--- /dev/null
+
+
+source("functions.r");
+
+# system("parse_rt_data.py 3 > rt_data.csv");
+#t <- read.csv('rt_data.csv', sep=',', header=TRUE)
+t <- read.csv('rt_data_2004-2010.csv', sep=',', header=TRUE)
+
+par(mfrow=c(2,1))
+
+h<-hist(log(log(t$replies)), breaks=50)
+lines(h$breaks[which(h$counts!=0)], h$counts[which(h$counts!=0)])
+h<-hist(log(log(log(t$replies))), breaks=50)
+lines(h$breaks[which(h$counts!=0)], h$counts[which(h$counts!=0)])
+
+
+par(mfrow=c(1,1))
+
+t2 <- t[which(t$complete == 1),]
+d <- (t2$lastreply - t2$start)/(60*60)
+
+#start_image("rt_hist_ttc_1000.png")
+#hist(d[which(d<1000)], xlab="hours from creation to last reply", breaks=30)
+#end_image()
+#
+#start_image("rt_hist_ttc_200.png")
+#hist(d[which(d<200)], xlab="hours from creation to last reply", breaks=30)
+#end_image()
+#
+#start_image("rt_hist_ttc_50.png")
+#hist(d[which(d<50)], xlab="hours from creation to last reply", breaks=30)
+#end_image()
+#
+#start_image("rt_hist_ttc_10.png")
+#hist(d[which(d<10)], xlab="hours from creation to last reply", breaks=30)
+#end_image()
+#
+#d2 <- (t2$lastreply - t2$start)
+#h<-hist(log(d2), plot=F, breaks=50)
+#lines(h$breaks[which(h$counts!=0)], h$counts[which(h$counts!=0)])
+
+
+# this doesn't work as I would like. I think the bins aren't as I expect
+#h <- hist(d, plot=F, breaks=c(seq(0,max(d)+1, .1)))
+#plot(h$counts, log="x", pch=20, col="blue",
+# main="Log-normal distribution",
+# xlab="Value", ylab="Frequency")
+
+#plot(log(d2))
+#plot(ecdf(d2))
+
+tstamp_45 <-unclass(as.POSIXct("2005-01-01", origin="1960-01-01"))[1]
+tstamp_56 <-unclass(as.POSIXct("2006-01-01", origin="1960-01-01"))[1]
+tstamp_67 <-unclass(as.POSIXct("2007-01-01", origin="1960-01-01"))[1]
+tstamp_78 <-unclass(as.POSIXct("2008-01-01", origin="1960-01-01"))[1]
+tstamp_89 <-unclass(as.POSIXct("2009-01-01", origin="1960-01-01"))[1]
+tstamp_90 <-unclass(as.POSIXct("2010-01-01", origin="1960-01-01"))[1]
+
+
+t_4 <- t2[which( t2$start < tstamp_45 ),]
+t_5 <- t2[which( t2$start >= tstamp_45 & t2$start < tstamp_56 ),]
+t_6 <- t2[which( t2$start >= tstamp_56 & t2$start < tstamp_67 ),]
+t_7 <- t2[which( t2$start >= tstamp_67 & t2$start < tstamp_78 ),]
+t_8 <- t2[which( t2$start >= tstamp_78 & t2$start < tstamp_89 ),]
+t_9 <- t2[which( t2$start >= tstamp_89 & t2$start < tstamp_90 ),]
+t_10 <- t2[which( t2$start >= tstamp_90 ),]
+
+par(mfrow=c(4,1))
+plot_rt_hist(t_4)
+plot_rt_hist(t_5)
+plot_rt_hist(t_6)
+plot_rt_hist(t_7)
+plot_rt_hist(t_8)
+plot_rt_hist(t_9)
+par(mfrow=c(1,1))
+
+start_image("rt_support_seasonal.png")
+par(mfrow=c(6,1))
+par(mai=c(.3,.3,.3,.3))
+
+# start dates on Sunday to align all weeks with weekend boundaries.
