Summary

Final take-homes

• The Markdown language is a useful tool for altering the format and structure of plain text

• Markdown is best implemented in R via RStudio using the rmarkdown R package

• R Markdown documents permit dynamic integration of formatted plain text, R code and its outputs, in turn supporting reproducible software development, data analysis and workflows

Acknowledgements and additional resources

This workshop shares some thoughts and ideas from the following resources:

• The Reproducible Research in R course developed for the Monash Bioinformatics Platform

• The R Markdown Cookbook developed by Yihui Xie, Christophe Dervieux and Emily Riederer

• The Introduction to Markdown in Python workshop offered by University of Exeter’s Coding for Reproducable Research programme

• The R Markdown “cheat sheet” available through posit.

Creation of this workshop was also supported by ChatGPT.

Bonus task

Below are links to some tutorials from the microbiome R package for analysis of microbiome data (Bioconductor landing page here):

• Alpha diversity tutorial

• Beta diversity tutorial

• Community composition tutorial

Try to reproduce one of the above microbiome package tutorials of your choice using R Markdown.