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The Visual Display of Quantitative Information, by Edward R. Tufte
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The classic book on statistical graphics, charts, tables. Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs. data variation. Sources of deception. Aesthetics and data graphical displays. This is the second edition of The Visual Display of Quantitative Information. Recently published, this new edition provides excellent color reproductions of the many graphics of William Playfair, adds color to other images, and includes all the changes and corrections accumulated during 17 printings of the first edition.
- Sales Rank: #28788 in Books
- Published on: 2001-05-01
- Number of items: 1
- Binding: Paperback
- 200 pages
Most helpful customer reviews
5 of 5 people found the following review helpful.
A fantastic introduction - and a recommendation
By Todd
If you want to improve your ability to analyze and visualize data, Tufte's book is a great place to start.
The book provides an excellent blend of practice, tactical advice that can be applied to any Excel or PowerPoint graph - and discussions about the nature of data that seed ideas about more powerful and complex visualizations.
Being an old book, many of the examples are historic and hand-drawn. If you're looking for click-by-click advice on how to make your next PowerPoint prettier, you might get frustrated by this book. But if you're interested in making your graphs more information-rich and analysis-friendly, you've come to the right place.
If you're interested in this book, I also highly recommend another book - Visual Complexity by Manuel Lima - http://www.amazon.com/gp/product/1616892196/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=1616892196&linkCode=as2&tag=todmedblo-20&linkId=UHSORVFCZCO444LG
Lima's book is in many ways a modern counterpart to Tufte's, discussing analysis and visualization in a world of networks and big data. While both are fantastic reads for the data nut, I still believe that Tufte's is more useful for someone looking to get a first crash course at understanding data.
I hope you found this helpful!
9 of 9 people found the following review helpful.
Data decorators, data-ink, instant chartjunk, and naked women
By Abhinav Agarwal
Perhaps the single most accessible book on data visualizations. You are given a tour of the history of visualizations, the seminal contributions of people such as Playfair, Tukey, and others, a rogues' gallery of sorts of awful visualizations, a peek into small-multiples visualizations, and an exposition of the principles of good graphic design and visualizations. A must-have book for anyone interested in good data visualizations.
Tufte's contention is that a lack of adequate knowledge and expertise and a mistaken notion about numbers are to blame for bad visualizations. The principles of good visualizations, on the other hand, are few and simple. The book is all about exposing bad examples and enunciating these good principles, beautifully illustrated with examples, and printed on excellent quality paper.
Suggested Reading:
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Supplement this excellent book with at least the following, if you are interested in digging deeper into the area of data visualizations:
- Information Visualization, Second Edition: Perception for Design (Interactive Technologies)
- Information Dashboard Design: The Effective Visual Communication of Data
The rest of the review can be best told, in my opinion, through quotes from the book:
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"The theory of the visual display of quantitative information consists of principles that generate design options... The principles should not be applied rigidly or in a peevish spirit... and it is better to violate any principle than to place graceless or inelegant marks on paper. Most principles of design should be greeted with some skepticism." [page 191]
While seemingly a trivial matter, the issue of the size of charts, whether they should be tall or horizontal, Tufte states that "Graphics should tend toward the horizontal, greater in length than height..." and "Many graphics plot, in essence (cause and effect) and a longer horizontal helps to elaborate the workings of the causal variable in more detail." [pages 186, 187]
Time-series displays are at their best for big data sets with real variability. [page 30]
Chapter 2, "Graphical Identity" Is a stunning collection of graphs that distort, lie, deceive, and exhibit all manners of skills other than those required for data visualizations.
"Much of twentieth-century thinking about statistical graphics has been preoccupied with the question of how some amateurish chart might fool the naive viewer. ... At the core of the preoccupation with deceptive graphics was the assumption that data graphics were mainly devices for showing the obvious to the ignorant. ... The assumption led down two fruitless paths in the graphically barren years from 1930 to 1970: First, that graphics had to be "alive", "communicatively dynamic," overdecorated and exaggerated.. Second, that the main task of graphical analysis was to detect and denounce deception." [page 53]
"A graphic does not distort if the visual representation of the data is consistent with the numerical representation." [page 55]
Which leads to his definition of the term, "Lie Factor", which he defines as the "size of the effect shown in graphic" divided by "size of effect in chart".
