Package 'tibble' reference manual

Title:	Simple Data Frames
Description:	Provides a 'tbl_df' class (the 'tibble') with stricter checking and better formatting than the traditional data frame.
Authors:	Kirill Müller [aut, cre] , Hadley Wickham [aut], Romain Francois [ctb], Jennifer Bryan [ctb], RStudio [cph, fnd]
Maintainer:	Kirill Müller <[email protected]>
License:	MIT + file LICENSE
Version:	3.2.1.9044
Built:	2024-12-16 03:10:41 UTC
Source:	https://github.com/tidyverse/tibble

tibble: Simple Data Frames

Description

logo

Provides a 'tbl_df' class (the 'tibble') with stricter checking and better formatting than the traditional data frame.

Details

The tibble package provides utilities for handling tibbles, where "tibble" is a colloquial term for the S3 tbl_df class. The tbl_df class is a special case of the base data.frame class, developed in response to lessons learned over many years of data analysis with data frames.

Tibble is the central data structure for the set of packages known as the tidyverse, including dplyr, ggplot2, tidyr, and readr.

General resources:

Website for the tibble package: https://tibble.tidyverse.org
Vectors chapter in Advanced R (2nd edition), specifically the Data frames and tibbles section

Resources on specific topics:

Create a tibble: tibble(), as_tibble(), tribble(), enframe()
Inspect a tibble: print.tbl(), glimpse()
Details on the S3 tbl_df class: tbl_df
Package options: tibble_options

Author(s)

Maintainer: Kirill Müller [email protected] (ORCID)

Authors:

Hadley Wickham [email protected]

Other contributors:

Romain Francois [email protected] [contributor]
Jennifer Bryan [email protected] [contributor]
RStudio [copyright holder, funder]

Add columns to a data frame

Description

This is a convenient way to add one or more columns to an existing data frame.

Usage

add_column(
  .data,
  ...,
  .before = NULL,
  .after = NULL,
  .name_repair = c("check_unique", "unique", "universal", "minimal")
)
add_column(
  .data,
  ...,
  .before = NULL,
  .after = NULL,
  .name_repair = c("check_unique", "unique", "universal", "minimal")
)

Arguments

`.data`	Data frame to append to.
`...`	<`dynamic-dots`> Name-value pairs, passed on to `tibble()`. All values must have the same size of `.data` or size 1.
`.before`, `.after`	One-based column index or column name where to add the new columns, default: after last column.
`.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.

Examples

# add_column ---------------------------------
df <- tibble(x = 1:3, y = 3:1)

df %>% add_column(z = -1:1, w = 0)
df %>% add_column(z = -1:1, .before = "y")

# You can't overwrite existing columns
try(df %>% add_column(x = 4:6))

# You can't create new observations
try(df %>% add_column(z = 1:5))

# add_column ---------------------------------
df <- tibble(x = 1:3, y = 3:1)

df %>% add_column(z = -1:1, w = 0)
df %>% add_column(z = -1:1, .before = "y")

# You can't overwrite existing columns
try(df %>% add_column(x = 4:6))

# You can't create new observations
try(df %>% add_column(z = 1:5))

Add rows to a data frame

Description

This is a convenient way to add one or more rows of data to an existing data frame. See tribble() for an easy way to create an complete data frame row-by-row. Use tibble_row() to ensure that the new data has only one row.

add_case() is an alias of add_row().

Usage

add_row(.data, ..., .before = NULL, .after = NULL)
add_row(.data, ..., .before = NULL, .after = NULL)

Arguments

`.data`	Data frame to append to.
`...`	<`dynamic-dots`> Name-value pairs, passed on to `tibble()`. Values can be defined only for columns that already exist in `.data` and unset columns will get an `NA` value.
`.before`, `.after`	One-based row index where to add the new rows, default: after last row.

Examples

# add_row ---------------------------------
df <- tibble(x = 1:3, y = 3:1)

df %>% add_row(x = 4, y = 0)

# You can specify where to add the new rows
df %>% add_row(x = 4, y = 0, .before = 2)

# You can supply vectors, to add multiple rows (this isn't
# recommended because it's a bit hard to read)
df %>% add_row(x = 4:5, y = 0:-1)

# Use tibble_row() to add one row only
df %>% add_row(tibble_row(x = 4, y = 0))
try(df %>% add_row(tibble_row(x = 4:5, y = 0:-1)))

# Absent variables get missing values
df %>% add_row(x = 4)

# You can't create new variables
try(df %>% add_row(z = 10))
# add_row ---------------------------------
df <- tibble(x = 1:3, y = 3:1)

df %>% add_row(x = 4, y = 0)

# You can specify where to add the new rows
df %>% add_row(x = 4, y = 0, .before = 2)

# You can supply vectors, to add multiple rows (this isn't
# recommended because it's a bit hard to read)
df %>% add_row(x = 4:5, y = 0:-1)

# Use tibble_row() to add one row only
df %>% add_row(tibble_row(x = 4, y = 0))
try(df %>% add_row(tibble_row(x = 4:5, y = 0:-1)))

# Absent variables get missing values
df %>% add_row(x = 4)

# You can't create new variables
try(df %>% add_row(z = 10))

Coerce lists, matrices, and more to data frames

Description

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 class '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 class 'list'
as_tibble(
  x,
  validate = NULL,
  ...,
  .rows = NULL,
  .name_repair = c("check_unique", "unique", "universal", "minimal")
)

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

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

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

## Default S3 method:
as_tibble(x, ...)

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

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

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

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

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

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

## Default S3 method:
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.

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))
as_tibble_row(list(c = "three", d = list(4:5)))
as_tibble_row(1:3, .name_repair = "unique")

as_tibble_col(1:3)
as_tibble_col(
  list(c = "three", d = list(4:5)),
  column_name = "data"
)
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))
as_tibble_row(list(c = "three", d = list(4:5)))
as_tibble_row(1:3, .name_repair = "unique")

as_tibble_col(1:3)
as_tibble_col(
  list(c = "three", d = list(4:5)),
  column_name = "data"
)

Format a character vector

Description

Constructs a character vector that can be formatted with predefined minimum width or without width restrictions, and where the abbreviation style can be configured.

