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
#> # ... with 22 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 most commonly created by do(), 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
#> # ... with 1 more rowsIt 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, 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:
l <- replicate(26, sample(100), simplify = FALSE)
names(l) <- letters
microbenchmark::microbenchmark(
as_tibble(l),
as.data.frame(l)
)
#> Unit: microseconds
#> expr min lq mean median uq max
#> as_tibble(l) 520.511 555.2405 596.2015 580.798 612.1915 1116.805
#> as.data.frame(l) 2932.765 3060.7465 3429.1367 3113.495 3253.2700 8814.272
#> neval cld
#> 100 a
#> 100 bThe 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 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 × 1
#> x
#> <int>
#> 1 1
#> 2 2
#> 3 3
#> 4 4
#> # ... with 996 more rowsYou 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:
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 column 'a'
#> NULLAlso, tibbles ignore the drop argument.