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.
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
#> # ℹ 16 more rowsThis makes it easier to use with list-columns:
tibble(x = 1:3, y = list(1:5, 1:10, 1:20))
#> # A tibble: 3 × 2
#> x y
#> <int> <list>
#> 1 1 <int [5]>
#> 2 2 <int [10]>
#> 3 3 <int [20]>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:
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.
To complement 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:
l <- replicate(26, sample(100), simplify = FALSE)
names(l) <- letters
timing <- bench::mark(
as_tibble(l),
as.data.frame(l),
check = FALSE
)
timing#> # 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 1599.648
#> 2 as.data.frame(l) 0.000791522 0.0016640039 0.001098172 0.007652914 600.9601
#> # ℹ 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 0.5080494
#> 2 -4 1.524148
#> 3 -3 4.572444
#> 4 -2 13.71733
#> 5 -1 41.152
#> 6 0 123.456
#> 7 1 370.368
#> 8 2 1111.104
#> 9 3 3333.312
#> 10 4 9999.936
#> # ℹ 96 more rowsNumbers 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 n rows, print only the first
m rows. Use options(pillar.print_max = Inf) to
always show all rows.
options(pillar.width = n): use n
character 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.
See ?pillar::pillar_options and
?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:
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 $:
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] 1
df2 <- tibble(abc = 1)
df2$a
#> Warning: Unknown or uninitialised column: `a`.
#> NULLHowever, tibbles respect the drop argument if it is
provided:
data.frame(a = 1:3)[, "a", drop = TRUE]
#> [1] 1 2 3
tibble(a = 1:3)[, "a", drop = TRUE]
#> [1] 1 2 3Tibbles do not support row names. They are removed when converting to a tibble or when subsetting:
df <- data.frame(a = 1:3, row.names = letters[1:3])
rownames(df)
#> [1] "a" "b" "c"
rownames(as_tibble(df))
#> [1] "1" "2" "3"
tbl <- tibble(a = 1:3)
rownames(tbl) <- letters[1:3]
#> Warning: Setting row names on a tibble is deprecated.
rownames(tbl)
#> [1] "a" "b" "c"
rownames(tbl[1, ])
#> [1] "1"See 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 in `tibble()`:
#> ! 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.