Environmental Data Science: two modules in R Shiny and RMarkdown
Environmental Data Science is a third-year undergraduate course of ~20 students within the Environmental Data Science major at a public R1 state university. Key skills developed in this course include advanced R coding, environmental data wrangling, visualization, and interpretation, and data-driven modeling. Students are expected to have basic to intermediate R coding skills upon enrollment in the course. The instructor designed a two-week unit (four 75-minute class periods) using Macrosystems EDDIE ecological forecasting materials. The dual goals of the unit were to introduce students to the emerging field of ecological forecasting as well as to better understand model uncertainty and how to calculate it. During the first week, students completed Introduction to Ecological Forecasting using the R Shiny app. During the second week, students completedUnderstanding Uncertainty in Ecological Forecasts using RMarkdown. This format permitted students to be introduced to a new concept (ecological forecasting) in a user-friendly interface (R Shiny), and then subsequently apply this new knowledge to a more in-depth task (uncertainty quantification) while reinforcing and developing coding skills (in RMarkdown).