+year_hist(t_4, "2004", "2003/12/28", "2005/1/7", 85)
+year_hist(t_5, "2005", "2005/1/2", "2006/1/7", 85)
+year_hist(t_6, "2006", "2006/1/1", "2007/1/7", 85)
+year_hist(t_7, "2007", "2006/12/31", "2008/1/7", 85)
+year_hist(t_8, "2008", "2007/12/30", "2009/1/7", 85)
+year_hist(t_9, "2009", "2008/12/28", "2010/1/30", 85)
+end_image()
+
+par(mai=c(0.7,0.7,0.7,0.7))
+par(mfrow=c(1,1))
+
+
+tstamp <-unclass(as.POSIXct("2008-01-01", origin="1960-01-01"))
+t_67 <- t2[which( t2$start < tstamp[1] ),]
+t_89 <- t2[which( t2$start >= tstamp[1] ),]
+
+
+# install.packages('sn')
+require(sn)
+par(mfrow=c(6,1))
+par(mai=c(0.3,0.3,0.3,0.3))
+
+#start_image("rt_hist_ttc_1000.png")
+time_hist <- function (t, lessthan, year, log=T, breaks=30, xlim=c(-4,10), ylim=c(0,150))
+{
+ d <- (t$lastreply - t$start)/(60*60)
+ main = sprintf("Histogram of d<%s for %s", lessthan, year);
+ if ( log )
+ {
+ d <- log(d[which(d<lessthan)])
+ avg <- round(mean(d), 2)
+ main = sprintf("Histogram of d<%s for %s : %s", lessthan, year, avg);
+ #h<-sn.mle(d, xlab="Hours to Complete Ticket", breaks=30, main=main, xlim=xlim, ylim=ylim)
+ h<-sn.mle(y=d)
+ } else {
+ h<-hist(d[which(d<lessthan)], xlab="Hours to Complete Ticket", breaks=30, main=main, xlim=xlim, ylim=ylim)
+ }
+ return (h);
+}
+tstamp <-unclass(as.POSIXct("2007-05-01", origin="1960-01-01"))
+t_7a <- t_7[which(t_7$start < tstamp),]
+t_7b <- t_7[which(t_7$start >= tstamp),]
+
+#end_image()
+h4<-time_hist(t_4, 10000, "2004")
+h5<-time_hist(t_5, 10000, "2005")
+h6<-time_hist(t_6, 10000, "2006")
+#h7<-time_hist(t_7, 10000, "2007")
+h7a<-time_hist(t_7a, 10000, "2007")
+h7b<-time_hist(t_7b, 10000, "2007")
+h8<-time_hist(t_8, 10000, "2008")
+h9<-time_hist(t_9, 10000, "2009")
+
+tstamp <-unclass(as.POSIXct("2009-09-01", origin="1960-01-01"))
+m_9a <- m_9[which(m_9$start < tstamp),]
+m_9b <- m_9[which(m_9$start >= tstamp),]
+
+split_by_time <- function (t, datestr)
+{
+ tstamp <-unclass(as.POSIXct(datestr, origin="1960-01-01"))
+ a <- t[which(t$start < tstamp),]
+ b <- t[which(t$start >= tstamp),]
+ v<- list('before'=a, 'after'=b)
+ return (v);
+}
+
+mh7 <- time_hist(m_7, 10000, '2007')
+
+sm_8 <- split_by_time(m_8, "2008-07-01")
+#mh8a <- time_hist(rbind(m_7, m_8$before, m_8$after), 10000, '2008')
+#mh8a <- time_hist(rbind(m_7[which(log((m_7$lastreply-m_7$start)/(60*60))>2),]), 10000, '2008')
+# m_7 is junk data
+
+mh_8 <- time_hist(sm_8$before, 10000, '2008')
+
+sm_9 <- split_by_time(m_9, "2009-09-01")
+
+mh_89 <- time_hist(rbind(sm_8$after, sm_9$before), 10000, '2009')
+mh_9 <- time_hist(sm_9$after, 10000, '2009')
+
+
+x<-seq(-8,10,0.01)
+#x<- exp(x)/24
+
+#my7<-dsn(x, dp=cp.to.dp(mh7$cp))
+my8<-dsn(x, dp=cp.to.dp(mh_8$cp))
+my89<-dsn(x, dp=cp.to.dp(mh_89$cp))
+my9<-dsn(x, dp=cp.to.dp(mh_9$cp))
+
+y4<-dsn(x, dp=cp.to.dp(h4$cp))
+y5<-dsn(x, dp=cp.to.dp(h5$cp))
+y6<-dsn(x, dp=cp.to.dp(h6$cp))
+y7a<-dsn(x, dp=cp.to.dp(h7a$cp))
+y7b<-dsn(x, dp=cp.to.dp(h7b$cp))
+y8<-dsn(x, dp=cp.to.dp(h8$cp))
+y9<-dsn(x, dp=cp.to.dp(h9$cp))
+
+start_image("rt_time_to_resolve.png")
+par(mfrow=c(1,1))
+par(mai=c(1.0,0.7,0.7,0.7))
+# monitor
+plot(x, my9, col='blue', type='l', axes=F, xlab="Days to Resolve", ylab="Density")
