Data Cleaning and Preparation in R

Raw data is rarely ready for analysis right out of the box, often containing errors, missing values, or inconsistent formatting. Learning to identify and fix these issues is the most critical step in any data professional's workflow, ensuring that the conclusions drawn from data are accurate and reliable. This course provides a structured approach to identifying data quality issues and applying programmatic solutions to resolve them. You will move from understanding basic data structures to implementing sophisticated cleaning pipelines that ensure your analysis is built on a solid foundation. By focusing on reproducible workflows, you will learn how to turn chaotic spreadsheets into structured data ready for modeling. What you'll learn: - Understand data types and convert between formats to ensure computational accuracy - Apply range and categorical constraints to identify and handle out-of-bounds values - Identify and resolve duplicate records using exact and partial matching techniques - Handle missing data systematically by identifying patterns and applying imputation strategies - Clean and standardize string data using modern text manipulation tools - Implement record linkage to merge disparate datasets with inconsistent naming conventions - Practice tidy data principles to restructure datasets for efficient downstream analysis The course begins with fundamental definitions of data quality and the philosophy of tidy data before moving into practical text-based exercises. You will learn to use the modern R ecosystem to automate repetitive tasks, handle messy strings, and join datasets that don't perfectly align. This course is designed for beginners who have a basic grasp of R syntax and want to focus on the practicalities of data preparation. No prior experience in data engineering or advanced statistics is required. Start building your data cleaning toolkit today.