🧩 Tutorial Overview

The session walked through the evolution of common data manipulation patterns and how the new functions help simplify logic that previously required more complex workarounds.

We explored:

• how when_any() and when_all() help express conditional logic more clearly
  
• how to rethink workflows that previously relied on case_match()
  
• improvements to filtering logic that make pipelines easier to read
  
• practical examples grounded in realistic data workflows

As often happens in R-Ladies Rome sessions, the emphasis was not on memorizing functions but on building intuition. The goal was to leave participants with a clearer mental model of how to approach data transformations.

🎥 Recording

🎬 Watch the Recording

The recording is suitable both for participants who attended live and for anyone wanting a practical overview of how the newest dplyr features fit into everyday work.

🧠 What You’ll Learn

By watching the recording, you will learn how to:

• understand the design ideas behind the newest dplyr functions
  
• simplify conditional filtering logic
  
• improve readability of transformation pipelines
  
• choose clearer patterns for data manipulation
  
• write tidyverse code that is easier to maintain and share

📦 Resources & Materials

• Intro Presentation
• Slides
• dyplr 1.2.0
• TidyData Tutor

🔊 About the Speaker

Isabella Velásquez works in Developer Relations at Posit PBC, where she helps the data science community adopt modern tools and workflows. Her work connects programming, communication, and data analysis, with a strong focus on making data science tools approachable and practical.

🔊 About the Tutorial

This session is part of the R-Ladies Rome Tutorials series, where we focus on practical tools that support reproducible and sustainable data workflows.

Our goal is always the same: to help people move from knowing functions to understanding workflows. Whether someone is just starting with R or refining their practice, we aim to provide sessions that strengthen both technical skills and confidence.

Keep learning and exploring, subscribe to our YouTube channel, and revisit past events on rladiesrome.org!

📊 R-Ladies Rome in Numbers

Our 2025 snapshot highlights both growth and momentum:

• 2,500+ Meetup members
• 1,200+ total RSVPs in 2025
• Highest RSVP event: 416 attendees
• Average RSVPs per event significantly above historical average
• Recurring monthly activity throughout the year

Compared to our earlier years, engagement has increased steadily, with 2025 showing stronger and more consistent participation across events. What began as a local initiative has evolved into a structured, recurring learning ecosystem.

💡 What We Built Together

Throughout the year, we delivered:

• Hands-on workshops in R fundamentals and data wrangling
  
• Sessions on reproducible workflows with Quarto and GitHub
  
• Applied talks in health analytics and machine learning
  
• Community-driven discussions and collaborative learning spaces

Our focus has always been practical. We do not just present slides — we work with code, data, and real analytical reasoning.

🔐 Our Principles Remain the Same

Growth has not changed our core values.

📈 Sustained Engagement, Not One-Off Events

One of the strongest signals in 2025 was recurring participation.

Our engagement is not driven by single viral events, but by consistent attendance, returning participants, and structured programming. This indicates community maturity — learners are not just attending; they are staying.

🌍 Part of a Global Movement

R-Ladies Rome is part of the global R-Ladies network and supported by the open-source ecosystem, including the R Consortium and The Linux Foundation.

We are local in presence, but global in reach.

🚀 Looking Ahead to 2026

The momentum of 2025 sets the stage for an exciting year ahead.

In 2026, we aim to:

• Expand advanced technical workshops
  
• Strengthen collaborations with universities and industry
  
• Increase digital content and recorded sessions
  
• Continue building structured learning pathways

Our mission remains clear: empower people to move from learning R to building meaningful, reproducible, real-world projects.

🤝 Join Us

Whether you are just starting with R or already working professionally in data science, there is a place for you in this community.

Join us.
Speak with us.
Collaborate with us.

Let’s continue building an inclusive, technically strong, and open data science community in Rome — and beyond.

R-Ladies Rome Organizers
Federica Gazzelloni & Rafaela Ribeiro
www.rladiesrome.org

🧩 Tutorial Overview

The session was delivered by Priyanka Schnell, Chief Technology Officer at Insurfox. With experience in enterprise-scale digital systems and aerospace manufacturing, Priyanka guided us through how sensor data, physics models, and anomaly detection can work together in production.

The context: robotic drilling in A321 wing assembly. A single snapped drill bit can cause burrs or delamination, potentially scrapping a wing skin valued at €150,000. Traditional maintenance policies replaced drill bits after a fixed number of holes — a strategy that often led to premature replacements or unexpected failures.

