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:

if (requireNamespace("microbenchmark", quiet = TRUE)) {
  l <- replicate(26, sample(100), simplify = FALSE)
  names(l) <- letters

#> Loading required namespace: microbenchmark

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 two key differences between tibbles and data frames: printing and subsetting.


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:

tibble(x = 1:1000)
#> # A tibble: 1,000 x 1
#>       x
#>   <int>
#> 1     1
#> 2     2
#> 3     3
#> 4     4
#> # ... with 996 more rows

You can control the default appearance with options:


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:

df1 <- data.frame(x = 1:3, y = 3:1)
class(df1[, 1:2])
#> [1] "data.frame"
class(df1[, 1])
#> [1] "integer"

df2 <- tibble(x = 1:3, y = 3:1)
class(df2[, 1:2])
#> [1] "tbl_df"     "tbl"        "data.frame"
class(df2[, 1])
#> [1] "tbl_df"     "tbl"        "data.frame"

To extract a single column use [[ or $:

#> [1] "integer"
#> [1] "integer"

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

df <- data.frame(abc = 1)
#> [1] 1

df2 <- tibble(abc = 1)
#> Warning: Unknown or uninitialised column: 'a'.

tibbles also ignore the drop argument.