Visualize This: The FlowingData Guide to Design, Visualization, and Statistics

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.

I bought this book for my son, per his request, as a Christmas gift and he loves it. He is a game designer and works with data - data on players, on the games, and uses data to present information to his colleagues, boss and clients. This book exceeded his expectations he said. What he said he likes about it:* teaches how to scrape data from web pages* teaches the nuts and bolts* teaches you how to do it - not just a textbook approach of what visualization is* super applied* makes it easy to extract value* feels like you are being tutored rather than just taught about it

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.

While some of the links/sources are getting a little outdated (i.e. links to data sources from the web), the content of this book is great. The author provides some really easy to follow tutorials for creating good looking visualizations using a combination of the R programming language and Adobe Illustrator. I have a design background and don't consider myself great with code by any means, and I was figure out the R stuff thanks to this book (with only a little googling).

Good insights and organization of various techniques of visualizations, but the author tries to cover too many different technologies in the examples, my opinion. One time the text may describe how to scrape a web site in Python (including code for BeautifulSoup), then later how to use Tableau, Flash, Javascript and etc. I don't mind surveys, but why include code if you are surveying the landscape? And because of the limited length, the author can't go into sufficient detail to do any of these justice.

Visualization is still not covered very well. This is one of the books to help. The big difference: It is not beholden to any one system. Rather than trying to do everything in R or another statistics language this one shows you how to make publication quality graphics by importing R png files into photoshop and others and really make them shine.Personally, I do all my figure labels, legends and annotations in photoshop after generating the graphic in R. It is so much faster and prettier.

I'm a big fan of Mr. Yau's Flowing Data blog and purchased this book as soon as it came out. There is no doubt in his knowledge and understanding of visualization and this is a great resource for those actually creating visualizations. I myself am not a programmer and so a lot of the jargon and code suggestions are not really relevant to what I'm looking for. This should absolutely not detract from Mr. Yau's book, if you want to learn how to create visualizations, then this is the book for you. I just think there may be an opportunity for another volume.

A good intro book - the wiley prints i have bought so far are indian cheap paper versions - but this is printed beautifully in colour

I needed textbook and this is it.

Lo consiglio a chi vuole trovare spunti efficaci ed assolutamente "d'avanguardia" sul data management e la coesione tra input ed output

Nathan Yau est l'un des jeunes experts de ce jeune domaine. Féru de stats et de design, l'auteur contribue beaucoup à la réflexion sur la création de dataviz. Certaines pages restent quand même très basiques. Ce n'est pas non plus un livre de recettes : pas de scripts, d'algos, etc... mais une démonstration de la façon dont l'auteur approche chaque cas d'analyse de données. Ça manque toutefois un peu de profondeur.

I bought this book on a Friday with delivery the following day. All of that weekend I wasn't able to put the book down and even now I am always flicking through the pages for inspiration. It is a must read for anyone who handles data and/or prepares reports based on data, and is simply beautiful in its presentation. It is clear that every aspect of this book has been carefully considered, from the typeface to the page layout.This book will open your eyes to what is possible once you move away from Microsoft Excel. As a professional analyst and data modeller, I have been using Excel for years but was growing frustrated with its limitations. In this book, Nathan Yau uses R, Python and Adobe Illustrator (though I personally prefer the open-source Inkscape equivalent) to show just what can be achieved with a little imagination and creativity.I have given this five stars. Although it would have been nice to have more complex walk-throughs from raw data to final graphic as suggested in other reviews, to do so would have required the reader to have a solid foundation in R and Python programming. To include the required learning material in these programming languages so as to bring the reader up to speed as a programmer, as well as containing the excellent material it already does contain, would have required a book three or four times the thickness. If we were then to add a needed introductory statistics course into the book as well...I think therefore to penalise the book for focusing purely on the creation of great looking graphics is a bit harsh especially when it says "Visualise", "design" and "visualisation" in the title.That said there is a plethora of free PDF guides to R and Python (and Inkscape) legally available for download from the internet and of a high, publishable quality. These guides will take the reader from basic programming to intermediate level and beyond. See the documentation page on the R website or google "A Byte of Python" for an excellent, and free, beginners guide to Python programming.So all in all this book will not teach you how to be a great R/Python programmer or statistician for that matter, but it will give you more than enough inspiration to motivate you away from Excel charts and towards teaching yourself powerful professional techniques that will make your presentations/reports stand out and make you a great data visualiser.A simply beautiful book.