Data Cleaning and Wrangling in R

Real-world data is rarely tidy, often arriving with missing values, inconsistent formatting, and awkward structures. Mastering practical data preparation techniques in R ensures your downstream exploratory analysis and modeling are accurate and dependable. In this comprehensive written course, you will develop the practical skills needed to diagnose messy datasets, standardize text, manage relational data, and reshape tables into compliant formats using modern tidyverse practices. What you'll learn: - Understand core data cleaning definitions, tidy data principles, and common quality pitfalls. - Reshape and restructure tables effectively using modern pivoting and wrangling tools. - Identify, filter, and impute missing or anomalous observations with consistent strategies. - Manipulate and clean character strings, categorical variables, and unstandardized text. - Combine multiple relational datasets using robust joins and functional programming concepts. - Organize reproducible data preparation scripts using current R workflow standards. You will start with foundational terminology and concepts before working through clear, text-based explanations and illustrative code patterns designed for real-world scenarios. This course is ideal for beginners, aspiring data analysts, and researchers who want to build a dependable foundation in R data cleaning without complex prerequisites. Begin reading today to gain total confidence in preparing your data for analysis.