An R package for animated biplots.

Ganey R & Nienkemper-Swanepoel J (2025). moveEZ: Animated Biplots.moveEZ, R package version 1.1.1, https://CRAN.R-project.org/package=moveEZ.
 

Install from Github

GitHub

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

Background

• Consider a dataset \({\bf{X}}\) comprising of \(n\) observations and \(p\) continuous variables, along with an additional variable representing “time”.

• 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.

• The goal of moveEZ is to address this 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.

Biplots

• Singular Value Decomposition

• Representing samples

• Representing variables

Climate Data

Iturbide et al. 2020. An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets. Earth System Science Data 12(4):2959-2970.

Source: https://sites.ualberta.ca/~ahamann/data/climateaf.html

Climate Data

PCA biplot

library(biplotEZ)
bp <- biplot(Africa_climate, scaled = TRUE) |> 
  PCA(group.aes = Africa_climate$Region) |> 
  samples(opacity = 0.8, 
          col = scales::hue_pal()(10)) |> 
  axes(col="black") |> 
  plot()

Fixed Variable Frame

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.

moveplot()

Using the previously created PCA biplot object bp, the moveplot() function enables animation of the sample points over time. This function is piped with several key arguments:

• time.var: Specifies the name of the variable in the dataset that represents the temporal or sequential dimension. In this case, the variable “Year” relates to the time variable.

• group.var: Indicates a grouping variable used for colour-coding. In this case, the variable “Region” relates to the group variable.

• hulls: A logical argument that determines whether to display individual sample points or to draw convex hulls around each group.

• scale.var: A numerical value to scale the vectors.

move: A logical argument that controls whether the biplot is animated. If set to TRUE, the sample points are animated across time. If set to FALSE, the function returns a faceted plot showing a static biplot for each time level.
shadow: A logical argument that controls whether samples of previous time points are still displayed as time moves forward (burn-in of previous states). Only works when move = TRUE and hulls = FALSE.

moveplot()

bp |> moveplot(time.var = "Year", group.var = "Region", hulls = FALSE, move = FALSE)

# Object of class biplot, based on 960 samples and 9 variables.
# 6 numeric variables.
# 3 categorical variables.

moveplot(): Insider tip!

overall_time <- Africa_climate |> 
  mutate("Overall" = as.factor(rep("Overall",
  nrow(Africa_climate))))

bp_overall <- biplot(overall_time, scaled = TRUE) |> 
  PCA(group.aes = overall_time$Region)

bp_overall |> moveplot(time.var = "Overall", 
  group.var = "Region", hulls = FALSE, move = FALSE)

# Object of class biplot, based on 960 samples and 10 variables.
# 6 numeric variables.
# 4 categorical variables.

moveplot(): shifting perspective

bp |> moveplot(time.var = "Region", group.var = "Region", hulls = FALSE, move = FALSE)

# Object of class biplot, based on 960 samples and 9 variables.
# 6 numeric variables.
# 3 categorical variables.

moveplot()

bp |> moveplot(time.var = "Year", group.var = "Region", hulls = TRUE, move = TRUE)

moveplot()

bp |> moveplot(time.var = "Year", group.var = "Region", hulls = FALSE, move = TRUE, shadow = TRUE)

Dynamic Frame