rOpenSci - open tools for open science

Celebrating the rOpenSci Community

rOpenSci — Tue, 29 Sep 2026 00:00:00 +0000

rOpenSci turns 15 and I’ve been a part of this community for a good chunk of that time. As I reflected on those years, a lot of great memories came to mind, and I realized some of them taught me some lessons that I would love to share.

Stretch your comfort zone

Early in 2018 I came across this post:

Apply to attend rOpenSci unconf 2018!
We’re organizing unconf18 to bring together scientists, developers, and open data enthusiasts from academia, industry, government, and non-profits to get together for a couple of days to hack on various projects and generally enrich our community.

In hindsight I was a good fit. I had recently transitioned from academia to my first job as a research software engineer. And yet, did I think I was a solid candidate? Oh no, I had lots of doubts.

But I know the greatest things often happen a little stretch outside your comfort zone. So I applied anyway, got invited, and spent two days collaborating with an incredible bunch of people. Some of them were already rockstars, many others eventually became influential in their own niche, and all of them had this great attitude that at the time I lacked words to describe. I now do: they were respectful and kind.

Be respectful and kind

In 2022 rOpenSci launched the first cohort of the rOpenSci Champions Program. By then I already had a few years of experience as an associate editor of rOpenSci software peer-review, and Yani invited me to talk to our champions about the process.

As I prepared that talk and discussed some ideas with Yani, she made me realize how much the rOpenSci community cares about the way we communicate with one another¹. So much so that our guides for reviewers and editors open with this message:

rOpenSci’s community is our best asset. We aim for reviews to be open, non-adversarial, and focused on improving software quality. Be respectful and kind! See our reviewers’ guide and code of conduct for more.

Coming from such a deeply technical guide, that opening might surprise you. But it makes sense to me now; broken code is way easier to fix than broken human relationships.

Contribute in your own way

At rOpenSci you can contribute in so many ways. We have a community contributing guide but here’s a list of some of my own contributions:

Attend events.
Help to welcome and onboard new members.
Ask or answer questions on Slack, or share or discuss ideas or jobs.
Improve our documentation, e.g. fix a typo.
Write a blog post.
Translate or review some work in your native language.
Pilot a new process.
Author, review, or edit an R package.
Lead a workshop.
Build a tool to enhance some process.
Give and get support, e.g. mentor, review grant applications or talks, advice or recommendations for jobs, help unavailable or overwhelmed people.

As you can see, most of my contributions did not involve any code, and only a few required an invitation. Some of them are the kind of thing you could add to your CV, and the most important ones only belong in your heart.

Attract great people

Great communities are made of great people, and you can play an active role in growing it in the direction you want. If you know someone that would fit in and enjoy the rOpenSci community, you can try to find a way to bring them in. For example, I’ve encouraged and helped people to submit and review packages. Also as an editor I have the occasional privilege to nominate other editors. Using this super-power I’ve attracted two editors and I now get to enjoy interacting with them quite regularly.

Keep the human in your human connections

AI is changing the way we contribute to open source software. But we don’t know much about its effect on the open communities around that software. Earlier this year rOpenSci published a preliminary set of policies, and you may want to watch rOpenSci’s newsletter and this Openscapes community call:

Open Communities in the Age of AI
{Open communities} help us connect on a human level around science and data and the things that we are passionate about. They change careers, they change lives. (…) But now, people are not necessarily finding their communities and getting the benefit of those communities in the way that they used to, because they’re more easily able to get help and answers by using AI. (…) These connections and communities are more important than ever, as we need to find ways to support each other and ourselves as AI rapidly changes the landscape right under our feet.

Personally, over the past year I’ve used AI heavily and learned several lessons. The most relevant here is that I don’t want AI to impersonate me or the other person in a human-to-human connection. The humans of rOpenSci are real and wonderful people. Here are some that I’ve recently met in person:

Noam Ross, Yanina Bellini Saibene and me at the 2024 CZI meeting in Boston, USA.

Monica Gerber, Stefanie Butland, Sean Kross, Julia Stewart Lowndes, Kelly O’Briant and me at the 2024 Posit conference in Seattle, USA.

Luis D. Verde Arregoitia, Diana Garcia Cortes and me at the Georgia Aquarium after the 2025 Posit conference in Atlanta, USA.

Ronny A. Hernández Mora and me at Ronny’s family gathering near San José, Costa Rica in 2025.

Beatriz Milz and me after coffee and pastries in São Paulo, Brazil in 2025.

Thanks for joining me in celebrating rOpenSci. And if, like me, you self-identify with our community, then happy birthday to you!

Two books I like are How to Win Friends and Influence People and Crucial Conversations (Third Edition): Tools for Talking When Stakes Are High. ↩︎

rOpenSci News Digest, September 2026

rOpenSci — Mon, 28 Sep 2026 00:00:00 +0000

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

Yani and Mark at “Open Communities in the age of AI”

rOpenSci community manager Yani and software-review lead Mark will participate in the upcoming Openscapes community call on October 1st (this Thursday!) at 9:30AM PT (16:30 UTC) entitled “Open Communities in the Age of AI”, together with Mara Averick, Senior Developer Advocate at Quansight and Hadley Wickham, Chief Scientist at Posit.

Event page, including link for free registration.

Champions Program update

Our current 2026–2027 cohort has completed the training phase and is now focused on developing their individual projects with the support of their mentors, as well as on their outreach activities.

