README.md
In moveEZ: Animated Biplots

moveEZ

The goal of moveEZ is to create animated biplots.

Installation

You can install the development version of moveEZ from GitHub with:

library(devtools)
install_github("MuViSU/moveEZ")

Background

Consider a dataset ${\mathbf{X}}$ comprising $n$ observations and $p$ continuous variables, along with an additional variable representing “time.” This time variable need not correspond to chronological time; it could just as well represent another form of ordered index, such as algorithmic iterations or experimental stages.

A natural approach is to construct separate biplots for each level of the time variable, enabling the user to explore how samples and variable relationships evolve across time. However, when the time variable includes many levels, this quickly results in an overwhelming number of biplots.

This package addresses that challenge by animating a single biplot across the levels of the time variable, allowing for dynamic visualisation of temporal or sequential changes in the data.

The animation of the biplots—currently limited to PCA biplots—is based on two conceptual frameworks:

Fixed Variable Frame moveplot(): A biplot is first constructed using the full dataset ${\bf{X}}$, and the animation is achieved by slicing the observations according to the “time” variable. In this approach, the variable axes remain fixed, and only the sample points are animated over time.
Dynamic Frame moveplot2() and moveplot3(): Separate biplots are constructed for each time slice of the data. Both the sample points and variable axes evolve over time, resulting in a fully dynamic animation that reflects temporal changes in the underlying data structure. The differences between these functions are highlighted in the subsequent sections.

Please have a look at the vignettes for a full illustration of how these functions work.

Still to Come!

We are actively working to develop and enhance the dynamic plotting capabilities of these functions to expose and detect changes in observations and variables over time.

Stay tuned for updates!