Week 1 - Welcome to IDS
Get acquainted with the course, the technology, the workflow, and the skills you will acquire throughout the semester :toolbox:
- Watch the videos
- Complete the readings
- Visit the course on Learn to join RStudio Cloud
- Complete the Getting to Know you survey
- Complete the assignments
- Participation in the Extra Credit opportunity is optional, but highly encouraged
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.
Note: This week’s assignments will not be marked, they are provided as practice and to help you get acquainted with the material without the pressure of grades. Formative feedback will be provided. Even though there is no penalty for not completing them, you are urged to complete them so that you are prepared for next week and beyond.
|Due this week|
|OQ 00||Syllabus data||Sun, 27 Sep, 23:59 UK|
|Due next week|
|Lab 00||Hello IDS!||Tue, 29 Sep, 16:00 UK|
|HW 00||Pet names||Thur, 1 Oct, 16:00 UK|
|EC||Extra credit||Multiple (see assignment)|
|📖||R4DS: Chp 2, Introduction||Required|
|📖||IMS: Chp 1, Sec 1.1 and 1.2||Required|
|📄||How to read an R help page||Optional|
|📄||rOpenSci’s Reproducibility Guide||Optional|
|📄||1,500 scientists lift the lid on reproducibility||Optional|
|🖋||How R changed me as an analyst||Optional|
This week we worked with the Himalayan climbing expeditions data 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 01 - Himalayan Climbing Expeditions.
|Bonus||Text analysis of chat transcript from the session!|
Interactive R tutorials
The following interactive R tutorials is designed to give you more practice with R. If you’re struggling with any of the topics covered this week, we strongly recommend you work through it.
|Data Visualization Basics||Extra practice|