- Series: Statistics and Computing
- Hardcover: 500 pages
- Publisher: Springer; 1st ed. 2008. Corr. 2nd printing 2009 edition (August 10, 2009)
- Language: English
- ISBN-10: 0387759352
- ISBN-13: 978-0387759357
- Product Dimensions: 6.1 x 1.1 x 9.2 inches
- Shipping Weight: 2.2 pounds (View shipping rates and policies)
- Average Customer Review: 16 customer reviews
- Amazon Best Sellers Rank: #547,942 in Books (See Top 100 in Books)
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Software for Data Analysis: Programming with R (Statistics and Computing) 1st ed. 2008. Corr. 2nd printing 2009 Edition
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John Chambers turns his attention to R, the enormously successful open-source system based on the S language. His book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stages, starting with simple functions. More advanced programming techniques can be added as needed, allowing users to grow into software contributors, benefiting their careers and the community. R packages provide a powerful mechanism for contributions to be organized and communicated. This is the only advanced programming book on R, written by the author of the S language from which R evolved.
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The text assumes that the reader is familiar with packages, generic functions, model fitting formulae, and much of the base functions and libraries. The first instance of an interaction with the R system in this text (Section 2.2, page 13 in my copy) does not quite work if you copy and paste it! The next chapter starts with "constructing a fairly complicated linear model." Again, the code snippet there will not work if you just type it in, and there is no detailed explanation of what the code snippet actually does (but it would be "obvious" to some one experienced with statistical analysis in this language). Still another example is chapter 9 which describes (mostly S4) object classes. I doubt anyone without considerable experience with object oriented programming and the generic function mechanism in R would be able to make sense of this chapter without a lot of effort; consider, for example, that the term "slot" does not even have any entry in the index!
I found the writing style formal, hard to read, and somewhat turgid. There are many seemingly bizzare choices of examples or topics, most notably an introduction to perl programming! I ended up comparing the text with the paper "Evaluating the design of the R language" from the ECOOP 2012 proceedings (easily found on the web). In a few pages that paper seemed to provide a considerable portion of the insight that this book contains, but without the somewhat overwrought philosophizing and Star Trek references. I cannot help but think a better editor would have helped improve this book tremendously. So I have to say that the book was a bit of a let down for me.
I did find parts of this book truly outstanding and enjoyable. In my opinion the final chapter, titled "How R Works", should be required reading for any serious R programmer. The early chapters that dealt with debugging and organizing packages, as opposed to merely detailing language features, were very insightful. The focus is always on why the language works the way it does, and how it was intended to be used. Yes, this book can be considered the "Prime Directive" for R programmers!
In the end this is a book that has definitely found a place on my bookshelf, but it is one I cannot really love. It's hard to read, and meanders too much. But it sprinkles enough truly insightful information through its four hundred odd pages that it is worth reading at least once, and perhaps many more times.
I agree with the reviewers who say it's chatty, but that makes it very readable. You don't have to work every example to understand the points the book is making. Likewise, it _is_ cross referenced to death, but it's easy enough to read over the links, and when you're trying to make sense of something, the cross references do take you to the right information to round out a picture.