Plot Objects

Overview

The lionfish package offers multiple different types of display elements. These have to be provided to the interactive_tour function that launches the GUI. Each plot object is a list containing a type and an object. The type specifies, which kind of display should be generated. The object provides additional information required to construct the display. The plot objects then have to be stored in a list that can be given to the interactive_tour function. The currently supported displays are:

Examples for all displays can be found below.

Setup

# Load required packages
library(tourr)
library(lionfish)
if (requireNamespace("flexclust")) {library(flexclust)}

# Initialize python backend
if (check_venv()){
init_env(env_name = "r-lionfish", virtual_env = "virtual_env")
} else if (check_conda_env()){
init_env(env_name = "r-lionfish", virtual_env = "anaconda")
}

Dataset 1 - Flea data

# Load dataset
data("flea")

# Prepare objects for later us
clusters_flea <- as.numeric(flea$species)
flea_subspecies <- unique(flea$species)

# Standardize data and calculate half_range
flea <- apply(flea[,1:6], 2, function(x) (x-mean(x))/sd(x))
feature_names_flea <- colnames(flea)
half_range_flea <- max(sqrt(rowSums(flea^2)))

Dataset 2 - Winter activities data

if (requireNamespace("flexclust")) {
# Load dataset and set seed
data("winterActiv")
set.seed(42)

# Perform kmeans clustering 
clusters_winter <- stepcclust(winterActiv, k=6, nrep=20)
clusters_winter <- clusters_winter@cluster

# Get the names of our features
feature_names_winter <- colnames(winterActiv)
}
#> 6 : * * * * * * * * * * * * * * * * * * * *

Currently supported display types

One dimensional tour

To display one dimensional tours, they first have to be generated and saved using the save_history function of the tourr package. For more information on the available types of tours please visit tour path construction.

Example plot object

guided_tour_flea_1d <- save_history(flea,
                                    tour_path=guided_tour(holes(),1))
#> Target: 1.530, 28.9% better
#> Target: 1.607, 5.0% better
#> Target: 1.629, 1.4% better
#> Target: 1.638, 0.6% better
#> Target: 1.653, 0.9% better
#> Target: 1.656, 0.1% better
#> No better bases found after 25 tries.  Giving up.
#> Final projection:
#>  0.438
#> -0.281
#> -0.247
#> -0.532
#>  0.338
#> -0.520

obj_flea_1d_tour <- list(type="1d_tour",          # type of display
                         obj=guided_tour_flea_1d) # 1d tour history

Interactivity

Two dimensional tour

To display two dimensional tours, they first have to be generated and saved using the save_history function of the tourr package. For more information on the available types of tours please visit tour path construction.

Example plot object

grand_tour_flea_2d <- save_history(flea,
                                   tour_path = grand_tour(d=2))

obj_flea_2d_tour <- list(type="2d_tour",         # type of display
                         obj=grand_tour_flea_2d) # 2d tour history

Interactivity

Scatterplot

To display a scatter plot, the features to be displayed on the x and y axis have to be provided in form of a two dimensional vector.

Example plot object

obj_flea_scatter <- list(type="scatter",          # type of display
                         obj=c("tars1", "tars2")) # x and y axis of plot

Interactivity

Histogram

To display a histogram, the feature to be displayed on the x axis has to be provided in form of a two dimensional vector.

Example plot object

obj_flea_histogram <- list(type="hist", # type of display
                           obj="head")  # x axis of histogram

Interactivity

Mosaic

To display a mosaic plot, one has to provide whether the subgroups/clusters should be on the x or y axis, either with “subgroups_on_y” or “subgroups_on_x”.

Example plot object

obj_winter_mosaic <- list(type="mosaic",   # type of display
                  obj=c("subgroups_on_y")) # whether subgroups x or y axis

Interactivity

Currently there is no way of directly interacting with the mosaic plot.

Heatmap

To display a heatmap, the metric to be calculated and plotted has to be selected. One can choose between “Total fraction”, “Intra cluster fraction” and “Intra feature fraction”.

Consider the matrix [ C = \left[ \begin{array}{cccc} c_{11} & c_{12} & \dots & c_{1p} \ c_{21} & c_{22} & \dots & c_{2p} \ \vdots & \vdots & \ddots & \vdots \ c_{k1} & c_{k2} & \dots & c_{kp} \end{array} \right] ]

where \(c_{ij}, i=1, ..., k\) (number of clusters); \(j=1, ..., p\) (number of features) are a summary of each feature in each cluster. In case of binary data \(c_{ij}\) are the positive counts of the cluster/feature combination.

Then the total fraction is calculated by \(f_{ij}^{o} = \frac{c_{ij}}{n}\)

the intra cluster fraction by \(f_{ij}^{c} = \frac{c_{ij}}{n_{i}}\) and

the intra feature fraction by \(f_{ij}^{f} = \frac{c_{ij}}{n_{j}}\),

where \(n_i, n_j\) are the row and column totals.

Example plot object

obj_winter_heatmap <- list(type="heatmap",          # type of display
                           obj=c("Total fraction")) # initial metric

Interactivity

Categorical cluster interface

To display a categorical cluster interface, the metric to be calculated and plotted has to be selected. One can choose between “Total fraction”, “Intra cluster fraction” and “Intra feature fraction”. For details see “Heatmap”

Example plot object

obj_winter_cat_clust <- list(type="cat_clust_interface", # type of display
                             obj=c("Total fraction"))    # initial metric

Interactivity

Generating the displays

The various plot objects can the be displayed with the interactive_tour function.

# interactive_tour call of flea dataset. Insert plot objects of your liking.
if (interactive()){
interactive_tour(data=flea,
                 feature_names=feature_names_flea,
                 plot_objects=list(obj_flea_2d_tour),
                 half_range=half_range_flea,
                 n_plot_cols=2,
                 preselection=clusters_flea,
                 n_subsets=3,
                 preselection_names=flea_subspecies,
                 display_size=5)
}

# interactive_tour call of winterActiv dataset. Insert plot objects of your liking.
if (interactive()){
interactive_tour(data=winterActiv,
                 feature_names=feature_names_winter,
                 plot_objects=list(obj_winter_cat_clust),
                 half_range=3,
                 n_plot_cols=2,
                 preselection=clusters_winter,
                 n_subsets=10,
                 display_size=5)
}