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
|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.
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|