How to Get Organized with RStudio Projects and Git

Use RStudio Projects and Git to organize your data science projects

Would you like to better organize your data science projects following a simple and consistant workflow?

In my new YouTube video, I show you how to use RStudio Projects and Git version control to follow a consistant file structure and track all the changes in your code.

You will learn how to:

• use RStudio projects and connect them to Git and GitHub (the easy way);

• use RStudio Git panel view to commit, push and pull files with 1 click;

• see an example of a data science project file structure.

Watch the video now:

Data science projects can quickly become chaotic without proper organization and version control. In this tutorial, you’ll learn how to use RStudio Projects and Git to maintain clean, organized, and portable data science workflows.

The Problem: Disorganized Project Folders

Many data scientists end up with folders that look like this:

• analysis.R

• new-analysis.R

• new-analysis-version2.R

• ideas-from-document.R

• Mixed files from different projects in the same directory

This disorganized approach leads to confusion, lost work, and difficulty collaborating with others. Let’s fix this using RStudio Projects and Git version control.

Creating Your First RStudio Project

Step 1: Start a New Project

1. Click on the New Project button in the top-right corner of RStudio

2. You’ll see three options:

   • New Directory: Creates a new folder

   • Existing Directory: Uses an existing folder

   • Version Control: Connects to Git (we’ll cover this later)

Step 2: Choose Project Type

Select New Directory, then choose from various project templates: - Empty new project (recommended for beginners) - R package - Shiny application - Quarto projects - And other specialized templates

Step 3: Configure Your Project

1. Directory name: This becomes your project folder name (e.g., “organized”)

2. Subdirectory: Choose where to save your project

   • Pro tip: Create a dedicated folder for all your data science projects

   • Consider syncing with cloud storage (OneDrive, Google Drive, etc.)

3. Optional settings:

   • Git repository (we’ll set this up separately)

   • renv for package management (recommended for advanced users)

Click Create Project to finish setup.

Understanding Project Benefits

Automatic Working Directory

RStudio Projects automatically set your working directory to the project root. This means:

# Instead of complex file paths like this:
read_csv("/Users/username/long/path/to/project/data/data.csv")

# You can use simple relative paths:
read_csv("data/data.csv")

This makes your projects portable – you can move the entire folder anywhere and the code still works.

Project File Structure

The .Rproj file in your project folder tells RStudio this is an RStudio Project. Benefits include:

• Easy project switching via the top-right dropdown menu

• Automatic working directory management

• Project-specific settings and history

• Better organization across multiple projects

Recommended Folder Structure

For consistency across projects, follow R package conventions:

your-project/
├── data/           # Raw and processed data
├── R/              # R scripts and functions
├── docs/           # Documentation
├── outputs/        # Results, plots, reports
├── README.md       # Project description
└── your-project.Rproj

Connecting Git to RStudio Projects

Why Use Git?

Git helps you:

• Track changes to your code over time

• Collaborate with others

• Back up your work online

• Revert to previous versions when needed

Method 1: Add Git to Existing Project

If you already have an RStudio Project, use the usethis package:

library(usethis)
use_git()
use_github(private = TRUE)  # Set private = TRUE for private repos

Important: The private argument defaults to FALSE, making your repository public!

Method 2: Create Project with Git (Recommended)

This approach avoids common Git setup issues:

1. Create GitHub repository first:

   • Go to GitHub.com and create a new repository

   • Add a description

   • Choose public or private

   • Copy the HTTPS URL

2. Clone in RStudio:

   • Choose New Project > Version Control > Git

   • Paste the repository URL

   • RStudio will automatically name the project after your repository

   • Choose your subdirectory location

Working with Git in RStudio

The Git Panel

Once connected, you’ll see a Git tab in RStudio showing: - Modified files - New files - Files ready to commit

Using .gitignore

The .gitignore file tells Git which files to ignore. Common entries:

# Credentials and sensitive data
credentials.R
*.env

# Large data files
data/large-dataset.csv

# Temporary files
.Rhistory
.RData

The Git Workflow

1. Stage files: Check the boxes next to files you want to commit

2. Commit: Click “Commit” and write a descriptive message

3. Push: Upload your changes to GitHub

Viewing Changes

RStudio’s Git integration shows you: - Which files have been modified - Exactly what changed in each file (diff view) - When you right-click a file and select “Diff selected files”

This helps you: - Spot unintended changes or typos - Review your work before committing - Understand what’s changed since your last commit

Best Practices Summary

1. Always use RStudio Projects for better organization and portability

2. Connect Git early in your project lifecycle

3. Use consistent folder structures across projects

4. Write meaningful commit messages to track your progress

5. Regularly commit and push your work

6. Review diffs before committing to catch errors

See you in another tutorial!