[R] How to speed up nested for loop computations

Thu Aug 10 18:13:46 CEST 2006

Dear all,
	here is the result of R.Version():

 > R.Version()
$platform
[1] "powerpc-apple-darwin8.6.0"

$arch
[1] "powerpc"

$os
[1] "darwin8.6.0"

$system
[1] "powerpc, darwin8.6.0"

$status
[1] ""

$major
[1] "2"

$minor
[1] "3.1"

$year
[1] "2006"

$month
[1] "06"

$day
[1] "01"

$`svn rev`
[1] "38247"

$language
[1] "R"

$version.string
[1] "Version 2.3.1 (2006-06-01)"

 >

	I have the following code to produce boxplots of some experimental  
data, but it's really slow (I'm a newbie of R and the quality of my  
code is not really high-level!). Could you give me some guidelines  
(or examples) on how to remove those nested for loops (maybe using  
the "apply" function) so to speed-up the computation?

---BEGIN CODE---

optimal_values<-read.table("optimal_values_80.txt",header=TRUE)
resPIR2OPT<-read.table("parallel_independent_2- 
opt_80_100.txt",header=TRUE)
resSEQ2OPT<-read.table("sequential_2-opt_80_800.txt",header=TRUE)
resSEQ22OPT<-read.table("sequential2_2-opt_80_100.txt",header=TRUE)
resFC1x102OPT<-read.table("fc.1.x.10_2-opt_80_100.txt",header=TRUE)
resFC26102OPT<-read.table("fc.2.6.10_2-opt_80_100.txt",header=TRUE)
resFC27102OPT<-read.table("fc.2.7.10_2-opt_80_100.txt",header=TRUE)
resFC28102OPT<-read.table("fc.2.8.10_2-opt_80_100.txt",header=TRUE)
resFC29102OPT<-read.table("fc.2.9.10_2-opt_80_100.txt",header=TRUE)
resFC36102OPT<-read.table("fc.3.6.10_2-opt_80_100.txt",header=TRUE)
resFC37102OPT<-read.table("fc.3.7.10_2-opt_80_100.txt",header=TRUE)
resFC38102OPT<-read.table("fc.3.8.10_2-opt_80_100.txt",header=TRUE)
resFC39102OPT<-read.table("fc.3.9.10_2-opt_80_100.txt",header=TRUE)
resHC1x102OPT<-read.table("hc.1.x.10_2-opt_80_100.txt",header=TRUE)
resHC26102OPT<-read.table("hc.2.6.10_2-opt_80_100.txt",header=TRUE)
resHC27102OPT<-read.table("hc.2.7.10_2-opt_80_100.txt",header=TRUE)
resHC28102OPT<-read.table("hc.2.8.10_2-opt_80_100.txt",header=TRUE)
resHC29102OPT<-read.table("hc.2.9.10_2-opt_80_100.txt",header=TRUE)
resHC36102OPT<-read.table("hc.3.6.10_2-opt_80_100.txt",header=TRUE)
resHC37102OPT<-read.table("hc.3.7.10_2-opt_80_100.txt",header=TRUE)
resHC38102OPT<-read.table("hc.3.8.10_2-opt_80_100.txt",header=TRUE)
resHC39102OPT<-read.table("hc.3.9.10_2-opt_80_100.txt",header=TRUE)
resRW1x102OPT<-read.table("rw.1.x.10_2-opt_80_100.txt",header=TRUE)
resRW26102OPT<-read.table("rw.2.6.10_2-opt_80_100.txt",header=TRUE)
resRW27102OPT<-read.table("rw.2.7.10_2-opt_80_100.txt",header=TRUE)
resRW28102OPT<-read.table("rw.2.8.10_2-opt_80_100.txt",header=TRUE)
resRW29102OPT<-read.table("rw.2.9.10_2-opt_80_100.txt",header=TRUE)
resRW36102OPT<-read.table("rw.3.6.10_2-opt_80_100.txt",header=TRUE)
resRW37102OPT<-read.table("rw.3.7.10_2-opt_80_100.txt",header=TRUE)
resRW38102OPT<-read.table("rw.3.8.10_2-opt_80_100.txt",header=TRUE)
resRW39102OPT<-read.table("rw.3.9.10_2-opt_80_100.txt",header=TRUE)
resUR1x102OPT<-read.table("ur.1.x.10_2-opt_80_100.txt",header=TRUE)
resUR26102OPT<-read.table("ur.2.6.10_2-opt_80_100.txt",header=TRUE)
resUR27102OPT<-read.table("ur.2.7.10_2-opt_80_100.txt",header=TRUE)
resUR28102OPT<-read.table("ur.2.8.10_2-opt_80_100.txt",header=TRUE)
resUR29102OPT<-read.table("ur.2.9.10_2-opt_80_100.txt",header=TRUE)
resUR36102OPT<-read.table("ur.3.6.10_2-opt_80_100.txt",header=TRUE)
resUR37102OPT<-read.table("ur.3.7.10_2-opt_80_100.txt",header=TRUE)
resUR38102OPT<-read.table("ur.3.8.10_2-opt_80_100.txt",header=TRUE)
resUR39102OPT<-read.table("ur.3.9.10_2-opt_80_100.txt",header=TRUE)

