Department of Statistics


The R Project

The Department of Statistics of The University of Auckland is well known for being the birthplace of the R Project.

Founders of the R Project are, at the time senior lecturers R. Gentleman and R. Ihaka, now one of our Associate Professors. The R Project is a language and environment for statistical computing and graphics. It is widely taught around the world and is being used by Ivy League Universities, Google, our second-year Statistics students, and even by school children.

Today the R Project is maintained by the R Development core team which includes our staff members Ross Ihaka, Paul Murrell, and Thomas Lumley. Many of our other staff write packages or applications using R.

We use R in several of our undergraduate and postgraduate courses.

  • STATS 201/207/208: Data Analysis for example uses R to undertake the analyses. This provides a gentle guided introduction to using the R environment for statistical computing.
  • STATS 220: Statistical Computing offers a more detailed look at the use of software to accomplish statistical analysis. In addition to R students learn other useful tools including perl.

Books about R


Our staff have written books on the use of the R language. A short selection of interests include:

  • Curran, J.M. (2010) Introduction to Data Analysis with R for Forensic Scientists, ISBN: 978-1420088267
  • Murrell, P (2005) R Graphics, ISBN: 978-1584884866
  • Murrell, P Introduction to Data Technologies www.stat.auckland.ac.nz/~paul/ItDT

Ihaka & Gentleman’s 1996 paper introducing R has been cited by some 4,635 papers as of 2010.

The comprehensive R archive network


The Comprehensive R Archive Network (CRAN) is a globally-distributed repository of add-ons for the R Project. We host a local copy which you can access at http://cran.stat.auckland.ac.nz/.

To use this repository, either select the “New Zealand” mirror, or try use the following command in R before installing packages: options(repos = “http://cran.stat.auckland.ac.nz”);

Please note that your local CRAN will be the fastest. A full list of mirrors may be found on CRAN.

Mirrors list on CRAN