Week 4) Build analysis-ready data, review pull requests, and automate with Make

Learning Goals

• Coding: engineer analysis-ready data with mutate(), rank(), distinct(), group_by(), summarise(), left_join(), inner_join(), timestamp conversion, and lag().
• Collaboration: work in two branches, commit in small steps, and complete pull request and pull request review workflows.
• Automation: structure scripts with Setup-Input-Transform-Output and run a multi-step make workflow with a working clean rule.

Preparation before class

• Please read chapter 7 of “Marketing Analytics: A Modern Toolkit” (and optionally from the Appendix: “SQL”).
• Complete the code quests for week 4.

Tutorial

• Tutorial: advanced data engineering + collaboration through PR1/PR2 + automation with make and make clean.

Coaching session

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

After the lecture

• Complete all exercises from the tutorial shared with students this week.

Optional practice

• Re-run your workflow from scratch and verify your commit history tells a clear coding + automation story.
• 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)