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regressionModelsVarsPlots_21.R
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#########################################################
#
# Author: Jeff Hemsley
#
# Purpose: code for my disertation
# Description: reads in data cleaned by processRTEs_vX.R
# and fits a regression model.
#
#
# Regression notes for write up:
# Faraway, J. J. (2004). Linear models with R (Vol. 63). Chapman and Hall/CRC.
#
# Non constance variance can be delt with by transforming the responce
# varaible, though this makes interpritation difficult. P. 56
# sqrt is good for count vars
#
# http://www.statmethods.net/stats/rdiagnostics.html
# http://people.duke.edu/~rnau/testing.htm
# http://www.basic.northwestern.edu/statguidefiles/linreg_ass_viol.html
include.files.dir <- log.file.path <- "c:/r/aDiss/processRTEs/"
# contains global variable settings
process.rtes.config.file.name <- "processRTEs_CONFIG_v0.R"
source(paste(include.files.dir, process.rtes.config.file.name, sep=""))
library(car)
library(faraway)
library(MASS)
regression.plot.dir <- "C:/r/aDiss/descriptivePlots/"
track.words.file <- "c:/r/aDiss/processRTEs/tracking.v8.R"
source(track.words.file)
track.8.words <- tolower(track.8.words)
length(track.8.words)
rdata.file <- "C:/r/aDiss/processRTEs/ConstructedRTEdata_201405151457.Rdata"
rdata.file <- "C:/r/aDiss/processRTEs/ConstructedRTEdata_201405201715.Rdata"
# has gini in it:
# rdata.file <- "C:/r/aDiss/processRTEs/ConstructedRTEdata_201406051526.Rdata"
load(rdata.file)
rte.meta.df$origin.user.lower <- tolower(rte.meta.df$origin.user)
colnames(rte.meta.df)
tmp.dates <- format(strptime(rte.meta.df$start.date, created.ts.format.string), "%d%b%Y")
rte.meta.df$Nov15 <- ifelse(tmp.dates == "15Nov2011", 1, 0)
rte.meta.df$Nov17 <- ifelse(tmp.dates == "17Nov2011", 1, 0)
track.list.users.index <- which(rte.meta.df$origin.user.lower %in% track.8.words)
occ.initiators <- unique(rte.meta.df$origin.user[track.list.users.index])
occ.rtes <- rte.meta.df[track.list.users.index,]
dim(occ.rtes) # 436, 34
colnames(occ.rtes)
#########################################################
#
# Functions
#
#########################################################
# # # #
# tri plot function for transformed vars
TransformTriPlot <- function(x, y, outliers, xlab, ylab, transform.var="log", fname="", log.y=TRUE, plot.file=T) {
#outliers <- high.alpha.index
#x <- rtes$occ.users + 2
#y <- rtes$alpha
#ylab <- xlab<-""
#summary(x)
#fname="test"
#log.y <- F
if (plot.file == TRUE) {
plot.file.name.png <- paste(regression.plot.dir, "TriPlot_", fname, ".png", sep="")
png(plot.file.name.png, width=625, height=230, pointsize=16)
}
tmp.col <- rep(rgb(64, 153, 255, maxColorValue=255), length(x))
tmp.col[outliers] <- "red"
par(mfrow=c(1,3))
plot(x, y, col=tmp.col, bty="n", main=paste("Nontransformed\ncor=", round(cor(x,y), 3), sep=""), ylab=ylab, xlab=xlab)
abline(lm(y~x), col="black", lwd=1)
