Reading very large text files into R

Tim and others,

A point to consider is that there are various algorithms in the functions
used to read in formatted data into data.frame form and they vary. Some do
a
look-ahead of some size to determine things and if they find a column that
LOOKS LIKE all integers for say the first thousand lines, they go and read
in that column as integer. If the first floating point value is thousands
of
lines further along, things may go wrong.

So asking for line/row 16 to have an extra 16th entry/column may work fine
for an algorithm that looks ahead and concludes there are 16 columns
throughout. Yet a file where the first time a sixteenth entry is seen is at
line/row 31,459 may well just set the algorithm to expect exactly 15
columns
and then be surprised as noted above.

I have stayed out of this discussion and others have supplied pretty much
what I would have said. I also see the data as flawed and ask which rows
are
the valid ones. If a sixteenth column is allowed, it would be better if all
other rows had an empty sixteenth column. If not allowed, none should have
it.

The approach I might take, again as others have noted, is to preprocess the
data file using some form of stream editor such as AWK that automagically
reads in a line at a time and parses lines into a collection of tokens
based
on what separates them such as a comma. You can then either write out just
the first 15 to the output stream if your choice is to ignore a spurious
sixteenth, or write out all sixteen for every line, with the last being
some
form of null most of the time. And, of course, to be more general, you
could
make two passes through the file with the first one determining the maximum
number of entries as well as what the most common number of entries is, and
a second pass using that info to normalize the file the way you want. And
note some of what was mentioned could often be done in this preprocessing
such as removing any columns you do not want to read into R later. Do note
such filters may need to handle edge cases like skipping comment lines or
treating the row of headers differently.

As some have shown, you can create your own filters within a language like
R
too and either read in lines and pre-process them as discussed or continue
on to making your own data.frame and skip the read.table() type of
functionality. For very large files, though, having multiple variations in
memory at once may be an issue, especially if they are not removed and
further processing and analysis continues.

Perhaps it might be sensible to contact those maintaining the data and
point
out the anomaly and ask if their files might be saved alternately in a
format that can be used without anomalies.

Avi

-----Original Message-----
From: R-help <r-help-bounces at r-project.org> On Behalf Of Ebert,Timothy
Aaron
Sent: Friday, September 30, 2022 7:27 AM
To: Richard O'Keefe <raoknz at gmail.com>; Nick Wray <nickmwray at gmail.com>
Cc: r-help at r-project.org
Subject: Re: [R] Reading very large text files into R

Hi Nick,
   Can you post one line of data with 15 entries followed by the next line
of data with 16 entries?

Tim