To get the equivalent of what your loop does, you could use
lapply(data[,3:5],function(x)x/ave(x,data$plateNo,FUN=mean))
but you might find the output of
sapply(data[,3:5],function(x)x/ave(x,data$plateNo,FUN=mean))
to be more useful.
- Phil Spector
Statistical Computing Facility
Department of Statistics
UC Berkeley
spec...@stat.berkeley.edu
On Fri, 11 Mar 2011, hi Berven wrote:
Hello all,
I'm new to R and trying to figure out how to perform calculations on a large dataset (300 000
datapoints). I have already made some code to do this but it is awfully slow. What I want to do is
add a new column for each "rep_ " column where I have taken each value and divide it by
the mean of all values where "PlateNo" is the same. My data is in the following format:
data
PlateNo
Well
rep_1
rep_2
rep_3
1
A01
1312
963
1172
1
A02
10464
6715
5628
1
A03
3301
3257
3281
1
A04
3895
3350
3496
1
A05
8731
7389
5701
2
A01
7893
6748
5920
2
A02
2912
2385
2586
2
A03
985
785
809
2
A04
1346
1018
1001
2
A05
794
314
486
To generate it copy:
a <- c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2)
b <- c("A01", "A02", "A03", "A04", "A05", "A01", "A02", "A03", "A04", "A05")
c <- c(1312, 10464, 3301, 3895, 8731, 7893, 2912, 985, 1346, 794)
d <- c(963, 6715, 3257, 3350, 7389, 6748, 2385, 785, 1018, 314)
e <- c(1172, 5628, 3281, 3496, 5701, 5920, 2586, 809, 1001, 486)
data <- data.frame(plateNo = a, Well = b, rep_1 = c, rep_2 = d, rep_3 = e)
Here is the code I have come up with:
rows <- length(data$plateNo)
reps <- 3
norm <- list()
for (rep in 1:reps) {
x <- paste("rep_",rep,sep="")
normx <- paste("normalised_",rep,sep="")
for (row in 1:rows) {
plateMean <-
mean(data[[x]][data$plateNo == data$plateNo[row]])
wellData <- data[[x]][row]
norm[[normx]][row] <- wellData /
plateMean
}
}
Any help or tips would be greatly appreciated!
Thanks,
Haakon
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