The formatting is applied when the vector is printed or formatted, and also in a tibble column.

set_char_opts() adds formatting options to an arbitrary character vector, useful for composing with other types.

Usage

char(
  x,
  ...,
  min_chars = NULL,
  shorten = c("back", "front", "mid", "abbreviate")
)

set_char_opts(
  x,
  ...,
  min_chars = NULL,
  shorten = c("back", "front", "mid", "abbreviate")
)
char(
  x,
  ...,
  min_chars = NULL,
  shorten = c("back", "front", "mid", "abbreviate")
)

set_char_opts(
  x,
  ...,
  min_chars = NULL,
  shorten = c("back", "front", "mid", "abbreviate")
)

Arguments

`x`	A character vector.
`...`	These dots are for future extensions and must be empty.
`min_chars`	The minimum width to allocate to this column, defaults to 15. The `"pillar.min_chars"` option is not consulted.
`shorten`	How to abbreviate the data if necessary: `"back"` (default): add an ellipsis at the end `"front"`: add an ellipsis at the front `"mid"`: add an ellipsis in the middle `"abbreviate"`: use `abbreviate()`

Examples

# Display as a vector:
char(letters[1:3])

# Space constraints:
rand_strings <- stringi::stri_rand_strings(10, seq(40, 22, by = -2))

# Plain character vectors get truncated if space is limited:
data_with_id <- function(id) {
  tibble(
    id,
    some_number_1 = 1, some_number_2 = 2, some_number_3 = 3,
    some_number_4 = 4, some_number_5 = 5, some_number_6 = 6,
    some_number_7 = 7, some_number_8 = 8, some_number_9 = 9
  )
}
data_with_id(rand_strings)

# Use char() to avoid or control truncation
data_with_id(char(rand_strings, min_chars = 24))
data_with_id(char(rand_strings, min_chars = Inf))
data_with_id(char(rand_strings, min_chars = 24, shorten = "mid"))

# Lorem Ipsum, one sentence per row.
lipsum <- unlist(strsplit(stringi::stri_rand_lipsum(1), "(?<=[.]) +", perl = TRUE))
tibble(
  back = char(lipsum, shorten = "back"),
  front = char(lipsum, shorten = "front"),
  mid = char(lipsum, shorten = "mid")
)
tibble(abbr = char(lipsum, shorten = "abbreviate"))

# Display as a vector:
char(letters[1:3])

# Space constraints:
rand_strings <- stringi::stri_rand_strings(10, seq(40, 22, by = -2))

# Plain character vectors get truncated if space is limited:
data_with_id <- function(id) {
  tibble(
    id,
    some_number_1 = 1, some_number_2 = 2, some_number_3 = 3,
    some_number_4 = 4, some_number_5 = 5, some_number_6 = 6,
    some_number_7 = 7, some_number_8 = 8, some_number_9 = 9
  )
}
data_with_id(rand_strings)

# Use char() to avoid or control truncation
data_with_id(char(rand_strings, min_chars = 24))
data_with_id(char(rand_strings, min_chars = Inf))
data_with_id(char(rand_strings, min_chars = 24, shorten = "mid"))

# Lorem Ipsum, one sentence per row.
lipsum <- unlist(strsplit(stringi::stri_rand_lipsum(1), "(?<=[.]) +", perl = TRUE))
tibble(
  back = char(lipsum, shorten = "back"),
  front = char(lipsum, shorten = "front"),
  mid = char(lipsum, shorten = "mid")
)
tibble(abbr = char(lipsum, shorten = "abbreviate"))

Converting vectors to data frames, and vice versa

Description

enframe() converts named atomic vectors or lists to one- or two-column data frames. For a list, the result will be a nested tibble with a column of type list. For unnamed vectors, the natural sequence is used as name column.

deframe() converts two-column data frames to a named vector or list, using the first column as name and the second column as value. If the input has only one column, an unnamed vector is returned.

Usage

enframe(x, name = "name", value = "value")

deframe(x)
enframe(x, name = "name", value = "value")

deframe(x)

Arguments

`x`	A vector (for `enframe()`) or a data frame with one or two columns (for `deframe()`).
`name`, `value`	Names of the columns that store the names and values. If `name` is `NULL`, a one-column tibble is returned; `value` cannot be `NULL`.

Value

For enframe(), a tibble with two columns (if name is not NULL, the default) or one column (otherwise).

For deframe(), a vector (named or unnamed).

Examples

enframe(1:3)
enframe(c(a = 5, b = 7))
enframe(list(one = 1, two = 2:3, three = 4:6))
deframe(enframe(3:1))
deframe(tibble(a = 1:3))
deframe(tibble(a = as.list(1:3)))
enframe(1:3)
enframe(c(a = 5, b = 7))
enframe(list(one = 1, two = 2:3, three = 4:6))
deframe(enframe(3:1))
deframe(tibble(a = 1:3))
deframe(tibble(a = as.list(1:3)))

Printing tibbles

Description

One of the main features of the tbl_df class is the printing:

Tibbles only print as many rows and columns as fit on one screen, supplemented by a summary of the remaining rows and columns.
Tibble reveals the type of each column, which keeps the user informed about whether a variable is, e.g., ⁠<chr>⁠ or ⁠<fct>⁠ (character versus factor). See vignette("types") for an overview of common type abbreviations.