Meanwhile, the 2025–2026 cohort will wrap up their journey with a closing community call, Más Allá del Código: muestra abierta de los proyectos de nuestros campeon(a|e)s (Beyond the Code: an open showcase of our Champions’ Projects). At the call, the Champions will present their final projects, from creating, improving, and reviewing R packages to the outreach work that brought those projects to their communities. They’ll also share what their projects led to, including scholarships, new collaborations, and professional growth. The event is in Spanish. The call will take place on Monday, October 12 at 15:00 UTC. Come get inspired, ask your questions live, and help us celebrate our growing open-source community!

We’re still celebrating our 15th anniversary! 🎉

In July, we started to share stories from members of our community about their experiences with rOpenSci. Our second story features Yi-Chin Sunny Tseng and her connection with rOpenSci. Read it on our blog: Happy Birthday rOpenSci — My Journey from First-Time Developer to Current Opportunities Stay tuned for more stories from our community as we continue celebrating 15 years of rOpenSci!

rOpenSci at LatinR 2026 in Medellín, Colombia. See you there!

Registration for LatinR 2026 (November 11–13, Universidad de Antioquia, Medellín) is now open, and rOpenSci will have a strong presence.

Jeroen will give a talk on R-Universe. Yani will lead a workshop on R package development and give a talk on the Champions Program’s open curriculum. Nic Crane is one of the conference’s keynote speakers. Evelia Lorena Coss Navarrete, one of our Champions, will talk about the package she developed during the program. Several other rOpenSci folks — Natalia Da Silva, Luis Verde, and Francisco Cardozo, among others — will also be there. Jeroen, Francisco, and Yani will take part in the hackathon during the conference.

Join us in Medellín!

Coworking

Read all about coworking!

Tuesday October 6th, 09:00 Americas Pacific (16:00 UTC) “Writing Tests & Testing in R”, with Yanina Bellini Saibene and co-host Olivier Leroy.
- Explore how to write tests for R and add some tests to your work or packages
- Meet co-host, Olivier Leroy, and chat about testing
Tuesday November 3rd, 09:00 Australia Western (01:00 UTC) “Climate Science in R”, with Steffi LaZerte and co-host Elio Campitelli.
- Explore how R is used to study the climate
- Meet co-host, Elio Campitelli, and discuss Climate Science in R
Tuesday December 8th^*, 14:00 Europe Central (12:00 UTC) “Code Linting in R”, with Steffi LaZerte and co-host Etienne Bacher.
- Read up on Code Linting and apply some linters to your R code
- Meet co-host, Etienne Bacher, and discuss code linting in general, or flir and Jarl in particular
  * Note that December coworking is a week later than usual

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 📦

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

ciecl, developed by Rodolfo Tasso Suazo: Tools for working with the International Classification of Diseases (ICD-10 Chile official MINSAL/DEIS v2018). Includes optimized SQL search with SQLite, fuzzy matching of medical terms (Jaro-Winkler), Charlson and Elixhauser comorbidity calculation, WHO ICD-11 API integration, and hierarchical code validation. Data from Centro FIC Chile DEIS https://deis.minsal.cl/centrofic/. It has been reviewed by Maëlle Salmon and Yanina Bellini.
brapiR2, developed by Joash Joshua Ayo: Provides pipe-friendly, stateless read access to the Breeding API (BrAPI) v2.1 specification, an open community standard for plant breeding data interchange maintained by the BrAPI project https://brapi.org. Wraps 32 of the 37 BrAPI v2.1 entities across all four modules, Core, Germplasm, Phenotyping, and Genotyping, covering 49 of the specifications 138 retrieval (GET and search) endpoints and returning tidy tibbles ready for analysis. Write and update endpoints are out of scope by design. Features include automatic pagination, async search handling, response caching, parallel batch fetching, and convenience functions for genomic selection workflows (e.g. dosage matrix extraction). Designed for plant breeders and bioinformaticians who need programmatic access to plant breeding databases that implement the BrAPI’ v2 specification. It has been reviewed by David Waring and Jenna Hershberger.

Discover more packages, read more about Software Peer Review.

New versions

The following twenty-two packages have had an update since the last newsletter: visdat (v0.6.1), nycOpenData (v0.2.3), ruODK (v1.6.0), stats19 (v4.1.0), npi (v0.3.1), promoutils (v0.7.0), brapiR2 (v0.2.0), ciecl (v1.0.0), bibtex (v0.5.3), ckanr (v0.9.0), osmapiR (v0.2.6), distionary (v0.2.0), GLMMcosinor (v0.2.2), reviser (v0.3.1), autotest (v0.2), occCite (v0.6.3), readODS (v2.3.6), osmdata (v0.4.1), goodpractice (v1.2.0), galamm (v0.4.1), RAMEN (v2.1.2), and EDIutils (v3.0.1).

