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#install.packages("mclust")
#install.packages("plyr")
#install.packages("stringr")
#install.packages("igraph")
#install.packages("stringi")
#install.packages("magrittr")
#install.packages("dplyr")
#install.packages("sna")
#install.packages("RColorBrewer")
#install.packages("visNetwork")
#source("http://bioconductor.org/biocLite.R")
#biocLite("RBGL")
#biocLite("graph")library(mclust)## Package 'mclust' version 5.4
## Type 'citation("mclust")' for citing this R package in publications.library(RColorBrewer)
library(sna)## Loading required package: statnet.common##
## Attaching package: 'statnet.common'## The following object is masked from 'package:base':
##
## order## Loading required package: network## network: Classes for Relational Data
## Version 1.13.0 created on 2015-08-31.
## copyright (c) 2005, Carter T. Butts, University of California-Irvine
## Mark S. Handcock, University of California -- Los Angeles
## David R. Hunter, Penn State University
## Martina Morris, University of Washington
## Skye Bender-deMoll, University of Washington
## For citation information, type citation("network").
## Type help("network-package") to get started.## sna: Tools for Social Network Analysis
## Version 2.4 created on 2016-07-23.
## copyright (c) 2005, Carter T. Butts, University of California-Irvine
## For citation information, type citation("sna").
## Type help(package="sna") to get started.library(graph)## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following object is masked from 'package:statnet.common':
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## order## The following objects are masked from 'package:stats':
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## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
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## anyDuplicated, append, as.data.frame, cbind, colMeans,
## colnames, colSums, do.call, duplicated, eval, evalq, Filter,
## Find, get, grep, grepl, intersect, is.unsorted, lapply,
## lengths, Map, mapply, match, mget, order, paste, pmax,
## pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce,
## rowMeans, rownames, rowSums, sapply, setdiff, sort, table,
## tapply, union, unique, unsplit, which, which.max, which.min##
## Attaching package: 'graph'## The following object is masked from 'package:sna':
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## degreelibrary(igraph)##
## Attaching package: 'igraph'## The following objects are masked from 'package:graph':
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## degree, edges, intersection, union## The following objects are masked from 'package:BiocGenerics':
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## normalize, union## The following objects are masked from 'package:sna':
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## betweenness, bonpow, closeness, components, degree,
## dyad.census, evcent, hierarchy, is.connected, neighborhood,
## triad.census## The following objects are masked from 'package:network':
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## %c%, %s%, add.edges, add.vertices, delete.edges,
## delete.vertices, get.edge.attribute, get.edges,
## get.vertex.attribute, is.bipartite, is.directed,
## list.edge.attributes, list.vertex.attributes,
## set.edge.attribute, set.vertex.attribute## The following objects are masked from 'package:stats':
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## decompose, spectrum## The following object is masked from 'package:base':
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## unionlibrary(readr)
library(plyr)##
## Attaching package: 'plyr'## The following object is masked from 'package:graph':
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## join## The following object is masked from 'package:network':
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## is.discretelibrary(stringr)##
## Attaching package: 'stringr'## The following object is masked from 'package:graph':
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## boundarylibrary(stringi)
library(magrittr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
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## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:igraph':
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## as_data_frame, groups, union## The following object is masked from 'package:graph':
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## union## The following objects are masked from 'package:BiocGenerics':
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## combine, intersect, setdiff, union## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionSamsung <- read_rds("Samsung.RDS")
tData <- data.frame(1:length(Samsung$screenName),Samsung$screenName,Samsung$tweettext)
tData$Samsung.screenName <- as.character(tData$Samsung.screenName)
tData$Samsung.tweettext <- as.character(tData$Samsung.tweettext)createList <- function(tData) {
# Reads data
nData <-
tData %>%
set_colnames(c("id", "screenname", "tweet")) %>%
tbl_df()
# Extracts poster information
retweeterPoster <-
nData %>%
mutate(is_retweeted = stri_detect_regex(tweet, "(RT|via)((?:\\b\\W*@\\w+)+)")) %>%
filter(is_retweeted) %>%
rowwise() %>%
do({
# Gets retwitter
who_retweet <-
stri_extract_first_regex(.$tweet, "(RT|via)((?:\\b\\W*@\\w+)+)")[[1]] %>%
stri_extract_first_regex("@[a-zA-Z0-9_]{1,}") %>%
stri_replace_all_fixed("@", "")
# Returns pair
data_frame(who_post = .$screenname, who_retweet = who_retweet,
combi = stri_c(sort(c(.$screenname, who_retweet)), collapse = " "))
}) %>%
ungroup() %>%
group_by(combi) %>%
summarize(from = min(who_post, who_retweet),
to = max(who_post, who_retweet),
weight = n()) %>%
ungroup() %>%
select(-combi)
# Returns results
retweeterPoster
}
retweeterPoster <- createList(tData)m <- ftM2adjM(ft = as.matrix(retweeterPoster[, 1:2]), W = retweeterPoster$weight, edgemode = "directed")
g1 <- as(m, "graphNEL")
# Calculate centrality
node <- data.frame(nodes(g1))
node$betweenness <- sna::betweenness(m)
node$degree <- sna::degree(m)
sortlist <- node[order(-node$degree),]
head(sortlist, 10)# Defines clusters for nodes in 3 groups by degree centrality
node %<>%
mutate(size = log(node$degree)) %>%
mutate(size = ifelse(size == -Inf, 1, size))
N = 3
node %<>%
mutate(group = Mclust(size, G = N)$classification)library(visNetwork)
gnode <- data.frame(node$nodes.g1.)
