1 Prerequisites (the boring stuff)

1.1 Module overview

This is a 10-credit Stage 2 module. This module introduces the principles of data visualisation, the grammar of graphic, and software for data visualisation. You will learn and acquire skills in data exploration and visualisation. By the end of the module, you will be able to process and clean raw data sets, apply the grammar of graphics and visualise the data in the most appropriate way.

All materials are available on the Canvas page: https://ncl.instructure.com/courses/66981.

1.2 Module Contacts

1.3 Materials

  • Lecture materials are available in the Modules area on the Canvas page.
  • As this is a new module, materials will be updated as we go along. Announcements will be made when this happens.

1.5 Schedule

The following teaching sessions take place during the teaching weeks:

  • Tuesday 10-11am: Lecture @ Herschel Building Lecture Theatre 2
  • Friday 1-2pm: Lecture @ Herschel Building Lecture Theatre 2
  • Friday 4-5pm: Computing practical @ Stephenson Building Computer Cluster, Room 2.007

Below is a preliminary schedule of the chapters we are going to cover.

Week commencing Teaching Week Chapter
Jan 26th 1 2
Feb 2nd 2 3
Feb 9th 3 4
Feb 16th 4 5, 6
Feb 23rd 5 7
Mar 2nd 6 8, 9
Mar 9th 7 10
Mar 16th 8 10
Apr 20th 9 10
Apr 27th 10 11
May 4th 11 Revision

1.6 Assessment

Below is the schedule of the assignments, each of which is worth 10% of the whole module mark:

  • Coursework 1:
    • Questions available from: 4/2 (Wed) 4pm
    • Submission deadline: 18/2 (Wed) 4pm
  • Coursework 2:
    • Questions available from: 25/2 (Wed) 4pm
    • Submission deadline: 11/3 (Wed) 4pm
  • Coursework 3:
    • Questions available from: 18/3 (Wed) 4pm
    • Submission deadline: 29/4 (Wed) 4pm

Lastly, there will be a digital exam, worth 70% of the whole module mark, during the summer exam period.

1.6.1 Late work policy

Below are the highlights of the late work policy; please check the module Canvas page for details.

Policy on Late or Missed Coursework and Missed Tests:  For normal written coursework, a deadline extension of up to 7 days can be requested (by means of submitting a PEC form); work submitted within 7 days of the deadline without good reason will be marked for reduced credit (following the University sliding scale). You should note that no work can be accepted more than 7 days after the original deadline; where work cannot be submitted by this time, the PEC Committee may agree instead to ‘discount’ or ‘exempt’ the work (although this would not be routine). For details of the policy (including procedures in the event of illness etc.) please consult the Mathematics, Statistics & Physics Community pages on Canvas, under: Assessment Information, Late Work and Missed Assessments.

1.7 Syllabus

The syllabus is on both the Canvas page and the module catalogue.

The materials on the Canvas page are sufficient for completing the assignments. If you would like to read further, there are lots of good texts on visualisation with R. The following books that are available both in the library and online are suitable for the material we cover:

  1. R Graphics Cookbook (2nd Edition) by Chang (2018)
  2. ggplot2: Elegant Graphics for Data Analysis by Wickham (2016)
  3. Interactive web-based data visualization with R, plotly, and shiny by Sievert (2020)

1.8 FAQ

Q: What software do I need for this module?

A: You will need R for this module, and RStudio is recommended as the integrated development environment (IDE) as this is what we use in class. If you are using a university computer, both R and RStudio should already be installed. If you are using your own computer, you can download R from CRAN and RStudio from Posit.

Q: Can I use Python instead of R?

A: While Python is a powerful language for data science, data visualisation is one aspect where R is particularly strong compared to Python. The ggplot2 package in R provides a coherent and elegant framework for creating graphics that is unmatched in Python. For this reason, and for consistency in teaching and assessment, we will be using R throughout this module.

Q: Where can I get help if I’m stuck?

A: You have several options:

  1. attend the timetabled practical sessions where demonstrators are available to help;
  2. come to my office hour (see Module Contacts above);
  3. post questions on the Canvas discussion board where your peers or I can help.

Q: Can I use AI tools (e.g. ChatGPT, GitHub Copilot) for this module?

A: I might use AI tools myself to help illustrate concepts and generate examples in these notes. There is no way to stop you from using AI tools for your own learning, but bear in mind that:

  1. you will need to sit an exam under controlled conditions where AI tools are not available, and
  2. AI-generated code can contain errors or use outdated syntax. You are responsible for understanding and verifying any code you submit.
    Please also refer to the University’s guidance on the responsible use of AI.

Q: Why should I learn to code when AI can write code for me?

A: Two reasons:

  1. This module teaches you the framework and principles of data visualisation, not just the syntax. Understanding why certain visualisations work better than others, and what makes a good graphic, requires human judgement that AI cannot replace.
  2. Even if AI writes code for you, you need a basic understanding of programming to review, debug, and modify that code. Blindly trusting AI-generated code without understanding it is a recipe for errors and miscommunication.

References

Chang, Winston. 2018. R Graphics Cookbook: Practical Recipes for Visualizing Data. 2nd ed. O’Reilly Media.

Sievert, Carson. 2020. Interactive Web-Based Data Visualization with R, Plotly, and Shiny. Chapman & Hall/Crc the R Series. CRC Press.

Wickham, Hadley. 2016. ggplot2: Elegant Graphics for Data Analysis. Use R! Springer.