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R for SAS and SPSS Users
R is a powerful and free software system for data analysis and graphics, with over 1,200 add-on packages available. This book introduces R using SAS and SPSS terms with which you are already familiar. It demonstrates which of the add-on packages are most like SAS and SPSS and compares them to Râ s built-in functions. It steps through over 30 programs written in all three packages, comparing and contrasting the packagesâ differing approaches. The programs and practice datasets are available for download. The glossary defines over 50 R terms using SAS/SPSS jargon and again using R jargon. The table of contents and the index allow you to find equivalent R functions by looking up both SAS statements and SPSS commands. When finished, you will be able to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses. "This is a really great book. It is easy to read, quite comprehensive, and would be extremely valuable to both regular R users and users of SAS and SPSS who wish to switch to or learn about Râ ¦An invaluable reference." - David Hitchcock, University of South Carolina "Thanks for writing R for SAS and SPSS Users--it is a comprehensible and clever document. The graphics chapter is superb!" - Tony N. Brown, Vanderbilt University "This is a Rosetta Stone for SPSS and SAS users to start learning R quickly and effectively." - Ralph O'Brien, ASA Fellow "I am a professional SAS and SPSS programmer and found this book extremely useful." - Tony Chu, Public Policy Research Data Analyst
487 pages, Hardcover
First published October 17, 2008
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Displaying 1 - 2 of 2 reviews
August 28, 2009
R for SAS and SPSS users is a sight for sore eyes for anyone in the statistical analysis community. Bob manages to take a genuinely complex topic (e.g., programming in R), and transform it into something manageable to learn. The reason this book is, in my view, so accessible, is that the author made a conscious decision not to embed to the equations for all of the statistics into the text, which isn't true in other R books, that discuss the equations but never bother to tell you how to do it in R, which is hardly trivial sometimes.
If I have two criticisms it's that 1) there are two chapters in the book that essentially repeat themselves. The author claims that there are differences in the way the data frame is setup, and I guess that's true, but the actual text repeats, which makes this difference difficult to spot. Second, it would be nice to see exercises, and an answer key, so that this book could be used for an undergraduate stats course.
If I have two criticisms it's that 1) there are two chapters in the book that essentially repeat themselves. The author claims that there are differences in the way the data frame is setup, and I guess that's true, but the actual text repeats, which makes this difference difficult to spot. Second, it would be nice to see exercises, and an answer key, so that this book could be used for an undergraduate stats course.
December 19, 2009
I'm the author of this book, posting a review by professor Warren Lambert of Vanderbilt University. You can read many more reviews of this at the book's main web site or by searching on the book's title. From Dr. Lambert:
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 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 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 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.
I keep 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.
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 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 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 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.
I keep 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.
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