[R] Conditional read-in of data

Gabor Grothendieck ggrothendieck at gmail.com
Wed Nov 4 14:15:33 CET 2009


1. You can pipe your data through gawk (or other scripting language)
process as in:
http://tolstoy.newcastle.edu.au/R/e5/help/08/09/2129.html

2. read.csv.sql in the sqldf package on CRAN will set up a database
for you, read the file into the database automatically defining the
layout of the table, extract a portion into R based on an sql
statement that you provide and then destroy the database all in one
statement.  It uses the sqlite database which is included in the
RSQLite R package that it depends on so there is nothing to separately
install.
See ?read.csv.sql in the package and also see example 13 on the home page:
http://sqldf.googlecode.com


On Wed, Nov 4, 2009 at 12:07 AM, mnstn <pavan.namd at gmail.com> wrote:
>
> Hello All,
> I have a 40k rows long data set that is taking a lot of time to be read-in.
> Is there a way to skip reading even/odd numbered rows or read-in only rows
> that are multiples of, say, 10? This way I get the general trend of the data
> w/o actually reading the entire thing. The option 'skip' in read.table
> simply skips the first n rows and reads the rest. I do understand that once
> the full data set (40k rows) is read-in, I can manipulate the data. But the
> bottle-neck here is the first read/scan of data.
>
> I searched in the forum using key words (conditional skip/skip reading
> rows/skip data/conditional data read) etc. but couldn't find relevant
> conversations. I apologize if this has already been discussed since it does
> seem hard to imagine that nobody has come across this problem yet.
>
> Any suggestions/comments are welcome.
> Thanks,
> mnstn
> --
> View this message in context: http://old.nabble.com/Conditional-read-in-of-data-tp26191091p26191091.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> R-help at 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.
>




More information about the R-help mailing list