Question

I have a script that has a bunch of quality control checksums and it got caught on a dataset that had no need to remove any samples (rows) due to quality control. However, this script gave me an unexpected result of a dataframe with zero rows. With example data, why does this work:

data(iris)
##get rid of those pesky factors
iris$Species <- NULL
med <- which(iris[, 1] < 4.9)
medtemp <- iris[-med, ]
dim(medtemp)
[1] 134   4

but this returns a dataframe of zero rows:

small <- which(iris[, 1] < 4.0)
smalltemp <- iris[-small, ]
dim(smalltemp)
[1] 0 4

As does this:

x <- 0
zerotemp <- iris[-x, ]
dim(zerotemp)
[1] 0 4

It seems that the smalltemp dataframe should be the same size as iris since there are no rows to remove at all. Why is this?

Answer 1

Copied verbatim from Patrick Burns’s R Inferno p. 41 (I hope this constitutes “fair use” — if someone objects I’ll remove it)

negative nothing is something

> x2 <- 1:4
> x2[-which(x2 == 3)]
[1] 1 2 4

The command above returns all of the values in x2 not equal to 3.

> x2[-which(x2 == 5)]
numeric(0)

The hope is that the above command returns all of x2 since no elements are
equal to 5. Reality will dash that hope. Instead it returns a vector of length
zero.
There is a subtle difference between the two following statements:

x[]
x[numeric(0)]

Subtle difference in the input, but no subtlety in the difference in the output.
There are at least three possible solutions for the original problem.

out <- which(x2 == 5)
if(length(out)) x2[-out] else x2

Another solution is to use logical subscripts:

x2[!(x2 %in% 5)]

Or you can, in a sense, work backwards:

x2[ setdiff(seq along(x2), which(x2 == 5)) ]