https://github.com/pythonhealthdatascience/rdesrap_stroke
Reproducible analytical pipeline (RAP) for R discrete-event simulation (DES) implementing the Stroke Capacity Planning Model from Monks et al. 2016.
https://github.com/pythonhealthdatascience/rdesrap_stroke
Last synced: 5 months ago
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Reproducible analytical pipeline (RAP) for R discrete-event simulation (DES) implementing the Stroke Capacity Planning Model from Monks et al. 2016.
- Host: GitHub
- URL: https://github.com/pythonhealthdatascience/rdesrap_stroke
- Owner: pythonhealthdatascience
- License: other
- Created: 2025-06-30T09:49:02.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-07-10T15:14:55.000Z (6 months ago)
- Last Synced: 2025-07-10T17:43:09.610Z (6 months ago)
- Language: R
- Size: 64 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# Stroke capacity planning model: R DES RAP
[](https://doi.org/10.5281/zenodo.15863376)
[](https://github.com/pythonhealthdatascience/rdesrap_stroke/actions/workflows/R-CMD-check.yaml)
[](https://github.com/pythonhealthdatascience/rdesrap_stroke/actions/workflows/lint.yaml)
[](https://orcid.org/0000-0002-6596-3479)
## Repository overview
This repository provides a reproducible analytical pipeline (RAP) that implements a real-life stroke service capacity planning model in R. It serves as a practical example, fully reproducible and aligned with RAP principles, based on the following study:
> Monks T, Worthington D, Allen M, Pitt M, Stein K, James MA. A modelling tool for capacity planning in acute and community stroke services. BMC Health Serv Res. 2016 Sep 29;16(1):530. doi: [10.1186/s12913-016-1789-4](https://doi.org/10.1186/s12913-016-1789-4). PMID: 27688152; PMCID: PMC5043535.
Model diagram:
## Installation
Clone the repository:
```{.r}
git clone https://github.com/pythonhealthdatascience/rdesrap_stroke.git
cd rdesrap_stroke
```
Set up the R environment using `renv` (recommended):
```{.r}
renv::init()
renv::restore()
```
If you encounter issues restoring the exact environment, you can install dependencies from `DESCRIPTION` and generate your own lock file:
```{.r}
renv::init()
renv::install()
renv::snapshot()
```
## How to run
The simulation code is in the `R/` folder as a local package. There are files executing the model and analysing the results in `rmarkdown/`.
**Install the local package:**
```{.r}
devtools::install() # Needed if running in parallel
devtools::load_all() # Sufficient if running sequentially
library(simulation)
```
**Run a single simulation:**
```{.r}
param <- create_parameters(number_of_runs = 1L)
single_results <- runner(param = param)
```
**Run multiple replications:**
```{.r}
param <- create_parameters(number_of_runs = 5L)
single_results <- runner(param = param)
```
**Run all analyses (from command line):**
```{.r}
bash run_rmarkdown.sh
```
**Run tests:**
```{.r}
devtools::test()
```
**Lint code:**
```{.r}
lintr::lint_dir()
```
### Generating the results from the article
The original study used Simul8. Each of the outputs from that article have been replicated in this repository using R:
* Figure 1. Simulation probability density function for occupancy of an acute stroke unit.
* Figure 3. Simulated trade-off between the probability that a patient is delayed and the no. of acute beds available.
* Table 2. Likelihood of delay. Current admissions versus 5% more admissions.
* Table 3. Results of pooling of acute and rehab beds.
* Supplementary Table 1. Likelihood of delay. Current admissions versus No Complex neurological patients.
* Supplementary Table 3. Likelihood of delay. Current admissions versus ring fenced acute stroke beds.
To generate these, simply execute `rmarkdown/analysis.Rmd`.
**Examples**
**Figure 1**
Original:
From this repository:
**Figure 3**
Original:
From this repository:
## Run time and machine specification
The run time for this analysis (`notebooks/analysis.Rmd`) is **1m 38s** seconds. This was on an Intel Core i7-12700H, 32GB RAM, Ubuntu 24.04.1.
The other notebooks generate results for tests and illustrate other functionality (e.g. importing parameters from csv, running with logs), and these just take a second or two.
## Citation
If you use this repository, please cite either the GitHub repository or Zenodo:
> Heather, A. (2025). Stroke capacity planning model: R DES RAP. GitHub. https://github.com/pythonhealthdatascience/rdesrap_stroke.
>
> Heather, A. (2025). Stroke capacity planning model: R DES RAP. Zenodo. https://doi.org/10.5281/zenodo.15863376.
**Contributors:**
**Amy Heather** - developed the repository.
* [](https://orcid.org/0000-0002-6596-3479)
* [](https://github.com/amyheather)
## Licence
MIT Licence. See `LICENSE.md` for details.
## Acknowledgements
This repository was developed with thanks to a few others sources. These are acknowledged throughout in the relevant scipts, and also summarised here:
**TODO**
## Funding
This project was developed as part of the project STARS: Sharing Tools and Artefacts for Reproducible Simulations. It is supported by the Medical Research Council [grant number [MR/Z503915/1](https://gtr.ukri.org/projects?ref=MR%2FZ503915%2F1)].