On 26-01-2013, at 19:43, emorway <emor...@usgs.gov> wrote:
> I'm wondering if I need to use a function other than sapply as the following
> line of code runs indefinitely (or > 30 min so far) and uses up all 16Gb of
> memory on my machine for what seems like a very small dataset (data attached
> in a txt file wells.txt
> <http://r.789695.n4.nabble.com/file/n4656723/wells.txt> ). The R code is:
>
> wells<-read.table("c:/temp/wells.txt",col.names=c("name","plc_hldr"))
> wells2<-wells[sapply(wells[,1],function(x)length(strsplit(as.character(x),
> "_")[[1]])==2),]
>
> The 2nd line of R code above gets bogged down and takes all my RAM with it:
> <http://r.789695.n4.nabble.com/file/n4656723/memory_loss.png>
>
> I'm simply trying to extract all of the lines of data that have a single "_"
> in the first column and place them into a dataset called "wells2". If that
> were to work, I then want to extract the lines of data that have two "_" and
> put them into a separate dataset, say "wells3". Is there a better way to do
> this than the one-liner above?
Read your file with
wells<-read.table("wells.txt",col.names=c("name","plc_hldr"),
stringsAsFactors=FALSE)
Remove all non underscores with
w.sub <- gsub("[^_]+","",wells[,1])
then select elements of w.sub with 2 underscores and a single underscore with
u.2 <- which(w.sub=="__")
u.1 <- which(w.sub=="_")
and use u.1 and u.2 to select the appropriate rows of wells.
I tried to select rows containing 1 or 2 underscores with grep regular
expressions but that appeared to be more difficult than I had expected.
The method above is quick.
Berend
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