if (log.y == TRUE) {
y <- log10(y)
ylab <- paste("log(", ylab, ")", sep="")
}
if (transform.var == "log") {
x <- log10(x)
} else if (transform.var == "sqrt") {
x <- sqrt(x)
#y <- sqrt(y)
}
plot(x, y, col=tmp.col, bty="n", main=paste("Transformed\ncor=", round(cor(x,y), 3), sep=""), ylab=ylab, xlab=paste(transform.var, "(", xlab, ")", sep=""))
abline(lm(y~x), col="black", lwd=1)
x <- x[-outliers]
y <- y[-outliers]
plot(x, y, col=tmp.col[-outliers], bty="n", main=paste("Transformed\nno outliers\ncor=", round(cor(x,y), 3), sep=""), ylab=ylab, xlab=paste(transform.var, "(", xlab, ")", sep=""))
abline(lm(y~x), col="black", lwd=1)
par(mfrow=c(1,1))
if (plot.file == TRUE) {
dev.off()
}
}
#########################################################
#
# REGRESSION:: variable plots & transformations
#
#########################################################
rtes <- occ.rtes
# remove any rtes that failed to get a closeness or alpha
closeness.na.index <- which(is.na(rtes$follow.prop.closeness))
alpha.na.index <- which(is.na(rtes$alpha))
rtes <- rtes[-(union(closeness.na.index, alpha.na.index)),]
#rtes.bak <- rtes
#rtes <- rtes.bak
#rtes <- rtes[-outs,]
rtes <- rtes[sample(1:dim(rtes)[1]),]
row.names(rtes) <- 1:nrow(rtes)
dim(rtes)
summary.main.text <- paste("Output Variable Correlation Matrix\nn = ", dim(rtes)[1], sep="")
plot.file.name.png <- paste(regression.plot.dir, dim(rtes)[1], "_NewALL_VarsCorrMat.png", sep="")
plot.file.name.pdf <- paste(regression.plot.dir, dim(rtes)[1], "_NewALL_VarsCorrMat.pdf", sep="")
png(plot.file.name.png, width=800, height=800, pointsize=18)
#pdf(plot.file.name.pdf, width=12, height=12, pointsize=10)
pairs(~follower.delta+alpha+follow.prop.closeness
+window.size+peak.at+peak.rate
+follower.win.delta+follower.mean+tweet.overlap+occ.users
+occupy.time.marker+occ.peak.time.marker
#+size+window.size+peak.at+peak.rate+longest.path+count.pre.peak
# +follower.sum+follower.mean+tweet.overlap+occ.users
# +occupy.time.marker+occ.peak.time.marker+Nov15+Nov17
, data=rtes
, lower.panel=panel.smooth, upper.panel=panel.cor, diag.panel = panel.hist, text.panel=my.text.panel
, lwd=2, pch=20, col=adjustAlpha("dodgerblue4", .3) # col=adjustAlpha("forestgreen", .3)
, main="")
mtext(text=summary.main.text, side=3, line=1)
dev.off()
# # # #
# high alphas
high.alpha.index <- which(rtes$alpha > 2.3)
high.alpha.index <- intersect(which(rtes$occupy.time.marker > 150), high.alpha.index)
length(high.alpha.index)
late.set <- rtes[high.alpha.index,]
colnames(late.set)
late.set[,c("start.date")]
add.days <- late.set$occ.peak.time.marker - late.set$occupy.time.marker
tmp.date <- as.POSIXlt(late.set$start.date)
tmp.date$hour <- tmp.date$hour + (24 * add.days)
sort(tmp.date)
# # # #
# broken occupytime functions
occ.time.lh <- occ.time.rh <- rtes$occupy.time.marker
occ.time.lh <- occ.time.rh <- rtes$occ.peak.time.marker
occ.time.lh[-high.alpha.index] <- 0
occ.time.rh[high.alpha.index] <- 0
