Measures the speed of downloading from different CRAN mirrors

Estimates the speed of each CRAN mirror by measuring the time it takes to download the NEWS file.

cranometer(ms = getCRANmirrors(all = FALSE, local.only = FALSE), ...)

Arguments

ms	- the output of getCRANmirrors. Defaults to using all of the mirrors.
...	not in use

Value

a data.frame with details on mirror sites and the time it took to download their NEWS file.

Details

It works by downloading the latest NEWS file (288 Kbytes at the moment, so not huge) from each of the mirror sites in the CRAN mirrors list. If you want to test it on a subset then call getCRANmirrors yourself and subset it somehow.

It runs on the full CRAN list and while designing this package I've yet to find a timeout or error so I'm not sure what will happen if download.file fails. It returns a data frame like you get from getCRANmirrors but with an extra 't' column giving the elapsed time to get the NEWS file.

CAVEATS: if your network has any local caching then these results will be wrong, since your computer will probably be getting the locally cached NEWS file and not the one on the server. Especially if you run it twice. Oh, I should have put cacheOK=FALSE in the download.file - but even that might get overruled somewhere. Also, sites may have good days and bad days, good minutes and bad minutes, your network may be congested on a short-term basis, etc etc.

There may also be a difference in reliability, which would not so easily be measured by an individual user.

Later that year, Barry also wrote Cranography. See: https://www.maths.lancs.ac.uk/~rowlings/R/Cranography/.

See also

freegeoip, myip, cranometer

Author

Barry Rowlingson <b.rowlingson@lancaster.ac.uk>

Examples

if (FALSE) {
# this can take some time
x <- cranometer() 

time_order <- order(x$t)

# a quick overview of the fastest mirrors
head(x[time_order,c(1:4, 9)], 20)

# a dotchart of the fastest mirrors
with(x[rev(time_order),],
 dotchart(t, labels =Name,
 cex = .5, xlab = "Timing of CRAN mirror")
 )

# tail(geonames_df)
# tail(x)
require(plyr)
ss <- !(x$Name == "0-Cloud")
gvis_df <- ddply(x[ss,], .(CountryCode), function(xx) {
  ss <- which.min(xx$t) 
  if(length(ss) == 0) ss <- 1
  data.frame(time = xx$t[ss], name = xx$Name[ss] )
})
gvis_df <- gvis_df[!is.na(gvis_df$time), ]

require2("googleVis")
Geo<-gvisGeoMap(gvis_df,
              locationvar = "CountryCode",
               numvar="time",
               hovervar = "name",
               options=list(
                            colors='[0xA5EF63, 
                             0xFFB581, 0xFF8747]')
              )
# Display chart
plot(Geo) 
}