useR! 2024

Best practices for reproducible analyses help to make our work easier and more reliable. However, there is frequently an initial hurdle to overcome to set up an analysis environment well, and this task becomes progressively harder as work takes shape and gains in complexity. To solve this, we present cookiecutter, an R package and RStudio plugin following the popular Python standard (Greenfield et al., 2022) for creating project templates. It helps create structured work environments that adhere to best practices and build on common helpers (e.g. workflow tools), while leaving room for flexibility and customisation through a guided setup wizard. Users with more specialised needs can adapt, create and (optionally) publish their own templates, contributing back to the wider data science community. Our goal is to encourage researchers and analysts to structure their projects from the get-go, by using accessible templates that support them in creating uncluttered projects and organised workflows. Ultimately, we hope that this will increase the adoption of best practices, and more robust research generally.