Writing

Blood, guts, gore and the data fairy

The O’Reilly press folks passed along this review (PDF) of Visualizing Data from USENIX magazine. I really appreciated this part:

My favorite thing about Visualizing Data is that it tackles the whole process in all its blood, guts, and gore. It starts with finding the data and cleaning it up. Many books assume that the data fairy is going to come bring you data, and that it will either be clean, lovely data or you will parse it carefully into clean, lovely data. This book assumes that a significant portion of the data you care about comes from some scuzzy Web page you don’t control and that you are going to use exactly the minimum required finesse to tear out the parts you care about. It talks about how to do this, and how to decide what the minimum required finesse would be. (Do you do it by hand? Use a regular expression? Actually bother to parse XML?)

Indeed, writing this book was therapy for that traumatized inner child who learned at such a tender young age that the data fairy did not exist.

Wednesday, July 23, 2008 | iloveme, parse, reviews, vida  
Book

Visualizing Data Book CoverVisualizing Data is my book about computational information design. It covers the path from raw data to how we understand it, detailing how to begin with a set of numbers and produce images or software that lets you view and interact with information. Unlike nearly all books in this field, it is a hands-on guide intended for people who want to learn how to actually build a data visualization.

The text was published by O’Reilly in December 2007 and can be found at Amazon and elsewhere. Amazon also has an edition for the Kindle, for people who aren’t into the dead tree thing. (Proceeds from Amazon links found on this page are used to pay my web hosting bill.)

Examples for the book can be found here.

The book covers ideas found in my Ph.D. dissertation, which is basis for Chapter 1. The next chapter is an extremely brief introduction to Processing, which is used for the examples. Next is (chapter 3) is a simple mapping project to place data points on a map of the United States. Of course, the idea is not that lots of people want to visualize data for each of 50 states. Instead, it’s a jumping off point for learning how to lay out data spatially.

The chapters that follow cover six more projects, such as salary vs. performance (Chapter 5), zipdecode (Chapter 6), followed by more advanced topics dealing with trees, treemaps, hierarchies, and recursion (Chapter 7), plus graphs and networks (Chapter 8).

This site is used for follow-up code and writing about related topics.

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