A new version of the ropensci magick package has been released to CRAN. Magick is a package for Advanced Image-Processing in R. It wraps the ImageMagick STL which is perhaps the most comprehensive open-source image processing library available today. Our original announcement has more details. New features This new version now includes a beautiful vignette which gives an overview of the main functionality to get you started!

The rOpenSci project based at the University of California, Berkeley seeks to hire a postdoctoral scholar to work on the research activities funded by the grant titled “Fostering the next generation of sustainable software and reproducible research practices in the scientific community”. The project develops open source software to promote reproducible research practices in the scientific community.

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.

The new magick package is an ambitious effort to modernize and simplify high-quality image processing in R. It wraps the ImageMagick STL which is perhaps the most comprehensive open-source image processing library available today. The ImageMagick library has an overwhelming amount of functionality. The current version of Magick exposes a decent chunk of it, but being a first release, documentation is still sparse.

We are excited to announce a paper describing rotl, our package for theOpen Tree of Life data, has beenpublished. The fullcitation is: Michonneau, F., Brown, J. W. and Winter, D. J. (2016), rotl: an Rpackage to interact with the Open Tree of Life data. Methods EcolEvol. doi: https://doi.org/10.1111/2041-210X.12593 The paper, which is freely available, describes the package and the datait wraps in detail.

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.

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 rOpenSciunder 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 theresources to do the necessary work to continue and grow rOpenSci. This is one of the most difficult challenges forrOpenSci and for many other research software projects.

Code review, in which peers manually inspect the source code of softwarewritten by others, is widely recognized as one of the best tools for findingbugs in software. Code review is relatively uncommon in scientific softwaredevelopment, though.

Geospatial data input/output, manipulation, and vizualization are tasks that are common to many disciplines. Thus, we’re keenly interested in making great tools in this space. We have an increasing set of spatial tools, each of which we’ll cover sparingly. See the cran and github badges for more information.

The rOpenSci team is growing, thanks in part to our recent funding. We recently welcomed Jeroen Ooms on the software development side and today we’re thrilled to announce a position for community manager. Our mission is to expand access to scientific data and promote a culture of reproducible research and sustainable research software.