Introduction to ggplot2 (Oct 2026)

A focused, practical introduction to creating clear and informative data visualisations in R using the ggplot2 package. Participants learn how ggplot2 uses the grammar of graphics to build plots from data, aesthetic mappings and graphical layers. They then apply this framework to explore relationships, distributions, differences between groups and the composition of categorical data.

Using the built-in mpg dataset throughout, participants progressively create and refine scatterplots, density plots, histograms, boxplots and bar charts. The workshop also covers adding labels, using facets, improving readability and saving completed plots. The emphasis is on selecting and constructing visualisations that answer a particular question and communicate results clearly.

This workshop focuses on data visualisation and does not cover statistical analysis.

Learning Objectives

By the end of this workshop, participants will be able to:

• Explain how data, aesthetic mappings and geometric objects combine to form a ggplot2 visualisation.

• Create scatterplots to explore relationships between numerical variables.

• Distinguish between mapping an aesthetic to a variable and setting an aesthetic to a fixed value.

• Add and arrange graphical layers to reveal patterns in data.

• Create density plots and histograms to explore the distribution of numerical variables.

• Create and interpret boxplots to compare distributions across groups.

• Use bar charts to display categorical counts and proportions.

• Select an appropriate plot type for relationships, distributions, group comparisons and composition.

• Improve visualisations using informative labels, facets and simple theme adjustments.

• Save completed plots to an image file using ggsave().

FAQs

Who should attend?

Researchers and analysts from any discipline who have basic experience using R and want to develop practical skills in exploring and communicating data with ggplot2. It is particularly suitable for people who can work with data frames and run R scripts but have limited experience creating visualisations in R.

Are there prerequisites?

Prior experience with R and the RStudio interface is required. Participants should be comfortable running scripts, working with variables and data frames, and using basic R functions. The fundamentals of R programming will not be covered.

Participants who are new to R should first complete R for Reproducible Scientific Analysis or have equivalent experience.

R and RStudio must be installed before the session, along with the tidyverse package. Installation instructions will be provided on registration. A full-function computer running Windows, Mac or Linux is required. Chromebooks and tablets are not suitable. A second monitor is strongly recommended. A reliable internet connection suitable for video conferencing is essential.

How can I contact the organiser with any questions?

You can email training@qcif.edu.au.

What's the cancellation policy?

Cancellations are accepted up to three working days before the start of the workshop. If you do not join on the day without having cancelled beforehand, you may be blocked from attending any future QCIF training workshops.