Software Peer Review

There are seventeen recently closed and active submissions and 4 submissions on hold. Issues are at different stages:

Two at ‘6/approved’:
- brapiR2, A Tidyverse-Native Client for the BrAPI v2 (Breeding API) Specification. Submitted by Joash Joshua Ayo.
- ciecl, International Classification of Diseases ICD-10/ICD-11 for Chile. Submitted by Rodolfo Tasso.
Three at ‘5/awaiting-reviewer(s)-response’:
- ibger, Access the IBGE Aggregate Data API from R. Submitted by Andre Leite Wanderley.
- RAQSAPI, A Simple Interface to the US EPA Air Quality System Data Mart API. Submitted by mccroweyclinton-EPA.
- coevolve, Fit Bayesian Generalized Dynamic Phylogenetic Models using Stan. Submitted by Scott Claessens. (Stats).
One at ‘4/review(s)-in-awaiting-changes’:
- rcrisp, Automate the Delineation of Urban River Spaces. Submitted by Claudiu Forgaci. (Stats).
Four at ‘3/reviewer(s)-assigned’:
- camtrapReport, Camera-Trap Report Generator. Submitted by Elham Ebrahimi.
- nert, Curated Access to TERN Environmental Raster Data. Submitted by Max Moldovan.
- rfastlowess, High-Performance LOWESS Smoothing for R. Submitted by Amir Valizadeh. (Stats).
- camtrapReport, Camera-Trap Report Generator. Submitted by Elham Ebrahimi.
Two at ‘2/seeking-reviewer(s)’:
- grumpy, Read NumPy .npy and .npz Files. Submitted by Hugo Gruson.
- tezr, Access Thesis Metadata from Turkiye’s National Thesis Center. Submitted by Emrah Er.
Five at ‘1/editor-checks’:
- ocean3d, Three-Dimensional Marine Spatial Analysis. Submitted by Jay Matsushiba.
- corila, Sparse modelling with grouped and correlated features allowing for privileged information. Submitted by Armin Rauschenberger. (Stats).
- tarpolyglot, Run Python, Julia, Rust, and C++ Inside targets Pipeline Steps. Submitted by Pierre9344.
- OptSurvCutR, Optimal Survival Cut-Point Discovery for Time-to-Event Analysis with OptSurvCutR. Submitted by Payton Yau. (Stats).
- HydraR, Stateful Agentic Orchestration for Scientific Reproducibility. Submitted by Ignatius Pang.

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

On the blog

Happy Birthday rOpenSci — My Journey from First-Time Developer to Current Opportunities by Yi-Chin Sunny Tseng. Yi-Chin Sunny Tseng shares how the rOpenSci Champions Program helped her grow from a first-time R package developer into an open science contributor, creating tools for biodiversity research and discovering new opportunities along the way.

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?.

charlatan, create fake data in R. Issue for volunteering.

Calls for contributions

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 information for R package developers. 👀

{covr2gh}: Coverage summary as GitHub comment

The covr2gh package by Dragoș Moldovan-Grünfeld provides an automated way to summarise the impact of a pull request (PR) on test coverage directly within GitHub. See it in action in a PR in the tfrmt R package.

Deprecation messages for package data

Hugo Gruson wrote an exhaustive post about the deprecation of data in an R package. The post features the delayedAssign() function.

A refactoring story featuring people

Athanasia Mo Mowinckel published “Why ggseg Atlases Became Function Calls”, where she explains how she made data into objects into a package in order to allow re-exporting them. She furthermore tells how she got to that conclusion by trying out different solutions and discussing with other package developers in the rOpenSci Slack workspace.

usethis 3.2.2

The usethis package was updated on CRAN. The changelog features many quality of life improvements around Git and GitHub, and a new use_readme_qmd() function for drafting a README in the Quarto format.

Last words

Thanks for reading! If you want to get involved with rOpenSci, check out our Contributing Guide. This guide will help direct you to the right place, whether you want to make code contributions, non-code contributions, or contribute in other ways such as through sharing use cases. You can also support our work through donations.

If you haven’t subscribed to our newsletter yet, you can do so though our signup form. Until it’s time for our next newsletter, you can keep in touch with us through our website, Mastodon, or LinkedIn. See you soon!

Happy Birthday rOpenSci — My Journey from First-Time Developer to Current Opportunities

rOpenSci — Tue, 15 Sep 2026 00:00:00 +0000

What? People would fund me and mentor me building my own R package?

My first time hearing about rOpenSci was, interestingly, at a bird conference. At an ornithological conference in 2023, I was chatting with my bird friend / colleague Steffi LaZerte, who highly recommended that I apply for the rOpenSci Champions program given my strong interest in developing R packages. I mentioned my concerns and hesitation about whether I am “qualified” to make an R package, which at that time sounded like what professional developers do. She simply asked, “Can you write functions in R?” “Yes, I can.” “Then you are good to go! R packages are simply putting functions together in an organized way!” So here we go, I took the motivation and sent out my application for the 2024 rOpenSci cohort. A few months later, I was selected as one of the 20 participants around the world; I remember the strong excitement when I received the confirmation letter from rOpenSci. I was probably even more excited than when I got my PhD admission!

bbsTaiwan R package – a combination of my dreams

I have been an avid R user for a long time, and I am particularly fascinated by the open science nature that comes with it. My proposed package, “bbsTaiwan”, aims to solve a common issue with an open-source dataset, the Breeding Bird Survey (BBS) data of Taiwan. While the BBS dataset is publicly available online and stored in Global Biodiversity Information Facility, it is structured in a specific machine-readable format that requires a certain amount of data wrangling to make it usable for downstream modeling and ecological inference. bbsTaiwan is a data package as well as a wrangling toolbox, and it can be particularly useful for undergraduate and graduate students, as well as researchers who need BBS data for their dissertations. Most importantly, I see it as a crucial way to contribute my skills back to my beloved homeland - Taiwan. I want to use this package to show my care and love for the land.

