Training courses, interactive workshops, and outreach work, covering programming, statistics, and visualisation.
These interactive workshops have been delivered in-person and remotely to individuals and small groups of up to 14 people. Some have also been be delivered online for groups of up to 500 people.
Introduction to data analysis with R
An introductory level course for beginners to R, covering data import, summary statistics, and visualisation.
An intermediate R course for people who want to build their first R package. There are many benefits to turning your R scripts or functions into a package, like making your code easier to re-use, easier to share with others, easier to document, and easier to test. But the process of writing a package can feel intimidating, especially if you haven’t done it before. But it doesn’t need to!
An intermediate R course for people who want to build their first R package. There are many benefits to turning your R scripts or functions into a package, like making your code easier to re-use, easier to share with others, easier to document, and easier to test. But the process of writing a package can feel intimidating, especially if you haven’t done it before. But it doesn’t need to!
Learning outcomes:
Know what things you need to make a package and how to create them
How to write functions (in a package-friendly way) and add them to a package
How to write documentation and examples for functions
Best practices for package development
How to share your package with other people
Prerequisites: The session aims to be introductory, so you don’t need any previous experience of building R packages (or even writing functions!) but some basic knowledge of R will be useful.
A course covering random forests, support vector machines, and LASSO regression and how to implement them in {tidymodels}. Suitable for those with some experience of R and statistics.
A course covering random forests, support vector machines, and LASSO regression and how to implement them in {tidymodels}. Suitable for those with some experience of R and statistics.
Learning outcomes:
Be able to use some common machine learning techniques such as Lasso regression, random forests and support vector machines
Fit these models in R using {tidymodels}
Understand common concepts of machine learning such as cross-validation, hyperparameter tuning and model evaluation
Prerequisites: Familiarity with some statistical concepts such as correlation, variability, and simple linear regression. Being reasonably comfortable with data wrangling using {dplyr} and {tidyr}.
An intermediate-level course covering styling HTML and PDF documents, for those with some experience of Quarto.
Learning outcomes:
Know how to customise Quarto HTML outputs (including documents and revealjs
Understand what Quarto extensions are, how to install them, and use them for styling documents
slides) using CSS and PDF documents using LaTeX
Know what the components of Quarto extensions are and be able to create a simple style extension to make your documents look more professional and recognisable
Prerequisites: (Ideally) able to create simple Quarto documents. No prior knowledge of CSS or LaTeX required.
An introductory-level course on Quarto (an open-source scientific and technical publishing system that allows you to combine text, images, code, plots, and tables in a fully-reproducible document).
An introductory-level course on Quarto (an open-source scientific and technical publishing system that allows you to combine text, images, code, plots, and tables in a fully-reproducible document).
Learning outcomes:
Understand what Quarto is and why it is useful for reproducible reporting
Be able to create simple HTML documents, PDFs, and revealjs presentations, with embedded code
Be able to set different options for: code execution, figure options, animation, and more!
Prerequisites: Basic knowledge of R, Python, Julia, or ObservableJS.