When programming in R, I generally pass data around by reading and writing text files (typically, CSV files). The ubiquity of CSV files means that many different types of software will open them easily (e.g., Notepad, Excel, TextEdit, etc.). However, if the data structure is not flat or contains other attributes, then writing to CSV requires flattening and/or dropping attributes. The general solution to writing data to a file while retaining structure and attributes is serialization.

The Delta Simulation Model II (DSM2) is a hydrodynamic model used for Sacramento-San Joaquin Delta planning and management. When working with DSM2 output, I frequently need to look up the location of channels, nodes, and stations in the model. A map of all of the channels, nodes, and stations (and more) is provided with DSM2 as a PDF file, but that file is not easy to search.

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 reactiveValues(), observe(), and observeEvent().

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.