I have been spending more and more time learning R because a lot of the statistical procedures used in bioinformatics are being made available (most times exclusively) via R and Bioconductor. As I keep learning more about R, I am continually impressed with its capabilities and wondered why I did not learn it earlier. Do not make the same mistake, learn R as soon as possible if you are serious about data analysis!
For those coming from a biological background (like myself) and want to learn R with respect to data analysis and visualisation of high throughput sequencing data, have a look at the material provided by the UC Davis Bioinformatics Core.
The Bioinformatics Data Skills book also has a nice chapter on getting started with R (among other bioinformatic topics).
You can click on the R tag, to retrieve most of my posts related to R. I say most because I am sure I have forgotten to add the R tag to a few posts. Lastly, as with the rest of my site, I use this site as a learning tool for myself so please view everything with a grain of salt (and please let me know where I have erred!).
Books
- R for Data Science
- Advanced R
- R Packages
- Advanced R Solutions
- Mastering Software Development in R
- R Markdown: The Definitive Guide
- R Graphics Cookbook
Must read
There are some must read articles available at the R Manuals page, such as An Introduction to R and R Data Import/Export.
Links to R resources
- A bunch of useful R commands that I have aggregated at my R wiki.
- swirl is a software package for the R statistical programming language. Its purpose is to teach users statistics and R simultaneously and interactively.
- A course on data Analysis and visualisation course
- Tutorials from Sean Davis
- A Survival Guide to Data Science with R
- Nice R Code
- A gentle introduction to R
Useful R packages
Bioconductor packages
- ctc
- edgeR
- DESeq
- baySeq
- GO.db
- GOstats
- biomaRt
- Ringo
- ShortRead
- org.Hs.eg.db
- goseq
- Rsamtools
- GenomicRanges
- IRanges
- CAGEr
CRAN packages
- gplots
- ggplot2
- tm
- seqinr
- data.table
Great to have stumbled on your blog. Your post on PCA really helped, since i have been wrecking my brain like no tomorrow. Cheers!
Hi Siewfong,
Glad it helped! A PCA is not the easiest thing to grasp; sometimes I have to look back at the post to remind myself.
Cheers,
Dave
Now the challenge is to understand my results, and explain that. Will try to have fun there…… 🙂
Have a good day and keep up the (generous) good work!
~2 years later, I found this post https://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/ which was extremely useful in understanding PCA. I thought I share it with you.
100 percent agree with you !
R is pretty suitable for us who are biological background like you and me, help us to avoid taking a roundabout course both in our study and work .
however, It’s really not easy for most of us to learn it well by ourselves, we should keep practice and communicate with each other.
I am lucky to see you blog, Thank you again .
Call me Jimmy