+axis(1, labels=c(0.0001, 0.01, 0.1, 1, 5, 20, 100), at=c(0.0001, 0.01, 0.1, 1, 5, 20, 100))
+axis(2)
+lines(x, my8, col='dodgerblue')
+lines(x, my7, col='turquoise')
+abline(v=x[which(my8==max(my8))])
+abline(v=x[which(my9==max(my9))])
+
+# heavy
+lines(x, y7a, col='green3')
+lines(x, y4, col='green4')
+lines(x, y5, col='greenyellow')
+
+abline(v=x[which(y4==max(y4))])
+abline(v=x[which(y5==max(y5))])
+abline(v=x[which(y7a==max(y7a))])
+
+# light
+lines(x, y7b, col='orange', type='l')
+lines(x, y6, col='orange3')
+lines(x, y8, col='firebrick2')
+lines(x, y9, col='firebrick4')
+
+abline(v=x[which(y7b==max(y7b))])
+abline(v=x[which(y6==max(y6))])
+abline(v=x[which(y8==max(y8))])
+abline(v=x[which(y9==max(y9))])
+
+end_image()
+
+whisker <- function (x0,y0,sd, length=0.05)
+{
+ arrows(x0, y0, x0, y0+sd, code=2, angle=90, length=length)
+ arrows(x0, y0, x0, y0-sd, code=2, angle=90, length=length)
+}
+
+whisker2 <- function (x0,y0, y0_high, y0_low, col="black", length=0.05)
+{
+ arrows(x0, y0, x0, y0_high, code=2, angle=90, length=length, col=col)
+ arrows(x0, y0, x0, y0_low, code=2, angle=90, length=length, col=col)
+}
+
+start_image("rt_aggregate_times.png")
+par(mfrow=c(1,1))
+par(mai=c(1,1,1,1))
+par(mar=c(5,4,4,4))
+
+s_list <- c(1519, 1596, 1112, 1591, 1019, 815)
+m_list <- c(0,0,0, 119, 229, 251)
+x_tick_list <- c(1, 2.5, 4, 5.5, 7, 8.5)
+x_tt_resolve_list <- c(1, 2.5, 4, 5.2,5.8, 7, 8.5)
+y_tt_resolve_list <- c( x[which(y4==max(y4))],
+ x[which(y5==max(y5))],
+ x[which(y6==max(y6))],
+ x[which(y7a==max(y7a))],
+ x[which(y7b==max(y7b))],
+ x[which(y8==max(y8))],
+ x[which(y9==max(y9))])
+
+
+y_mean_list <- c( h4$cp['mean'],
+ h5$cp['mean'],
+ h6$cp['mean'],
+ h7a$cp['mean'],
+ h7b$cp['mean'],
+ h8$cp['mean'],
+ h9$cp['mean'])
+
+y_sd_list <- c( h4$cp['s.d.'],
+ h5$cp['s.d.'],
+ h6$cp['s.d.'],
+ h7a$cp['s.d.'],
+ h7b$cp['s.d.'],
+ h8$cp['s.d.'],
+ h9$cp['s.d.'])
+
+days_tt_resolve <- exp(y_tt_resolve_list)/24
+days_tt_resolve_low <- exp(y_tt_resolve_list-y_sd_list)/24
+days_tt_resolve_high <- exp(y_tt_resolve_list+y_sd_list)/24
+
+
+my_mean_list <- c( mh_8$cp['mean'],
+ mh_89$cp['mean'],
+ mh_9$cp['mean'])
+
+my_sd_list <- c( mh_8$cp['s.d.'],
+ mh_89$cp['s.d.'],
+ mh_9$cp['s.d.'])
+
+mx_tt_resolve_list <- c(7, 8, 8.5)
+my_tt_resolve_list <- c(x[which(my8==max(my8))],
+ x[which(my89==max(my89))],
+ x[which(my9==max(my9))] )
+
+mdays_tt_resolve <- exp(my_tt_resolve_list)/24
+mdays_tt_resolve_low <- exp(my_tt_resolve_list-my_sd_list)/24
+mdays_tt_resolve_high <- exp(my_tt_resolve_list+my_sd_list)/24
+
+
+days_y_sd_list <- exp(y_sd_list)/24
+mdays_y_sd_list <- exp(my_sd_list)/24
+
+days_y_sd_list <- exp(y_sd_list)/24
+mdays_tt_resolve <- exp(my_tt_resolve_list)/24
+
+plot(x_tt_resolve_list, days_tt_resolve, type='p', pch=c(22), axes=FALSE,
+ log='y', ylim=c(.01,350), xlab="Year", ylab='')
+#points(x_tt_resolve_list, days_tt_resolve, pch=c(22))
+
+lines(c(x_tt_resolve_list[1:2], x_tt_resolve_list[4]), c(days_tt_resolve[1:2], days_tt_resolve[4]), col='red')
+lines(c(x_tt_resolve_list[3], x_tt_resolve_list[5:7]), c(days_tt_resolve[3], days_tt_resolve[5:7]), col='green')
