speed up subsetting with certain conditions

Hi folks,

I am working on a project that requires subsetting of a found file 
based on some known file. The known file contains several lines like 
below:

chr1    3237546    3237547    rs52310428    0    +
chr1    3237549    3237550    rs52097582    0    +
chr2    4513326    4513327    rs29769280    0    +
chr2    4513337    4513338    rs33286009    0    +

where the first column can be chr2, chr1, chr12 etc... The second and 
third are numbers (cordinates). The found file contains lines like:

chr1    3213435    G    C
chr1    3237547    T    C
chr1    3237549    G    T
chr2    4513326    A    G
chr2    4513337    C    G

where the first column, again, can be chr1, chr2, chr12 etc... and the 
second is a number. What I have to do is to separate the found file to 
two files: one (foundY) contains lines that have the same first column 
and the second column in range of the two columns 2 and 3 of any line 
of known file, and one (foundN) contains lines that do not meet the 
previous condition. For the two examples above, foundN will be the 
first line, and foundY will be the next 4 lines.

What I came up with is this algorithm:

* get the uniq item in the first column of found file (chr1, chr2, 
chr12, chr13 etc...)
* for each of the uniq item, set subset of the known file and the 
found file that have same first column, then scanning each item in the 
known subset to see if any line meets any condition

The code is like below:

########## CODE START###########
# import known and found files to data frames
known <- read.table( "known.txt", sep="\t", header=FALSE )
found <- read.table( "found.txt", sep="\t", header=FALSE, fill=TRUE )

# get the uniq item in first column of found file
found.Chr <- as.character(found[!duplicated(found[[1]]),1])

# create two empty result data frames
foundN <- found[0,]
foundY <- found[0,]

# scan for each of the uniq items
for ( iChr in found.Chr ) {
  # subset of known and found with specific item
  found.iChr <- found[found[[1]]==iChr,]
  known.iChr <- known[known[[1]]==iChr,]

  # scan through all found subset items
  if ( nrow(known.iChr)>0 ) {
    for ( i in 1:nrow(found.iChr) ) {
      if ( nrow(known.iChr[known.iChr[[3]]>=found.iChr[i,2] & 
known.iChr[[2]]<=found.iChr[i,2],])==0 ) {
          foundN <- rbind( foundN, found.iChr[i,] )
      } else {
          foundY <- rbind( foundN, found.iChr[i,] )
      }
    }
  }
}

########## CODE END###########

The code works well, but I tested it for only small known and found 
files. When trying with larger files (the known file can contains ~ 15 
million lines, the found ~ 15k lines), it takes like hrs to run.

I want to speed up the process, and I believe there must be a better 
algorithm to do this with R. My questions are:

* any body has a better algorithm or comments or suggestion?

* I read (google) that matrices work faster than data frame. Can I use 
matrices for this case? (is matrices for numbers only?)
* I read (google) that I should avoid rbind, and prelocate data frame 
for faster speed. How would I do that in this case?

Thank you very much in advance,

Bests,

D.