setup_analysis_project: Set up analysis project folder and script files

In LukasWallrich/timesaveR: Functions to Accelerate (Academic) Data Analysis and Reporting

Set up analysis project folder and script files

Description

This sets up simple folder structure and template files for an analysis project.

Usage

setup_analysis_project(
  folder = here::here(),
  analyses = c("data_prep", "analyses", "presentation"),
  pipeline_name = "outputs",
  code_folder = FALSE,
  standard_packages = c("magrittr", "here", "dplyr"),
  github_packages = NULL
)

Arguments

folder	Root folder of the project to be set up. Defaults to here::here()
analyses	Character vector of analysis steps. R files will be set up in order.
pipeline_name	Name of folder for outputs from each analysis step
code_folder	Logical. Should code files be placed in /code sub-folder? Otherwise, are placed in root folder
standard_packages	Character vector of packages to be loaded at start of each analysis file.
github_packages	Character vector of packages to be loaded and installed from Github if needed at start of each analysis file.

Details

The template includes two helpers worth highlighting. The take_note() function allows to add character strings to a note file that is saved at the end of each script. Usage should follow the first example: take_note("Note created:", timestamp(quiet = TRUE)). Note that this modifies notes in place, so the result should not be assigned.

Then there is the ⁠0_run_all.R⁠ file, which runs all analysis scripts in order and documents when this last happened.

Source

The structure is based on https://towardsdatascience.com/how-to-keep-your-research-projects-organized-part-1-folder-structure-10bd56034d3a, with some simplifications and additions.

LukasWallrich/timesaveR documentation built on Nov. 29, 2024, 4:47 a.m.