Data Carpentry for Biologists

Version Control

Notes

Before class

  • Setup class organization at Github
    • Check the Allow members to create repositories for this organization permission
    • Set the Default permissions for the organization to None if you want to avoid students accessing each others repositories
  • Have students create a GitHub account and email their username to the instructor.
  • Add students’ username to organization.

For class

  • Download Gaeta_etal_CLC_data.csv.

  • Either arrange to have a teaching partner to attend class or be logged into GitHub as another user in the browser for collaboration demos.

  • Open the following links in a browser and zoom in to make the images fill the screen.

Live coding demo and assignment are intertwined and designed to work in order.

Introduction

Motivation

  • Who has a directory on their computer with a bunch of filenames
  • Get rid of messy folders and track changes to things like data files and code in a more manageable way.

Benefits of version control

  • Track changes (but better)
    • Tracks every change ever made in groups called commits
      • Every commit stores the full state of all of your files at that time
    • Never lose anything
      • Revert or restore to any commit
      • Easily unbreak your code/data/manuscript
      • No more file name changes
  • Collaboration
    • Work on things simultaneously
      • See what changes others have made
      • Everyone has the most recent version of everything

Version control using Git & RStudio

Create a Git repo

  1. Navigate to Github in a web browser and login.
  2. Click the + at the upper right corner of the page and choose New repository.
  3. Choose the class organization (e.g., dcsemester) as the Owner of the repo.
  4. Fill in a Repository name that follows the form FirstnameLastname.
  5. Select Private.
  6. Select Initialize this repository with a README.
  7. Click Create Repository.

Connect to the Git repo in RStudio

  1. From new GitHub repository, click green Clone or download button -> Click the Copy to clipboard button.
  2. In RStudio, File -> New Project -> Version Control -> Git
  3. Paste copied URL in Repository URL:.
  4. Leave Project directory name: blank; automatically given repo name.
  5. Choose where to Create project as subdirectory of:.
  6. Click Create Project.
  7. Check to make sure you have a Git tab in the upper right window.

Install

install.packages(c('dplyr', 'usethis', 'gitcreds'))

Introduce yourself to Git

library(usethis)

use_git_config(user.name = "[name]", user.email = "[email]")

That was Exercise 1 - Set-up Git. Have students confirm that this all worked and fix any issues.

First commits

Commit data

  • Download the data file Gaeta_etal_CLC_data.csv to your project directory.
  • Add the data file to version control
  • Two step process:
  1. Add the data file (checkbox)
  2. Commit it
  • Git -> Select Gaeta_etal_CLC_data.csv.
  • Commit with message.
    • Add fish size and growth rate data
  • History:
    • One commit
    • Changes too large to see

Commit R script

  • Read in data to new R script.
library(dplyr)

fish_data = read.csv("Gaeta_etal_CLC_data.csv")
  • Save as fish-analysis.R.
  • Git -> Select fish-analysis.R.
    • Changes in staged files will be included in next commit.
    • Can also see changes by selecting Diff
  • Commit with message.
    • Start script comparing fish length and scale size
  • History:
    • Two commits
    • See what changes were made to fish-analysis.R

Building a history

  • fish-analysis.R doesn’t currently show on the Git tab
    • No saved changes since last commit
  • Add some more code to fish-analysis.R
    • Create new categorical size column
fish_data_cat = fish_data |>
  mutate(length_cat = ifelse(length > 200, "big", "small"))
  • Save fish-analysis.R.
  • Now we see the file on the Git tab.
    • M indicates that it’s been modified.
  • To commit these changes, we need to stage the file.
    • Check the box next to fish-analysis.R.
  • Commit with message.
    • Add categorical fish length column
  • History:
    • Three commits
    • Each fish-analysis.R commit shows the additions we made in that commit.
  • Modify this code in fish-analysis.R
    • Change category cut-off size
fish_data_cat = fish_data %>% 
  mutate(length_cat = ifelse(length > 300, "big", "small"))
  • Save file -> stage -> commit
    • Change size cutoff for new column
    • Green sections for added lines, red for deleted
    • Git works line by line.
      • The previous version of the line is shown as deleted.
      • The new version of the line is shown as added.

Do Exercise 2 - First Solo Commit and Exercise 3 - Second Solo Commit

Instructor also do exercises

Committing multiple files

  • Commits can include multiple files at once
  • Let’s move our data file into a data subdirectory
  • New Folder -> data
  • Checkbox Gaeta_etal_CLC_data.csv -> More -> Move
  • Change code to read from new subdirectory
fish_data = read.csv("data/Gaeta_etal_CLC_data.csv")
  • Changes to R script indicated by M
  • Original datafile has a red D next to it which indicates “deleted”
  • New, untracked, data directory
  • git initially thinks we’ve deleted Gaeta_etal_CLC_data.csv and created a new Gaeta_etal_CLC_data.csv file in a new directory.
  • Click on both the old and new files to stage them

  • git then recognizes that we have moved (or renamed) the file by making the two files into one and marking this with an R for “rename”.