Instead of relying on static thresholds, the session introduced a digital twin approach.

We explored:

• how high-frequency sensor data (torque, vibration, speed, feed rate) can be transformed into meaningful features
  
• why Specific Cutting Energy helps detect “wasted energy” before functional failure
  
• how regime normalization allows comparison across aluminum and titanium cutting
  
• how Isolation Forest can identify deviations without labeled failure data
  
• why persistence rules (e.g., “6 of 12”) reduce alert noise
  
• how early detection within the P–F interval provides actionable lead time

The session included a live walkthrough in Google Colab using simulated data to demonstrate the workflow step-by-step.

🎥 Recording

🎬 Watch the Recording

The recording is suitable both for participants who attended live and for anyone interested in understanding how anomaly detection and digital twins operate in high-stakes environments.

🧠 What You’ll Learn

By watching the recording, you will learn how to:

• understand what a digital twin means in practical data terms
  
• engineer physics-informed features from raw sensor signals
  
• apply unsupervised anomaly detection in production settings
  
• use persistence logic to stabilize alert systems
  
• think beyond time-based rules toward dynamic maintenance strategies
  
• connect domain knowledge with statistical modeling

📦 Resources & Materials

• Intro Presentation
• Tutorial Slides and Colab Code

🔊 About the Tutorial

This session is part of the R-Ladies Rome Tutorials series, which focuses on practical workflows and real-world data applications.

Our goal is to show how data science connects to operational systems — where decisions have financial and technical consequences. We aim to make advanced topics accessible without removing the rigor behind them.

Keep learning and exploring, subscribe to our YouTube channel, and revisit past events on rladiesrome.org!

🧩 Tutorial Overview

The tutorial was presented by Federica Gazzelloni, Lead Organizer of R-Ladies Rome. We started from the environment most learners find confusing: the command line.

In the first section, we clarified the difference between terminal and shell, and why this matters when learning Git. Many Git “mysteries” disappear once you understand that the terminal is just the window, while the shell is the interpreter running inside it. We also covered what to expect across platforms (macOS zsh, bash commonly used in RStudio, and typical Windows setups via WSL).

From there, we moved to Git fundamentals and framed Git as a local tool that works without accounts or internet access. The tutorial explained what a repository really is (a folder containing a hidden .git directory) and how Git records change through the staged workflow: working directory → staging area → commit history.

The practical part of the session walked through the essential local commands, git init, git status, git add, git commit, git log, git diff.

Finally, we positioned GitHub as the collaboration layer: remotes, push/pull, and how GitHub enables teamwork when you already understand local Git.

🎥 Recording

🎬 Watch the Recording

The recording is suitable both for participants who attended live and for anyone who wants to follow the workflow at their own pace.

🧠 What You’ll Learn

By watching the recording and working through the guide, you will learn how to:

• distinguish clearly between terminal and shell, and work comfortably from the command line
• understand Git as a local history of your project, not “a cloud thing”
• move confidently through the core Git states: modified → staged → committed
• use essential commands for day-to-day work (status, add, commit, log, diff)
• understand how GitHub fits into the workflow once local Git is solid
• explore branching and merging as a way to manage parallel work

Next steps:

• undo mistakes safely using the right tool for the situation (restore, reset, revert) - check the material below 👇

📦 Resources & Materials

• Workshop guide (PDF)
• Slides
• RLadiesRome Chapter Intro

🔊 About the Tutorial

This tutorial is part of the R-Ladies Rome Tutorials series, created to support practical, reproducible workflows for data work. The goal is to make core tooling approachable without oversimplifying it, so participants leave with a mental model they can actually use in real projects.

Keep learning and exploring,subscribe to our YouTube channel and revisit past events on rladiesrome.org!

🩺 Workshop Overview

The workshop was led by Rafaela Ribeiro Lucas, Federica Gazzelloni, and Lucy Michaels, organisers of R-Ladies Rome, who jointly guided participants through a complete exploratory analysis workflow using a simulated diabetes dataset.

In the first part of the workshop, Rafaela focused on descriptive statistics, using the simulated diabetes dataset to demonstrate how numerical summaries provide essential grounding for any analysis. Alongside measures of central tendency, variability, and distribution, she introduced prevalence calculations to show how key health indicators are derived and interpreted in practice. Contingency tables were used to explore relationships between categorical variables, allowing participants to examine how diabetes status varies across demographic groups, while chi-squared tests were discussed as a way to formally assess whether observed differences are likely to reflect underlying associations rather than random variation.

