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
Às 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
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