Introductory description

This module introduces students to contemporary practice of data visualisation. The module takes a hand-on approach where concepts are introduced using the grammar of graphics (ggplot2) framework implemented in R.

This module is offered as an optional module to Statistics students and as an unusual option to students from other departments, space permitting. Student taking this module from other departments must have taken ST120 Introduction to Probability and ST121 Statistics Laboratory.

Pre-registration is required. Please see the module page for details.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

1. Data visualisation in the pre-informatics age. Historical examples of good pen-and-ink graphics.
2. Tufte's principles of good graphical design: graphical excellence; graphical integrity; avoiding chart junk; data-ink maximisation.
3. File formats for graphics: vector and and bitmap graphics; portability; embedding graphics in documents.
4. Wilkinson's grammar of graphics. A pattern language for visualizations that allows us to build visualisations from reusable components.
5. Graphics in R using ggplot2, an implementation of Wilkinson's grammar of graphics in R.
6. Colour theory. Colour spaces. Classification of colour palettes. Accessibility for colour blind readers.
7. Interactive graphics using Shiny.

Learning outcomes

By the end of the module, students should be able to:

• Critically appraise data visualisations created by third parties
• Create their own improved versions of flawed visualisations in R
• Choose appropriate visualisations for different tasks
• Create novel visualisations in R from a given data set and a description of the required output

Indicative reading list

Wickham, H (2009) Ggplot2: elegant graphics for data analysis

C.O. Wilke (2019) Fundamentals of data visualization: a primer on making informative and compelling essential

Nussbaumer Knaflic. C (2019) Storytelling with Data

Tufte, ER (2001) The visual display of quantitative information

Wilkinson, L (200) The grammar of graphics

Cleveland, WS (1993) Visualizing data

View reading list on Talis Aspire

Interdisciplinary

This module requires students to develop programming and data-analytic skills for solving real-world visualisation problems from a wide variety of disciplines.

Subject specific skills

1. Demonstrate facility with data processing and visualisation methods in R
2. Create readable, valid, reliable, and modular code for visualisation
3. Analyze problems, abstracting their essential information formulating them using appropriate programming
   concepts to facilitate their solution.
4. Demonstrate programming skills and knowledge of fundamental programming concepts, both explicitly and by
   applying them to the solution of real-world problems

Transferable skills

1. Problem solving skills: The module requires students to solve problems presenting their conclusions as logical and coherent arguments.
2. Written communication skills: Students complete written assessments that require precise and unambiguous communication in the manner and style expected in mathematical sciences.
3. Verbal communication skills: Students are encouraged to discuss and debate formative assessment and lecture material within small-group tutorials sessions. Students can continually discuss specific aspects of the module with the module leader. This is facilitated by statistics staff office hours.
4. Team working and working effectively with others: Students are encouraged to discuss and debate formative assessment and lecture material within small-group tutorials sessions.
5. Professionalism: Students work autonomously by developing and sustaining effective approaches to learning, including time-management, organisation, flexibility, creativity, collaboratively and intellectual integrity.