Convert data objects to a tibble with defaults optimized for spreadsheet (2D) presentation
Source:R/entibble.R
entibble.RdDefault behaviors:
Always includes rownames in tibble, if available
Does not repeat input elements to obtain common lengths
Arranges lists with uniform arrays into columns of a single tibble
A list of ragged elements becomes a two column tibble where the first column contains names and the second column is a nested list
Is unconcerned with duplicate column names unless specified by user with .name_repair
Usage
entibble(
...,
.rowname = "rowname",
.name_repair = c("minimal", "check_unique", "unique", "universal", "unique_quiet",
"universal_quiet")
)Arguments
- ...
object or expression to convert to a tibble
- .rowname
string; to name the column containing rownames. If rownames are not present in the dataframe,
.rownameis ignored.- .name_repair
Treatment of problematic column names:
"minimal": No name repair or checks, beyond basic existence,"unique": Make sure names are unique and not empty,"check_unique": (default value), no name repair, but check they areunique,"universal": Make the namesuniqueand syntactica function: apply custom name repair (e.g.,
.name_repair = make.namesfor names in the style of base R).A purrr-style anonymous function, see
rlang::as_function()
This argument is passed on as
repairtovctrs::vec_as_names(). See there for more details on these terms and the strategies used to enforce them.
Details
@details
Built to work with the function xl() to produce 2D output for spreadsheets.
entibble() is liberal about names, such as duplicate column names, and tries to produce
flat 2D data in place of nested data.
Examples
letters |> entibble()
#> # A tibble: 26 × 1
#> letters
#> <chr>
#> 1 a
#> 2 b
#> 3 c
#> 4 d
#> 5 e
#> 6 f
#> 7 g
#> 8 h
#> 9 i
#> 10 j
#> # ℹ 16 more rows
# naming examples
(example_data <- letters |> purrr::set_names(LETTERS))
#> A B C D E F G H I J K L M N O P Q R S T
#> "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t"
#> U V W X Y Z
#> "u" "v" "w" "x" "y" "z"
example_data |> entibble()
#> # A tibble: 26 × 2
#> rowname example_data
#> <chr> <chr>
#> 1 A a
#> 2 B b
#> 3 C c
#> 4 D d
#> 5 E e
#> 6 F f
#> 7 G g
#> 8 H h
#> 9 I i
#> 10 J j
#> # ℹ 16 more rows
example_data |> entibble(.rowname = 'a user-specified name')
#> # A tibble: 26 × 2
#> `a user-specified name` example_data
#> <chr> <chr>
#> 1 A a
#> 2 B b
#> 3 C c
#> 4 D d
#> 5 E e
#> 6 F f
#> 7 G g
#> 8 H h
#> 9 I i
#> 10 J j
#> # ℹ 16 more rows
example_data |> entibble(.name_repair = ~c('name_A', 'name_B'))
#> # A tibble: 26 × 2
#> name_A name_B
#> <chr> <chr>
#> 1 A a
#> 2 B b
#> 3 C c
#> 4 D d
#> 5 E e
#> 6 F f
#> 7 G g
#> 8 H h
#> 9 I i
#> 10 J j
#> # ℹ 16 more rows
entibble(example_data, example_data)
#> # A tibble: 26 × 4
#> rowname example_data rowname example_data
#> <chr> <chr> <chr> <chr>
#> 1 A a A a
#> 2 B b B b
#> 3 C c C c
#> 4 D d D d
#> 5 E e E e
#> 6 F f F f
#> 7 G g G g
#> 8 H h H h
#> 9 I i I i
#> 10 J j J j
#> # ℹ 16 more rows
entibble(x = 1:3, x = 11:13, .name_repair = 'unique_quiet')
#> # A tibble: 3 × 2
#> x...1 x...2
#> <int> <int>
#> 1 1 11
#> 2 2 12
#> 3 3 13
mtcars |> entibble(.rowname = 'vehicle')
#> # A tibble: 32 × 12
#> vehicle mpg cyl disp hp drat wt qsec vs am gear carb
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 Mazda RX4 … 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 Hornet 4 D… 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 Hornet Spo… 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 Duster 360 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 Merc 240D 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 Merc 280 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # ℹ 22 more rows
