Note: the current release of Bioconductor is version 3.3, which works with R version 3.3.0. If you use older versions of R and Bioconductor, please update those.

Install R from CRAN.

Open the R session, and install some additional packages that are not in the base distribution.

install.packages("devtools")
install.packages("gplots")
install.packages("knitr")
install.packages("RColorBrewer")

Install the basic Bioconductor packages by typing

source("http://bioconductor.org/biocLite.R")
biocLite()

(also check out the Bioconductor installation page). Now install some additional Bioconductor packages:

biocLite("affy")
biocLite("affydata")
biocLite("Biobase")
biocLite("bumphunter")
biocLite("DESeq2")
biocLite("genefilter")
biocLite("GenomicRanges")
biocLite("hgfocus.db")
biocLite("limma")
biocLite("oligo")
biocLite("org.Hs.eg.db")
biocLite("org.Rn.eg.db")
biocLite("preprocessCore")
biocLite("qvalue")
biocLite("simpleaffy")
biocLite("SpikeInSubset")
biocLite("SpikeIn")
biocLite("sva")
biocLite("vsn")

Make sure to install/update all dependencies when you are prompted.

In addition, we will use some custom packages from Rafa's github repository:

library(devtools)
install_github("ririzarr/rafalib")
install_github("genomicsclass/dagdata")

The code on the class page is in R markdown, which can easily be run in the freely available environment R Studio. You can also strip the code from the markdown files with the purl function from the knitr package:

library(knitr)
purl("foo.Rmd")

Also, binaries for some of the data sets for a local download are here.