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).