#TidyTuesday 

A collection of visualizations for Tidy Tuesdays, built as a Quarto website with R and Python support. Forked from Guillaume Noblet @ gnoblet/TidyTuesday and adapted from Cédric Scherer @ z3tt/TidyTuesday.

Website

Visit the live gallery on my personal website here.

Contributions

My contributions to the #TidyTuesday challenge, a weekly social data project focused on crafting meaningful and beautiful visualizations using tools from the {tidyverse} ecosystem.

Contributions in chronological order (click to expand)

• Challenges 2026
  • No contributions yet — stay tuned!

Development Setup

Prerequisites

• R (≥4.4.0)
• Python (≥3.13)
• Quarto
• uv (Python package manager)

Quick Start

1. Clone the original repository:

   git clone https://github.com/gnoblet/TidyTuesday.git
   cd TidyTuesday

2. Run the setup script:

   ./setup-dev.sh

3. Preview the website:

   quarto preview

Creating New Visualizations

Quick Template Creation

Use the included script new-viz-from-template.sh to generate a new visualization from the template (from the top-level directory):

# Usage: ./scripts/new-viz-from-template.sh <date> <week> <language> <title>

# Create a new R analysis
./scripts/new-viz-from-template.sh 2025-07-22 29 r "MTA Permanent Art Catalog"

# Create a new Python analysis  
./scripts/new-viz-from-template.sh 2025-01-09 2 python "posit::conf talks"

# Example with single-word title
./scripts/new-viz-from-template.sh 2024-09-24 39 r "Olympiad"

Arguments:

• date: ISO date for the challenge (YYYY-MM-DD), e.g., 2024-12-15
• week: TidyTuesday week number (e.g., 29)
• language: Programming language (r or python)
• title: Title for the analysis (wrap in quotes if it contains spaces)

This will create:

• A .qmd file inside a structured folder: challenges/YYYY/YYYY-MM-DD_WW_Title/language/
• All placeholders automatically replaced with your specified values
• Ready-to-edit analysis structure

Template Structure

Each generated visualization includes:

• Overview: Brief summary of the dataset, analysis goals, and main findings
• Dataset: Description of the data source, structure, and how the data is loaded
• Analysis: Data cleaning, wrangling, and any exploratory steps
• Plotting: Code and explanation for generating the main visualization(s)
• Technical Summary: Tools, packages, and libraries used in the analysis
• Visualization: The final rendered plot or output image
• Future Directions: Suggestions for further analysis, improvements, or data limitations
• TidyTuesday References: Links to relevant TidyTuesday repositories and resources

Manual Creation

You can also manually create new visualizations:

1. Copy template.qmd to your desired location
2. Replace all {{PLACEHOLDER}} values
3. Add your analysis code
4. Render with quarto render filename.qmd

Automatic Gallery Integration

• New analyses automatically appear in the gallery when you render the site
• No manual updates needed - the gallery scans for .qmd files dynamically
• Consistent formatting across all projects

Manual Setup

R Environment (renv)

# Restore R packages
R -e "renv::restore()"

Python Environment (uv)

# Create virtual environment
uv venv .venv
source .venv/bin/activate

# if using fish instead of bash
source .venv/bin/activate.fish

# Install dependencies
uv sync

Build Website

# Render the website
quarto render

# Preview locally
quarto preview

Project Structure

├── .github/workflows/        # GitHub Actions for deployment
├── challenges/               # Challenge folders by year and week
│   └── 2025/
│       └── 2025-07-22_29_MTA_Permanent_Art_Catalog/
│           ├── data/
│           ├── r/ | python/  # R or Python analysis
│           │   └── 2025-07-22_29_MTA_Permanent_Art_Catalog.qmd
│           └── viz
│               └── gif/
│                   ├── frames/
│                   └── viz.gif
├── _quarto.yml               # Quarto configuration
├── README.md                 # Project README
├── references.bib            # Bibliography file for citations
├── renv/                     # R environment directory
├── renv.lock                 # R package lockfile
├── scripts/
│   ├── load_tidytuesday_data.py  # Script to load TidyTuesday data in Python-based .qmds
│   ├── load_tidytuesday_data.R   # Script to load TidyTuesday data in R-based .qmds
│   ├── mcc.R
│   ├── new-viz-from-template.sh  # Script to create a new project
│   ├── setup-dev.sh              # Development setup script
│   ├── template.qmd              # Starter Quarto template
│   └── viz_aesthetics.R
├── _site/                    # Quarto rendered website (ignored)
├── tidytuesday.Rproj          # TidyTuesday .Rproj file
├── tidytuesdayR.pdf          # TidyTuesday cheat sheet
├── .venv/                    # Python virtual environment (ignored)
├── _quarto.yml               # Quarto configuration

Deployment

The website is automatically deployed to GitHub Pages when changes are pushed to the main branch. The GitHub Actions workflow:

1. Sets up R with renv for package management
2. Sets up Python with uv for package management
3. Installs system dependencies
4. Renders the Quarto website
5. Deploys to GitHub Pages

Adding New Projects

Use the new-viz-from-template.sh script to add new projects. It creates the proper folder structure and updates _quarto.yml automatically.

• R projects go in challenges/YYYY/YYYY-MM-DD_WW_Title/r/
• Python projects go in challenges/YYYY/YYYY-MM-DD_WW_Title/python/

Package Management

• R packages: Managed by renv
• Python packages: Managed by uv
• Repository: Fast Linux binaries from p3m.dev

License

This project is open source and available under the MIT License.