• Training Manual
  • Welcome
    • Lesson Materials
      • Software-Carpenty
      • Data-Carpentry
      • The Carpentries
      • DataCamp
      • R for Data Science
  • I Project setup
  • 1 Infrastructure
    • 1.1 Project Template
  • 2 Documentation and Metadata
    • 2.1 Code documentaion
      • 2.1.1 lintr
    • 2.2 Metadata
    • 2.3 README files
      • 2.3.1 Example File
  • II Version Control
  • 3 Version Control
    • 3.1 Git
    • 3.2 Git setup
    • 3.3 Git on your own
    • 3.4 Working with remotes
    • 3.5 Git with branches
    • 3.6 Collaborating with Git
    • 3.7 Protecting branches
    • 3.8 Help! (FAQ)
      • 3.8.1 General workflow
      • 3.8.2 Git push rejected (master)
      • 3.8.3 Accidently did work on master:
      • 3.8.4 Get changes from master on your branch
      • 3.8.5 Remote server (e.g., GitLab, GitHub, Bitbucket, etc) shows merge conflict
      • 3.8.6 Remove data/files from history
  • III RStudio
  • 4 RStudio
    • 4.1 Restarting RStudio Session
      • 4.1.1 Within RStudio
      • 4.1.2 From the terminal
    • 4.2 Accessing folders
      • 4.2.1 Outside of Home
  • IV R
  • 5 Functions
    • 5.1 Writing Functions
      • 5.1.1 Fahrenheit to Kelvin
      • 5.1.2 Kelvin to Celsius
      • 5.1.3 Fahrenheit to Celsius
    • 5.2 Testing Functions
    • 5.3 Exercise
    • 5.4 Checking values
    • 5.5 dot-dot-dot …
  • 6 Conditionals
    • 6.1 if statements
    • 6.2 If else statements
    • 6.3 Dealing with NA
    • 6.4 Fizzbuzz
      • 6.4.1 Vectorized conditionals
      • 6.4.2 Multiple conditions
    • 6.5 Exercise
  • 7 Iteration
    • 7.1 Broadcasting
    • 7.2 For loops
      • 7.2.1 Pre allocating in a loop
    • 7.3 purrr (map)
      • 7.3.1 Exercise
    • 7.4 Fitting models
    • 7.5 Apply (in base R)
      • 7.5.1 lapply
      • 7.5.2 sapply
      • 7.5.3 vapply
      • 7.5.4 mapply
      • 7.5.5 apply (2-dimensions)
    • 7.6 Safely dealing with failure
    • 7.7 Possibly and quietly succeeds
    • 7.8 Mapping over different arguments
    • 7.9 Exercise
    • 7.10 Walk
    • 7.11 Other predicates
  • V Modeling
  • 8 Linear Models
    • 8.1 Fit a linear model
      • 8.1.1 Base R
      • 8.1.2 Tidymodels
    • 8.2 Predict
    • 8.3 Error metrics
    • 8.4 Train Test Split
    • 8.5 Exercise
      • 8.5.1 Solution
  • 9 Workflows
    • 9.1 Update formula
    • 9.2 Update Model
    • 9.3 Get workflow components
  • 10 Purrr + tidymodel objects
    • 10.1 Exercise: Train on split data
    • 10.2 Pass the split object
    • 10.3 Question
    • 10.4 Exercise
    • 10.5 map functions
    • 10.6 Exercise
    • 10.7 unnest
  • 11 Other models
    • 11.1 Exercise
      • 11.1.1 Solution
    • 11.2 Decision trees
    • 11.3 KNN
    • 11.4 Selecting your metrics
  • 12 Recipes
    • 12.1 PCA
      • 12.1.1 Prep/bake
      • 12.1.2 Dummy variables
      • 12.1.3 Step novel
      • 12.1.4 remove 0 variance
      • 12.1.5 PCA
    • 12.2 Exercise
      • 12.2.1 Solution
    • 12.3 Put it all together
      • 12.3.1 Solution
  • VI Publishing + Communication
  • 13 RMarkdown
    • 13.1 html
    • 13.2 pdf
    • 13.3 word
    • 13.4 powerpoint
    • 13.5 ioslides
    • 13.6 beamer
    • 13.7 tufte
    • 13.8 rolldown
    • 13.9 rticles
    • 13.10 flexdashboard
    • 13.11 pagedown
    • 13.12 bookdown
    • 13.13 learnr
  • 14 Bookdown
    • 14.1 Components of a book
      • 14.1.1 Section titles:
    • 14.2 Rendering M-K vs K-M
    • 14.3 Looking at the book while you work
    • 14.4 Figures
    • 14.5 Tables
    • 14.6 Cross refrence chapters
  • References
  • Published with bookdown

Training Manual

Chapter 3 Version Control

Software-Carpentry Git Lesson

DataCamp Courses:

  • Introduction to Git for Data Science
  • Working with the RStudio IDE (Part 2) – Chapter 2: Version Control

Quick References:

  1. Software-Carpentry Reference
  2. Git Cheat Sheet (by Github)
  3. Jenny Bryan’s “Happy Git and GitHub for the useR”
  4. Git interaction from NDP Software
  5. Learn Git Branching

Git flight rules for when you mess up