Building directly on these exploratory insights, Federica then showed how data visualisation can be used to deepen understanding and communicate findings clearly. Using ggplot2, she demonstrated how to make histogram, density and boxplots, and how to do little improvements with tools such as scales_ and labs functions. She then added insights about layout—affect interpretation, and how plots can support analytical reasoning rather than simply summarising results. This part of the session reinforced the idea that visualisation is an integral component of analysis, closely tied to both exploration and explanation.

The final part of the workshop was led by Lucy, who introduced clustering as a way to explore heterogeneity within the population. Focusing on the intuition behind k-prototypes clustering, Lucy explained how this approach allows analysts to work simultaneously with numerical and categorical variables, a common challenge in health and demographic data. Rather than presenting clustering as a definitive classification tool, the discussion emphasised interpretation, limitations, and the role of clustering as a complementary method that can reveal structure and risk profiles not immediately visible through standard summaries or models.

Together, these three perspectives laid the groundwork for subsequent modelling stages. The session demonstrated how exploratory analysis, visualisation, and clustering naturally lead to modelling questions, such as identifying factors associated with diabetes status or understanding variation across population subgroups.

🎥 Recording

🎬 Watch the Recording

The recording is suitable both for participants who attended the live session and for those who wish to follow the full workflow at their own pace.

🧠 What You’ll Learn

Participants will gain a clear understanding of how to approach exploratory data analysis in R in a structured and defensible way, particularly when working with health-related data. The session demonstrates how to move from raw data to informed analytical questions, how to justify cleaning and preparation decisions, and how to use visualisation as an integral part of the reasoning process rather than as a final reporting step.

📦 Resources & Materials

• Workshop materials and code: https://rladiesrome.github.io/Principles-of-data-Analysis-in-R/02-exploring-diabetes-data.html

• R-Ladies Rome events and resources: https://www.meetup.com/rladies-rome

🔊 About the Workshop

This workshop is part of the Principles of Data Analysis in R series developed by R-Ladies Rome. The series aims to provide practical, reproducible, and conceptually grounded guidance for applied data analysis, with a strong emphasis on transparency, interpretation, and real-world relevance.

No prior experience with medical datasets is required, and all code is explained step by step, making the material accessible to a broad audience of R users.

Keep learning and exploring—subscribe to our YouTube channel and revisit past events on rladiesrome.org!

🐈 Workshop Overview

Nicola walked participants through the entire process of building a data visualisation in R with ggplot2, starting from a brand new dataset — the popular TidyTuesday cats dataset. Throughout the session, she demonstrated how to explore data, sketch visual ideas, and refine designs to communicate insights clearly and beautifully.

Participants learned how to customise plots with themes, colours, and fonts, transforming simple graphics into polished, publication-ready visualisations.

🎥 Recording

🎬 Watch the Recording

Whether you’re new to data visualisation or looking to refine your creative process, this talk offers practical steps and artistic inspiration to enhance your ggplot2 workflow.

🧠 What You’ll Learn

• How to move from data exploration to final design using ggplot2
• Techniques for customising fonts, colours, and layouts
• Ways to create aesthetic yet effective visualisations that communicate clearly

📦 Resources & Materials

• Nicola’s code examples and resources: https://nrennie.rbind.io/talks/rladies-rome-ggplot2/slides.html
• R-Ladies Rome event details: https://www.meetup.com/rladies-rome
• Connect with Nicola on LinkedIn: https://www.linkedin.com/in/nicola-rennie/

🔊 About the Speaker

Nicola Rennie is a data visualisation specialist with a background in statistics and data science. She focuses on how to communicate complex quantitative ideas effectively using open-source tools, including R, D3, and Svelte. Nicola is the author of The Art of Visualization with ggplot2, co-author of the Royal Statistical Society’s Best Practices for Data Visualisation, and serves on the Editorial Board of Significance magazine.

Keep learning and exploring—subscribe to our YouTube channel and revisit past events on rladiesrome.org!

📊 Talk Overview

In this engaging presentation, Kelly guided us through how machine learning models are transforming decision-making in the Buy Now, Pay Later (BNPL) landscape. She demonstrated live Python code examples, showing how predictive models can provide fast, scalable, and reliable assessments of user creditworthiness — even when traditional credit data is limited.