The mentor and the learning

I was very lucky to have Eunseop Kim as my mentor during the Champions program. He was extremely patient, supportive, and understanding. Eunseop has a background in statistics and is very fluent in package development. He provided me with a lot of freedom on the project and emphasized that he would be there to support and help if I had any questions along my journey. I sought suggestions from him frequently in terms of package design, usability, resources, and references. He not only taught me how to make R packages, but he also showed me the importance and power of being a supportive mentor. Being able to study references on how other people developed their packages improved my own philosophy of making packages as well.
One rule that still sticks with me until today is “be kind.” Be kind to your users, be kind to your collaborators, and be kind to your colleagues. If we can put extra thought and effort into thinking from other people’s perspectives (what users might like to see and what makes a package easy to use), then it will be a tool that people genuinely enjoy using.

rOpenSci program as a path to future opportunities

The rOpenSci Champions program not only opened my door to R package development, but it also provided me with invaluable opportunities. I was selected as an “Opportunity Scholar” for posit::conf(2024), where I connected with fellow programmers and was inspired further by the possibilities of this programming language. After the Champions program, I was presenting in the rOpenSci Community Call “From Novice to Contributor: Making and Supporting First-Time Contributions to FOSS”, where I had a chance to share my experience and contribute back to rOpenSci. Furthermore, I received support from Natural Sciences and Engineering Research Council of Canada and the R Consortium to create my second R package, birdnetTools, in collaboration with the Cornell Lab of Ornithology. It was a dream come true!! None of this would have been possible if I hadn’t participated in the rOpenSci Champions program a few years ago. Meanwhile, my story with rOpenSci will continue.

Thank you to the community at rOpenSci. Thank you for bringing excitement, fun, and care to this world.

rOpenSci News Digest, August 2026

rOpenSci — Fri, 28 Aug 2026 00:00:00 +0000

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

Champions Program update

Our Champions are making great progress! 🌟 They’ve now completed the training on good open source software development practices, package development, and peer review, and are moving on to explore community building and communications. Meanwhile, mentoring is underway with monthly meetings helping Champions move their projects forward. We’re already starting to see some exciting results: first versions of packages are taking shape, and some Champions will soon be sharing their work at LatinR! Stay tuned for more updates as their projects continue to grow! 🚀

R-Universe updates

💪 R-Universe has started building and checking packages for Windows ARM64. Read more in our tech note.

🧬 R-Universe is now part of the infrastructure of the Bioconductor submission process.

We’re still celebrating our 15th anniversary! 🎉

In July, we started to share stories from members of our community about their experiences with rOpenSci. Our first story features Eunseop Kim and his connection with rOpenSci. Read it on our blog: From Peer Review to Mentorship: My rOpenSci Story Stay tuned for more stories from our community as we continue celebrating 15 years of rOpenSci!

Coworking

Read all about coworking!

Tuesday September 1st, 14:00 Europe Central (12:00 UTC) “Getting to Know SORTEE”, with Steffi LaZerte and co-host Ed Ivimey-Cook.
- Visit SORTEE (Society for Open, Reliable, and Transparent Ecology and Evolutionary Biology).
- Meet co-host, Ed Ivimey-Cook, and learn more about SORTEE and how you might get involved.
Tuesday October 6th, 09:00 Americas Pacific (16:00 UTC) “Writing Tests & Testing in R”, with Yanina Bellini Saibene and co-host Olivier Leroy.
- Explore how to write tests for R and add some tests to your work or packages
- Meet co-host, Olivier Leroy, and chat about testing
Tuesday November 3rd, 09:00 Australia Western (01:00 UTC) TBA, with Steffi LaZerte and co-host TBA.
Tuesday December 8th, 14:00 Europe Central (12:00 UTC) “Code Linting in R”, with Steffi LaZerte and co-host Etienne Bacher.
- Read up on Code Linting and apply some linters to your R code
- Meet co-host, Etienne Bacher, and discuss code linting in general, or flir and Jarl in particular * Note that December coworking is a week later than usual

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 📦

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

lakefetch, developed by Jeremy Lynch Farrell: Calculates fetch (open water distance) and wave exposure metrics for lake sampling points. Downloads lake boundaries from OpenStreetMap, calculates directional fetch using a ray-casting approach, and optionally integrates National Hydrography Dataset (NHD) data https://www.usgs.gov/national-hydrography for hydrological context including outlet and inlet locations. Can estimate lake depth from surface area using empirical relationships, and integrate historical weather data for cumulative wave energy calculations. Includes an optional interactive shiny application for visualization. It has been reviewed by Jorrit Mesman and Kelly Hondula.
galamm, developed by Øystein Sørensen: Estimates generalized additive latent and mixed models using maximum marginal likelihood, as defined in Sorensen et al. (2023) https://doi.org/10.1007/s11336-023-09910-z, which is an extension of Rabe-Hesketh and Skrondal (2004)s unifying framework for multilevel latent variable modeling https://doi.org/10.1007/BF02295939. Efficient computation is done using sparse matrix methods, Laplace approximation, and automatic differentiation. The framework includes generalized multilevel models with heteroscedastic residuals, mixed response types, factor loadings, smoothing splines, crossed random effects, and combinations thereof. Syntax for model formulation is close to lme4 (Bates et al. (2015) https://doi.org/10.18637/jss.v067.i01) and PLmixed’ (Rockwood and Jeon (2019) https://doi.org/10.1080/00273171.2018.1516541). It has been reviewed by Nicholas Clark and David Lawrence Miller.
EpiStrainDynamics, developed by Saras Windecker together with Oliver Eales, James McCaw, and Freya Shearer: EpiStrainDynamics is a statistical framework developed for inferring temporal trends of multiple pathogens from routinely collected surveillance data. It has been reviewed by Sangeeta Bhatia and Joshua Lambert.
RAMEN, developed by Erick I. Navarro-Delgado together with Keegan Korthauer and Michael S. Kobor: Using population data, RAMEN identifies which genetic (G), environmental (E), additive (G+E) or interaction (GxE) model better explains DNA methylation levels in genome-wide locations with high DNA methylation variability. It has been reviewed by Lluís Revilla Sancho and Ulduz Vafadarshamasbi.

