Hi
I do not see much logic in your removal of "outliers".
you can easily find which values differ from previous one by more than 15
myts[c(FALSE,abs(diff(myts$x))>15),]
but I did not understand why do you keep values from row 8 and 10.
Your example can be solved by
myts$y[myts$x>15]<-1
myts$x<-myts$x*(myts$x<15)
but it probably is not what you want.
Cheers
Petr
> -----Original Message-----
> From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of
> jeff6868
> Sent: Monday, March 02, 2015 12:11 PM
> To: r-help at r-project.org
> Subject: [R] How to speed up a double loop?
>
> Dear R-users,
>
> I would like to speed up a double-loop I developed for detecting and
> removing outliers in my whole data.frame. The idea is to remove data
> with a
> too big difference with the previous value. If detected, this test must
> be
> done here on maximum the next 10 values following the last correct one
> (and
> put an index on another column).
>
> It works well on a small data frame, but really too slowly for my real
> DF
> with 500 000 rows.
> Here's a fake data example and the double-loop:
>
> myts <- data.frame(x=c(1,2,50,40,30,40,100,1,50,1,2,3,3,5,4),y=NA)
>
> for(jj in 1:(nrow(myts)-10)){
> for(nn in ((jj+1):(jj+10))) {
> if((!is.na(myts[jj,1])) & (!is.na(myts[nn,1])) &
> (abs((myts[nn,1])-(myts[jj,1]))>15))
> { myts[nn,2] <- 1
> myts[nn,1] <- NA } } }
>
> Can somebody explain me how can I speed this up easily? I heard about
> vectorization but I don't really understand how it works.
>
>
>
>
> --
> View this message in context: http://r.789695.n4.nabble.com/How-to-
> speed-up-a-double-loop-tp4704054.html
> Sent from the R help mailing list archive at Nabble.com.
>
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