentionally add dimensionality where it is not intended, clutter presentation with inessential information, and frustrate decision making by making data comparison difficult.

He uses a couple of concepts to illustrate these pitfalls.

- Chartjunk: the junk visual information that decorates but adds no meaning to a graphic
- Data-Ink: a ratio of data presented to ink used. When all ink conveys data the ratio is 1:1.
- Lies: a score representing the “size of effect suggested by ink used” divided by the “actual size of effect in the data” — the higher the number, the bigger the lie

The terms are catchy, easy to understand, and copiously illustrated throughout the text. There are plenty of negative examples used to illustrate what is wrong and some truly inspired exemplars of what goes well (see the two examples above).

Tufte also attempts to fix some faulty graphics by showing how to apply guidelines like 1) draw attention to the substance of the data, 2) erase non-data-ink, 3) eliminate chartjunk, 4) supply context, 5) don’t add visual dimensions unless it maps to actual data points. The result of these revisions are elegant and sometimes sparse minimal looking graphics, some more usable than others.

I’m not always convinced that Tufte improves the graphics he works on because some reduce enough visual information to be difficult to interpret. He claims that his revisions work, however, because he can understand them and so should any reasonable consumer of data. He also admits that the revised graphics are unconventional but learnable. That may be true, but is the effort needed to learn non-conventional presentation much of an improvement over the presumed errors of interpretation that coincide with wasteful and poorly design but conventional graphics? The evidence just isn’t there to prove his point. Frankly, I was confused by less than 25% of the “bad” graphics that he showed and those only because they were egregiously bad (e.g., using 3-D graphics to show 1 dimension of data). The others were conventional and easily interpretable in their conventionality, even if wasteful of digital ink.

So who knows. Some nice guidelines here but to be used as usability warrants. show less

According to Edward Tufte, the purpose of graphics is, “Not the complication of the simple; rather (…) the revelation of the complex.” And his The Visual Display of Quantitative Information, first self-published nearly 30 years ago, is now a bible -- a sort of The Elements of Style applied to information graphics.

Tufte reviews how information can be presented (i.e. a minimal amount via a sentence; a moderate amount via a table; a huge amount via a graphic) and then turns his attention to graphics -- from their beginnings in cartography to how to achieve graphic excellence today.

He urges a multi-disciplinary approach, cautioning that, “Allowing artist-illustrators to control the design and content of statistical graphics is show more almost like allowing typographers to control the content, style, and editing of prose.” He touches on psychology and cognition. He rails against using graphic design to deceive, and enlightens readers by pulling numerous examples of misrepresentation from prominent media. He devotes a large part of the book to improving the effectiveness of graphs by urging the elimination of “chart junk” (e.g. moiré-effect cross-hatching) and numerous other sources of “non-data ink.” In fact, a chapter wherein he strips away seemingly necessary text, frames, hatch marks, etc. (leaving little more than an ether vapor but in the process simplifying and clarifying the meaning) is revelatory.

So many books I've read recently have referenced Tufte, and I'm glad to have finally read him directly. Highly recommended. show less

Edward Tufte's first salvo for sanity in data design. I will admit that I read the book mainly for his historical graph-porn—examples of exemplary works of design and visual analysis. But what ended up being the most enjoyable chapter was when he took some common forms and deconstructed them: took them apart, removed the unnecessary pieces, sparingly annotated what was left, and then presented it as an immensely improved graph. While design can churn out results that are best described as magical, it's always great to see the thinking involved, and the skill and process by which something mediocre is slowly shaped into something great.

"The Visual Display of Quantitative Information" by Edward R. Tufte

Rarely do I find a book that I would call beautiful, but this meets the criteria, both as a physically appealing book, apropos to the purpose of the book, and an informationally dense, and well presented one. A favorite quote of mine, from Zen and the Art of Motorcycle Maintenance, where the protagonist says; "I remember... remarking about the analytic craftsmanship displayed." This was my reaction to Tufte's book.

The book manages to decompose graphical presentation of data into categories other than the x- and y-axes, and instead talks about multifunctional elements and data density. The book reimagines the nature of numerical information using a graphical design show more perspective, with a healthy dose of common sense as to how graphs are used, and a veritable treasure trove of examples of both good and bad design.

This book, along with "How Buildings Learn," by Stewart Brand, is a rare example of a narrow focus with an incredibly broad appeal. This book is not for the narrow specialist in constructing the sometimes obscurely complex graphics displayed, but rather for anyone who is interested in the data presented to them, and certainly anyone who produces this data in any form. show less

I think this is my favourite of the three Tuftes volumes I've read. It really gets down to the principles of what makes a good graphic and I'm relieved to hear that for small quantities of data, he feels a table does the job perfectly well. I'm not sure I agree with everything he says. Some of the 'non-data ink' he describes as redundant is helpful because it follows familiar conventions. We know how to read the data because we are used to the point where the x and y axis meet, for example. But I'm still awed by the clarity of his writing and ideas and the sheer beauty of this book's production.

This book should serve as a great inspiration for anyone who undertakes designing charts or any kind of data visualisation. This is not a technical step-by-step guide for using particular tools or techniques but rather an overview of principles that apply when producing high quality data graphics.

Immaculately designed and packed with fantastic illustrations of good and bad approaches to visualisation, this book is a pleasure to read and absorb. I found that it worked well both when reading just a couple of pages at a time and when immersing myself in it for a longer period of time.

This is one of those books that I know I will be revisiting for reference in the future.