I have a dataset that has many columns which are NA or constant, and so I
remove them like so:
same <- sapply(dataset, function(.col){
all(is.na(.col)) || all(.col[1L] == .col)
})
dataset <- dataset[!same]
This works GREAT (thanks to the r-users list archive I found this)
however, then when I do my data sampling like so:
testSize <- floor(nrow(x) * 10/100)
test <- sample(1:nrow(x), testSize)
train_data <- x[-test,]
test_data <- x[test, -1]
test_class <- x[test, 1]
It is now possible that test_data or train_data contain columns that are
constants, however as one dataset they did not.
So the solution for me is to just re-run lines to remove all constants......not
a problem, but is this normal? is this how I should
be handling this in R? many models I am attempting to use (SVM, lda, etc)
don't like if a column has all the same value.......
so as a beginner, this is how I am handling it in R, but I am looking for
someone to sanity check what I am doing is sound.
Brian
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