  • Commit: Move data file into subdirectory

Do Exercise 4 - Commit Multiple Files.

Instructor also do exercise

Git as a time machine

Experiment with impunity

fish_data_cat = fish_data %>% 
  mutate(length_cat = ifelse(length > 300, "large", "small"))
  • Save and show changes are staged

  • More -> Revert -> Yes

  • Get previous state of a file
    • History -> select commit -> View file @ ...
    • Save file over current file
    • Copy and paste relevant piece into current file

Delete with impunity

  • Both of these also work for deleted files
  • Close the upper left window with the fish-analysis.R.
  • Choose the File tab in the lower right window.
  • Select fish-analysis.R -> Delete -> Yes
  • Stage deleted file -> More -> Revert -> Yes

GitHub Remotes

Draw diagram to link local machine with GitHub origin.

  • So far we’ve worked with a local Git repository.
  • One of the big benefits of version control is easy collaboration.
  • To do this, we synchronize our local changes with a remote repository called origin.
  • Our remote repository is on GitHub.
    • By far the most popular hosted version control site
    • Public and private hosted repositories
    • Private free for students and academics
    • https://education.github.com/
      • For the assignment, we’re using private repositories that we made at the beginning.

Push to a remote

Connect to GitHub

  • To push to your remote we first have to connect to GitHub, which is a little tricky
  • First, log in to GitHub in your browser
  • Then create a GitHub token, this is like a special password just for one computer
usethis::create_github_token()
  • Select defaults
  • Create token

  • Copy token

  • Now add this token our local git setup so that it can use it to connect to GitHub
gitcreds::gitcreds_set()
  • Paste your password

Push

  • Push sends your recent commits to the origin remote.

Draw push arrow on diagram on board from local to origin.

  • Before a Push your commits show in your local history but not on the remote.

Show local commit history and lack of history in remote.

  • To Push to your remote, select the Push button at the top of the Git tab.
  • Now your changes and commit history are also stored on the remote.

Show local commits now on origin.

Do Exercise 5 - Pushing Changes.

Have students email a link to their repo to their instructor once they have finished Pushing Changes

The instructor should then commit the following code to their repo with the commit message: Plot histogram of scale length by categorical size

ggplot(fish_data_cat, aes(x = scalelength, fill = length_cat)) +
  geom_histogram()

Either you (logged in as another user) or your teaching partner should make the same change to your respository

Pulling

  • Big advantage to remotes is easy collaboration
  • Avoids emailing files and shared folders where you are never sure if you actually have the most recent version
  • Makes it easy to see what collaborators have done
  • Automatically combines non-overlapping changes
  • While I’ve been talking, a collaborator has added a plot of scale size and fish length to the code.

Show origin with collaborator commit.

Add collaborator local repo to diagram and pull arrow from origin to locals.

  • Pull the changes from the remote repo with the Pull button on the Git tab

Show updates to history following Pull and run code

Do Tasks 3-6 in Exercise 6 - Pulling and Pushing.

Merges

Demo merges either with a partner or by logging into GitHub as another user in the browser.

  • What happens if two people make changes at the same time?
    • If they edit different parts of the code git will combine them automatically
    • If they edit the same areas of the code this requires human intervention

  • Merges

  • You decide to change the number of histogram bins to 10
geom_histogram(bins = 10)
  • Your collaborator reassesses the measurement device and decides it is accurate down to 0.5 mm and pushes the change to the remote repository [make this change in the remote]
filter(scalelength >= 0.5)
  • You try to push your change
  • Get an error that shows someone else has made a change & you need to incorporate it to push
  • Pull
  • Merge happens automatically
  • You have both sets of changes
  • Remote still only has collaborators changes
  • Push to add the merged version to the remote

Merge conflicts

  • If both you and your collaborator edit the same location in the code git doesn’t know how to combine the changes.

  • A human has to make this kind of decision.

  • You decide to change "big" to "large"
mutate(length_cat = ifelse(length > 300, "large", "small"))
  • Your collaborator changes the size threshold and pushes to the remote
mutate(length_cat = ifelse(length > 250, "big", "small"))
  • You attempt to push your changes
  • Merge conflict when pulling collaborators changes
  • This shows as U for “unmerged” in RStudio
  • First block of code is your version
  • Second block is the version on the remote
  • Combine into a single block that includes everything
mutate(length_cat = ifelse(length > 250, "large", "small"))
  • Click check box next to file
  • Commit indicating that it is a merge
  • Still not on remote yet
  • Push

Full GitHub flow

  • Collaborating on Github can get more complex with “forks” and “branches.

Optional: Redraw diagram with local, origin, and upstream. Arrows from origin to/from upstream are pull requests and merges.

Show an example of a working repository with branches and forks. Navigate to pull requests.