The R package ecosystem for natural language processing has been flourishing in recent days. R packages for text analysis have usually been based on the classes provided by the NLP or tm packages. Many of them depend on Java. But recently there have been a number of new packages for text analysis in R, most notably text2vec, quanteda, and tidytext. These packages are built on top of Rcpp instead of rJava, which makes them much more reliable and portable.

We are excited to announce a paper describing rotl, our package for the Open Tree of Life data, has been published. The full citation is: Michonneau, F., Brown, J. W. and Winter, D. J. (2016), rotl: an R package to interact with the Open Tree of Life data. Methods Ecol Evol. doi: https://doi.org/10.1111/2041-210X.12593 The paper, which is freely available, describes the package and the data it wraps in detail. Rather than rehash the information here, we will use this post to briefly introduce the goals of the package and thank some of the people that helped it come to be.

Travis is a continuous integration service which allows for running automated testing code everytime you push to GitHub. Hadley’s book about R packages explains how and why R package authors should take advantage of this in their development process. The build matrix Travis is now providing support for multiple operating systems, including Ubuntu 14.04 (Trusty) and various flavors of Mac OS-X. Jim Hester has done a great job of tweaking the travis R-language build script to automate building and checking of R packages on the various platforms.

On April 21st and 22nd of 2016, we had 40 members of the R community gather in Brisbane, Australia, with the goal of reproducing the rOpensci Unconference events that have been running with great success in San Francisco since 2014. Like every event organisers ever, we went through the usual crisis: Where will it be? Will anyone actually show up? Is the problem space over venue, date, attendees, catering, sponsors convex?

I’m happy to announce that I’ve started a project with rOpenSci under their recent award from the Helmsley Foundation. My work with rOpenSci will focus on sustainability of the project itself. Sustainability can be defined as having the resources to do the necessary work to continue and grow rOpenSci. This is one of the most difficult challenges for rOpenSci and for many other research software projects. rOpenSci has a very broad and very ambitious goal, as stated on their web site, “Transforming science through open data.