This Is Not The Michael You're Looking For's Reviews > Visualize This: The FlowingData Guide to Design, Visualization, and Statistics

Visualize This by Nathan Yau
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Feb 29, 12

bookshelves: nonfiction, science, math-stats, own
Read from January 16 to February 29, 2012 — I own a copy, read count: 1

Visualize This is a book about designing visualizations for data ("graphs" more or less, although there are visualizations which are not, strictly speaking, graphs). The focus of the book was not what I expected; given that the author is a graduate student in statistics, I expected the book to have more of a scientific focus. Instead, it is mostly focused on designing visualizations for websites and/or newspapers and magazines. While there can be a lot of overlap between these tasks and more directly scientific output, the focus made the book less useful and interesting to me than I had hoped it would be.

In some sense, the book is schizophrenic. It talks a lot about visual design, but is more holistic than anything one might consider to be visualization theory. At many times the book is more focused on how you create a graphic (i.e., specific software, tools, code, etc.) rather than what the graph should or should not contain. The tool use is mostly focused on a combination of R and Illustrator (or Inkscape), but occasionally wanders (for little apparent reason at times) into Python, Javascript, Flash, ActionScript, and other similarly random packages. Since it's not a textbook on their use, I question how useful these examples are for the greater purpose of learning. Is the book about what graphs should (and should not) look like to maximally display the information/tell the story you wish to convey or is about the mechanics of getting a computer to draw a specific style? It tries to be both and, therefore, somewhat fails at both. It is also a bit overly redundant at times; since many of the chapters were explicitly designed to allow a reader to jump in and read independent of the rest, the same basic tasks and ideas are often introduced in multiple chapters, which is frustrating to someone who sits down to read it cover-to-cover.

This is not to imply the book does not contain useful information, it certainly does. It introduces a decent variety of graph styles, many of which are likely unfamiliar to readers. It does discuss valuable resources of which the reader may be unaware, such as Inkscape (a free, open-source alternative to Adobe Illustrator) and 0to255.com (a website for helping choose color schemes). When it does slip into visualization theory the advice is solid and to the point (e.g., how to scale bubble plots so the area of the bubbles properly reflects relatives sizes).

Although I was clearly disappointed in the text and have many criticisms, in the end it has enough advantages to be a worthwhile read, particularly for those who are complete newcomers to visualization or who have a particular interest in data visualization for the web.
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01/16/2012 page 22
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