Beyond the technical side, the talk also explored the ethical and inclusive dimensions of algorithmic credit scoring, emphasizing transparency, fairness, and access in financial decision-making.

🎥 Recording

🎬 Watch the Video Now

This talk is packed with insights for data scientists, fintech professionals, and anyone interested in applied ML for responsible innovation.

🧠 What You’ll Learn

• How to build and evaluate ML models for BNPL credit scoring
• Ways to handle high-volatility and short-term behavioral data
• The role of ethics, inclusion, and fairness in financial ML models
• Practical Python code demonstrations for end-to-end modeling

📦 Resources & Materials

• Talk material:

  • Data: https://tinyurl.com/27awzehk
  • Code: https://github.com/Kelly002/model_credit_score/blob/main/Modelo_Score_Credito.ipynb

• R-Ladies Rome Intro Presentation: https://tinyurl.com/bdht95ss
  

• Stay updated via Meetup: https://www.meetup.com/rladies-rome

🔊 About the Speaker

Kelly Marques is a data scientist from São Paulo (Brazil) specializing in machine learning for finance. With a strong background in predictive analytics and model ethics, she focuses on developing scalable, transparent, and human-centered AI systems for real-world applications.

Keep learning and exploring — subscribe to our YouTube channel and revisit past events on rladiesrome.org!

📊 Talk Overview

We love exploring new ways of working with data, and sometimes that means stepping outside of R to see how other tools can connect and enrich our workflows.

While our heart and mission remain with R, as data scientists and statisticians we often work in environments where multiple tools coexist. Understanding how to bridge these ecosystems — R, Python, Stata — helps us collaborate better, think more critically about methodology, and expand our problem-solving toolkit.

This event is not about selling or promoting software. It’s about learning, sharing knowledge, and keeping our community up-to-date with evolving data science practices.

🎥 Recording

🎬 Watch the Video Now

Whether you’re a researcher, designer, or data enthusiast, this recording is packed with practical tools you can adapt immediately.

🧠 What You’ll Learn

In this talk, Dr. Chuck Huber demonstrates how to use Python within Stata, showcasing the seamless integration between these two powerful tools. You’ll learn how to:

• Set up and configure Python to work with Stata
• Connect Python with Stata commands and .ado files
• Explore real-world examples of using Python in Stata for data analysis and visualization

By the end of this session, you’ll have a solid understanding of how to harness the strengths of both Stata and Python to enhance your data analysis workflows.

📦 Resources & Materials

• Talk slides: https://tinyurl.com/statapython

• R-Ladies Rome Intro Presentation: https://tinyurl.com/yk79a9yw

• Stay updated via Meetup: https://www.meetup.com/rladies-rome

🔊 About the Speaker

Chuck Huber is Director of Statistical Outreach at StataCorp and Adjunct Associate Professor of Biostatistics at the Texas A&M School of Public Health and at the New York University School of Global Public Health.

He produces instructional videos for the Stata Youtube channel, writes blog entries, develops online NetCourses and gives talks about Stata at conferences and universities. He has published in the areas of neurology, human and animal genetics, alcohol and drug abuse prevention, nutrition and birth defects.

Dr. Huber currently teaches survey sampling at NYU and introductory biostatistics at Texas A&M where he previously taught categorical data analysis, survey data analysis, and statistical genetics.

Keep learning and exploring—subscribe to our YouTube channel and revisit past events on rladiesrome.org!

📊 Workshop Overview

In this easy-going workshop, participants explored how to build interactive web-based dashboards that enable teams—whether in academic or business settings—to present information via multiple graphs and tables. The session covered techniques to filter, sort, visualize data, uncover trends, and present results with both function and aesthetic appeal using R, Quarto, Shiny, Markdown, and geospatial data. Some prior programming experience was helpful, though not required.

🎥 Recording

🎬 Watch the Video Now

Whether you’re a researcher, designer, or data enthusiast, this recording is packed with practical tools you can adapt immediately.

