data_analysis_template

Preferred template for a reproducible data analysis workflow using R and Python.

Overview

Overview of project goes here

Setup

Minimum Prerequisites

• Python installed and available on PATH
• R installed and available on PATH

Setup Steps

1. Copy the project folder by either:

   Option 1: Clone from GitHub

   git clone <repository-url> <local-folder>
   

   Option 2: Download manually

   • From the repository home page, click the green Code button
   • Select Download ZIP
   • Extract to a desired location on your local machine

2. Navigate to the project root folder and run:

   setup.bat
   

   This will execute setup.ps1, which:

   • Verifies that Python and R are installed and available on PATH
   • Installs uv (if needed) and syncs the Python environment
   • Installs renv (if needed) and restores the R environment

Result

After setup completes:

• All Python dependencies are installed and synced
• The R environment is restored
• The project is ready to run (see 'scripts/run_analysis_example.r' for example of orchestration script)

Folder Description

config/

Configuration files defining global paths and shared settings used across the project.

data/

Structured storage for all data used and generated in the project.

• raw/ → immutable source data (tracked in git if appropriate)
• pipeline/ → outputs used between processing steps
• interim/ → temporary, diagnostic, or QC outputs
• final/ → analysis-ready datasets

All non-raw data is generated from scripts and should not be manually edited.

doc/

Project documentation, including:

• methodological notes
• draft reports and supporting materials

results/

Outputs generated from analysis, including:

• figures and tables for reporting
• model outputs and summaries
• publication-ready materials

scripts/

Executable scripts that define and run the analysis workflow.

• includes pipeline steps
• serves as entry points for running analyses

src/

Reusable source code and helper functions used by scripts.

• avoids duplication across scripts

Notes

• All data processing should be reproducible by running scripts
• Avoid manual edits to generated data or results
• Use src/ for reusable logic and scripts/ for execution

Running the Pipeline (example)

Add instructions here for running the full pipeline (e.g., via R or Python script).