On Mar 11, 2011, at 8:41 AM, Benjamin Stier wrote:
Hi Francisco,
Thanks for your solution. It runs pretty fast compared to my for
loop. Here
is a comparison of system.time():
system.time(splitVals <- by(serv, dates, aggregateDf ))
user system elapsed
1.129 0.218 1.348
system.time(
Hi Francisco,
Thanks for your solution. It runs pretty fast compared to my for loop. Here
is a comparison of system.time():
system.time(splitVals <- by(serv, dates, aggregateDf ))
user system elapsed
1.129 0.218 1.348
system.time(... my long for loop...)
user system elapsed
276.
On Mar 10, 2011, at 8:23 AM, Benjamin Stier wrote:
Hello list!
I have a data.frame which looks like this:
serv
datum op.read op.write read write
1 2011-01-29 10:00:00 00 0 0
2 2011-01-29 10:00:01 00 0 0
3 2011-01-29 10:00:02 0
Benjamin,
A more elegant "R-style" solution would be to use one of R's
"apply"/aggregation routines, of which there are many. For example, the "by"
function can split a data.frame by some factor/categorical variable(s), and
then apply a function to each "slice". The result can then be pieced back
Hello list!
I have a data.frame which looks like this:
> serv
datum op.read op.write read write
1 2011-01-29 10:00:00 00 0 0
2 2011-01-29 10:00:01 00 0 0
3 2011-01-29 10:00:02 00 0 0
4 2011-01-29 10:00:03
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