Look at your data plots

layd_plots.r

points_per_hour<-2000
layd_raw_transition<-
data.frame(timestamp=factor(c(rep(seq(0,11),points_per_hour),
                       rep(seq(12,23),points_per_hour))),
           latency=c(rgamma(12*points_per_hour,shape=3,scale=60),
                     rgamma(12*points_per_hour*0.9,shape=2,scale=60),
                     rgamma(12*points_per_hour*0.1,shape=8,scale=60)),
           type=(c(rep("before",12*points_per_hour),
                   rep("after",12*points_per_hour))))

library(ggplot2)

# A timeseries of boxplots
ggplot(layd_raw_transition,aes(y=latency))+geom_boxplot(aes(x=timestamp,group=timestamp))

# The heatmap
ggplot(layd_raw_transition,aes(timestamp,latency))+stat_bin2d(binwidth=c(1,50))

# A "violin" plot.  I wanted this to work but it had a nasty Moire effect.
ggplot(layd_raw_transition,aes(latency))+ geom_ribbon(aes(ymax=..count../2, ymin=-..count../2,), binwidth=40,stat="bin")+facet_wrap(~timestamp,nrow=1)+coord_flip()

# Faceted frequency polygons
ggplot(layd_raw_transition,aes(latency))+geom_freqpoly(binwidth=20)+facet_wrap(~type,ncol=1)

# Boil down into a time series
layd_ts_transition<-ddply(layd_raw_transition,"timestamp", summarize, avg_latency=mean(latency), count=length(latency))

# The timeseries I showed in the preso
ggplot(layd_ts_transition,aes(timestamp,avg_latency))+
  geom_line(aes(group=1),size=3,color="#92959c")+
  geom_point(size=5,shape=15,colour="#4f4e5f")+
  scale_x_discrete(breaks=seq(0,23,4))+
  ylim(0,200)+xlab("Time")+ylab("Average Latency (ms)")

# The heatmap chart I showed in the preso
ggplot(layd_raw_transition,aes(timestamp,latency))+stat_bin2d(binwidth=c(1,50))+
  geom_line(data=layd_ts_transition,aes(timestamp,avg_latency,group=1),size=3,color="#92959c")+
  scale_x_discrete(breaks=seq(0,23,4))+
  xlab("Time")+ylab("Average Latency (ms)")