Reproducible Discrete-Event Simulation in Python and R

Last modified

July 18, 2025

๐Ÿ’ก About this resource

This practical guide helps you build discrete-event simulation models (DES) which are reproducible and part of a robust reproducible analytical pipeline (RAP) - a structured, automated, and repeatable approach to analysis. Learn more โ†’

Packages we use:

Tool Purpose Link
๐Ÿ SimPy DES in Python https://simpy.readthedocs.io/en/latest/
๐Ÿงฎ simmer DES in R https://r-simmer.org/

๐Ÿ—บ๏ธ Why use this guide?

  • Build models that last. Set up your projects in a way thatโ€™s easy to run, revisit, and reuse - whether itโ€™s tomorrow or next year.

  • Informed by best practice. It adheres to recommendations from Heather et al. 2025 and the โ€œLevels of RAPโ€ framework from the NHS RAP Community of Practice. . Learn more โ†’

  • Not just for healthcare. The coding and reproducibility tips are useful to all kinds of models, fields, and analysis techniques.

Developed as part of the STARS project

Author: Amy Heather (GitHub) (ORCID).

Reviewers: Nav Mustafee (GitHub) (ORCID)

Licences: All code is under the MIT Licence. Text is CC-BY-SA 4.0.

Progress tracker: Complete, unless marked โŒ.