# 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)