What can a data frame do that a tibble cannot?

Question

Fans of the Tidyverse regularly give several advantages of using tibbles rather than data frames. Most of them seem designed to protect the user from making mistakes. For example, unlike data frames, tibbles:

• Don't need a ,drop=FALSE argument to not drop dimensions from your data.
• Will not let the $ operator do partial matching for column names.
• Only recycle your input vectors if they are of exactly length one.

I'm steadily becoming convinced to replace all of my data frames with tibbles. What are the primary disadvantages of doing so? More specifically, what can a data frame do that a tibble cannot?

Preemptively, I would like to make it clear that I am not asking about data.table or any big-picture objections to the Tidyverse. I am strictly asking about tibbles and data frames.

Answer 1

From the trouble with tibbles, you can read :

there isn’t really any trouble with tibbles

However,

Some older packages don’t work with tibbles because of their alternative subsetting method. They expect tib[,1] to return a vector, when in fact it will now return another tibble.

This is what @Henrik pointed out in comments.

As an example, the length function won't return the same result:

library(tibble)
tibblecars <- as_tibble(mtcars)
tibblecars[,"cyl"]
#> # A tibble: 32 x 1
#>      cyl
#>    <dbl>
#>  1     6
#>  2     6
#>  3     4
#>  4     6
#>  5     8
#>  6     6
#>  7     8
#>  8     4
#>  9     4
#> 10     6
#> # ... with 22 more rows
length(tibblecars[,"cyl"])
#> [1] 1
mtcars[,"cyl"]
#>  [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4
length(mtcars[,"cyl"])
#> [1] 32

Other example :

• base::reshape not working with tibbles

Invariants for subsetting and subassignment explains where the behaviour from tibble differs from data.frame.

These limitations being known, the solution given by Hadley in interacting with legacy code is:

A handful of functions don’t work with tibbles because they expect df[, 1] to return a vector, not a data frame. If you encounter one of these functions, use as.data.frame() to turn a tibble back to a data frame: