National Archive of Computerized Data on Aging

What is R?

R acts as an alternative to traditional statistical packages such as SPSS, SAS, and Stata such that it is an extensible, open-source language and computing environment for Windows, Macintosh, UNIX, and Linux platforms. Such software allows for the user to freely distribute, study, change, and improve the software under the Free Software Foundation?s GNU General Public License. It is a free implementation of the S programming language, which was originally created and distributed by Bell Labs. However, most code written in S will run successfully in the R environment. R performs a wide variety of basic to advanced statistical and graphical techniques at little to no cost to the user. These advantages over other statistical software encourage the growing use of R in cutting edge social science research.

Where can I obtain R?

Installation files for Windows, Mac, and Linux can be found at the Web site for the Comprehensive R Archive Network, http://cran.r-project.org/. The site also contains documentation for downloading and installing the software on different operating systems. There is no cost for downloading and using R.

Where can I find more information on R?

Books

Braun, W. and Murdoch, D. (2007). A First Course in Statistical Programming with R. Cambridge, MA: Cambridge University Press.
Chambers, J. M. (1998). Programming with Data: A Guide to the S Language. Murray Hill, NJ: Bell Laboratories.
Dalgaard, P. (2008). Introductory Statistics with R (2nd edition). New York: Springer.
Everitt, B., and Hothorn, T. (2006). A Handbook of Statistical Analyses Using R. Boca Raton, FL: Chapman & Hall/CRC.
Faraway, J. J. (2005). Linear Models with R. Boca Raton, FL: Chapman & Hall/CRC.
Faraway, J. J. (2006). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Boca Raton, FL: Chapman & Hall/CRC.
Fox, J. (2002). An R and S-Plus Companion to Applied Regression. Thousand Oaks, CA : Sage Publications.
Muenchen, R. A. (2009). R for SAS and SPSS Users. Springer Series in Statistics and Computing. New York: Springer.
Murrell, P. (2005). R Graphics. Boca Raton, FL: Chapman & Hall/CRC.
Pinheiro, J. C. and Bates, D. M. (2004). Mixed Effects Models in S and S-Plus. New York: Springer.
Spector, P. (2000). Data Manipulation with R. New York: Springer.
Venables, W. N., and Ripley, B. D. (2002). Modern Applied Statistics with S. Fourth Edition. New York: Springer.
Zuur, A. F., Ieno, E. N., and Meesters, E. H. W. G. (to be published 2009). A Beginner's Guide to R. Use R. New York: Springer.

Web Resources

Quick-R site
The Omega Project for Statistical Computing
The R Project for Statistical Computing
The R Journal

Seminal Journal article

Ihaka, R., and Gentleman, R. (1996). R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5(3):299-314.

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