plot(table(rtes$occ.peak.time.marker))
#1 - 80, 81 - 150, 150 up
octm.1 <- ifelse(rtes$occ.peak.time.marker > 80, 0, rtes$occ.peak.time.marker)
octm.2 <- rtes$occ.peak.time.marker
octm.2[octm.2 < 81] <- 0
octm.2[octm.2 > 149] <- 0
octm.3 <- ifelse(rtes$occ.peak.time.marker < 150, 0, rtes$occ.peak.time.marker)
rtes[which(rtes$occ.peak.time.marker == 150),]
occ.t.lh <- ifelse(rtes$occ.peak.time.marker < 150, rtes$occ.peak.time.marker, 0)
occ.t.rh <- ifelse(rtes$occ.peak.time.marker < 150, 0, rtes$occ.peak.time.marker)
plot(occ.t.lh, occ.t.rh)
days.in <- 90
occ.first.80 <- ifelse(rtes$occ.peak.time.marker < days.in, rtes$occ.peak.time.marker, 0)
occ.after.80 <- ifelse(rtes$occ.peak.time.marker < days.in, 0, rtes$occ.peak.time.marker)
#cbind(occ.time.rh, occ.time.lh)
# # # #
# vector for high alpha colors
tmp.col <- rep("black", dim(rtes)[1])
tmp.col[high.alpha.index] <- "red"
late.high.alphas <- rep(0, dim(rtes)[1])
late.high.alphas[high.alpha.index] <- 1
plot(rtes$occ.peak.time.marker, rtes$alpha, col=tmp.col, cex=w.size.L/2)
summary(rtes$follower.delta)
follower.delta.L <- log10(rtes$follower.delta)
follower.win.delta.L <- rtes$follower.win.delta
follower.win.delta.L <- follower.win.delta.L + abs(min(follower.win.delta.L)) + 1
follower.win.delta.L <- log10(follower.win.delta.L)
plot(sort(rtes$follower.win.delta))
alpha.L <- log10(rtes$alpha)
closeness.L <- log(rtes$follow.prop.closeness)
size.L <- log10(rtes$size)
w.size.L <- log10(rtes$window.size)
peak.at <- rtes$peak.at
peak.at[peak.at == 0] <- 1
peak.at.L <- log10(peak.at)
peak.rate.L <- log10(rtes$peak.rate)
mean.followers.L <- log10(rtes$follower.mean)
occ.users.sqrt <- sqrt(rtes$occ.users + 1)
occupy.time.marker.L <- log10(rtes$occupy.time.marker)
#plot(table(rtes$occupy.time.marker))
occ.peak.time <- log10(rtes$occ.peak.time.marker)
occ.peak.time.L <- log10(rtes$occ.peak.time.marker)
overlap.sqrt <- sqrt(rtes$tweet.overlap)
######################################
#
# DECRIPTIVES PLOTS & SUMMARIES
#
######################################
# "rte.ids" "origin.user" "num.bots" "occ.users" "start.date"
# "occupy.time.marker" "occ.peak.time.marker" "follower.start" "follower.delta" "follower.win.delta"
# "follower.percent.delta" "follower.sum" "follower.mean" "size" "window.size"
# "first.rt.lag" "last.rt.at" "peak.at" "count.pre.peak" "peak.rate"
# "post.peak" "post.window" "occ.start.minute" "occ.end.minute" "alpha"
# "KS.p" "poission.p" "wait.closeness" "follow.prop.closeness" "follow.prob.closeness"
# "post.peak.closeness" "longest.path" "chains" "tweet.overlap" "tweet.overlap.minutes"
# "origin.user.lower" "Nov15" "Nov17"
colnames(rtes)
dim(rtes) # 429 RTEs
sum(rtes$window.size) # 76,419
sum(rtes$size) # 84,972
table(rtes$origin.user)
plot(table(rtes$window.size))
plot(table(rtes$alpha))
plot(table(rtes$follower.win.delta))
plot(table(rtes$peak.rate))
plot(table(rtes$occ.peak.time.marker))
summary(rtes$occ.peak.time.marker)
colnames(rtes)
