R is a powerful and free software system for data analysis and graphics, with over 1,200 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 30 programs written in all three packages, comparing and contrasting the packages' differing approaches. The programs and practice datasets 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.

Robert A. Muenchen is the manager of the Statistical Consulting Center at the University of Tennessee and has 28 years of experience as a consulting statistician. He has served on the advisory boards of SPSS Inc. and the Statistical Graphics Corporation.

"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, University of South Carolina

"Thanks for writing R for SAS and SPSS Users--it is a comprehensible and clever document. The graphics chapter is superb!"

• Tony N. Brown, Vanderbilt University

"This is a Rosetta Stone for SPSS and SAS users to start learning R quickly and effectively."

• Ralph O'Brien, ASA Fellow

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

• Tony Chu, Public Policy Research Data Analyst

Klappentext

While SAS and SPSS have many things in common, R is very different. My goal in writing this book is to help you translate what you know about SAS or SPSS into a working knowledge of R as quickly and easily as possible. I point out how they differ using terminology with which you are familiar, and show you which add-on packages will provide results most like those from SAS or SPSS. I provide many example programs done in SAS, SPSS, and R so that you can see how they compare topic by topic. When finished, you should be able to use R to: Read data from various types of text files and SAS/SPSS datasets. Manage your data through transformations or recodes, as well as splitting, merging and restructuring data sets. Create publication quality graphs including bar, histogram, pie, line, scatter, regression, box, error bar, and interaction plots. Perform the basic types of analyses to measure strength of association and group differences, and be able to know where to turn to cover much more complex methods.

Inhalt

Introduction. The five main parts of SAS and SPSS.- Programming conventions.- Typographic conventions.- Installing & updating R.- Running R.- Help and documentation.- Programming language basics.- Data Acquisition.- Selecting Variables - Var, Variables=.- Selecting observations - where, if select if, filter.- Selecting both variables and observations.- Converting data structures.- Data management.- Recoding variables. Value labels or formats (& measurement level).- Variable labels.- Generating data.- How R stores data.- Managing your files and workspace.- Graphics overview.- Traditional graphics.- The ggplot2 package.- Statistics.- Summary.- Conclusion.- Appendix A.- Appendix B.- Appendix C.- Bibliography.