Introduction: From Clustering to Density Plots

 library(clustNet)

Clustering

First, we need to learn the networks. Here, we simulate data from three clusters. This process takes around two minutes on a local PC.

 library(clustNet)
 
 # Simulate data
k_clust <- 3 # numer of clusters
ss <- c(400, 500, 600) # samples in each cluster
simulation_data <- sampleData(k_clust = k_clust, n_vars = 20, n_samples = ss)
sampled_data <- simulation_data$sampled_data
 
 # Network-based clustering
cluster_results <- get_clusters(sampled_data, k_clust = k_clust)

Visualization of networks

We can visualize the networks as follows.

 # Load additional pacakges to visualize the networks
 library(ggplot2)
 library(ggraph)
 library(igraph)

## 
## Attaching package: 'igraph'

## The following objects are masked from 'package:stats':
## 
## decompose, spectrum

## The following object is masked from 'package:base':
## 
## union

 library(ggpubr)
 
 # Visualize networks
 plot_clusters(cluster_results)

Visualization of networks

Finally, we can create a density plot of our clustering.

 # Load additional pacakges to create a 2d dimensionality reduction
 library(car)

## Loading required package: carData

 library(ks)

## 
## Attaching package: 'ks'

## The following object is masked from 'package:igraph':
## 
## compare

 library(graphics)
 library(stats)
 
 # Plot a 2d dimensionality reduction
 density_plot(cluster_results)