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
Something like this should work:
num <- apply(yourData, 2, function(x) sum(is.na(x)) < 375)
yourData <- youData[, num]
On Sun, 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
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-bl
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