Printing can be tweaked for a one-off call by calling print() explicitly and setting arguments like n and width. More persistent control is available by setting the options described in pillar::pillar_options. See also vignette("digits") for a comparison to base options, and vignette("numbers") that showcases num() and char() for creating columns with custom formatting options.

As of tibble 3.1.0, printing is handled entirely by the pillar package. If you implement a package that extends tibble, the printed output can be customized in various ways. See vignette("extending", package = "pillar") for details, and pillar::pillar_options for options that control the display in the console.

Usage

## S3 method for class 'tbl_df'
print(
  x,
  width = NULL,
  ...,
  n = NULL,
  max_extra_cols = NULL,
  max_footer_lines = NULL
)

## S3 method for class 'tbl_df'
format(
  x,
  width = NULL,
  ...,
  n = NULL,
  max_extra_cols = NULL,
  max_footer_lines = NULL
)
## S3 method for class 'tbl_df'
print(
  x,
  width = NULL,
  ...,
  n = NULL,
  max_extra_cols = NULL,
  max_footer_lines = NULL
)

## S3 method for class 'tbl_df'
format(
  x,
  width = NULL,
  ...,
  n = NULL,
  max_extra_cols = NULL,
  max_footer_lines = NULL
)

Arguments

`x`	Object to format or print.
`width`	Width of text output to generate. This defaults to `NULL`, which means use the `width` option.
`...`	These dots are for future extensions and must be empty.
`n`	Number of rows to show. If `NULL`, the default, will print all rows if less than the `print_max` option. Otherwise, will print as many rows as specified by the `print_min` option.
`max_extra_cols`	Number of extra columns to print abbreviated information for, if the width is too small for the entire tibble. If `NULL`, the `max_extra_cols` option is used. The previously defined `n_extra` argument is soft-deprecated.
`max_footer_lines`	Maximum number of footer lines. If `NULL`, the `max_footer_lines` option is used.

Examples

print(as_tibble(mtcars))
print(as_tibble(mtcars), n = 1)
print(as_tibble(mtcars), n = 3)

print(as_tibble(trees), n = 100)

print(mtcars, width = 10)

mtcars2 <- as_tibble(cbind(mtcars, mtcars), .name_repair = "unique")
print(mtcars2, n = 25, max_extra_cols = 3)


print(nycflights13::flights, max_footer_lines = 1)
print(nycflights13::flights, width = Inf)

print(as_tibble(mtcars))
print(as_tibble(mtcars), n = 1)
print(as_tibble(mtcars), n = 3)

print(as_tibble(trees), n = 100)

print(mtcars, width = 10)

mtcars2 <- as_tibble(cbind(mtcars, mtcars), .name_repair = "unique")
print(mtcars2, n = 25, max_extra_cols = 3)


print(nycflights13::flights, max_footer_lines = 1)
print(nycflights13::flights, width = Inf)

Row-wise matrix creation

Description

Create matrices laying out the data in rows, similar to matrix(..., byrow = TRUE), with a nicer-to-read syntax. This is useful for small matrices, e.g. covariance matrices, where readability is important. The syntax is inspired by tribble().

Usage

frame_matrix(...)
frame_matrix(...)

Arguments

...

<dynamic-dots> Arguments specifying the structure of a frame_matrix. Column names should be formulas, and may only appear before the data. These arguments are processed with rlang::list2() and support unquote via rlang::!! and unquote-splice via rlang::!!!.

Value

A matrix.

Examples

frame_matrix(
  ~col1, ~col2,
  1,     3,
  5,     2
)
frame_matrix(
  ~col1, ~col2,
  1,     3,
  5,     2
)

Test if the object is a tibble

Description

This function returns TRUE for tibbles or subclasses thereof, and FALSE for all other objects, including regular data frames.

Usage

is_tibble(x)
is_tibble(x)

Arguments

x

An object

Value

TRUE if the object inherits from the tbl_df class.

Build a list

Description

lst() constructs a list, similar to base::list(), but with some of the same features as tibble(). lst() builds components sequentially. When defining a component, you can refer to components created earlier in the call. lst() also generates missing names automatically.

See rlang::list2() for a simpler and faster alternative without tibble's evaluation and auto-name semantics.

Usage

lst(...)
lst(...)

Arguments

...

<dynamic-dots> A set of name-value pairs. These arguments are processed with rlang::quos() and support unquote via rlang::!! and unquote-splice via rlang::!!!. Use ⁠:=⁠ to create columns that start with a dot.

Arguments are evaluated sequentially. You can refer to previously created elements directly or using the rlang::.data pronoun. To refer explicitly to objects in the calling environment, use rlang::!! or rlang::.env, e.g. !!.data or .env$.data for the special case of an object named .data.

Value

A named list.

Examples

# the value of n can be used immediately in the definition of x
lst(n = 5, x = runif(n))

# missing names are constructed from user's input
lst(1:3, z = letters[4:6], runif(3))

a <- 1:3
b <- letters[4:6]
lst(a, b)

# pre-formed quoted expressions can be used with lst() and then
# unquoted (with !!) or unquoted and spliced (with !!!)
n1 <- 2
n2 <- 3
n_stuff <- quote(n1 + n2)
x_stuff <- quote(seq_len(n))
lst(!!!list(n = n_stuff, x = x_stuff))
lst(n = !!n_stuff, x = !!x_stuff)
lst(n = 4, x = !!x_stuff)
lst(!!!list(n = 2, x = x_stuff))
# the value of n can be used immediately in the definition of x
lst(n = 5, x = runif(n))

# missing names are constructed from user's input
lst(1:3, z = letters[4:6], runif(3))

a <- 1:3
b <- letters[4:6]
lst(a, b)

# pre-formed quoted expressions can be used with lst() and then
# unquoted (with !!) or unquoted and spliced (with !!!)
n1 <- 2
n2 <- 3
n_stuff <- quote(n1 + n2)
x_stuff <- quote(seq_len(n))
lst(!!!list(n = n_stuff, x = x_stuff))
lst(n = !!n_stuff, x = !!x_stuff)
lst(n = 4, x = !!x_stuff)
lst(!!!list(n = 2, x = x_stuff))

Tibble constructor and validator

Description

Creates or validates a subclass of a tibble. These function is mostly useful for package authors that implement subclasses of a tibble, like sf or tsibble.

new_tibble() creates a new object as a subclass of tbl_df, tbl and data.frame. This function is optimized for performance, checks are reduced to a minimum. See vctrs::new_data_frame() for details.

validate_tibble() checks a tibble for internal consistency. Correct behavior can be guaranteed only if this function runs without raising an error.

