camartinezbu/cookiecutter-r-project

A template for R projects made using cookiecutter.

cookiecutter-r-project

A template for R projects made using cookiecutter.

Structure

.
├── LICENSE
├── .Rprofile
├── .gitignore
├── README.md
├── analysis
├── data
│   ├── external
│   ├── interim
│   ├── processed
│   └── raw
├── etl
├── notebooks  
├── references
├── reports
├── viz
│   └── figures
└── {{cookiecutter.project_slug}}.Rproj

• LICENSE
  • License file for the project.
  • Availiable options include MIT and BSD-3-Clause.
• .Rprofile
  • Stores environment variables for local R projects.
• .gitignore
  • Ignores R user profile temporary files.
• README.md
  • Project specific readme.
• analysis
  • R code that involves analysis on already-cleaned data. Code for cleaning data should go in etl.
  • Multiple analysis files are numbered sequentially.
• data
  • This is the directory used to store all of the project's data. All files should go into one of the following folders.
  • data/external
    • Data from third party sources.
  • data/interim
    • Intermediate data that has been transformed.
  • data/processed
    • The final, canonical data sets for analysis.
  • data/raw
    • The original, inmutable data dump.
• etl
  • ETL (extract, transform, load) scripts for reading in source data, cleaning and standardizing it to prepare for analysis go here.
    • Multiple ETL files are numbered sequentially.
    • Joins are included in ETL process.
• notebooks
  • Any R Markdown files go here.
• references
  • Data dictionaries, manuals, and all other exploratory materials.
• reports
  • Generated analysis as HTML, PDF, LaTeX, etc.
• viz
  • Graphics and visualization development specific work should go here.
    • Multiple viz files are numbered sequentially.
  • viz/figures
    • Generated graphics and figures to be used in reporting.
• {{cookiecutter.project_slug}}.Rproj
  • This is the .Rproj file that can be used with RStudio to work within the project.

Requirements

• Conda
• Cookiecutter Python package

This can be installed using either

pip install cookiecutter

or

conda install -c conda-forge cookiecutter

Installation

In the folder where you want to generate the project, run:

cookiecutter https://github.com/camartinezbu/cookiecutter-r-project

Credits

This template was designed based on jvelesmagic's Cookiecutter Conda Data Science and AP's R Cookicutter.

Extra

Check out a similar template for python here.