some thoughts about reproducibility

Reproducibility.md

Reproducibility in R and Marketing Research

Ensuring reproducibility in R-based marketing research projects involves adopting practices that enable others (or your future self) to replicate and extend your analysis.

Language-Agnostic Tips

• Version Control (e.g., Git):

  • Use Git to track changes in your project files. This keeps the project history intact and makes collaboration easier.
  • Always create clear commit messages to describe changes effectively.
  • IMHO: branching/forking and other git-features are overkill for small projects.

• Reproducibility:

  • Document your system environment (e.g., operating system, R version, compiler version).
  • Set seeds for random processes (e.g., set.seed(123) in R) to ensure consistent outputs.
  • Save intermediary outputs, such as pre-processed data or model results, to avoid recalculating results unnecessarily. Makes it easy to compare intermediary outputs.

• Backups:

  • Leverage platforms like GitHub or GitLab for source code backups.
  • For larger files (e.g., datasets >100MB), use dedicated object storage solutions (e.g., AWS S3 or Google Drive).
  • consider committing also your R workspace as serialized object.
  • Store data in compressed formats (e.g., .zst) for faster loading and saving.

• Trackability:

  • Use clear labels and file organization for all components of your project. For example:
    • /data for raw and processed datasets.
    • /scripts for analysis scripts.
    • /results for output files, such as visualizations or tables.
  • Clearly document each step in the analysis pipeline.

R-Specific Tips

• Avoid Side-Effects:

  • Instead of importing entire libraries, use namespace-qualified calls. For example:

    readr::read_csv("file.csv")

    This prevents conflicts between similarly named functions across packages.

• File Formats and Editors:

  • Use Quarto over R Markdown (Rmd) for writing reports and reproducible documents. Quarto supports multiple languages, has better integration with modern tools, and is easier to version control.
  • Avoid Jupyter notebooks (.ipynb) for version control-heavy workflows, as they are harder to merge and track in Git.

• Environment Versioning:

  • Use renv (R environment) to snapshot and lock package versions. This ensures that others can replicate your analysis in the same R environment.

    renv::init() # Initializes a project-specific library
    renv::snapshot() # Records the state of your library

  • Include the renv.lock file in your version control to share the exact package dependencies.

• Reproducible Workflows:

  • Organize your code into modular scripts, such as:
    • data_preparation.R
    • analysis.R
    • visualization.R

• Scalable Collaboration:

  • Write functions for repetitive tasks instead of duplicating code.