R for SAS and SPSS Users (Statistics and Computing),   ISBN:9780387094175

Customer Rating: 

I've used SAS for 16 years and have found the transition to R to be fairly difficult. This book has helped a lot. It's well organized and I've found myself turning to it as a go to source for how to get things done. The online documentation for R is probably its weakest characteristic and you need a book like this. In all other respects I have found the book quite useful and would buy additional books by the author if they were available.

Customer Rating: 

I wanted to write this book but Robert Muenchen did a much better job. The people who developed S at Bell Labs and then the group that copied it for R didn't think much about the messy data of real studies in research and industry. Also working with row-based packages like SAS and SPSS puts one in a certain mindset. Muenchen successfully guides people in this mindset to take advantage of the flexibility of R.

The sequence of chapters lets a person start running R, access and modify data, and then graph or analyze. He answers questions that come up in daily work such as selecting variables and observations, changing names, and handling missing values. Too many books and classes show interesting functions without prioritizing their use. If it is beyond the scope of the book, he refers to websites and other books.

The connection to SAS and SPSS made this book like a bright light for me. For example for years I struggled with the idea of a 'factor'. But when he explained that it was like a 'formatted variable' it clicked. He keeps the other languages in the background so I think someone who didn't know them could still use the book.

I have a few small criticisms. There are a few editing errors in the book. Someone looking to do a specialized analysis would be better off with a different resource since the statistics section is short. The ggplot package looks interesting but a more extensive traditional graphics section would be more useful.

Customer Rating: 

This book really is a superb reference for looking up how to do things in R. As an experienced SAS user - an ordinary guy using statistics for work, not a statistician - who recently branched out, I found that R's very different mindset made for a formidable learning curve. My discovery of this book flattened the learning curve dramatically and has saved me dozens of hours. I found the book to be a far more accessible treatment of R than other resources and I have little doubt that those coming to R from backgrounds other than SAS or SPSS will similarly find it valuable. Although it is worth reading the book cover to cover, sections are structured so that it is easy to jump in wherever some help is needed. The table of contents effectively points the way to major topics and the index is implemented well. Explanations are clear and examples are abundant: Muenchen generally shows multiple ways to accomplish the same or similar tasks. These varied approaches not only help cement understanding of how R works, but give the reader an abundance of models from which to work.

Customer Rating: 

This book 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. The author goes into the important fundamental differences in how the two languages think about and process data.[...]

There is also very good coverage of R graphics (especially the set of functions in ggplot2 that are wildly useful and rarely mentioned in other books). 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 presentation 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.

Customer Rating: 

I've used and taught SAS and SPSS since about 1982. It seems to me that much of the new statistical developments are coming out in the open-source R language, rather than business-prediction software like SAS or SPSS. The number of new statistical packages in R is rapidly increasing, including packages supported by high quality textbooks. SAS and SPSS offer "business intelligence" -- software to help businessmen predict the future -- rather than cutting-edge tools for serious 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 to help the experienced SAS/SPSS programmer learn R. Since R is object-oriented, it "thinks" quite differently from SAS and SPSS, and you spend as much time unlearning old ways of thinking 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 in SAS, SPSS, and other software. This makes his explanations seem intuitive and natural without the "one hand clapping" feeling you get from R "help" messages. The book is not only a good introduction but it 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. Each chapter has 3 tables of similar code in SAS, SPSS, and R. These tables may help it serve as a "lookup book" during programming.

I keep a text file of the book's examples open in my editor when I write R code so that I can cut and paste working code from the book rather than doing trial and error on minor details. This same cut-and-paste approach works with SAS, SPSS, and other software.

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