The problem I was looking for a way to compile an RMarkdown document and have the filename of the resulting PDF or HTML document contain the name of the input data that it processed. That is, if I compiled the analysis.Rmd file, where in that file it did some analysis and reporting on data001.txt, I’d want the resulting filename to look something like data001.txt.analysis.html.

I forgot where I originally found the code to do this, but I recently had to dig it out again to remind myself how to draw two different y axes on the same plot to show the values of two different features of the data. This is somewhat distinct from the typical use case of aesthetic mappings in ggplot2 where I want to have different lines/points/colors/etc. for the same feature across multiple subsets of data.

At our most recent R user group meeting we were delighted to have presentations from Mark Lawson and Steve Hoang, both bioinformaticians at Hemoshear. All of the code used in both demos is in our Meetup’s GitHub repo.

Anscombe’s quartet comprises four datasets that have nearly identical simple statistical properties, yet appear very different when graphed. Each dataset consists of eleven ( x , y ) points. They were constructed in 1973 by the statistician Francis Anscombe to demonstrate both the importance of graphing data before analyzing it and the effect of outliers on statistical properties. Let’s load and view the data.

Joanna Zhao’s and Jenny Bryan’s R graph catalog is meant to be a complement to the physical book, Creating More Effective Graphs, but it’s a really nice gallery in its own right. The catalog shows a series of different data visualizations, all made with R and ggplot2. Click on any of the plots and you get the R code necessary to generate the data and produce the plot.

I recently learned about the microbenchmark package while browsing through Hadley’s advanced R programming book. I’ve done some quick benchmarking using system.time() in a for loop and taking the average, but the microbenchmark function in the microbenchmark package makes this much easier.

A colleague needed some help getting Illumina BeadArray gene expression data loaded into R for data analysis with limma. Hopefully whoever ran your arrays can export the data as text files formatted as described in the code below. If so, you can import those text files directly using the beadarray package.

Last week I ran a one-day workshop on RNA-seq data analysis in the UVA Health Sciences Library. I set up an AWS public EC2 image with all the necessary software installed.

I talked a little bit about tidy data my recent post about dplyr, but you should really go check out Hadley’s paper on the subject. R expects inputs to data analysis procedures to be in a tidy format, but the model output objects that you get back aren’t always tidy. The reshape2, tidyr, and dplyr are meant to take data frames, munge them around, and return a data frame.

TL;DR? We started an R Users group, awesome community, huge turnout at first meeting, lots of potential. --- I've sat through many hours of meetings where faculty lament the fact that their trainees (and the faculty themselves!) are woefully ill-prepared for our brave new world of computing- and data-intensive science.