Dear R-Helpers,
I have a large data matrix (9707 rows, 60 columns), which contains missing
data. The matrix looks something like this:
1) X X X X X X NA X X X X X X X X X
2) NA NA NA NA X NA NA NA X NA NA
3) NA NA X NA NA NA NA NA NA NA
5) NA X NA X X X NA X X X X NA X
..
9708) X NA NA X NA NA X X NA NA X
.and so on. Notice that every row has a varying number of entries, all rows
have at least one entry, but some rows have too much missing data. My goal
is to filter out/remove rows that have ~5 (this number is yet to be
determined, but let's say its 5) missing entries before I run pearsons to
tell me correlation between all of the rows. The order of the columns does
not matter here.
I think that I might need to test each row for a "data, at least one NA,
data" pattern?
Is there some kind of way of doing this? I am at a loss for an easy way to
accomplishing this. Any suggestions are most appreciated!
John Morrow
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