Usage

new_tibble(x, ..., nrow = NULL, class = NULL, subclass = NULL)

validate_tibble(x)
new_tibble(x, ..., nrow = NULL, class = NULL, subclass = NULL)

validate_tibble(x)

Arguments

`x`	A tibble-like object.
`...`	Name-value pairs of additional attributes.
`nrow`	The number of rows, inferred from `x` if omitted.
`class`	Subclasses to assign to the new object, default: none.
`subclass`	Deprecated, retained for compatibility. Please use the `class` argument.

Construction

For new_tibble(), x must be a list. The nrow argument may be omitted as of tibble 3.1.4. If present, every element of the list x should have vctrs::vec_size() equal to this value. (But this is not checked by the constructor). This takes the place of the "row.names" attribute in a data frame. x must have names (or be empty), but the names are not checked for correctness.

Validation

validate_tibble() checks for "minimal" names and that all columns are vectors, data frames or matrices. It also makes sure that all columns have the same length, and that vctrs::vec_size() is consistent with the data.

Examples

# The nrow argument can be omitted:
new_tibble(list(a = 1:3, b = 4:6))

# Existing row.names attributes are ignored:
try(validate_tibble(new_tibble(trees, nrow = 3)))

# The length of all columns must be compatible with the nrow argument:
try(validate_tibble(new_tibble(list(a = 1:3, b = 4:6), nrow = 2)))
# The nrow argument can be omitted:
new_tibble(list(a = 1:3, b = 4:6))

# Existing row.names attributes are ignored:
try(validate_tibble(new_tibble(trees, nrow = 3)))

# The length of all columns must be compatible with the nrow argument:
try(validate_tibble(new_tibble(list(a = 1:3, b = 4:6), nrow = 2)))

Format a numeric vector

Description

Constructs a numeric vector that can be formatted with predefined significant digits, or with a maximum or fixed number of digits after the decimal point. Scaling is supported, as well as forcing a decimal, scientific or engineering notation. If a label is given, it is shown in the header of a column.

The formatting is applied when the vector is printed or formatted, and also in a tibble column. The formatting annotation and the class survives most arithmetic transformations, the most notable exceptions are var() and sd().

set_num_opts() adds formatting options to an arbitrary numeric vector, useful for composing with other types.

Usage

num(
  x,
  ...,
  sigfig = NULL,
  digits = NULL,
  label = NULL,
  scale = NULL,
  notation = c("fit", "dec", "sci", "eng", "si"),
  fixed_exponent = NULL,
  extra_sigfig = NULL
)

set_num_opts(
  x,
  ...,
  sigfig = NULL,
  digits = NULL,
  label = NULL,
  scale = NULL,
  notation = c("fit", "dec", "sci", "eng", "si"),
  fixed_exponent = NULL,
  extra_sigfig = NULL
)
num(
  x,
  ...,
  sigfig = NULL,
  digits = NULL,
  label = NULL,
  scale = NULL,
  notation = c("fit", "dec", "sci", "eng", "si"),
  fixed_exponent = NULL,
  extra_sigfig = NULL
)

set_num_opts(
  x,
  ...,
  sigfig = NULL,
  digits = NULL,
  label = NULL,
  scale = NULL,
  notation = c("fit", "dec", "sci", "eng", "si"),
  fixed_exponent = NULL,
  extra_sigfig = NULL
)

Arguments

`x`	A numeric vector.
`...`	These dots are for future extensions and must be empty.
`sigfig`	Define the number of significant digits to show. Must be one or greater. The `"pillar.sigfig"` option is not consulted. Can't be combined with `digits`.
`digits`	Number of digits after the decimal points to show. Positive numbers specify the exact number of digits to show. Negative numbers specify (after negation) the maximum number of digits to show. With `digits = 2`, the numbers 1.2 and 1.234 are printed as 1.20 and 1.23, with `digits = -2` as 1.2 and 1.23, respectively. Can't be combined with `sigfig`.
`label`	A label to show instead of the type description.
`scale`	Multiplier to apply to the data before showing. Useful for displaying e.g. percentages. Must be combined with `label`.
`notation`	One of `"fit"`, `"dec"`, `"sci"`, `"eng"`, or `"si"`. `"fit"`: Use decimal notation if it fits and if it consumes 13 digits or less, otherwise use scientific notation. (The default for numeric pillars.) `"dec"`: Use decimal notation, regardless of width. `"sci"`: Use scientific notation. `"eng"`: Use engineering notation, i.e. scientific notation using exponents that are a multiple of three. `"si"`: Use SI notation, prefixes between `1e-24` and `1e24` are supported.
`fixed_exponent`	Use the same exponent for all numbers in scientific, engineering or SI notation. `-Inf` uses the smallest, `+Inf` the largest fixed_exponent present in the data. The default is to use varying exponents.
`extra_sigfig`	If `TRUE`, increase the number of significant digits if the data consists of numbers of the same magnitude with subtle differences.

