Network Analysis Sample Viz
2025-05-21
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##install.packages(“tidyverse”) install.packages(“igraph”) install.packages(“ggraph”)
# Load necessary libraries
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.4.3## Warning: package 'ggplot2' was built under R version 4.4.3## Warning: package 'tibble' was built under R version 4.4.3## Warning: package 'tidyr' was built under R version 4.4.3## Warning: package 'readr' was built under R version 4.4.3## Warning: package 'purrr' was built under R version 4.4.3## Warning: package 'dplyr' was built under R version 4.4.3## Warning: package 'stringr' was built under R version 4.4.2## Warning: package 'forcats' was built under R version 4.4.3## Warning: package 'lubridate' was built under R version 4.4.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## unionlibrary(ggraph)## Warning: package 'ggraph' was built under R version 4.4.3# Create a mock edge list: from - to relationships
edges <- tribble(
~from, ~to,
"Partner A", "Partner B",
"Partner A", "Partner C",
"Partner B", "Partner D",
"Partner C", "Partner D",
"Partner D", "Partner E",
"Partner E", "Partner F",
"Partner F", "Partner G",
"Partner G", "Partner H",
"Partner H", "Partner I",
"Partner A", "Partner J",
"Partner C", "Partner F",
"Partner B", "Partner H",
"Partner J", "Partner K",
"Partner K", "Partner L",
"Partner L", "Partner M",
"Partner M", "Partner N",
"Partner N", "Partner O",
"Partner O", "Partner P",
"Partner P", "Partner Q"
)
# Create graph object
g <- graph_from_data_frame(edges, directed = FALSE)
# Plot the network using ggraph
ggraph(g, layout = 'fr') +
geom_edge_link(alpha = 0.6, color = "gray") +
geom_node_point(size = 5, color = "skyblue") +
geom_node_text(aes(label = name), repel = TRUE, size = 4) +
theme_void() +
ggtitle("Mock Network of Partners")
# Calculate degree (number of connections)
V(g)$degree <- degree(g)
# View top connected partners
sort(V(g)$degree, decreasing = TRUE)## [1] 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 1 1# Calculate betweenness centrality
V(g)$betweenness <- betweenness(g)
# View top brokers in the network
sort(V(g)$betweenness, decreasing = TRUE)## [1] 65.416667 63.000000 60.000000 55.000000 48.000000 39.000000 34.666667
## [8] 28.000000 25.333333 21.083333 15.000000 12.333333 10.166667 3.916667
## [15] 1.083333 0.000000 0.000000# Calculate closeness centrality
V(g)$closeness <- closeness(g)
# View most central partners by proximity
sort(V(g)$closeness, decreasing = TRUE)## [1] 0.018867925 0.018518519 0.017543860 0.017241379 0.016666667 0.016129032
## [7] 0.014925373 0.014925373 0.014925373 0.014492754 0.013157895 0.012820513
## [13] 0.012820513 0.012195122 0.011235955 0.009803922 0.008547009# Plot with node size based on degree
ggraph(g, layout = 'fr') +
geom_edge_link(alpha = 0.4, color = "gray") +
geom_node_point(aes(size = degree), color = "steelblue") +
geom_node_text(aes(label = name), repel = TRUE, size = 3) +
scale_size_continuous(range = c(3, 10)) +
theme_void() +
ggtitle("Partner Network: Node Size by Degree")
# Detect communities using Louvain algorithm
communities <- cluster_louvain(g)
V(g)$community <- communities$membership
# Plot with color by community
ggraph(g, layout = 'fr') +
geom_edge_link(alpha = 0.5) +
geom_node_point(aes(color = as.factor(community)), size = 5) +
geom_node_text(aes(label = name), repel = TRUE, size = 3) +
theme_void() +
ggtitle("Partner Network: Colored by Community")
library(visNetwork)## Warning: package 'visNetwork' was built under R version 4.4.3# Create nodes and edges from igraph object
nodes <- data.frame(id = V(g)$name,
label = V(g)$name,
group = communities$membership,
value = V(g)$degree)
edges <- as_data_frame(g, what = "edges")visNetwork(nodes, edges) %>%
visNodes(scaling = list(min = 10, max = 30)) %>%
visEdges(arrows = "to") %>%
visOptions(highlightNearest = TRUE,
nodesIdSelection = TRUE) %>%
visLayout(randomSeed = 123) %>%
visPhysics(stabilization = TRUE)