README.md

In ccjolley/cRimson: Social Network Analysis from Crimson Hexagon Exports

cRimson

Tools for social media analysis (together with Crimson Hexagon)

Installation

install.packages("devtools")
library(devtools)
devtools::install_github("ccjolley/cRimson")
library(cRimson)

Examples

I've included a sample dataset called sample_tweets; you can convert it to a graph using:

library(dplyr)
g <- ws_to_graph(sample_tweets) %>%
  graph_lcc()

To get a simple visualization of the network that highlights major influencers (using the PageRank algorithm):

sma_plot(g)

If you want to see exactly who those major influencers are, you can get a color-coded bar chart:

sma_bar(g)

If you need to use a metric other than PageRank, you can specify it using the highlight argument:

library(igraph)
b <- betweenness(g)
sma_plot(g,highlight=b)
sma_bar(g,highlight=b)

The cRimson package also contains tools for the analysis and visualization of communities, building upon the wide variety of community detection algorithms offered by igraph. To visualize a community breakdown:

fg <- g %>%
    as.undirected() %>%
    simplify() %>%
    fastgreedy.community()
community_plot(g,fg)

One way to capture what is happening in these communities is to find the "mayor" of each community -- the node that ranks the highest according to some graph metric (PageRank by default). To see exactly where these mayors are located in the network, use the extra argument in sma_plot() and sma_bar(). Note that color-coding is the same as in the plot above.

m <- mayors(g,fg)
sma_plot(g,extra=m)
sma_bar(g,extra=m)

Comparing this plot to the community plot above will give you a sense of how the communities "led" by each of these handles extend beyond those with whom they have direct interactions.

ccjolley/cRimson documentation built on May 13, 2019, 2:16 p.m.