Skill
R
Statistical programming language used for data analysis, statistical modeling, network analysis, and visualization in research contexts.
I use R primarily in research and data analysis contexts, leveraging its mature statistical ecosystem and visualization tooling.
The main project where R played a central role is the gender bias in movie ratings study, conducted jointly with Barbara and presented at Sapienza in July 2025. The analysis combined:
- Descriptive statistics — average ratings segmented by voter gender, film genre, and voter occupation
- Effect size measurement — Cohen’s d to quantify the standardized difference in rating distributions between male and female voters across film categories defined by star gender
- Geographic segmentation — relative frequency and average rating mapped by US state via choropleth visualization
- Network analysis — fast-greedy community detection on actor and voter network samples, plus centrality metrics (betweenness, degree, closeness, eigenvector centrality) on the actor graph
R’s strength in statistical testing and its ggplot2-based visualization ecosystem made it the natural choice for presenting these results clearly and reproducing standard academic analysis workflows. Python handled the data collection, cleaning, and pipeline orchestration; R handled the statistical layer and final visualization.