Examples

# Display as a vector
num(9:11 * 100 + 0.5)

# Significant figures
tibble(
  x3 = num(9:11 * 100 + 0.5, sigfig = 3),
  x4 = num(9:11 * 100 + 0.5, sigfig = 4),
  x5 = num(9:11 * 100 + 0.5, sigfig = 5),
)

# Maximum digits after the decimal points
tibble(
  x0 = num(9:11 * 100 + 0.5, digits = 0),
  x1 = num(9:11 * 100 + 0.5, digits = -1),
  x2 = num(9:11 * 100 + 0.5, digits = -2),
)

# Use fixed digits and a currency label
tibble(
  usd = num(9:11 * 100 + 0.5, digits = 2, label = "USD"),
  gbp = num(9:11 * 100 + 0.5, digits = 2, label = "£"),
  chf = num(9:11 * 100 + 0.5, digits = 2, label = "SFr")
)

# Scale
tibble(
  small  = num(9:11 / 1000 + 0.00005, label = "%", scale = 100),
  medium = num(9:11 / 100 + 0.0005, label = "%", scale = 100),
  large  = num(9:11 / 10 + 0.005, label = "%", scale = 100)
)

# Notation
tibble(
  sci = num(10^(-13:6), notation = "sci"),
  eng = num(10^(-13:6), notation = "eng"),
  si  = num(10^(-13:6), notation = "si"),
  dec = num(10^(-13:6), notation = "dec")
)

# Fixed exponent
tibble(
  scimin = num(10^(-7:6) * 123, notation = "sci", fixed_exponent = -Inf),
  engmin = num(10^(-7:6) * 123, notation = "eng", fixed_exponent = -Inf),
  simin  = num(10^(-7:6) * 123, notation = "si", fixed_exponent = -Inf)
)

tibble(
  scismall = num(10^(-7:6) * 123, notation = "sci", fixed_exponent = -3),
  scilarge = num(10^(-7:6) * 123, notation = "sci", fixed_exponent = 3),
  scimax   = num(10^(-7:6) * 123, notation = "sci", fixed_exponent = Inf)
)

#' Extra significant digits
tibble(
  default = num(100 + 1:3 * 0.001),
  extra1 = num(100 + 1:3 * 0.001, extra_sigfig = TRUE),
  extra2 = num(100 + 1:3 * 0.0001, extra_sigfig = TRUE),
  extra3 = num(10000 + 1:3 * 0.00001, extra_sigfig = TRUE)
)
# Display as a vector
num(9:11 * 100 + 0.5)

# Significant figures
tibble(
  x3 = num(9:11 * 100 + 0.5, sigfig = 3),
  x4 = num(9:11 * 100 + 0.5, sigfig = 4),
  x5 = num(9:11 * 100 + 0.5, sigfig = 5),
)

# Maximum digits after the decimal points
tibble(
  x0 = num(9:11 * 100 + 0.5, digits = 0),
  x1 = num(9:11 * 100 + 0.5, digits = -1),
  x2 = num(9:11 * 100 + 0.5, digits = -2),
)

# Use fixed digits and a currency label
tibble(
  usd = num(9:11 * 100 + 0.5, digits = 2, label = "USD"),
  gbp = num(9:11 * 100 + 0.5, digits = 2, label = "£"),
  chf = num(9:11 * 100 + 0.5, digits = 2, label = "SFr")
)

# Scale
tibble(
  small  = num(9:11 / 1000 + 0.00005, label = "%", scale = 100),
  medium = num(9:11 / 100 + 0.0005, label = "%", scale = 100),
  large  = num(9:11 / 10 + 0.005, label = "%", scale = 100)
)

# Notation
tibble(
  sci = num(10^(-13:6), notation = "sci"),
  eng = num(10^(-13:6), notation = "eng"),
  si  = num(10^(-13:6), notation = "si"),
  dec = num(10^(-13:6), notation = "dec")
)

# Fixed exponent
tibble(
  scimin = num(10^(-7:6) * 123, notation = "sci", fixed_exponent = -Inf),
  engmin = num(10^(-7:6) * 123, notation = "eng", fixed_exponent = -Inf),
  simin  = num(10^(-7:6) * 123, notation = "si", fixed_exponent = -Inf)
)

tibble(
  scismall = num(10^(-7:6) * 123, notation = "sci", fixed_exponent = -3),
  scilarge = num(10^(-7:6) * 123, notation = "sci", fixed_exponent = 3),
  scimax   = num(10^(-7:6) * 123, notation = "sci", fixed_exponent = Inf)
)

#' Extra significant digits
tibble(
  default = num(100 + 1:3 * 0.001),
  extra1 = num(100 + 1:3 * 0.001, extra_sigfig = TRUE),
  extra2 = num(100 + 1:3 * 0.0001, extra_sigfig = TRUE),
  extra3 = num(10000 + 1:3 * 0.00001, extra_sigfig = TRUE)
)

Tools for working with row names

Description

While a tibble can have row names (e.g., when converting from a regular data frame), they are removed when subsetting with the [ operator. A warning will be raised when attempting to assign non-NULL row names to a tibble. Generally, it is best to avoid row names, because they are basically a character column with different semantics than every other column.

These functions allow to you detect if a data frame has row names (has_rownames()), remove them (remove_rownames()), or convert them back-and-forth between an explicit column (rownames_to_column() and column_to_rownames()). Also included is rowid_to_column(), which adds a column at the start of the dataframe of ascending sequential row ids starting at 1. Note that this will remove any existing row names.

Usage

has_rownames(.data)

remove_rownames(.data)

rownames_to_column(.data, var = "rowname")

rowid_to_column(.data, var = "rowid")

column_to_rownames(.data, var = "rowname")
has_rownames(.data)

remove_rownames(.data)

rownames_to_column(.data, var = "rowname")

rowid_to_column(.data, var = "rowid")

column_to_rownames(.data, var = "rowname")

Arguments

`.data`	A data frame.
`var`	Name of column to use for rownames.

