colSums(is,na(x) ) can replace your function and negative indexing can
eliminate the unwanted columns:
x[-(colSums(is.na(x)) > 375)]
or equivalently:
x[(colSums(is.na(x)) <= 375)]
You could (destructively) assign the result to x if you are brave.
--
David Winsemius
On Jan 18, 2009, at 9:55 AM, Josh B wrote:
Hello,
I have a dataset (named "x") with many (966) columns. What I would
like to do is delete any columns that do not have at least 375 non-
blank observations (i.e., the cells have some value in them besides
NA).
How can I do this? I have come up with the following code to _count_
the non-blank observations in each column, but how would I adapt
this code to _delete_ columns from the dataset if they do not have
at least 375 non-blank observations?
lapply(x, function(d)
{
d.2<- na.omit(d)
count<- length(d.2)
}
)
Many thanks in advance,
Josh B.
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