Introduction to the R Statistical Computing Environment

Instructor(s):

  • John Fox, McMaster University

This workshop will be offered in an online video format.

The R statistical programming language and computing environment has become the de-facto standard for writing statistical software among statisticians and is widely used in the social sciences and elsewhere -- it is now possibly the most popular statistical software in the world. R is a free, open-source implementation of the S language, and is available for Windows, macOS, 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. Commercial statistical packages can also be expensive.

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 and to the RStudio IDE (interactive development environment), a powerful front-end to R. The first six 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 three sessions pick up where the basic lectures leave off, and are intended to provide the background required to use R more flexibly 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.

Fee and Registration: This course is part of the second four-week session. Please see our fee chart on our Registration page for the cost of attending one (or both) four-week session(s). Participants who enroll in a four-week session may take as many courses (workshops and lectures) as desired during the session for which they are enrolled. Participants in the four-week sessions are also welcome to attend all of the lectures and discussions offered in the Blalock Lecture Series.

Tags: R, open-source

Course Sections

Section 1

Location: Online -- Video format,

Date(s): July 21 - July 31

Time: 5:30 PM - 7:30 PM

Instructor(s):

  • John Fox, McMaster University

Syllabus: