Books on R

• J Albert, Bayesian Computation with R (2nd edn), New York, etc.: Springer-Verlag 2009 (SF 4 ALB)
• R A Becker, J M Chambers and A R Wilks, The new S language: a programming environment for data analysis and graphics, Pacific Grove, CA: Wadsworth & Brooks/Cole Advanced Books & Software 1988 (SK 59 S/B). This is also called the "Blue Book"
• R S Bivand, E J Pebesma and V Gòmez-Rubio, Applied Spatial Analysis with R, New York, etc.: Springer-Verlag 2008.
• W J Braun and D J Murdoch, A First Course in Statistical Programming with R, Cambridge: Cambridge University Press 2007
• J M Chambers, Programming with Data, New York, etc.: Springer, New York 1998 (SK 59 S/C). This is also called the "Green Book"
• J M Chambers, Software for data analysis : programming with R, New York, etc.: Springer 2008 (SK 59 R/C)
• J M Chambers and T J Hastie (eds), Statistical Models in S, Boca Raton, FA: Chapman & Hall/ CRC, New York 1992 (SK 59 S/C). This is also called the "White Book"
• M J Crawley, The R Book, Chichester: Wiley 2007 (SK 59 R/C).
• D Cook and D F Swayne, Interactive and Dynamic Graphics for Data Analysis with R and GGobi, Springer-Verlag 2007.
• S P Cowpertwait and A Metcalfe, Introductory Time Series with R, Springer-Verlag 2009 (available as an e-book from: http://dx.doi.org/10.1007/978-0-387-88698-5)
• P Dalgaard, Introductory Statistics with R, New York, etc.: Springer-Verlag 2002 (SK 59 R/D)
• B S Everitt, [A Handbook of] Statistical Analyses using S-plus, Boca Raton, FL, etc: Chapman & Hall/ CRC 1994 (SK 59 S/E)
• B Everitt, An R and S-PLUS companion to multivariate analysis, London, etc.: Springer 2005 (SF 2 EVE)
• B Everitt and S Rabe-Hesketh, Analyzing medical data using S-Plus, New York, etc.: Springer-Verlag 2001 (SF 1.Y EVE)
• J J Faraway, Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models, Boca Raton, FL, etc.: Chapman & Hall/CRC 2006 (SK 59 R/F)
• J Fox, An R and S-Plus Companion to Applied Regression, Thousand Oaks, CA: Sage (SF 2.5 FOX)
• C Gaetan and X Gueyon, Spacial Statistics and Modeling, Springer 2010
• P J Good, Introduction to statistics through resampling methods and R/S-PLUS, Hoboken, NJ, etc.: Wiley 2005 (S 9.6 GOO)
• R M Heiberger, R Through Excel, Springer-Verlag 2009.
• P D Hoff, A First Course in Bayesian Statistical Methods, Springer 2009 (e-book available from: http://dx.doi.org/10.1007/978-0-387-92407-6)
• S Huet, Statistical tools for nonlinear regression : a practical guide with S-PLUS and R examples (2nd edn), New York, etc.: Springer-Verlag 2004 (SF 2.5 HUE)
• R Kabacoff, R in Action, Manning 2010.
• R Ihaka and R Gentleman, "R: A language for data analysis and graphics", Journal of Computational and Graphical Statistics, 5 (1996), 299-314, 1996 (available via JSTOR Arts and Science complement). The original published description of the R project, now dated but still worth looking at
• A Krause and M Olson, The Basics of S and S-PLUS (2nd edn), New York, etc: Springer-Verlag 2000 (SK 59 S/K)
• K Kleinman and N J Horton, SAS and R: Data Management, Statistical Analysis, and Graphics, Chapman and Hall/CRC, 2009.
• U Ligges, Programmieren mit R (3rd edn), Springer 2009
• S P Millard, A Krause (eds), Applied statistics in the pharmaceutical industry : with case studies using S-Plus, New York, etd.: Springer-Verlag 2001 (SF 1.Y MIL)
• R A Muenchen, R for SAS and SPSS Users, Springer 2009
• R A Muenchen and J M Hilbe, R for Stata Users, Springer 2010.
• P Murrell, R Graphics, Boca Raton, FL, etc.: Chapman & Hall/CRC 2006 (SK 59 R/M)
• G P Nason, Wavelet Methods in Statistics with R, Springer-Verlag 2008.
• S Pekar and M Brabec, Moderni analyza biologickych dat. 1. Zobecnene linearni modely v prostredi R, Prague: Scientia 2009.
• G Petris, Dynamic Linear Models with R, Springer 2009.
• J C Pinheiro and D M Bates, Mixed-effects models in S and S-PLUS, New York, etc.: Springer-Verlag 2000 (SK 59 S/P)
• S S Qian, Environmental and Ecological Statistics with R, Chapman and Hall/CRC 2009.
• R Development Core Team, The, The R reference manual : base package (2 vols), Bristol : Network Theory Ltd., 2003 (SK 59 R/R)
• J Ramsey, G Hooker and S Graves, Functional Data Analysis with R and MATLAB, Springer-Verlag 2009.
• M L Rizzo, Statistical computing with R, Boca Raton, FL, etc.: Chapman and Hall/CRC 2008 (SK 59 R/R)
• C Robert and G Casella, Introducing Monte Carlo Methods with R, Springer 2010 (S 9.93 ROB)
• P Spector, Data Manipulation with R, New York, etc.: Springer-Verlag 2008 (SK 59 R/S)
• M H Stevens, A Primer of Ecology with R, Springer 2009 (available as an e-book from: http://dx.doi.org/10.1007/978-0-387-89882-7)
• M D Ugarte, A F Militino and A Arnholt, Probability and Statistics with R, Boca Racon, FL, etc.: Chapman & Hall/CRC 2008
• K Varmuza and K Filzmoser, Introduction to multivariate statistical analysis in chemometrics, Boca Raton, FL: CRC Press 2009 (available as an e-book from: http://www.myilibrary.com?id=199373
• W N Venables, An introduction to R : notes on R: a programming environment for data analysis and graphics, version 1.9.1, Bristol : Network Theory, 2004 (SK 59 R/V)
• W N Venables and B D Ripley, Modern Applied Statistics with S (4th edn), New York, etc: Springer 2002 (SK 59 S/V). There is also a supplement called 'R' Complements to Modern Aplied Statistics with S-Plus which is on the web at http://www.stats.ox.ac.uk/pub/MASS4/
• H D Vinot (ed.), Advances in Social Science Research Using R, Springer 2010
• E Zivot and J Wang, Modeling financial time series with S-Plus, New York, etc.: Springer-Verlag 2003 (SK 59 S/V)
• H Wickham, ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag 2009.
• A F Zuur and E N Ieno, A Beginner's Guide to R, Springer-Verlag 2009.

http://www.r-project.org/

including

• An Introduction to R, based on the former "Notes on R", which gives an introduction to the language and how to use R for doing statistical analysis and graphics
• A draft of The R language which defines and documents the language per se, that is, the objects that it works on, and the details of the expression evaluation process, which are useful to know when programming R functions
• Writing R Extensions which covers how to create your own packages, write R help files, and the foreign language (C, C++, Fortran, ...) interfaces
• R Data Import/Export which describes the import and export facilities available either in R itself or via packages which are available from CRAN
• R Installation and Administration
• R Internals: a guide to the internal structures of R and coding standards for the core team working on R itself
• The R Reference Index which contains all help files of the R standard and recommended packages in printable form