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DES RAP Book: Reproducible Discrete-Event Simulation in Python and R
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
Contact: Amy Heather - https://github.com/amyheather - a.heather2@exeter.ac.uk
Keywords: Automated testing, discrete-event simulation, Reproducibility, Reproducible Environment, Reproducible Research, Reproducible Science, reproduce, reproducible research, Reproducible Analytical Pipeline, RAP, Health Services, Python for Data Analysis, R Programming, Simulation, Open source code, Open Source Software, Open Science, Open Access, SimPy, simmer
Target audience: Researchers, Research Software Engineers, Analysts, Postgraduate students
Resource type: Book, Coding, Computer Science, Computer Software, Course materials, case studies, course materials, Data Science, Documentation, E-Learning, E-learning, Education, Educational Resource, e-Learning, e-learning, educational materials, examples, FREE online course, How-to guide, handbook, hands-on tutorial, Jupyter notebook, knowledgebase, Online material, Open educational resource, online course, online modules, online tutorial, Programming, Training materials, 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
Contributors: Tom Monks, Nav Mustafee, Alison Harper, Fatemeh Alidoost, Rob Challen, 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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