Dear rOpenSci friends, it’s time for our monthly news roundup!Some housekeeping first:We’ve been changing the infrastructure of our newsletter a bit so please update your RSS and JSON feeds to https://ropensci.org/tags/newsletter/index.xml and https://ropensci.org/tags/newsletter/index.json, respectively.You can read this post on our blog.Now let’s dive into the activity at and around rOpenSci!rOpenSci HQ Our R-universe project now has its own
A week ago we held a Community Call discussing rOpenSci Statistical Software Testing and Peer Review.This call included speakers Noam Ross, Mark Padgham, Anna Krystalli, Alex Hayes, and John Sakaluk.
The R-universe build system The R-universe system is a complex effort, consisting of numerous frontend and backend pieces that operate across various platforms. A key challenge in developing such a system is managing overall complexity by finding ways to reduce the problem into smaller, loosely coupled components, which can be thought of, and developed, somewhat independently.
rOpenSci Software Peer Review’s guidance is gathered in an online book and keeps improving!To find out what’s new in our dev guide 0.6.0, you can read the changelog,or this blog post for more digested information. On our way to Spanish!
Scientists rarely cite research software they use as part of a research project. As a consequence, the software and the time spent developing and maintaining it becomes an invisible scholarly contribution. Furthermore, this lack of visibility means that incentives to produce high quality, sustainable software are missing. Among many reasons why software is not cited, one is the lack of a clear citation information from package developers.
A new R-package, coder, has been developed, peer-reviewed by rOpenSci, accepted by CRAN, and published in a paper by the Journal of Open Source Software (JOSS). In this blog post, I will explain why this package might be useful for (epidemiological/medical/health care related) research. Clinical mess Once upon a time, in countries not far from ours, there were MDs and nurses making up funny names for any diseases they encountered.
Make 1 -like pipelines enhance the integrity, transparency, shelf life, efficiency, and scale of large analysis projects.With pipelines, data science feels smoother and more rewarding, and the results are worthy of more trust. targets install.packages("targets") The targets 2 package is a new pipeline toolkit for R.It recently cleared software review, and it is now on CRAN.
rOpenSci community calls are online events with the fundamental goal of strengthening our community.
It has been a while since we posted an update about magick, but behind the scenes we are constantly tweaking and improving this package, which has become a very mature and complete toolkit for image processing in R. Over the past year, we did 6 CRAN releases, containing many small features and fixes, but perhaps more importantly, the package is getting betting better due to updates of the underlying ImageMagick library.
More and more R packages access resources on the web, and play crucial roles in workflows.Examples from the rOpenSci suite of packages include rromeo, GSODR, qualtRics, rnassqs, and many, many others.Like for all other packages, appropriate unit testing can make them more robust.However, unit testing of these packages can bring special challenges: dependence of tests on a good internet connection, testing in the absence of authentication
