Welcome to Data Analysis in the Geosciences.

This website will be used extensively in this course. Lecture notes will be posted here, with the most recent lecture appearing in this space. Previous lectures will be accessible through the list at the left, as will all problem sets and handouts, including the syllabus.

If you need to contact me, the best way is to click on the email link at the left, under Contact Us. Please leave the subject line as 8370.

In the first few class periods, we will be learning the statistical software R. There will be no lectures notes for this, as we will follow the handout, A Short R Tutorial. I recommend that everyone install R on their own computers. R is available for OS X, Linux, and Windows platforms, and can be downloaded at no cost from the R Project for Statistical Computing.

There’s an ever-growing list of R-related books available, but here are a few I’ve found useful. Springer is publishing several titles on R, including Borcard et al. (below) on numerical ecology, as well as volumes on phylogenetics, time series, spatial data analysis, multivariate data visualization, and bayesian computation.

Adler, J., 2009. R in a Nutshell. O'Reilly Media, 640 p. ISBN: 978–0-596–80170–0

Bolker, B.M., 2008. Ecological Models and Data in R. Princeton University Press, Princeton, 396 p. ISBN 978–0-691–12522–0

Borcard, D., F. Gillet, and P. Legendre, 2011. Numerical Ecology with R. Springer, New York, 306 p. ISBN 978–1-4419–7975–9

Crawley, M.J., 2005. Statistics: An Introduction Using R. John Wiley & Sons, West Sussex, 327 p. ISBN 0–470–02298–1. This is our textbook.

Crawley, M.J., 2013. The R Book. John Wiley & Sons, West Sussex, 1051 p. ISBN 978–0470–97392–9. A lap breaker, but a fantastic, comprehensive volume. If I could own only one R book, this would be it.

Dalgaard, P., 2002. Introductory Statistics with R. Springer, New York, 267 p. ISBN 0–387–95475–9

Maindonald, J., and J. Braun, 2003. Data Analysis and Graphics Using R: An Example-Based Approach. Cambridge University Press, Cambridge, 362 p. ISBN 0–521–81336–0

Matloff, N., 2011. The Art of R Programming. No Starch Press, 373 p. ISBN 978–1-59327–384–2. If you have programmed before, you will appreciate this approach.

Murrell, P., 2006. R Graphics. Chapman & Hall / CRC, Boca Raton, 301 p. ISBN 1–58488–486-X. A great source when you want to get serious with the graphics capabilities of R.

The R Core Development Team, 2004. The R Reference Manual Base Package, Volumes 1 and 2. Network Theory Limited, Bristol. ISBN 0–9546120–0-0. Note that this is just a hard-copy version of the help files that come with R. You might find it easier to browse and the proceeds help cover the development costs of R. Think of it as a donation to a good cause.

Teetor, P., 2011. R Cookbook. O'Reilly Media, 436 p. ISBN 978–0-596–80915–7.

Venables, W.N., D.M. Smith, and the R Development Core Team, 2009. An Introduction to R. ISBN 3–900051–12–7. This is available as a free pdf download, and is a solid reference. It’s often the first place I look.

Verzani, J., 2005. Using R for Introductory Statistics. Chapman & Hall / CRC, Boca Raton, 414 p. ISBN 1–58488–4509