Friday, September 22, 2023 From rOpenSci (https://ropensci.org/blog/2023/09/22/news-september-2023/). Except where otherwise noted, content on this site is licensed under the CC-BY license.
Dear rOpenSci friends, it’s time for our monthly news roundup!
You can read this post on our blog. Now let’s dive into the activity at and around rOpenSci!
Thanks to some help from George Stagg, we added experimental support for building WebAssembly binary packages. This makes it possible to install packages in webr, directly from the R-universe.
For instance:
webr::install(
'jsonlite',
repos = c(
'https://jeroen.r-universe.dev',
'https://repo.r-wasm.org'
)
)
This only works if the package and all of its dependencies support webassembly. For CRAN packages you can look at https://repo.r-wasm.org/. For other packages… you’ll have to give it a try!
Carolina Pradier’s package eph was approved after a review process in Spanish! Congratulations to Carolina, and thanks to editor Mauro Lepore, reviewers Guadalupe Gonzalez and Denisse Fierro Arcos, and mentor Athanasia Monika Mowinckel. eph is a package which helps process data from the Argentina household survey.
We are excited we received 123 applications from 41 countries on 5 continents!
We are very grateful to everyone who submitted their proposals to our program.
The review process is starting.
Stay tuned for updates!
Read all about coworking in our recent post!
Join us for social coworking & office hours monthly on first Tuesdays! Hosted by Steffi LaZerte and various community hosts. Everyone welcome. No RSVP needed. Consult our Events page to find your local time and how to join.
And remember, you can always cowork independently on work related to R, work on packages that tend to be neglected, or work on what ever you need to get done!
Do you have a project idea that is likely to have a broad impact on the R community and has a focused scope? Don’t miss the twice-yearly R Consortium Call for Proposals! Past funded projects include rOpenSci projects like the HTTP testing in R book, and work on the babeldown package.
As global movements, Open Source and Open Science face language-based exclusion as most resources are in English. This affects scientists and research software engineers working in R, particularly those who don’t have English as their first language.
rOpenSci multilingual efforts aim to lower access barriers, democratize quality resources, and increase the possibilities of contributing to open software and science. We successfully piloted our Spanish-language peer review and the localization to Spanish of our comprehensive guide to software development, with Portuguese translation underway.
Maëlle Salmon, Paola Corrales, and Elio Campitelli, will share the rOpenSci Multilingual project details on this call. Maëlle will present the R packages that allow us to have our content in several languages. Then Elio and Paola will share the translation workflow and show the Translation Guide written to document the process.
The following two packages recently became a part of our software suite:
eph, developed by Carolina Pradier together with Diego Kozlowski, Pablo Tiscornia, Guido Weksler, Natsumi Shokida, and German Rosati: Tools to download and manipulate the Permanent Household Survey from Argentina (EPH is the Spanish acronym for Permanent Household Survey). e.g: get_microdata() for downloading the datasets, get_poverty_lines() for downloading the official poverty baskets, calculate_poverty() for the calculation of stating if a household is in poverty or not, following the official methodology. organize_panels() is used to concatenate observations from different periods, and organize_labels() adds the official labels to the data. The implemented methods are based on INDEC (2016) http://www.estadistica.ec.gba.gov.ar/dpe/images/SOCIEDAD/EPH_metodologia_22_pobreza.pdf. As this package works with the argentinian Permanent Household Survey and its main audience is from this country, the documentation was written in Spanish. It is available on CRAN.
ohun, developed by Marcelo Araya-Salas: Facilitates the automatic detection of acoustic signals, providing functions to diagnose and optimize the performance of detection routines. Detections from other software can also be explored and optimized. Araya-Salas et al. (2022) doi:10.1101/2022.12.13.520253. It is available on CRAN. It has been reviewed by Alec L. Robitaille, and Sam Lapp.
Discover more packages, read more about Software Peer Review.
