Overview

A practical introduction to the R programming language for researchers with little or no prior coding experience. Using real-world data throughout, participants learn to write clean, reproducible analysis scripts in RStudio. Topics covered include data structures, manipulation, visualisation and reporting. The emphasis is on building good habits from the start: organised projects, modular code and documented workflows that can be shared, rerun and built upon.

Note: this workshop focuses on the fundamentals of R as a programming language and does not cover statistical analysis.

This workshop will run over four consecutive mornings from Monday 12 October to Thursday 15 October 2026 from 09:00 AM to 01:00 PM AEST each day.

Who Should Attend

Researchers from any discipline looking for more capable and automated data analysis than spreadsheets can offer. No programming experience is required.

Prerequisites

No prior programming experience is required. Participants must have R and RStudio installed on their computer before the first session. Installation instructions will be provided on registration. A full-function computer (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.

Learning Objectives

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

• Navigate the RStudio interface and use it to write, run and organise R scripts.

• Set up and manage a well-structured RStudio project that supports reproducible analysis.

• Understand and work with R's core data types and structures, including vectors, lists and data frames.

• Read data into R, explore its structure and subset it effectively.

• Use control flow (conditionals and loops) to automate repetitive tasks.

• Write reusable functions that follow best-practice software design principles.

• Manipulate and reshape data frames using the dplyr and tidyr packages.

• Produce publication-quality graphics using ggplot2.

• Generate reproducible reports combining code, output and narrative using knitr and R Markdown.

• Apply good software practices including modular code, consistent style and meaningful documentation.

Course Activities

All sessions are hands-on. Participants work through exercises using the Gapminder dataset, a real-world dataset of global development indicators, to practise each concept as it is introduced. Activities include:

• Guided live-coding exercises where participants write and run code alongside the instructor.

• Structured exercises to explore and subset data frames using R's indexing syntax.

• Building a ggplot2 visualisation incrementally, adding layers to improve and refine a plot.

• Writing and testing custom functions with built-in argument checking.

• Transforming a dataset between wide and long formats using tidyr.

• Summarising grouped data using dplyr's split-apply-combine workflow.

• Producing a final reproducible report in R Markdown that integrates code, results and written interpretation.

Materials and Tools

• R (latest version) | Free download from r-project.org

• RStudio Desktop | Free download from posit.co

• Workshop materials are based on the Software Carpentry curriculum R for Reproducible Scientific Analysis, licensed under CC-BY 4.0

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.

Contact the host of R for Reproducible Scientific Analysis (Oct 2026)

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