Keynotes

Abstract

The talk examines the use of R for empirical research, university teaching, and community-oriented training related to Ukraine during the Russian full-scale invasion. It focuses on how R is applied in applied research projects, in classroom instruction, and in initiatives such as the Workshops for Ukraine series when work is affected by repeated disruption. Examples will be used to illustrate how R supports empirical analysis, the preparation of teaching materials and assignments, and the coordination of collaborative and community activities under unstable conditions. The talk will also draw on feedback from R learners and users in Ukraine to distinguish between challenges that are specific to wartime circumstances and those that are likely to be common across learning environments.

Abstract

R grew out of the PC, Internet, and Open Source revolution in the 1990s. I will give an account of the early history of R, and then outline the development principles of R Core, with special focus on release management. However, the R Core Team is not alone in the governance of the R project, other major actors being the CRAN team, the R Foundation and the R Consortium. I discuss their contributions with a view to sustainability and possible improvements.

Abstract

When crossing a busy street, we understand that keeping our eyes open isn't optional—it's how we stay safe. Yet when building complex models, we often choose to work blind. Some of this is understandable — visualising high-dimensional data is genuinely difficult. But cultural attitudes matter too: there's a lingering belief that "looking at the data" compromises objectivity, and a tendency to view diagnostics as about as appealing as cleaning house. Yet the landscape is shifting. Visualising high dimensions is getting easier, and explainability is now considered as important as predictive accuracy.

This talk demonstrates how to open our eyes when building models. I'll begin with the Rashomon quartet, showing how visualising the simulated training data reveals why four equally high-performing models yield strikingly different interpretations - an example of visualising fitted models relative to observed data.. I'll then tackle a critical challenge: local model explanations often conflict, and interactive graphics in high dimensions can help determine which explanations are trustworthy. Finally, I'll outline practical ways to integrate these visual methods into the tidymodels workflow, making visualisation a natural part of model development.

Abstract

Spatial data are central to understanding environmental change, social processes, and their interactions. Maps are not only visual products of analysis, but tools that shape how problems are framed, how phenomena are perceived, and how decisions are made. R has become a widely used environment for spatial data science because it combines interactive analysis, open development, statistical rigor, and strong visualization within reproducible workflows. This keynote will review the current state of spatial work in R, highlighting mature and interoperable tools, intuitive workflows, and available documentation and learning resources.

Abstract

Every R script, package, and analysis rests on a foundation of community labor that often goes unseen. While R is widely celebrated for its technical power, its longevity depends just as much on the people who teach, maintain, translate, mentor, and organize around it. In this keynote, I will draw on my leadership at The Carpentries to share stories and insights about building and sustaining global open-source communities. I will discuss how contribution shows up in many forms, how community care and governance shape technicalecosystems, and why investing in people is essential to the future of R.

This talk is an invitation to pause, reflect, and reimagine our relationship to the tools we use. Whether you are an R package maintainer, educator, researcher, or new user, you will leave with a deeper understanding of how your participation matters and how giving back can strengthen both the R community and your own professional journey.