On Aug 17, 2011, at 8:14 PM, mark_horo wrote:
Hi I'm trying to speed my loop up. Any Suggestions?? At the moment
it takes a
few days to run.
THE CODE
We are not surprised.
Rainfall_dataset <- read.table("1km_grid_nzmg.csv",
header=TRUE, sep=",", na.strings="NA", dec=".", strip.white=TRUE)
---------------------------------------------------------------------------------------------------
for(i in 1:11) {
for (j in 3:12) {
# What is x ??? Where is y dimensioned???
# whatever the answers, this would be faster --->
y[ ,1] <- x[i,j, ]
for (k in 1:273107) {
# and this is no longer needed in the inner loop y[k,1] <- x[i,j,k]
print(y)
# t <- Rainfall_dataset
# print(t)
p<-cbind(t,y)
???? You are just writing out a set of vectors as the first column to
identical copies of the entire RainfalL_dataset?
#y<-cbind(t,y) Huh???
#print(y)
}
# This where you write out array y to a
csv file
# Save output as a csv file
site <- paste("site",i,"-",j)
csvfile <- paste(site,sep=".","csv"); print(csvfile)
path<- paste("c:/Data/",csvfile)
print(path)
write.csv(p,file=path,
row.names = TRUE)
}
}
To me this looks like a 11*10* 273107 == 30MB set of files with no
real added information. Surely a database solution must be better.
--
David Winsemius, MD
West Hartford, CT
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