govcookiecutter

A cookiecutter template for analytical, Python-, or Python and R-based projects within Her Majesty's Government, and wider public sector.

This template helps to set up standardised project structures, and includes security features using pre-commit hooks.

It also provides an Agile, centralised, and lightweight analytical quality assurance (AQA) process. Pull or merge request templates are used to nudge users to complete this process. This helps meet HM Government best practice on producing quality analysis, as defined in the Aqua Book.

For reasons why we developed govcookiecutter, read the blog post, and watch the live demonstration from March 2021 on version 0.5.3.

Getting started

First, make sure your system meets the requirements. Next, open your terminal, navigate to the directory where you want your new repository to exist. Then run the following command for the latest stable release:

cookiecutter https://github.com/best-practice-and-impact/govcookiecutter.git

or for a specific branch, tag, or commit SHA {SPECIFIC}, run:

cookiecutter https://github.com/best-practice-and-impact/govcookiecutter.git --checkout {SPECIFIC}

Follow the prompts; if you are asked to re-download govcookiecutter, input yes. Default responses are shown in the squared brackets; to use them, leave your response blank, and press enter.

Once you've answered all the prompts, your project will be created. Then:

1. Set up a Python virtual environment — there are many ways to set up a virtual environment, so we'll let you decide what's best for you!
2. In your terminal, navigate to your new project, and initialise Git

   git init

3. Install the necessary packages using pip and the pre-commit hooks:

   pip install -r requirements.txt
   pre-commit install

   or use the make command:

   make requirements

4. Stage all your project files, and make your first commit

   git add .
   git commit -m "Initial commit"

Once you've completed these steps, consider making some optional changes before kicking off your project development.

Requirements to create a cookiecutter template

Contributors have some additional requirements!

To get started your system should meet the following requirements:

1. Python 3.6.1+ installed
2. R 4.0.4+ installed (optional)¹
3. The cookiecutter package installed

Installing the cookiecutter package

There are many ways to install the cookiecutter package. Our recommendation is to install it at the system or user level, rather than as a Python package with pip or conda. This ensures it is isolated from the rest of your system, and always available.

For macOS, open your terminal, and install cookiecutter with Homebrew:

brew install cookiecutter

For Debian/Ubuntu, use the following commands:

sudo apt-get install cookiecutter

Otherwise, you can install cookiecutter with pip — you may wish to create a virtual environment first:

python -m pip install --user cookiecutter

Optional changes to consider post-project creation

Here are some suggested changes to make before your first commit:

• consider using the cruft package to integrate future govcookiecutter releases

  pip install cruft
  cruft link https://github.com/best-practice-and-impact/govcookiecutter

• make sure the README.md reflects what you want to do with your project
• have a look inside the docs/aqa folder, as you may want to modify some of this analytical quality assurance documentation (AQA), for example the AQA plan
• (if present) confirm that the pull or merge request template checklists meet your requirements
  • These can be found at .github/pull_request_template.md (GitHub), or in .gitlab/merge_request_templates folder (GitLab)

Licence

Unless stated otherwise, the codebase is released under the MIT License. This covers both the codebase and any sample code in the documentation. The documentation is © Crown copyright and available under the terms of the Open Government 3.0 licence.

Contributing

If you want to help us build, and improve govcookiecutter, view our contributing guidelines.

Acknowledgements

This template is based off the DrivenData Cookiecutter Data Science project. Specifically, it uses similar data and src folder structures, and a modified version of the help commands in the Makefiles.

Footnotes

1. Only for combined Python and R projects, if selected in the prompts during project creation. ↩