# [R] loop over large dataset

Federico Calboli f.calboli at imperial.ac.uk
Fri Jul 1 13:31:48 CEST 2005

```Hi All,

I'd like to ask for a few clarifications. I am doing some calculations
over some biggish datasets. One has ~ 23000 rows, and 6 columns, the
other has ~620000 rows and 6 columns.

I am using these datasets to perform a simulation of of haplotype
coalescence over a pedigree (the datestes themselves are pedigree
information). I created a new dataset (same number of rows as the
pedigree dataset, 2 colums) and I use a looping functions to assign
haplotypes according to a standrd biological reprodictive process (i.e.
meiosis, sexual reproduction).

My code is someting like:

off = function(sire, dam){ # simulation of reproduction, two inds
sch.toll = round(runif(1, min = 1, max = 2))
dch.toll = round(runif(1, min = 1, max = 2))
s.gam = sire[,sch.toll]
d.gam = dam[,dch.toll]
offspring = cbind(s.gam,d.gam)
# offspring
}

for (i in 1:dim(new)[1]){
if(ped[i,3] != 0 & ped[i,5] != 0){
zz = off(as.matrix(t(new[ped[i,3],])),as.matrix(t(new[ped[i,5],])))
new[i,1] = zz[1]
new[i,2] = zz[2]
}
}

I am also attribution a generation index to each row with a trivial
calulation:

for(i in atres){
genz[i] = (genz[ped[i,3]] + genz[ped[i,5]])/2 + 1
#print(genz[i])
}

My question then. On the 23000 rows dataset the calculations take about
5 minutes. On the 620000 rows one I kill the process after ~24 hours,
and the the job is not finished. Why such immense discrepancy in
execution times (the code is the same, the datasets are stored in two
separate .RData files)?

Any light would be appreciated.

Federico

PS I am running R 2.1.0 on Debian Sarge, on a Dual 3 GHz Xeon machine
with 2 gig RAM. The R process uses 99% of the CPU, but hardly any RAM
for what I gather from top.

--
Federico C. F. Calboli
Department of Epidemiology and Public Health
Imperial College, St Mary's Campus
Norfolk Place, London W2 1PG

Tel  +44 (0)20 7594 1602     Fax (+44) 020 7594 3193

f.calboli [.a.t] imperial.ac.uk
f.calboli [.a.t] gmail.com

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