# nests ragged data
entibble(example_data, !!!letters)
#> # A tibble: 27 × 2
#> rowname `list(ragged_elements)`
#> <chr> <list>
#> 1 example_data <tibble [26 × 2]>
#> 2 a <tibble [1 × 1]>
#> 3 b <tibble [1 × 1]>
#> 4 c <tibble [1 × 1]>
#> 5 d <tibble [1 × 1]>
#> 6 e <tibble [1 × 1]>
#> 7 f <tibble [1 × 1]>
#> 8 g <tibble [1 × 1]>
#> 9 h <tibble [1 × 1]>
#> 10 i <tibble [1 × 1]>
#> # ℹ 17 more rows
# spreads conforming rows and lists of conforming rows
entibble(x = 1:3, y = 11:13)
#> # A tibble: 3 × 2
#> x y
#> <int> <int>
#> 1 1 11
#> 2 2 12
#> 3 3 13
entibble(x = 1:3, y = 10:14)
#> # A tibble: 2 × 2
#> rowname `list(ragged_elements)`
#> <chr> <list>
#> 1 x <tibble [3 × 1]>
#> 2 y <tibble [5 × 1]>
# be somewhat careful about conforming-row inputs; it is different from `tibble()`
enlist(head(iris), tail(mtcars)) |> entibble()
#> # A tibble: 6 × 17
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species rowname mpg cyl
#> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 5.1 3.5 1.4 0.2 setosa Porsche… 26 4
#> 2 4.9 3 1.4 0.2 setosa Lotus E… 30.4 4
#> 3 4.7 3.2 1.3 0.2 setosa Ford Pa… 15.8 8
#> 4 4.6 3.1 1.5 0.2 setosa Ferrari… 19.7 6
#> 5 5 3.6 1.4 0.2 setosa Maserat… 15 8
#> 6 5.4 3.9 1.7 0.4 setosa Volvo 1… 21.4 4
#> # ℹ 9 more variables: disp <dbl>, hp <dbl>, drat <dbl>, wt <dbl>, qsec <dbl>,
#> # vs <dbl>, am <dbl>, gear <dbl>, carb <dbl>
as.matrix(list(1:10,11:20)) |> entibble()
#> # A tibble: 10 × 2
#> `1` `2`
#> <int> <int>
#> 1 1 11
#> 2 2 12
#> 3 3 13
#> 4 4 14
#> 5 5 15
#> 6 6 16
#> 7 7 17
#> 8 8 18
#> 9 9 19
#> 10 10 20
# if you want to ensure separate lists, call [purrr::map()] or [list_iron()]
enlist(head(iris), tail(mtcars)) |> purrr::map(entibble)
#> $`head(iris)`
#> # A tibble: 6 × 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#>
#> $`tail(mtcars)`
#> # A tibble: 6 × 12
#> rowname mpg cyl disp hp drat wt qsec vs am gear carb
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Porsche 914… 26 4 120. 91 4.43 2.14 16.7 0 1 5 2
#> 2 Lotus Europa 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
#> 3 Ford Panter… 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
#> 4 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6
#> 5 Maserati Bo… 15 8 301 335 3.54 3.57 14.6 0 1 5 8
#> 6 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2
#>
enlist(head(iris), tail(mtcars)) |> list_iron(.f = entibble)
#> $`enlist(head(iris), tail(mtcars))|head(iris)`
#> # A tibble: 6 × 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#>
#> $`enlist(head(iris), tail(mtcars))|tail(mtcars)`
#> # A tibble: 6 × 12
#> rowname mpg cyl disp hp drat wt qsec vs am gear carb
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Porsche 914… 26 4 120. 91 4.43 2.14 16.7 0 1 5 2
#> 2 Lotus Europa 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
#> 3 Ford Panter… 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
#> 4 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6
#> 5 Maserati Bo… 15 8 301 335 3.54 3.57 14.6 0 1 5 8
#> 6 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2
#>
# makes multi-dimensional tables 2D:
Titanic |> dim()
#> [1] 4 2 2 2
Titanic |> entibble()
#> # A tibble: 32 × 5
#> Class Sex Age Survived Freq
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 1st Male Child No 0
#> 2 2nd Male Child No 0
#> 3 3rd Male Child No 35
#> 4 Crew Male Child No 0
#> 5 1st Female Child No 0
#> 6 2nd Female Child No 0
#> 7 3rd Female Child No 17
#> 8 Crew Female Child No 0
#> 9 1st Male Adult No 118
#> 10 2nd Male Adult No 154
#> # ℹ 22 more rows