Practical Data Cleaning and Preparation with R

Raw data is rarely ready for analysis, often filled with missing values, inconsistent formatting, and duplicate entries. Mastering the art of data cleaning in R is the essential first step to turning messy datasets into reliable, actionable insights. This text-based course guides you through the foundational concepts of data preparation, helping you write clean, reproducible R code to restructure and sanitize your data. You will transition from struggling with messy spreadsheets to confidently preparing analysis-ready datasets. What you'll learn: - Understand the core principles of tidy data and the basic data cleaning workflow in R. - Identify and handle missing values, duplicates, and structural anomalies in your datasets. - Manipulate and clean text data, format dates, and convert data types using modern R packages. - Reshape, filter, and merge data tables to prepare them for downstream analysis. - Apply modern best practices for writing readable, reproducible data preparation scripts. The course begins with fundamental definitions of data quality and tidy structure, then progresses step-by-step through practical cleaning techniques using popular modern library functions, concluding with a complete end-to-end data preparation workflow. Designed entirely for beginners, this course requires no prior programming experience, though a basic familiarity with data spreadsheets is helpful. Start reading today to build a strong foundation in data cleaning and elevate your analytical skills.