Input and output data analysis for system dynamics modelling using the tidyverse libraries of R

Evaluating the sustainability of complex health system transformation in the context of population ageing: An empirical system dynamics study

“…For that reason, we have found it more efficient to implement the model and conduct the numerical experimentations on a programming language platform, which in this case is Python 2.7.11. Although the common practice is to use purpose specific software, the use of programming languages has been recently introduced in the field of system dynamics (Duggan, 2018;Duggan, 2019;Dural-Selcuk et al, 2019). Our choice of implementation platform provided us with the flexibility to work with a higher level of granularity and the opportunity to demonstrate that it is feasible to work with a large number of stock disaggregation within the SD modelling paradigm.…”

Exploring the opportunity of using machine learning to support the system dynamics method: Comment on the paper by Edali and Yücel

“…However, a possible barrier to entry for the use of machine learning methods within the system dynamics modelling process is the perceived complexity of the area and often the focus on big data problems involving real‐time analysis, for example, image processing. While there are machine learning problems that require highly computation approaches such as deep learning (Sengupta et al, 2020), many machine learning methods can be usefully applied to simulation data and explored using open‐source libraries such as R (Duggan, 2018, 2019).…”

“…As an illustrative example of how a machine learning method can be applied to time series data, and by extension, output from a system dynamics model, consider the following example using the R language (Duggan, 2018(Duggan, , 2019. It is based on a weather and energy data set for the Republic of Ireland (Duggan, 2020), which contains hourly weather observations for 2017, which include rainfall (rain), mean sea-level pressure (msl) and predominant wind direction in degree (wwdir).…”

Using R libraries to facilitate sensitivity analysis and to calibrate system dynamics models

“…The R script uses the packages dplyr , tidyr and ggplot2, all of which have already been discussed for use in system dynamics modeling (Duggan, ). The initial code includes these libraries and the three functions contained in the file tidy_sens.R, and, for convenience, creates variables that refer directly to the three Vensim files…”

“…This paper builds on an initial exploration of how R can benefit system dynamics (Duggan, ), and further demonstrates R's potential as a tool for analysts “to ask meaningful questions about their applications, quickly and flexibly” (Chambers, ). Three additional uses of R that can support system dynamics modelers are presented: (i) transforming sensitivity output data from Vensim to tidy data format , so as to facilitate ease of processing of large stochastic simulation data sets, with an illustration using the statistical screening method (Ford and Flynn, ); (ii) implementing system dynamics models using the R package deSolve (Soetaert et al ., )—a package that also facilitates implementing array‐based models; (iii) performing model calibration using the R package FME (Soetaert and Petzoldt, ).…”