Coerce lists, matrices, and more to data frames

as_tibble() turns an existing object, such as a data frame or matrix, into a so-called tibble, a data frame with class tbl_df. This is in contrast with tibble(), which builds a tibble from individual columns. as_tibble() is to tibble() as base::as.data.frame() is to base::data.frame().

as_tibble() is an S3 generic, with methods for:

• data.frame: Thin wrapper around the list method that implements tibble's treatment of rownames.

• matrix, poly, ts, table

• Default: Other inputs are first coerced with base::as.data.frame().

as_tibble_row() converts a vector to a tibble with one row. If the input is a list, all elements must have size one.

as_tibble_col() converts a vector to a tibble with one column.

Usage

as_tibble(
  x,
  ...,
  .rows = NULL,
  .name_repair = c("check_unique", "unique", "universal", "minimal"),
  rownames = pkgconfig::get_config("tibble::rownames", NULL)
)

# S3 method for data.frame
as_tibble(
  x,
  validate = NULL,
  ...,
  .rows = NULL,
  .name_repair = c("check_unique", "unique", "universal", "minimal"),
  rownames = pkgconfig::get_config("tibble::rownames", NULL)
)

# S3 method for list
as_tibble(
  x,
  validate = NULL,
  ...,
  .rows = NULL,
  .name_repair = c("check_unique", "unique", "universal", "minimal")
)

# S3 method for matrix
as_tibble(x, ..., validate = NULL, .name_repair = NULL)

# S3 method for table
as_tibble(x, `_n` = "n", ..., n = `_n`, .name_repair = "check_unique")

# S3 method for NULL
as_tibble(x, ...)

# S3 method for default
as_tibble(x, ...)

as_tibble_row(
  x,
  .name_repair = c("check_unique", "unique", "universal", "minimal")
)

as_tibble_col(x, column_name = "value")

Arguments

x

A data frame, list, matrix, or other object that could reasonably be coerced to a tibble.

...

Unused, for extensibility.

.rows

The number of rows, useful to create a 0-column tibble or just as an additional check.

.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 are unique,

• "universal": Make the names unique and syntactic

• a function: apply custom name repair (e.g., .name_repair = make.names for names in the style of base R).

• A purrr-style anonymous function, see rlang::as_function()

This argument is passed on as repair to vctrs::vec_as_names(). See there for more details on these terms and the strategies used to enforce them.

rownames

How to treat existing row names of a data frame or matrix:

• NULL: remove row names. This is the default.

• NA: keep row names.

• A string: the name of a new column. Existing rownames are transferred into this column and the row.names attribute is deleted. No name repair is applied to the new column name, even if x already contains a column of that name. Use as_tibble(rownames_to_column(...)) to safeguard against this case.

Read more in rownames.

_n, validate

For compatibility only, do not use for new code.

n

Name for count column, default: "n".

column_name

Name of the column.

Row names

The default behavior is to silently remove row names.

New code should explicitly convert row names to a new column using the rownames argument.

For existing code that relies on the retention of row names, call pkgconfig::set_config("tibble::rownames" = NA) in your script or in your package's .onLoad() function.

Life cycle

Using as_tibble() for vectors is superseded as of version 3.0.0, prefer the more expressive as_tibble_row() and as_tibble_col() variants for new code.

See also

tibble() constructs a tibble from individual columns. enframe() converts a named vector to a tibble with a column of names and column of values. Name repair is implemented using vctrs::vec_as_names().

Examples

m <- matrix(rnorm(50), ncol = 5)
colnames(m) <- c("a", "b", "c", "d", "e")
df <- as_tibble(m)

as_tibble_row(c(a = 1, b = 2))
#> # A tibble: 1 × 2
#>       a     b
#>   <dbl> <dbl>
#> 1     1     2
as_tibble_row(list(c = "three", d = list(4:5)))
#> # A tibble: 1 × 2
#>   c     d        
#>   <chr> <list>   
#> 1 three <int [2]>
as_tibble_row(1:3, .name_repair = "unique")
#> New names:
#> • `` -> `...1`
#> • `` -> `...2`
#> • `` -> `...3`
#> # A tibble: 1 × 3
#>    ...1  ...2  ...3
#>   <int> <int> <int>
#> 1     1     2     3

as_tibble_col(1:3)
#> # A tibble: 3 × 1
#>   value
#>   <int>
#> 1     1
#> 2     2
#> 3     3
as_tibble_col(
  list(c = "three", d = list(4:5)),
  column_name = "data"
)
#> # A tibble: 2 × 1
#>   data        
#>   <named list>
#> 1 <chr [1]>   
#> 2 <list [1]>