you can even use a simple for-loop, e.g.,
m1 <- cbind(1:5,1:5,1:5)
out <- m1
for(i in 1:nrow(out))
out[i, ] <- cumsum(out[i, ])
which seems to be faster than apply(m1, 1, cumsum), i.e.,
m1 <- m1[rep(1:5, each = 1e04), ]
library(rbenchmark)
benchmark(
"apply" = apply(m1, 1, cumsum),
"for" = {out <- m1; for(i in 1:nrow(out)) out[i, ] <- cumsum(out[i,
])},
replications = 50, order = "relative"
)
I hope it helps.
Best,
Dimitris
On 4/14/2010 2:18 PM, Eleni Rapsomaniki wrote:
Dear R-helpers,
I have a huge data-set so need to avoid for loops as much as possible. Can
someone think how I can compute the result in the following example (that uses
a for-loop) using some version of apply instead (or any other similarly
super-efficient function)?
example:
#Suppose a matrix:
m1=cbind(1:5,1:5,1:5)
#The aim is to create a new matrix with every column containing the cumulative
sum of all previous columns.
m2=m1
for(i in 2:ncol(m1)){
m2[,i]=apply(m1[,1:i],1,sum)
}
m2
Many thanks in advance
Eleni Rapsomaniki
Research Associate
Strangeways Research Laboratory
Department of Public Health and Primary Care
University of Cambridge
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--
Dimitris Rizopoulos
Assistant Professor
Department of Biostatistics
Erasmus University Medical Center
Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
Tel: +31/(0)10/7043478
Fax: +31/(0)10/7043014
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
