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.]