**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.

__Books__

These two
books have a clear slant towards biologist seeking to learn R:

ÒA BeginnerÕs
guide to RÓ

Zuur, Ieno,
& Meesters

http://www.springer.com/us/book/9780387938363

ÒGetting
start with R: An introduction for biologistsÓ

Beckerman
& Petchey

__R reference card:__

An
extremely dense summary of R functions and programming syntax.

http://cran.r-project.org/doc/contrib/Short-refcard.pdf

__Comprehensive introductions to R for
beginners:__

**Nicely
explained, very user-friendly, and comprehensive overview, from the very
beginning.

http://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf

**A beginnerÕs introduction, emphasizing data types and structures

http://www.cran.r-project.org/doc/manuals/R-intro.pdf

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.

http://www.r-tutor.com/r-introduction

The Quick-R site
gives barebones examples to a wide variety of topics and basic skills in R.

http://www.statmethods.net/index.html

The Ôfor
DummiesÕ 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:

http://www.dummies.com/how-to/computers-software/programming/R.html

and the entire book is available
electronically via the KU library.

An
introduction to using R, broken into several distinct tutorials with worked
examples.

http://www.cyclismo.org/tutorial/R/

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.

http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual

Blog-based
tutorial of R, oriented towards standard statistical tests

http://rtutorialseries.blogspot.com/2009/10/r-tutorial-series-introduction-to-r_11.html

**A thorough
crowd-sourced ÔmanualÕ of R programming.
Covers many topics not typically included in beginning tutorials. Full of great info for
both beginners and experienced practitioners.

http://en.wikibooks.org/wiki/R_Programming

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.

http://www.computerworld.com/article/2884322/learn-r-programming-basics-with-our-pdf.html?nsdr=true

Comprehensive
but fairly technical overview of R, from the R-project itself. HTML based. Good as a reference but not ideal as a
tutorial.

http://cran.r-project.org/doc/manuals/R-intro.html

basic R concepts & mechanics,
organized as Frequently Asked Questions

http://statistics.ats.ucla.edu/stat/r/faq/

A
huge list of R tutorials, including some of the above, and many more.

http://cran.r-project.org/other-docs.html

__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:*

*http://blog.revolutionanalytics.com/2012/12/coursera-videos.html*

*All of the PengÕs
lectures are available on his youtube channel. Look at ÔplaylistsÕ.*

*http://www.youtube.com/user/rdpeng*

Here is
another comprehensive set of R video tutorials from a person I know nothing
about. But the pace is more gentle than PengÕs and it is
broken into smaller units. There
are slightly sarcastic subtitles that appear in the videos as well, which hopefully
wonÕt 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.

http://www.youtube.com/playlist?list=PL8BE0E317807A9A21

**Graphics**: Plotting

**Very good
progression demonstrating basic plotting functions with R

http://www.harding.edu/fmccown/r/

**Diverse
collection of example plots with code.
Excellent for getting started with new and different
forms of data visualization.

http://gallery.r-enthusiasts.com/

*(Note, as of late August,
2013, this page is not accessible.
Comments on the galleryÕs
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
ÔwaybackÕ machine. Be patient, this loads very slowly:*

*http://web.archive.org/web/20120902082441/http://gallery.r-enthusiasts.com/thumbs.php )*

**A
thoughtful and practical overview with demonstrations of multipanel
plots in R with base-level graphics

http://seananderson.ca/courses/11-multipanel/multipanel.pdf

A
cheat-sheet to graphical layouts in R.

http://research.stowers-institute.org/mcm/plotlayout.pdf

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.

http://www.r-bloggers.com/how-to-plot-a-graph-in-r/

Blogpost giving examples and functions for
making error bars on barplotsÉ arguably an Achilles
heel of R plotting.

http://monkeysuncle.stanford.edu/?p=485

Website to
support Paul MurrellÕs book on R Graphics, with several code examples

http://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html

explanation of *par* values in a plot region

http://research.stowers-institute.org/efg/R/Graphics/Basics/mar-oma/index.htm

A condensed
overview of base-level graphics in R

http://pmc.ucsc.edu/~mclapham/Rtips/graphing.htm

**Graphics**: Colors

Colors in R,
especially blending for a continuous range, like in a heatmap

http://simplystatistics.org/2011/10/17/colors-in-r/

http://menugget.blogspot.com/2011/11/define-color-steps-for-colorramppalette.html#more

**Handy color
reference charts, plus good explanations of color-related functions.

__http://research.stowers-institute.org/efg/R/Color/Chart/__

A brief
overview of
colors in R, with a handy custom function to make picking colors
easier

http://aviadklein.wordpress.com/2010/05/21/color-choosing-made-easy/

**Bioconductor**:

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:

http://www.bioconductor.org/help/mailing-list/

**A
comprehensive introduction to analyzing genomic & high-throughput (Illumina) data in R.

http://www.bioconductor.org/help/course-materials/2012/SeattleMay2012/Bioconductor-tutorial.pdf

A course
website with tutorials, heavily oriented towards microarray analysis.

http://bcb.dfci.harvard.edu/~aedin/courses/Bioconductor/

Again,
another set of examples and tutorials aimed at microarray analysis with R.

http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual

**Text Processing in R:**

http://en.wikibooks.org/wiki/R_Programming/Text_Processing

**Dates & Times:**

A nice
overview of date & time classes and functions on a single page

http://www.stat.berkeley.edu/classes/s133/dates.html

**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.

http://www.burns-stat.com/documents/books/the-r-inferno/

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

http://biostat.mc.vanderbilt.edu/wiki/pub/Main/SvetlanaEdenRFiles/regExprTalk.pdf

A
friendly introduction to debugging tools in R.

http://seananderson.ca/2013/08/23/debugging-r.html

A thorough
over view of ÔapplyÕ-type functions

http://nsaunders.wordpress.com/2010/08/20/a-brief-introduction-to-apply-in-r/

Good blog
entry on parallelization in R

http://www.r-bloggers.com/a-no-bs-guide-to-the-basics-of-parallelization-in-r/

Demo of how
to include arguments from the commandline when
running R scripts

http://www.r-bloggers.com/including-arguments-in-r-cmd-batch-mode/

Blog entries
on supplying arguments on the commandline to R
scripts

http://www.r-bloggers.com/including-arguments-in-r-cmd-batch-mode/

http://shihho.wordpress.com/2012/11/30/r-how-to-run-r-scripts-in-batch-mode-with-arguments/

A
blog on R and various topics in statistical computing.
Lots of good food for thought and computing tips.