2 · Data wrangling

The analysis workflow, tidy data, importing and reshaping

Author

Eduard (Eddie) Bukin

Before anything can be estimated it has to be read in and reshaped. This topic covers the workflow a data analysis follows, what makes a dataset tidy, and the readr / readxl / dplyr tools you will use in every later exercise.

Slides

Deck Covers
🖥 Workflow, tidy data, wrangling The data-analysis workflow and what makes a dataset tidy
🖥 Wrangling with dplyr Extended version of the deck above, working through the full dplyr verb set
🖥 Import data Reading CSV and Excel with readr and readxl; cleaning names with janitor

Exercise

Exercise What you practise
📋 AE03-01 Import, clean, summarise, plot Reading messy Excel and CSV files, cleaning column names, first summaries and plots

📦 Download the bundle: ae03-data-wrangling — the RStudio project with all four AE03 exercises and their five datasets.

Workflow and tidy data

Read

Watch (optional)

Importing data

Read

Watch (optional)

Wrangling with dplyr

dplyr comes up in every exercise from here on, so there is no separate session for it — use this list to learn it alongside everything else.

Read

Practise

Visualising with ggplot2

Like dplyr, ggplot2 is practised throughout rather than in one session.

Read

Practise

Watch (optional)

Where the course goes next

← 1 · Introduction · → 3 · Describing data