This page will feature slides from our sessions, recordings of the various sessions (for review), exercises for you to complete, and their solutions (all to follow). Prior to the module, please install up-to-date versions of R and RStudio on the computer you will use during the summer institute. Both are free.

• R is available from this list of sites.
• RStudio is available here.

Script files are posted following each session; these will contain our R code for the exercises. To make them work on your computer, remember to modify file names and locations appropriately. Also note that many different 'correct' solutions are possible.

The module has 10 sessions, each of 90 minutes. The basic format for a session is:

• 45 minutes of lecture material. These will be recorded and made available as soon as possible.
• 30 minutes of exercises for you to try, with others in the class
• 15 minute discussion of exercises, where the instructors will present possible solutions and answer questions

Before trying to read data into your R session, we recommend looking at it first, in a text editor. Is the data comma- or tab-delimited? Does it have a 'header' row containing variable names?

• crab.csv, crab.txt
• lawschool.csv, lawschool.txt
• mammals.csv, mammals.txt
• othercrab.csv
• salary.csv, salary.txt
• titanic.csv, titanic.txt
• titaniclong.csv, titaniclong.txt

• The R site, which includes the Comprehensive R Archive Network (CRAN) of downloads and packages
• A reference card of common R commands (and a slightly longer reference card)
• A cheat sheet for ggplot
• The R Graphics Gallery has a good selection of examples - most are more complicated than those seen in our slides
• Some R mailing lists - joining the announce list is recommended. CrossValidated is also helpful (but search it before posting)
• To change languages, put a .Renviron file in the startup working directory - use en for English, or de, it (To get the working directory, use getwd())

• P. Dalgaard, Introductory Statistics with R: Springer, 2nd Edition 2008 [good if you haven't seen much statistics before, though it uses very simple methods, and doesn't emphasize programming much. There are several other texts at this level]
• Matloff, The Art of R Programming, 2011; goes from introductory programming up to a high level.
• Braun and Murdoch, A First Course in Statistical Programming with R, CUP 2007 [A different emphasis from the other books here, good for those new to programming]