dear R wizards: here is the strange question for the day. It seems to me
that nrow() is very slow. Let me explain what I mean:
ds= data.frame( NA, x=rnorm(10000) ) ## a sample data set
> system.time( { for (i in 1:10000) NA } ) ## doing nothing takes
virtually no time
user system elapsed
0.000 0.000 0.001
## this is something that should take time; we need to add 10,000 values
10,000 times
> system.time( { for (i in 1:10000) mean(ds$x) } )
user system elapsed
0.416 0.001 0.416
## alas, this should be very fast. it is just reading off an attribute of
ds. it takes almost a quarter of the time of mean()!
> system.time( { for (i in 1:10000) nrow(ds) } )
user system elapsed
0.124 0.001 0.125
## here is an alternative way to implement nrows, which is already much
faster:
> system.time( { for (i in 1:10000) length(ds$x) } )
user system elapsed
0.041 0.000 0.041
is there a faster way to learn how big a data frame is? I know this sounds
silly, but this is inside a "by" statement, where I figure out how many
observations are in each subset. strangely, this takes a whole lot of
time. I don't believe it is possible to ask "by" to attach an attribute to
the data frame that stores the number of observations that it is actually
passing.
pointers appreciated.
regards,
/iaw
--
Ivo Welch (ivo.we...@brown.edu, ivo.we...@gmail.com)
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