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R for SAS, SPSS and STATA Users

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  • 52 Videos
  • 196 Exercises
  • 16 hours 
  • 28,804 Participants
  • 14450 XP


Bob Muenchen
Bob Muenchen

Robert A. Muenchen is the author of R for SAS and SPSS Users and, with Joseph M. Hilbe, R for Stata Users. He is a consulting statistician with over 30 years of experience and is currently the manager of the Research Computing Support (formerly the Statistical Consulting Center) at the University of Tennessee. He holds a B.A. in Psychology and an M.S. in Statistics. Bob has conducted research for a variety of public and private organizations and has assisted on more than 1,000 graduate theses and dissertations. He has written or coauthored over 70 articles published in scientific journals and conference proceedings. Bob has served on the advisory boards of the SAS Institute, SPSS Inc., the Statistical Graphics Corporation and PC Week Magazine. His suggested improvements have been incorporated into SAS, SPSS, JMP, STATGRAPHICS and several R packages.

Course Description

If you already know SAS, SPSS or Stata, you don’t need to spend time learning how to analyze data; you need a course that focuses on translating your knowledge into R. This comprehensive course introduces R jargon using the language you're familiar with.

1Introduction Free

This section introduces R and describes how it integrates the five main parts of SAS, SPSS and Stata into a powerful, comprehensive system.

Installing & Maintaining R 

The software you’re familiar with is a complete software package. However, R is downloaded and installed in pieces. This chapter tells you how to find parts of R that match your current software and how to install them.

Help & Documentation 

SAS Institute, IBM (makers of SPSS) and Statacorp all act as one-stop-shops for documentation and support. With R, top-notch documentation and support are also available... if you know where to look! This chapter gives you the best options.

RStudio Basics 

There are many ways to control R, but RStudio is the most popular by far. This brief chapter covers what you need to know to get started.

Controlling Functions 

You can control the way analyses are run in ways that are very similar to your current software, or you can use an object oriented approach that’s unique to R. This section covers the alternatives.

Selecting Observations 

This section covers the two most common ways to select observations in R, and it points out that the way you specify the logic in those selections follows slightly different rules.

Selecting Variables & Observations 

The previous chapters discussed the selection of variables and observations. Here, we'll cover techniques on how to do both at the same time.


R is unique in its ability to create new variables from variables stored in multiple datasets at once. This section covers three different ways to specify transformations, pointing out the advantages of each.

Writing Functions 

Writing functions in R is very similar to writing macros in SAS, SPSS and Stata. However the resulting functions are much more integrated into the package, more like the “procs” or “commands” of other software. The downside to this though, is that functions are required to do “by group” processing. This section will guide you through the basic steps of both.

High Quality Output 

R’s output by default looks pretty bad! But don’t worry, there are add-on packages that produce beautiful publication-quality tables. This section shows how they work.

Ways to Run R 

R can be run in many ways, from simple point-and-click user interfaces to deep integration with other stat packages. This section briefly covers the integration of R into Alteryx, Excel, KNIME, R Commander, RapidMiner, Rattle, SAS, SPSS, and Stata.