Overview:
R is a free, open source, object-oriented software system for statistical analysis and graphics. The home page for R is www.r-project.org. You can download the base R system and documentation there. Statisticians contribute to R by writing "packages" for specific problems.
The "free" is the good part of R. Also good is the fact that R can run under the Windows, Mac OS X, and Linux operating systems. The bad part is the very steep learning curve to master the software.
Fortunately, R Commander provides a good GUI (Graphical User Interface) that automates the importation of data, some simple transformations of variables, and elementary (mainly) statistical analyses and graphics. One advantage of R Commander is it echos the R commands from the point and click interface, so you can use this to learn R. You can also edit commands and write your own commands in the Script window of R. Everything that can be done in R can also be done in R Commander. The home page for R Commander is socserv.mcmaster.ca/jfox/Misc/Rcmdr. The best way to install R Commander is to start R and use the Install Packages option under the Packages menu. The first time you load it, you may be prompted to download some other packages required by Rcmdr. Just follow the instructions in the windows to download these.
Necessary Bookmarks:
The R project for Statistical Computing: This is the “home page” for R. Most documentation here is on the more technical side.
The Comprehensive R Archive Network (CRAN): Place to download R. The search link is very helpful. Click on it and then type Excel in the search field. You will see many links related to reading an Excel file into R, most of which advise you (correctly) to first output the data as a tab-delimited or comma-delimited file and them import that file into R.
Quick-R: A site developed by Robert Kabakoff. You can learn statistics without leaving this site!. It includes R code showing you how to perform basic data input and management, elementary and somewhat advanced statistics, and graphics.
UCLA Academic Technology Servies R pages: A rtre3asure-trove of examples and modules for learning R. The UCLA ATS website is also an essential one repository for information on many other statistical packages.
An Introduction to R by Venables and Smith. A more technical introduction to the language and its structure that is useful if you are programming in R. The authors have turned these notes into an inexpensive book that can be ordered through Amazon or other web sites.
Other Info:
Many books and web sites have learning material on R. Do a web search on "R Introduction Statistics" (or similar keywords) to find a large number of resources, many of them free. There is also a ton of R examples over the web. Do a web search on “boxplot R” or “regression R” and you will come up with many examples of code that you can adapt for your situation.
Tom Short has developed very useful reference cards for R. Usually, they are available at www.Rpad.org. In case you have problems with that server, you can get a copy here.
At CU, Matt Keller teaches a course on R. You can examine his slides and lecture notes here here.