+#lines(mx_tt_resolve_list, mdays_tt_resolve)
+#points(mx_tt_resolve_list, mdays_tt_resolve, pch=c(24))
+
+lines(mx_tt_resolve_list, mdays_tt_resolve, col='blue')
+points(mx_tt_resolve_list, mdays_tt_resolve, pch=c(24))
+
+ticks<-c(0,0.01, 0.1, 0.5,1,2,4,7,21, 28, 7*8, 7*16)
+
+axis(1, labels=c('2004', '2005', '2006', '2007', '2008', '2009'), at=x_tick_list)
+axis(2, labels=ticks, at=ticks)
+mtext("Days to Resolve Message", 2, line=3)
+#axis(2, labels=ticks, at=ticks)
+#for (i in 1:length(days_y_sd_list) ) {
+# whisker(x_tt_resolve_list[i], days_tt_resolve[i], days_y_sd_list[i])
+#}
+#for (i in 1:length(mdays_y_sd_list) ) {
+# whisker(mx_tt_resolve_list[i], mdays_tt_resolve[i], mdays_y_sd_list[i])
+#}
+for (i in c(1,2,4) ) {
+ whisker2(x_tt_resolve_list[i], days_tt_resolve[i],
+ days_tt_resolve_high[i], days_tt_resolve_low[i], col='red')
+}
+for (i in c(3,5,6,7) ) {
+ whisker2(x_tt_resolve_list[i], days_tt_resolve[i],
+ days_tt_resolve_high[i], days_tt_resolve_low[i], col='green')
+}
+for (i in 1:length(mdays_y_sd_list) ) {
+ whisker2(mx_tt_resolve_list[i], mdays_tt_resolve[i],
+ mdays_tt_resolve_high[i], mdays_tt_resolve_low[i], col='blue')
+}
+
+abline(h=21,col='grey90')
+abline(h=2,col='grey90')
+abline(h=0.5,col='grey80')
+
+legend(1, .05,
+ cex=0.7,
+ legend=c("Unstable Periods", "Stable Periods", "MyOps Messages"),
+ pch=c(22, 22, 24),
+ col=c('red', 'green', 'blue'),
+ lty=c(1, 1,1), merge=T)
+end_image()
+# install.packages('UsingR')
+require(UsingR)
+
+m<-min(t_4$start)
+d<-data.frame(
+ '2004'=t_4$start-m,
+ '2005'=t_5$start-m,
+ '2006'=t_6$start-m)
+simple.violinplot(d)
+
+par(mfrow=c(3,3))
+par(mai=c(.3,.3,.3,.3))
+sp <- function (t)
+{
+ d <- (t$lastreply-t$start)/(60*60*24)
+ simple.violinplot(log(d))
+}
+sp(t_4)
+sp(t_5)
+sp(t_6)
+sp(t_7)
+sp(t_8)
+sp(t_9)
+sp(m_8)
+sp(m_89)
+sp(m_9)
+
+
+t3 <- add_year (t2)
+m3 <- add_year (m2)
+
+par(mfrow=c(1,2))
+par(mai=c(.5,.5,.5,.5))
+t4<-t3[which((t3$lastreply-t3$start)/(60*60*24) < 20),]
+t4<-t3
+simple.violinplot(log((lastreply-start)/(60*60*24)) ~ year, data=t4)
+
+m3[which((m3$lastreply-m3$start)< 0),]
+m4<-m3[which((m3$lastreply-m3$start)/(60*60*24) < 100),]
+simple.violinplot(log((lastreply-start)/(60*60*24)) ~ year, data=m4, log='y')
+
+meanof <- function (t, year)
+{
+ tx <- t[which(t$year == year),]
+ r<-sn.em(y=log((tx$lastreply-tx$start)/(60*60*24)))
+ return (r)
+}
+
+t_sd <- p
+t_p <- c( meanof(t3,2004)$cp['mean'],
+ meanof(t3,2005)$cp['mean'],
+ meanof(t3,2006)$cp['mean'],
+ meanof(t3,2007)$cp['mean'],
+ meanof(t3,2008)$cp['mean'],
+ meanof(t3,2009)$cp['mean'],
+ meanof(t3,2010)$cp['mean'])
+points(t_p)
+for (i in 1:length(t_sd) ) {
+ whisker(i, t_p[i], exp(t_sd[i]))
+}
+
+
+
+
+
+
+#for (i in 1:length(y_tt_resolve_list) ) {
+# whisker(x_tt_resolve_list[i], scale_by*y_tt_resolve_list[i], scale_by*2)
+#}
+#for (i in 1:length(my_tt_resolve_list) ) {
+# whisker(mx_tt_resolve_list[i], scale_by*my_tt_resolve_list[i], scale_by*2)
+#}
+
+#
+#end_image()
+#par(mfrow=c(2,1))
+#plot_rt_hist(t_67)
+#plot_rt_hist(t_89)
+par(mfrow=c(1,1))
+
git://git.onelab.eu / monitor.git / blobdiff