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on January 10, 2012
I was really hoping for a book on how to abstract data sets into visualizations, with concrete programming examples. In other words, "ask yourself these questions about the data; with these answers (or those), the data is best visualized in these formats. Now, let's implement".

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.
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on August 8, 2011
I really enjoyed this book. It is absolutely beautifully printed and the examples are well made and well explained. There are a couple of things I would have liked to see done a little differently.

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 Even with my outdated copy, not everything worked for me.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.
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on January 1, 2012
If you are looking for a book on how to use Illustrator and R to create various visual elements, this book may be for you. It shows you examples using various tools, and is more of a guide to creating charts and other elements using those tools. This book is not for people not interested in using R or Illustrator or Excel for that matter. If you are looking for a book that will outline how you should THINK about presenting data that you have, and give you constructive ideas, this book is not for you. The FlowingData blog has a bunch of great tips for that, so that is what I was expecting. However, when I saw a bunch of screenshots and sample code, this is more of a programming guide than a design book.

In addition, the first chapter in the preview is not representative of the book, IMO.

A more apt title would be "How to use Adobe Illustrator and R to Create Charts".

If you are looking for a 'How To' guide, this might very well be your book. I'm rating it low because I was expecting a book that would give me design principles and guides, not show me code samples and tool screenshots.
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on July 20, 2011
This is a nice addition to the books on data visualization. It will be particulary useful for people wanting to learn R (the lingua franca of statisticians) to create good looking visualizations. The writing style is crisp and conversational and is organized around the kind of things one might want the data to communicate: time series, part-to-whole comparisons, relationships, etc. It does not require any expertise in programming or statistics to understand.
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on September 24, 2011
I have been teaching data management and visualization for 12 years and I have never seen a book that covers visualization so well for such a broad audience. It braids together the very best tools of the trade for scientific data visualization, graphic design concepts and "how-to" advice. It gives a friendly introduction to tools like R, Illustrator, XML, Python (with BeautifulSoup), JSON, etc. (and the toolkit goes on-and-on). Also it gives complete working code examples to show how to scrape data from the web for analysis and visualize the info without swamping the reader with details. It has a HUGE set of references and free tools for getting interesting data-sets (everything from sports to science to politics to health), reformatting data and making graphics that are ready for mass media or scientific publication.

There is very little to complain about here except the fact that the author shows off Illustrator instead of its less expensive competitors. I had avoided Illustrator because of cost and the nasty learning curve but now, thanks to this book, I am using it to edit my SAS and R graphics that were "almost perfect." Happily this book has great examples for showing how to manipulate/clean up scientific graphics without getting bogged down in the endless complexity that is Illustrator.

So, this is all around beautiful, friendly and worth every cent if you need to make high quality graphics.
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on February 2, 2015
One star for this, thought it might be an okay or even excellent book for what it sets out to do.

One star, though, because the publisher declines to make clear anywhere on the cover that one must be able to code in R to understand this book. I mistakenly thought this was just a brainier substitute for Dona Wong's excellent but low-level "The Wall Street Journal Guide to Information Graphics: The Dos and Don'ts of Presenting Data, Facts, and Figures."

Not the case. The text is written, often, in computer code. (Not in a sidebar, not in an appendix, etc.)

It's a coding book.
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on July 12, 2011
I'm a long-time fan of Nathan Yau's FlowingData blog, so I pre-ordered this book as soon as I saw that it was coming. This book is exactly what I hoped that it would be: a collection of introductions to various analyses, data sources, and absolutely gorgeous examples of data visualization.
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on December 29, 2013
The book introduces several interesting stepping stones of the process of showing your data more attractively to audience or telling you story better (some times at all even). However, the book is tailored through by computer codes (indeed scripts) that allow to generate discussed examples of the visualisation automatically in web browser from raw files. Even though I can code and I understood (and acknowledged the elegance) of the codes, they still somehow punch the visualization value added of the book. If you can convince yourself not to freak out about scripts (and accompanying passages) in the text, I strongly recommend to read this book, as I am already very advanced I this matter and still I have found it resourceful reading. If you puke upon seeing math equations or computer codes, please avoid this book for the benefit of your own health.
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on March 28, 2012
The book is very well written, full of tips, tricks, how-to, and things that we should all be thinking about. As you may guess the figures are fab but are ULTRA-LOW RESOLUTION meaning that you can't read the well designed figures that are central to the book. Zooming in on these low res kindle rendered figures will make you want to gauge out your eyes.

Get the paper version of this book NOT the kindle version.

I recommend the book for applied statisticians, especially at the early masters student level so that they know how to make decent plots when they need to use them.
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on December 18, 2013
Nathan Yau maintains one of the most famous blog about data visualization ( He is also author of the book Visualize This: The FlowingData guide to design, visualization and statistics. The book is of course well illustrated, full in colors. It is based on the main idea that one can find patterns through well chosen data visualization. He covers topics such as data scrapping, formatting, basic graphs and multidimensional scaling (MDS) among others.

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.
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