Discover more packages, read more about Software Peer Review.

New versions

The following twenty-five packages have had an update since the last newsletter: DataSpaceR (v1.0.2), RAMEN (v2.1.1), frictionless (v1.3.0), cffr (v1.4.2), nodbi (v0.15.0), reviser (v0.2.0), writexl (v2.0.1), rangr (v1.0.10), lightr (v2.1.0), gert (v2.4.1), c14bazAAR (5.3.0), landscapetools (v0.6.3), comtradr (v1.0.6), lingtypology (v1.1.26), textreuse (v1.0.2), sofa (v0.4.2), pkgstats (v0.2.4), refsplitr (v1.2.3), npi (v0.3.0), rerddap (v1.3.0), stantargets (0.1.3), openalexR (v3.1.0), ernest (v1.2.5), mregions2 (v1.1.3), and spiro (v0.2.4).

Software Peer Review

There are eighteen recently closed and active submissions and 5 submissions on hold. Issues are at different stages:

Five at ‘6/approved’:
- EpiStrainDynamics, Infer temporal trends of multiple pathogens. Submitted by Saras Windecker. (Stats).
- lakefetch, Calculate Fetch and Wave Exposure for Lake Sampling Points. Submitted by jeremylfarrell.
- RAMEN, RAMEN: Regional Association of Methylome variability with the Exposome and geNome. Submitted by Erick Navarro-Delgado.
- priorsense, Prior Diagnostics and Sensitivity Analysis. Submitted by Noa Kallioinen. (Stats).
- galamm, Generalized Additive Latent and Mixed Models. Submitted by Øystein Sørensen. (Stats).
Two at ‘5/awaiting-reviewer(s)-response’:
- RAQSAPI, A Simple Interface to the US EPA Air Quality System Data Mart API. Submitted by mccroweyclinton-EPA.
- ciecl, International Classification of Diseases ICD-10/ICD-11 for Chile. Submitted by Rodolfo Tasso.
Two at ‘4/review(s)-in-awaiting-changes’:
- rcrisp, Automate the Delineation of Urban River Spaces. Submitted by Claudiu Forgaci. (Stats).
- coevolve, Fit Bayesian Generalized Dynamic Phylogenetic Models using Stan. Submitted by Scott Claessens. (Stats).
Two at ‘3/reviewer(s)-assigned’:
- brapiR2, A Tidyverse-Native Client for the BrAPI v2 (Breeding API) Specification. Submitted by Ayo.
- ibger, Access the IBGE Aggregate Data API from R. Submitted by Andre Leite Wanderley.
Three at ‘2/seeking-reviewer(s)’:
- grumpy, Read NumPy .npy and .npz Files. Submitted by Hugo Gruson.
- tezr, Access Thesis Metadata from Turkiye’s National Thesis Center. Submitted by Emrah Er.
- rfastlowess, High-Performance LOWESS Smoothing for R. Submitted by Amir Valizadeh. (Stats).
Four at ‘1/editor-checks’:
- camtrapReport, Camera-Trap Report Generator. Submitted by Elham Ebrahimi.
- nert, Curated Access to TERN Environmental Raster Data. Submitted by Max Moldovan.
- OptSurvCutR, Optimal Survival Cut-Point Discovery for Time-to-Event Analysis with OptSurvCutR. Submitted by Payton Yau. (Stats).
- HydraR, Stateful Agentic Orchestration for Scientific Reproducibility. Submitted by Ignatius Pang.

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

On the blog

Software Review

From Peer Review to Mentorship: My rOpenSci Story by Eunseop Kim. From submitting a package, to reviewing one, to mentoring a Champion: my path with rOpenSci.
FOSS Tools for Lazy Editors by Steffi LaZerte. How we streamlined the editing of our blog posts using 4 open-source tools that you could adopt too.
The Journey of {nycOpenData}: From Classroom to Community by Christian Martinez.
From Census Data to Demographic Analysis with ARcenso: A Reproducible Workflow in R by Andrea Gomez Vargas and Emanuel Ciardullo. How to Access and Process the 1970 and 1980 Argentine Censuses Using R. Other languages: De datos censales a análisis demográficos con ARcenso: un flujo de trabajo reproducible en R (es).

Tech Notes

An API for Everything There Is to Know About Packages by Maëlle Salmon. Use cases of the R-Universe APIs.
Windows ARM64 comes to R-universe by Jeroen Ooms.

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?.

charlatan, create fake data in R. Issue for volunteering.

Calls for contributions

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 information for R package developers. 👀

From Rd files to Quarto

Edgar Ruiz from Posit released pkgsite, a package for converting your package’s .Rd files to Quarto. It creates qmd files that you can integrate as you want in a Quarto website.

Mutation testing, fuzzy testing

First of all, a reminder in case you confuse the two concepts…

👽️ Mutation testing: you run tests on mutated version of the code.

🚮 Fuzzy testing: you run tests with all sorts of inputs to the code.

