In perl the 'unpack' command makes it very easy to parse fixed fielded data.
On Fri, Feb 5, 2010 at 9:09 PM, Gabor Grothendieck <ggrothendi...@gmail.com> wrote: > Note that the filter= argument on read.csv.sql can be used to pass the > input through a filter written in perl, [g]awk or other language. > For example: read.csv.sql(..., filter = "gawk -f myfilter.awk") > > gawk has the FIELDWIDTHS variable for automatically parsing fixed > width fields, e.g. > http://www.delorie.com/gnu/docs/gawk/gawk_44.html > making this very easy but perl or whatever you are most used to would > be fine too. > > On Fri, Feb 5, 2010 at 8:50 PM, Vadlamani, Satish {FLNA} > <satish.vadlam...@fritolay.com> wrote: >> Hi Gabor: >> Thanks. My files are all in fixed width format. They are a lot of them. It >> would take me some effort to convert them to CSV. I guess this cannot be >> avoided? I can write some Perl scripts to convert fixed width format to CSV >> format and then start with your suggestion. Could you let me know your >> thoughts on the approach? >> Satish >> >> >> -----Original Message----- >> From: Gabor Grothendieck [mailto:ggrothendi...@gmail.com] >> Sent: Friday, February 05, 2010 5:16 PM >> To: Vadlamani, Satish {FLNA} >> Cc: r-help@r-project.org >> Subject: Re: [R] Reading large files >> >> If your problem is just how long it takes to load the file into R try >> read.csv.sql in the sqldf package. A single read.csv.sql call can >> create an SQLite database and table layout for you, read the file into >> the database (without going through R so R can't slow this down), >> extract all or a portion into R based on the sql argument you give it >> and then remove the database. See the examples on the home page: >> http://code.google.com/p/sqldf/#Example_13._read.csv.sql_and_read.csv2.sql >> >> On Fri, Feb 5, 2010 at 2:11 PM, Satish Vadlamani >> <satish.vadlam...@fritolay.com> wrote: >>> >>> Matthew: >>> If it is going to help, here is the explanation. I have an end state in >>> mind. It is given below under "End State" header. In order to get there, I >>> need to start somewhere right? I started with a 850 MB file and could not >>> load in what I think is reasonable time (I waited for an hour). >>> >>> There are references to 64 bit. How will that help? It is a 4GB RAM machine >>> and there is no paging activity when loading the 850 MB file. >>> >>> I have seen other threads on the same types of questions. I did not see any >>> clear cut answers or errors that I could have been making in the process. If >>> I am missing something, please let me know. Thanks. >>> Satish >>> >>> >>> End State >>>> Satish wrote: "at one time I will need to load say 15GB into R" >>> >>> >>> ----- >>> Satish Vadlamani >>> -- >>> View this message in context: >>> http://n4.nabble.com/Reading-large-files-tp1469691p1470667.html >>> Sent from the R help mailing list archive at Nabble.com. >>> >>> ______________________________________________ >>> R-help@r-project.org mailing list >>> https://stat.ethz.ch/mailman/listinfo/r-help >>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >>> and provide commented, minimal, self-contained, reproducible code. >>> >> > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Jim Holtman Cincinnati, OH +1 513 646 9390 What is the problem that you are trying to solve? ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
