just reverse what you did before. newdata <- data newdata[] <- NA newdata[,!apply(is.na(data), 2, any)] <- myfunction(data_no_NA)
On Fri, Mar 27, 2015 at 1:13 AM, Jatin Kala <jatin.kala...@gmail.com> wrote: > Hi, > I've got a rather large matrix of about 800 rows and 600000 columns. > Each column is a time-series 800 long. > > Out of these 600000 time series, some have missing values (NA). > I want to strip out all columns that have one or more NA values, i.e., only > want full time series. > > This should do the trick: > data_no_NA <- data[,!apply(is.na(data), 2, any)] > > I now use data_no_NA as input to a function, which returns output as a > matrix of the same size as data_no_NA > > The trick is that i now need to put these columns back into a new 800 by > 600000 empty matrix, at their original locations. > Any suggestions on how to do that? hopefully without having to use loops. > I'm using R/3.0.3 > > Cheers, > Jatin. > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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 -- To UNSUBSCRIBE and more, see 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.
