Public Health Data Analysis on Foodborne Disease Risk Factors Using R

This project analyzes self-reported data on foodborne illness among individuals, with a focus on identifying demographic and environmental risk factors. The analysis was conducted using R programming with an emphasis on data cleaning, recoding categorical variables, statistical summaries, and structured reporting aligned with public health research standards. Dataset Source: A structured dataset titled ENVIRONMENTALANDSOCI_DATA_2025-06-05_1030.csv, containing responses from a health behavior survey. Variables included gender, religion, education level, water sources, sanitation, and experience with foodborne illness. Tools & Libraries Used: tidyverse for data manipulation and visualization gtsummary for statistical summaries and formatted tables here for consistent file referencing apaTable for APA-style reporting Analysis Objectives: Clean and recode demographic and environmental data for interpretability Describe the population distribution by key factors such as gender, education, religion, and marital status Evaluate associations between water/sanitation factors and reported foodborne illness Present the findings in well-structured Word-ready tables using gtsummary and APA formats Key Steps & Components: Data Cleaning & Recoding Used mutate() and dplyr::recode() to transform coded variables (e.g., gender: 1 → Male, 2 → Female) Handled categorical variables like religion, marital status, education level for clarity Descriptive Statistics Summarized demographic distributions (e.g., majority Christian, mostly educated up to secondary/tertiary level) Generated frequency tables and cross-tabulations for environmental exposure variables Foodborne Illness Assessment Analyzed the proportion of individuals reporting illness Compared illness rates across groups (e.g., by water source, handwashing practices, and food storage) Reporting Produced publication-ready tables using gtsummary::tbl_summary() Report was rendered as a clean Word document with interpretable tables for policymakers or public health officials Outcomes & Insights: Demonstrated correlation between poor sanitation indicators (e.g., unsafe water source) and self-reported foodborne disease Identified key demographic segments for targeted interventions Delivered a professional-quality report using reproducible R Markdown workflows This project showcases your ability to: Conduct public health data cleaning and statistical exploration using R Translate coded survey data into readable, policy-relevant summaries Apply structured R workflows for reproducible reporting