RMarkdown & Github

Conducting Reproducible Research by R MarkDown and Github

Why?
From rOpenSci

“The aim of practising reproducible computational research is to expose more of the research workflow to our audience. This makes it easier for them to make a more informed assessment of our methods and results, and makes it easier for them to adapt our methods to their own research.”

http://ropensci.github.io/reproducibility-guide/sections/introduction/

Guards against

• Academic fraud
• Erroneous interpretation 

Allows for

• Independent verification through transparency
• Critique of methods
• Critique of results 

Promotes

• New techniques
• Good scientific practice

How to we produce reproducible research?

Lots of different ways, but in Geographic Information / Spatial Data Science, two tools – Markdown and Github – are making openness and reproducibility far more straightforward

RMarkdown

• RMarkdown documents enable the integration of text (commentary) with code and the outputs of that code.
• RMarkdown documents can be ‘knited’ into a range ofdifferent output formats:
• https://rmarkdown.rstudio.com/lesson-2.html

RMarkdown – Output Formats

• HTML–Webpages
• PDFdocuments
• MS Word Documents
• Books (see bookdown.org)
• ScientificPapers formatted to particular journal styles
• Slide-decks
• InterpretiveDance*

*doesn’t actually output to interpretive dance - yet

Rmarkdown – Reproducibility Built In

• RMarkdown forces you to carry out your analysis properly – it will only compile the end document if you have included everything required (packages, data, code etc.).
• Analysis and commentary are integrated so it is possible to describe the full workflow from data import > processing > analysis > results > interpretation/conclusions.
• Anyone viewing your RMarkdown document will be able to reproduce exactly what you have done and through the commentary, understand how and why you have done it.

GitHub

• A web-based service for storing your code and, importantly, versions of it.
• Git is a versioning tool (one of many, but probably the most popular – others include SVN).
• Versioning is vital when working on collaborative projects, however it can also be useful when updating your own projects.
• GitHub is becoming the de facto location for storing code for others to use and view on the web (although others exist like Bitbucket and Gitlab)
• You will produce and critically compare TWO maps produced using 2 different pieces of software.
• One map will be created using a GUI-based piece of GIS Software, such as ArcMap or QGIS.
• One map will be produced using predominantly code- based software such as R or MapBox.
• The accompanying text will evaluate both finished products and the cartographic/GIS/data science work-flows used to produce them. Images of the two maps should be embedded in an R Markdown .Rmd document titled “AssessmentPart1”. Once complete, you will upload your completed“AssessmentPart1.Rmd” file to a repo on your personal GitHub page.