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.
Tools for calling C from R - for sessions 7/8- Windows: Resources for builing R packages are available for download here. Make sure you get the version for your (current) version of R. We recommend allowing the installer to add these tools to your default Path; once you have installed the the tools, your first command entered at the command line should be e.g.
path C:\Program Files\R\R-3.4.1\bin\x64;%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. - Mac OS X: see this page
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, graphics (R code for color wheels and boxes in the slides) (Exercises: .docx, .pdf) (R script file)
Session 2 Simulation, debugging, timing (Examples of timing and profiling) (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) If you don't have code for Session 4's exercise, cut and paste just the functions from this script file.
- The usual R script file
- A zip file of the directories it makes - with some editing
- Finally a tarball of the package's source code that you can load into an R session.
- The roxygen2 package, from RStudio, is another very straightforward way to write packages from scratch
- Also consider using Markdown, particularly for writing vignettes. RStudio makes it easy to make Markdown output; here are some introductory notes on Markdown
Session 6, XML (SVG Funnel plot example) (Exercises: .docx, .pdf). If you don't have Google Earth installed but do have internet access and a Google account, you can upload your KML file to Google's My Maps feature. (R script file)
Session 7, Embedding C code (R script for the convolve example, including some Command Line commmands; it uses convolve.c. See also R script for the convolve example using .Call, which uses convolve2.c - no exercises for this session).
For C operations on arrays, see this short example that uses the inline package. Also see Rcpp for other ways to use R objects in C code
Session 8, Lab exercise (Exercises: .docx, .pdf) (R script file)
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)
- [Session 1] SEAflights.csv - the documentation for this file is on this website. See also airportlocations.csv
- [Session 2] amd.txt - use read.table() to load this large-ish dataset (146 people, 9132 SNPs) into your current R session. The genotype data are coded as -1/0/1/2, -1 are missing values
- [Session 4]
- ROC commands for you to adapt, and make generic;
- sequence.rda for question 3
- [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]
- Matloff, The Art of R Programming, 2011; goes from introductory programming up to a high 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, 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]
- Hadley Wickham, Advanced R is designed primarily for R users who want to improve their programming skills and understanding of the language. It is also useful for programmers coming to R from other languages, as it explains some of R's quirks and shows how some parts that seem horrible do have a positive side. [See also its official book site]