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Programme & Objectives

Workshop Overview

Have you had the experience of writing a paper and discovering at some point that the numerical results don't match between versions? Or have you prepared conference presentations and got tired of going back and forth with running the analysis and copying the plots to the slides? 

In this workshop, we will look at how to create research outputs in multiple formats, while ensuring the reproducibility of your analysis, using a scripting approach as part of the open research workflow. While we will focus on the use of R this approach can be applied to other programming languages.

Target Audience

Academics and PGR students who use statistical data analysis in their research. Prior knowledge of statistical programming (in any language or software) is preferred, but is not required. 

Priority will be given to colleagues who are employed by the University if the course reaches capacity.

Learning Objectives 

At the end of the workshop participants will be able to:

• Understand the basic principles of computational reproducibility and literate programming, as part of the open research workflow.
• Create simple yet reproducible documents without separate code files.
• Create outputs in multiple formats (e.g. pdf, html, word) using the same script files (in R Markdown or Quarto).
• Outline the steps to make the statistical analyses in their current research reproducible.

Programme 

The workshop programme is as follows: 

• Introduction to computational reproducibility and literate programming
• Demonstration of basic usage of RStudio
• Demonstration of creating outputs using R Markdown and/or Quarto
• Q&A session and practical
• Conclusion and follow-on directions

Duration: 2 hours

Facilitator: Clement Lee

Location: HDB.1.06 Learning Lab, Henry Daysh Building

Notes

Participants are encouraged to:

• Bring their own laptop, with R (https://cran.r-project.org) and RStudio (https://posit.co/download/rstudio-desktop/) installed prior to the workshop. You will need to install R first before installing RStudio. However, no knowledge of R or RStudio is required.
• Bring their own datasets (preferably csv or xlsx files) or analyses (either the code or the document describing the analyses).

R is a programming language for data analysis, statistics and graphics. It is open source, free, and very widely used by professional statisticians. RStudio is a free, open source integrated development environment (IDE) for R. It has a user-friendly interface and provides powerful tools for writing code. We will use RStudio to work with R.