R Template Script of Epidemiological Data Cleaning & Reporting

A reproducible R workflow for importing, reviewing, cleaning, summarizing, and exporting epidemiological datasets. It is based on what I learned from completing Applied Epi's Free R Tutorials.

Overview

This repository contains a modular, end-to-end R script designed to support rapid and reproducible data cleaning workflows for public health and clinical analytics. It includes:

• Importing raw line list data using project-safe file paths (here)
• Standardizing variable names and harmonizing inconsistent data entries
• Fixing dates, cleaning categorical values, and constructing analytic variables
• Creating age groups and case definition classifications
• Producing exploratory summaries and quick visualizations
• Building and exporting formatted tables using flextable + officer
• Saving cleaned datasets for downstream analysis or modeling

This template is designed for reusability, teaching, and real-world public health data operations.

Features

• Reproducible structure using clear pipe-based cleaning chains
• Clean naming conventions powered by janitor::clean_names()
• Standardized date handling with lubridate
• Case definitions & categorical recoding using dplyr::case_when()
• Age group creation with epikit::age_categories()
• Beautiful summary tables exported to Word (.docx)
• Project-safe paths via here() for fully portable scripts
• Optional PNG table export using webshot

Example Output

The script automatically generates:

• data/clean/linelist_clean.csv — cleaned, analysis-ready dataset
• outputs/summary_table.docx — formatted table created with flextable (can also specify PPTX, HTML, or PNG)

Folder Structure

project/
├── data/
│ ├── raw/
│ └── clean/
├── scripts/
└── outputs/

Requirements

The script uses pacman::p_load() to simplify package management. Required packages include:

• tidyverse
• janitor
• epikit
• skimr
• flextable
• officer
• here
• rio
• webshot

Purpose

This repository serves both as a portfolio artifact and as a teaching-friendly reference for students, practitioners, and analysts working with public health datasets. The script emphasizes clarity, reproducibility, and real-world epidemiological cleaning challenges.

Feel free to copy, adapt, and reuse this workflow.

License

MIT License