Training and Workshops

These workshops can be delivered in-person or remotely, and will be tailored to your existing skillset and area of application. They can be delivered to individuals or small groups (up to 14 people).

Data Visualisation

Data visualisation is essential for exploratory analysis, and for effectively communicating results.

More effective data visualisation

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

R

R is a programming language that can be used for many things, commonly for statistical modelling and data visualisation.

Introduction to data analysis with R

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: 6 hours.

Course website: nrennie.rbind.io/training-intro-to-r

Introduction to Shiny

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: 1 hours

Course website: nrennie.rbind.io/training-intro-to-shiny

Writing better R code

Learning outcomes:

  • Understand how write to better code that is well-structured and easier to read.
  • Be able to organise multiple R scripts in a project.
  • Know how to manage dependencies on different R packages.

Pre-requisites:

  • Basic knowledge of R.

Time: 2 hours

Course website: nrennie.rbind.io/training-better-r-code

Git and GitHub for R users

Learning outcomes:

  • Understand why version control is necessary and useful.
  • Be able to collaborate on code using Git and GitHub from RStudio.
  • Edit code, track changes, and review code using GitHub.

Pre-requisites:

  • Basic knowledge of R.

Time: 2 hours

Course website: nrennie.rbind.io/training-git-r

Quarto

Quarto is an open-source scientific and technical publishing system that allows you to combine text, images, code, plots, and tables in a fully-reproducible document. Quarto has support for multiple languages including R, Python, Julia, and Observable JS. It also works for a range of output formats such as PDFs, HTML documents, websites, and presentations.

Introduction to Quarto

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

Styling documents using Quarto extensions

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

Course website: nrennie.rbind.io/training-quarto-extensions

Statistics and Modelling

Introduction to machine learning with {tidymodels}

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