🧠 What You’ll Learn

• How to assemble interactive dashboards using Quarto and Shiny
  
• Methods for effective data presentation: filtering, sorting, and trend exploration
  
• Techniques for adding geospatial data and aesthetic polish to your dashboards

📦 Resources & Materials

• Workshop slides and code (GitHub):

  • https://dsollbergerquarto.netlify.app/posts/dslc_quarto_dashboards/quarto_dashboards
  • https://github.com/dsollberger/q_dashboards

• R-Ladies Rome Intro Presentation: https://rladiesrome.quarto.pub/august242025

• Stay updated via Meetup: https://www.meetup.com/rladies-rome

🔊 About the Speaker

Derek Sollberger is a lecturer at CSML (Center for Statistics and Machine Learning), specializing in Data Science, AI Methods, and Bayesian Analysis. A Posit Certified Tidyverse Instructor, Derek brings experience from leading R workshops and consulting on pedagogy-driven data projects.

Keep learning and exploring—subscribe to our YouTube channel and revisit past events on rladiesrome.org!

💬 Talk Overview

This session explores how health data can be made truly useful to those navigating diagnoses, treatment options, and care plans. Rita shares real-world examples that highlight the disconnect between how data is typically presented (for researchers and clinicians) and how it could better serve patients themselves.

Through a lens of empathy and inclusion, she invites us to reflect on how scientific and clinical communication can be reimagined to support patient understanding, trust, and agency.

🎥 Recording

🎬 Watch the Video Now

The event was conducted in Italian, and the video is in Italian with English subtitles. Whether you’re a data scientist, healthcare professional, or simply interested in how data can empower patients, this talk is a must-watch.

🧭 Key Topics

• How to design data visualizations that center patient needs
• The role of empathy and accessibility in scientific communication
• Why inclusive design is critical for health equity
• Examples of both effective and ineffective communication tools

📚 Resources & Materials

🔗 Slides: https://github.com/ritagior/R-LadiesRome

🌐 Rita’s work:

• https://ritagiordano.co.uk/
• https://github.com/ritagior

📝 R-Ladies Rome Intro Slides: https://rladiesrome.quarto.pub/july222025

📅 Stay updated: https://www.meetup.com/rladies-rome

👩‍🏫 About the Speaker

Rita Giordano is a data visualization designer with a strong focus on accessibility and clarity in public health and medical communication. She collaborates with organizations across Europe to help turn complex data into useful, understandable information—especially for those most affected.

💬 Let’s keep working to make data accessible to everyone. Subscribe to our YouTube channel and explore more events at rladiesrome.org!

#DataViz #HealthCommunication #InclusiveDesign #RLadiesRome #RStats #EmpathyInDesign

📊 Workshop Overview

This talk explores the fundamentals of time series forecasting using linear regression models. We’ll cover how to transform time series data into a supervised learning format and utilize feature engineering techniques to model various components of the series, including trends, seasons, outliers, and structural breaks. This is a beginner-level workshop, and prior knowledge of time series is not needed beyond understanding how linear regression works.

🎥 Recording

🎬 Watch the Video Now

Whether you’re brushing up on regression or just getting started with time series, this session is packed with practical insight and real-world examples.

🧠 What You’ll Learn

• How to frame time series as a supervised learning problem
• Why feature engineering matters: trends, seasonality, outliers, and breaks
• How to apply linear models to time-dependent data
• Tips for model validation and performance assessment

📦 Resources & Materials

🔗 Slides & resources: https://github.com/RamiKrispin/r-ladies-rome-workshop

🌐 Learn more:

• Rami’s Book: https://ramikrispin.github.io/atsaf/

• Rami’s Book Hands-On Time Series Analysis with R: Perform time series analysis and forecasting using R:

  • https://github.com/RamiKrispin
  • https://www.amazon.com/dp/1788629159

📝 R-Ladies Rome Intro Presentation: https://rladiesrome.quarto.pub/july032025

📅 Stay updated: https://www.meetup.com/rladies-rome

🔊 About the Speaker

Rami Krispin leads the data science and engineering team at Apple Finance - Services and Infra. His team builds advanced statistical and ML models to support Apple’s strategic decisions. Rami is also an open-source contributor and author, with a special focus on scalable forecasting tools. He’s currently working on his next book: Applied Time Series Analysis and Forecasting.

💬 Keep learning and sharing—subscribe to our YouTube channel and check out more past events on rladiesrome.org!