At useR! 2026, both topics were covered:

mutation testing in Beyond Code Coverage: Mutation Testing in R with mutator by Assanali Amandykov, Pierre Donat-Bouillud.
fuzzy testing in Fuzz-testing R-based research software for robustness by Marco Colombo.

roxyreqs

Also at useR!, Moritz Lang and collaborators introduced roxyreqs, a package for adding roxygen2-like documentation to testthat. Slides.

checktor, a new helper for CRAN submissions

If you want to submit your package to CRAN, you can get help through the CRAN cookbook, the collaborative list maintained by ThinkR and now a new package, checktor by James Balamuta! Read more in James’ post.

Interesting AI reads

Recommendations When Using LLM-backed Generative AI Systems for FOSS Contributions by Software Freedom Conservancy, shared by Will Gearty.
Who cleans up after the vibe-coding party? by Sam Learner in the Financial Times.
AI Tooling and Open Source in which Nic Crane discusses “how AI tooling affects open source, the actions maintainers have been taking to address the less positive aspects, and emerging policies that open source projects are implementing around the topic of AI-generated pull requests”.

Last words

From Census Data to Demographic Analysis with ARcenso: A Reproducible Workflow in R

rOpenSci — Mon, 10 Aug 2026 00:00:00 +0000

Read it in: Español.

Population censuses are a key source of information for understanding the composition of populations and how they change across regions within a country. They provide essential evidence for research, the design and evaluation of public policies, and informed decision-making.

Working with census data typically involves several preliminary steps: identifying what information is available, downloading the data, organizing it, and preparing it for analysis. When this information is scattered across multiple sources and provided in different formats, the process can become complex.

ARcenso blog homepage

ARcenso is an R package developed as part of the rOpenSci Champions Program to facilitate access to Argentine census data and simplify its analysis.

In this article, the ARcenso development team demonstrates how to work with the historical census data available through the package in a simple, reproducible way.

The goal is to conduct a demographic analysis using official national data from the 1970 and 1980 censuses, combining visualisations and tables to explore the structure of the population—that is, how it is distributed by age and sex. This type of analysis helps characterise demographic changes over time, providing key information for research and the design of public policies.

Getting started

To run the code examples from this article on your own computer, make sure you have the following packages installed:

# If you don't have pak installed
install.packages("pak")

# Install ARcenso from GitHub
pak::pkg_install("soyandrea/arcenso")

# Install the required packages from CRAN
install.packages(c("dplyr", "tidyr", "ggplot2", "gt"))

Next, we load the packages needed to work with the census data and build indicators for the analysis:

library(arcenso) # access census data
library(dplyr) # data manipulation
library(tidyr) # data tidying and transformation
library(ggplot2) # data visualisation
library(gt) # table formatting

Accessing census data

Now that the required packages are installed and loaded, we can use ARcenso to access census data from the 1970 and 1980 censuses. The package allows users to retrieve information by census year, topic, and geographic area of interest, using the official geographic codes defined by the National Institute of Statistics and Censuses (INDEC) of Argentina .

The check_repository() function helps identify the datasets available in the package. The topic argument specifies the subject of interest (for example, population structure), while geo_code identifies the geographic area.

If you are unsure which values are available for these arguments, you can explore the package metadata. The geo_metadata object contains the available geographic areas and their corresponding codes, while census_metadata provides information about the topics and tables included in the package.

In this example, we use population structure data (topic = "estructura") for the entire country (geo_code = "00"). As a first step, we use check_repository() to see which datasets are available.

check_repository(topic = "estructura", geo_code = "00")

# A tibble: 4 × 3
id_cuadro anio titulo
<chr> <dbl> <chr>
1 1970_00_estructura_01 1970 Cuadro 1. Total del país. Población total, por gr…
2 1980_00_estructura_01 1980 Cuadro G3. Centros urbanos según tamaño y poblaci…
3 1980_00_estructura_02 1980 Cuadro G1. Total del país. Población total según …
4 1980_00_estructura_03 1980 Cuadro G2. Total del país. Población según sexo y…

The output of check_repository() lists the available years for the selected combination of topic and geographic area, together with the corresponding table identifiers (IDs) and their titles. Based on this information, we select the tables 1970_00_estructura_01 and 1980_00_estructura_03, which contain tabulations of the country’s total population by sex and age group and will be used throughout this analysis.

Tip: You can also explore the available data interactively using arcenso_app(). Once you find the table you need, simply copy its ID and use it in your analysis.

Interactive census data explorer with arcenso_app()

Data preparation

Once the tables of interest have been identified, we use get_census() to import them directly into the R session using their table identifiers.

As the structures of the two tables are not identical, we perform a series of transformations to harmonise the variables and create a consistent basis for comparison across censuses. In particular, we recode the age categories into five-year groups to ensure a common structure and add a column identifying the census year.

1970 Census

For the 1970 census, the data are already grouped into five-year age groups, so we only need to adjust the format of some labels and keep the variables relevant to the analysis.

