R is a statistical software program like SPSS or SAS. Unlike these programs, however, R is a free, open-source code program. As licensing fees increase, many people are attracted to the “free” aspect, but “open-source code” is the most important aspect. Having access to the base program's code, hundreds of programmers and statisticians around the world freely donate their time to create “packages” that add to the functionality of R. As a result, R is able to perform all the functions of SPSS, SAS, MPLUS, Stata, AMOS, LISREL, and HLM.
More information and introductions on how to use R can be freely found here:
- http://www.r-project.org/ (click on “Manuals”)
- http://www.statmethods.net/
The R Workshop will be offered in multiple sessions that build on one another in terms of content and complexity. In total, there will be four sessions this fall. All sessions will be videotaped and the power point slides will be available to download as a pdf file.
Session 1
Session 2
Session 3
Session 4
Helpful Resources
The following websites contain some useful resources:
- "Quick-R" is a guide to help reduce the learning curve for R: www.statmethods.net
- Answers to frequently asked questions from the R experts: http://stackoverflow.com/questions/tagged/r
- Paul Johnson teaches courses on R and freely shares his course materials here: http://pj.freefaculty.org/guides/
- Frank Harrell also teaches courses on using R for regression. His materials (including R code and examples) can be found here: http://biostat.mc.vanderbilt.edu/wiki/pub/Main/RmS/course2.pdf
Resource Books:
- The classic reference guide and companion to the MASS package: William Venables and Brian Ripley, Modern Applied Statistics with S, 4th ed. More information here: http://www.stats.ox.ac.uk/pub/MASS4/
- A readable and comprehensive overview of regression with R examples. A companion to the car package: John Fox, Companion to Applied Regression, 2nd ed. More information here: http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/
- A detailed guide to regression, including survival analysis, and multivariate and logistic regressions. A companion to the rms package: Frank Harrell, Regression Modeling Strategies. More information here: http://biostat.mc.vanderbilt.edu/wiki/Main/RmS
- A detailed guide to conducting mixed effects models in R (i.e., multilevel, hierarchical linear models). A companion to the nlme package: Pinheiro and Bates, Mixed Effects Models in S and S-Plus. More information here: http://cm.bell-labs.com/cm/ms/departments/sia/project/nlme/MEMSS/index.html
