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SAS and R: Data Management, Statistical Analysis, and Graphics 1st Edition

4.3 out of 5 stars 12 customer reviews
ISBN-13: 978-1420070576
ISBN-10: 1420070576
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Editorial Reviews

About the Author

Ken Kleinman is an associate professor at Harvard Medical School. His research deals with clustered data analysis, surveillance, and epidemiological applications.

Nicholas J. Horton is an associate professor of statistics at Smith College. His research interests include longitudinal regression models and missing data methods.

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Product Details

  • Hardcover: 343 pages
  • Publisher: Chapman and Hall/CRC; 1 edition (July 21, 2009)
  • Language: English
  • ISBN-10: 1420070576
  • ISBN-13: 978-1420070576
  • Product Dimensions: 10 x 7 x 0.9 inches
  • Shipping Weight: 1.7 pounds
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (12 customer reviews)
  • Amazon Best Sellers Rank: #1,159,011 in Books (See Top 100 in Books)

Customer Reviews

Top Customer Reviews

Format: Hardcover Verified Purchase
This book is a really helpful reference. I'm the author of "R for SAS and SPSS Users", and I thought you might be interested in how these two books differ.

"SAS and R" is a well-crafted dictionary of how to do things in both SAS and R. For each topic the authors clearly and concisely show how to perform that task in SAS, then in R. They typically provide a paragraph of description for each. The brevity of explanation allows the authors to cover a wider range of topics. If you needed to know more about a topic, at least they have given you a good start and you'll know what SAS statements or R functions to pursue. That's helpful information, especially in R. Each chapter concludes with example programs with output which demonstrate the topics covered. Output for both packages is shown. The book does include brief introductions to both SAS and R in the appendices but, as the authors state in the preface, their book is not meant to be read cover to cover. However, unlike a standard dictionary, the entries are organized by category, so reading several entries in a row is usually helpful.

"R for SAS and SPSS Users" is a step-by-step introductory text, meant to be read in order. I assume you already know SAS or SPSS, and the only discussion of them is used to help you learn R. Rather than a paragraph of explanation per topic, I typically provide several pages, stepping through complete example programs, and pointing out where beginners typically make mistakes (often caused by expecting R to work more like SAS or SPSS). However, given that added explanation, the range of topics is narrower. I do include programs in all three at the end of each topic, but I provide detailed explanations for only the R programs. To save space, I show only the R output.
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Format: Hardcover Verified Purchase
I am a long time SAS user who is surrounded by R experts. As such, I have been looking, for years, for a dictionary to translate between R and SAS. That is what this book is designed to do and it is absolutely excellent for this purpose. It covers all the SAS data manipulation and graphics procedures and functions that I use all the time and it shows how to do them in R. Happily the book is very up to date and the most modern (9.21) SAS graphics procedures (like sgplot) are covered.

The organization and indexing are fantastic. There is a table of contents, an index with SAS vocabulary, an index of R vocabulary and an overall index. Using these tools you can quickly find the procedure/methods that you want to accomplish and get parallel code snippets in both languages along with annotation to say what the differences are between the two implementations. In addition to the pure dictionary organization there are extended examples working through the analysis and visualization of a large data set.

The book is not a textbook on the fundamental differences between R and SAS, like the different approach to objects and data sets. For a real text on that take a look at R for SAS and SPSS Users (Statistics and Computing).

Amazon will not let me post the link to the book's website but if you search the web for the authors' last names and Smith (as in the liberal arts college in Northampton Mass) you should be able to see it. There you will find a PDF with the table of contents, code snippets and lots of supporting material.

This book is a must for people moving to R from SAS (or the other direction) and it should be excellent for people needing a dictionary to find functions/procedures to do data manipulation and graphic(s) tasks in either language.
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Format: Hardcover Verified Purchase
I'm a R programmer who has some familiarity with SAS.
I knew early-on that SAS is a mountain to climb, I was looking for something that would assist me in handing tasks between the 2-systems. This book is the one.

Excellent examples and numerous explanations makes this a no-brainer for people using either system and wanting to learn the other.
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By wlj on September 23, 2012
Format: Hardcover Verified Purchase
If you already know how to use SAS this can be looked at as a nice cookbook. I had it checked out from the University and found it helpful enough that I went ahead and bought it. My biggest problem with R has been that its code moves faster than its documentation. And, while the syntax is actually pretty simple in a lot of ways, if you already know SAS you get a pretty concise description of the problem(s) they are working on as well as mechanisms for resolving them in either system.

Much shorter than most of the books I have seen on R, but it can start with the assumption that you know how to do things in general and just want to see how to do them in a new system.
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Format: Hardcover Verified Purchase
I gave the book 5 stars because that's what those who use it tend to give it. This is a case where it would be good to have reviews without stars because this review is not so much about the book, but the concept.

I know SAS and statistical methods well. And I can manipulate data if I'm in the mood. But, the time has come to add R to my skills and I wanted something that might make things easier. So, I saw the stellar reviews for this book and decided to give it a try.

What I discovered is that the concept doesn't work for me. Since I know the methods well, I don't need to refer to how to do something in SAS in order to understand how to do it in R. Things get further complicated because there are often many ways to do something in SAS, so for someone well-versed in SAS going to R, again what matters is the particular task, not any particular way of accomplishing it in SAS.

Let me try an analogy. I think of SAS and R as languages. I speak SAS but want to become a *native* speaker of R. That means I have to learn to think in R rather than by attempt a work-for-word translation from SAS. The word-for-word translation might work for closely related languages (the statistical analysis portions of SAS and SPSS, for example [or maybe even Stata and R?]), but not for two so different as SAS and R. In computer language terms it's like translating Fortran into APL.

I can see how this approach might work for someone who is on the same footing in both R and SAS as a way to compare and contrast, but as a way for going from one to the other, what works best for me is starting with a clean slate.
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