plot(rtes$peak.at + 1), rtes$peak.rate)
plot(rtes$peak.at, rtes$occ.peak.time.marker)
plot(rtes$peak.rate, rtes$occ.peak.time.marker)
cor.test(rtes$peak.at, rtes$occ.peak.time.marker)
cor.test(rtes$peak.rate, rtes$occ.peak.time.marker)
plot(peak.at.L, peak.rate.L)
plot(peak.at.L, occ.peak.time.L)
plot(peak.rate.L, occ.peak.time.L)
library(scatterplot3d)
s3d <-scatterplot3d(peak.at.L, occ.peak.time.L, peak.rate.L, pch=16, highlight.3d=TRUE,
type="h", main="3D Scatterplot")
fit.3d <- lm(peak.rate.L ~ peak.at.L + occ.peak.time.L)
s3d$plane3d(fit.3d)
s3d <-scatterplot3d(peak.at.L, occ.peak.time.L, rtes$alpha, pch=16, highlight.3d=TRUE,
type="h", main="3D Scatterplot")
fit.3d <- lm(rtes$alpha ~ peak.at.L + occ.peak.time.L)
s3d$plane3d(fit.3d)
s3d <-scatterplot3d(peak.at.L, peak.rate.L, occ.peak.time.L, pch=16, highlight.3d=TRUE,
type="h", main="3D Scatterplot")
fit.3d <- lm(occ.peak.time.L ~ peak.at.L + peak.rate.L + rtes$alpha)
fit.3d <- lm(occ.peak.time.L ~ peak.at.L + peak.rate.L + rtes$alpha)
fit.3d <- lm(closeness.L ~ poly(occ.peak.time.marker,2), data=rtes)
s3d$plane3d(fit.3d)
summary(fit.3d)
pairs(~alpha+peak.at+peak.rate+occ.peak.time.marker
#+size+window.size+peak.at+peak.rate+longest.path+count.pre.peak
# +follower.sum+follower.mean+tweet.overlap+occ.users
# +occupy.time.marker+occ.peak.time.marker+Nov15+Nov17
, data=rtes
, lower.panel=panel.smooth, upper.panel=panel.cor, diag.panel = panel.hist, text.panel=my.text.panel
, lwd=2, pch=20, col=adjustAlpha("dodgerblue4", .3) # col=adjustAlpha("forestgreen", .3)
, main="")
tmp.df <- data.frame(alpha=rtes$alpha, closeness=rtes$follow.prop.closeness, PeakTime=rtes$peak.at, PeakRate=rtes$peak.rate, OccupyDay=rtes$occ.peak.time.marker)
cor(tmp.df) # get correlations
as.matrix(cor(tmp.df))
cor.test(rtes$alpha, rtes$follow.prop.closeness)
cor.test(rtes$follower.delta, rtes$follow.prop.closeness)
summary(lm(rtes$follower.delta~rtes$follow.prop.closeness+rtes$size))
library(Hmisc)
my.corrs <- rcorr(as.matrix(tmp.df), type="pearson")
names(my.corrs)
my.corrs$r <- round(my.corrs$r, 3)
my.corrs$P <- round(my.corrs$P, 3)
corr.mat <- my.corrs$r
corr.mat[lower.tri(my.corrs$P)] <- my.corrs$P[lower.tri(my.corrs$P)]
par(bty="n")
boxplot(rtes$alpha, horizontal = T, col="gold", bty="n", cex=1.2, pch=8)
boxplot(rtes$alpha, horizontal = T, col="gold", bty="n", cex=1.2, pch=8
, border="white")
#tmp.col <- rep(rgb(64, 153, 255, maxColorValue=255), length(rtes$alpha))
tmp.col <- rep("orange", length(rtes$alpha))
tmp.col[high.alpha.index] <- "red"
points(rtes$alpha[high.alpha.index], rep(1, length(rtes$alpha[high.alpha.index])), pch = 1, col="red")
stripchart(rtes$alpha, pch=20, method="stack", col=tmp.col)
library(vioplot)
vioplot(rtes$alpha, names=c("alpha"), col="gold", drawRect=FALSE, horizontal = T)
title("Violin Plots of Miles Per Gallon")
colnames(rtes)
library(aplpack)
bagplot(rtes$occ.peak.time.marker, rtes$alpha, xlab="Occ Day", ylab="Alpha",
