# [R] convert for loop into apply()

Bill.Venables at csiro.au Bill.Venables at csiro.au
Sun Aug 3 07:41:11 CEST 2008

```Here is a way to speed up your toy example:

_________________
a1 <- data.frame(id = 1:6,
cat = paste('cat', rep(1:3, c(2,3,1))),
st = c(1, 7, 30, 40, 59, 91),
en = c(5, 25, 39, 55, 70, 120))

a2 <- data.frame(id = paste('probe', 1:8),
cat = paste('cat', rep(1:3, c(2,3,3))),
st = c(1, 9, 20, 38, 53, 70, 80, 95),
en = c(6, 15, 36, 43, 58, 75, 85, 98))

c1 <- outer(a2\$st , a1\$en , "<=")
c2 <- outer(a2\$en , a1\$st , ">=")
c3 <- outer(a2\$cat, a1\$cat, "==")

a1\$coverage <- colSums(c1*c2*c3)
__________________

This won't work in one step if a1 has 30000 rows and a2 has 200000,
unless you memory size is approximately infinite, so you will need a
loop.  Suppose you can handle 1000 probes at a time.  You might be able
to get away with something like this:
__________________

chunk <- 1000  ### make as large as possible!

checkCoverage <- function(a1, a2)
colSums(outer(a2\$st , a1\$en , "<=") *
outer(a2\$en , a1\$st , ">=") *
outer(a2\$cat, a1\$cat, "=="))

coverage <- numeric(N <- nrow(a2))
m2 <- 0
while(m2 < N) {
m1 <- m2 + 1
m2 <- min(m2 + chunk, N)
coverage[m1:m2] <- checkCoverage(a1, a2[m1:m2, ])
}
a1\$coverage <- coverage
__________________

(Warning: untested code.)

Failing that, go to C code and be done with it.

BTW, why did you think apply() was going to be useful here?

Bill Venables
CSIRO Laboratories
PO Box 120, Cleveland, 4163
AUSTRALIA
Office Phone (email preferred): +61 7 3826 7251
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mailto:Bill.Venables at csiro.au
http://www.cmis.csiro.au/bill.venables/

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
On Behalf Of Anh Tran
Sent: Saturday, 2 August 2008 4:04 PM
To: rlist
Subject: [R] convert for loop into apply()

Hi all,I know this topic has came up multiple times, but I've never
fully
understand the apply() function.

Anyway, I'm here asking for your help again to convert this loop to
apply().

I have 2 data frames with the following information: a1 is the fragment
that
is need to be covered, a2 is the probes that cover the specific
fragment.

I need to count the number of probes cover every given fragment (they
need
to have the same cat ID to be on the same fragment)

a1<-data.frame(id=c(1:6), cat=c('cat 1','cat 1','cat 2','cat 2','cat
2','cat
3'), st=c(1,7,30,40,59,91), en=c(5,25,39,55,70,120));
a2<-data.frame(id=paste('probe',c(1:8)), cat=c('cat 1','cat 1','cat
2','cat
2','cat 2','cat 3','cat 3','cat 3'), st=c(1,9,20,38,53,70,80,95),
en=c(6,15,36,43,58,75,85,98));
a1\$coverage<-NULL;

I came up with this for loop (basically, if a probe starts before the
fragment end, and end after a fragment start, it cover that fragment)

for (i in 1:length(a1\$id))
{
a1\$coverage[i]<-length(a2[a2\$st<=a1\$en[i]&a2\$en>=a1\$st[i]&a2\$cat==a1\$cat
[i],]\$id);
}

> a1\$coverage
[1] 1 1 2 2 0 1

This loop runs awefully slow when I have 200,000 probes and 30,000
fragments. Is there anyway I can speed this up with apply()?

This is the time for my for loop to scan through the first 20 record of
my
dataset:
user  system elapsed
2.264   0.501   2.770

I think there is room for improvement here. Any idea?

Thanks
--
Regards,
Anh Tran

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