April 17, 2020 From rOpenSci (https://deploy-preview-121--ropensci.netlify.app/blog/2020/04/17/news-apr2020/). Except where otherwise noted, content on this site is licensed under the CC-BY license.
1 staff-contributed package passed software peer review.
On March 16, we paused new submissions for software peer review for 30 days (possibly longer) in the interest of reducing load on reviewers and editors in light of the COVID-19 crisis.
5 new packages from the community and our staff are on CRAN.
ghql - General purpose GraphQL client for R. Author: Scott Chamberlain
osfr - Interface to the Open Science Framework (OSF). Author: Aaron Wolen
outsider.base - Base package for outsider. Author: Dom Bennett
parzer - Parse messy geographic coordinates. Author: Scott Chamberlain
taxadb - A High-Performance Local Taxonomic Database Interface. Author: Kari Norman
parzer: Parse Messy Geographic Coordinates Tech Note - Scott Chamberlain
10 Things We Learned in Creating the Blog Guide with bookdown - Stefanie Butland, Maëlle Salmon
2 Months in 2 Minutes - rOpenSci News, February 2020 - Stefanie Butland
rOpenSci’s Leadership in #rstats Culture - Julia Stewart Lowndes
opentripplanner: Fast and Easy Multimodal Trip Planning in R with OpenTripPlanner Tech Note - Malcolm Morgan
Supercharge your GitHub Actions Experience with tic Tech Note - Patrick Schratz
76 published works cited or used rOpenSci software (listed in individual newsletters)
2 use cases for our packages or resources were posted in our discussion forum Look for microdemic, magick
Have you used an rOpenSci package? Share your use case and we’ll tweet about it.
We have a discussion forum (using Discourse) for the rOpenSci community. It’s a really nice way to have conversations on the internet. From time to time we’ll highlight recent discussions of interest.
Peter Desmet asked a question that many R package maintainers run in to: What if raw data in package is too large?
Emilio Bruna asked: What is best practice when a dependency is removed from CRAN?
Python 101: Learning About Lists - A good introduction to lists in Python; they’re kind of like R lists. It’s an important data structure to know if you’re going to be learning Python
Necessary & Sufficient - A good read on how to think about testing in general
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