Last week I taught a three-hour introduction to R workshop for life scientists at UVA’s Health Sciences Library.

I broke the workshop into three sections:

In the first half hour or so I presented slides giving an overview of R and why R is so awesome. During this session I emphasized reproducible research and gave a demonstration of using knitr + rmarkdown in RStudio to produce a PDF that can easily be recompiled when data updates.

In the second (longest) section, participants had their laptops out with RStudio open coding along with me as I gave an introduction to R data types, functions, getting help, data frames, subsetting, and plotting. Participants were challenged with an exercise requiring them to create a scatter plot using a subset of the built-in *mtcars* dataset.

We concluded with an analysis of RNA-seq data using the DESeq2 package. We started with a count matrix and a metadata file (the modENCODE *pasilla* knockout data packaged with DESeq2), imported the data into a *DESeqDataSet* object, ran the DESeq pipeline, extracted results, and did some basic visualization (MA-plots, PCA, volcano plots, etc). A future day-long course will cover RNA-seq in more detail (intro UNIX, alignment, & quantitation in the morning; intro R, QC, and differential expression analysis in the afternoon).

I wrote the course materials using knitr, rendered using Jekyll, hosted as a GitHub project page. The rendered course materials can be found at the link below, and the source is on GitHub.

**Course Materials: Introduction to R for Life Scientists**

Slides:

Cheat Sheet:

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