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 slides for these sessions contain minor changes to the printed version
** This session covers different material to the printed version

Session 1, Introductions, reading in data (exercises) (R script file)

Session 2; Learning to Draw (exercises) (R script file)

Session 3; Data Manipulation (exercises) (R script file)

Session 4; Model Fitting (exercises) (R script file)

Session 5*; Permutation Tests (exercises) (R script file)

Session 6; Writing Loops (exercises) (R script file)

Session 7**; Handling Large Datasets (exercises) (R script file)

Session 8*; Bioconductor #1 (exercises) (R script file)

Special Exercise: This is a more in-depth programming problem, for you to try on Tuesday night; we'll discuss it in the final session (PowerPoint) (R script file)

Session 9; Bioconductor #2 (exercises) (R script file)

Session 10; Special Exercise review (see above), interfacing R to other software

Bonus Tracks! In the printed notes, there is a session on haplotypes. Here are some exercises, and a script file with one way to program them. You may also find this documentation useful.

- in alphabetical order.

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?

AMDchrom1.Rdata -- see session 8
annt.txt
bpdata.csv
data.vsn.csv
example-pheno.txt  
example-pheno.csv 
example-snp.txt
foursnps.csv
foursnps.txt
genepi.txt
justsnps.txt
niehs.csv
psa.txt
salary.dat
sampleinfo.csv
sisg.nc -- see session 7
swirl.zip -- see session 8

• The R site, which includes the Comprehensive R Archive Network (CRAN) of downloads and packages [this is a local mirror]
• Bioconductor is a collection of R packages for bioinformatics/genomics. It will be helpful to download and install the 'base' Bioconductor packages before sessions 8/9/10
• A reference card of common R commands (and a slightly longer reference card)
• 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]
• J. M. Chambers, Programming with Data: A Guide to the S Language. New York: Springer, 1998. [reference for programmers who find 'thinking' in R difficult] Describes the S language, on which R is based. Also known as "The Green Book"
• J. M. Chambers, Software for Data Analysis: Programming with R. New York: Springer, 2008. [also for programmers; it begins with simple interactive use and gradually progresses to advanced use of R]
• Gentleman et al, Bioinformatics and Computational Biology Solutions Using R and Bioconductor: Springer 2005 [guides you through practical bioinformatics data analysis using the Bioconductor toolkit, emphasizing microarrays]
• Hahne et al, Bioconductor Case Studies; Springer 2008 [Several fairly advanced examples using R and Bioconductor, emphasizing genomic data]
• 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]