COVID-19 R and SQL Analysis Project

🔍 Overview

This project presents a comprehensive exploratory and statistical analysis of global COVID-19 data using R. The goal is to showcase advanced data wrangling, visualization, and statistical testing skills that are directly relevant to data analyst roles.

Data is sourced from Our World in Data (OWID), covering over 200 countries with detailed metrics on COVID-19 cases, deaths, testing, vaccinations, policy response, and population demographics.

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🎯 Objectives

• Ingest and clean real-world COVID-19 data
• Conduct time-series and comparative visual analysis
• Apply statistical tests to identify meaningful patterns
• Present insights clearly for decision-making or public health communication

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🧰 Tools Used

• R / RStudio for analysis and visualization
• tidyverse for data wrangling
• ggplot2 and plotly for visualizations
• zoo for moving averages
• Kendall for nonparametric trend testing
• GitHub for version control and portfolio presentation

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📂 Project Structure

covid19-r-analysis/
├── script.r              # Full R analysis script
├── queries.sql           # Supporting SQL queries for MySQL ingestion
├── figures/              # Output plots used in README
├── README.md             # This file

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📊 Key Analyses

✅ Time Series Trends

• Daily new cases and deaths in the US
• 7-day moving average smoothing
• Multi-country trend comparisons

📈 Policy Response vs. Case Rates

• Policy stringency index compared to new case trajectories
• Vaccination rates by country and their effect

📉 Statistical Testing

• T-test comparing US and Germany case rates
• Mann-Kendall test for monotonic trend detection in US cases
• Pearson correlation between policy stringency and case rate in the US

📊 Summary Metrics

• Top 10 countries by total cases
• Case fatality rates plotted in log–log scale
• Fully vaccinated population by country

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📷 Example Visualizations

US 7-Day Rolling Average of Cases

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🚀 How to Run

1. Clone the repository:

git clone https://github.com/tylermaire/covid19-r-analysis.git

2. Open script.r in RStudio
3. Install packages:

install.packages(c("tidyverse", "lubridate", "plotly", "zoo", "Kendall"))

4. Make sure owid-covid-data.csv is in the project root
5. Run the script to generate all visuals and results

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📌 Why This Project Matters

This project shows proficiency in:

• Cleaning and joining real-world datasets
• Extracting insights through visual analysis
• Using statistical methods for validation
• Presenting findings in a reproducible, portfolio-ready format

Whether applying for a role in healthcare analytics, public policy, or data-driven business roles, this project reflects the analytical mindset and technical ability of a professional data analyst.

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📎 Data Source

• Our World in Data COVID-19 Dataset
• License: CC BY 4.0

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👨‍🔬 Author

Tyler Maire
Bioinformatics & Data Analytics
University of Florida Entomology Department
GitHub: @tylermaire

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📈 Future Improvements

• Add Shiny dashboard for interactivity
• Expand to US state-level analysis
• Apply predictive modeling or clustering for trend detection

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