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How to Use This Template

1. Install Dependencies

To use this template, you’ll need to install these dependencies:

Cookiecutter can be installed with pip by or conda depending on how you manage your Python packages:

$ pip install cookiecutter

or

$ conda config --add channels conda-forge
$ conda install cookiecutter

2. Start a New Project

To start a new data science project first run:

$ cookiecutter gh:TalusBio/cookiecutter-data-science

After running the command you will be directed through a series of prompts to finish setting up your project. The directory structure of your new project will look like this: 

├── Makefile                       <- Makefile with commands like `make data` or `make env`
├── README.md                      <- The top-level README for developers using this project.
├── data                           <- The original, immutable data.
├── docs                           <- Manuscript drafts, presentations, etc.
├── notebooks                      <- Jupyter notebooks and/or analysis scripts. 
│   └── wfondrie                   <- Create a subdirectory with your username.
│       └── 2022-11-01_my-analysis <- Create a dated subdirectory for each analysis.
│           ├── notebook.ipynb     <- The analysis notebook or script.
│           └── figures            <- A subdirectory to put the generated figures
├── environment.yml                <- Specifies the dependencies to build a conda environment.
│                                     Create the environment with `make env && conda activate ./envs`
├── pyproject.toml                 <- Specifies Python configuration for our local Python package.
├── src                            <- Source code for the local Python package to use in this project.
│   └── __init__.py                <- Makes src a Python module
└── .env                           <- Define sensitive environment variables. This is intentionally 
                                      ignored by git by default. Do not commit this file!

Feel free to modify these directories and files as best fits your needs. For example, if your project does not use Python, you may want to remove a couple files: pyproject.toml and src/__init__.py.

3. Customize and Do Cool Things

Your first task in your new project should be to edit environment.yaml, so that it contains all of the software dependencies for your project. Once it is ready, create your project environment with:

$ make env

Then follow the instructions to activate your new environment. With your environment prepared and activated, you’re ready to start your analyses!