[R] handling big data set in R

[shu zhang]

Hello R users,

I'm wondering whether it is possible to manage big data set in R? I
have a data set with  3 million rows and 3 columns (X,Y,Z), where X is
the group id. For each X, I need to run 2 regression on the submatrix.
I used the function "split":

datamatrix<-read.csv("datas.csv", header=F, sep=",")
dim(datamatrix)
# [1] 2980523  3
names(datamatrix)<-c("X","Y","Z")

attach(datamatrix)

subX<-split(X, X)
subY<-split(Y,X)
subZ<-split(Z,X)
n<-length(subdata)  ### number of groups
s1<-s2<-rep(NA, n)  ### vector to store the regression slope

for (i in 1:n){
   a<-table(Y[[i]])
   table.x<-as.numeric(names(a))
   table.y<-as.numeric(a)
   fit1<-lm(table.y~table.x)    ##### find the slope of the histogram of y
   s1[i]<-fit$coefficients[2]

   fit2<-lm(subY[[i]]~subZ[[i]])  ####### regress y on z
   s2[i]<-fit$coefficients[2]
}


But my R died before completing the loop... (I've thought about doing
it in SAS, but I don't know how to write a loop combined with a PROC
REG...)

One thing that might be helpful is that my data set has already been
sorted based on X. I don't know whether this can be any helpful for
managing the dataset.

Any suggestion would be appreciated!


Thanks!
-Shu