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The other day I was having a quick look to the newspapers and I stumbled on this article. Apparently, Pornhub (a website whose mission should be pretty clear) have analysed the data on their customers and found out that the town of Ware (Hertfordshire) has more demand for online porn than any other UK town. According to PornHub, a Ware resident will last 10 minutes 37 seconds (637 seconds) on its adult website, compared with the world average time of 8 minutes 56 seconds (just 536 seconds).Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Comments have gone both ways, with Ware being dubbed (in a somewhat derogatory way) the “Britain capital of porn”, while some people have highlighted the better “performance”, shall we say, of Ware viewers (who, ahem, lasted on line nearly 2 minutes longer than the average viewer).
The whole data (or at least an excerpt) are available from The Guardian website and so I have very, very quickly played around with them. In particular, I think that it’s kind of weird that the analysis focussed just on the maximum value; so I had a quick look at the entire distribution.
Interestingly, it appears that not just Ware, but basically all of the British towns in the dataset are above the world average; some are even very close to the porn capital! So do the same comments made for Ware apply to the whole of the UK?
After downloading the data to a file “PornUse.csv“, you can easily recreate the graph with this very simple R code:
boxplot(time_seconds~Region,main=”Time spent online viewing porn (in secs)”,ylab=”Time (seconds)”)
abline(h=536,lwd=2,lty=2)
ord <- order(time_seconds,decreasing=TRUE)
for (i in 1:3) {
text(1,time_seconds[ord[i]],Town[ord[i]],cex=.6,pos=4)
}
ord2 <- order(time_seconds,decreasing=FALSE)
text(4,time_seconds[ord2[4]],Town[ord2[4]],cex=.6,pos=4)
for (i in 1:3) {
text(1,time_seconds[ord2[i]],Town[ord2[i]],cex=.6,pos=4)
}
ord <- order(time_seconds,decreasing=TRUE)
for (i in 1:3) {
text(1,time_seconds[ord[i]],Town[ord[i]],cex=.6,pos=4)
}
ord2 <- order(time_seconds,decreasing=FALSE)
text(4,time_seconds[ord2[4]],Town[ord2[4]],cex=.6,pos=4)
for (i in 1:3) {
text(1,time_seconds[ord2[i]],Town[ord2[i]],cex=.6,pos=4)
}
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