SISG, Intro to R

Slides, exercises from sessions

Session 1, Introductions, reading in data (exercises)

Session 2; Learning to Draw (exercises) (R script file for one way to do the exercise questions)

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

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

Special exercise! This is a more in-depth programming problem, for you to try tonight; we'll talk more about this in the final sessions. Here's the data you will need - note that the genotypes are coded 0/1/2, with missing values coded as -1. (R script file)

Session 5; Writing Loops (exercises) (R script file)

Session 6; Permutation tests (exercises) (R script file)

Session 7; Bioconductor #1 (exercises) (R script file)

Session 8; Bioconductor #2 (exercises) (R script file)

Datasets

AMDchrom1.Rdata -- download this to your machine and use load() - not the more usual read.table() or read.csv()

sisg.nc -- download this to your machine - and see Session 8

Other links

A reference card of common 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())

Books

J. M. Chambers, Programming with Data: A Guide to the S Language. New York: Springer, 1998. [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] Chambers's newest book has not yet been published but will be out shortly.
P. Dalgaard, Introductory Statistics with R: Springer, 2002 [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]
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, emphasising microarrays]


SISG 2008, Auckland R and Bioconductor: Computing for Statistical Genetics Instructor: Thomas Lumley, Ken Rice Email: see thomas or ken, in class Downloading Bioconductor `source("http://bioconductor.org/biocLite.R") biocLite()` This takes some time! - but you only need to do it once. When you need a new package, use e.g. `source("http://bioconductor.org/biocLite.R") biocLite("siggenes")` Slides, exercises from sessions Session 1, Introductions, reading in data (exercises) Session 2; Learning to Draw (exercises) (R script file for one way to do the exercise questions) Session 3; Data Manipulation (exercises) (R script file) Session 4; Model Fitting (exercises) (R script file) Special exercise! This is a more in-depth programming problem, for you to try tonight; we'll talk more about this in the final sessions. Here's the data you will need - note that the genotypes are coded 0/1/2, with missing values coded as -1. (R script file) Session 5; Writing Loops (exercises) (R script file) Session 6; Permutation tests (exercises) (R script file) Session 7; Bioconductor #1 (exercises) (R script file) Session 8; Bioconductor #2 (exercises) (R script file) Datasets AMDchrom1.Rdata -- download this to your machine and use `load()` - not the more usual `read.table()` or `read.csv()` sisg.nc -- download this to your machine - and see Session 8 foursnps.csv foursnps.txt genepi.txt justsnps.txt sampleinfo.csv bpdata.csv example-pheno.csv psa.txt salary.dat niehs.csv swirl.zip data.vsn.csv annt.txt Other links R site Bioconductor A reference card of common 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()`) Books J. M. Chambers, Programming with Data: A Guide to the S Language. New York: Springer, 1998. [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] Chambers's newest book has not yet been published but will be out shortly. P. Dalgaard, Introductory Statistics with R: Springer, 2002 [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] 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, emphasising microarrays]
Send mail to: (see thomas or ken in class)