Actually your reply was very helpful because it surfaced ways in which you were partially right and I was partially wrong.

I highlighted the nesting issue in constructing versus coercing (which is correct and does have implications for what you're trying to do) but actually in your example the distinction is broken because of a different edge case

Which is to say the following:

  ncol(iris) # 5
  ncol(as_tibble(iris)) # 5
  ncol(tibble(iris) # 1

  iris$Species # Works
  as_tibble(iris)$Species # Works
  tibble(iris)$Species # Errors because of nesting

  iris[, "Species"] # Works
  tibble(iris)[, "Species"] # Doesn't work
  as_tibble(iris)[, "Species"] # Works
 

However, you're correct that because the subset operator for tibble doesn't drop dimensions, length gets you the number of columns rather than the number of observations. This does speak to the fact that length is a pretty shitty function to begin with, but I concede you're partially correct there.

You are also correct that because class labels are not contractual, there is no guarantee that having the data.frame fallback label means stuff behaves identically (for instance, you could add the data.frame label to any data structure and the data.frame dispatch stuff would not work properly). My point was that in the case of a tibble, a tibble is literally a data frame with an additional class label. If you remove that class label, it's exactly identical.

But your example and linked discussion does highlight a way in which I'm wrong; the subset function is overridden for something with a tibble class label. That's true and could produce edge cases I hadn't considered.

Apologies for any hostility in my original reply.