This folder contains a shiny app which is designed to help novice learners understand the nature of data, how to visualise data using 2D plots, and how to execute generalized linear models (GLiMs). I advocate an approach of data -> plots -> analyses.
The tabs are to be taken in order. First, understand individual variables (data). Second, understand relationships between multiple variables (plots). Third, determine significance of and parameters for these relationships (analyses). Start with Gaussian data and progress from there.
The apps utilise R, and in particular the
ggplot2
package, which follows the "grammar of graphics" framework for data
visualisation. The apps provide a GUI for learning theories of data, data
visualisation and GLiMs, but display corresponding R code to implement these,
which (I hope) is a softer approach to introducing learners to coding for the
first time. Accompanying exercises and
lecture slides supplement learning by reinforcing the lessons learned through
the app, and encouraging students to go beyond the foundation of the app to work
with new data, new variables, new plots, new analyses, unknown arguments,
new funtions, etc., usw., osv.
Many of the data examples are of a biological / ecological / conservation / evolution nature, because that's what I do and that's what I teach. If you want to fork and replace with your own data, I think that should work provided they're in the correct format for working with read_csv.
I teach the course using the excellent
Getting Started With R
textbook by
Andrew Beckerman,
Dylan Childs and
Owen Petchey.
I recommend it.
The app is available here:
ShinyGLiM