JayCrlt / GitHub_Tutorial

This repository presents a global overview of why GitHub, and especially Git+R should be used by everyone, helping for reproducibility. This tutorial is provided with a class of ~ 1h30.

GitHub tutorial

This tutorial is presented by Jérémy Carlot, as a result of his post-doctoral fellowship with the Laboratoire d'Océanographie de Villefranche sur mer at IMEV in France and supervised by Nuria Teixido, Steeve Comeau and Jean-Pierre Gattuso.

Participants in 2023:

• Mégane Ballin
• Jordi Boada
• Diego Moreno Moran
• Elisabet Nebot-Colomer
• Romane Torchy
• Jana Verdura

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RMarkDown Basics

This document is a RMarkdown.
It will be presented at the beginning, and can / should be used to explain to your readers the purpose of this repository.

Because you are working in ecology 🌱, one of the best use you can do, is to use a GitHub Repository to store:

• Your Data 📝
• Your Code 💻
• Your Figures 📊

Note that you can use emojis to be more friendly.
You have two options; if you know the emoji code, then you can use it. For example, if you want a folder, you can use the code :file_folder:. If you don't know the code, you can still copy/paste from an emoji website.
You can also refer some keywords to another page using brackets and the URL. To do so, put brackets around the word, or the group of words you want to highlight, followed by the URL in parenthesis.

There are plenty of ways to help your reader and to make your repository fancier. If you're not used to RMarkdown, I recommend having a look at RMarkdown Cheat Sheet

You can also make tables, in certain cases, if you feel the need. Below I show you a silly example, but it can be terrific for data curation use (e.g. here)

Table 1. Silly example

People	Age	Size
Jérémy	30 yrs	173 cm
Valentin	28 yrs	176 cm

A well-structured Rmakdown README is key for reproducibity

But one of the highest strengths of RMarkdown, is that you can write down some ideas or start writing down even for a meeting presentation, including (or not) your R analysis and your outputs. Most of the time, you will work with chunks. If you are interested, we can work on this during another tutorial.

If I mentioned the chunks, it's because I highly suggest launching in the console the sessionInfo() function once your final R script is done. Then you can copy/paste the different pieces of information into a triple `.
It will highly help the reproducibility of your analyse with your pairs.

R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.2.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

It is worth to build some hierarchy into your repository,
I recommend getting always:

• a single R folder 📁, hosting for your RProj and your R scripts.
• a single Data folder 📁, hosting for your data you are willing to share.
• a single Results folder 📁, hosting for your raw or edited Figures.

Here is an example of a perfectly reproducible repository.

From here, we can move on and work directly with a case study using R: Tutorial R+Git