REFERENCES AND RESOURCES
There are multitudinous resources available on the S language and on R, and the number is growing daily, it seems. The ones listed here are the ones I am familiar with, or have used in the creation of these tutorials. I have listed them in what I consider to be a best-first order, occasionally with annotations. You can take the annotations with however many grains of salt you care to.
R Development Core Team. (2006). R: A language and environment for statistical computing. Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org. [Without whom there would be no R.] Dalgaard, P. (2002). Introductory Statistics With R. New York: Springer. [THE best resource for learning R I have seen, bar none! If you want to learn R, you should have this book. Now out in a 2nd, and even better, edition, 2008. Also available on Kindle.] Venables, W. N., Smith, D. M., and the R Core Development Team. (2006). An Introduction to R. Notes on R: A Programming Environment for Data Analysis and Graphics, Version 2.4.0 (2006-10-03). Vienna: R Foundation for Statistical Computing. [An updated version of this will come with the R download. Look in the doc folder. I can't really recommend it for raw beginners, but once you get your feet wet, this is one of the best reference sources around.] Ugarte, M. D., Militino, A. F., & Arnholt, A. T. (2008). Probability and Statistics with R. Boca Raton, FL: Chapman & Hall. [An excellent--and thick--introduction to both statistics and R, which begins at the beginning and progresses through more advanced topics than the Dalgaard book.] Verzani, J. (2005). Using R for Introductory Statistics. Boca Raton, FL: Chapman & Hall. [A good stat book as well as a very good introduction to R. An older, less complete version is available at CRAN in PDF format: http://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf] Fox, J. & Weisberg, H. S. (2011). An R Companion to Applied Regression (2nd ed.). Thousand Oaks, CA: Sage. [Outstanding! Listed below Ugarte and Verzani only because the coverage is less comprehensive.] Crawley, M. J. (2005). Statistics: An Introduction Using R. Chichester, England: Wiley. Crawley, M. J. (2007). The R Book. Chichester, England: Wiley. [More about R than any sane person would ever want to know! My only problem with this book is that it is very Windows-centric.] Maindonald, J. & Braun, J. (2007). Data Analysis and Graphics Using R--An Example Based Approach (2nd ed.). New York: Cambridge University Press. [A more advanced introduction than either Dalgaard or Ugarte et al. A 3rd edition is now available, 2010, although I haven't seen it.] Long, J. D. (2012). Longitudinal Data Analysis for the Behavioral Sciences Using R. Thousand Oaks, CA: Sage. [I need to read this book again. Very clear and helpful for longitudinal data analysis, and I can't say that about a whole lot of sources on this topic!] Zieffler, A. S., Harring, J. R., & Long, J. D. (2011). Comparing Groups: Randomization and Bootstrap Methods Using R. Hoboken, NJ: Wiley. [A very well written and easily comprehended book on this somewhat difficult topic.] Baron, J. & Li, Y. (2006). Notes on the use of R for psychology experiments and questionnaires. On-line at http://www.psych.upenn.edu/~baron/rpsych/rpsych.html. [This appears to have been updated in 2011. A book has been published based on this online resource: Li, Y. & Baron, J. (2012). Behavioral Research Data Analysis with R. New York: Springer. Also available as an e-book. I have some reservations about the book, but I do have it, and I do use it.] Everitt, B. S. & Hothorn, T. (2006). A Handbook of Statistical Analysis Using R. Boca Raton, FL: Chapman & Hall. [A 2nd edition, 2010, is available. I haven't seen it.] Murrell, P. (2006). R Graphics. Boca Raton, FL: Chapman & Hall. [To date, THE book on R graphics. A 2nd edition, 2011, is available. I haven't seen it.] Fox, J. (2002). An R and S-Plus Companion to Applied Regression. Thousand Oaks, CA: Sage. [More general in scope and more R oriented than the following book. The 2nd addition of this is listed separately above, but this is also quite good if it's all you can get your hands on.] Faraway, J. J. (2005). Linear Models with R. Boca Raton, FL: Chapman & Hall. [The complete dope on linear models, incorporating analysis of covariance and anova. A newer edition has very recently been published, or is about to be, but I haven't seen it.] Wright, D. B & London, K. (2009). Modern Regression Techniques Using R: A Practical Guide for Students and Researchers. London: Sage. [Depends a little to much on optional packages if you ask me, but if you don't mind that sort of thing, it's a pretty good little book.] Rizzo, M. L. (2008). Statistical Computing with R. Boca Raton, FL: Chapman & Hall. [Dense and mathematically a bit heavy for social science types, but a good resource for those interested in the specialized area of statistical computing.] Canty, A. J. (2002). Resampling Methods in R: The boot Package. R News, vol. 2/3. On-line at URL: http://cran.r-project.org/doc/Rnews/. ---------- Following is a list of non-R-using stat books, but still useful. ---------- Sheskin, D. J. (2004). Handbook of Parametric and Nonparametric Statistical Procedures (3rd ed.). Boca Raton, FL: Chapman & Hall. [Not an R book, but this is the first or second source I go to with statistical questions. The 5th edition was published in 2011.] Freedman, D., Pisani, R., & Purves, R. (2007). Statistics (4th ed.). New York: Norton. [Why isn't everyone using this book to teach general statistics?] Ramsey, F. L. & Schafer, D. W. (2013). The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed.). Boston: Brooks/Cole Cengage Learning. [If you're a complete statistics newbie, get Freedman, et al. Otherwise, this is the book you want!] Howell, D. C. (2007). Statistical Methods for Psychology (6th ed.) Belmont,CA: Thomson-Wadsworth. [The best stat book I am aware of dedicated to social science issues, esp. psychology. The 8th edition came out in 2012, and the publisher is now called Cengage Learning.] Maxwell, S. E. & Delaney, H. D. (2004). Designing Experiments and Analyzing Data: A Model Comparison Perspective (2nd ed.). New York: Taylor & Francis. [Equal in quality to Howell but not for a first course! In some ways less comprehensive and less hand-holding but definitely authoritative in so far as issues concerning ANOVA are concerned.] Fox, J. (2008). Applied Regression Analysis and Generalized Linear Models. Los Angeles: Sage. [Want to learn GLMs? This is the best resource I know of, at least for social science types. It is not an R book, however.] Chatfield, C. (2003). The Analysis of Time Series: An Introduction (6th ed.). Boca Raton, FL: Chapman & Hall. [This book has been recommended to me as an excellent and fairly elementary introduction to time series. I haven't read it so can make no further comments.] Montgomery, D. C. (1977). Design and Analysis of Experiments (4th ed.). New York: Wiley. [Not a book on R, but very useful for experimental design. Oriented towards engineering, so you better know your math!] Hand, D. J., Daly, F., Lunn, A. D., McConway, K. J., & Ostrowski, E. (eds.). (1993). A Handbook of Small Data Sets. Boca Raton, FL: Chapman & Hall/CRC. [A few of the data sets used in these tutorials came from this excellent resource.]
revised 2016 February 8