The R Statistical Computing Environment: The Basics and Beyond (Berkeley, CA)
The R statistical programming language and computing environment has become the de facto standard for writing statistical software among statisticians. Over the past few years, it also has made substantial inroads in the social sciences. 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 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. Statisticians and others (including a number of social scientists) have taken advantage of the extensibility of R to contribute more than 5000 freely available "packages" of documented R programs and data to CRAN (the Comprehensive R Archive Network) and many others to the Bioconductor package archive. R is also particularly capable in the area of statistical graphics.
The goal of this five-day workshop is to introduce R and R programming. Each day will combine four to five hours of lectures and demonstrations with two to three hours of hands-on labs. We will begin with a basic overview of and introduction to R, including statistical modeling in R -- in effect, using R as a statistical package. After that, the workshop picks up where the basic material leaves off, and provides the background required to use R seriously for sophisticated data analysis, programming, and presentation. Topics to be covered in the workshop include an introduction to R programming and the design of custom statistical graphs, unlocking the power of the R statistical programming environment.
Fee: Members = $1500; Non-members = $3000