gnode<- setNames(gnode, "id")
gnode$shape <- "dot"
gnode$shadow <- TRUE # Nodes will drop shadow
gnode$title <- node$nodes.g1. #Click to show title
gnode$label <- node$degree # Node label by degree centrality
gnode$group <- node$group
gnode$size <- gnode$group*3 # Node size by group
gnode$color.background <- c("slategrey", "tomato", "gold")[gnode$group]
visNetwork(
gnode,
setNames(retweeterPoster, c("from", "to", "weight"))
) %>%
visOptions(highlightNearest = TRUE,
selectedBy = "group")library(mclust)
library(RColorBrewer)
library(sna)
library(graph)
library(igraph)
library(readr)
library(plyr)
library(stringr)
library(stringi)
library(magrittr)
library(dplyr)
Samsung2 <- read_rds("Samsung2.RDS")
tData <- data.frame(1:length(Samsung2$screenName),Samsung2$screenName,Samsung2$tweettext)
tData$Samsung2.screenName <- as.character(tData$Samsung2.screenName)
tData$Samsung2.tweettext <- as.character(tData$Samsung2.tweettext)createList <- function(tData) {
# Reads data
nData <-
tData %>%
set_colnames(c("id", "screenname", "tweet")) %>%
tbl_df()
# Extracts poster information
retweeterPoster <-
nData %>%
mutate(is_retweeted = stri_detect_regex(tweet, "(RT|via)((?:\\b\\W*@\\w+)+)")) %>%
filter(is_retweeted) %>%
rowwise() %>%
do({
# Gets retwitter
who_retweet <-
stri_extract_first_regex(.$tweet, "(RT|via)((?:\\b\\W*@\\w+)+)")[[1]] %>%
stri_extract_first_regex("@[a-zA-Z0-9_]{1,}") %>%
stri_replace_all_fixed("@", "")
# Returns pair
data_frame(who_post = .$screenname, who_retweet = who_retweet,
combi = stri_c(sort(c(.$screenname, who_retweet)), collapse = " "))
}) %>%
ungroup() %>%
group_by(combi) %>%
summarize(from = min(who_post, who_retweet),
to = max(who_post, who_retweet),
weight = n()) %>%
ungroup() %>%
select(-combi)
# Returns results
retweeterPoster
}
retweeterPoster <- createList(tData)m <- ftM2adjM(ft = as.matrix(retweeterPoster[, 1:2]), W = retweeterPoster$weight, edgemode = "directed")
g1 <- as(m, "graphNEL")
# Calculate centrality
node <- data.frame(nodes(g1))
node$betweenness <- sna::betweenness(m)
node$degree <- sna::degree(m)
sortlist <- node[order(-node$degree),]
head(sortlist, 10)# Defines clusters for nodes in 3 groups by degree centrality
node %<>%
mutate(size = log(node$degree)) %>%
mutate(size = ifelse(size == -Inf, 1, size))
N = 3
node %<>%
mutate(group = Mclust(size, G = N)$classification)library(visNetwork)
gnode <- data.frame(node$nodes.g1.)
gnode<- setNames(gnode, "id")
gnode$shape <- "dot"
gnode$shadow <- TRUE # Nodes will drop shadow
gnode$title <- node$nodes.g1. #Click to show title
gnode$label <- node$degree # Node label by degree centrality
gnode$group <- node$group
gnode$size <- gnode$group*3 # Node size by group
gnode$color.background <- c("slategrey", "tomato", "gold")[gnode$group]
visNetwork(
gnode,
setNames(retweeterPoster, c("from", "to", "weight"))
) %>%
visOptions(highlightNearest = TRUE,
selectedBy = "group")