[R] SAS "datalines" or "cards" statement equivalent in R?

[Gabor Grothendieck]

strapply in the gsubfn package can parse any input format that can be
described using a regular expression and generally requires only one
line of code to do the parsing.  See:  http://gsubfn.googlecode.com

On Mon, Dec 7, 2009 at 12:37 PM, Marshall Feldman <marsh at uri.edu> wrote:
> I totally agree with Barry, although it's sometimes convenient to
> include data with analysis code for debugging and/or documentation purposes.
>
> However, the example actually applies equally to separate data files. In
> fact, the example is from the U.S. Bureau of Labor Statistics at
> ftp://ftp.bls.gov/pub/time.series/sm/, which contains nothing but data
> and documentation files. At issue is not where the data come from, but
> rather how to parse relatively complex data organized inconsistently.
> SAS has built-in the ability to parse five different organizations of
> data: list (delimited), modified list, column, formatted, and mixed (see
> http://www.masil.org/sas/input.html). It seems R can parse such data,
> but only with considerable work by the user. It would be great to have a
> function/package that implements something with as easy (hah!) and
> flexible as SAS.
>
>    Marsh
>
> Barry Rowlingson wrote:
>> On Mon, Dec 7, 2009 at 3:53 PM, Marshall Feldman <marsh at uri.edu> wrote:
>>
>>> Regarding the various methods people have suggested, what if a typical
>>> tab-delimited data line looks like:
>>>
>>>     SMS11000000000000001 1990 M01 688.0
>>>
>>> and the SAS INPUT statement is
>>>
>>>   INPUT survey $ 1-2 seasonal $ 3 state $ 4-5 area $ 6-10 supersector $
>>> 11-12 @13 industry $8. datatype $ 21-22  year period $ value footnote $ ;
>>>
>>> Note that most data lines have no footnote item, as in the sample.
>>>
>>> Here (I think) we'd want all the character variables to be read as factors,
>>> possibly "year" as a date, and "value" as numeric.
>>>
>>
>>  Actually I'm surprised that nobody has yet said what a clearly
>> bonkers thing it is to mix up your data and your analysis code in a
>> single file. Now suppose you have another set of data you want to
>> analyse with the same code? Are you going to create a new file and
>> paste the new data in? You've now got two copies of your analysis code
>> - good luck keeping corrections to that code synchronised.
>>
>>  This just seems like horrendously bad practice, which is one reason
>> it's kludgy in R. If it was good practice, someone would surely have
>> written a way to do it neatly.
>>
>>  Keep your data in data files, and your functions in .R function
>> files. You'll thank me later.
>>
>> Barry
>>
>
>
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>
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