#TimeSeries #Forecasting #RStats #RLadiesRome #LearningTogether #RamiKrispin

🎙️ Opening Talk

⚡ Lightning Talks – Short, Sharp & Full of Insight

The energy kept flowing with a series of lightning talks, each just 5 minutes long, where community members shared projects, tools, and reflections:

• Marco Guerra presented “Automatic Detection of Large-Scale Travelling Ionospheric Disturbances using GNSS Data and Image Processing Techniques”
• Massimo Viola shared insights on “A custom TreeQL Python-based Web Service for Database Access”
• Federico Palma illustrated “Normalized Semantic Chunker”
• Egon Ferri closed the session with a thought-provoking reflection on learning curves and language choice titled: “Flying SOLID-ly: Python design principles explained with birds”

🫱🏽🫲🏿 Networking, Connections & Pizza Plans

After the talks, participants stayed to mingle, exchange ideas, and build connections across communities. Many of us continued the evening outside the venue for a multi-community dinner—a true Roman finale to a collaborative and inspiring meetup.

📺 Missed the Event? Watch the Recording!

💡 You can still watch the full event recording here: 📺 Watch on YouTube

📚 Resources & Follow-Up

• Slides: Speaking Many Languages: Finding Power and Joy in R and Python
• Normalized-Semantic-Chunker

“Grazie to all who attended, presented, volunteered, and made this evening so special. Whether you were with us at INGV or joined online from across the globe, we’re grateful to be building this community with you. Stay tuned for more events and opportunities to learn, share, and grow together.”

📬 Want to get involved or speak at the next event? Reach out through PyData Roma Capitale, R-Ladies Rome, or the Rome R Users Group.

💡 Key Takeaways

In this session, we explored:

• 🧩 Why small, well-labeled commits improve traceability, reduce merge conflicts, and support collaboration
• 🧠 How to structure your work to commit in logical, meaningful units
• 🔧 How to fix and refine commit history with tools like git commit --amend and git rebase
• 🧪 How to experiment safely using saperlipopette, a package for practicing Git workflows in R projects
• 🎤 Bonus Q&A with community tips, questions, and shared real-world use cases

🎥 Watch the Recording

Missed the event? No problem—you can catch the full session on YouTube:

Don’t forget to like and subscribe to our R-Ladies Rome YouTube channel!

📦 Materials & Resources

• 🖥️ Slides from the talk
• ✍️ GitHub: Write Better Commits, Build Better Projects
• 🧰 Positron Debugging – Lisa M. Mowinckel

For continued learning: - 📘 Pro Git Book (free) - 🧪 Git for Data Science - 🧭 GitHub Learning Lab - 🧾 GitHub Cheat Sheet (PDF)

🧵 Why This Talk Mattered

At R-Ladies Rome, we’re committed to making tools like Git approachable and useful—especially for data scientists, researchers, and R users in biostatistics, public health, and beyond.

In this session, Maëlle showed us how even small improvements in commit practices can make a big impact: not just in terms of technical tidiness, but in our ability to tell the story of our work. Her message was simple but powerful:

Git is more than a tool—it’s a practice.

She helped us see version control not as a chore, but as an ally in building reproducible, transparent, and collaborative projects.

🤝 Get Involved!

• Want to speak at a future event? Drop your email in the comments!
• Have someone in mind? Suggest a speaker you’d love to see.
• Stay connected on Meetup, YouTube, and GitHub

Our events are free, open-source, and beginner-friendly. Every month, we share kick-start resources so you can keep learning after the meetup.

Let’s keep learning, coding, and sharing—one small commit at a time. 💜

💡 What We Covered

During the session, we explored:

• The similarities and differences between R and Python
• How to perform common data analysis tasks in Python
  
• An introduction to popular Python libraries like pandas, scikit-learn, and seaborn

We also demoed basic workflows, provided a starter script, and answered audience questions live.

📊 What You Said

We asked participants three short questions using Slido. Here’s what they shared:

📦 Workshop Materials

All workshop materials are freely available:

• 🎤 Presentation Slides
  
• 📖 Workshop Booklet
  
• 💻 GitHub Repository

You’ll find setup instructions, sample datasets, and the Python scripts we used during the event.

🎥 Recording

You can rewatch the full event on our YouTube channel:

🎬 Watch the Video Now Simply click on the video below to catch up on the discussion:

Whether you missed the event or just want a refresher, it’s all there for you.

