Importing and Cleaning Data with R

Raw data is rarely ready for analysis, and data professionals spend most of their time cleaning and preparing it. To get accurate insights, you must first know how to import datasets correctly and resolve real-world inconsistencies. This course provides a clear, step-by-step pathway to mastering data preparation using R, the premier language for statistical computing. You will begin by understanding foundational data structures, file formats, and tidy data principles before moving on to hands-on manipulation. By reading through clear explanations and structured code examples, you will learn to tackle common data headaches like missing values, incorrect data types, and duplicate entries. You will also discover modern R practices, including using type-safe operations and writing clean, reproducible data pipelines that save hours of manual work. What you'll learn: - Import data efficiently from CSVs, Excel spreadsheets, and relational databases - Clean and standardize inconsistent text entries, dates, and categorical variables - Handle missing data and structural anomalies systematically without losing critical information - Reshape and merge complex datasets using modern tidyverse packages - Convert data types safely to ensure accurate calculations and modeling - Apply reproducible workflows to automate your data preparation steps The course starts with essential terminology and the anatomy of a clean dataset. From there, you will progress through importing techniques, data-cleaning workflows, and reshaping strategies using industry-standard packages. This course is designed for absolute beginners to R, requiring no prior programming experience. Start your journey toward stress-free data preparation today.