[R] How to make this for() loop memory efficient?

[iliketurtles]

##I have 2 columns of data. The first column is unique "event IDs" that
represent a phone call made to a customer.
###So, if you see 3 entries together in the first column like follows:

matrix(c("call1a","call1a","call1a") )

##then this means that this particular phone call  (the first call that's
logged in the data set) was transferred 
##between 3 different "modules" before the call was terminated.

##The second column is a numerical description of the module the call
started with and then got transferred to prior to ##call termination. Now,
I'll construct a ##representative array of the type of data I'm dealing with
(the real data set goes ##on for X00,000s of rows):
##(Ignore how I construct the following array, it’s completely unrelated to
how the actual data set was constructed). 


a<-sapply(1:50,function(i){paste("call",i,sep="",collapse="")})
development.a<-seq(1,40,3)
development.a2<-seq(1,40,5)
a[development.a]<-a[development.a+1]
a[development.a2]<-a[development.a2+1]
a[1:2]<-"call2a";a[3]<-"call3a";a[4:5]<-"call5a";a[6:8]<-"call8a";a[9]<-"call9a"
b<-c(920010,960010,820009,920010,960500,970050,930010,920010,960500,970050,930900,870010,840010,960500,920010,970050,930010,960500,920010,970050,930010,960010,920010,940010,960010,970010,960500,920010,970050,930010,960500,920010,970050,930010,960500,920010,970050,930010,920010,960500,970050,930010,920009,960500,970050,930009,940010,960500,960500,960500)
data<-as.data.frame(cbind(a,b))
colnames(data)<-c("phone calls","modules")
dim(data)
print(data[1:10,]) #sample of 10 rows

# Note that in the real data set, data[,2] ranges from 810,000 to 999,999.
I've been tasked with the following:
# "For each phone call that BEGINS with the module which is denoted by 81
(i.e. of the form 81X,XXX), what is the expected number of modules in these
calls?"
#Then it's the same question for each module beginning with 82, 83, 84.....
all the way until 99. 
#I've created code that I think works for this, but I can't actually run it
on the whole data set. I left it for 30 minutes and it only had about #5% of
the task completed (I clicked "STOP" then checked my output to see if I did
it properly, and it seems correct).
#I know the apply() family specializes in vector operations, but I can't
figure out how to complete the above question in any way other than #loops.

L<-data

A<-array(0,dim=c(19,2));rownames(A)<-seq(81,99,1)
A<-data.frame(A)

 for(i in 1:(nrow(L)-1))
 {
  if(L[(i+1),1]!=L[i,1])
  {
   
A[paste(strsplit(as.character(L[i+1,2]),"")[[1]][1:2],sep="",collapse=""),1]<-
    { 
     
A[paste(strsplit(as.character(L[i+1,2]),"")[[1]][1:2],sep="",collapse=""),1]+length(grep(as.character(L[i+1,1]),L[,1],value=FALSE)) 
#aggregate number of modules in the calls that begin with XX (not yet
averaged). 
    }
   
A[paste(strsplit(as.character(L[i+1,2]),"")[[1]][1:2],sep="",collapse=""),2]<-
    {
     
A[paste(strsplit(as.character(L[i+1,2]),"")[[1]][1:2],sep="",collapse=""),2]+1
    }
  }
   
 }

#If I can get this code to be more memory efficient such that I can do it on
a 400,000 row data set, I can do, for example,

A[17,1]/A[17,2]

#and I'll arrive at the mean number of modules per call where the call
starts with a module that starts with 97.

A[17,1] 
#is 10, which means that, out of every single call that started with a
module of 97X,XXX,
#they went through 10 modules in total. 

A[17,2] 
#is 6, which means that there was 6 calls in total that began with a 97X,XXX
module.

#Hence,


A[17,1]/A[17,2]

#is the average number of modules that were executed in all the calls that
began with a 97X,XXX module.


-----
----

Isaac
Research Assistant
Quantitative Finance Faculty, UTS
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