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R for SAS and SPSS Users (Statistics and Computing), by Robert A. Muenchen
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R is a powerful and free software system for data analysis and graphics, with over 5,000 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 R's built-in functions. It steps through over 30 programs written in all three packages, comparing and contrasting the packages' 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 new edition has updated programming, an expanded index, and even more statistical methods covered in over 25 new sections.
- Sales Rank: #680638 in Books
- Published on: 2011-07-26
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x 2.06" w x 6.14" l, 2.30 pounds
- Binding: Hardcover
- 686 pages
Review
From the reviews of the second edition:
“This is a greatly expanded second edition of a text that has already proved widely popular. The explanation is careful and detailed. It uses SAS and SPSS terminology, matching it with R terminology … . A glossary translates R terminology into terminology that is likely to be more familiar to SAS and SPSS users. … a wide-ranging and carefully compiled source of information on R. It is a strongly recommended addition to the library of anyone who comes to R from SAS or SPSS.” (John H. Maindonald, International Statistical Review, Vol. 80 (1), 2012) From the Back Cover
R is a powerful and free software system for data analysis and graphics, with over 4,000 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 R's built-in functions. It steps through over 50 programs written in all three packages, comparing and contrasting the packages' differing approaches.
The glossary defines R terms using SAS/SPSS terminology and again using R terminology. The table of contents and the index allow you to find equivalent R functions by looking up both SAS statements and SPSS commands. The second edition adds 216 pages of new topics.
�"I found the book extremely helpful…The material is laid out in a way that makes it very accessible. Because of this I recommend this book to any R user regardless of his or her familiarity with SAS or SPSS...For new R users it will demystify many aspects, and for existing R users it will have many answers to those questions you have been too afraid to ask in public."
--The American Statistician
�"…�an excellent introduction to R…the book meticulously covers data management, data structures, programming, graphics and basic statistical analysis in R. The prose is clear, the examples tied to their SPSS and SAS analogs. The handling of both traditional and newer “ggplot2” graphics is comprehensive: SPSS and SAS users will undoubtedly find lots to like. "
--Information Management�
�"As a long time SAS user this book makes the task of transition to R much more palatable and appealing. It also greatly reduces the time to get up and running in R effectively."
--Technometrics
“It is great to see this book in a second edition. It serves nicely as an introduction to R, irrespective of whether they are familiar with SAS or SPSS. I have long been a fan of programming by example and the book is full of excellent ones.”
--Graham Williams, Author, Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery
About the Author
Robert A. Muenchen is the manager of the Statistical Consulting Center at the University of Tennessee and has 28 years of experience as a consulting statistician. He has served on the advisory boards of SPSS Inc. and the Statistical Graphics Corporation.
Most helpful customer reviews
12 of 13 people found the following review helpful.
Great read for SPSS or SAS users learning R
By E. Warren 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 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.
8 of 8 people found the following review helpful.
From SAS to R made easy
By I Teach Typing
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.
5 of 5 people found the following review helpful.
A Superb Reference for Using and Learning R
By Wayne Richter
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.
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