rOpenSci News Digest, October 2024

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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!

rOpenSci HQ

Fostering open science in Latin America: CZI awards funds for sustainable research software development

Exciting News! The Chan Zuckerberg Initiative has awarded rOpenSci a new grant to foster sustainable scientific software as a pillar of Open Science in Latin America by building capacity and community. With this $340K grant, we’re planning to launch a Spanish-language version of our Champions Program, along with other new initiatives to make sustainable software development more accessible to researchers across the region. Read more in our announcement.

R-Universe interactive tour

Each package page on R-universe now features a guided tour built with intro.js, so that you might get an overview of all information presented on package pages. To start the tour, click on the “Website Tour” button on the navbar – note that it does not work on mobile devices. Example: xml2 package.

The rOpenSci community at upcoming events

rOpenSci Community Manager Yanina Bellini Saibene will deliver a keynote talk at BioNT Community Event & CarpentryConnect-Heidelberg 2024, on November 14th in Heidelberg, Germany.

Several members of the rOpenSci community will be part of this year’s LatinR—Latin American Conference About the Use of R in R&D!

Check the full list of talks and other events were rOpenSci members will be participating.

rOpenSci at UK Research Software Engineers conference

The eighth annual UK Research Software Engineering conference took place in Newcastle from September 3rd to 5th. Mark Padgham presented a talk on “rOpenSci statistical software peer-review: The importance and challenge of community engagement”. The talk was second in a two-part session devoted to software peer review, and followed a walk-through demonstration of the process of submissing to Journal of Open Source software. Find Mark’s slides.

Our first community call in Portuguese!

A comunidade R fala português 🕓 Wednesday, 06 November 2024 13:00 UTC With Pedro Faria, Beatriz Milz, Francesca Belem Lopes Palmeira.

The R Community is carrying out several translations into Portuguese of different resources: books, packages and guides.

In this community call, three leaders from the Portuguese-speaking community will present their experiences leading and being part of these projects and share how we can participate in these efforts, including rOpenSci Developer Guide translation project.

This community call will be followed by a traslaton at LatinR, for those who want to contribute.

Looking for Maintainers to Support First-Time Contributors

Making your first contribution to Open Source can be both empowering and intimidating. As such, we’re exited to announce a special series of activities to support first-time contributors to rOpenSci packages! 🎉

Are you an rOpenSci package maintainer who would like to help someone make their first contribution?

See our blog post for more details and how to sign up (by November 24).

Coworking

Read all about coworking!

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!

Software 📦

New packages

The following five packages recently became a part of our software suite:

  • pkgmatch, developed by Mark Padgham: Find R packages matching either descriptions or other R packages.

  • rsi, developed by Michael Mahoney: Downloads spatial data from spatiotemporal asset catalogs (STAC), computes standard spectral indices from the Awesome Spectral Indices project (Montero et al. (2023) doi:10.1038/s41597-023-02096-0) against raster data, and glues the outputs together into predictor bricks. Methods focus on interoperability with the broader spatial ecosystem; function arguments and outputs use classes from sf and terra, and data downloading functions support complex CQL2 queries using rstac. It is available on CRAN. It has been reviewed by Felipe Carvalho and Michael Sumner.

  • allcontributors, developed by Mark Padgham together with Chris Hartgerink: Acknowledge all contributors to a project via a single function call. The function appends to a README or other specified file(s) a table with names of all individuals who contributed via code or repository issues. The package also includes several additional functions to extract and quantify contributions to any repository. It is available on CRAN.

  • chopin, developed by Insang Song together with Kyle Messier: Geospatial data computation is parallelized by grid, hierarchy, or raster files. Based on future and mirai parallel backends, terra and sf functions as well as convenience functions in the package can be distributed over multiple threads. The simplest way of parallelizing generic geospatial computation is to start from par_pad_* functions to par_grid, par_hierarchy, or par_multirasters functions. Virtually any functions accepting classes in terra or sf packages can be used in the three parallelization functions. A common raster-vector overlay operation is provided as a function extract_at, which uses exactextractr, with options for kernel weights for summarizing raster values at vector geometries. Other convenience functions for vector-vector operations including simple areal interpolation (summarize_aw) and summation of exponentially decaying weights (summarize_sedc) are also provided. It has been reviewed by Alec L. Robitaille and Eric R. Scott.

  • eDNAjoint, developed by Abigail G. Keller: Models integrate environmental DNA (eDNA) detection data and traditional survey data to jointly estimate species catch rate (see package vignette: https://ednajoint.netlify.app/). Models can be used with count data via traditional survey methods (i.e., trapping, electrofishing, visual) and replicated eDNA detection/nondetection data via polymerase chain reaction (i.e., PCR or qPCR) from multiple survey locations. Estimated parameters include probability of a false positive eDNA detection, a site-level covariates that scale the sensitivity of eDNA surveys relative to traditional surveys, and catchability coefficients for traditional gear types. Models are implemented with a Bayesian framework (Markov chain Monte Carlo) using the Stan probabilistic programming language. It is available on CRAN. It has been reviewed by Chitra M. Saraswati and Saras M. Windecker.

Discover more packages, read more about Software Peer Review.

New versions

The following eleven packages have had an update since the last newsletter: pkgmatch (v0.4.0), rsi (v0.3.0), charlatan (v0.6.1), git2r (v0.35.0), nodbi (v0.10.7), rgbif (v3.8.1), riem (v0.3.2), ruODK (v1.5.0), spatsoc (v0.2.6), targets (1.8.0), and tidyhydat (v0.7.0).

Software Peer Review

There are thirteen recently closed and active submissions and 6 submissions on hold. Issues are at different stages:

Find out more about Software Peer Review and how to get involved.

On the blog

Calls for contributions

Calls for maintainers

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?.

Calls for contributions

Also refer to our help wanted page – before opening a PR, we recommend asking in the issue whether help is still needed.

Package development corner

Some useful tips for R package developers. 👀

Using property-based testing in R

Want to add more variation to your test cases? Don’t miss Etienne Bacher’s post Using property-based testing in R where he explains “Property-based testing is a way of testing where we give random inputs to the function we want to test and we want to ensure that no matter the inputs, the output will respect some properties.”. The post includes examples.

Useful functions for comparing versions

If you need to compare versions in your code, for instance installed version and version at which a feature was added, these two functions might prove handy:

Create and use a custom roxygen2 tag

In a blog post, Maëlle Salmon describes how to create and use a simple custom roxygen2 tag that adds a section to the Rd file.

Last words

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. You can also support our work through donations.

If you haven’t subscribed to our newsletter yet, you can do so via a form. Until it’s time for our next newsletter, you can keep in touch with us via our website and Mastodon account.

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