reading very large files

Hi.

General idea:

1. Open your file as a connection, i.e. con <- file(pathname, open="r")

2. Generate a "row to (file offset, row length) map of your text file,
i.e. a numeric vector 'fileOffsets' and 'rowLengths'.  Use readBin()
for this. You build this up as you go by reading the file in chunks
meaning you can handles files of any size.  You can store this lookup
map to file for your future R sessions.

3. Sample a set of rows r = (r1, r2, ..., rR), i.e. rows =
sample(length(fileOffsets)).

4. Look up the file offsets and row lengths for these rows, i.e.
offsets = fileOffsets[rows].  lengths = rowLengths[rows].

5. In case your subset of rows is not ordered, it is wise to order
them first to speed up things.  If order is important, keep track of
the ordering and re-order them at then end.

6. For each row r, use seek(con=con, where=offsets[r]) to jump to the
start of the row.  Use readBin(..., n=lengths[r]) to read the data.

7. Repeat from (3).

/Henrik

On 2/2/07, juli g. pausas <pausas at gmail.com> wrote:

Hi all,
I have a large file (1.8 GB) with 900,000 lines that I would like to read.
Each line is a string characters. Specifically I would like to randomly
select 3000 lines. For smaller files, what I'm doing is:

trs <- scan("myfile", what= character(), sep = "\n")
trs<- trs[sample(length(trs), 3000)]

And this works OK; however my computer seems not able to handle the 1.8 G
file.
I thought of an alternative way that not require to read the whole file:

sel <- sample(1:900000, 3000)
for (i in 1:3000)  {
un <- scan("myfile", what= character(), sep = "\n", skip=sel[i], nlines=1)
write(un, "myfile_short", append=TRUE)
}

This works on my computer; however it is extremely slow; it read one line
each time. It is been running for 25 hours and I think it has done less than
half of the file (Yes, probably I do not have a very good computer and I'm
working under Windows ...).
So my question is: do you know any other faster way to do this?
Thanks in advance

Juli

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