When building a simulation model in R, I might want to group related input parameters into a data structure. For example, in a life cycle model with resident and anadromous fish, you might use different fecundity parameters for each life history type. One option is to create different objects for each fecundity parameter.
I have recently started learning Racket. For a first task, I tried to build a simple age-structured population model. I hit a stumbling block and reached out to the helpful folks on the Racket mailing list. In this post, I recap the mailing list exchange with a target audience of R programmers that are interested in learning more about Racket.
Shiny Scorekeeper is a basketball scorekeeper app built with the Shiny web framework for R. I needed a new app for scoring video of my son's basketball games and I decided it would be a good learning experience to try to build my own. In this post, I describe using DataTables from the DT package as the interface to the CRUD (create-read-update-delete) features in Shiny Scorekeeper. The post assumes familiarity with features of Shiny apps, particularly
For many years, I've had intentions of learning another programming language. I would guess that I've done 70-80% of my programming work in R and 20-30% in NetLogo. Those two languages have served me well and I haven't yet been in a position where I was required to learn a new language for work. Lately, I've been thinking about my professional development goals and how learning a new programming language might fit into those goals.
In developing the DSM2 HYDRO Viz Tool, we were faced with deciding how to deploy a Shiny app that required interaction with large local files. I first heard about the possibility of using Electron to deploy Shiny apps as standalone desktop applications in this talk by Katie Sasso, but it wasn't until I discovered the R Shiny Electron (RSE) template that I decided to take the plunge.