10 Resources

Over the past few years there has been a hige development of reproducible research tools using R.

10.1 RStudio tools and packages

Since the development of R and RStudio (and a magnitude of other IT changes happening at the same time) there are now tools for working with issues to do with reproducibility. There are many blogs from a simple web search.

Here is a collection of the posts I have drawn inspiration from:

• R bloggers post: Jeromy Anglim
• Reproducible workflows in RStudio
• R workflow: Mara Averick
• Methods in Ecology
• workflow general tips
• Data Science and R/Python
• Truely reproducible
• EEB313H1

10.2 Scientific publications

There are many many different posts on reproducible workflows. This document collects the current resources available in R and RMarkdown. I have developed this document from a combination of different git repositories:

• BES guidelines as a start

• Added Wickhams etc paper

• Best practices for Scientific Computing (http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001745)

• Good enough practices for Scientific Computing (https://swcarpentry.github.io/good-enough-practices-in-scientific-computing/)

• 10 simple rules for reproducible computational research: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003285

• A quick guide to organizing computational biology projects: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000424

• Ten Simple Rules for Digital Data Storage (http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005097)

• The Reproducible Research CRAN Task View: (https://cran.r-project.org/web/views/ReproducibleResearch.html)

10.3 Workflow options

However because of this my workflow has a distinctly ecological feel along with the tidyverse approach of tooling. I apologise for this in advance. If you are not a R user I would recommend finding another workflow with the same components.

• Packaging data publication

• Tidytools package