load packages

library(tidyverse)

## ── Attaching packages ───────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 3.1.0       ✔ purrr   0.3.0  
## ✔ tibble  2.0.1       ✔ dplyr   0.8.0.1
## ✔ tidyr   0.8.2       ✔ stringr 1.4.0  
## ✔ readr   1.3.1       ✔ forcats 0.4.0

## ── Conflicts ──────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

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

## 
## Attaching package: 'igraph'

## The following objects are masked from 'package:dplyr':
## 
##     as_data_frame, groups, union

## The following objects are masked from 'package:purrr':
## 
##     compose, simplify

## The following object is masked from 'package:tidyr':
## 
##     crossing

## The following object is masked from 'package:tibble':
## 
##     as_data_frame

## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum

## The following object is masked from 'package:base':
## 
##     union

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

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:igraph':
## 
##     groups

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

Game of Throwns- links

got_nodes <- read_csv("got_nodes.csv")

## Parsed with column specification:
## cols(
##   id = col_character(),
##   name = col_character()
## )

got_links <- read_csv("got_links.csv")

## Parsed with column specification:
## cols(
##   from = col_character(),
##   to = col_character()
## )

Game of Throwns Network– Discover network

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

Density of network

got_network %>% 
  edge_density()

## [1] 0.0545183

Histogram of Game of Throwns Network links

got_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.

Get network diameter

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

## [1] 7

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

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

got_nodes

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

Network Link of Game of Thrones. Hover over each dot and segment to understand relationship of characters.

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

Find communities

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

Node are now color coded by group membership

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

```