I (like Lambert's review) am in the middle of reading the book but wanted to chime in early to say that it is absolutely excellent. The focus is on the data manipulation and processing that goes on before analysis. As a longtime SAS user, this is the major stumbling block for me using R. The parallels and discrepancies across the languages are clearly pointed out with solid code examples. The book covers basic syntax but more importantly it goes way beyond saying this is the syntax for an "if" statement in SAS and this is an "if" statement in R. He goes into the important fundamental differences in how the two languages think about and process data. You can get at this with Data Manipulation with R (Use R) but the coverage here is ideal for SAS users (and probably for SPSS folks). There is also very good coverage of R graphics. The coverage of statistics is limited to only one chapter. So, do not get the book if you only want to learn the ins-and-outs of R stats. Happily that chapter covers the most commonly done statistics so even in its short form it should help everyone.

While the book is geared toward someone with experience in SAS or SPSS, I think it would be excellent for anyone learning R. The links to the point and click versions of R (R commander, Rattle or JGR) are invaluable for anyone starting out.

The author is actively maintaining the book's website. So be sure to grab the errata and his notes.

I'm now reading this book (CH 14 of 24) and will write more thoroughly later when I finish. I have read the early 80 page version available for free here: [..]

I'm used and taught SAS and SPSS since about 1982, and like many statisticians, it seems to me that much of the new statistical developments are coming out in the free R language, rather than SAS or SPSS. The number of new statistical packages in R is rapidly increasing, including packages supported by high quality textbooks. It also seems to me that SAS and SPSS have, oxymoronically, become "business intelligence" rather than cutting-edge tools for academic research.

There are many good books for R experts, and good beginners books are starting to come out. Before Muenchen's book, there was nothing for the experienced SAS/SPSS programmer to learn R. Since R is object-oriented, it "thinks" quite differently from SAS and SPSS, and you spend as much time unlearning old approaches as learning new ones.

The author of R FOR SAS AND SPSS USERS knows how SAS/SPSS programmers think, since he is one of us and has spent decades at UT teaching people to manage and analyze data. This makes his explanations seem intuitive and natural without the "one hand clapping" feeling you get from R "help" messages. It is not only a good introduction but goes into considerable detail to cover basic and intermediate R programming. The style is simple and lucid. Unlike some R material, the book is rich in concrete examples during exposition. Each chapter has 3 tables of similar code in SAS, SPSS, and R, which may help it serve as a "lookup book" during programming.

My conclusion so far: If you have some years with SAS or SPSS and you want to learn R, this will be your #1 book.

I'm the author posting this review written by Ajay Ohri at DecisionStats.com. Cheers, Bob Muenchen