Links to R sites and tutorials

Objective

This page provides links to a variety of websites and materials related to the R statistical programming environment.

Description

R is an open-source programming language and environment designed for statistical analysis. Add-on packages extend the capabilities of the base software to specialized domains relevant to this course, such as analysis of high-throughput sequencing data from chromatin structure or conformation experiments, RNA expression experiments, or genome data retrieval and annotation.

Keys

Each R installation should have built-in help files, available as either text from the R prompt in response to the command ?topic or in HTML format in response to the command help.start().

Exercises

Exercises are built into many of the tutorials and beginner’s guides listed below in Additional Resources.

Additional Resources

• The R Project home page
• The Comprehensive R Archive Network (CRAN)
• The web version of R manuals (which also available as part of local R installations)
• The Contributed Documentation section of the R web site, which includes links to over a dozen book-length (> 100 page) tutorials and guides, some with example code and application-specific scripts included. There are also links to several shorter documents, tutorials, and beginner’s guides.
• R For Data Science is an online book by Garrett Grolemund and Hadley Wickham; printed copies of the book are available from O’Reilly Publishing as well. Wickham is an advocate for simplification and streamlining of many aspects of data analysis in R, and has written several packages of tools to implement his ideas. This book is based on his approach and uses his packages, so some methods for accomplishing particular tasks will be presented differently here than in other learning materials you may find.
• Video tutorials for many aspects of R set-up and use are available on Youtube - those by Tutorlol focus on the Windows interface, but are still useful guides to R.
• Google R coding style guide. These are not requirements, unless you work for Google, but they do make R code easier to read and understand.
• The Bioconductor web site - Bioconductor is a add-on to R that provides packages specifically targeted to molecular biology and genomics research.
• The help link on the Bioconductor home page leads to a page with links to Workflows, which are summaries of packages suitable for analysis of several types of genomic data, including microarrays, high-throughput sequencing, flow cytometry, eQTL analysis, differential gene expression, and genome annotation.
• The Bioconductor help page also includes links to Community Help Resources, including Thomas Girke’s excellent R and Bioconductor Manual at UC Riverside.

Last modified 2 January 2019. Edits by Ross Whetten, Will Kohlway, & Maria Adonay.