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

Nathan Yau maintains one of the most famous blog about data visualization (flowingdata.com). 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.

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.

This book contains great information, but good luck keeping the pages in it. It will start falling apart within a week, and I am NOT rough on books either. I treat my books very respectfully. I literally just opened it for the second time and pages started falling out, and they continue to. Buy the ebook... not the paperback.

This book was exactly what other readers promised it would be: A broad introduction to the topic and a very solid, comprehensive technical reference book. For those with no design or development experience, you might look elsewhere for an introduction to data visualization but, for designers like myself, it was spot-on. The author does a very nice job of being conversational about a topic that doesn't easily lend itself to that style of writing. The visuals are good, the examples are topical and the references extremely valuable. I really enjoyed it. I would love to have an audio book copy, so that I could listen to the dialog while studying the visuals...that would probably be the perfect way to absorb and internalize the information.

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 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.

This is one of the better books on visualization that is on the market. Yau's understanding of data visualization is enlightening for the common data user. If you want to add data visualization to your toolbox then this is THE book to have on your reference shelf.

I bought this book on a whim and have slightly mixed feelings about it. All in all, I like it as I was very keen to learn about using other packages to present my data (and it gives a good and thorough overview of R and Adobe Illustrator in the book – both of which I am interested in using – as well as suggestions for other open source software e.g. Inkscape and paid software too). I was getting fed up of doing all my analysis in Excel – it’s not only clunky for large datasets, but I felt the output looked boring/dated and desperately wanted to make my analysis more inviting and interesting. I wasn’t sure which packages I should think about using given I have used a combination of SAS, excel and SQL before. The book has definitely given an overview of what’s out there - plenty of it is open source so I feel I can experiment without the worry of cost.Good points about the book:The layout and graphics in the book and clean and inviting and the author gives suggestions on how to label and present graphs and charts to make sure the audience understand them (this is all fairly standard stuff, but if the target audience is non-analysts then these points need to be reiterated). He also clearly explains when certain graphs will/won’t work; this isn’t ground-breaking stuff (I found myself agreeing with most of what he says) and for the seasoned analyst this is probably too basic, but it’s useful to get an idea of where a different type of graph/chart might work for your analysis. It gave me some great ideas for a big project I am working on.Bad points about the book:At times, it felt like the main focus was an intro to R. If you’ve got a decent background in R this book will no doubt be too basic for you and you are better off downloading a trial for Adobe or getting Inkscape and fiddling around with your graphs to make them look more interesting – if this is what you want to do.The writing style is very informal and a bit too ‘chatty’ for my liking (perhaps all books are heading this way?) I imagine this approach would work in a video format or for a lecture, but in a book I prefer a more formal style. The casual language in the book didn’t work for me.Things I am not sure about:I am not entirely sure who this book is aimed at – analysts or non-analysts, or both? If you’ve got an analytical background, the content might be too simplistic for you, but if you want fresh ideas because the presentation of your analysis feels a bit stale then this book will help.Buy this book if you’ve got an analytical background and are wanting to learn how to do very basic data analysis in R and then improve the graphs in Adobe (as the first 6 chapters do a lot of this!) As others have said, plenty of code is provided so you can plot basic graphs in R. If you want to practice examples, there are links to the datasets on the author’s website so you can easily read the data into R and create the output yourself. If you're a non analyst, you might want to get a more technical book first before you reach for this so you're clear on the basics (e.g. what a histogram is and how you interpret it).

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.

This book concentrates on the visual presentation of numerical data. Most books on this subject that are already out there either focus on the data or the presentation but rather uniquely this one picks up on how these two ends of the spectrum meet in the middle. The techniques for extracting data from various sources, exploring the data and then selecting clear visualisations that enable further exploration and discovery are all presented in clear, practical examples that can be worked through. You won't find any meaningless data-porn here, just modern techniques for developing elegant and beautiful visualisations!The book seems to be aimed at beginning or lower-intermediate data designers who are comfortable with either design or statistics (but not necessarily both) but it's good refreshing read even if you're already familiar with the content as the text contains many insightful thoughts from the author. I'm giving it 4 rather than 5 stars as it would've been nice if the book was rounded off with with one or two more challenging examples.

The book is definitely nice for people who want to get their hands dirty and start visualising right away. The author gives simple, easy-to-understand tutorials on how to use the different software packages (mainly R and Adobe Illustrator). The extent of links and other references is big and sure enough offers a lot of future possibilities. The book is nicely printed, the illustrations are good quality which helps a lot when reading.The downside for me was that I was looking more for theoretical concepts behind it (my own research is in data sonification and I wanted to see what visualists do). The author seems (at least to me) to speak for personal experiences, he does not refer to scientific parameters which is a bit of a miss to me. As such, the most interesting part was the first chapter on data gathering.Conclusion: do you want to get a quickstart with visualisation, read this book. If you want to read something more theoretical, look further. The links to different datasites are interesting nevertheless

I bought this book to get a different viepoint on how to generate graohics which were not just your usual two colour bar charts etc. and this book doesn't dissapoint!. The author presents a dazzling array of options together with the R code required to generate them but some of them are definately not for the novice user. Overall the book has an esy style and can be dipped into as required. Explanations are good and it is well worth the money.

I'm doing an MSc in Data Science and this book was fantastic for looking at visualisations. There are good pictures, code examples and written to take you on a journey of visualisation discovery. I have read it twice now and I consider it the best I have read .. so far.

Filled full of great content for design tips, appropriate charts to use in certain situations. I read it quickly and was able to learn a lot of useful things.

Die Internetseite des Autors ist mitunter eine Goldgrube. Das Buch ist dagegen eine Enttäuschung. Es ist oberflächlich, total nicht originell und banal. Wenn man ein Gespür dafür entwickeln möchte, wie Daten effektiv visualisiert werden können, dann kauft man sich das "R Graphics Cookbook" und ggf. noch "R in Action", um sich mit der Programmiersprache R vertraut zu machen.Fazit: Für das Buch leider nur ein Punkt. Und nochmal: Die Website des Autors ist eine ausdrückliche Empfehlung.

Visual analytics practitioners can't afford to ignore this book.

Just an awesome book for visualisation professionals

This book describes some basics for creating graphs. It is well organized and provides a good introduction to creating graphs; also hints at what you should avoid in graphs. It is however a book more suitable for beginners.

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

Not very detailed, but good enough for anyone to start on the subject. Audience should be beginner to intermediate.

Great primer

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