Welcome!

Welcome to the resources for the data visualisation session by Dr Nicola Rennie at the Mathematics for our Future Climate CDT! The website for the session can be found at nrennie.rbind.io/mfc-cdt-data-viz, where you will also find a copy of the slides.

Engaging and effective data visualisations

Data visualisation can be a very effective method of communicating research findings to broad audiences. However, good data visualisation requires appreciation and careful consideration of your audience, a chart’s purpose, and its technical design elements. It also involves a creative element, where we make choices about the story we want to tell. Our design decisions are then driven by the need to convey that story most effectively to our audience.

By the end of this session, you’ll:

Understand why data visualisation is an important aspect of research communication,
Be able to choose an appropriate chart type for a specific audience, and
Know how to design effective charts that still look good,
and be able to create effective data visualisations using R and/or Python.

Session plan

Careers in data visualisation (15 mins): Slides, questions
Introduction to data visualisation (30 mins): Slides, interactive discussion
Overview of software for charts (15 mins): Slides, interactive discussion
BREAK: 10 mins
Choosing a chart type (50 mins): Live code demonstration in Python, exercise, and discussion
LUNCH: 60 mins
Styling charts (50 mins): Live code demonstration in R, exercise, and discussion
Wrapping up and questions (10 mins)

Software prerequisites

For the interactive coding parts of this session, we will be creating some visualisations either using ggplot2 in R or plotnine in Python.

Please make sure you have installed the following packages:

R

tidyverse

Optional:

gghighlight
ggtext
ggview

Python

pandas
matplotlib.pyplot
plotnine

Further training

View other available training courses and workshops at nrennie.rbind.io/projects/training-workshops.