main="Bagplot of Alpha and Occupy Day", cex=.75, col.baghull="gray85", col.loophull="gray95", show.whiskers=F)
bagplot(rtes$follow.prop.closeness, rtes$alpha, xlab="Occ Day", ylab="Alpha",
main="Bagplot of Alpha and Occupy Day", cex=.75, col.baghull="gray85", col.loophull="gray95", show.whiskers=F)
######################################
# ALPHA
#
TransformTriPlot(rtes$follow.prop.closeness, rtes$alpha, high.alpha.index, xlab="closeness", ylab="alpha", transform="log", fname="AlphaClose", log.y=F)
TransformTriPlot(rtes$size, rtes$alpha, high.alpha.index, xlab="size", ylab="alpha", transform="log", fname="AlphaSize", log.y=F)
TransformTriPlot(rtes$window.size, rtes$alpha, high.alpha.index, xlab="window size", ylab="alpha", transform="log", fname="AlphaWinSize", log.y=F)
TransformTriPlot(rtes$peak.at + 1, rtes$alpha, high.alpha.index, xlab="peak time", ylab="alpha", transform="log", fname="AlphaPeakAt", log.y=F)
TransformTriPlot(rtes$peak.at + 1, rtes$alpha, high.alpha.index, xlab="peak time", ylab="alpha", transform="sqrt", fname="AlphaPeakAtSqrt", log.y=F)
TransformTriPlot(rtes$peak.rate, rtes$alpha, high.alpha.index, xlab="peak rate", ylab="alpha", transform="log", fname="AlphaPeakRate", log.y=F)
TransformTriPlot(rtes$occ.users + 2, rtes$alpha, high.alpha.index, xlab="occupy users", ylab="alpha", transform="log", fname="AlphaOccUsers", log.y=F)
TransformTriPlot(rtes$occ.users + 1, rtes$alpha, high.alpha.index, xlab="occupy users", ylab="alpha", transform="sqrt", fname="AlphaOccUsersSqrt", log.y=F)
TransformTriPlot(rtes$follower.mean, rtes$alpha, high.alpha.index, xlab="mean followers", ylab="alpha", transform="log", fname="AlphaMeanFollow", log.y=F)
TransformTriPlot(rtes$tweet.overlap + 1, rtes$alpha, high.alpha.index, xlab="tweet.overlap", ylab="alpha", transform="log", fname="AlphaOverlap", log.y=F)
TransformTriPlot(rtes$tweet.overlap + 1, rtes$alpha, high.alpha.index, xlab="tweet.overlap", ylab="alpha", transform="sqrt", fname="AlphaOverlapSqrt", log.y=F)
TransformTriPlot(rtes$occupy.time.marker, rtes$alpha, high.alpha.index, xlab="occupy Day", ylab="alpha", transform="log", fname="AlphaOccTime", log.y=F)
TransformTriPlot(rtes$occupy.time.marker, rtes$alpha, high.alpha.index, xlab="occupy Day", ylab="alpha", transform="sqrt", fname="AlphaOccTimeSqrt", log.y=F)
TransformTriPlot(rtes$occ.peak.time.marker, rtes$alpha, high.alpha.index, xlab="occupy Peak Day", ylab="alpha", transform="log", fname="AlphaOccPeakTime", log.y=F)
TransformTriPlot(rtes$occ.peak.time.marker, rtes$alpha, high.alpha.index, xlab="occupy Peak Day", ylab="alpha", transform="sqrt", fname="AlphaOccPeakTimeSqrt", log.y=F)
######################################
# FOLLOWER DELTA
#
plot(log10(rtes$follow.prop.closeness), log10(rtes$follower.win.delta))
hist(rtes$follower.win.delta)
plot(table(rtes$follower.win.delta))
follow.delta <- rtes$follower.win.delta + 1 + abs(min(rtes$follower.win.delta))
summary(follow.delta)