📚 Learn More: Python Resources for Beginners

If you’re ready to keep going, here are some recommended resources:

• Python for Data Analysis (Wes McKinney) – The classic book for learning pandas and data wrangling in Python.
• Real Python – A comprehensive site full of beginner tutorials and hands-on examples.
• Python Basics – Simple and direct explanations of core Python concepts.
• DataCamp’s Python tracks – Interactive exercises for data science beginners.
• The Carpentries Python Lessons – Friendly workshops teaching foundational skills.
• Kaggle Python Micro-Course – Great for learning through applied exercises.

If you prefer video: - Corey Schafer YouTube Channel – Detailed and beginner-friendly. - freeCodeCamp Python course – Full-length intro course, highly rated.

📌 Tip: Pick one small project and try to replicate it in Python from R. This hands-on approach accelerates learning.

💜 Thank You

Thank you to everyone who attended, participated, and asked questions. Your curiosity and enthusiasm made this event a success!

Stay tuned for upcoming R-Ladies Rome events — and until then, happy coding 🐍💜

Catch the Recording

We’re excited to share that the recording of Federica Gazzelloni’s insightful talk is now available on our YouTube channel!

Catch the Recording

We’re excited to share that the recording of Isabella Velásquez’s insightful talk, “Interactive R, Python, and Shiny in the Browser with Quarto and Shinylive”, is now available on our YouTube channel!

Catch the Recording: Deathly Dataviz

We’re excited to share that the recording of Eli Holder’s insightful talk, “Deathly Dataviz: How Public Health Dashboards Can Backfire, and What We Can Do Instead”, is now available on our YouTube channel!

A Year of Highlights in Numbers

• 12 incredible events hosted.
  
• A growing community with 1.2K+ members on Meetup.
  
• Over 2K RSVPs from members eager to learn, connect, and grow.
  
• Dozens of expert speakers and workshop leaders from across the globe.

This year, we witnessed extraordinary moments that reminded us of the power of community.

Inspiring Selection of Talks and Hands-On Learning

Throughout the year, we explored a variety of fascinating topics:

• Reproducible Environments with Docker: Bruno Rodrigues guided attendees in creating reproducible workflows using Docker.
• Building a Website in R: Federica Gazzelloni led a workshop on using R Markdown and Shiny to build interactive websites.
• Quarto Dashboards: Isabella Velasquez demonstrated creating dynamic dashboards with Quarto, showcasing practical applications.
• Extending the Data Science Workflow: {vetiver} and {pins} - Isabel Zimmerman introduced the applications of the {vetiver} and {pins} packages for enhanced data workflows.
• Debugging in R: Shannon Pileggi shared valuable tips and techniques for troubleshooting and resolving common programming errors.

Each event was an opportunity to learn new skills, gain fresh perspectives, and connect with a global network of R enthusiasts.

Building a Thriving Community

R-Ladies Rome is more than just a series of meetups—it’s a growing, inclusive community where members share ideas, ask questions, and support one another. The energy, engagement, and curiosity of our members have made every event special.

Gratitude for Our Speakers and Participants

We want to thank our speakers, who generously shared their knowledge and insights, and our members, whose enthusiasm and participation made this year unforgettable.

You are the heart of R-Ladies Rome, and your dedication to learning and collaboration continues to inspire us. THANK YOU 🤗

Looking Ahead to 2025

We’re kicking off 2025 with an exciting event featuring Eli Holder on Thursday, January 9th:

More interesting events to come:

• Introduction to Bayesian Modeling: A session that introduced foundational concepts in Bayesian statistics and demonstrated how to apply them using R.
• Data Visualization Best Practices: A workshop on crafting effective visualizations with {ggplot2} and communicating data insights.
• Shiny Apps for Interactive Data Exploration: Learn how to build interactive web apps for data analysis with Shiny.
• Machine Learning with {tidymodels}: Hands-on exploration of machine learning workflows using the tidymodels framework.
• Introduction to Time Series Analysis: Techniques and R tools for analyzing and forecasting time series data.

Stay tuned for more details and join us for what promises to be an insightful session!

Thank You and Happy Holidays

As we wrap up 2024, we want to express our deepest gratitude to everyone who has been part of R-Ladies Rome’s journey. Thank you for your support, engagement, and contributions.

Let’s continue learning, growing, and building together in 2025. Happy holidays from all of us at R-Ladies Rome! 🎉

Stay connected with us on our website and social media for updates on upcoming events. We can’t wait to see you in the new year!