Week 1 - Welcome to IDS

Get acquainted with the course, the technology, the workflow, and the skills you will acquire throughout the semester :toolbox:



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.

00 Meet the course team 02:36
01 Welcome to IDS! 15:07
02 AE: First dataviz 08:10
03 Course information 26:17
04 Meet the toolkit: course operation 10:45
05 Meet the toolkit: programming 34:17
06 Meet the toolkit: version control and collaboration 11:24


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)


📄 Course syllabus Required
📖 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.

Session artifacts .Rmd     .md
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