- Series: Statistics and Computing
- Hardcover: 686 pages
- Publisher: Springer; 2nd ed. 2011 edition (July 26, 2011)
- Language: English
- ISBN-10: 1461406846
- ISBN-13: 978-1461406846
- Product Dimensions: 6.1 x 2.1 x 9.2 inches
- Shipping Weight: 3.4 pounds (View shipping rates and policies)
- Average Customer Review: 15 customer reviews
- Amazon Best Sellers Rank: #785,271 in Books (See Top 100 in Books)
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R for SAS and SPSS Users (Statistics and Computing) 2nd ed. 2011 Edition
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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. "
"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."
“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
Top customer reviews
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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. And -- quite critically -- the book gives you enough grounding in the basics that you can understand R's help files. Once you can understand those help files, you can do a lot more.
However, I was very frustrated at times by the sheer number of typos in the book. Since R is a language where a misplaced comma can make a huge difference, and readers of this book are entering code from the book's pages, a typo can be maddening for the reader/user, because it will produce errors as output instead of the expected result. In fact once I realized typos were common I actually started using them as a kind of test of my knowledge... "hmmm, that won't work because..." . The author does maintain a pdf list of corrections on his web site. I wrote him with one, which he added to the list, but there were more I didn't report.
All in all, the book does its job very well, once you get savvy about the typos. Recommended.
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.
While other books give emphasis on how to do particular statistical and graphing techniques, they tend to omit details on how to import and manipulate variables and observations in order to undertake the statistical analysis. I find that data preparation is around 90% of my analysis time, so not having this information has a major effect on my productivity. This book covers all that missing detail, as well as some facets of statistical analysis as well. The chapters and sections are well laid out in a logical sequence, and the bonus for the kindle is being able to search for terms.
Robert Muenchen is a good writer as well: plain English explanations are given along with the code. He also gives examples of equivalent SAS and SPSS code so you can see the differences between them and R.
If you are coming to R from a SAS or SPSS background, even if you have other R reference material, I recommend you purchase this book.