Workshop overview

Motivation

This image was created by Scriberia for The Turing Way community and is used under a CC-BY licence (DOI 10.5281/zenodo.3332807).

Two main topics

• Research compendia
• R packages

• Code on github https://github.com/selinaZitrone/YoMos2020
• Slides on https://selinazitrone.github.io/YoMos2020/index.html

Motivations for a better project organization and workflow

• Reproduciblity 🔃 (for you and others)
• Reliablity 🏋 (will it work again?)
• Re-usablity ⚙ (don’t re-invent the wheel)
• Visiblity 🔍 (let others see and use your work)

How? Use a research compendium!

This image was created by Scriberia for The Turing Way community and is used under a CC-BY licence (DOI 10.5281/zenodo.3332807).

What is a research compendium?

This is a collection of all digital parts of a research project including data, code, texts (protocols, reports, questionnaires, metadata). The collection is created in such a way that reproducing all results is straight forward.

(Definition from The Turing Way)

Excursion to the TTW website

Excursion to Karthik Ram’s talk at RStudio::conf 2019

Mavick et al. 2018

• It’s an 📦 R Package!
• packages generating similar project directory structures:
  • rrtools
  • ProjectTemplate
  • prodigenr
  • workflowr

Advantages of using R Packages

• benefit from quality control mechanisms built around R packages (R CMD check)
• easy documentation
• additional packages available to make your life easier
  • usethis: workflow package to automate tasks like project setup etc.
  • pkgdown: build a quick and easy website for your package
  • goodpractice: gives you advice about good practices when building R packages
  • testthat: add unit testing to your package
  • … and many more

Hands-on I

Create a research compendium with the R package structure

Version control

Git as a distributed version control system (VCS)

• complete and long-term history of every file in your project
  • go back to specific versions of your project
• collaborate in teams
  • branching and merging
  • tracability: each change is commented and can be traced back to a developer
• host your repository on remote server (GitHub, Bitbucket, GitLab, …)
  • open-source collaborative development
  • visibility: get new collaborators, feedback, bug reports

Hands-on II

Add version control to the research compendium

Automated testing

What?

• unit tests: functions that run a small amount of code: “units”
• input: defined and fixed
• compares output with an expected output
• returns true/false

Why?

• fewer bugs
• better code (needs to be testable)
• confidence and trust

How?

• write tests either before or after implementing your code
• when you found a bug: before fixing, write a test that would have detected it
• use a test framework, e.g. in R: testthat package (Hadley Wickham)

Hands-on III

Unit tests

Continous integration (CI)

“If it hurts, do it more often” (Martin Fowler)

Continous integration (CI)

Idea

• shared code repository (e.g. on github)
• push code frequently (every day)
• automated builds and tests after each commit
• immediate feedback if something doesn’t work anymore
• however, can be time consuming and might be overkill for small or solo projects

Github Actions for CI (and other automation tasks)

• supports mac-OS, windows, linux, docker containers
• integration with github, so no separate setup and authentication
• free for open source, academic or educational repos
• 20 concurrent jobs (40 with pro), 6 h per job, 72 h per workflow
• self host to remove the per job limit
• very easy to setup with R using the usethis package

This image was created by Scriberia for The Turing Way community and is used under a CC-BY licence (DOI 10.5281/zenodo.3332807).

Hands-on IV

Github Actions for R CMD check

Reproducibility and Accessability

Another excursion to Karthik Ram’s talk at RStudio::conf 2019

Holepunch

Hands-on V

Publish your repository on Binder with a live RStudio server instance