Arthur Gailes 10/14/2020

The folders in this project are:

• data - all the data you have collected or been given to analyze.
  • raw - data from external sources, not to be edited.
  • tidy - organized, munged, prunded, and/or joined data for analysis. I usually add this to .gitignore, since it should all be reproducable by the package code.
• analysis - scripts for tests, internal reports, exploratory analysis and graphs. Naming convention is a number (for ordering), the creator’s initials, and a short “-” delimited description, e.g. “1.0-jqp-initial-data-exploration”.
• references - data dictionaries, manuals, and all other explanatory materials.
• products - Anything that is published or distributed
  • figures - publicized graphs, charts, etc
  • reports - written reports (.docx, .pdf, etc)
  • data - publicized datasets (.csv, .xlsx, etc)
• README - project root documentation
• .gitignore - instructions for git to ignore files.
• R - R package functions, if necessary (see below)
• src - python scripts and functions

Obviously all folder structure is flexible; this is only a starting point to make sure our analyses orbit around a different structure.

To set up an R package directory, run this code in README.Rmd or in the console (making sure you’re in the correct directory)

library(here)
knitr::opts_knit$set(root.dir = here())
# usethis::create_tidy_package(path=getwd(), "your_package_name")

Note: all .gitkeep files can be deleted; they’re only here so the folders will show up.

Based on:

• https://peerj.com/preprints/3192.pdf
• https://nicercode.github.io/blog/2013-04-05-projects/
• https://drivendata.github.io/cookiecutter-data-science/#directory-structure