Try this:
dataset <- subset(dataset, grepl("(SAO |FL-15)", REC.TYPE))
On Sun, Mar 3, 2013 at 1:11 AM, Matt Borkowski <mathias1...@yahoo.com> wrote:
> Let me start by saying I am rather new to R and generally consider myself to
> be a novice programmer...so don't assume I know what I'm doing :)
>
> I have a large matrix, approximately 300,000 x 14. It's essentially a 20-year
> dataset of 15-minute data. However, I only need the rows where the column
> I've named REC.TYPE contains the string "SAO " or "FL-15".
>
> My horribly inefficient solution was to search the matrix row by row, test
> the REC.TYPE column and essentially delete the row if it did not match my
> criteria. Essentially...
>
>> j <- 1
>> for (i in 1:nrow(dataset)) {
>> if(dataset$REC.TYPE[j] != "SAO " && dataset$RECTYPE[j] != "FL-15") {
>> dataset <- dataset[-j,] }
>> else {
>> j <- j+1 }
>> }
>
> After watching my code get through only about 10% of the matrix in an hour
> and slowing with every row...I figure there must be a more efficient way of
> pulling out only the records I need...especially when I need to repeat this
> for another 8 datasets.
>
> Can anyone point me in the right direction?
>
> Thanks!
>
> Matt
>
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--
Jim Holtman
Data Munger Guru
What is the problem that you are trying to solve?
Tell me what you want to do, not how you want to do it.
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