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DES RAP Book: Reproducible Discrete-Event Simulation in Python and R

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An open, self-paced training resource that teaches how to design, implement, and share discrete-event simulation (DES) models in Python and R as part of a reproducible analytical pipeline (RAP).

DOI: https://doi.org/10.5281/zenodo.17094155

Licence: MIT License

Keywords: Automated testing, Health Services, Open Access, Open Science, Open Source Software, Open source code, Python for Data Analysis, R Programming, RAP, Reproducibility, Reproducible Analytical Pipeline, Reproducible Environment, Reproducible Research, Reproducible Science, SimPy, Simulation, discrete-event simulation, reproduce, reproducible research, simmer

Target audience: Researchers, Research Software Engineers, Analysts, Postgraduate students

Resource type: Book, Coding, Computer Science, Computer Software, Course materials, Data Science, Documentation, E-Learning, E-learning, Education, Educational Resource, FREE online course, How-to guide, Jupyter notebook, Online material, Open educational resource, Programming, Training materials, Tutorial, case studies, course materials, e-Learning, e-learning, educational materials, examples, handbook, hands-on tutorial, knowledgebase, online course, online modules, online tutorial, Tutorial, tutorials, workflow

Version: v0.4.0

Status: Active

Prerequisites:

Basic programming in Python or R (functions, packages, simple scripts).
Familiarity with probability and basic statistics.

Learning objectives:

  • Setting up version control and reproducible environments for DES RAP projects
  • Structuring simulation projects as reusable packages
  • Managing inputs, parameters, and experiments in a reproducible way
  • Building DES models with entities, processes, randomness, and logging
  • Performing output analysis, warm-up, replications, and scenario/sensitivity analysis
  • Applying verification, validation, testing, and quality assurance to simulation models
  • Automating checks with linters and continuous integration
  • Documenting, licensing, citing, and archiving DES models for reuse

Date created: 2025-04-10

Date modified: 2026-02-06

Date published: 2025-09-10

•• intermediate

Authors: Amy Heather

Contributors: Alison Harper, Fatemeh Alidoost, Nav Mustafee, Rob Challen, Tom Monks, Tom Slater

Scientific topics: Computer science, Data visualisation, Data management, FAIR data, Informatics, Open science, Statistics and probability, Version control, Workflows

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