Studies of muscle physiology often rely on closed-source, proprietary software for not only recording data but also for data wrangling and analyses. Although specialized software might be necessary to record data from highly-specialized equipment, data wrangling and analyses should be free from this constraint.
To the uninitiated, software testing may seem variously boring, daunting or bogged down in obscure terminology. However, it has the potential to be enormously useful for people developing software at any level of expertise, and can often be put into practice with relatively little effort. Our 1-hour Call will include two speakers and at least 20 minutes for Q &
Today we are pleased to announce that we have received new funding from the Gordon and Betty Moore Foundation. The $894k grant will help us improve infrastructure for R packages and enable us to move towards a science first package ecosystem for the R community. You may have already noticed some developments on this front when we announced our automated documentation server back in June.
In May 2019 version 0.2.0 of tidync was approved by rOpenSci and accepted to CRAN. Here we provide a quick overview of the typical workflow with some pseudo-code for the main functions in tidync. This overview is enough to read if you just want to try out the package on your own data.
Theme song: PSA by Jay-Z We announced the testing version of skimr v2 onJune 19, 2018. After more than ayear of (admittedly intermittent) work, we’re thrilled to be able to say thatthe package is ready to go to CRAN. So, what happened over the last year? Andwhy are we so excited for v2? 🔗Wait, what is a “skimr”? skimr is an R package for summarizing your data.
In early September, the version 2.0.0 of rmangal was approved byrOpenSci, four weeks later it made it to CRAN. Following-up on our experience wedetail below the reasons why we wrote rmangal, why we submitted our package torOpenSci and how the peer review improved our package.
🔗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.
If you have an R package on CRAN, you probably know about CRAN checks. Each package on CRAN, that is not archived on CRAN 1 , has a checks page, like this one for ropenaq:https://cloud.r-project.org/web/checks/check_results_ropenaq.html The table above is results of running R CMD CHECK on the package on a combination of different operating systems, R versions and compilers.
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.
Image processing is one of the core focus areas of rOpenSci. Over the last few months we have released several major upgrades to core packages in our imaging suite, including magick, tesseract, and av. This post highlights a few cool new features.
