- Paperback: 384 pages
- Publisher: Wiley; 1 edition (July 20, 2011)
- Language: English
- ISBN-10: 0470944889
- ISBN-13: 978-0470944882
- Product Dimensions: 7.3 x 0.9 x 9 inches
- Shipping Weight: 2 pounds (View shipping rates and policies)
- Average Customer Review: 81 customer reviews
- Amazon Best Sellers Rank: #69,108 in Books (See Top 100 in Books)
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Visualize This: The FlowingData Guide to Design, Visualization, and Statistics 1st Edition
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From the Back Cover
See your data in new ways
Our world is awash in data. To mean anything, it must be presented in a way that enables us to interpret, analyze, and apply the information. One of the best ways to do that is visually.
Nathan Yau is a pioneer of this innovative approach. In this book, he offers you dozens of ideas for telling your story with data presented in creative, visual ways. Open the book, open your mind, and discover an almost endless variety of ways to give your data new dimensions.
Learn to present data with visual representations that allow your audience to see the unexpected
Find the stories your data can tell
Explore different data sources and determine effective formats for presentation
Experiment with and compare different visualization tools
Look for trends and patterns in your data and select appropriate ways to chart them
Establish clear goals to guide your visualizations
Visit the companion web site at www.wiley.com/go/visualizethis for code samples, data files you can download, and interactive examples to show you how visualization works
About the Author
Nathan Yau is a PhD candidate in Statistics at UCLA and a lifelong data junkie. His goal is to make data available and useful to those who aren't necessarily data experts, and he focuses on data visualization and personal data collection. You can follow his visualization experiments at http://flowingdata.com.
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Top customer reviews
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Instead, I found it to be a kind of "circular" logic (visualize data in good ways is important... here is some data visualized in a good way... now doesn't that show how important it is - and it's cool... btw here is a code snippet). It is almost like the book is just trying to convince me that data visualization can be powerful and cool. I know that - that's why I bought this, I wanted to learn the tools and techniques to determine the best and most innovative way to visualize data sets, not how the author has visualized existing data sets he has dealt with.
Interesting enough to borrow if you see it on a friend's desk, but I don't think I'd purchase it again if I had the opportunity.
First, every example uses Adobe Illustrator to make the visualization look as good as they do. In order to complete the exercises, you must have Illustrator. Nathan does explain that it can be obtained at a discount or you can an older version, but it's still a pretty big financial investment. If I hadn't been able to dig up a old copy, Illustrator 9, I would have been out of luck. Even with my outdated copy, not everything worked for me. If he had included at least a couple of examples with the open source Inkscape, this would have been a 5 star rating.
The second thing I would have liked to see a little different is more statistical info to go along with the visualizations. We often visualize data to help make decisions. Nathan shows how to display a LOESS line to see the best fit for the curve, but he stops there. Maybe discussing R² ( correlation coefficient) analysis to determine whether the values are are a good match would help me feel better about analyzing the data beyond just visualization.
That said, this is an extremely well written book and easily deserves 4 stars. Dig up an old copy of Illustrator (preferably CSx versions) and enjoy this book.
The book describes several visualization methods. For each topic, Yau starts with a quick overview of the technique. He then follows with programming details (for example using R). He eventually shows the way from standard R graphics to nice visualizations using Illustrator. The book is thus very practical, with few place for theoretical concepts.
Yau provides several good advices such as the importance to question your data. The books contains tips and tricks for preparing and programming graphics. It is sometimes more of a R user manual than a general book on the topic. To be noted the excellent Chapter 7, about visualizing multi-dimensional data. This book is a must-have for people who want to prepare nice graphics in R. For expert users, the book is too straightforward (out of the last few chapters). For others, it’s a nice non-theoretical journey in the world of data visualization.