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.
