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This is the repository of the PDS paper "The FAIRification of research in real-world evidence: A practical introduction to reproducible analytic workflows using Git and R"

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FAIRification of pharmacoepidemiological research using Git

DOI

Transparency and reproducibility in conducting healthcare database studies in pharmacoepidemiology are critical scientific requirements for meaningful research. To increase the adoption of transparent and reproducible workflows in real-world evidence (RWE) studies using the version control systems (VCS), this tutorial aims to give a practical introduction tailored for pharmacoepidemiologists on how to set up, structure, and implement workflows using Git, the most frequently used VCS to date.

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This repository hosts all documents and files for the manuscript project on...

The FAIRification of pharmacoepidemiological research: A practical introduction to reproducible analytical workflows using git and R

This manuscript is being drafted as part of the 2023 call for papers by the journal Pharmacoepidemiology and Drug Safety (PDS) focusing on Pharmacoepidemiology Research Reproducibility.

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Repository details

This repository contains the following sub-directories/-files:

  • README.md: Short description and information about the project.

  • manuscript: Sub-directory where manuscript and supplementary material are stored.

  • renv: Project-specific R package library. Fore more information, please visit the renv website.

  • renv.lock: renv lockfile that keeps track of all R packages and version.

  • .gitignore: File to specify which files should not be tracked via git.

  • .Rprofile: Environment file for R projects to store things like paths or keys (you can also setup an .Renviron)

  • .git: Sub-directory where all Git changes are tracked

Dependencies

To reproduce R scripts and Quarto files in this repository, git clone or fork it and run renv::restore().

About

This is the repository of the PDS paper "The FAIRification of research in real-world evidence: A practical introduction to reproducible analytic workflows using Git and R"

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