An ioslides statistics story that asks whether more weekly practice goes with a higher free-throw percentage. Using a simulated dataset of 40 players, it covers Pearson correlation, hypothesis testing, confidence intervals, and simple linear regression, with the math written in LaTeX. It includes three ggplot2 visualizations and an interactive 3D Plotly chart, and ends with what the results do and don’t tell us. Built in R Markdown with ggplot2, plotly, and dplyr.

NO, superstars, no problem. PSG have shown that football isnt about names. Its about the system.

Voici nos sources pour notre exposé de macroéconomie, prévu en octobre 2026. Membres du groupe : Liam Sergent, Zachary Wenner, Baptiste Pedrono.

CodeBase

Rmarkdown & Leaflet

This project explores the relationship between basketball practice time and player performance using data analysis and visualization in R. Using a simulated dataset of 40 basketball players, it examines correlations between practice hours and performance metrics through Pearson correlation, linear regression, and data visualizations. The project includes static charts created with ggplot2 and an interactive 3D visualization created with Plotly to help identify patterns and relationships in the data.