library(tidyverse)

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library(igraph)                    

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library(visNetwork)                
library(DT)
library(plotly)

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1. Read the data into links and nodes, and create the network in igraph with graph_from_data_frame().
   
2. Find the density of the network.
   
3. Make a histogram of the distances in the network.
   
4. Find the legnth of the diameter of the network.
   
5. Mutate the following into the nodes data: label, title, degree, and set value = degree.
   
6. Mutate betweenness into the links data, and set it to value.
   
7. Create a diagram with visNetwork, with a title and options set for a menu and highlightNearest.
   

8. Find the communities in the network, mutate a group column in the nodes data, and create another diagram displaying the colored groups.

9. Read the data into links and nodes

got_nodes <- read_csv("got_nodes.csv")

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got_links <- read_csv("got_links.csv")

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Greate network:

got_network <- graph_from_data_frame(got_links, 
                                           vertices = got_nodes, 
                                           directed = F)

2. Find network density:

got_network %>% 
  edge_density()

## [1] 0.0545183

3. A histogram of the distances in the network:

got_network %>% 
  distances() %>% 
  as.vector() %>%              
  as_tibble() %>%              
  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.
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4. Network diameter:

got_network %>% 
  get_diameter() %>% 
  length()

## [1] 7

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

got_nodes <- got_nodes %>% 
  mutate(label = name) %>% 
  mutate(title = name) %>% 
  mutate(degree = degree(got_network)) %>% 
  mutate(value = degree)

got_nodes

6. Mutate betweenness into the links data, and set it to value.

got_links <- got_links %>% 
  mutate(betweenness = edge_betweenness(got_network)) %>% 
  mutate(value = betweenness) 

got_links 

7. Diagram:

visNetwork(got_nodes, 
           got_links, 
           main = "Game of thrones network analysis") %>% 
  visIgraphLayout(layout = "layout_nicely") %>% 
  visOptions(highlightNearest = T, nodesIdSelection = T)

8. Find communities:

got_nodes <- got_nodes %>% 
  mutate(group = membership(infomap.community(got_network)))

Network diagram wiht communities:

visNetwork(got_nodes, 
           got_links, 
           main = "Game of thrones network analysis") %>% 
  visIgraphLayout(layout = "layout_nicely") %>% 
  visOptions(highlightNearest = T, nodesIdSelection = T, selectedBy = "group")