An R Reproducibility Toolkit for the practical researcher

Dates

14-16-18 March 2022

 

Due to the COVID-19 outbreak, this course will be held online

 

Course overview

With considerable effort, you wrote R code to analyze your data and generate a final document or report to present the results. You then give the code to a colleague for them to evaluate it, they don’t know which packages they need to install, they don’t have the necessary data and they don’t know if the correct script is report_final.Rmd or report_final_final.Rmd. Even after your guidance, they find that the code does not run on their machine. Worse, when you try to run it yourself a few weeks later, you realize that a package update "broke" your code and now it doesn't run.
This course will help you avoid those issues. You will learn how to organize a project to speed up collaboration and maximize its reproducibility by leveraging existing tools in the R ecosystem --such as RMarkdown, renv, and others--, version control and working environments.

 

 

Target audience and assumed background

This course is intended for researchers, data scientists, and anyone who uses R to generate documents and who wants to collaborate with other people (or themselves in the future) with the minimum amount of pain possible.
Basic prior experience with R is recommended. If you have ever read data and generated a graph or table based on it, you have everything you need to participate.

 

Learning outcomes

By the end of this course, participants will be able to:
Create an R project that outputs a reproducible document.
Create and manage a reproducible environment that specifies packages and packages versions.
Track changes with git.
Collaborate with others and themselves with GitHub.
Create and publish containers.

Program

Daily schedule:
3-6 pm (Berlin time): live lectures, live coding, live exercises.

Asynchronous homework support via Slack.

 

Monday– Classes from 3-6 PM Berlin time

 

  • RStudio projects
    • Folder structure
    • R Package structure
    • Automate stuff with rrtools and friends.
    • Here::here.
    • Code documentation
  • R markdown. 
    • Intro.
    • Rticles
    • Using LaTeX templates (pandoc templates)
    • Parameterised reports
  • Managing dependencies
    • renv

 

 

Wednesday– Classes from 3-6 PM Berlin time

 

  • Sharing data
    • Data repositories (DOI and details)
    • Access to data from code
  • Git and GitHub
    • Setup and basic ideas
    • Basic workflow (add + commit)
    • Collaboration (forks, pull requests, etc..)
    • Repo documentation (README, Licence, code of conduct).

 

 

Friday– Classes from 3-6 PM Berlin time

 

  • Containers
    • Docker
    • My binder
    • Rstudio cloud
    • Others
  • Docker
    • Create a container with a Dockerfile
    • Docker + renv
    • Publish container on dockerhub

 

Instructors

Paola Corrales: Ph.D. student at the University of Buenos Aires. She studies atmospheric sciences applying data assimilation techniques to improve short-term forecasts of severe events in Argentina. Trainer and instructor for The Carpentries and an RStudio certified instructor. She also develops openly licensed materials to teach and learn R from scratch. 

More information about Paola: https://paocorrales.github.io/.

 

 

Elio Campitelli:  Ph.D. student at the University of Buenos Aires in atmospheric sciences and an R package developer. They apply open science principles with a strong emphasis on reproducibility by publicly making all the code and data available. They are a founding member of the R User Group in Buenos Aires and an outstanding contributor to the LatinR conference (as recognized by LatinR founding chairs). They maintain several open-source R packages (e.g., ggnewscale; metR) and contribute to other packages, such as data.table and ggplot2.

More information about Elio: https://eliocamp.github.io

 


Cost overview

Package 1

 

380 €


Should you have any further questions, please send an email to info@physalia-courses.org

Cancellation Policy:

 

> 30  days before the start date = 30% cancellation fee

< 30 days before the start date= No Refund.

 

Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.