Value

column_to_rownames() always returns a data frame. has_rownames() returns a scalar logical. All other functions return an object of the same class as the input.

Examples

# Detect row names ----------------------------------------------------
has_rownames(mtcars)
has_rownames(trees)

# Remove row names ----------------------------------------------------
remove_rownames(mtcars) %>% has_rownames()

# Convert between row names and column --------------------------------
mtcars_tbl <- rownames_to_column(mtcars, var = "car") %>% as_tibble()
mtcars_tbl
column_to_rownames(mtcars_tbl, var = "car") %>% head()

# Adding rowid as a column --------------------------------------------
rowid_to_column(trees) %>% head()

# Detect row names ----------------------------------------------------
has_rownames(mtcars)
has_rownames(trees)

# Remove row names ----------------------------------------------------
remove_rownames(mtcars) %>% has_rownames()

# Convert between row names and column --------------------------------
mtcars_tbl <- rownames_to_column(mtcars, var = "car") %>% as_tibble()
mtcars_tbl
column_to_rownames(mtcars_tbl, var = "car") %>% head()

# Adding rowid as a column --------------------------------------------
rowid_to_column(trees) %>% head()

Subsetting tibbles

Description

Accessing columns, rows, or cells via $, [[, or [ is mostly similar to regular data frames. However, the behavior is different for tibbles and data frames in some cases:

[ always returns a tibble by default, even if only one column is accessed.
Partial matching of column names with $ and [[ is not supported, and NULL is returned. For $, a warning is given.
Only scalars (vectors of length one) or vectors with the same length as the number of rows can be used for assignment.
Rows outside of the tibble's boundaries cannot be accessed.
When updating with ⁠[[<-⁠ and ⁠[<-⁠, type changes of entire columns are supported, but updating a part of a column requires that the new value is coercible to the existing type. See vctrs::vec_slice() for the underlying implementation.

Unstable return type and implicit partial matching can lead to surprises and bugs that are hard to catch. If you rely on code that requires the original data frame behavior, coerce to a data frame via as.data.frame().

Usage

## S3 method for class 'tbl_df'
x$name

## S3 method for class 'tbl_df'
x[[i, j, ..., exact = TRUE]]

## S3 method for class 'tbl_df'
x[i, j, drop = FALSE, ...]

## S3 replacement method for class 'tbl_df'
x$name <- value

## S3 replacement method for class 'tbl_df'
x[[i, j, ...]] <- value

## S3 replacement method for class 'tbl_df'
x[i, j, ...] <- value
## S3 method for class 'tbl_df'
x$name

## S3 method for class 'tbl_df'
x[[i, j, ..., exact = TRUE]]

## S3 method for class 'tbl_df'
x[i, j, drop = FALSE, ...]

## S3 replacement method for class 'tbl_df'
x$name <- value

## S3 replacement method for class 'tbl_df'
x[[i, j, ...]] <- value

## S3 replacement method for class 'tbl_df'
x[i, j, ...] <- value

Arguments

`x`	A tibble.
`name`	A name or a string.
`i`, `j`	Row and column indices. If `j` is omitted, `i` is used as column index.
`...`	Ignored.
`exact`	Ignored, with a warning.
`drop`	Coerce to a vector if fetching one column via `tbl[, j]` . Default `FALSE`, ignored when accessing a column via `tbl[j]` .
`value`	A value to store in a row, column, range or cell. Tibbles are stricter than data frames in what is accepted here.

Details

For better compatibility with older code written for regular data frames, [ supports a drop argument which defaults to FALSE. New code should use [[ to turn a column into a vector.

Examples

df <- data.frame(a = 1:3, bc = 4:6)
tbl <- tibble(a = 1:3, bc = 4:6)

# Subsetting single columns:
df[, "a"]
tbl[, "a"]
tbl[, "a", drop = TRUE]
as.data.frame(tbl)[, "a"]

# Subsetting single rows with the drop argument:
df[1, , drop = TRUE]
tbl[1, , drop = TRUE]
as.list(tbl[1, ])


# Accessing non-existent columns:
df$b
tbl$b

df[["b", exact = FALSE]]
tbl[["b", exact = FALSE]]

df$bd <- c("n", "e", "w")
tbl$bd <- c("n", "e", "w")
df$b
tbl$b


df$b <- 7:9
tbl$b <- 7:9
df$b
tbl$b

# Identical behavior:
tbl[1, ]
tbl[1, c("bc", "a")]
tbl[, c("bc", "a")]
tbl[c("bc", "a")]
tbl["a"]
tbl$a
tbl[["a"]]
df <- data.frame(a = 1:3, bc = 4:6)
tbl <- tibble(a = 1:3, bc = 4:6)

# Subsetting single columns:
df[, "a"]
tbl[, "a"]
tbl[, "a", drop = TRUE]
as.data.frame(tbl)[, "a"]

# Subsetting single rows with the drop argument:
df[1, , drop = TRUE]
tbl[1, , drop = TRUE]
as.list(tbl[1, ])


# Accessing non-existent columns:
df$b
tbl$b

df[["b", exact = FALSE]]
tbl[["b", exact = FALSE]]

df$bd <- c("n", "e", "w")
tbl$bd <- c("n", "e", "w")
df$b
tbl$b


df$b <- 7:9
tbl$b <- 7:9
df$b
tbl$b

# Identical behavior:
tbl[1, ]
tbl[1, c("bc", "a")]
tbl[, c("bc", "a")]
tbl[c("bc", "a")]
tbl["a"]
tbl$a
tbl[["a"]]

`tbl_df` class

Description

The tbl_df class is a subclass of data.frame, created in order to have different default behaviour. The colloquial term "tibble" refers to a data frame that has the tbl_df class. Tibble is the central data structure for the set of packages known as the tidyverse, including dplyr, ggplot2, tidyr, and readr.

