Now, lets get the data into R and plot it.
-[For clarity, I'm not showing the R code, but it's available in the source cdoe for this post.]
+[For clarity, I'm not showing the R code, but it's available in the source code for this post.]
-[[!sweavealike results="hide" fig=1 echo=0 code="""
+[[!sweavealike width=800 fig=1 echo=0 nooutput=1 results="hide" code="""
+require(lattice)
reporting.rate <- read.table("data/bug_reporting_rate.txt")
colnames(reporting.rate) <- c("bug","epoch")
reporting.rate <- data.frame(reporting.rate)
-reporting.rate$bug <- as.numeric(gsub("\\.report","",gsub(".*\\/","",reporting.rate$bug)))
+reporting.rate$bug <- as.numeric(gsub("\\\\.report","",gsub(".*\\\\/","",reporting.rate$bug)))
reporting.rate <- reporting.rate[order(as.numeric(reporting.rate$bug)),]
### this is the number of bugs submitted per second
### however, for bug numbers less than about 100000, this number is wrong.
(reporting.rate$epoch[-1]-reporting.rate$epoch[-nrow(reporting.rate)]))
reporting.rate$day <- as.POSIXct(reporting.rate$epoch,origin="1970-01-01")
### show the reporting rate from 2003 onward with a lowess line
-xyplot(rate~day,reporting.rate[reporting.rate$epoch >= 1041408000,],
+print(xyplot(rate~day,reporting.rate[reporting.rate$epoch >= 1041408000,],
panel=function(x,y,col,...){
panel.xyplot(x,y,col="cyan",...);
panel.loess(x,y,col="red",...);},
- ylim=c(0,0.005),
+ ylim=c(0,0.004),
ylab="Bugs per Second",
- xlab="Time")
+ xlab="Time"))
"""]]
From the plot (Bugs reported per second over time with a red loess fit