Week 4: Engineering data sets

Learning goals

• Apply data wrangling techniques using the Tidyverse to clean, transform, and prepare datasets for analysis.
• Structure R scripts into modular components (setup, input, transformation, output) to facilitate reproducibility and automation.
• Implement common data operations (e.g., merging, aggregating, reshaping) and integrate basic programming concepts in R.
• Develop new variables and features (feature engineering) to enhance the analysis and understanding of datasets.

Preparation before the lecture

Lecture

Laptop required!

• Tutorial (view slides, view complete tutorial)

Coaching session

• Work on your team goals for this course week (see coaching #3 on your workplan).

After the lecture

• After-class exercises ( View, Download; right click - download file as)
• Reading: Data preparation process and challenges
• Optional (for students that need extra guidance in developing R skills):
  • DataCamp Introduction to Tidyverse (chapter 1 and 3)
  • DataCamp Cleaning Data in R (chapter 1 and 2)
  • DataCamp Joining Data with dplyr (chapter 1 and 2)

Having a hard time with Datacamp, or seeking to practice more?

Then check this material on manipulating data using dplyr and tidyr!