Adding to what Nick said, extra lines like those described often are in some
comment format like beginning with "#" or some consistent characters
that can be filtered out using comment.char='#' for example in
read.csv() or comment="string" in the tidyverse function read_csv().
And, of course you can skip lines if that makes sense albeit it can be tricky
with header lines.
-----Original Message-----
From: R-help <r-help-bounces at r-project.org> On Behalf Of Rui Barradas
Sent: Sunday, September 18, 2022 6:19 PM
To: Nick Wray <nickmwray at gmail.com>; r-help at r-project.org
Subject: Re: [R] removing non-table lines
Helo,
Unfortunatelly there are many files with a non tabular data section followed by
the data. R's read.table has a skip argument:
skip
integer: the number of lines of the data file to skip before beginning to read
data.
If you do not know how many lines to skip because it's not always the same
number, here are some ideas.
Is there a pattern in the initial section? Maybe a end-of-section line or maybe
the text lines come in a specified order and a last line in that order can be
detected with a regex.
Is there a pattern in the tables' column headers? Once again a regex might
be the solution.
Is the number of initial lines variable because there are file versions?
If there are, did the versions evolve over time, a frequent case?
What you describe is not unfrequent, it's always a nuisance and error prone
but it should be solvable once patterns are found. Inspect a small number of
files with a text editor and try to find both common points and differences.
That's half way to a solution.
Hope this helps,
Rui Barradas
?s 20:39 de 18/09/2022, Nick Wray escreveu:> Hello - I am having to download lots of rainfall and temperature data
> in csv form from the UK Met Office. The data isn't a problem -
it's
> in nice columns and can be read into R easily - the problem is that in
> each csv there are 60 or so lines of information first which are not
> part of the columnar data. If I read the whole csv into R the column
> data is now longer in columns but in some disorganised form - if I
> manually delete all the text lines above and download I get a nice
> neat data table. As the text lines can't be identified in R by line
> numbers etc I can't find a way of deleting them in R and atm have to
> do it by hand which is slow. It might be possible to write a
> complicated and dirty algorithm to rearrange the meteorological data
> back into columns but I suspect that it might be hard to get right and
consistent across every csv sheet and any errors
> might be hard to spot. I can't find anything on the net about this -
has
> anyone else had to deal with this problem and if so do they have any
> solutions using R?
> Thanks Nick Wray
>
> [[alternative HTML version deleted]]
>
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