R Statistical Programming Language

The R Project provides a comprehensive, free, open source statistical programming language and environment based on the S language. R is the name of both the language and the environment in which you generally use the language. It’s an interactive environment where the commands you enter generate immediate results that you can use to guide your analyses.

> x <- c(5,4,3,2,5)
> mean(x)

There is an active development and support community using R and it’s been growing more popular in both academic and business communities.

Books & Articles on R

  • Albert, J. (2009). Bayesian computation with R (2nd ed.). New York: Springer.
  • Baayen, R. H. (2008). Analyzing linguistic data : a practical introduction to statistics using R. Cambridge, UK ; New York: Cambridge University Press.
  • Braun, J., & Murdoch, D. J. (2007). A first course in statistical programming with R. Cambridge ; New York: Cambridge University Press.
  • Crawley, M. J. (2007). The R book. Chichester, England ; Hoboken, N.J.: Wiley.
  • Dalgaard, P. (2008). Introductory statistics with R (2nd ed.). New York: Springer.
  • Gentle, J. E. (2009). Computational statistics. New York: Springer.
  • Good, P. I. (2005). Introduction to statistics through resampling methods and R/S-PLUS. Hoboken, N.J.: Wiley-Interscience.
  • Kabacoff, R. I. (2011). R in Action: Data Analysis and Graphics with R.
  • Leipzig, J., & Li, X.-Y. (2009). Data mashups in R. from http://oreilly.com/catalog/9780596804770/
  • Nolan, D. A., & Speed, T. P. (2000). Stat labs : mathematical statistics through applications. New York: Springer.
  • Owen, W. J. (2006). Using R in an Undergraduate Mathematical Statistics Sequence [Electronic Version]. InterStat, from http://interstat.statjournals.net/YEAR/2006/abstracts/0605002.php
  • Owen, W. J. (2007). The R Guide [Electronic Version], from http://cran.r-project.org/doc/contrib/Owen-TheRGuide.pdf
  • Petris, G. (2009). Dynamic linear models with R. New York: Springer.
  • Rossiter, D. G. (2009). Introduction to the R Project for Statistical Computing for use at ITC [Electronic Version], from http://cran.r-project.org/other-docs.html
  • Torgo, L. (2003). Data Mining with R: Learning by Case Studies. LIACC-FEP, University of Porto.
  • Trosset, M. W. (2009). An introduction to statistical inference and its applications with R. Boca Raton: Chapman & Hall/CRC.
  • Vance, A. (2009, January 7). Data Analysts Captivated by R’s Power. New York Times, from http://www.nytimes.com/2009/01/07/technology/business-computing/07program.html
  • Vance, A. (2009, January 8). R You Ready for R. New York Times, from http://bits.blogs.nytimes.com/2009/01/08/r-you-ready-for-r/
  • Verzani, J. simpleR – Using R for Introductory Statistics.
  • Warnes, G. R., & Gorjanc, G. (2008). gdata: Various R programming tools for data manipulation.

Support for R

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