Webinar Highlights & Recording: System Dynamics Modeling with R.

System Dynamics is a powerful approach to understanding complex systems in various fields, such as public health, ecology, and economics. Prof. Jim Duggan, an expert in applying System Dynamics and data science methods to public health, recently hosted a webinar on System Dynamics Modeling with R. In this session, Prof. Duggan provided insights into R’s capabilities for data analysis, visualization, and manipulation, as well as building and running Ordinary Differential Equation (ODE) models. Additionally, he discussed using R’s Shiny framework for creating interactive web apps and the prospect of forming a joint digital science tools group for R and Python users.

Exploring R’s Capabilities

Prof. Duggan showcased R’s potential by introducing packages such as ggplot2 and dplyr for data manipulation and visualization, deSolve for building models, and Tidyverse for data analysis. Moreover, he demonstrated creating interactive web apps using the Shiny package.

Visualizing Data with ggplot2 and GGCloud2

Ggplot2, a library from the Tidyverse, enables users to create graphs in R. Prof. Duggan demonstrated creating scatter plots with different visual properties, such as color, using the ggplot function with specific arguments. He also highlighted GGCloud2 as a flexible visualization tool that adds a fourth dimension to a plot using the facetwrap function.

Tidying Data in R

Prof. Duggan emphasized the importance of tidying data for easier processing in R. He showcased the use of pivot_longer for converting wide data into a tidy data format and dplyr as a powerful package for summarizing data. Techniques like grouping, summarizing, and data transformation can be performed using Tidyverse tools.

Building and Running ODE Models

R can be used to build and run ODE models, as Prof. Duggan demonstrated by defining stocks using vectors and generating models automatically with the deSolve package. He also discussed how Markov Chain Monte Carlo (MCMC) can be used to calibrate data using System Dynamics models, helping to find parameters from a dataset.

Interactive Web Apps with Shiny

Shiny, R’s framework for creating interactive web apps, was another key focus of the webinar. Prof. Duggan explained how Shiny’s reactive programming automatically updates outputs based on input changes, requiring minimal HTML, CSS, and JavaScript knowledge. The server function links inputs to outputs and generates new graphs based on input changes.

Conclusion

Prof. Jim Duggan’s webinar on System Dynamics Modeling with R provided valuable insights into the capabilities of R for data analysis, visualization, manipulation, and ODE model building. The session was an excellent resource for those interested in exploring R’s applications in System Dynamics and data science.

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