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R network analysis packages. Use for graph analysis, social network analysis, network visualization, and community detection.
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Based on SOC occupation classification
R language data analysis and visualization skill. Use when user asks to (1) run R scripts or code, (2) install/update R packages, (3) perform data analysis with R, (4) create visualizations with ggplot2/plotly, (5) statistical analysis, (6) data manipulation with tidyverse/dplyr/data.table. Triggers on keywords like "R语言", "R脚本", "ggplot", "tidyverse", "数据分析", "可视化".
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| name | r-network |
| description | R network analysis packages. Use for graph analysis, social network analysis, network visualization, and community detection. |
| Sub-skill | Description |
|---|---|
| r-network-analysis | igraph, tidygraph, centrality, communities |
| r-network-viz | ggraph, visNetwork, networkD3 |
| r-network-dynamic | networkDynamic, ndtv, temporal networks |
Network and graph analysis in R.
| Package | Description |
|---|---|
| igraph ★ | Comprehensive network analysis |
| tidygraph ★ | Tidy API for graphs |
| network | Basic relational data tools |
| sna | Social network analysis |
| Package | Description |
|---|---|
| ergm | Exponential random graph models |
| latentnet | Latent position/cluster models |
| manynet | Many network types |
| Package | Description |
|---|---|
| networkDynamic | Dynamic/temporal networks |
| ndtv | Animated network visualization |
| netdiffuseR | Network diffusion analysis |
| Package | Description |
|---|---|
| ggraph ★ | Grammar of graphics for graphs |
| visNetwork ★ | Interactive visualization (vis.js) |
| networkD3 | D3 network graphs |
| autograph | Automagic network plotting |
| Package | Description |
|---|---|
| tnet | Weighted/two-mode networks |
| rgexf | Export to GEXF (Gephi) |
# igraph basics
library(igraph)
# Create graph
g <- graph_from_data_frame(edges, directed = TRUE, vertices = nodes)
# Or from adjacency matrix
g <- graph_from_adjacency_matrix(adj_matrix)
# Basic metrics
vcount(g) # Number of vertices
ecount(g) # Number of edges
degree(g) # Degree centrality
betweenness(g) # Betweenness centrality
closeness(g) # Closeness centrality
page_rank(g)$vector # PageRank
# Community detection
communities <- cluster_louvain(g)
membership(communities)
modularity(communities)
# Shortest paths
shortest_paths(g, from = "A", to = "B")
distances(g)
# Plot
plot(g,
vertex.size = degree(g) * 2,
vertex.color = membership(communities),
edge.arrow.size = 0.5)
# tidygraph + ggraph
library(tidygraph)
library(ggraph)
tg <- as_tbl_graph(g) %>%
activate(nodes) %>%
mutate(
centrality = centrality_degree(),
community = group_louvain()
)
ggraph(tg, layout = "fr") +
geom_edge_link(alpha = 0.5) +
geom_node_point(aes(size = centrality, color = factor(community))) +
geom_node_text(aes(label = name), repel = TRUE) +
theme_graph()
# Interactive visualization
library(visNetwork)
visNetwork(nodes, edges) %>%
visOptions(highlightNearest = TRUE) %>%
visLayout(randomSeed = 123)
# Network statistics
transitivity(g) # Clustering coefficient
diameter(g) # Network diameter
graph.density(g) # Density
assortativity_degree(g) # Degree assortativity
library(igraph)
# Load data
edges <- read.csv("edges.csv")
nodes <- read.csv("nodes.csv")
g <- graph_from_data_frame(edges, vertices = nodes)
# Centrality analysis
nodes$degree <- degree(g)
nodes$betweenness <- betweenness(g)
nodes$eigenvector <- eigen_centrality(g)$vector
# Community detection
comm <- cluster_louvain(g)
nodes$community <- membership(comm)
# Key players
head(nodes[order(-nodes$betweenness), ])
library(ggraph)
library(tidygraph)
tg <- as_tbl_graph(g) %>%
activate(nodes) %>%
mutate(importance = centrality_pagerank())
# Force-directed layout
ggraph(tg, layout = "fr") +
geom_edge_link(aes(alpha = weight)) +
geom_node_point(aes(size = importance)) +
theme_void()
# Circular layout by community
ggraph(tg, layout = "linear", circular = TRUE) +
geom_edge_arc(aes(alpha = weight)) +
geom_node_point(aes(color = factor(community))) +
coord_fixed()