fastscript.R

# Packages needed for the script
library(igraph)
library(ggplot2)
library(htmltools)
library(RCy3)
library(htmlwidgets)

# Set a seed for reproducibility. You can choose a number of your preference
set.seed(106)

# Produce a random Network and visualize it
graph <- erdos.renyi.game(200, p = 0.05, directed = FALSE)
plot(graph, main = "Network")

# Modify the layout of the Network for better visualization, and create a PNG file
layout <- layout.graphopt(graph)
plot(
    graph,
    layout = layout,
    main = "Network_graphopt_style",
    vertex.label.cex = 0.8,
    vertex.size = 8,
)
#Create a PNG file of the stylized plot
png("Network_Graphopt_Style.png", width = 1000, height = 1000)
plot(
    graph,
    layout = layout,
    main = "Network_graphopt_style",
    vertex.label.cex = 0.8,
    vertex.size = 8,
)
dev.off()
# Assign HTML coordinates to the stylized Network for the HTML report
network_graphopt_style_dependency <- htmltools::htmlDependency(
    "network_graphopt_style", "1.0.0", src = "network_graphopt_style.png", script = FALSE,
    stylesheet = FALSE
)
network_graphopt_style_html <- sprintf('<div><img src="%s" alt="network graphopt style"></div>', network_graphopt_style_dependency$src)

# WARNING: Cytoscape must be open before launching the script
# Connect to Cytoscape and upload the Network
cytoscapePing()
createNetworkFromIgraph(graph, title = "Network", collection = "Maastricht_Assignment")

# Extract the information needed for the analysis of the network
degree_info <- degree(graph) # Number of edges of each node
closeness_info <- closeness(graph) # Closeness to the center of each node
betweenness_info <- betweenness(graph) # Crucial level of connectivity between nodes 
clustering_info <- transitivity(graph) # Tendencies of each node to form a group

# Create the plot of the Degree level
data_deg <- data.frame(Node = 1:length(degree_info), Degree = degree_info)
degree_graph <- ggplot(data_deg, aes(x = Degree)) +
    geom_histogram(binwidth = 1, fill = "blue", color = "black", alpha = 0.7) +
    labs(x = "Degree", y = "Frequency") +
    theme(axis.title=element_text(size=9)) +
    ggtitle("Degree Distribution") +
    theme(plot.title = element_text(hjust = 0.5))
degree_graph  
ggsave("degree_graph.png", degree_graph, device = "png", width = 5, height = 3)
# Create a plot of the Betweeness level
data_bet <- data.frame(Node = 1:length(betweenness_info), Betweenness = betweenness_info)
mean_betweenness <- mean(betweenness_info)
betw_graph <- ggplot(data_bet, aes(x = Node, y = Betweenness)) +
    geom_bar(stat = "identity", fill = "blue", alpha = 0.7) +
    geom_hline(yintercept = mean_betweenness, linetype = "dashed", color = "red", size = 1) +
    labs(x = "Node", y = "Betweenness") +
    theme(axis.title=element_text(size=8)) +
    ggtitle("Betweenness Distribution with Mean") +
    theme_minimal() +
    theme(plot.title = element_text(hjust = 0.5))
betw_graph
ggsave("betw_graph.png", betw_graph, device = "png", width = 5, height = 3)

# Assign HTML coordinates to the plots for the HTML report
degree_graph_dependency <- htmltools::htmlDependency(
    "degree_graph", "1.0.0", src = "degree_graph.png", script = FALSE,
    stylesheet = FALSE
)
betw_graph_dependency <- htmltools::htmlDependency(
    "betw_graph", "1.0.0", src = "betw_graph.png", script = FALSE,
    stylesheet = FALSE
)
degree_graph_html <- sprintf('<div><img src="%s" alt="Degree Graph"></div>', degree_graph_dependency$src)
betw_graph_html <- sprintf('<div><img src="%s" alt="Betw Graph"></div>', betw_graph_dependency$src)

# Produce the HTML report file
report <- paste0(
    "<style>",
    "  body { font-family: 'Arial', sans-serif; font-size: 28px; color: #333; }",
    "  h1, h2 { color: #008080; }",
    "  p { line-height: 1.6; }",
    "  .highlight { background-color: #FFFF00; }",
    "  .center { text-align: center; }",
    "</style>",
    "<h1>Network Analysis Report</h1>",
    network_graphopt_style_html,
    "<h2>Basic Information</h2>",
    "<p>Number of Nodes: ", vcount(graph), " (the number of nodes in the network.)</p>",
    "<p>Number of Edges: ", ecount(graph), " (the number of edges connecting the nodes in the network.)</p>",
    "<h2>Degree Distribution</h2>",
    "<p>Mean Degree: ", mean(degree_info), " (average of the degrees of the nodes in the graph. High values = more connected network.)</p>",
    degree_graph_html,
    "<p>Standard Deviation of Degree: ",  sd(degree_info), " (standard deviation of node degrees. High values = heterogeneity in the degree distribution.)</p>",
    "<h2>Closeness Centrality</h2>",
    "<p>Mean Closeness Centrality: ", mean(closeness_info), " (average of closeness centrality values across all nodes. High values = nodes in the network are closer to each other in terms of the shortest path length.)</p>",
    "<h2>Betweenness Centrality</h2>",
    "<p>Mean Betweenness Centrality: ", mean(betweenness_info), " (average betweenness centrality across all nodes in the graph. High values = nodes in the network have a more crucial role in connecting different parts.)</p>",
    betw_graph_html,
    "<h2>Clustering Coefficient</h2>",
    "<p>Global Clustering Coefficient: ", clustering_info, " (degree to which nodes in a graph tend to cluster together. High values = higher tendency for nodes in the network to form clusters or groups)</p>"
)

writeLines(report, "network_analysis_report.html")

# Produce a report file for visualize the result in R
report_data <- c(
    "Network Analysis Report",
    "Number of Nodes:",vcount(graph),"(the number of nodes in the network.)",
    "Number of Edges:",ecount(graph),"(the number of edges connecting the nodes in the network.)",
    "Mean Degree:",mean(degree_info),"(average of the degrees of the nodes in the graph. High values = more connected network)",
    "Standard Deviation of Degree:",sd(degree_info),"(standard deviation of node degrees. High values = heterogeneity in the degree distribution.)",
    "Mean Closeness Centrality:",mean(closeness_info),"(average of closeness centrality values across all nodes. High values = nodes in the network are closer to each other in terms of the shortest path length.)",
    "Mean Betweenness Centrality:",mean(betweenness_info),"(average betweenness centrality across all nodes in the graph. High values = nodes in the network have a more crucial role in connecting different parts.)",
    "Global Clustering Coefficient:",clustering_info,"(degree to which nodes in a graph tend to cluster together. High values = higher tendency for nodes in the network to form clusters or groups)"
)
cat(report_data, sep = "\n")