Best Practices for Data Visualisation
Insights, advice, and examples (with code) to make data outputs more readable, accessible, and impactful
London School of Hygiene and Tropical Medicine
11 April 2024
Introduction
In this session we will cover…
why you should visualise data;
some guidelines for making better charts;
examples of good and bad charts!
But first, some background!
How did the guide come about?
A survey in 2021 asked RSS members their views on Significance magazine.
Respondents were asked, “What aspects of content could be improved?”
- “Better, more consistent charts… I’d like to see a house style like The Economist”
- “The plots sometimes look amateurish…”
- “The figures are often difficult to read…”
Help wanted
We put out a call:
“RSS publications seek data visualisation expert to develop best practice guidance”
We wanted a guide that would:
- Help contributors develop data visualisations that are high quality, readable, effective at conveying information, and fulfil their intended purpose.
- Summarise and link to authoritative advice on chart styles and formats for different types of data.
- Show how to override software defaults in common data visualisation software and packages.
Skip forward quite a few months…
How the guide is structured
Who’s reading the guide?
Since July 2023, the guide has recorded…
- 50,000 views
- 14,000 users
- visitors from 152 countries
Meet Nicola
Lecturer in Health Data Science within the Centre for Health Informatics, Computing, and Statistics.
Background in statistics, operational research, and data science consultancy.
Collaborate with local NHS trusts on data science projects.
Co-author of Royal Statistical Society’s Best Practices for Data Visualisation guidance.
Why visualise data?
Data visualisation has two main purposes:
- Exploratory data analysis and identifying data issues
- Communicating insights and results
Communicating insights with data visualisation
Grab attention
Visualisations stand out. If a reader is short on time or uncertain about whether a document is of interest, an attention-grabbing visualisation may entice them to start reading.
Improve access to information
Textual descriptions can be lengthy and hard to read, and are frequently less precise than a visual depiction showing data points and axes.
Summarise content
Visual displays allow for summarising complex textual content, aiding the reader in memorising key points.
Communicating insights with data visualisation
John Snow collected data on cholera deaths and created a visualisation where the number of deaths was represented by the height of a bar at the corresponding address in London.
This visualisation showed that the deaths clustered around Broad Street, which helped identify the cause of the cholera transmission, the Broad Street water pump.
Snow. 1854.
Why do pie charts have a bad reputation?
Good data visualisation isn’t just based on opinions…
Why do 3D charts have a bad reputation?
On the plot on the left, how tall is the bar?
What are you trying to communicate?
Data visualisations must serve a purpose.
Ask yourself:
- What is the purpose?
- Does the visualisation support the purpose?
- Is it quick, accurate, and intuitive?
A frequent aim: comparison.
Elements of charts
- Layout
- Aspect ratio
- Lines
- Points
- Colours
- Axes
- Symbols
- Legends
- Orientation
- Auxiliary elements
- Dimensionality
Layouts, aspect ratios, and axes
