Temporal Analysis of Discussion Posts
Load data
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionload("../digs-reply-network.Rdata")
glimpse(net_reply)## Observations: 514
## Variables: 4
## $ date <chr> "2016-01-25", "2016-01-25", "2016-01-25", "2016-01-25",...
## $ week <chr> "1", "1", "1", "1", "1", "2", "2", "2", "2", "1", "1", ...
## $ source <chr> "19235", "24712", "25034", "24979", "25382", "23971", "...
## $ target <chr> "25382", "19235", "24422", "24633", "25507", "24010", "...Create networks for weeks
unique(net_reply$week)## [1] "1" "2" "5" "3" "6" "4" "7" "8" "9" "10" "11" "12" "13" NAlibrary(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.net_1 <- network(as.matrix(net_reply %>% filter(week == "1") %>% select(source, target)), directed = TRUE)
net_2 <- network(as.matrix(net_reply %>% filter(week == "2") %>% select(source, target)), directed = TRUE)
net_3 <- network(as.matrix(net_reply %>% filter(week == "3") %>% select(source, target)), directed = TRUE)
net_4 <- network(as.matrix(net_reply %>% filter(week == "4") %>% select(source, target)), directed = TRUE)
net_5 <- network(as.matrix(net_reply %>% filter(week == "5") %>% select(source, target)), directed = TRUE)
net_6 <- network(as.matrix(net_reply %>% filter(week == "6") %>% select(source, target)), directed = TRUE)
net_7 <- network(as.matrix(net_reply %>% filter(week == "7") %>% select(source, target)), directed = TRUE)
net_8 <- network(as.matrix(net_reply %>% filter(week == "8") %>% select(source, target)), directed = TRUE)
net_9 <- network(as.matrix(net_reply %>% filter(week == "9") %>% select(source, target)), directed = TRUE)
net_10 <- network(as.matrix(net_reply %>% filter(week == "10") %>% select(source, target)), directed = TRUE)
net_11 <- network(as.matrix(net_reply %>% filter(week == "11") %>% select(source, target)), directed = TRUE)
net_12 <- network(as.matrix(net_reply %>% filter(week == "12") %>% select(source, target)), directed = TRUE)
net_13 <- network(as.matrix(net_reply %>% filter(week == "13") %>% select(source, target)), directed = TRUE)
# net_all <- network(as.matrix(net_reply %>% select(source, target)), directed = TRUE)Create a temporal network
library(networkDynamic)##
## networkDynamic: version 0.9.0, created on 2016-01-12
## Copyright (c) 2016, Carter T. Butts, University of California -- Irvine
## Ayn Leslie-Cook, University of Washington
## Pavel N. Krivitsky, University of Wollongong
## Skye Bender-deMoll, University of Washington
## with contributions from
## Zack Almquist, University of California -- Irvine
## David R. Hunter, Penn State University
## Li Wang
## Kirk Li, University of Washington
## Steven M. Goodreau, University of Washington
## Jeffrey Horner
## Martina Morris, University of Washington
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("networkDynamic").net_list = list(net_1, net_2, net_3, net_4, net_5, net_6, net_7, net_8, net_9, net_10, net_11, net_12, net_13)
tnet <- networkDynamic(network.list = net_list, vertex.pid="vertex.names")## Neither start or onsets specified, assuming start=0
## Onsets and termini not specified, assuming each network in network.list should have a discrete spell of length 1
## Argument base.net not specified, using first element of network.list instead
## Initialized network of size 21 inferred from number of unique vertex.pids
## Created net.obs.period to describe network
## Network observation period info:
## Number of observation spells: 1
## Maximal time range observed: 0 until 13
## Temporal mode: discrete
## Time unit: step
## Suggested time increment: 1head(as.data.frame(tnet))## onset terminus tail head onset.censored terminus.censored duration
## 1 0 1 1 19 FALSE FALSE 1
## 2 0 1 11 1 FALSE FALSE 1
## 3 0 4 15 9 FALSE FALSE 4
## 4 5 6 15 9 FALSE FALSE 1
## 5 0 1 13 10 FALSE FALSE 1
## 6 6 7 13 10 FALSE FALSE 1
## edge.id
## 1 1
## 2 2
## 3 3
## 4 3
## 5 4
## 6 4Visualize a temporal network
library(ndtv)## Loading required package: animation## Loading required package: sna## Loading required package: statnet.common## 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.##
## ndtv: version 0.10.0, created on 2016-5-6
## Copyright (c) 2016, Skye Bender-deMoll, University of Washington
## with contributions from
## Martina Morris, University of Washington
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("ndtv").# render.animation(tnet)
render.d3movie(tnet,output.mode = 'htmlWidget')## No slice.par found, using## slice parameters:
## start:0
## end:13
## interval:1
## aggregate.dur:1
## rule:latest## Calculating layout for network slice from time 0 to 1## Calculating layout for network slice from time 1 to 2## Calculating layout for network slice from time 2 to 3## Calculating layout for network slice from time 3 to 4## Calculating layout for network slice from time 4 to 5## Calculating layout for network slice from time 5 to 6## Calculating layout for network slice from time 6 to 7## Calculating layout for network slice from time 7 to 8## Calculating layout for network slice from time 8 to 9## Calculating layout for network slice from time 9 to 10## Calculating layout for network slice from time 10 to 11## Calculating layout for network slice from time 11 to 12## Calculating layout for network slice from time 12 to 13## Calculating layout for network slice from time 13 to 14## caching 10 properties for slice 0## caching 10 properties for slice 1## caching 10 properties for slice 2## caching 10 properties for slice 3## caching 10 properties for slice 4## caching 10 properties for slice 5## caching 10 properties for slice 6## caching 10 properties for slice 7## caching 10 properties for slice 8## caching 10 properties for slice 9## caching 10 properties for slice 10## caching 10 properties for slice 11## caching 10 properties for slice 12## caching 10 properties for slice 13## Loading required namespace: htmlwidgets## loading ndtv-d3 animation widget...# compute.animation(tnet, animation.mode='MDSJ', default.dist=38)
filmstrip(tnet, frames = 14, displaylabels=FALSE)## No coordinate information found in network, running compute.animation
proximity.timeline(tnet,default.dist=6,
mode='sammon',labels.at=17,vertex.cex=4)## collapsing slice networks ...## computing vertex positions using 1D sammon layout ...## computing positions for slice 1## computing positions for slice 2## computing positions for slice 3## computing positions for slice 4## computing positions for slice 5## computing positions for slice 6## computing positions for slice 7## computing positions for slice 8## computing positions for slice 9## computing positions for slice 10## computing positions for slice 11## computing positions for slice 12## computing positions for slice 13## rendering splines for each vertex ...
Compute some basic temporal stats
Counts of the number of edges forming (edge onset at time point) and dissolving (edge terminus at time point):
library(tsna)
plot( tEdgeFormation(tnet) )
plot( tEdgeDissolution(tnet) )
Graph density:
plot( tSnaStats(tnet,'gden') )
Credit: ndtv workshop