**Book Description**

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 data sets 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.

** **

**Reviews and Comments from Readers **

"This is a greatly expanded second edition of a text that has already proved widely popular...[it] is a wide ranging and carefully compiled source of information on R...a strongly recommended addition to the library of anyone who comes to R from SAS or SPSS."

-International Statistical Review, 80, 1, 176-204, by John H. Maindonald

"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."

-Technometrics, February 2011, Vol. 53, No. 1, by Roger Sauter, PhD, CQE, Math Modeler, Boeing Commercial Airplanes

"...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, by Jennifer Brown, PhD, Head, Department of Mathematics and Statistics, University of Canterbury

"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

"I'm recommending your excellent book to many people."

-Frank Harrell,
PhD, Chair of the Department of Biostatistics, Vanderbilt University
and author of the popular R packages, Hmisc, Design and rms

*"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, Steve Miller, June 15, 2010

"**Fantastic guide** for those who are very familiar with SAS and SPSS."

-Michael Wexler, Author of The Net Takeaway

"Thank you for writing this book! I've been a SAS programmer for around
15 years, a SPSS programmer for around 4 years, and I also program in
VBA, and coming to R from this background has been an exercise in
frustration. R is just so different in how it handles data, and its
command syntax, I found that having previous programming experience was
not really an advantage. I was wondering about the value of this book as
I already have the MASS book at home and The R Book, and it was the
need to recode a lot of variables was the activity that made me purchase
this book.

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."

-M. Gosse

"If you are an experienced SAS or SPSS user, if you have committed to
learning R, and if you find the initial steps perplexingly difficult
despite all the R books you have bought, then *R for SAS and SPSS Users*, by Robert Muenchen, is the book for you.
It fills a niche in between the simple R introductions (e.g. Dalgaard,
Introductory Statistics with R), and complex R books (e.g. Pinheiro and
Bates, Mixed-effects models in S and S-PLUS [very like R]; Ramsay and
Silverman, Functional Data Analysis.) It has much overlap with Spector,
Data Manipulation with R, but it puts the data manipulation in the
context of data analysis tasks that you are trying to accomplish.

Starting
at a low level of difficulty, the book shows how to install R,
including installing the contributed packages **and their
dependencies.** It shows how to run R programs in batch and in
graphical user interfaces, how to use help, and it supplies some nice
programs (on the book's web page) to get started. By working a little
each day, the reader can build skill and a library of programs. The
book shows how to convert your (probably large and complex) SAS and
SPSS data sets to R data structures for analysis. Assuming that you
already deploy many skills and techniques when doing an analysis in SAS
or SPSS, it explains how to do those tasks in R: how to select
variables and cases using several techniques (by name, by logic, by
subscripting from arrays); how to perform transformations on the
variables; how to "restructure" data (convert variables to cases and
cases to variables for different ways of analyzing what are
repeated-measures data); how to sort a file; how to analyze a file BY a
sort variable (SAS) or split variable (SPSS); how to add labels to
variables; how to match-merge sorted files. For all of these tasks,
there are small program files in triplicate, performing the exact same
operation on the exact same data in R, in SAS, and in SPSS. The book
describes how to use the intermediate and final computed values from an
analysis in subsequent calculations; and how to find out what all the
intermediate computed values are.

After that the author shows
how to do many common graphical tasks: scatter plot matrices; titles,
colors, legends; how to label points on graphs; add fitted lines with
confidence bands, or confidence ellipses to scatterplots; rescale axes
to logarithmic; and much more. After this solid foundation in data
management (about 270 pp.) and graphing (100 pp.), the author presents
elementary statistics: cross-tabulation, linear regression, Wilcoxon
tests, and more.

If you are like me, you have found the
transition to different data structure the hardest step, the step most
like learning a new language. This book is great help in making that
transition.

There are some things that this book is not. It is
not about how to do complex data analyses in R; however, you will be
able to master the books that explain those analyses after you have
mastered this book (and you'll probably be better able to make good use
of Crawley, The R Book.) The basic text is about 450 pp., and it would
have been an impossibly long book if it had tried to cover all
important techniques. It is not a book on how to use SAS or SPSS if you
are a master of R: it assumes fluency with SAS or SPSS from the start,
and proficiency at data analysis in SAS or SPSS, but it introduces R at
an elementary level and works slowly upward from there."

I recommend it highly.

-Matthew Marler, PhD

"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 for
the experienced SAS/SPSS programmer to learn R. Since R is
object-oriented, it "thinks" quite differently from SAS and SPSS, and
you spend as much time unlearning old approaches 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 during exposition. Each chapter has 3 tables of similar code
in SAS, SPSS, and R, which may help it serve as a "lookup book" during
programming.

I keep 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."

-Warren Lambert, Behavioral Statistics Coordinator, Statistics and Methodology Core;
Senior Research Associate, Center for Evaluation and Program
Improvement (CEPI); Senior Associate in Biostatistics, Vanderbilt University

"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."

-Wayne Richter

"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."

-Raymond Balise, PhD, Stanford University

"I wanted to write this book, but Robert Muenchen did a much better job!"

-Georgette Asherman, founder, Direct Effects

"**Honorable Mention: Best R Book for existing SAS/SPSS users**.

This is an excellent book on doing statistics with R - even if you are
not an experienced user of SPSS or SAS. But if you ARE an SPSS or SAS
user, this book will really excite you - it puts everything in R in a
familiar context, and will help you get going with R much faster than
any other book we have seen."

