R Computing Page

Here are the notes that I created for my R short course at the Odum Institue.

Getting Started:

First, download the latest version of R at: http://cran.r-project.org/. At the top of the page, click on your operating system. If you use Windows, click "base" on the next screen, and then "R-2.6.0-win32.exe" on the final screen. If you use Mac, I think "R-2.6.0.dmg" will serve your needs. Note: The number in the filename will increase as new versions are released. If you use Linux, please see my page on using the Ubuntu operating system as a scientific working environment.
Second, after you install R with the program you have downloaded, install the following packages: car, RWinEdt (if you have WinEdt already installed), MASS, and foreign. To do this, enter into the R command prompt: install.packages("packageName"), where the double quotes are included and packageName will be replaced by the individual, case-sensitive names of each of these four packages. You may be prompted to select a cran mirror, any of which should be suitable.
Finally, download the files below--RIntro.pdf and the relevant data files--into a folder you want to work in as you learn R.
Note: If you ever update your version of R, this is done by downloading and re-installing the newest version, as just described. The major downside of this is the loss of previously installed packages. Install_Packages_Post_Update.R is a program by Evan Parker-Stephen that installs commonly-used packages, so it can save you some time after installing R for the first time or re-installing the latest edition.

Once you have set-up, consider these documents to get your feet wet:

The R website, http://www.r-project.org/, which is helpful for downloading R, installing packages, and finding help. Under "Manuals," the document entitled "An Introduction to R" is particularly useful.
The notes to my short course at the Odum Institute. Some examples use human rights data in either Stata or ASCII format. My thanks to Luke Keele & Evan Parker-Stephen for sharing earlier notes that contributed heavily to this file.
An R cheat sheet.
For a more detailed introduction, John Fox's Introduction to Statistical Computing in R is excellent.
For continuing questions about R, R-Seek and R Wiki are useful online sources.

Running R over a computing cluster:

If you have the option to submit your R code as a batch to a Linux-based computing cluster, you may find this useful if your code either requires more memory than a desktop computer or if the code runs very slowly.
Bev Wilson has written a cheat sheet based on his use of UNC's Emerald cluster. It explains the commands to use at the terminal to successfully submit your R program.

If you are interested in graphics in R, consider the following sources:

An introduction to graphics in R written by Evan Parker-Stephen.
Bill Jacoby's website for Blalock Lectures on statistical graphics using the "lattice" package.
A website with a number of tips and tricks.
Rcolor.pdf & ColorChart.pdf visually display all of the colors available in R.

If you are interested in maximum likelihood estimation in R, consider these sources:

Marco Steenbergen's notes on programming MLE in R and Stata. I personally find R more elegant on MLE, but either program can do it.
mle_plotting.R This is example code for deriving an MLE estimator in R and for creating simple graphs, including predicted probabilities of a logit model.
teach_figures.R Example code on programming MLE, using the "glm" command, and plotting model output.

Some of the interesting user-created R packages include:

"effects" which John Fox has written several papers about, posted at the following links: polytomous-effect-displays and effect-displays-revised
"nlme" Linear and Nonlinear Mixed Effects Models, which performs hierarchical modeling nicely.
"pscl" Political Science Computational Laboratory for Bayesian statistics, particularly ideological scaling.
"MCMCpack" also for Bayesian statistics, offering Bayesian solutions to a wide range of models.
For time series analysis, Shumway and Stoffer now have a book out entitled "Time Series Analysis and Its Applications With R Examples".
The following link offers several free R books.