Live app: https://arbitrary.shinyapps.io/final-project-repositories-sophiebalint/_w_6adb59bd/
Timeline: Apr 2023 – Jun 2023
Affiliation: University of Washington Information School
Stack: R (analysis & visualization), Shiny (interactive app)
This repository documents the project and links to the live app. The source code is not public; this case study summarizes scope, methods, and results.
This project analyzes how economic factors relate to COVID-19 vaccination rates across U.S. regions. We combined publicly available CDC vaccination data with regional economic indicators to explore correlations and trends, then published an interactive Shiny app so users can slice the data dynamically.
- Bring together vaccination and economic data into a single, clean dataset for analysis.
- Quantify and visualize relationships between vaccination rates and selected economic measures.
- Provide a self-serve interactive interface (Shiny) for regional exploration and comparison.
- Produce a Data Nutrition Label to document data provenance, coverage, limitations, and appropriate use.
- Sources: CDC vaccination rates and publicly available economic indicators at regional levels.
- Integration: Normalized field names and merged disparate inputs into one CSV to maintain integrity and simplify downstream analysis.
- Quality checks: Verified joins, handled missing values, standardized region identifiers, and ensured consistent date coverage.
(Note: Exact raw datasets are not redistributed in this repository.)
- Exploratory analysis and statistical summaries in R to examine correlations between vaccination rates and economic variables.
- Visualizations (time series, regional comparisons) to surface disparities and patterns.
- Interactive Shiny app to filter by region and variables, with dynamic plots and tables.
- Data Nutrition Label created to capture data lineage, assumptions, collection windows, and known caveats.
- Open the live app: https://arbitrary.shinyapps.io/final-project-repositories-sophiebalint/_w_6adb59bd/
- Choose a region and one or more economic indicators to compare against vaccination rates.
- Use the controls to filter time windows or subgroups; charts and tables update interactively.
- Hover over points to see values; export views where available.
- Identified correlations between vaccination rates and selected socioeconomic measures that varied across regions.
- The app supports regional comparisons and makes disparities visible to non-technical audiences.
- The Data Nutrition Label clarifies how to interpret results responsibly and where caution is warranted.
(Quantitative effect sizes are not included here; see the app for visual exploration.)
- Observational analysis: correlations do not imply causation.
- Data coverage may vary by region and date; some indicators have missing or lagged values.
- Granularity is limited to the most consistent regional breakdowns available across sources.
- Consolidated multi-source data into a single CSV with integrity checks.
- Built the interactive Shiny app for dynamic exploration.
- Performed statistical analysis in R and authored a Data Nutrition Label to document transparency and limitations.
- Summarized insights relevant to public health and socioeconomic context.
Intro & Data Sources
Vaccines vs. Income (by manufacturer)
Income-Normalized Vaccine Plot
Correlation Matrix
Fully Normalized Vaccine Plot
Takeaways & About
University of Washington Information School. Data from the CDC and public economic datasets (attributed within the app and Data Nutrition Label).