# Country Total
poblacion_1970 <- get_census(id = "1970_00_estructura_01")

pob_1970 <- poblacion_1970 |>
 filter(sexo != "Total") |>
 mutate(
 censo = 1970,
 grupo_de_edad = case_when(
 grupo_de_edad == "0-4" ~ "00-04",
 grupo_de_edad == "5-9" ~ "05-09",
 TRUE ~ grupo_de_edad
 )
 ) |>
 rename(grupo_edad = grupo_de_edad) |>
 select(censo, sexo, grupo_edad, poblacion)

1980 Census

In contrast, the 1980 census table reports age as single years and includes additional information that is not relevant to this analysis. We therefore select the categories of interest and aggregate the data into five-year age groups to make them comparable with the 1970 census.

poblacion_1980 <- get_census(id = "1980_00_estructura_03")

pob_1980 <- poblacion_1980 |>
 filter(urbano_rural == "Total", sexo != "Total", edad != "Total") |>
 mutate(
 censo = 1980,
 edad_num = ifelse(edad == "85 y más", 85, as.numeric(edad)),
 grupo_edad = case_when(
 edad_num %in% c(0:4) ~ "00-04",
 edad_num %in% c(5:9) ~ "05-09",
 edad_num %in% c(10:14) ~ "10-14",
 edad_num %in% c(15:19) ~ "15-19",
 edad_num %in% c(20:24) ~ "20-24",
 edad_num %in% c(25:29) ~ "25-29",
 edad_num %in% c(30:34) ~ "30-34",
 edad_num %in% c(35:39) ~ "35-39",
 edad_num %in% c(40:44) ~ "40-44",
 edad_num %in% c(45:49) ~ "45-49",
 edad_num %in% c(50:54) ~ "50-54",
 edad_num %in% c(55:59) ~ "55-59",
 edad_num %in% c(60:64) ~ "60-64",
 edad_num %in% c(65:69) ~ "65-69",
 edad_num %in% c(70:74) ~ "70-74",
 edad_num %in% c(75:79) ~ "75-79",
 edad_num %in% c(80:84) ~ "80-84",
 TRUE ~ "85 y más"
 )
 ) |>
 select(censo, sexo, grupo_edad, poblacion)

Building the integrated database

Once both tables have been processed, we combine them into a single dataset and define the final structure of the variables, preparing the data for visualisation and the analysis of demographic indicators.

# We combine both geographic areas and prepare the variables for use
poblacion <-
 bind_rows(
 pob_1970,
 pob_1980
 ) |>
 mutate(
 poblacion = as.numeric(poblacion),
 sexo = factor(sexo, levels = c("Varones", "Mujeres")),
 grupo_edad = factor(
 grupo_edad,
 levels = c(
 "00-04",
 "05-09",
 "10-14",
 "15-19",
 "20-24",
 "25-29",
 "30-34",
 "35-39",
 "40-44",
 "45-49",
 "50-54",
 "55-59",
 "60-64",
 "65-69",
 "70-74",
 "75-79",
 "80-84",
 "85 y más"
 )
 )
 )

Population structure

Now that we have a dataset combining both censuses, with population totals organised by census year, sex, and age group, we can consistently compare the population composition across the two census years.

head(poblacion)


 # A tibble: 6 × 4
 censo sexo grupo_edad poblacion
 <dbl> <fct> <fct> <dbl>
 1 1970 Varones 00-04 1196950
 2 1970 Mujeres 00-04 1158350
 3 1970 Varones 05-09 1163050
 4 1970 Mujeres 05-09 1133950
 5 1970 Varones 10-14 1114300
 6 1970 Mujeres 10-14 1086850

Organising the population by sex and five-year age groups allows us to analyse its structure using a variety of visualisations. In this case, we use a population pyramid, which makes it easier to interpret both dimensions together.

Population pyramid

A population pyramid displays the distribution of the population by age and sex simultaneously, typically showing males on the left and females on the right. By representing the proportion of people in each five-year age group, it provides a clear visual summary of the population structure.

In this case, we calculated the relative distribution of the population within each census, allowing us to compare the population structure between 1970 and 1980 regardless of total population size. The shape of the pyramid also allows us to identify broad demographic patterns, such as a higher concentration of younger age groups or a relatively older population profile.

We then calculated the relative distribution of the population in each census and constructed a population pyramid to compare the population structure of the two censuses.

# Dataset for the Population Pyramid
piramide <- poblacion |>
 group_by(censo, sexo) |>
 mutate(
 poblacion_rel = if_else(
 sexo == "Varones",
 -poblacion / sum(poblacion),
 poblacion / sum(poblacion)
 )
 ) |>
 ungroup()

# Comparison Pyramid
piramide |>
 ggplot(aes(x = poblacion_rel, y = grupo_edad, fill = sexo)) +
 geom_col() +
 facet_wrap(~censo, ncol = 2) +
 scale_fill_manual(values = c("#00f59b", "#7014f2")) +
 scale_x_continuous(
 labels = function(x) paste0(abs(round(x * 100, 1)), "%"),
 limits = c(-0.15, 0.15),
 breaks = seq(-0.15, 0.15, by = 0.05)
 ) +
 labs(
 title = "Gráfico 1. Estructura de la población por sexo y grupo quinquenal de edad.",
 subtitle = "Argentina. Años 1970 y 1980",
 x = "Porcentaje",
 y = "Grupo quinquenal de edad",
 caption = "Fuente: INDEC, Censo Nacional de Población 1970 y 1980. Procesado con ARcenso.",
 fill = "Sexo"
 ) +
 theme_bw() +
 theme(
 legend.position = "bottom",
 strip.text = element_text(face = "bold", size = 12)
 )

Figure 1. Population structure by sex and five-year age group. Argentina. Year 1970 and 1980. Source: INDEC, Censo Nacional de Población 1970 y 1980. Processed with ARcenso.

Both censuses show a young population structure, with a high concentration of people in the younger age groups. However, by 1980, a slight shift towards the adult age groups is already apparent, suggesting the early stages of population aging.

