Week 3 - Wrangling and tidying data

Data wrangling, joining, and tidying 🔧


  • Watch the videos
  • Complete the readings
  • Complete the assignments
  • (Optional) Complete the Course feedback survey (not graded)
  • (Optional) Participate in the Virtual exchange (extra credit)
    • Task details to be posted
    • Zoom call on Friday, 9 Oct, 19:00-20:00 UK time


You have two options for watching the course videos, on YouTube or on MediaHopper. You can also find a playlists for all course videos on YouTube here and on MediaHopper here.

01 Keeping up with IDS: Week 3 6:23
02 Tidy data 6:46
03 Grammar of data wrangling 13:13
04 Working with a single data frame 32:15
05 Working with multiple data frames 17:26
06 Tidying data 20:14
07 AE: Hotels + Data wrangling 30:58


Due this week
Lab 01 Plastic waste Tue, 6 Oct, 16:00 UK
OQ 02 Data wrangling Sun, 11 Oct, 23:59 UK
Due next week
Lab 02 Take a sad plot and make it better Tue, 13 Oct, 16:00 UK
HW 01 All about Edinburgh Thur, 15 Oct, 16:00 UK
EC Extra credit Multiple (see assignment)

If you’re having difficulty accessing your HW or Lab repo, see troubleshooting advice here.


📖 R4DS::Chp 4 - Workflow: basics Required
📖 R4DS::Chp 5 - Data transformation Required
📖 IMS::Chp 2 - Summarizing and visualizing data (If you haven’t read it last week!) Required


The data come from TidyTuesday. TidyTuesday is a weekly social data project for the R community. Read more about TidyTuesday here and see people’s contributions on Twitter under the #tidytuesday hashtag.

You can find starter code for this session on RStudio Cloud, in the project titled Code Along 03 - College tuition, diversity, and pay.

Session artifacts .Rmd     .md

Interactive R tutorials

The following are interactive R tutorials, designed to give you more practice with R. These are optional, but the “Airbnb listings in Edinburgh” dataset show up in your next homework assignment as well, so you might want to go through that one so that you can gain familiarity with it. If you’re struggling with any of the topics covered this week, we strongly recommend you work through the second tutorial as well.

Road Traffic Accidents Related to HW 01
Work with data Extra practice