Make your functions compa-tibble


Hugo Gruson


May 27, 2024

tibbles are described as “a modern reimagining of the data.frame, with subtle differences in the way the default data.frame behaviour, based on years of experience”.

Users will sometimes go from data.frame to tibbles and vice-versa interchangeably. And the subtle differences mentioned above can produce errors or unexpected results.

Even if you verify that the user provided input is a data.frame (via inherits(input, "data.frame"), it may end up being a tibble rather than a “standard” data.frame as tibbles still inherit from data.frame.

data(starwars, package = "dplyr")
[1] "tbl_df"     "tbl"        "data.frame"

A simple option when you write functions (as when developing packages) may seem to force users to use either data.frame or tibble:

f_only_tibbles <- function(x) {
  if (!inherits(x, "tbl_df")) {
    stop("x must be a tibble")
  # ...

f_only_dataframes <- function(x) {
  if (identical(class(x), "data.frame")) {
    stop("x must be a data.frame, without any other subclass")
  # ...

But being overly strict in inputs can be discouraging to users and this extra friction is not necessary. After all, tibbles are data.frames, and we can make sure we support them alongside data.frames.

In this post, we will see how to make your functions compa-tibble, i.e., how you can support invisibly both data.frame and tibble as inputs.

df  <- cars
tbl <- tibble::as_tibble(cars)

Don’t rely on drop = TRUE being the default

This is probably the most important point of this post and the most common offender. This can arise when you want to pass a column to function expecting a vector, such as mean() in the following example:

# Example function to compute the mean of a specific column.
mean_col <- function(data, col_index) {
  mean(data[, col_index])

mean_col(df, 1)
[1] 15.4
mean_col(tbl, 1)
Warning in mean.default(data[, col_index]): argument is not numeric or logical:
returning NA
[1] NA

If we want to support both standard data.frames and tibbles, we have to adjust the mean_col() source slightly, and explicitly specify that we want the extracted column to be returned as a vector:

compatibble_mean_col <- function(data, col_index) {
  mean(data[, col_index, drop = TRUE])

compatibble_mean_col(df, 1)
[1] 15.4
compatibble_mean_col(tbl, 1)
[1] 15.4

Don’t rely on partial matching

By default, many R mechanisms will not always require typing the entire argument or column name, a mechanism known as partial matching. But partial matching is often criticized as fragile, and prone to unexpected behaviours when the code is updated. As a result, it has been disabled in tibbles:

mean_speed <- function(data) {
  # "s" will be partially matched to "speed". But only in standard data.frames

[1] 15.4
Warning: Unknown or uninitialised column: `s`.
Warning in mean.default(data$s): argument is not numeric or logical: returning
[1] NA

The good news is that you can ensure you don’t rely on partial matching by setting options(warnPartialMatchDollar = TRUE) in your .Rprofile. It can also be set temporarily in the current session:

options(warnPartialMatchDollar = TRUE)
Warning in data$s: partial match of 's' to 'speed'
[1] 15.4

If you’re using testthat, you can also set this as part of your continuous integration by creating a special setup-options.R file in tests/testthat/:

options(warnPartialMatchDollar = TRUE)

The warnings here lead us to creating a more robust, and compa-tibble version of mean_speed(), which doesn’t rely on partial matching:

compatibble_mean_speed <- function(data) {

[1] 15.4
[1] 15.4

If, for some reason, you really need to support partial matching, you will need to manually handle it for tibbles:

mean_speed_partialmatch <- function(data) {
  col <- charmatch("s", colnames(data))

[1] 15.4
[1] 15.4

Other differences, and how to deal with them

We have presented the most common issues I encountered in my own packages when trying to add support for tibbles but the tibble package provides a longer vignette with all the differences between tibbles and data.frames. To get fully compa-tibble, I encourage you go through it and identify patterns you tend to use in your code and which lead to different behaviours in data.frame and tibbles.

Because of the many (admittedly less common than the ones presented here) differences, the only way to properly ensure you have full compa-tibbility and you don’t break it with future updates, is to actually run tests with both data.frames and tibbles.

At the moment, the simplest option is probably to run the same tests on manually crafted inputs: one data.frame, and its tibble equivalent. If you really want to avoid the repetition, you could have a look at the patrick R package, which allows to run parametrized tests.

In the future, I will probably recommend doing this via mutation testing. The autotest R package for example proposes the automatic detection of data.frame inputs, and the automatic mutation of these inputs to other rectangular formats, which could include tibbles.