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:
It never changes an input’s type (i.e., no more
stringsAsFactors = FALSE!).
tibble(x = letters) #> # A tibble: 26 × 1 #> x #> <chr> #> 1 a #> 2 b #> 3 c #> 4 d #> 5 e #> 6 f #> 7 g #> 8 h #> 9 i #> 10 j #> # … with 16 more rows
This makes it easier to use with list-columns:
List-columns are often created by
tidyr::nest(), but they can be useful to create by hand.
It never adjusts the names of variables:
It evaluates its arguments lazily and sequentially:
tibble(x = 1:5, y = x ^ 2) #> # A tibble: 5 × 2 #> x y #> <int> <dbl> #> 1 1 1 #> 2 2 4 #> 3 3 9 #> 4 4 16 #> 5 5 25
It never uses
row.names(). The whole point of tidy data is to store variables in a consistent way. So it never stores a variable as special attribute.
It only recycles vectors of length 1. This is because recycling vectors of greater lengths is a frequent source of bugs.
tibble(), tibble provides
as_tibble() to coerce objects into tibbles. Generally,
as_tibble() methods are much simpler than
as.data.frame() methods. The method for lists has been written with an eye for performance:
#> # A tibble: 2 × 14 #> expression min mean median max `itr/sec` #> <chr> <bench_tm> <bench_tm> <bench_tm> <bench_tm> <dbl> #> 1 as_tibble(l) 0.000287696 0.0006251376 0.000327178 0.004508219 1600. #> 2 as.data.fram… 0.000791522 0.0016640039 0.001098172 0.007652914 601. #> # … with 8 more variables: mem_alloc <bnch_byt>, n_gc <dbl>, n_itr <int>, #> # total_time <bench_tm>, result <list>, memory <list>, time <list>, #> # gc <list>
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.
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, and uses font styles and color for highlighting:
tibble(x = -5:100, y = 123.456 * (3 ^ x)) #> # A tibble: 106 × 2 #> x y #> <int> <dbl> #> 1 -5 0.508 #> 2 -4 1.52 #> 3 -3 4.57 #> 4 -2 13.7 #> 5 -1 41.2 #> 6 0 123. #> 7 1 370. #> 8 2 1111. #> 9 3 3333. #> 10 4 10000. #> # … with 96 more rows
Numbers are displayed with three significant figures by default, and a trailing dot that indicates the existence of a fractional component.
You can control the default appearance with options:
options(pillar.print_max = n, pillar.print_min = m): if there are more than
nrows, print only the first
options(pillar.print_max = Inf)to always show all rows.
options(pillar.width = n): use
ncharacter slots horizontally to show the data. If
n > getOption("width"), this will result in multiple tiers. Use
options(pillar.width = Inf)to always print all columns, regardless of the width of the screen.
?tibble_options for the available options,
vignette("types") for an overview of the type abbreviations,
vignette("numbers") for details on the formatting of numbers, and
vignette("digits") for a comparison with data frame printing.
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
Tibbles are also stricter with
$. Tibbles never do partial matching, and will throw a warning and return
NULL if the column does not exist:
df <- data.frame(abc = 1) df$a #>  1 df2 <- tibble(abc = 1) df2$a #> Warning: Unknown or uninitialised column: `a`. #> NULL
However, tibbles respect the
drop argument if it is provided:
data.frame(a = 1:3)[, "a", drop = TRUE] #>  1 2 3 tibble(a = 1:3)[, "a", drop = TRUE] #>  1 2 3
Tibbles do not support row names. They are removed when converting to a tibble or when subsetting:
vignette("invariants") for a detailed comparison between tibbles and data frames.
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:
tibble(a = 1, b = 1:3) #> # A tibble: 3 × 2 #> a b #> <dbl> <int> #> 1 1 1 #> 2 1 2 #> 3 1 3 tibble(a = 1:3, b = 1) #> # A tibble: 3 × 2 #> a b #> <int> <dbl> #> 1 1 1 #> 2 2 1 #> 3 3 1 tibble(a = 1:3, c = 1:2) #> Error: #> ! Tibble columns must have compatible sizes. #> • Size 3: Existing data. #> • Size 2: Column `c`. #> ℹ Only values of size one are recycled.
This also extends to tibbles with zero rows, which is sometimes important for programming:
Unlike data frames, tibbles don’t support arithmetic operations on all columns. The result is silently coerced to a data frame. Do not rely on this behavior, it may become an error in a forthcoming version.
tbl <- tibble(a = 1:3, b = 4:6) tbl * 2 #> a b #> 1 2 8 #> 2 4 10 #> 3 6 12