Resources for learning R (an incomplete list)
Compiled by James Walters, for use by BIOL701: Basic programming in R for Biologist.
University of Kansas, Ecology & Evolutionary Biology
Last updated 9/15/2013
Note that this list will be expanded over time.
These two books have a clear slant towards biologist seeking to learn R:
“A Beginner’s guide to R”
Zuur, Ieno, & Meesters
“Getting start with R: An introduction for biologists”
Beckerman & Petchey
R reference card:
An extremely dense summary of R functions and programming syntax.
Comprehensive introductions to R for beginners:
**Nicely explained, very user-friendly, and comprehensive overview, from the very beginning.
**A beginner’s introduction, emphasizing data types and structures
A clear and concise introduction to R. Data structures and data types are explained succinctly and lucidly, followed by numerous examples of standard statistical analyses. This site exists to promote the sale of an eBook focusing on Bayesian statistics in R.
The Quick-R site gives barebones examples to a wide variety of topics and basic skills in R.
The ‘for Dummies’ series does a surprisingly good job with R. While I feel like their overall learning progression is too gentle, resulting in a piecemeal learning experience, the explanations of any given particular topic are often quite good and very accessible. Much of their content is free, though not coherently organized:
and the entire book is available electronically via the KU library.
An introduction to using R, broken into several distinct tutorials with worked examples.
Another comprehensive overview, starting from scratch, formatted as HTML pages. Ultimately arcs towards using bioconductor. Somewhat terse in places; as much a manual as a tutorial.
Blog-based tutorial of R, oriented towards standard statistical tests
**A thorough crowd-sourced ‘manual’ of R programming. Covers many topics not typically included in beginning tutorials. Full of great info for both beginners and experienced practitioners.
A quick-moving walk through many of the R basics, including simple forays in ggplot2. Contains a huge list of OTHER resources for learning R.
Comprehensive but fairly technical overview of R, from the R-project itself. HTML based. Good as a reference but not ideal as a tutorial.
basic R concepts & mechanics, organized as Frequently Asked Questions
A huge list of R tutorials, including some of the above, and many more.
Video & Multimedia
**Codeschool offers a superb basic introduction to R. Maybe the best one-stop introduction to R that currently exists for total newbies.
Roger Peng developed an introduction to R for Coursera. This is a collection of about 40 video lectures ranging from 4 to 40 minutes. It offers an excellent and thorough grounding in using R.
This blog post organizes the lectures by week & topic:
All of the Peng’s lectures are available on his youtube channel. Look at ‘playlists’.
Here is another comprehensive set of R video tutorials from a person I know nothing about. But the pace is more gentle than Peng’s and it is broken into smaller units. There are slightly sarcastic subtitles that appear in the videos as well, which hopefully won’t offend anyone. But the information on R is pretty good. Note that if you dig around among all the youtube videos posted by this user (GordonAnthonyDavis), there are two videos on graphing use the ggplot package not included in the playlist linked here.
**Very good progression demonstrating basic plotting functions with R
**Diverse collection of example plots with code. Excellent for getting started with new and different forms of data visualization.
(Note, as of late August, 2013, this page is not accessible. Comments on the gallery’s facebook page indicate the author is revising and improving it. In the meantime, you can actually access a copy of the old version via the InternetArchives ‘wayback’ machine. Be patient, this loads very slowly:
**A thoughtful and practical overview with demonstrations of multipanel plots in R with base-level graphics
A cheat-sheet to graphical layouts in R.
Another nice set of examples of customizing the layouts of graphics using base-level par() settings
Another brief but helpfully demonstrative example for tweaking details like plotting symbols and axes.
Blogpost giving examples and functions for making error bars on barplots… arguably an Achilles heel of R plotting.
Website to support Paul Murrell’s book on R Graphics, with several code examples
explanation of par values in a plot region
A condensed overview of base-level graphics in R
Colors in R, especially blending for a continuous range, like in a heatmap
**Handy color reference charts, plus good explanations of color-related functions.
A brief overview of colors in R, with a handy custom function to make picking colors easier
Of course, the bioconductor homepage is an extensive repository of useful resources for learning.
Help page for bioconductor, including a search window for searching all bioconductor list-serve entries:
**A comprehensive introduction to analyzing genomic & high-throughput (Illumina) data in R.
A course website with tutorials, heavily oriented towards microarray analysis.
Again, another set of examples and tutorials aimed at microarray analysis with R.
Text Processing in R:
Dates & Times:
A nice overview of date & time classes and functions on a single page
For extended learning and useful tidbits
**The R-Inferno is a remarkable resource of very good and sophisticated advice about many common pitfalls that occur when using R. This is particular good for folks who have mastered the foundations of R and want to become more expert. Also, it is an amazing merging of the quantitative with the literary.
Hadley Wickham of ggplot fame has an on-line book: Advanced R Programming. Currently freely available to all!
An online journal covering a wide range of topics, all related to R. Includes many articles with hints & help for improving your proficiency with R.
Regular expressions in R
A friendly introduction to debugging tools in R.
A thorough over view of ‘apply’-type functions
Good blog entry on parallelization in R
Demo of how to include arguments from the commandline when running R scripts
Blog entries on supplying arguments on the commandline to R scripts
A blog on R and various topics in statistical computing. Lots of good food for thought and computing tips.