R Workshops @ UQ
We have been conducting several R Workshops each year since 2012. We are all mathematical ecologists who have learnt data analysis and data science through application to real-world problems in R. We initiated these workshops because we enjoy using R, we find it invaluable in our research, and we wanted to pass on what we had learnt. In particular, because we are self-taught, we want to help others avoid some of the mistakes we made when we were setting out to learn and use R. We have now taught >1,500 students at these workshops, and we look forward to helping the next generation of R programmers and applied statisticians learn the skills they need to meet the demands of the modern research environment.
What will you get from our workshops?
Our workshops are interactive, informative, and fun. We provide all the code, datasets and notes — the notes provide an invaluable reference guide for you in the future.
We have workshops that cater to all experience levels, from Introductory to Advanced. During each workshop, we have several tutors to ensure you have support to help debug your code if you encounter any problems. And we try to work at a pace that allows participants to follow along — acknowledging that there is a diversity of experience in every room.
If you’d like to see more of what previous attendees have said about us please have a look at the Testimonials page.
In each workshop, we usually have a mix of postgraduate students and researchers from:
- Universities (including UQ, UniSC, Griffith, SCU, CSU)
- Government Departments (including QLD Dept of Environment, QLD Dept of Agriculture and Fisheries, NSW Dept of Climate Change, Energy, the Environment and Water, and NSW Dept of Primary Industries and Regional Development)
- CSIRO and AIMS
- Private industry (Educators, Consultants)
Introducing the 2025 Workshops
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Building your Data Science Skills in R
These workshops will explore intermediate and advanced topics such as data wrangling, writing functions, using GitHub, graphics, mapping and building shiny apps.
This workshop series will run 14-18 July 2025 on campus at the University of Queensland.
More details of the July workshops will be made available shortly. If you want to be notified of updates you can join our mailing list here.
Day 1 (Monday 14th July) Data Wrangling 1:* Data wrangling with the tidyverse (pipes, joins, summarise, dates, strings), Open Data Science (writing in markdown, using GitHub) and getting help with your coding (writing a reprex).
Day 2 (Tuesday 15th July) Data Wrangling 2:* Functional Programming (Structuring your code, writing functions, anonymous functions, debugging), tidy evaluation, mapping functions to data (purrr and furrr) and basics of building an R Package.
Day 3 (Wednesday 16th July) Visualising your data and model results: * Intermediate/Advanced ggplot (geoms, inheritance, annotations, legends, colormaps etc), patchwork (wrapping plots, lists of plots, merging elements, updating multiple plots at once) and publication ready plotting (exporting, size, resolution etc.).
Day 4 (Thursday 17th July) Spatial Analysis and Mapping:* Getting started with spatial data (projections, coordinate reference systems, spatial types), Vector Data using sf (manipulation and plotting), Raster data using terra (manipulation and plotting), building beautiful (and interactive) maps for communication (leaflet, tmap).
Day 5 (Friday 18th July) Introduction to Shiny: * An introduction to building interactive web applications, fundamentals of reactive programming, constructing user interfaces, updating themes, deploying shiny apps to servers.
*TBC
COMING SOON: prioritizr: Practical Conservation Planning in R
Join us for a comprehensive 1-day workshop designed to empower you with the tools and knowledge to effectively use the prioritizr R package for systematic conservation planning. Whether you’re a seasoned conservation scientist or new to the field, this workshop will provide you with a practical foundation for making informed decisions about where to invest conservation efforts.
More details will be made available in due course. If you want to be notified of updates you can join our mailing list here.
What you’ll learn:
- Core Principles of Systematic Conservation Planning: We’ll delve into the fundamental concepts that underpin effective conservation planning, including representation, persistence, and cost-effectiveness.
- Hands-on Introduction to prioritizr: Through guided exercises and real-world examples, you’ll learn how to load data, define conservation features, and set up optimization problems within the prioritizr framework.
