Data Cleaning in R: Advanced Techniques for Clean Datasets

Real-world data is rarely clean, structured, or complete. To perform accurate analysis, you must know how to transform messy text, parse complex API responses, and handle missing values without losing critical information. This course equips you with the practical skills to tackle complex data preparation tasks in R. You will transition from basic data cleaning to handling sophisticated data structures and statistical imputation with confidence. In this course, you will learn to: 1. Apply regular expressions to parse, match, and clean complex text patterns. 2. Parse and manipulate semi-structured JSON data retrieved from web APIs. 3. Apply map and anonymous functions using modern functional programming techniques to automate repetitive cleaning tasks. 4. Resolve missing data challenges using statistical imputation rather than simply deleting valuable records. 5. Integrate external datasets seamlessly to enrich and complete your existing data. The course starts with essential terminology and setup before moving step-by-step through text processing, API integration, and advanced imputation techniques. You will learn through clear written explanations and practical code examples designed for immediate application. This course is designed for R users who know the basics of data frames and want to elevate their data preparation skills. No advanced programming experience is required. Start writing cleaner, more efficient R code today.