"Another way to confuse data variation with design variation is to use areas to show one-dimensional data" [page 69]
An example cited is the depiction of "the rate of inflation", for which, "graphs show currency shrinking on two dimensions, even though the value of money is one-dimensional." [page 70]
A very important observation quoted in Chapter 3 comes from Howard Weiner - "Perhaps the reason is an increase in the perceived need for graphs ... without a concomitant increase in training in their construction." [page 79]
Tufte elaborates: "Nearly all those who produce graphics for mass publication are trained exclusively in the fine arts and have had little experience with the analysis of data. ..." "... many graphic artists believe that statistics are boring and tedious. It then follows that decorated graphics must pep up, animate, and all too often exaggerate what evidence there is in the data." [page 79]
And "The doctrine of boring data serves political ends, helping to advance certain interests over others in bureaucratic struggles for control of a publication's resources. ... as the art bureaucracy grows, style replaces content. And the word people, having lost space in the publication to data decorators, console themselves... " [page 80]
Tufte defines "data-ink" in Ch 4 ("Theory of Data Graphics") as "the non-erasable core of a graphic, the non-redundant ink arranged in response to variations in the numbers represented
Data-ink ration = data-ink / total ink used to print the graphic" [page 93]
So, it should not come as a surprise, when Tufte takes a single bar with a value label at the top of the bar, and states that "the labeled, shaded bar of the bar chart, for example, unambiguously locates the altitude in size separate ways." [page 96].
Chapter 5 - "Charkjunk: Vibrations, Grids, and Ducks" is perhaps the most humorous chapter, as the title itself suggests. A quote from Johnathan Swift, indicting 17th-century cartographers, says it all - "With save pictures fill their gaps, And o'er unhabitable downs, Place elephants for want of towns." [page ] ouch!
"This may well be the worst graphic ever to find its way into print:" [page 118] refers to a "series of weird three-dimensional displays appearing in the magazine American Education in the 1970s (that) delighted connoisseurs of the graphically preposterous. Here five colors report, almost by happenstance, only five pieces of data..." [page 118]
You may not, and I certainly did not agree with Tufte's suggestions for maximizing the data-ink efficiency of the box-plot, in the chapter on "Data-ink Maximization", but they are worth examining nonetheless. However, his redesign of the bar chart, with a border and other accouterments, on pages 126-128, are excellent.
Many examples of bad visualizations cited in the book are from the "New York Times", so it is sort of reassuring when you see that the quality of visualizations on the NYT has improved a lot, and are frequently the objects of animated discussions. There may be hope, after all.
The review title, explained, at least part thereof:
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And what about that slightly inappropriate word in the title of the review?
Tufte writes that an art director with overall responsibility for the design of over 3,000 graphics annually had this to say - "graphics are intended to more to lure the reader's attention away from the advertising than to explain the news in any detail. 'Unlike the advertisements,' he said, 'at least we don't put naked women in our graphics.' " [page 80] We must be all thankful for small mercies, I suppose.
3 of 3 people found the following review helpful.
The Landmark Book on Conveying Information Graphically
By Karen & Aneil Mishra
I own all of Edward Tufte's books, and regularly order his booklets for my MBA students. The reason is simple: to make good decisions, and to help others make good decisions, one must convey data as information and not simply as numbers, words, or even pictures. Business periodicals regularly violate the admonitions we learned in our introductory statistics courses, including failing to use zero as the bottom of any scale (these periodicals don't use zero in order to exaggerate changes). The reason that intelligent people convey data inappropriately is either to deliberately distort it, or because they've failed to read Tufte's books.
Once you've purchased this first book by Tufte, you will never look at charts or other graphical displays without a jaundiced eye. You will also will begin to be more honest in how you convey information to others. You will make better decisions, and you will raise the standard for other communicators and decision makers. Life and death decisions do get made on the basis of data, and not just in the sciences and medicine. Buy this book and you will have a very tough time putting it down.
Aneil Mishra
[...]
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