TransformTriPlot(rtes$alpha, follow.delta, high.alpha.index, xlab="closeness", ylab="follower.win.delta", transform="log", fname="followClose", log.y=T)
TransformTriPlot(rtes$follow.prop.closeness, follow.delta, high.alpha.index, xlab="closeness", ylab="follower.win.delta", transform="log", fname="followClose", log.y=T)
TransformTriPlot(rtes$size, follow.delta, high.alpha.index, xlab="size", ylab="follower.win.delta", transform="log", fname="followSize", log.y=T)
TransformTriPlot(rtes$window.size, follow.delta, high.alpha.index, xlab="window size", ylab="follower.win.delta", transform="log", fname="followWinSize", log.y=T)
TransformTriPlot(rtes$peak.at + 1, follow.delta, high.alpha.index, xlab="peak time", ylab="follower.win.delta", transform="log", fname="followPeakAt", log.y=T)
TransformTriPlot(rtes$peak.at + 1, follow.delta, high.alpha.index, xlab="peak time", ylab="follower.win.delta", transform="sqrt", fname="followPeakAtSqrt", log.y=T)
TransformTriPlot(rtes$peak.rate, follow.delta, high.alpha.index, xlab="peak rate", ylab="follower.win.delta", transform="log", fname="followPeakRate", log.y=T)
TransformTriPlot(rtes$occ.users + 2, follow.delta, high.alpha.index, xlab="occupy users", ylab="follower.win.delta", transform="log", fname="followOccUsers", log.y=T)
TransformTriPlot(rtes$occ.users + 1, follow.delta, high.alpha.index, xlab="occupy users", ylab="follower.win.delta", transform="sqrt", fname="followOccUsersSqrt", log.y=T)
TransformTriPlot(rtes$follower.mean, follow.delta, high.alpha.index, xlab="mean followers", ylab="follower.win.delta", transform="log", fname="followMeanFollow", log.y=T)
TransformTriPlot(rtes$tweet.overlap + 1, follow.delta, high.alpha.index, xlab="tweet.overlap", ylab="follower.win.delta", transform="log", fname="followOverlap", log.y=T)
TransformTriPlot(rtes$tweet.overlap + 1, follow.delta, high.alpha.index, xlab="tweet.overlap", ylab="follower.win.delta", transform="sqrt", fname="followOverlapSqrt", log.y=T)
TransformTriPlot(rtes$occupy.time.marker, follow.delta, high.alpha.index, xlab="occupy Day", ylab="follower.win.delta", transform="log", fname="followOccTime", log.y=T)
TransformTriPlot(rtes$occupy.time.marker, follow.delta, high.alpha.index, xlab="occupy Day", ylab="follower.win.delta", transform="sqrt", fname="followOccTimeSqrt", log.y=T)
TransformTriPlot(rtes$occ.peak.time.marker, follow.delta, high.alpha.index, xlab="occupy Peak Day", ylab="follower.win.delta", transform="log", fname="followOccPeakTime", log.y=T)
TransformTriPlot(rtes$occ.peak.time.marker, follow.delta, high.alpha.index, xlab="occupy Peak Day", ylab="follower.win.delta", transform="sqrt", fname="followOccPeakTimeSqrt", log.y=T)
#####################################################
summary(fit)
fit.coef <- fit$coefficients
fit.ci <- confint(fit)
ci.low <- fit.ci[,1]
ci.hi <- fit.ci[,2]
conf.int.df <- cbind(ci.low, fit.coef, ci.hi)
var.index <- 6
x.min <- min(conf.int.df[var.index,])
