Tibbles are a modern take on data frames. They keep the features that have stood the test of time, and drop the features that used to be convenient but are now frustrating (i.e. converting character vectors to factors).


tibble() is a nice way to create data frames. It encapsulates best practices for data frames:


To complement tibble(), tibble provides as_tibble() to coerce objects into tibbles. Generally, as_tibble() methods are much simpler than as.data.frame() methods, and in fact, it’s precisely what as.data.frame() does, but it’s similar to do.call(cbind, lapply(x, data.frame)) - i.e. it coerces each component to a data frame and then cbinds() them all together.

as_tibble() has been written with an eye for performance:

The speed of as.data.frame() is not usually a bottleneck when used interactively, but can be a problem when combining thousands of messy inputs into one tidy data frame.

Tibbles vs data frames

There are three key differences between tibbles and data frames: printing, subsetting, and recycling rules.


When you print a tibble, it only shows the first ten rows and all the columns that fit on one screen. It also prints an abbreviated description of the column type:

You can control the default appearance with options:

  • options(tibble.print_max = n, tibble.print_min = m): if there are more than n rows, print only the first m rows. Use options(tibble.print_max = Inf) to always show all rows.

  • options(tibble.width = Inf) will always print all columns, regardless of the width of the screen.


Tibbles are quite strict about subsetting. [ always returns another tibble. Contrast this with a data frame: sometimes [ returns a data frame and sometimes it just returns a vector:

To extract a single column use [[ or $:

Tibbles are also stricter with $. Tibbles never do partial matching, and will throw a warning and return NULL if the column does not exist:

As of version 1.4.1, tibbles no longer ignore the drop argument:


When constructing a tibble, only values of length 1 are recycled. The first column with length different to one determines the number of rows in the tibble, conflicts lead to an error. This also extends to tibbles with zero rows, which is sometimes important for programming: