Writing

Salary vs. Performance for 2009

go tigersI’ve just posted the updated version of Salary vs. Performance for the 2009 baseball season. I had hoped this year to rewrite the piece to cover multiple years, have a couple more analysis options, and even to rebuild it using the JavaScript version of Processing (no Java! no plug-in!), but a busy spring has upended my carefully crafted but poorly implemented plans.

Meanwhile, my inbox has been filling with plaintive comments like this one:

Will you be updating this site for this year? It’s the first year I think my team, the Giants would have a blue line instead of a red line.

How can I ignore the Giants fans? (Or for that matter, their neighbors to the south, the Dodgers, who perch atop the list as I write this.)

More about the project can be found in the archives. Visualizing Data explains the code and how it works, and the code itself is amongst the book examples.

Thursday, July 2, 2009 | inbox, salaryper  
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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