Introduction to the R Statistical Computing Environment
Instructor(s):
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.
The lectures provide an introduction to the R statistical computing environment. They do not assume any prior knowledge of R. The first week covers basic introduction to R, like data management, packages, and simple analyses. The following week will introduce modeling, the design custom statistical graphs, and a preliminary introduction to the R programming language. There will be two lab sessions (one each week) to help students apply materials to different questions.
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.
Tags: R, statistical computing, programming, statistical graphics, statistical modeling
Course Sections
Section 1 Location: ICPSR -- Ann Arbor, MI Date(s): June 27 - July 7 Time: 5:30 PM - 7:30 PM Instructor(s): |