The Comprehensive R Archive Network (CRAN) site provides Windows, OS X, and Linux binary installers for the reference implementation of R. Ubuntu users can also follow these instructions to install R using the apt package management system.

An optimized, parallel version of R is available from Revolution Analytics. UT students can obtain a free academic subscription by registering with their "@utexas.edu" email address. Warning: The Revolution R distribution may be one or more revisions behind the CRAN distribution, which may cause problems when installing certain extension pacakges.

RStudio is a free visual interface and integrated development environment (IDE) for R. StatET is a plug-in extension for developing R scripts using the Eclipse IDE platform.

Rattle provides a nice visual interface to various data mining packages in R. You can install this package by starting R and running the following the commands (the ">" indicates the R prompt):

> install.packages("rattle", dep=c("Suggests"))
> library('rattle')
> rattle()

Update (13 Feb. 2013): A demo R script for basic classifiers has been added.

Update (29 Jan. 2013): The R script used for the in-class RStudio demo is now available.

Multiple resources for learning R are available on the web. A suggested list (from this CMU data analysis course) includes:

• "Introduction to R" (also available as a PDF): Official tutorial from the people who brought you R.
• John Verzani, "SimpleR" (PDF): Short guide on using R for basic statistics
• Quick R (website): Guide developed for people familiar with commerical packages like SAS or SPSS; clearly written and useful even for people who have never used those commerical systems.
• Thomas Lumley, "R Fundamentals and Programming Techniques" (large PDF)
• Patrick Burns, "The R Inferno" (PDF): "If you are using R and you think you're in hell, this is a map for you." While not a tutorial, this book identifies gotchas and other such things you should be aware of when programming in R.
• Revolution Analytics has a blog post on translating Python and MATLAB to R.
• Lecture notes on "Writing Functions in R" and "Rewriting Your Code" (to have readble R functions).

Even if you have some experience coding, you should read this page providing minimal advice on programming.

There are also several books on R and statistical computing, including:

• W. John Braun and Duncan J. Murdoch, A First Course in Statistical Programming with R (Cambridge University Press, 2008; ISBN 9780521694247)
• Joseph Adler, R in a Nutshell (O'Reilly, 2009; ISBN 9780596801700)
• Norman Matloff, The Art of R Programming (No Starch Press, 2011; ISBN 9781593273842)
• John M. Chambers, Software for Data Analysis: Programming with R (Springer, 2008; ISBN 978-0-387-75935-7)

Many (if not all) of the algorithms covered in this introductory course have already been implemented in various add-on packages that are available from CRAN. Related packages have been gathered into various task views, including one for machine learning and one for graphics.

As a general rule, you should try to find an R package that implements an algorithm before implementing it yourself. Example code for using R packages to solve machine learning/data mining problems can be found in various places, including the "Data Mining Algorithms in R" Wikibook.

Bioconductor is another large R package repository. Most of these packages are specifically for bioinfomatics analyses, but several might be useful in more general contexts.