for loop - improve speed

Question

I have the following piece of code.it is too slow right now. How can I rewrite it to improve speed? (in vectorized form , using apply functions or any other form)

my dataframe is called urban.

bcolumn.pattern <- '^b[0123456789][0123456789]'
bcolumn.index = grep(bcolumn.pattern, names(urban))
bcolumn.nrow <- dim(urban)[1]

for (k in bcolumn.index){
for (l in( 1 :bcolumn.nrow))
if (    is.nan(urban [l, ][ ,k])    )     
{urban [l, ][ ,k] <- 0 }

If you want us help optimize, we'll need a reproducible example. http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example — Roman Luštrik, Mar 28 '14 at 10:13
Your subsetting is confusing. Why don't you simply use `urban[l, k]`? That would be easier to read and faster, too. But see droopy's answer for better solutions. — Roland, Mar 28 '14 at 10:32

droopy · Answer 1 · 2014-03-28T10:39:12.873

2

one possibility :

# if urban is a data.frame
urban[,bcolumn.index] <- lapply(urban[,bcolumn.index], function(x) {x[is.nan(x)] <- 0; x})
# if urban is a matrix
urban[,column.index][is.nan(urban[, column.index])] <- 0

edited Mar 28 '14 at 10:39

answered Mar 28 '14 at 10:26

droopy

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thanks @droopy but it did not work out. Are you sure about lapply? because urban[,bcolumn.index] is a dataframe not a list. – Hamideh Mar 28 '14 at 11:25
with this example it works : x – droopy Mar 28 '14 at 13:07

for loop - improve speed

1 Answers1