Shareable and reproducible reporting with R and Rmarkdown
The course is suitable for graduate students or professionals who are already beginner to intermediate users of R. In particular, participants should be familiar with the “tidyverse” suite of packages and “ggplot2”. The module “Engaging Data with R” is the perfect precursor to this module.
In the field of Data Science, R is an incredible resource for engaging with data in order to extract meaningful information that can be used to make actionable insights across a wide variety of scientific disciplines and businesses. Once a data scientist has explored their data and begun to build models, ideally there needs to be a way to communicate the results to collaborators and colleagues in a way that is efficient and automated. In this module, we will cover: how to summarise model output into “tidy” tables, as well as, generating automated and reproducible reports. This course will focus on programming within the R “tidyverse” framework. Important to note: This course does not cover any form of statistical modelling (e.g. using statistical tests, predictive or causal modelling).
Participants will acquire the skills to:
Turn model output into easily digestible tables that can be used in a pipeline for visualisations and reporting
Create reports in “Rmarkdown” that incorporate plain text, plots, tables, and embedded code.
Generate automated and reproducible reports in different formats (e.g. html, pdf, and Microsoft Word).
Develop code for publication-ready tables in these reports.
There will be lectures, interwoven with practical exercises. Participants will be encouraged to follow along with exercises and programming on their own laptops.
Dr Roxanne Beauclair is a specialist in applying biostatistical methods to epidemiological data. She holds a PhD in this field from Ghent University, and has launched her own statistical consultancy company, Data Yarn based in Pretoria. She received training in Epidemiology (MPH) from the University of Cape Town in South Africa. She has been involved in an analytical capacity for several epidemiological studies. Over the past few years she has become an R enthusiast and enjoys learning new ways to improve upon statistical programmes by creating clean, reproducible, and legible code.