Transforming Healthcare Patient Data in R

Healthcare data is notoriously messy, filled with missing values, inconsistent formats, and complex patient records. Learning how to transform this data efficiently is a critical skill for anyone looking to work in clinical informatics, healthcare administration, or medical research. In this text-based course, you will learn the essential techniques of data wrangling using R. By reading our detailed explanations and studying real-world code snippets, you will gain the confidence to turn raw, chaotic patient records into clean, structured datasets ready for analysis. What you'll learn: - Understand foundational healthcare data structures and common data quality issues. - Clean messy patient records by handling missing values, duplicates, and inconsistent entries. - Convert and format critical data types, including clinical dates and patient factors. - Create new analytical variables using modern tidyverse functions. - Filter and subset patient cohorts based on specific clinical criteria. - Apply modern best practices for reproducible and clean data workflows in R. We begin with the fundamental concepts of healthcare data integrity and basic R syntax. From there, you will progress through structured chapters that show you how to systematically clean, reshape, and enrich patient data, ending with practical written exercises to test your comprehension. This course is designed for beginners, including healthcare professionals, researchers, and aspiring data analysts. No prior programming experience is required. Start reading today to build your healthcare data preparation skills.