Our community is our best asset

We are building a welcoming and diverse global community of software users and developers from a range of research domains. We aim to build capacity of novices, experts, and the “nexperts” in between. We welcome participation and civil conversations that adhere to our code of conduct. There are many ways to get involved.

Follow us on Twitter. Read our blog to learn about our software packages, best practices, events and the people who make up our community, or read our Contributing Guide to learn how you can contribute to rOpenSci as a user or developer. Subscribe to rOpenSci News to get semi-monthly updates on our activities sent to your inbox.

Ask and answer #rstats questions on our discussion forum, on Stackoverflow, or Twitter. Attend our Community Calls to hear about best practices, Q & As with well known developers, and to learn more about rOpenSci developments. These are free and open for anyone to attend and provide an opportunity to connect with rOpenSci community members around the world.

Use our software. Improve package documentation or code. Review a package. Submit your package for open software peer review. Apply to participate in our annual unconference.



Community Calls

We hold quarterly community calls open to everyone. Join us to hear about new projects, Q&As with well known developers, and to learn more about rOpenSci developments.

Commcalls Archive

Upcoming Events

Event Date Location Attendees Description
Reproducible workflows at scale with drake 2019/09/24 Community call (teleconference) 🌐 All are welcome Ambitious workflows in R, such as machine learning analyses, can be difficult to manage. A single round of computation can take several hours to complete, and routine updates to the code and data tend to invalidate hard-earned results. You can enhance the maintainability, hygiene, speed, scale, and reproducibility of such projects with the drake R package. drake resolves the dependency structure of your analysis pipeline, skips tasks that are already up to date, executes the rest with optional distributed computing, and organizes the output so you rarely have to think about data files. This talk demonstrates how to create and maintain a realistic machine learning project using drake-powered automation.
LatinR 2019 2019/09/25 - 2019/09/27 Santiago, CL 🇨🇱   Maëlle Salmon Maëlle Salmon will give a remote presentation (in Spanish!) on our open software peer review system.
rOpenSci Ozunconf19 2019/12/11 - 2019/12/13 Sydney, AU 🇦🇺   Steph Stammel ,   Karthik Ram 4th annual rOpenSci Ozunconf


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