can you provide reproducible code please?
even a fake example would help.
I would
1) set up a loop to read in each file from a directory
2) inside the loop chop up/ aggregate the data, each file in turn and spit
each new aggreagated file out to a directory using write.table(). This will
reduce the memory needed by only including the info you want. Make sure each
file is a data frame with the same names.
3) set up a new loop to read in each new small file and rbind them all
together to make your new "master file".
The R gurus may have a more parsimonious solution.
HTH
Simon.
----- Original Message -----
From: "SYKES, Jennifer" <jennifer.sy...@nats.co.uk>
To: <r-help@r-project.org>
Sent: Wednesday, May 13, 2009 11:45 AM
Subject: [R] read multiple large files into one dataframe
Hello
Apologies if this is a simple question, I have searched the help and
have not managed to work out a solution.
Does anybody know an efficient method for reading many text files of the
same format into one table/dataframe?
I have around 90 files that contain continuous data over 3 months but
that are split into individual days data and I need the whole 3 months
in one file for analysis. Each days file contains a large amount of
data (approx 30MB each) and so I need a memory efficient method to merge
all of the files into the one dataframe object. From what I have read I
will probably want to avoid using for loops etc? All files are in the
same directory, none have a header row, and each contain around 180,000
rows and the same 25 columns/variables. Any suggested packages/routines
would be very useful.
Thanks
Jennifer
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