WWALK’s Data & GitHub Crash Course

2026-05-19

Part I: Prep Work

Learning Goals

My goal for this workshop is to give everyone the tools to:

  • Confidently start a project in R
  • Manage files in a way that is reproducible and easy to understand
  • Allow people to document history/progress on their projects
  • Know one approach to publicly archiving projects

GitHub Caveats

  • Microsoft is a deeply evil company that profits from the war machine and the destruction of our planet
  • GitHub is a Microsoft company
  • There aren’t currently a lot of alternatives, but there will be eventually and you can check out this list if you’re interested

Privacy Caveats!!!

  • This lab has many examples of data that should not be uploaded to the internet, that does not mean that we can not use GitHub/Zenodo/reproducible science tools
  • We need to be extremely mindful, make sure our code is anonymized
  • Upload fake example data so that users can understand how our code works, with clear metadata that explains that the real data is unavailable

Software Installation

  • CHECK-IN: does everyone have everything working/installed?

Transparent Workflows

  • Ensuring that your workflow is transparent is important for:

    • Past/Current/Future You

    • WWALK Lab

    • Collaborators

    • Other grad students

    • Scientific Community

    • PUBLIC

Part II: R & RStudio

Project Management in R

Good file structure is important because it 1

  • Ensures the integrity of your data
  • Makes it easier to share your code with people
  • Makes it easier to upload your code/data with manuscript submission
  • Makes it easier to come back after a break

File Management for R

Best practices include (but are not limited to) 1

  • Use an R Project file so that your project is easily shareable
  • Always treat raw data as read-only
  • Store cleaned data in a separate folder (or distinguish clearly)
  • Treat output as disposable - you should always be able to re-generate with script
  • Have separate function and figure scripts

Cleaning Data in R

There are some tasks that do not need to be “as reproducible” (e.g., fixing typos) - these can be done in OpenRefine.

In general if you are:

  • Combining data sources

  • Making decisions about the data itself (e.g., removing or adding data)

  • Performing calculations

  • Renaming things

Do this in R (you will be grateful later!)

Basic File Structure

project
└───raw-data/
└───output/
└───R/
└───graphics/
└───README.md

Exercise: make a project

  • Let’s set up a new project using RStudio Projects

  • Add raw-data, output, R, and graphics folders

  • (Bonus: I recommend you have a folder on your computer dedicated to all R projects)

Part III: GitHub

GitHub & Version Control

Piled Higher and Deeper

GitHub & Version Control

  • GitHub is a website-software that documents your progress on a project and allows you to do version control

    • aka it takes snapshots of your progress across time so nothing gets lost

  • If you save rough drafts of your writing as you go along - that is version control

  • Really useful for when you want to go back/change your mind/re-run a test/etc.

  • Facilitates lower mental load + reproducible science + collaboration/sharing

Project Workflow with Git

biost@ts Git Tutorial

Project Workflow with Git + Others

biost@ts Git Tutorial

The Basics of GitHub

  • 5 basic jargon terms you need to know to use GitHub:
    • Repository/repo: your project
    • Clone: make a local copy of your project
    • Commit: describe and commit to any changes you’ve made
    • Push: send your changes to your online repo
    • Pull: incorporate any changes to your local repo
    • (BONUS branch: a side project)
  • We will do all these things today!

Exercise: make a repo!

github.com/repos

Exercise: make a repo!

Exercise: make a repo!

github.com/icrichmond/github-workshop

Cloning (Download An Existing Directory)

Committing

  • GitHub tracks the changes you make to your repository on your computer

  • After making changes, you have to select, describe, and commit them

Committing

Pushing

After committing, you push your changes to your remote repository

Pulling (Collaboration Station)

  • If you are collaborating on a project, where multiple people are contributing, make sure you pull from the remote repository before starting your work

  • Same button as push (ctrl + shift + P)

Bonus: Ignoring

  • the .gitignore file in your project directory allows you to force git to ignore specific files or folders

  • there is a syntax for specifying different file types or folders, which can be found in the link above

# Ignore all .txt files
*.txt

# But don't ignore important.txt
!important.txt

# Ignore all files in large directory
large/*

Part IV: Archiving Data

Lab Archiving

Archiving your project in the lab requires 4 things:

  1. Paper/thesis
  2. Clean data
  3. Metadata
  4. Code
  5. (Bonus: presentations you have given)

These things can be organized however you’d like, as long as they are easily understood by someone after you are gone.

Why Using Git is not Archiving

  • Does not have a DOI, so does not point to a specific moment in time

  • Can be changed continuously

  • Not dedicated to longevity

  • Can import GitHub repository to a true data archive

Public Archiving

  • Zenodo is a great option for archiving data

    • Easily links to GitHub repositories

    • Preserves file structures

    • Can be updated after reviews/changes with a new DOI

    • FREE

  • Other options include Dryad, figshare, and more topic-specific archives (e.g., GenBank)

  • As always, use what works for you

Zenodo

To connect and archive your code/data with Zenodo from GitHub, there are three main steps

  1. Link your GitHub to your Zenodo account, and toggle “On” for your repository
  2. Make a release of the project on GitHub
  3. Obtain DOI and project page from Zenodo

(see an example workthrough here)

NOTE: you do not need to use Git to use Zenodo, you can also upload local files

The Ultimate Combo Deal

Resources

This workshop - including examples & code can all be found here and formatted slides are here

Software Carpentry: R for Reproducible Scientific Analysis & Version Control with git

Data Carpentry: Data Analysis & Visualization in R for Ecologists & Data Organization in Spreadsheets for Ecologists

biost@ts: Version Control with Git and GitHub

Happy Git: happygitwithr

University of Bergen: Open Access to Research Data

Resources

Smart People I Know: Dr. Christie Bahlai’s Reproducible Quantitative Methods Course & Wildlife Ecology & Evolution Lab’s Guide by Alec Robitaille & Val Lucet’s Git Workshop