2015 Courses on the R Statistical Computing Environment

Image courtesy of www.r-project.org

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. R 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 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. R is also particularly capable in the area of statistical graphics.

In 2015, the ICPSR Summer Program is offering several courses on R:

FOUR-WEEK LECTURES

Session I: June 23 - July 3, 5:30 p.m.-7:30 p.m.
Introduction to the R Statistical Computing Environment
Location: Ann Arbor, MI
Instructor: John Fox, McMaster University
Description: 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 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.

Session II: July 21 - July 31, 5:30 p.m.-7:30 p.m.
Introduction to the R Statistical Computing Environment
Location: Ann Arbor, MI
Instructor: Kerem Ozan Kalkan, Eastern Kentucky University
Description: These lectures provide a basic introduction to the R environment. Specific topics will include: Elements and rules of the R language; R functions and objects; statistical models in R, including linear and generalized linear models; data manipulation; constructing statistical graphs; and using R packages. The overall objective is to provide some facility in the use of R, to a level that enables participants to employ this software for assignments and projects in other Summer Program courses.


THREE- TO FIVE-DAY WORKSHOPS

May 20 - 22, 9 a.m.-5 p.m.
R: Learning by Example
Location: Montreal, QC
Instructor: Kelly Gleason, University of Wisconsin at Milwaukee
Description: This course will introduce users to R by working through a number of example analyses commonly encountered in the social sciences. We use these examples as ways to motivate rudimentary topics such as data management, graphics, and finding help and other useful routines in R. We will cover linear models, generalized linear models, discrete choice models, and models for multivariate data. We will have ample time for participants to ask questions and work through examples themselves. Participants should leave the course with a good understanding of R and how to put it to use in their own research.

June 8 - 10, 9 a.m.-5 p.m.
R: Learning by Example
Location: Boulder, CO
Instructor: David Armstrong, University of Wisconsin at Milwaukee
Description: This course will introduce users to R by working through a number of example analyses commonly encountered in the social sciences. We use these examples as ways to motivate rudimentary topics such as data management, graphics and finding help and other useful routines in R. We will cover linear models, generalized linear models, discrete choice models, and models for multivariate data. We will have ample time for participants to ask questions and work through examples themselves. Participants should leave the course with a good understanding of R and how to put it to use in their own research.