Data Wrangling with dplyr: A Hands-On Interactive Course

Most of real analysis is wrangling: getting messy data in and bending it into a shape you can actually work with. This three-lesson interactive course teaches data wrangling in R from scratch, with the tidyverse, one small real-feeling dataset carried the whole way through.

Tutorials usually start at "call this function." This course starts one level deeper, with what a CSV actually is and what makes a table tidy, and builds up until you can filter, derive, group and summarise data with confidence and know exactly why each step is there.

Each lesson is a guided, interactive experience: you transform live tables in the browser, answer checkpoints, and write R as you go.

The three lessons

Lesson 1: Import a CSV and make it tidy

Read a real CSV into R with read_csv(), check the column types it picked, and fix the single most common import snag: a number column read as text. Then the three rules of tidy data and a live demo of one messy table becoming tidy.

Start Lesson 1: Importing and tidy data

Lesson 2: The core dplyr verbs

The verbs that do the everyday work, on one running data frame: filter() to keep rows, select() to keep columns, mutate() to derive new ones, and arrange(), distinct() and count() to order, dedupe and tally. The pipe chains them into a sentence you can read aloud.

Start Lesson 2: The dplyr verbs

Lesson 3: Group, summarise and clean

Split-apply-combine with group_by() and summarise(), deriving categories with case_when(), and handling missing values honestly, knowing when to drop and when to impute. Ends with the path to your Data Analyst certificate.

Start Lesson 3: Group, summarise and clean

Who this is for

You can run R and load a package with library(). You do not need any prior data-wrangling or tidyverse experience. By the end you will be able to take a raw file and turn it into a clean, tidy, summarised table ready for plotting or modelling.

What you will be able to do

• Read a CSV into R, check its column types, and fix a number column read as text
• State the three rules of tidy data and reshape a messy table to obey them
• Filter rows, select and derive columns, and chain steps with the pipe
• Group a table and compute summaries, derive categories, and handle missing values honestly

Ready? Begin with Lesson 1.