About Us

We teach R as a tool for data analysis, not as an abstract programming language. Our sessions focus on clear workflows, reproducible analysis, and interpretation of results using real-world examples.

The project is curated by Rafaela Ribeiro Lucas, Federica Gazzelloni, and Lucy Michaels. Together, we combine experience in statistics, data science, and applied research to deliver practical, accessible, and methodologically sound R training.

Bios of the Instructors

Rafaela Ribeiro Lucas Lucas is a Nutritionist with academic training from the Federal University of Pelotas (UFPel) and a postgraduate degree in Eating Behavior from Institute of Research, Teaching, and Health Management (iPGS). And a Master’s degree in Cardiology and Cardiovascular Sciences at the Federal University of Rio Grande do Sul (UFRGS) Her work is based on methodological and statistical applications in the investigation of cardiovascular risk factors and disease. She is passionate about Population Epidemiology, Epidemiological Surveillance, and analytical methods for longitudinal studies and complex survey designs.

Federica Gazzelloni is an independent researcher, statistician, and instructor specialising in data visualisation, statistical modelling, and data-driven storytelling. She leads R-Ladies Rome and co-organises the Rome R Users Group, promoting inclusive, hands-on learning for the local R and data-science community. Federica collaborates with international organisations, contributes to open-source projects, and develops educational resources spanning R, spatial analysis, and health metrics. She is passionate about building welcoming spaces where people can learn, experiment, and meet others who share a curiosity for data.

Lucy Michaels is a Data Scientist with academic training from Unitelma Sapienza, where she completed a Master’s degree in Analysis and Modeling of Data and Processes. She specialises in applied statistics, machine learning, and analytical modelling for complex real-world problems. Her recent work focuses on methodological and statistical approaches for understanding pedestrian-accident risk in urban environments, integrating multi-source datasets on visibility, weather, twilight phases, road conditions, and vehicle involvement. She is passionate about urban mobility analytics, public-health informatics, and data-driven decision-making for safer cities. Her interests include geospatial analysis, mixed-data clustering and designing reproducible analytical pipelines.