Tibbles

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).

Creating

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
#> # ℹ Use `print(n = ...)` to see more rows

This 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:

names(data.frame(`crazy name` = 1))
#> [1] "crazy.name"
names(tibble(`crazy name` = 1))
#> [1] "crazy name"

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.

Coercion

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` mem_a…¹
#>   <chr>       <bench_tm>  <bench_tm>   <bench_tm>  <bench_tm>      <dbl> <bnch_>
#> 1 as_tibble(… 0.000287696 0.0006251376 0.000327178 0.004508219 1599.648   1840  
#> 2 as.data.fr… 0.000791522 0.0016640039 0.001098172 0.007652914  600.9601 34584  
#> # … with 7 more variables: n_gc <dbl>, n_itr <int>, total_time <bench_tm>,
#> #   result <list>, memory <list>, time <list>, gc <list>, and abbreviated
#> #   variable name ¹mem_alloc
#> # ℹ Use `colnames()` to see all variable names

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.

Printing

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    
#> # … with 96 more rows
#> # ℹ Use `print(n = ...)` to see 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 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.

Subsetting

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 $:

class(df2[[1]])
#> [1] "integer"
class(df2$x)
#> [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)
df$a
#> [1] 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] 1 2 3
tibble(a = 1:3)[, "a", drop = TRUE]
#> [1] 1 2 3

Tibbles 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.

Recycling

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:

tibble(a = 1, b = integer())
#> # A tibble: 0 × 2
#> # … with 2 variables: a <dbl>, b <int>
#> # ℹ Use `colnames()` to see all variable names
tibble(a = integer(), b = 1)
#> # A tibble: 0 × 2
#> # … with 2 variables: a <int>, b <dbl>
#> # ℹ Use `colnames()` to see all variable names

Arithmetic operations

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