- Exploring Objective Functions: Discover the diverse range of objective functions available in prioritizr, such as maximizing feature representation or minimizing costs, and understand how to choose the right function for your specific conservation goals.
- Implementing Constraints and Penalties: Learn how to incorporate practical considerations like connectivity, adjacency, and socio-economic factors into your prioritizations using constraints and penalties.
- Real-World Applications and Case Studies: We’ll explore compelling case studies demonstrating how prioritizr has been successfully applied to address real-world conservation challenges.
- Troubleshooting and Best Practices: Gain valuable insights into common challenges and learn best practices for developing robust and defensible conservation plans.
- Q&A and Interactive Discussion: Engage with instructors and fellow participants to discuss your specific projects and challenges.
Who should attend?
- Conservation scientists and practitioners
- Environmental managers and policy-makers
- Researchers and students in ecology, geography, and related fields
- Anyone interested in using data-driven approaches for conservation planning
By the end of this workshop, you’ll be equipped with the practical skills and conceptual understanding necessary to leverage prioritizr for your own conservation projects.
Introduction to R and Statistical Modelling
Day 1 (Returning in February 2026): Introduction to R and the tidyverse
In the “Introduction to R and the tidyverse” workshop, we will start slowly. We will help familiarise you with R (the programming language), RStudio (the interface we recommend to program R in) and the tidyverse (a set of programming packages we use to speed up analysis and plotting). By the end of the day, you will be confidently loading datasets, writing basic code in R to manipulate the data, and plotting outputs from your analysis.
NOTE: If you are starting with “Day 1: Introduction to R and the tidyverse” we recommend you only register for Days 1-2 as Days 3-5 will cover more advanced material. After completing Days 1-2 we would encourage you to consider our July workshops for further tips/trick in R programming, and then come back the following February to complete the advanced statistics covered on Days 3-5.
Day 2 (Returning in February 2026): Linear modelling Today is all about learning the foundations of statistical modelling to analyse your data. We’ll start with simple linear models and explore why we fit models, learn how to interpret model output and how to upgrade your code to design more advanced linear models. We then learn how to select the best model, examining model diagnostics and plotting the output. Where model diagnostics indicate a violation of model assumptions, we consider what transformations might improve the model fit. We finish by learning how to fit Generalised Linear Models (GLMs) for binary and count response variables, predicting in link space compared to predictor space, and dealing with different error structures.
Day 3 (Returning in February 2026): Mixed Modelling
In this workshop you will learn how to fit Linear Mixed Models (LMMs), Generalised Linear Mixed Models (GLMMs), Generalised Additive Models (GAMs), and Generalised Additive Mixed Models (GAMMs). Mixed-effects models build on generalised linear models (GLMs) and have observations that are grouped in some way. This explicit recognition of grouping of observations within the model structure resolves many of the frequently encountered challenges associated with non-independence of observations, nested (hierarchical) designs, and spatial and temporal structuring. GAMs are extensions of GLMs and use flexible smoothers (wiggly lines) rather than mathematical equations to describe the relationship between the response and a set of predictors.
Day 4 (Returning in February 2026): Spatial modelling with temporal and spatial autocorrelation
This workshop will help you model more complex datasets. We will discuss autocorrelation and its consequences, the growing importance and accessibility of time series and spatial datasets (including time series, point-based spatial data, and aerial-based spatial data), and some of the key features of these data. We will cover structured random effects and penalised random effects, and how to begin modelling these with Generalised Additive Models (GAMs; spatial, temporal, spatiotemporal). This workshop will also cover model checking, plotting, extrapolation and forecasting.
Day 5 (Returning in February 2026): Multivariate statistics
This workshop will cover data analysis when you have multiple responses (i.e., multiple y-variables). We will discuss clustering (finding groups in data), ordination (displays multivariate data in fewer dimensions so you can more easily visualise patterns — such as non-metric MultiDimensional Scaling (nMDS) and Principal Components Analysis (PCA)), and how to infer environmental drivers of any patterns identified.