NHS RAP Cookiecutter Template

A Cookiecutter template for creating standardised NHS England Reproducible Analytical Pipeline (RAP) projects.

Overview

This template provides a standardised project structure for developing Reproducible Analytical Pipelines within NHS England. It includes pre-configured tooling for testing, linting, documentation, and dependency management following modern Python development practices.

What you get:

Standardised structure following RAP principles
Pre-configured tooling for testing, linting, and formatting
Multiple environment options (uv, conda, poetry, virtualenv, etc.)
NHS-branded documentation ready to customise
Security scanning with gitleaks pre-commit hook
Flexible configuration for different project needs

What is RAP?

Reproducible Analytical Pipelines (RAP) is a set of tools, principles, and techniques to help you improve your analytical processes. With RAP, you leverage open-source tools to make your work more efficient, more reusable, and less error-prone.

Learn more at the NHS RAP Community of Practice.

Core RAP Principles:

Principle	Description
Automation	Minimise manual, error-prone steps. Automate data retrieval, processing, and output generation.
Modular, reusable code	Write code in independent, loosely-coupled functions that can be tested and reused.
Transparency	Publish code openly under appropriate licences to improve trust and collaboration.
Open-source tools	Use Python, R, SQL, and other freely available languages instead of proprietary software.
Version control	Use Git to track changes, enable collaboration, and maintain an audit trail.
Good coding practices	Follow standards like PEP8, write clear documentation, and use logical project structures.
Testing	Implement automated tests to ensure reliability and catch errors early.
Peer review	Review code collaboratively to ensure quality, accuracy, and adherence to standards.

Installation

This package is not published to PyPI. Install directly from GitHub:

pipx (recommended for CLI tools):

pipx install git+https://github.com/nhsengland/nhse-rap-cookiecutter.git

See the pipx documentation for installation instructions.

uv:

uv tool install git+https://github.com/nhsengland/nhse-rap-cookiecutter.git

See the uv documentation for installation instructions.

pip:

pip install git+https://github.com/nhsengland/nhse-rap-cookiecutter.git

Quick Start

Using the CLI:

nhs-rap-template

Using cookiecutter directly:

cookiecutter gh:nhsengland/nhse-rap-cookiecutter

Configuration Options

The template prompts for the following:

Category	Option	Description	Choices
Project	project_name	Human-readable project name	Text
	repo_name	Repository name (default: lowercase project_name with underscores)	Text
	module_name	Python module name (default: repo_name with dashes converted to underscores)	Text
	description	Brief project description	Text
	author_name	Your full name	Text
	author_email	Your email address	Text
	organization_name	Your organisation name (default: NHS England)	Text
	team_name	Your team name	Text
	team_email	Team contact email	Text
	git_hosting_platform	Git hosting platform	github, gitlab, azure_devops, other
	repository_url	Repository URL (can override default)	Text
Python	python_version_number	Minimum Python version	3.10, 3.11, 3.12, 3.13
	environment_manager	Virtual environment tool	uv, virtualenv, conda, pipenv, pixi, poetry, none
Options	include_code_scaffold	Include example code modules	Yes, No
	linting_and_formatting	Code quality tools	ruff, flake8+black+isort
	open_source_license	Project licence	MIT, Apache-2.0, GPL-3.0, No license file
	docs	Documentation tool	mkdocs, none

Generated Project Structure

your-project/
├── data/
│   ├── external/        # Data from third-party sources
│   ├── interim/         # Intermediate transformed data
│   ├── processed/       # Final canonical datasets
│   └── raw/             # Original immutable data
├── docs/                # MkDocs documentation with NHS styling
├── models/              # Trained models and predictions
├── notebooks/           # Jupyter notebooks for exploration
├── references/          # Data dictionaries and documentation
├── reports/
│   └── figures/         # Generated graphics
├── scripts/             # Setup and utility scripts
│   └── setup_repository.py  # Automated repository setup
├── tests/
│   ├── unittests/       # Unit tests (pytest)
│   └── e2e/             # End-to-end integration tests
├── your_module/         # Source code package
│   ├── __init__.py
│   ├── config.py        # Configuration management
│   ├── dataset.py       # Data loading and processing
│   ├── features.py      # Feature engineering
│   ├── plots.py         # Visualisation functions
│   └── modeling/
│       ├── train.py     # Model training
│       └── predict.py   # Model inference
├── .env                 # Environment variables (not tracked in git)
├── .pre-commit-config.yaml  # Pre-commit hooks configuration
├── badges.toml          # Optional project badges
├── CODE_OF_CONDUCT.md   # Community guidelines
├── LICENSE              # Project license
├── LICENSE-OGL          # Open Government License for docs
├── Makefile             # Convenience commands
├── mkdocs.yml           # Documentation configuration
├── OPEN_CODE_CHECKLIST.md  # NHS England standards for publishing code
├── README.md
├── pyproject.toml       # Project configuration and dependencies
└── setup.cfg            # Legacy tool configuration (flake8 only)

Each directory serves a specific purpose in the RAP workflow. The structure follows data science best practices while accommodating NHS-specific requirements.

Next Steps

Getting Started - Step-by-step guide to your first project
Usage Guide - Configuration options and workflows
Contributing - Help improve the template

Licence

Unless stated otherwise, the codebase is released under the MIT Licence. This covers both the codebase and any sample code in the documentation.

Acknowledgements

This template is based on Cookiecutter Data Science, adapted for NHS England RAP standards and modern Python tooling.