I have a data frame of ~200 columns and ~20,000 rows where each column
consists of binary responses (0,1) and a 9 for missing data. I am
interested in finding the columns for which there are fewer than 100
individuals with responses of 0.
I can use an apply function to generate a table for each column, but I'm
not certain whether I can subset a list based on some criterion as
subset() is designed for vectors, matrices or dataframes.
For example, I can use the following:
tt <- apply(data, 2, table)
Which returns an object of class list. Here is some sample output from
tt
$R0235940b
0 1 9
2004 1076 15361
$R0000710a
0 9
2 18439
$R0000710b
0 1 9
3333 3941 11167
tt$R0000710a meets my criteria and I would want to be able to easily
find this instead of rolling through the entire output. Is there a way
to subset this list to identify the columns which meet the criteria I
note above?
Thanks,
Harold
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