Join and Reshape Data in R: A Hands-On Interactive Course

Real analysis almost never starts with one tidy table. This four-lesson interactive course teaches you to combine and reshape data of any shape: join keyed tables, pivot between long and wide, clean and match messy columns, and rectangle nested data into tidy rows.

This course picks up where Data Wrangling with dplyr leaves off. You can already filter, select and summarise one table; here you learn the moves that turn many awkward tables into the single, tidy frame that every chart and model wants.

Each lesson is a guided, interactive experience: you build the result step by step in the browser, answer checkpoints, and write R as you go.

The four lessons

Lesson 1: Joining tables

Combine two keyed tables and understand exactly what happens to the rows: the mutating joins (inner, left, right, full) and unmatched rows, the filtering joins (semi, anti), and when you need non-equi and rolling joins with join_by().

Start Lesson 1: Joining tables

Lesson 2: Pivoting long and wide

Reshape with pivot_longer() and pivot_wider(), and learn why long, tidy shape is what ggplot and models expect, and when wide is the right format for a report.

Start Lesson 2: Pivoting long and wide

Lesson 3: Splitting, uniting and fuzzy joins

Clean columns by splitting and uniting them (separate() / unite()), and join keys that do not match exactly with fuzzy matching, the everyday reality of real-world data.

Start Lesson 3: Splitting, uniting and fuzzy joins

Lesson 4: Nest, unnest and rectangling

Treat a column as a column of tables: nest() to pack each group into a list-column, map a summary or model over each, then unnest() back, and rectangle awkward nested or JSON-like data into tidy rows.

Start Lesson 4: Nest, unnest and rectangling

Who this is for

You can already build a dplyr pipeline on a single table (filter, select, mutate, summarise). You do not need any prior experience with joins, pivoting or nested data. By the end you will be able to take a pile of related, messy tables and turn them into one clean, analysis-ready frame.

What you will be able to do

• Combine tables with every join type, and predict exactly which rows survive
• Reshape between long and wide on purpose, and know which shape each task needs
• Split, unite and fuzzy-match messy columns and keys
• Rectangle nested and list-column data into tidy rows

Ready? Begin with Lesson 1.