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SAS and R: Data Management, Statistical Analysis, and Graphics Hardcover – July 21, 2009

ISBN-13: 978-1420070576 ISBN-10: 1420070576 Edition: 1st

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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: #1,190,354 in Books (See Top 100 in Books)

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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Customer Reviews

I was very excited when I saw this book and I think it has great utility.
Eclectic Reader
I recommend starting with "R for SAS and SPSS Users" to build a solid understanding of R, then use "SAS and R" to look up any additional topics.
Robert A. Muenchen
In addition to the pure dictionary organization there are extended examples working through the analysis and visualization of a large data set.
I Teach Typing

Most Helpful Customer Reviews

34 of 34 people found the following review helpful By Robert A. Muenchen on August 23, 2009
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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25 of 25 people found the following review helpful By I Teach Typing on July 29, 2009
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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11 of 12 people found the following review helpful By JoeT on June 2, 2010
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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Format: Hardcover Verified Purchase
This volume will best serve someone well versed in SAS and who wants a quick reference for common statistical tasks to be performed in R. The reverse is also true. What it does not do as well as I would have liked is a clear introduction to the datastep, contrasting common proceduers in SAS and R. Also I was hoping for more in-depth guidance on looping functions, producing simulations, etc.
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Format: Kindle Edition Verified Purchase
Don't buy the Kindle edition. It is missing the index page numbers. Useless, as that was supposed to be one the the great features of the book.
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Format: Hardcover Verified Purchase
if you know either SAS or R but not both this book is very useful. you can see how some process in one language is coded in the other
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