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Graphical Data Analysis with R (Chapman & Hall/CRC The R Series), by Antony Unwin
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See How Graphics Reveal Information
Graphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages and the R code is available at rosuda.org/GDA.
Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. This book guides you in choosing graphics and understanding what information you can glean from them. It can be used as a primary text in a graphical data analysis course or as a supplement in a statistics course. Colour graphics are used throughout.
- Sales Rank: #540852 in Books
- Published on: 2015-05-20
- Original language: English
- Number of items: 1
- Dimensions: 9.40" h x .70" w x 6.10" l, .0 pounds
- Binding: Hardcover
- 310 pages
Review
"Overall, the book is a very good introduction to the practical side of graphical data analysis using R. The presentation of R code and graphics output is excellent, with colours used when required. The book appears to be free of typographical and other errors, and its index is useful. Also, the book is well written and neatly structured. I enjoyed reading the book and can recommend it to anyone who wants to learn more about their data through graphics using R. It will also be a valuable asset for a library and as part of an undergraduate course in applied statistics." (Journal of the Royal Statistical Society, Series A)
"… an attractive addition to the current statistical graphics texts as it demonstrates what can be learned through graphs."
―Significance Magazine, February 2016
"… the strength of this book lies in the profound introduction to the topic of graphical data analysis. The comprehensive sectional introductions and overviews along with the ‘how-to’ might well be regarded as the modern update to Tukey’s 1977 landmark book."
―Biometrical Journal, December 2015
"Antony Unwin’s very clever new book … is well written, clearly by a practitioner with wide experience, gives generally good (though sometimes opinionated) advice, and includes R code for nearly all examples, as well as nice collections of additional exercises for each chapter … Beyond the content, Unwin also does an admirable job of conveying enthusiasm for data graphics."
―Journal of Educational and Behavioral Statistics, December 2015
"This text has the potential of bringing sophisticated visualization to a broad audience without resorting to mathematical formalizations or the skills of a graphics artist. It engages the reader with interesting graphics right from the start and overall is clear and unintimidating. Code for all examples is provided in the text and is available on a supporting website. What’s more, the code works as is, rather unusual and refreshing."
―Journal of Statistical Software, November 2015
"For statisticians and experts in data analysis, the book is without doubt the new reference work on the subject."
―Thomas Rahlf, datendesign-r.de
...would also be an excellent suggested additional reading for a pragmatic graphical data analysis-oriented course.
―Reijo Sund, Centre for Research Methods, University of Helsinki
About the Author
Antony Unwin is a professor of computer-oriented statistics and data analysis at the University of Augsburg. He is a fellow of the American Statistical Society, co-author of Graphics of Large Datasets, and co-editor of the Handbook of Data Visualization. His research focuses on data visualisation, especially in interactive graphics. His research group has developed several pieces of interactive graphics software and written packages for R.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Less practical than I hoped
By Dimitri Shvorob
This high-quality book was just what I needed after going through several titles from Packt, the publisher that said "bye bye" to editors and graphic designers, and is flooding the market with typo-ridden quickies by first-time authors. Now, this is a proper book, carefully written - with end-of-chapter exercises and a substantial bibliography - and nice to look at.
Unfortunately, it is also an unhurried, verbose book which teaches less than it could given its page count. Thoughtful text flows, and varied examples are shown; however, one at some point realizes that the variety of the datasets on display - found in various R packages - is not matched by the variety of chart types. The repertoire is surprisingly narrow. Bar charts dominate; I remember coverage of the parallel-coordinates plot, and of the mosaic plot, and two plots visualizing missing values ... what else? The book focuses on "ggplot" - good - but, though normally methodical, does not actually explain that "grammar of graphics", and anyway sticks to simple bar charts, so is not even in the same league as the "ggplot"-based books by Hadley Wickham and Winston Chang.
I try on the hat of a marketing person, and wonder about the book's "unique selling proposition": it's not the breadth, it's not the depth, what is it then? "Value for money" is a second question - you can buy both Wickham's and Chang's books for what this one costs. I would start with Chang's.
UPD. With the benefit of a little more life experience, I would say: don't spend your time on R at all. Python is the way to go.
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