The general ethos is that tibbles are lazy and surly: they do less and complain more than base data.frames. This forces problems to be tackled earlier and more explicitly, typically leading to code that is more expressive and robust.

Properties of `tbl_df`

Objects of class tbl_df have:

A class attribute of c("tbl_df", "tbl", "data.frame").
A base type of "list", where each element of the list has the same vctrs::vec_size().
A names attribute that is a character vector the same length as the underlying list.
A row.names attribute, included for compatibility with data.frame. This attribute is only consulted to query the number of rows, any row names that might be stored there are ignored by most tibble methods.

Behavior of `tbl_df`

How default behaviour of tibbles differs from that of data.frames, during creation and access:

Column data is not coerced. A character vector is not turned into a factor. List-columns are expressly anticipated and do not require special tricks. Internal names are never stripped from column data. Read more in tibble().
Recycling only happens for a length 1 input. Read more in vctrs::vec_recycle().
Column names are not munged, although missing names are auto-populated. Empty and duplicated column names are strongly discouraged, but the user must indicate how to resolve. Read more in vctrs::vec_as_names().
Row names are not added and are strongly discouraged, in favor of storing that info as a column. Read about in rownames.
df[, j] returns a tibble; it does not automatically extract the column inside. df[, j, drop = FALSE] is the default. Read more in subsetting.
There is no partial matching when $ is used to index by name. df$name for a nonexistent name generates a warning. Read more in subsetting.

See vignette("invariants") for a detailed description of the behavior.

Furthermore, printing and inspection are a very high priority. The goal is to convey as much information as possible, in a concise way, even for large and complex tibbles. Read more in formatting.

Build a data frame

Description

tibble() constructs a data frame. It is used like base::data.frame(), but with a couple notable differences:

The returned data frame has the class tbl_df, in addition to data.frame. This allows so-called "tibbles" to exhibit some special behaviour, such as enhanced printing. Tibbles are fully described in tbl_df.
tibble() is much lazier than base::data.frame() in terms of transforming the user's input.
- Character vectors are not coerced to factor.
- List-columns are expressly anticipated and do not require special tricks.
- Column names are not modified.
- Inner names in columns are left unchanged.
tibble() builds columns sequentially. When defining a column, you can refer to columns created earlier in the call. Only columns of length one are recycled.
If a column evaluates to a data frame or tibble, it is nested or spliced. If it evaluates to a matrix or a array, it remains a matrix or array, respectively. See examples.

tibble_row() constructs a data frame that is guaranteed to occupy one row. Vector columns are required to have size one, non-vector columns are wrapped in a list.

Usage

tibble(
  ...,
  .rows = NULL,
  .name_repair = c("check_unique", "unique", "universal", "minimal")
)

tibble_row(
  ...,
  .name_repair = c("check_unique", "unique", "universal", "minimal")
)
tibble(
  ...,
  .rows = NULL,
  .name_repair = c("check_unique", "unique", "universal", "minimal")
)

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

Arguments

...

.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.

Value

A tibble, which is a colloquial term for an object of class tbl_df. A tbl_df object is also a data frame, i.e. it has class data.frame.

Examples

# Unnamed arguments are named with their expression:
a <- 1:5
tibble(a, a * 2)

# Scalars (vectors of length one) are recycled:
tibble(a, b = a * 2, c = 1)

# Columns are available in subsequent expressions:
tibble(x = runif(10), y = x * 2)

# tibble() never coerces its inputs,
str(tibble(letters))
str(tibble(x = list(diag(1), diag(2))))

# or munges column names (unless requested),
tibble(`a + b` = 1:5)

# but it forces you to take charge of names, if they need repair:
try(tibble(x = 1, x = 2))
tibble(x = 1, x = 2, .name_repair = "unique")
tibble(x = 1, x = 2, .name_repair = "minimal")

## By default, non-syntactic names are allowed,
df <- tibble(`a 1` = 1, `a 2` = 2)
## because you can still index by name:
df[["a 1"]]
df$`a 1`
with(df, `a 1`)

## Syntactic names are easier to work with, though, and you can request them:
df <- tibble(`a 1` = 1, `a 2` = 2, .name_repair = "universal")
df$a.1

## You can specify your own name repair function:
tibble(x = 1, x = 2, .name_repair = make.unique)

fix_names <- function(x) gsub("\\s+", "_", x)
tibble(`year 1` = 1, `year 2` = 2, .name_repair = fix_names)

## purrr-style anonymous functions and constants
## are also supported
tibble(x = 1, x = 2, .name_repair = ~ make.names(., unique = TRUE))

tibble(x = 1, x = 2, .name_repair = ~ c("a", "b"))

# Tibbles can contain columns that are tibbles or matrices
# if the number of rows is compatible. Unnamed tibbled are
# spliced, i.e. the inner columns are inserted into the
# tibble under construction.
tibble(
  a = 1:3,
  tibble(
    b = 4:6,
    c = 7:9
  ),
  d = tibble(
    e = tibble(
      f = b
    )
  )
)
tibble(
  a = 1:3,
  b = diag(3),
  c = cor(trees),
  d = Titanic[1:3, , , ]
)

# Data can not contain tibbles or matrices with incompatible number of rows:
try(tibble(a = 1:3, b = tibble(c = 4:7)))

# Use := to create columns with names that start with a dot:
tibble(.dotted := 3)

# This also works, but might break in the future:
tibble(.dotted = 3)

# You can unquote an expression:
x <- 3
tibble(x = 1, y = x)
tibble(x = 1, y = !!x)

# You can splice-unquote a list of quosures and expressions:
tibble(!!!list(x = rlang::quo(1:10), y = quote(x * 2)))

# Use .data, .env and !! to refer explicitly to columns or outside objects
a <- 1
tibble(a = 2, b = a)
tibble(a = 2, b = .data$a)
tibble(a = 2, b = .env$a)
tibble(a = 2, b = !!a)
try(tibble(a = 2, b = .env$bogus))
try(tibble(a = 2, b = !!bogus))

