Chi siamo
R-Rome promotes open-source tools, collaborative learning, and accessible data science education through the R programming language.
Our group meets both in person and online to explore topics in R, data science, statistics, visualization, artificial intelligence, reproducible research, and advanced analytical tools. Whether you are just starting with R or already working professionally with data, our events are designed to support continuous learning, knowledge sharing, and community collaboration.
We believe learning should be open, practical, and lifelong. Our community encourages people from different backgrounds, disciplines, and experience levels to connect, learn together, and contribute to the broader open-source ecosystem.
Email: rome@rladies.org
Website: rladiesrome.org
More info & Contacts:
Twitter: @rladiesrome
LinkedIn: @R-Ladies Rome
GitHub: meetup-presentations_rome
YouTube: @rladiesrome
Mastodon: @rladiesrome@fosstodon.org
Our activities include workshops, talks, tutorials, study groups, and collaborative events focused on developing practical R and data science skills in an open and supportive environment. We strongly support the principles of open-source software, reproducible research, and free access to educational resources.
As a founding principle, participation in our events is free of charge. You can access presentations, R scripts, and projects through our GitHub repositories and follow our channels to stay updated on upcoming events and community initiatives.
Please make sure to read and comply with our Code of Conduct:
https://rladies.org/coc/
Please note that by participating in an R-Ladies event, you grant the organizers permission to use photographs, video recordings, and media content for communication, educational, promotional, and community-related purposes. This may include online publications, social media, newsletters, presentations, and related materials. If you do not wish to appear in photos or recordings, please inform one of the organizers.
— IN ITALIANO —
R-Rome è una comunità che promuove strumenti open source, apprendimento collaborativo e formazione accessibile nel campo della data science attraverso il linguaggio R.
Ci incontriamo sia di persona sia online per esplorare temi legati a R, statistica, data science, visualizzazione dei dati, intelligenza artificiale, ricerca riproducibile e strumenti avanzati di analisi. Che tu sia agli inizi o abbia già esperienza professionale con R e i dati, i nostri eventi sono pensati per favorire apprendimento continuo, condivisione delle conoscenze e collaborazione.
Crediamo in un apprendimento aperto, pratico e permanente. La nostra comunità incoraggia persone provenienti da percorsi, discipline e livelli di esperienza diversi a partecipare, imparare insieme e contribuire all’ecosistema open source.
La comunità organizza workshop, talk, tutorial, gruppi di studio ed eventi collaborativi per sviluppare competenze pratiche in R e nella data science in un ambiente aperto e inclusivo. Supportiamo fortemente i principi del software libero, della ricerca riproducibile e dell’accesso gratuito alle risorse educative.
Come principio fondante, la partecipazione agli eventi è gratuita. È possibile accedere a presentazioni, script R e progetti tramite i nostri repository GitHub e seguire i nostri canali per rimanere aggiornati sulle attività della comunità.
Assicurati di leggere e rispettare il nostro codice di condotta:
https://rladies.org/coc/
Si prega di notare che partecipando a un evento R-Ladies si concede agli organizzatori il diritto di utilizzare fotografie, registrazioni video e contenuti multimediali per finalità educative, comunicative, promozionali e legate alla comunità. Questo può includere pubblicazioni online, social media, newsletter, presentazioni e altri materiali correlati. Se non desideri comparire in foto o registrazioni, informa uno degli organizzatori.
Eventi futuri
1
Introduction to Machine Learning in Epidemiology with R
·OnlineOnlineHow can machine learning help us work with epidemiological data? And how do we know whether a predictive model is actually useful?
Join R-Ladies Rome for a practical, two-hour introduction to machine learning in epidemiology using R.
In this workshop, Federica Gazzelloni will introduce the main ideas behind supervised machine learning through an applied epidemiological example. Rather than focusing on a long list of algorithms, we will follow the complete machine-learning workflow: from defining an epidemiological question to training, evaluating and interpreting predictive models.
We will explore how different models approach the same prediction problem, starting with logistic regression as a baseline and moving to decision trees and random forests.
Using R, we will look at how to define a classification task, train models, generate predictions and evaluate their performance on unseen data.
Particular attention will be given to model evaluation and interpretation.Throughout the workshop, we will also consider an important distinction for epidemiological research:
Prediction is not the same as inference, and predictive importance does not imply causation.The workshop is inspired by the recent Machine Learning in Epidemiology study by Wright et al. (2026) and connects with Federica's book, Health Metrics and the Spread of Infectious Diseases: Machine Learning Applications and Spatial Modelling Analysis with R (CRC Press, 2025), where she introduces machine-learning applications in health and infectious-disease research using the `mlr` framework.
During the workshop, we will use the modern `mlr3` ecosystem and discuss how machine-learning workflows in R have evolved from `mlr` to `mlr3`.What we will cover
- What machine learning means in an epidemiological context
- From an epidemiological question to a prediction task
- Preparing data for machine learning
- Logistic regression as a baseline model
- Decision trees and random forests
- Training and evaluating models with `mlr3`
- Cross-validation and performance on unseen data
- Sensitivity, specificity, confusion matrices and ROC/AUC
- Variable importance and model interpretation
- Prediction versus explanation and causation
- Limitations, bias and data quality in epidemiological machine learning
Who is this workshop for?
The workshop is designed for R users interested in epidemiology, public health, health data or machine learning. Basic familiarity with R and data analysis is useful, but no previous machine-learning experience is required.
The session will combine explanation, live R coding and discussion, with an emphasis on practical and reproducible analysis.About the instructor
Federica Gazzelloni is an actuary, statistician, data scientist, author and instructor, and the founder and organiser of R-Ladies Rome. Her work spans health metrics, statistical modelling, machine learning, reproducible research and R.
She is the author of Health Metrics and the Spread of Infectious Diseases: Machine Learning Applications and Spatial Modelling Analysis with R, published by CRC Press in 2025.R-Ladies
R-Ladies is a worldwide organisation promoting gender diversity in the R community. R-Ladies Rome provides a welcoming space to learn, share knowledge and connect with people interested in R, data science, statistics and reproducible research.
Everyone is welcome to attend, regardless of gender identity or level of experience.20 partecipanti
Eventi passati
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