There are many books that teach you to use SAS or that teach you to use R. There is at least one book that teaches R to people who know SAS or SPSS (R for SAS and SPSS users by Robert Muenchen, and it’s very good).
Most of those try to teach you to use the program from the ground up, as it were. If we make an analogy to books about learning languages like French, these would be text books. The book I am reviewing today is very different. SAS and R: Data Management, Statistical Analysis and Graphics by Ken Kleinman and Nicholas J. Horton is more like a English-French dictionary, or perhaps a phrase book. Rather than try to teach the languages, textbook style, Kleinman and Horton try to list various tasks you might have, and how to do them in SAS and R. This is not a book to get if you know nothing about one of these languages. Nor is it a book to get if you want a formal course in a language (it does have two appendixes, one for SAS and one for R). But it is a very good book indeed if you know some SAS and some R, and have some tasks you need to accomplish in one or the other, or a task that you know how to do in one and want to do in another.
What makes or breaks a book like this is two things: First, the authors have to know what they are doing. They do. I learned a lot about both programs, just browsing through the book. Second, it has to be possible to find the material you want, when you want it. Here, too, this book is excellent. This is because of the extensive table of contents (7 pages) and three indexes: One for concepts, one for SAS commands, and one for R commands (33 pages in all).
I am sure I will use this book a lot – both to browse through and to find particular PROCs and functions and ways to do things.
Specialties: Regression, logistic regression, cluster analysis, statistical graphics, quantile regression.