r-reproducibility-guide

name	r-reproducibility-guide
description	Create reproducible research workflows with R and RMarkdown/Quarto
metadata	{"openclaw":{"emoji":"🔁","category":"tools","subcategory":"code-exec","keywords":["R programming","RMarkdown","reproducibility","Quarto","renv","computational reproducibility"],"source":"wentor-research-plugins"}}

Reproducible Research with R

A skill for creating fully reproducible research workflows in R using RMarkdown, Quarto, package management with renv, and project organization best practices. Covers literate programming, environment management, automated reporting, and sharing reproducible analyses.

Project Organization

Recommended Directory Structure

my-research-project/
  README.md
  my-project.Rproj         # RStudio project file
  renv.lock                 # Package versions (managed by renv)
  renv/                     # renv library directory
  data/
    raw/                    # Untouched original data
    processed/              # Cleaned, analysis-ready data
  R/
    01-clean.R              # Data cleaning functions
    02-analyze.R            # Analysis functions
    03-visualize.R          # Plotting functions
    utils.R                 # Helper functions
  analysis/
    main-analysis.Rmd       # Primary analysis notebook
    supplementary.Rmd       # Supplementary analyses
  output/
    figures/                # Generated plots
    tables/                 # Generated tables
    manuscript.pdf          # Compiled document
  Makefile                  # Reproducible build commands

Key Principles

1. Raw data is read-only (never modify original data files)
2. All processing steps are scripted (no manual spreadsheet edits)
3. Generated outputs can be deleted and recreated from source
4. Package versions are locked with renv
5. Random seeds are set for all stochastic operations
6. Paths are relative to project root (never absolute)