The following fourteen packages have had an update since the last newsletter: bold (v1.3.0), charlatan (v0.5.1), chromer (v0.6), eph (v1.0.0), europepmc (v0.4.3), geojsonio (v0.11.3), nodbi (v0.9.7), readODS (v2.1.0), rgbif (v3.7.8), spatsoc (v0.2.2), stplanr (v1.1.2), tarchetypes (0.7.8), targets (1.3.0), and weathercan (v0.7.1).
There are sixteen recently closed and active submissions and 3 submissions on hold. Issues are at different stages:
Two at ‘6/approved’:
eph, Argentina’s Permanent Household Survey Data and Manipulation Utilities. Submitted by Carolina Pradier.
ohun, Optimizing Acoustic Signal Detection. Submitted by Marcelo Araya-Salas.
Two at ‘4/review(s)-in-awaiting-changes’:
wmm, World Magnetic Model. Submitted by Will Frierson.
octolog, Better Github Action Logging. Submitted by Jacob Wujciak-Jens.
Seven at ‘3/reviewer(s)-assigned’:
fastMatMR, “fastMatMR: High-Performance Matrix Market File Operations in R”. Submitted by Rohit Goswami.
naijR, Operations to Ease Data Analyses Specific to Nigeria. Submitted by Victor Ordu .
rangr, Mechanistic Simulation of Species Range Dynamics. Submitted by Katarzyna Markowska.
mregions2, Access Data from Marineregions.org: The Marine Regions Gazetteer and the Marine Regions Data Products. Submitted by salvafern.
pangoling, Access to Large Language Model Predictions. Submitted by Bruno Nicenboim.
dfms, Dynamic Factor Models. Submitted by Sebastian Krantz.
fwildclusterboot, Fast Wild Cluster Bootstrap Inference for Linear Models. Submitted by Alexander Fischer. (Stats).
Three at ‘2/seeking-reviewer(s)’:
GLMMcosinor, Fit a cosinor model using a generalised mixed modelling framework. Submitted by Rex Parsons.
weatherOz, An API Client for Australian Weather and Climate Data Resources. Submitted by Rodrigo Pires.
bssm, Bayesian Inference of Non-Linear and Non-Gaussian State Space. Submitted by Jouni Helske. (Stats).
Two at ‘1/editor-checks’:
agromet, Índices y Estadísticos Climáticos e Hidrológicos. Submitted by Paola Corrales.
qualtdict, Generating Variable Dictionaries and Labelled Data Exports of Qualtrics. Submitted by lyh970817.
Find out more about Software Peer Review and how to get involved.
Meeting the Stars of the R-Universe: The R-Universe Against Diseases. by Yanina Bellini Saibene, Alejandra Bellini, Lucio Casalla, and Steffi LaZerte. . Other languages: Conociendo a las estrellas del Universo R: El universo R contra las enfermedades. (es).
Attract Contributors with ‘help wanted’ Issues by Maëlle Salmon, Yanina Bellini Saibene, and Steffi LaZerte. Tips on how to create and advertise help-wanted issues.
If you’re interested in maintaining any of the R packages below, you might enjoy reading our blog post What Does It Mean to Maintain a Package? (or listening to its discussion on the R Weekly highlights podcast hosted by Eric Nantz and Mike Thomas)!
Refer to our somewhat recent blog post to identify other packages where help is especially wished for! See also our help wanted page – before opening a PR, we recommend asking in the issue whether help is still needed.
Some useful tips for R package developers. 👀
Don’t miss our blog post Attract Contributors with ‘help wanted’ Issues! 😸
… with this evaluation tool.
Using a pkgdown configuration file to group and order functions on your package’s reference page is great for users, but also mean you need to maintain the file as pkgdown will error if a help topic is missing from the configuration.
If your package documentation is built by rOpenSci, it might be easier to miss a failure.
You can:
pkgdown::check_pkgdown() in a Git pre-commit hook;pkgdown::check_pkgdown() in a GitHub Actions workflow.As a reminder, the rjtools package will help you prepare a submission of a paper to the R Journal. This could be a good way to spread the word about a CRAN package of yours!
Thanks for reading! If you want to get involved with rOpenSci, check out our Contributing Guide that can help direct you to the right place, whether you want to make code contributions, non-code contributions, or contribute in other ways like sharing use cases.
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