The review trends of Yau's last book have already started with this edition: "too basic." Maybe we could graph the stats of those reviews, then look at the very topmost band of readers to find the "perfect" audience, vs. the large body of outliers who will trash this as oversimplistic. So, get into alpha, and visualize a bell curve, with "perfect for me" on Y and age/experience on x:
DON'T BUY IF:
--You're in the heavily skewed, lightly shaded, experienced right side of the curve, with even good basic experience in data presentation. I'd include any mid level manager who has decent powerpoints in this group. The colorful pictures are gorgeous, as in Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, and if you have a LOT of disposable income, you "could" buy it just for the pictorial ideas (paper is coated matte, images are 4 color, very high quality book production wise). If you're a post undergrad freshman, you might find the advice too basic. There also are a lot of discussions of data "types" but very little about psych. For example, starting a presentation with the statment "My purpose here is to INFORM" often gets audience hackles down if they're resistant to being sold or convinced-- not much of that is covered here.
--You're a graphic artist or graphic pro, unless, again, you're just looking for pictorial and presentation ideas, and not advice (the illustrations, as in the last edition, are stunning).
--You're very new to data presentation and aren't even sure whether red goes with green or tables are better than scatters in a given situation.
--You're, again, looking for VISUAL ideas to supercharge your presentations, NOT programming tips or even English advice on details. ONE EXCEPTION to this volume compared to Yau's last book: there ARE a good number of example visuals by artists other than Yau (although his are still astonishing), and in THOSE CASES, the author does give the website. In some cases, these are just bigger online pictures of the graphics, in others, there actually is an explanation of the techniques.
Now, for the good stuff. If you KNOW that this book is NOT for pros, you won't buy it, then downstar it because you're disappointed. JUST DON'T WASTE YOUR MONEY if you are looking for comparisons between R and visual basic, steps on translating LaTex and PostScript to .jpg, etc. The level of technical advice amounts to: "R is being used by more and more researchers and statisticians" (and that not until p. 283 of 290). There ARE a number of examples of open source and other software like indiemapper, GeoCommons, ArcGIS, Gephi, Imageplot, Treemap, Tilemill, etc. but the author only mentions them, and leaves you the autodidactic task of figuring out, for example, which do and don't work with Python, RSS, PHP, HTML5 and other pertinent questions pros would ask. But think about this: if you ARE very new to presentation, these tips WILL be eye openers and of great value, as you could surf for hours and not be able to compare or value what's worth it and not. At least beginners get a head start on what this very experienced statistician and author USED throughout the book.
The biggest problem I saw with previous reviews is that the purchasers seemed to expect detailed explanations of how the author created the stunning graphics. This is NOT that book. The software is still not always mentioned with each visual, and steps are really NEVER given that detail "how to" get that effect, let alone scripting, code, or even pseudocode. The book is truly more of a coffee table text showing best practices, as an artist would, but not giving a tutorial on techniques. I know you've watched some tutorials on YouTube that are really "show off" steps by the programmer, with no real intention to show you how to do it. This isn't that bad, as it does have many important "rules of thumb," especially on mistakes to avoid if you're a novice.
So, people who say this is a must buy, or people who say this is a waste of money are both wrong. The solution to that axis of opinion is an intesecting plane visual-- if you're relatively new, don't expect technical detail, and love to get visual ideas and inspiration, you won't go wrong with this volume. If you're expecting to learn tricks and tips in R vs. Excel, get dashboard and data texts on those specific programs instead, and you'll be much happier. Expect a lot of beauty, but not how to get there!!!
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