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3.7 out of 5 stars13
3.7 out of 5 stars
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on 16 July 2011
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.
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on 23 July 2012
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.
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on 27 May 2013
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
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on 9 February 2016
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).
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on 23 September 2013
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.
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on 9 August 2013
I don't usually review books but this one's utility leapt out at me as I leafed through it. Not only does it 1) provide a good introduction to some basic visualisation design principles and basic statistical/visual literacy (worth it in itself), it also provides 2) step by step instructions to enable the reader to put this knowledge into action using (mostly) free open source software - R, python, javascript etc - and in a reasonably accessible style with plenty of relevant illustrations (as you expect/need in a book about visualisation).

My main reservation about this book is that the author uses the "R" statistical software to do many of the manipulations. It is powerful and free but as he says himself "the software itself isn't very helpful in guiding new users"... and if you like "buttons and clicking and dragging" (which I do!) you will find it hard to use (p. 73). As a result you end up towards the end of the book with chunks like "the c()directive creates an empty vector, which you add to in each iteration [9 lines of code] then pass the reading_colors array into parallel instead of the lone #000000". This gives you Figure 7-24..." But some of this complexity is probably unavoidable if you want to get your hands dirty.
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on 6 May 2014
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.
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on 7 February 2013
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.
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on 12 February 2013
I was concerned that the intro to web-scraping makes no mention of the role of robots.txt and general netiquette around scraping.
The example scraper hits a 3rd party site in a tight loop with no delays - effectively a denial-of-service toolkit for the naiive programmer.

The topics on visualisations tend to whip through a variety of practical how-to examples using R and then tidying the results up in Illustrator. Protovis gets a mention but I think the action these days is increasingly around the newer d3.js framework which is not covered here. As for the theory behind effective visualisation I have seen more interesting explorations of the subject elsewhere e.g. Tamara Munzner's Vimeo presentation which covers the science behind human perception and how different encodings (color, size, position) vary in effectiveness as a result.
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on 27 October 2014
Probably a very logical book to the more technically patient of readers. I was hoping for something more 'visionary' about how to explain the key messages behind the data. This was not it. It's more my expectation from the title then this being a poor book.
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