R Notebook

library(tidyverse)
library(DT)
library(igraph)
library(rtweet)
library(visNetwork)
library(graphTweets)            # This package turns twitter data into edge & node data

1. Get the last couple hundred tweets from each member of the list.

governors <- lists_members(slug = "us-governors", owner_user = "TwitterGov")

governors_tweets <- get_timeline(cabinet$screen_name, n = 300)

Here I got the last 300 tweets from each member.

2. Decide on whether you want to create a network of retweets or mentions, and create the network.

# This creates the network with some commands from graphTweets
# The variables in the parentheses of gt_edges() are used, so in this case we're using retweets
# We're also keeping text of the tweets
governors_retweets_network <- governors_tweets %>% 
  gt_edges(screen_name, retweet_screen_name, text) %>% 
  gt_graph()


# This next line gets the nodes
governors_retweets_nodes <- as_data_frame(governors_retweets_network, what = "vertices")

# This adds some additional info to the nodes, so we get the names on hover
# and the size of the node is based on its degree, etc.
governors_retweets_nodes <- governors_retweets_nodes %>% 
  mutate(id = name) %>% 
  mutate(label = name) %>% 
  mutate(title = name) %>% 
  mutate(degree = degree(governors_retweets_network)) %>% 
  mutate(value = degree)

# This gets the edges, similar to how we got the nodes above
governors_retweets_edges <- as_data_frame(governors_retweets_network, what = "edges")

# This puts the text of the tweet itself into the edge
# so when you hover over a line in the diagram it will show the tweet
governors_retweets_edges <- governors_retweets_edges %>% 
  mutate(title = text)

# Creates the diagram
visNetwork(governors_retweets_nodes, governors_retweets_edges, main = "Retweet network of US governors") %>% 
  visIgraphLayout(layout = "layout_nicely") %>% 
  visEdges(arrows = "to")

This is the network showing of US governor’s retweets.

3. Get a count of nodes and edges, and get the density of the network.

governors_retweets_network %>% 
  vcount()

[1] 6

This is the number of nodes.

governors_retweets_network%>%
  ecount()

[1] 4

This is the number of edges.

governors_retweets_network%>%
  edge_density()

[1] 0.1333333

This is the edge density.

4. Create a data table of the list members, showing only their names and degree.

governors_retweets_nodes%>%
  select(name, degree)%>%
  datatable()

This is a datatable showing retweets just by name and degree.

5. Find communities and make a network diagram showing the communities. (You may make two diagrams, one with the communities and one without.)

governors_retweets_nodes <- governors_retweets_nodes %>% 
  mutate(group = membership(infomap.community(governors_retweets_network)))

Modularity is implemented for undirected graphs only.

visNetwork(governors_retweets_nodes, governors_retweets_edges, main = "US governors retweet network") %>% 
  visIgraphLayout(layout = "layout_nicely") %>% 
  visEdges(arrows = "to") %>%   
  visOptions(highlightNearest = T, nodesIdSelection = T, selectedBy = "group")

NA
NA

Here is a network diagram showing the communities.

# This creates the network with some commands from graphTweets
# The variables in the parentheses of gt_edges() are used, so in this case we're using retweets
governors_retweets_network <- governors_tweets %>% 
  filter(retweet_screen_name %in% governors$screen_name) %>%     # <- This is a new line and important.
  gt_edges(screen_name, retweet_screen_name, text) %>%             # It only keep retweets of other cabinet members
  gt_graph()


# This next line gets the nodes
governors_retweets_nodes <- as_data_frame(governors_retweets_network, what = "vertices")

# This adds some additional info to the nodes, so we get the names on hover
# and the size of the node is based on its degree, etc.
governors_retweets_nodes <- governors_retweets_nodes %>% 
  mutate(id = name) %>% 
  mutate(label = name) %>% 
  mutate(title = name) %>% 
  mutate(degree = degree(governors_retweets_network)) %>% 
  mutate(value = degree)

# This gets the edges, similar to how we got the nodes above
governors_retweets_edges <- as_data_frame(governors_retweets_network, what = "edges")

# This puts the text of the tweet itself into the edge
# so when you hover over a line in the diagram it will show the tweet
governors_retweets_edges <- governors_retweets_edges %>% 
  mutate(title = text)

# Creates the diagram
visNetwork(governors_retweets_nodes, governors_retweets_edges, main = "US governors retweet network") %>% 
  visIgraphLayout(layout = "layout_nicely") %>% 
  visEdges(arrows = "to")

Here is a network diagram without communities.