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In early September, the version 2.0.0 of rmangal was approved by rOpenSci, four weeks later it made it to CRAN. Following-up on our experience we detail below the reasons why we wrote rmangal, why we submitted our package to rOpenSci and how the peer review improved our package. Mangal, a database for ecological networks Ecological networks are defined as a set of species populations (the nodes of the network) connected through ecological interactions (the edges)....
rOpenSci HQ What would you like to hear about in an rOpenSci Community Call? We are soliciting your “votes” and new ideas for Community Call topics and speakers. Find out how you can influence us by checking out our new Community Calls repository. Videos, speaker’s slides, resources and collaborative notes from our Community Call on Reproducible Workflows at Scale with drake are posted. Help wanted!...
We want to know how you use rOpenSci packages and resources so we can give them, their developers, and your examples more visibility. It’s valuable to both users and developers of a package to see how it has been used “in the wild”. This goes a long way to encouraging people to keep up development knowing there are others who appreciate and build on their work. This also helps people imagine how they might use a package to address their research question, and provides some code to give them a head-start....
As announced in February, we now have an online book containing all things related to rOpenSci software review. Our goal is to update it approximately quarterly - it’s time to present the third version. You can read the changelog or this blog post to find out what’s new in our dev guide 0.3.0! Updates to our policies and guidance Scope We’ve introduced an important change for anyone thinking of submitting a package....
Teaching collaborative software development In the University of British Columbia’s Master of Data Science program one of the courses we teach is called Collaborative Software Development, DSCI 524. In this course we focus on teaching how to exploit practices from collaborative software development techniques in data scientific workflows. This includes appropriate use of the software life cycle, unit testing and continuous integration, as well as packaging code for use by others....
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