Data Visualisation - PSY00122M

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Module summary

Our modern lives generate huge amounts of data that are used to make key decisions, such as our performance in school, our medical histories and our consumer behaviour. In raw form, data are typically hard to make sense of - for example, how do you figure out whether patients are getting the care they need from a mound of medical data? This module will equip students with the skills to transform data into visualisations that answer important questions and tell compelling stories.

Module will run

Occurrence	Teaching period
A	Semester 1 2025-26

Module aims

Many modern organisations such as schools, medical systems, government agencies and businesses rely on human data to make key decisions. Typically, this information is impossible to make sense of in its raw, original form. Fortunately, simplifying data with good visualisations can tell a comprehensible and even compelling story. However, a bad visualisation can be unclear or even actively misleading. The module will walk you through the process turning a mound of data into a clear, meaningful picture. By the end of the course, you will be equipped to gain insights from data by choosing and implementing a form of visualisation that is appropriate for the kinds of data you’re working with and the kinds of questions you’re trying to answer. Moreover, you will have the skills to design and customise your visualisations to be clear and attractive to your audience. At each stage, you will gain hands-on experience with implementing a variety of visualisations on real-world datasets.

Module learning outcomes

• Formulate questions about data that can be answered with visualisations
• Choose a form of visualisation that is appropriate for the data structure and question
• Select how to map aspects of data to graphical aspects of visualisations, such as axes, bars, and colours
• Create visualisations by writing code in R
• Design visualisations so that they are clear and comprehensible to an audience
• Search for and identify new tools to enhance visualisations
• Create dashboards that combine multiple visualisations that work together to provide insight into a question

Module content

1. What is data visualisation and why use it

2. Intro to building visualisations in R using the “grammar of graphics” (ggplot2)

3. How to build different visualisations for different data structures and questions:

   • Distributions of data: histograms, density plots, violin plots, boxplots

   • Bar graphs: simple, stacked, grouped, diverging, polar

   • Line graphs

   • Relationships between variables: scatterplots, correlation matrices, adding trend lines

   • Other useful formats: heatmaps, pie charts, mosaic plots

4. Designing plots for clarity and attractiveness

5. Exporting plots to files such as pdf and image file formats

6. Finding extensions to ggplot2 and visualisation packages beyond ggplot2

7. Creating interactive data visualisation dashboards using shiny

Indicative assessment

Task	% of module mark
Essay/coursework	100

Special assessment rules

None

Indicative reassessment

Task	% of module mark
Essay/coursework	100

Module feedback

Marks will be available on e:vision.

Indicative reading

Fundamentals of Data Visualization: https://clauswilke.com/dataviz/index.html

Data Visualization: A Practical Introduction: https://socviz.co/index.html#preface