SAS and R: Data Management, Statistical Analysis, and Graphics
By Ken Kleinman, Nicholas J Horton

Publisher:   Chapman & Hall/CRC
Number Of Pages:   343
Publication Date:   2009-07-21
ISBN-10 / ASIN:   1420070576
ISBN-13 / EAN:   9781420070576

Product Description:
An All-in-One Resource for Using SAS and R to Carry out Common Tasks
Provides a path between languages that is easier than reading complete documentation
SAS and R: Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in both SAS and R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. The book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, and the creation of graphics, along with more complex applications.
Takes an innovative, easy-to-understand, dictionary-like approach
Through the extensive indexing, cross-referencing, and worked examples in this text, users can directly find and implement the material they need. The book enables easier mobility between the two systems: SAS users can look up tasks in the SAS index and then find the associated R code while R users can benefit from the R index in a similar manner. Demonstrating the code in action and facilitating exploration, the authors present extensive example analyses that employ a single data set from the HELP study. They offer the data sets and code for download on the book’s website.

Summary: A Great Reference
Rating: 5
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 much 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 very 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. 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. While I include some redundancy to facilitate using it as a reference, it is important to read it through at least once.
So for someone learning R, these books complement each other well. 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 the many topics that the other book did not cover.
For someone learning SAS, I recommend reading a book devoted to that topic, such as, "The Little SAS Book: A Primer", then using "SAS and R" to look up the many topics that book does not cover. "R for SAS and SPSS Users" is not a good choice for learning SAS or SPSS.
In either case, you'll probably need additional books devoted to the particular methods of analysis you need.


Summary: Really great for going from SAS to R
Rating: 5
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.