This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.

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

token <- create_token(
  app = "",
  consumer_key = "",
  consumer_secret = "",
  access_token = "",
  access_secret = "")

get_token("")        # this shows the token. make sure key is the same as consumer_key above
  1. First, gather the last 200 tweets from senators.
senate <- lists_members(slug = "us-senate", owner_user = "TwitterGov")

senate_tweets <- get_timeline(senate$screen_name, n = 200)
  1. Next, create a network of senator retweets and create a diagram.
senate_retweets_network <- senate_tweets %>% 
  filter(retweet_screen_name %in% senate$screen_name) %>%     # <- This is a new line and important.
  gt_edges(screen_name, retweet_screen_name, text) %>%             # It only keep retweets of other senate members
  gt_graph()


# This next line gets the nodes
senate_retweets_nodes <- as_data_frame(senate_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.
senate_retweets_nodes <- senate_retweets_nodes %>% 
  mutate(id = name) %>% 
  mutate(label = name) %>% 
  mutate(title = name) %>% 
  mutate(degree = degree(senate_retweets_network)) %>% 
  mutate(value = degree)

# This gets the edges, similar to how we got the nodes above
senate_retweets_edges <- as_data_frame(senate_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
senate_retweets_edges <- senate_retweets_edges %>% 
  mutate(title = text)

# Creates the diagram
visNetwork(senate_retweets_nodes, senate_retweets_edges, main = "US senate 
retweet network") %>% 
  visIgraphLayout(layout = "layout_nicely") %>% 
  visEdges(arrows = "to")
  1. Find the edges, nodes, and density of the senate retweet network.
senate_retweets_network %>%
  vcount()
[1] 164
senate_retweets_network %>%
  ecount()
[1] 736
senate_retweets_network %>%
  edge_density()
[1] 0.02753255
  1. Create a datatable that only shows the name and degree of the senators. Then, I placed it in descending order by degree.
senate_retweets_nodes %>%
  select(name, degree) %>%
  datatable()
  1. Finally, find the communities within the network, and create an iGraph.
senate_retweets_nodes <- senate_retweets_nodes %>% 
  mutate(group = membership(infomap.community(senate_retweets_network)))
Modularity is implemented for undirected graphs only.
visNetwork(senate_retweets_nodes, senate_retweets_edges, main = "US senate retweet network") %>% 
  visIgraphLayout(layout = "layout_nicely") %>% 
  visEdges(arrows = "to") %>%   
  visOptions(highlightNearest = T, nodesIdSelection = T, selectedBy = "group")
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