Getting Started with the R Data Analysis Package

Professor Norm Matloff
Dept. of Computer Science
University of California at Davis
Davis, CA 95616

R is a wonderful programming language for statistics and data management, used widely in industry, business, government, medicine and so on. And it's free, an open source product. The S language, of which R is essentially an open source version, won the ACM Software System Award in 1998.

Downloading R:

R is available for Linux, Windows and Mac systems.

You can download R from its home page.

For Ubuntu Linux or other Debian-related OSs, a more direct method is:

% sudo apt-get install r-base

Learning R:

There is a perception among some that R has a steep learning curve, but I disagree. True, R usage has its advanced aspects, but my recommendation is simply, just get started! Start simple, and then refine gradually.

Here are some resources that I would recommend for learning R:

• For Non-Geeks:

  There are numerous gentle online tutorials on R, such as:

  • This one at Potsdam University.
  • Just plug "R tutorial" into Google to see many more.

• For (Actual or Aspiring) Geeks:

  R syntax is similar to those of C, Python, PERL, etc. don't know these terms) object-oriented and has a functional programming philosophy. Here are some introductions to R from a programming perspective:

  • Of course, I'll mention my own tutorials on writing R code (though very little on the statistical aspects):
    • My 5-minute tutorial on R programming for those who know C.
    • Chapter 1 of my book on R software development, The Art of R Programming, NSP, 2011. This gives you an overview of the language, and walks you through a couple 5-minute R sessions.
    • Part of a very rough draft of that book. It is only about 50% complete, has various errors, and presents a number of topics differently from the final version, but should be useful.
    • The R programming Wiki.

• Many books are listed on the R home page.

R Programming Tools:

One of the most debated topics in R online discussions is that of programming tools for R, of which there are many. (Links to be added.)

• I'm not a fan of integrated development environments, but if you like IDEs, there are a number of them available, including a commercial product from Revolution Analytics (all of the rest in this list are open source), an R plugin from Eclipse called StatET, a new one called RStudio, and more established ones such as JGR, Rcmdr, RKward and so on.

  Currently the most popular, according to an April 2011 poll, are StatET and RStudio. The latter is new, but is gaining popularity quite quickly.

• There are packages for R programming in famous text editors, notably ESS for Emacs and vim-r for Vim. There are debugging tools that can be used with them: my own edtdbg (for Vim but soon to add Emacs) and tracebug (ESS).

People you can talk to:

• There are various mailing lists (start with R-help) shown on the R home page.
• There are R user groups in cities around the world. I'm active in the the San Francisco Bay Area group. We hold meetings once a month, with one or two speakers. Many attendees are new to R.
• Another online place to ask questions is Stack Overflow.