Training and Workshops

Learning outcomes:

• Understand the difference between R and RStudio, and how RStudio works.
• Be able to complete simple operations in R such as saving objects, performing calculations, and reading in data.
• Calculate summary statistics, and make simple plots of data.

Pre-requisites:

• None.

Time: 1-3 hours (depending on your existing experience).

Learning outcomes:

• Understand the different components of a Shiny app - UI and server.
• Be able to build a simple Shiny app with user inputs and dynamic outputs.
• Understand options for deployment - to get your Shiny app out into the world!

Pre-requisites:

• Basic knowledge of R.

Time: 2 hours.

Learning outcomes:

• Understand what an R package consists of, and how to create the basic framework for a package.
• Add documentation to the functions in your R packages.
• Be able to build and run unit tests for the functions in your package.

Pre-requisites:

• Comfortable using R and able to write your own (simple) functions.

Time: 3 hours.

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!

Pre-requisites:

• Basic knowledge of R, Python, Julia, or ObservableJS.

Time: 2 hours.

Learning outcomes:

• Understand what Quarto is and why it is useful for research.
• Be able to create simple Quarto documents in PDF and HTML.
• Create a (APA) style manuscript.
• Be able to integrate referencing, LaTeX equations, and other elements that are useful for academic publishing.

Pre-requisites:

• Able to run simple commands in either R or Python.

Time: 2 hours (can be combined with Quarto for teaching for a 4 hour workshop)

Learning outcomes:

• Be able to make revealjs slides for talks and lectures.
• Add interactive code blocks with webR or pyodide for teaching programming.
• Create a parameterised tutorial worksheet with features for showing and hiding solutions.
• Automate the production of tutorials for students.

Pre-requisites:

• Able to run simple commands in either R or Python.
• (Ideally) able to create simple Quarto documents.

Time: 2 hours (can be combined with Quarto for research for a 4 hour workshop)

Learning outcomes:

• Know how to customise Quarto HTML outputs (including documents and revealjs slides) using CSS and PDF documents using LaTeX.
• Understand what Quarto extensions are, how to install them, and use them for styling documents.
• 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.

Pre-requisites:

• (Ideally) able to create simple Quarto documents.
• No prior knowledge of CSS or LaTeX required.

Time: 2 hours.

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.

Pre-requisites:

• Familiarity with some statistical concepts such as correlation, variability, and simple linear regression.
• Being reasonably comfortable with data wrangling using {dplyr} and {tidyr}.

Time: 2 hours.

Learning outcomes:

• Know what generalised additive models (GAMs) are.
• Understand why and when GAMs might be appropriate for certain types of data.
• Be able to fit and evaluate GAMs using the {mgcv} package in R.
• Understand how to interpret the output from fitted models.

Pre-requisites:

• Basic knowledge of R.
• Familiarity with some statistical concepts such as correlation, variability, and simple linear regression.

Time: 2 hours.

Learning outcomes:

• Understand why data visualisation is necessary, and what it can be used for.
• Describe and apply principles of good data visualisation.
• Discuss the limitations of principles.

Pre-requisites:

• None.

Time: 1 hour.