This page will feature slides from our sessions, exercises for you to complete, and their solutions. Prior to the module, please install an up-to-date version of R on the laptop you will use during the summer institute. R is available from this site.
It may also be helpful to install the base Bioconductor packages; to download and install these, enter the following;
source("http://bioconductor.org/biocLite.R")
biocLite()
After doing this major download - once! - to download new Bioconductor packages (for example the hexbin package) use the following commands;
source("http://bioconductor.org/biocLite.R")
biocLite("hexbin")
(For sessions 7/8) Resources for builing R packages under windows are available for download here. Make sure you get the version for your (current) version of R. Once you have installed them, your first command entered at the command line should be e.g.
path C:\Program Files\R\R-2.15.0\bin;%PATH%
...which adds the directory containing R to your path. If you don't know the directory containing R on your machine, right-click on the R shortcut icon
Script files are posted following each session; these will contain our R code for the exercises. Many exercises will be open-ended - and have many different solutions; the code posted here illustrates possible approaches.
Session 1, Introductions, simulation, debugging, timing (Exercises: .docx, .pdf) (R script file)
Session 2, Graphics (R code for color wheels and boxes in the slides) (Exercises: .docx, .pdf) (R script file)
Session 3, Object Systems (No exercises for this session)
Session 4, Lab exercise (Exercises: .docx, .pdf) (R script file)
Session 5, Packages (Exercises: .docx, .pdf) (R script file) and the files it produces, which you can load into an R session)
Session 6, XML (SVG Funnel plot example) (Exercises: .docx, .pdf) (R script files: R script file)
Session 7, Embedding C code (R script for the convolve example, including some Command Line commmands convolve.c and R script for the convolve example using .Call convolve2.c- no exercises for this session)
Also see Rcpp for other ways to use R objects in C code
Session 8, Lab exercise (Exercises: .docx, .pdf) (R script file, Q1 R script file, Q2)
Session 9, Handling large datasets (no exercises for this session) (Example featuring RSQLite which uses the newexample.db database file)
Session 10, Lab exercise (Exercises: .docx, .pdf) (R script file)
- more to follow
- [Session 1] amd.txt - use read.table() to load this large-ish dataset into your current R session. The genotype data are coded as -1/0/1/2, -1 are missing values
- [Session 2] SEAflights.csv - the documentation for this file is on this website. See also airportlocations.csv
- [Session 4] ROC commands for you to adapt, and make generic;
- [Session 6] phiSITE767857.xml
- [Session 8] C code for you to embed in R;
- [Session 10]
- The R site, which includes the Comprehensive R Archive Network (CRAN) of downloads and packages [this link is to 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]
- Gentleman, R Programming for Bioinformatics; CRC Press 2008 [Most closely-aligned with this course!]
- 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]