R

IPython notebooks have become a defacto standard for presenting Python-based analyses and talks, as evidenced by recent Pycon and PyData events. As anyone who has used them knows, they are great for “reproducible research”, presentations, and sharing via the nbviewer. There are extensions connecting IPython to R, Octave, Matlab, Mathematica, SQL, among others. However, the brilliance of the design of IPython is in the modularity of the underlying engine (3 cheers to Fernando Perez and his team).

Publishers note: This blog was posted on August 1, 2013 on the Data Community DC blog, http://datacommunitydc.org/blog/2013/08/data-driven-presentations-using-slidify/ Presentations are the stock-in-trade for consultants, managers, teachers, public speakers, and, probably, you. We all have to present our work at some level, to someone we report to or to our peers, or to introduce newcomers to our work. Of course, presentations are passe, so why blog about it? There’s already PowerPoint, and maybe Keynote.

Prof. Atul Butte of Stanford University and colleagues just published two articles in Science Translational Research which got a fair amount of press. In fact I heard about the work on the radio on my commute to work. The research involves developing a computational method which can look at drug-disease interactions based on the NCBI GEO repository to discover potentially new uses for approved drugs. On reading the paper, I realized that their main computational tool is R, in particular the Bioconductor tools as well as pvclust and qvalue.

At the DC useR meetup last week, Marck Vaisman (@wahalulu) showed me a neat trick he’d learned to allow different facets in a faceted ggplot graph to have different plot types. The basis for this trick is this blog post in the Learn-R blog. Marck was trying to plot different statistics on our Meetup group’s membership on a faceted plot. Some of the variables were amenable to a step plot while others were more amenable to plotting using vertical lines.