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.
During the workshops, 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.
Who should attend?
We have workshops that cater to all experience levels, from Introductory to Advanced. Please see the descriptions to decide which workshops you should attend. 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
Welcome to the website for the 2025 R Workshops. For the first time, we are offering two weeks of workshops!!. The first in February 2025 will focus on statistical modelling, and the second workshop series in July 2025 will focus on data wrangling, data science and building shiny applications.
Week 1: February 2025 (Registration Open)
The workshop series will run 10-14 February 2025 on campus at the University of Queensland.
Registration is now open for our February 2025 Workshops. You can register here.
If you are not ready to attend one of our workshops, please feel free to sign up to our mailing list to stay abreast of future R Workshop updates. We will only send a couple of emails per year to advertise when the upcoming workshops have been released. This website will remain our main communication tool.
Day 1 (Monday 10th February): 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 in February 2026 to complete the advanced statistics covered on Days 3-5.
Day 2 (Tuesday 11th February): Linear modelling
Today is all about using statistical models 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 (Wednesday 12th February): 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 (Thursday 13th February): 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 (Friday 14th February): 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.
Week 2: July 2025
These workshops will build on the February workshops, exploring intermediate and advanced topics such as data wrangling, writing functions, using GitHub, graphics, mapping and building shiny apps.
More details of the July workshops will be made available after the February workshops. If you want to be notified of updates you can join our mailing list here.
Day 6 (Monday 14th July):* Data Wrangling 1 (Wrangling with the tidyverse, tidy evaluation, reprex)
Day 7 (Tuesday 15th July):* Data Wrangling 2 (Writing functions, using GitHub, purrr, furrr)
Day 8 (Wednesday 16th July):* Graphics and Mapping (Intermediate/Advanced ggplot, spatial mapping)
Day 9 (Thursday 17th July):* Introduction to Shiny
Day 10 (Friday 18th July):* Intermediate Shiny
*TBC