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R for SAS and SPSS Users (Statistics and Computing) Hardcover – October 24, 2008

4.5 out of 5 stars 13 customer reviews

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Editorial Reviews

Review

From the reviews

“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 R…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

"As his title suggests, Robert Muenchen crafted this to be a Rosetta Stone for SAS and SPSS users to start learning R quickly and effectively. Has he achieved this? Yes, and more." -Ralph O'Brien, Case Western Reserve University, ASA Fellow

“I am a professional SAS and SPSS programmer and found this book extremely useful.” - Tony Chu, Public Policy Research Data Analyst

"I found the book extremely helpful. Over the last few months I am regularly reaching for the book from my bookshelf to find sensible R code and to help with some data manipulation. 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 those dedicated SAS and SPSS users I especially recommend the book. As discussed in the Introduction section, the basics of the R language are very different from SAS and SPSS but this book’s layout, style, and content help with these differences. The ordering of the material is very user-friendly and sensible...To new R users, and to R users of some years experience, I recommend this book. 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, February 2010, Vol. 64, No. 1

"The title of this book accurately describes its goal: to teach SAS and SPSS users how to use R...The book is laid out well, with sensible features such as a separate font for programs; tables listing complete programs in all three languages; an index with entries that include main SAS or SPSS commands and procedures, allowing users to locate R equivalents fairly quickly; and appendices comparing the three languages’ attributes and procedures/packages. It is much easier to read and likely comparably more helpful than a manual...There is no question in my mind that this can be a very useful book for its intended audience." Biometrics, 65, 1313, December 2009

"R for SAS and SPSS Users provides an excellent introduction to R. As Muenchen, Manager of the Statistical Computing Center at the University of Tennessee, notes in the Preface, the SPSS and SAS platforms, introduced over 30 years ago, have much in common – but are very different than 10 year old R. The book's first chapters focus on gentle GUI's for R before taking on the language starting in Chapter 8. At that point 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. The appendixes contrast R jargon with SPSS/SAS and compare SPSS/SAS products with the corresponding R packages."  (Information Management, June 15, 2010)

“This book is designed for SAS/SPSS users interested in making a transition to R or wanting to add the additional capabilities of R to their repertoire. … book provides many simple how to s for new users of R and lots of comparative examples for users with knowledge of SAS and SPSS. … It is intended to help make the transition from SAS or SPSS to R as painless and smooth … .” (Roger M. Sauter, Technometrics, Vol. 53 (1), February, 2011)

From the Back Cover

 

 

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 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.

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.

"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 R…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

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Product Details

  • Series: Statistics and Computing
  • Hardcover: 487 pages
  • Publisher: Springer; 1 edition (October 24, 2008)
  • Language: English
  • ISBN-10: 0387094172
  • ISBN-13: 978-0387094175
  • Product Dimensions: 6.1 x 1.1 x 9.2 inches
  • Shipping Weight: 1.8 pounds
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (13 customer reviews)
  • Amazon Best Sellers Rank: #1,795,170 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

By E. Warren Lambert on January 6, 2009
Format: Hardcover Verified Purchase
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.
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Format: Hardcover Verified Purchase
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.
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Format: Hardcover Verified Purchase
This book worked for me, much better than free tutorials I've tried. I'm a long-time SPSS user who had used R occasionally for graphics, but for general use I had been stymied. I knew I was missing out on a lot of really powerful features in R. Finally I buckled down and followed this book word-for-word, even the parts I thought I didn't need.

Now I've used R confidently for several real-world projects and can't imagine going back to SPSS. And yes R is really much better -- the programmability, the ease of using "output" from one procedure as "input" to another, the ease of complex graphics, etc. The only place R seems to be inferior is in the task of quickly producing "presentable" tabular output for final reports, etc, though there are "packages" to help with that once you get more advanced.

This book got me there, but there are a few key things you should know about it, and R. First (for me anyway) R was just too difficult and different from SPSS to learn on a casual basis. To use R effectively, you ABSOLUTELY need a solid grounding in the many different data structures R uses and how to refer to the pieces you want to use. Basically, you need to know how to do all the boring but essential data processing and management tasks with complete confidence. After that, doing statistics and graphics come pretty quickly. But you can't skip any of the basics.

This book gives you that foundation, and along the way points out very useful information you won't find in a lot of web tutorials. For example, the great usefulness of R-based IDE's (I used RStudio). Also, the many places where you need to delve in to R's plethora of add-on-packages -- something that would be very confusing to work out on your own.
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Format: Hardcover
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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