Load packages

library(tidyverse)

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library(igraph)                    # This is the package to analyze the network

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library(visNetwork)                # Creates visualizations of the network
library(DT)
library(plotly)

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Read HP data

HP_links <- read.csv("HP_links.csv")
HP_nodes <- read.csv("HP_nodes.csv")

Create network

HP_network <- graph_from_data_frame(HP_links,
                                   vertices = HP_nodes,
                                   directed = F)

Find network density

HP_network %>%
  edge_density()

## [1] 0.1711538

Create a histogram of the distances of the network

HP_network %>% 
  distances() %>% 
  as.vector() %>%              # these two lines convert the distances matrix
  as_tibble() %>%              # to something plotly can graph
  plot_ly(x = ~value) %>% 
  add_histogram()

## Warning: Calling `as_tibble()` on a vector is discouraged, because the behavior is likely to change in the future. Use `enframe(name = NULL)` instead.
## This warning is displayed once per session.

Find the diameter of the network.

HP_network %>% 
  get_diameter() %>% 
  length()

## [1] 7

Mutate the following into the nodes data: label, title, degree, and set value = degree.

HP_nodes <- HP_nodes %>%
  mutate(label = name) %>%
  mutate(title = name) %>%
  mutate(degree = degree(HP_network)) %>%
  mutate(value = degree)

Find edge_betweeness for the HP_network

HP_links <- HP_links %>%
  mutate(betweeness = edge_betweenness(HP_network)) %>%
  mutate(value = betweeness)

Create a diagram

visNetwork(HP_nodes,
           HP_links,
           main = "Harry Potter Diagram") %>%
  visIgraphLayout(layout = "layout_nicely") %>%
  visOptions(highlightNearest = T, nodesIdSelection = T)

Find communities and create a diagram

HP_nodes <- HP_nodes %>%
  mutate(group = membership(infomap.community(HP_network)))

Network diagram with communities

visNetwork(HP_nodes,
           HP_links,
           main = "Harry Potter Diagram Featuring Communities") %>%
  visIgraphLayout(layout = "layout_nicely") %>%
  visOptions(highlightNearest = T, nodesIdSelection = T, selectedBy = "group")