res<-rbind 
(resFC1x102OPT,resFC26102OPT,resFC27102OPT,resFC28102OPT,resFC29102OPT,r 
esFC36102OPT,resFC37102OPT,resFC38102OPT,resFC39102OPT,resRW1x102OPT,res 
RW26102OPT,resRW27102OPT,resRW28102OPT,resRW29102OPT,resRW36102OPT,resRW 
37102OPT,resRW38102OPT,resRW39102OPT,resHC1x102OPT,resUR1x102OPT,resUR26 
102OPT,resUR27102OPT,resUR28102OPT,resUR29102OPT,resUR36102OPT,resUR3710 
2OPT,resUR38102OPT,resUR39102OPT,resPIR2OPT,resSEQ2OPT,resSEQ22OPT)


attach(res)
lalgo<-levels(idalgo)
linstance<-levels(instance)
ltry<-unique(try)
lcpu<-unique(cpu_id)

for (i in (1:length(linstance)))
{
	current_instance<-linstance[i]

	bestalgo<-list()
	for (j in (1:length(ltry)))
	{
		current_try<-ltry[j]	

		for (k in (1:length(lalgo)))
		{
			current_algo<-lalgo[k]
			
			res2<-res[res$instance==current_instance & res$try==current_try &  
res$idalgo==current_algo,]
# res2 contains for a given instance, a given try, and a given algo,  
all results
			res3<-res2[res2$best==min(res2$best),]
			res4<-res3[res3$time==min(res3$time),]
			if (nrow(res4)>1)
			{
				res4<-res4[1,]	
			}
			if (nrow(res4)==1)
			{
				res4$best<-(res4$best*100/optimal_values[optimal_values 
$instance==linstance[i],]$optimum)-100
     			print(res4)
				bestalgo<-rbind(bestalgo,res4)
			}
		}
	}	
	epsfile=paste(linstance[i],"_100_lim.eps",sep="")
	postscript(file=epsfile,onefile=TRUE,horizontal=TRUE)
	l<-split(bestalgo$best,list(bestalgo$idalgo))
	par(mar=c(5,5,5,3),cex.axis=0.7,las=2,mgp=c(4, 1, 0))
	title_plot=paste("100 iterations - instance ",linstance[i],sep="")
	boxplot(l,xlab="",ylab="% distance from best known solution",names=c 
(levels(bestalgo$idalgo)),main=title_plot,ylim=c(0,0.5))

	dev.off()
	epsfile=paste(linstance[i],"_100_nolim.eps",sep="")
	postscript(file=epsfile,onefile=TRUE,horizontal=TRUE)
	l<-split(bestalgo$best,list(bestalgo$idalgo))
	par(mar=c(5,5,5,3),cex.axis=0.7,las=2,mgp=c(4, 1, 0))
	title_plot=paste("100 iterations - instance ",linstance[i],sep="")
	boxplot(l,xlab="",ylab="% distance from best known solution",names=c 
(levels(bestalgo$idalgo)),main=title_plot)

	dev.off()			
	
}
detach(res)

---END CODE ---


	This is the output of the command str(res):

 > str(res)
`data.frame':	230200 obs. of  11 variables:
$ idalgo   : Factor w/ 31 levels "FC.1.x.10-2opt",..: 1 1 1 1 1 1 1 1  
1 1 ...
$ topo     : Factor w/ 7 levels "FC","RW","HC",..: 1 1 1 1 1 1 1 1 1  
1 ...
$ schema   : Factor w/ 12 levels "1.x.10","2.6.10",..: 1 1 1 1 1 1 1  
1 1 1 ...
$ ls       : int  2 2 2 2 2 2 2 2 2 2 ...
$ type     : Factor w/ 2 levels "Par","Seq": 1 1 1 1 1 1 1 1 1 1 ...
$ cpu_id   : int  0 0 0 0 0 0 0 0 0 0 ...
$ instance : Factor w/ 2 levels "lipa80a","tai80a": 1 1 1 1 1 1 1 1 1  
1 ...
$ try      : int  1 1 1 1 1 1 1 1 1 1 ...
$ best     : int  255434 255321 255296 255224 255181 255030 254985  
254961 254927 254897 ...
$ time     : num  0.09 0.09 0.09 0.18 0.27 0.46 1 1.37 1.42 1.66 ...
$ iteration: int  1 1 1 2 3 5 11 17 18 20 ...
 >

	Hoping that somebody could help me, accept my best regards.
----
Max MANFRIN
http://iridia.ulb.ac.be/~mmanfrin/