Hi all, I have a data frame with "clustered" rows as follows: Cu1 x1 y1 z1 ... Cu1 x2 y2 z2 ... Cu1 x3 y3 z3 ... # end of first cluster Cu1 Cu2 x4 y4 z4 ... Cu2 x5 y5 z5 Cu2 ... # end of second cluster Cu2 Cu3 ... ... "cluster"-size is 3 in the example above (rows making up a cluster are always consecutive). Is there any faster way to sample n clusters (with replacement) from this dataframe and build up a new data frame out of these sampled clusters? I use the "sample" function and a for-loop. Thanks in advance Armin
sampling from data.frame
4 messages · axionator, jim holtman, Charles C. Berry
Not sure exactly what you mean by 'sample' since you did not provide an example of the expected output, or input data that could be used. Here is an example of taking one sample from each cluster:
df <- data.frame(id=paste("C", rep(1:5, each=3), sep=''), data=1:15)
# sample 1 from each cluster
result <- lapply(split(seq(nrow(df)), df$id), function(.indx){
+ df[sample(.indx, 1),] + })
do.call(rbind,result)
id data C1 C1 1 C2 C2 4 C3 C3 9 C4 C4 11 C5 C5 15
result <- lapply(split(seq(nrow(df)), df$id), function(.indx){
+ df[sample(.indx, 1),] + })
do.call(rbind,result)
id data C1 C1 2 C2 C2 6 C3 C3 9 C4 C4 11 C5 C5 15
result <- lapply(split(seq(nrow(df)), df$id), function(.indx){
+ df[sample(.indx, 1),] + })
do.call(rbind,result)
id data C1 C1 3 C2 C2 4 C3 C3 8 C4 C4 10 C5 C5 13
On Tue, Dec 2, 2008 at 6:27 PM, axionator <axionator at gmail.com> wrote:
Hi all, I have a data frame with "clustered" rows as follows: Cu1 x1 y1 z1 ... Cu1 x2 y2 z2 ... Cu1 x3 y3 z3 ... # end of first cluster Cu1 Cu2 x4 y4 z4 ... Cu2 x5 y5 z5 Cu2 ... # end of second cluster Cu2 Cu3 ... ... "cluster"-size is 3 in the example above (rows making up a cluster are always consecutive). Is there any faster way to sample n clusters (with replacement) from this dataframe and build up a new data frame out of these sampled clusters? I use the "sample" function and a for-loop. Thanks in advance Armin
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Jim Holtman Cincinnati, OH +1 513 646 9390 What is the problem that you are trying to solve?
On Wed, 3 Dec 2008, axionator wrote:
Hi all, I have a data frame with "clustered" rows as follows: Cu1 x1 y1 z1 ... Cu1 x2 y2 z2 ... Cu1 x3 y3 z3 ... # end of first cluster Cu1 Cu2 x4 y4 z4 ... Cu2 x5 y5 z5 Cu2 ... # end of second cluster Cu2 Cu3 ... ... "cluster"-size is 3 in the example above (rows making up a cluster are always consecutive). Is there any faster way to sample n clusters (with replacement) from this dataframe and build up a new data frame out of these sampled clusters? I use the "sample" function and a for-loop.
Something like this: cl.samps <- sample( split( df, df$cluster ), n.samps, repl=TRUE ) do.call( rbind, cl.samps ) If you need to identify the samples from which the rows came (versus just the originating clusters): cl.samps2 <- lapply( seq(along=cl.samps), function(x) cbind( cl.samps[[ x ]], new.cluster = x ) ) do.call( rbind, cl.samps2 ) HTH, Chuck
Thanks in advance Armin
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Charles C. Berry (858) 534-2098
Dept of Family/Preventive Medicine
E mailto:cberry at tajo.ucsd.edu UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901