I'd first try plyr and see if it's efficient enough,
library(plyr)
listOfFiles <- list.files(pattern= ".txt")
d <- ldply(listOfFiles, read.table)
str(d)
alternatively,
d <- do.call(rbind, lapply(listOfFiles, read.table))
HTH,
baptiste
On 13 May 2009, at 12:45, SYKES, Jennifer wrote:
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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_____________________________
Baptiste Auguié
School of Physics
University of Exeter
Stocker Road,
Exeter, Devon,
EX4 4QL, UK
Phone: +44 1392 264187
http://newton.ex.ac.uk/research/emag
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