-Human Landscapes

"So
you decided to cut down on your statistical software expenses and
decided to get R, but the problem is you know SAS/SPSS and you need to
learn R fast enough to justify switching over. The ideal book for you
is *R for SAS and SPSS Users*. ...It's a really easy book, you
have the SAS Syntax, the corresponding SPSS Syntax and the R Syntax."

- Ajay Ohri, Author of DecisionStats

"One of the most clearly written, well designed, books I've
read on a programming language (of any variety or type) in my career.
And as a computer scientist, I've read quite a few! You seem to have a
knack for guessing ahead of time what problems R users will potentially
have and explaining to the reader, without talking down, how to get
around the problem."

- Andreas Stefik, PhD

Central Washington University

Department of Computer Science

"I've used SAS for 16 years and have found the transition to R to be
fairly difficult. This book has helped a lot. It's well organized and
I've found myself turning to it as a go to source for how to get things
done. The online documentation for R is probably its weakest
characteristic and you need a book like this. In all other respects I
have found the book quite useful and would buy additional books by the
author if they were available."

-David Young, Director
at Crisbal Company Limited

"In order to learn **R** quickly, I would suggest the following sequence:
read *An Introduction to R*, followed by *R for SAS and SPSS Users*.**"**

**- Robert I. Kobakoff, Ph.D., Author of the web site**

Quick-R for SAS/SPSS/Stata Users

(Muenchen suggests the reverse order, naturally!)

"I think the hands down best intro for R (and I have
the Dalgaard and Gelman and Hill books) is R for SAS and SPSS Users.
The thing that sells this one is that most people who want to get
into R are already users of either SAS or SPSS. What Muenchen does is
to track what you would normally do in those apps with how to do the
same thing in R. That means he has to explain why R does things (often
perversely) the way it does and he guides you to packages in R that
replicate SAS and SPSS routines very closely."

-Tracy Lightcap, PhD, Professor and Chair, Department of Political Science, LaGrange College

"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, Assistant Professor, Department of Statistics, University of South Carolina

"With
the integration of R and SPSS beginning with version 16 via the R
Plug-In, this is a timely book for SPSS users...This book does a great
job of leveraging prior knowledge of SPSS (or SAS) to get users started
in making the best use of R. R documentation tends to be written by
experts and for experts. This book is written by an expert for
beginners."

- Jon Peck, Technical Advisor and Principal Software Engineer, SPSS Inc.

"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

"If you are coming to R from a SAS or SPSS background then
*R for SAS and SPSS Users* is a good choice. Even if you are
not a SAS or SPSS user the book provides a straightforward
introduction to using R."

-Graham Williams, Developer of the Rattle data mining user interface for R

"*R
for SAS and SPSS Users* is a sight for sore eyes for anyone in the
statistical analysis community. Bob manages to take a genuinely complex
topic (e.g., programming in R), and transform it into something
manageable to learn." (5-star rating)

-Andreas, reviewer at Goodreads

I
am a statistician working at GlaxoSmithKline. As a long-time SAS user,
I really enjoyed reading your book, R fro SAS and SPSS Users, and I am
sure I will read it again and again when I have questions. I read some
free online books/articles on R before reading your book, and most of
them were difficult to understand. After reading your book, I use R
with confidence. Thank you very much for writing this great book."

Chun Huang, PhD

"R users with analytic backgrounds and experience with
software packages such as SAS and SPSS will do well to start with
Muenchen’s *R for SPSS and SAS users..."*

*Programming R*

"Thanks for writing *R for SAS and SPSS Users* - it is a comprehensible and clever
document. The graphics chapter is superb!"
- Tony N. Brown, Associate Professor, Department of Sociology, Vanderbilt University

"I am a professional SAS and SPSS programmer and
found this book extremely useful."

- Tony Chu, Public Policy Research Data Analyst

"I
have used SAS for 15 years in my ecology research and have decided to
move to R...thanks so much for writing your book - I have been banging
my head against a wall, despite a number of other books, trying to
figure out how to think in R and how it differs from SAS - your book
has been a fantastic help. I love the way you give the code and
terminology for both so I can see how to translate."

- Clare McArthur, University of Sydney

"It is very lucid, pitched at the right level, and aims for a workflow that I'm familiar with."

-Andrew Henderson

"Thanks for writing the document, "R for SAS and SPSS Users"...it's fantastic and I really appreciate it."

-Madan Gopal Kundu, Biostatistician, CDM, MACR, Ranbaxy Labs, Ltd.

"I like your book so much. I used it the reverse way. I learned a little
SAS."

-Manos Parzakonis, author of Stats Raving Mad

"The chapters on graphics are good introductions, with many examples, of two approaches to graphics in R."

-Joseph G. Voelkel, International Statistical Review (2009), 77, 3, 465-466

"Officially this is my third attempt to learn R and I must say things
are looking up this time...I am using the R for SAS and SPSS Users book as my bible and now...I’d
recommend this book to anybody wanting to learn R, even for those of you
who don’t know SAS or SPSS as the explanations are very clear..."

-A Pint of R

"Muenchen thinks deeply about what is useful...he knows what he is talking about!"

- Wolfgang Härdle, Humboldt-Universität zu Berlin, author of Applied Multivariate Statistical Analysis and many other books