Construction of demographic indicators

While the population pyramid provides an overall view of the population structure, demographic indicators offer summary measures that allow these patterns to be quantified and compared more precisely. In this section, we calculate two commonly used indicators to complement the visual analysis.

Aging index

The aging index compares the number of older adults (aged 65 years and over) with the number of children (aged 0–14 years). It provides a simple way to see whether the population is weighted more toward younger age groups or older age groups.

envejecimiento <- poblacion |>
 group_by(censo) |>
 summarise(
 poblacion_0a14 = sum(poblacion[
 grupo_edad %in% c("00-04", "05-09", "10-14")
 ]),
 poblacion_65ymas = sum(poblacion[
 grupo_edad %in% c("65-69", "70-74", "75-79", "80-84", "85 y más")
 ]),
 indice = round(poblacion_65ymas / poblacion_0a14 * 100, 0)
 )


gt(envejecimiento) |>
 tab_header(
 title = "Comparación del índice de envejecimiento",
 subtitle = "Argentina. Años 1970 y 1980"
 ) |>
 tab_spanner(
 label = "Población",
 columns = c(poblacion_0a14, poblacion_65ymas)
 ) |>
 fmt_number(
 columns = c(poblacion_0a14, poblacion_65ymas),
 decimals = 0,
 sep_mark = "."
 ) |>
 cols_label(
 poblacion_0a14 = "0 a 14 años",
 poblacion_65ymas = "65 años y más",
 indice = "Indice"
 ) |>
 tab_source_note(
 source_note = md(
 "**Fuente:** elaboración propia en base a datos de INDEC (Censos Nacionales de Población 1970 y 1980)."
 )
 )

Census	Population		Index
Aging Index Comparison
Argentina. 1970s and 1980s
Census	Ages 0 to 14	65 years and older	Index
1970	6,853,450	1,631,400	24
1980	8,480,768	2,290,564	27
Source: Author’s own calculations based on INDEC data (1970 and 1980 National Population Censuses).

The increase in the aging index reflects a change in the population age structure between 1970 and 1980, indicating a larger relative share of people aged 65 years and over compared with the younger population. This suggests a gradual aging of the population.

Female-to-Male ratio

This indicator shows how many women there are for every 100 men in a specific population group. Here, we calculate it for people aged 60 years and over, where differences between women and men tend to become more pronounced.

feminidad <- poblacion |>
 filter(
 grupo_edad %in% c("60-64", "65-69", "70-74", "75-79", "80-84", "85 y más")
 ) |>
 group_by(censo, grupo_edad, sexo) |>
 summarise(poblacion = sum(poblacion), .groups = "drop") |>
 pivot_wider(names_from = sexo, values_from = poblacion) |>
 mutate(
 indice_feminidad = round(Mujeres / Varones * 100, 0)
 ) |>
 select(-Varones, -Mujeres)


feminidad_plot <- feminidad |>
 pivot_wider(
 names_from = censo,
 values_from = indice_feminidad,
 names_prefix = "censo_"
 ) |>
 ggplot(aes(y = grupo_edad)) +
 geom_segment(
 aes(x = censo_1970, xend = censo_1980, yend = grupo_edad),
 color = "grey85",
 linewidth = 1
 ) +
 geom_point(
 aes(x = censo_1970),
 color = "#ff0f7b",
 size = 3
 ) +
 geom_point(
 aes(x = censo_1980),
 color = "#f89b29",
 size = 3
 ) +
 geom_text(
 aes(x = censo_1970, label = censo_1970),
 hjust = 1.4,
 size = 3
 ) +
 geom_text(
 aes(x = censo_1980, label = censo_1980),
 hjust = -0.4,
 size = 3
 ) +
 labs(
 x = "Mujeres por cada 100 varones",
 y = "Grupo de edad",
 title = "Cambio en el índice de feminidad de la población de 60 años y más",
 subtitle = "Argentina, Años 1970 y 1980",
 caption = "Fuente: INDEC, Censo Nacional de Población 1970 y 1980. Procesado con ARcenso."
 ) +
 theme_minimal()

feminidad_plot

Change in the female-to-male ratio among the population aged 60 and older. Argentina. Year 1970 and 1980. Source: INDEC, Censo Nacional de Población 1970 y 1980. Processed with ARcenso.

The female-to-male ratio shows a higher proportion of women in the older age groups. This difference becomes more pronounced between 1970 and 1980 across all the age groups analysed, reflecting patterns of higher female life expectancy.

What does this analysis tell us?

In demographic analysis, the most time-consuming part is often not calculating indicators or creating visualisations, but the work that comes beforehand: identifying the available data for each census, understanding how it is organised, and building a consistent framework that enables comparisons across census years.

In this example, that process involved locating the relevant tables, understanding the structural differences between the 1970 and 1980 censuses, and harmonising the variables based on the available information to create a comparable dataset. This is precisely what ARcenso is designed to simplify. The check_repository() function helps identify the available tables, get_census() imports the data into R in a structured format, and arcenso_app() provides an interactive way to explore the repository.

Once the data have been organised, the analysis becomes more accessible, reproducible, and easier to extend to new questions.

ARcenso is still a work in progress. Future developments will incorporate additional census years and continue expanding the package’s analytical capabilities. The package was created to make Argentine census data easier to discover, access, and analyse, while building on the collaborative efforts of the open-source community.

If you would like to explore further,check out the vignettes in the package, which include additional examples to work with census data.