Introduction to the R Statistical Computing Environment
The R statistical programming language and computing environment has become the de-facto standard for writing statistical software among statisticians and has made substantial inroads in the social sciences -- it is now possibly the most widely used statistical software in the world. R is a free, open-source implementation of the S language, and is available for Windows, Mac OS X, and Unix/Linux systems.
A statistical software "package," such as SPSS, is primarily oriented toward combining instructions, possibly entered via a point-and-click interface, with rectangular case-by-variable datasets to produce (often voluminous) output. Such packages make it easy to perform routine data analysis tasks, but they make it relatively difficult to do things that are innovative or nonstandard -- or to extend the built-in capabilities of the package.
In contrast, a good statistical computing environment makes routine data analysis easy and also supports convenient programming. R fulfills both of these requirements, and users can readily write programs that add to its already impressive facilities. Thousands of R add-on "packages," freely available on the Internet in the Comprehensive R Archive Network (CRAN), extend the capabilities of R to almost every area of statistical data analysis. R is also particularly capable in the area of statistical graphics.
These lectures provide an introduction to the R statistical computing environment. The first four lectures present a basic overview of and introduction to R, including to statistical modeling in R -- in effect, using R as a statistical package. The following five sessions pick up where the basic lectures leave off, and are intended to provide the background required to use R seriously for data analysis and presentation, including an introduction to R programming and to the design of custom statistical graphs, unlocking the power in the R statistical programming environment.
The overall objective is to provide some facility in the use of R, to a level that enables participants to employ the software for assignments and projects in other Summer Program courses as well as in their own work.
Fees: Consult the fee structure.
Location: ICPSR -- Ann Arbor, MI
Date(s): July 25 - August 4
Time: 5:30 PM - 7:30 PM