Exploratory Data Analysis & Visualization
Overview
Comprehensive coursework demonstrating advanced exploratory data analysis and visualization techniques using R. Projects span housing market analysis, quality-of-life metrics, and geospatial data exploration, with emphasis on statistical rigor and effective visual communication.
Visualization Techniques
- Distribution Analysis: Histograms, density plots, bar charts
- Relationship Exploration: Scatterplots, faceted regressions, hexagonal heatmaps
- Multivariate Analysis: PCA biplot, alluvial diagrams
- Time Series: Alluvial diagrams for temporal flow analysis
- And many more useful plots to analyze data, test assumptions and understand relationships!
Sample Visualizations
🔗 View HTML Report 1 🔗 View HTML Report 2 🔗 View HTML Report 3 🔗 View HTML Report 4
Technical Stack
R • ggplot2 • tidyverse • dplyr • ggalluvial • ggdensity • tidycensus • tmap
What I Learned
Effective data visualization is fundamental to extracting insights from complex datasets. This coursework reinforced that the right visualization doesn’t just display data, but reveals patterns, tests hypotheses, and communicates findings that raw numbers alone cannot convey. I developed both the technical skills to create sophisticated visualizations in R and the analytical judgment to choose the most appropriate visual approach for different data types and analytical questions.