Assignment overview

You will get the most out of this class if you:

  1. Engage with the readings and lesson materials
  2. Regularly practise using R

Each type of assignment in this class helps with one of these strategies.

Weekly exercises

Each session has lesson materials and fully annotated examples that teach and demonstrate how to do specific tasks in R. However, without practising these principles and writing code on your own, you won’t remember what you learn!

Each week you will download a small R project, work through a set of tasks in a Quarto file, and submit the rendered HTML to Moodle. The exercises have three parts:

  1. Weekly reflection — three short questions about what you learned and what was challenging this week
  2. R4NP interactive exercises — browser-based exercises from the course textbook that give you immediate feedback as you work
  3. Bonus tasks — optional but recommended coding tasks related to the week’s topic

Exercises will be graded using a check system:

  • ✔+: (115% in gradebook) All tasks attempted and mostly correct. Rendered document is clean and easy to follow. I will not assign these often.
  • ✔: (100% in gradebook) 70–99% complete and most answers are correct. This is the expected level of performance.
  • ✔−: (50% in gradebook) Less than 70% complete and/or most answers are incorrect. This indicates that you need to improve next time.

Note that this is essentially a pass/fail system. I am not grading your coding ability line by line, and I am not looking for perfect output. I am looking for good faith effort — try hard, do good work, and you will get a ✔.

You may (and should!) work together on the exercises, but you must submit your own work.

Data project

At the end of the course you will demonstrate your data science skills by completing an independent data project. You will find a dataset that interests you, explore it using the tools and techniques from the course, and write up your findings as a short report.

Complete details for the data project are here.

There is no final exam. The data project is your final assessment.

The project will not be graded using a check system. Instead I will use a rubric to assess four elements:

  1. Technical skills — appropriate use of R and the tidyverse
  2. Data wrangling — clean, well-organised, reproducible code
  3. Visualisation — clear and well-designed plots that communicate your findings
  4. Interpretation — a coherent written account of what the data shows

If you have engaged with the course content and completed the weekly exercises throughout the semester, you should do well with the data project.