x.max <- max(conf.int.df[var.index,])
ci.x <- c(conf.int.df[var.index,1], conf.int.df[var.index,3], conf.int.df[var.index,3], conf.int.df[var.index,1])
ci.y <- c(1,1,2,2)
coeff.x <- c(conf.int.df[var.index,2], conf.int.df[var.index,2])
coeff.y <- c(.9,2.1)
x <- seq(from=x.min - (x.min * .25) , to=x.max + (x.max * .25), length=10)
y <- seq(from=.5, to=2.5, length=10)
par(mar=c(3,8,1,1))
plot(x, y, type="n", bty="n", main="", ylab="", yaxt="n", xlab="")
polygon(ci.x, ci.y, col="orange", border="orange")
lines(coeff.x, coeff.y, lwd=2)
mtext(text=names(fit.coef[var.index]), side=2, las=1, line=0)
av.Plots(fit)
influencePlot(fit, id.method="identify", main="Influence Plot", sub="Circle size is proportial to Cook's Distance" )
qqPlot(fit.1, main="QQ Plot")
fit.out <- influence(fit.1)
qqnorml(fit.out$coef[,4])
halfnorm(cooks.distance(fit.1), 7)
prplot(fit.1,1)
prplot(fit.1,2)
prplot(fit.1,3)
prplot(fit.1,4)
prplot(fit.1,5)
prplot(fit.1,6)
prplot(fit.1,7)
fit <- lm(follow.delta^.19 ~ alpha + peak.rate.L + peak.at.L + follower.start.L + mean.followers.L + overlap.sqrt + poly(occ.peak.time.marker,2), data=rtes)
hist(follow.delta)
hist(rtes$alpha)
hist(rtes$peak.rate)
hist(rtes$peak.at)
hist(rtes$follower.start)
plot(sort(rtes$follower.start))
hist(rtes$follower.mean)
x <- 1:length(rtes$follower.mean)
y <- sort(rtes$follower.mean)
y <- sort(rtes$peak.rate)
plot(x, y)
plot(log10(x), log10(y))
# "rte.ids" "origin.user" "num.bots"
# "occ.users" "start.date" "occupy.time.marker"
# "follower.start" "follower.delta" "follower.percent.delta"
# "follower.sum" "follower.mean" "size"
# "window.size" "first.rt.lag" "last.rt.at"
# "peak.at" "count.pre.peak" "peak.rate"
# "post.peak" "post.window" "occ.start.minute"
# "occ.end.minute" "alpha" "KS.p"
# "poission.p" "wait.closeness" "follow.prop.closeness"
# "follow.prob.closeness" "post.peak.closeness" "longest.path"
# "chains" "tweet.overlap" "tweet.overlap.minutes"
# "origin.user.lower" "Nov15" "Nov17"
cor.test(rtes$follow.prop.closeness, rtes$follower.mean)
occ.user.table <- sort(table(rtes$origin.user))
occ.user.names <- names(occ.user.table)
occ.user.length <- length(occ.user.names)
for (i in 1:occ.user.length) {
}
cor.test(rtes$size, rtes$follower.delta)
cor.test(rtes$peak.rate, rtes$follower.delta)
cor.test(rtes$peak.rate, rtes$size)
cor.test(rtes$peak.rate, rtes$follow.prop.closeness)
cor.test(rtes$peak.rate, rtes$follower.mean)
cor.test(rtes$follower.mean, rtes$follow.prop.closeness)
cor.test(rtes$follower.win.delta, rtes$follow.prop.closeness)
plot(log10(rtes$follower.win.delta + 20), log10(rtes$follow.prop.closeness + 1))
colnames(rtes)
closeness <- round(rtes$follow.prop.closeness, 2)
hist(closeness)
summary(rtes$follow.prop.closeness)
plot(sort(rtes$follow.prop.closeness))
blue <- rgb(80, 165, 255, maxColorValue=255)
hist(rtes$size, breaks=20, col=blue, border=blue, main="Histogram of RTE sizes", xlab="size (number of tetweets)")
colnames(rtes)
followers