HT Sequence Analysis with R and Bioconductor is a great resource. Thank you Jeremy
I have been asked to recommend introductory books and resources to R and Bioconductor. My problem is just, I never read a book to learn R or Bioconductor, so I have no experience with this and cannot recommend one. I am interested in mainly introductory books, possibly targeting various groups of readers (computer scientists, molecular biologists, (bio-)statisticians), any recommendation appreciated.
For example, I used the following resources:
- The R-manuals, especially the R intro
- There are also a lot of contributed documents there on the R web site, but I didn't use.
- If a package from Bioconductor interests me, I read the package vignette.
- I read the Bioconductor mailing list, that helps to see what other people use.
- I have the "Venebles, Ripley. S Programming" book, that is hardly introductory.
Which books did you find helpful or completely useless to learn R/Bioconductor? For example: R Programming for Bioinformatics looks promising, anybody read it?
Or do you share my reluctance towards R-books and prefer online resources?
17 answers
Theresa Scott's Intro deserves a mention although it can be a bit verbose.
Introduction to the Fundamentals & Functionality of the R Programming Language
For Bioconductor and short read analysis I would stick with Girke's cookbook which I have found amazingly helpful. I would gladly have paid $100 for access to it.
Sequence Analysis with R and Bioconductor
Have not read R in a Nutshell yet but from what I saw in Joseph Adler's Baseball Hacks and the initial reviews I have a good feeling about it.
I would avoid R Programming for Bioinformatics as that book is really geared toward package development.
I co-wrote an O'Reilly Short Cut, "Data Mashups in R", that is designed to be a little more fun than some of the scientific stuff out there while still exploring data manipulation in R, using packages, XML, web services, rudimentary plotting, and even some statistics. It costs $5 if you are not a Safari subscriber.
HT Sequence Analysis with R and Bioconductor seems to be a great resource and I didn't find HyperGeometericTest over there. Anyway thanks to Jeremy for his detail descritpion
+1 for Adler's R in a Nutshell which I have found to be very helpful for getting to grips with data munging especially.
+1 for Adler's R in a Nutshell which I have found to be very helpful for getting to grips with R, especially data munging.
The Art of R Programming is a great book for both beginners and experts. It contains very few bells and whistles, but a lot of meticulous code examples and explanations.
+1 for the Google's R style guide.
Very basic, but it helped me get started, although I was mainly just interested in importing tabular data and plotting:
Zuur, Alain et al: A Beginner's Guide to R
Some online resources:
Quick-R a set of tutorials that I found to be to be a very lucid and practical introduction for general data analysis with R.
Data Analysis Examples at UCLA a nice matrix of various canonical analytical tasks in R as well as SAS, SPSS, etc.
R and BioConductor Manual a concise introduction to R for bioinformatics specifically
This is strange. Did some data get lost here on the transfer from old biostar or something?
I think this happened while transferring but if you will click on revisions, then you can see the links.
I've edited this answer to delete the <dd> and <dt> HTML tags responsible for this. It's actually not the first time I see these [?] instead of links, so I guess this should be issued in github.
Oh, I'm glad this got bumped--just yesterday I saw a new guide to R that might be useful:
I have R in a Nutshell on my desk and I use it at least weekly if not daily. Its well indexed with a lot of great examples and helps me save time by not searching online. The BioC chapter is decent, but quite short. I also bought Data mashups in R which I found to be a great purchase and a fun way to learn about GIS capabilities and the Yahoo API in addition to R.
I have to give a shout out for the "R Graph Gallery". When learning to make graphs/visualizations, this resource is indispensable. Sometimes when I'm trying to think of a way to visualize some data, I just browse through the gallery for inspiration. Then, if you find something you like, all the code to get you started is right there.
http://addictedtor.free.fr/graphiques/ (now dead)
UPDATED LINKS:
- http://gallery.r-enthusiasts.com/allgraph.php (new home of "R Graph Gallery")
- http://www.sr.bham.ac.uk/~ajrs/R/r-gallery.html
- http://research.stowers-institute.org/efg/R/
R programming for bioinformatics By Robert Gentleman
About R graphics, an introduction by Paul Murrell is very popular. And, you know what? He shares all the figures code and 3 chapters via his website. It's a very good one.
I have found these two helpful:
I love plotting:
ggplot is an amazing [R] package. I use it every day.
"Statistics Using R with Biological Examples" by Kim Seefeld helped me a lot
also "Applied Statistics for Bioinformatics using R" by Wim P. Krijnen
BTW, anyone gone through the book "Statistics and Data Analysis for Microarrays Using R and Bioconductor" by Sorin Draghici? From the Amazon preview I found it quite interesting
I just got a copy of Draghici's book and I found it to be FANTASTIC. I will possibly buy a copy soon and look for his upcoming title "An Introduction to Statistics and Data Analysis for Bioinformatics using R" to be released in Sep 15, 2012
A comment, not answering your question but which may help a lot people using R: If you are very annoyed that it is impossible to make a google search for R, use rseek.org. It is basicly R-specific google.
Introductory Statistics with R by Peter Dalgaard is a wonderful, brief introduction to basic statistical practice using R. If you want to know how to perform survival analysis, specify linear models, build plots, etc. it is a very clear guide. It is not bioinformatics-specific, and does not mention Bioconductor.
In general I've found the quality of the Springer books to be very high. If you're affiliated with a large university (e.g. UCSF), you may be able to browse their texts for free from your desk through SpringerLink.
Great pick. It really helped with both statistical modeling and R programming.
I upvoted David Quigley's recommendation of the Dalgaard book. I'd also recommend Modern Applied Statistics with S by Venables & Ripley. Despite the title, it's totally relevant to R. From the first page of the Introduction, "An Open Source system called R has emerged that provides an independent implentation of the S language. It is similar enough that almost all the examples in this book can be run under R." This book is pretty much the standard reference for R in book form.
R is derived from S, or vice versa as some would argue!
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