# Use tibble_row() to construct a one-row tibble:
tibble_row(a = 1, lm = lm(Height ~ Girth + Volume, data = trees))
# Unnamed arguments are named with their expression:
a <- 1:5
tibble(a, a * 2)

# Scalars (vectors of length one) are recycled:
tibble(a, b = a * 2, c = 1)

# Columns are available in subsequent expressions:
tibble(x = runif(10), y = x * 2)

# tibble() never coerces its inputs,
str(tibble(letters))
str(tibble(x = list(diag(1), diag(2))))

# or munges column names (unless requested),
tibble(`a + b` = 1:5)

# but it forces you to take charge of names, if they need repair:
try(tibble(x = 1, x = 2))
tibble(x = 1, x = 2, .name_repair = "unique")
tibble(x = 1, x = 2, .name_repair = "minimal")

## By default, non-syntactic names are allowed,
df <- tibble(`a 1` = 1, `a 2` = 2)
## because you can still index by name:
df[["a 1"]]
df$`a 1`
with(df, `a 1`)

## Syntactic names are easier to work with, though, and you can request them:
df <- tibble(`a 1` = 1, `a 2` = 2, .name_repair = "universal")
df$a.1

## You can specify your own name repair function:
tibble(x = 1, x = 2, .name_repair = make.unique)

fix_names <- function(x) gsub("\\s+", "_", x)
tibble(`year 1` = 1, `year 2` = 2, .name_repair = fix_names)

## purrr-style anonymous functions and constants
## are also supported
tibble(x = 1, x = 2, .name_repair = ~ make.names(., unique = TRUE))

tibble(x = 1, x = 2, .name_repair = ~ c("a", "b"))

# Tibbles can contain columns that are tibbles or matrices
# if the number of rows is compatible. Unnamed tibbled are
# spliced, i.e. the inner columns are inserted into the
# tibble under construction.
tibble(
  a = 1:3,
  tibble(
    b = 4:6,
    c = 7:9
  ),
  d = tibble(
    e = tibble(
      f = b
    )
  )
)
tibble(
  a = 1:3,
  b = diag(3),
  c = cor(trees),
  d = Titanic[1:3, , , ]
)

# Data can not contain tibbles or matrices with incompatible number of rows:
try(tibble(a = 1:3, b = tibble(c = 4:7)))

# Use := to create columns with names that start with a dot:
tibble(.dotted := 3)

# This also works, but might break in the future:
tibble(.dotted = 3)

# You can unquote an expression:
x <- 3
tibble(x = 1, y = x)
tibble(x = 1, y = !!x)

# You can splice-unquote a list of quosures and expressions:
tibble(!!!list(x = rlang::quo(1:10), y = quote(x * 2)))

# Use .data, .env and !! to refer explicitly to columns or outside objects
a <- 1
tibble(a = 2, b = a)
tibble(a = 2, b = .data$a)
tibble(a = 2, b = .env$a)
tibble(a = 2, b = !!a)
try(tibble(a = 2, b = .env$bogus))
try(tibble(a = 2, b = !!bogus))

# Use tibble_row() to construct a one-row tibble:
tibble_row(a = 1, lm = lm(Height ~ Girth + Volume, data = trees))

Package options

Description

Options that affect interactive display. See pillar::pillar_options for options that affect display on the console, and cli::num_ansi_colors() for enabling and disabling colored output via ANSI sequences like ⁠[3m[38;5;246m[39m[23m⁠.

Usage

tibble_options
tibble_options

Details

These options can be set via options() and queried via getOption(). For this, add a tibble. prefix (the package name and a dot) to the option name. Example: for an option foo, use options(tibble.foo = value) to set it and getOption("tibble.foo") to retrieve the current value. An option value of NULL means that the default is used.

Options for the tibble package

view_max: Maximum number of rows shown by view() if the input is not a data frame, passed on to head(). Default: 1000.

Examples

# Default setting:
getOption("tibble.view_max")

# Change for the duration of the session:
old <- options(tibble.view_max = 100)

# view() would show only 100 rows e.g. for a lazy data frame

# Change back to the original value:
options(old)

# Local scope:
local({
  rlang::local_options(tibble.view_max = 100)
  # view() would show only 100 rows e.g. for a lazy data frame
})
# view() would show the default 1000 rows e.g. for a lazy data frame
# Default setting:
getOption("tibble.view_max")

# Change for the duration of the session:
old <- options(tibble.view_max = 100)

# view() would show only 100 rows e.g. for a lazy data frame

# Change back to the original value:
options(old)

# Local scope:
local({
  rlang::local_options(tibble.view_max = 100)
  # view() would show only 100 rows e.g. for a lazy data frame
})
# view() would show the default 1000 rows e.g. for a lazy data frame

Row-wise tibble creation

Description

Create tibbles using an easier to read row-by-row layout. This is useful for small tables of data where readability is important. Please see tibble-package for a general introduction.

Usage

tribble(...)
tribble(...)

Arguments

...

<dynamic-dots> Arguments specifying the structure of a tibble. Variable names should be formulas, and may only appear before the data. These arguments are processed with rlang::list2() and support unquote via rlang::!! and unquote-splice via rlang::!!!.

Value

A tibble.

Examples

tribble(
  ~colA, ~colB,
  "a",   1,
  "b",   2,
  "c",   3
)

# tribble will create a list column if the value in any cell is
# not a scalar
tribble(
  ~x,  ~y,
  "a", 1:3,
  "b", 4:6
)


# Use dplyr::mutate(dplyr::across(...)) to assign an explicit type
tribble(
  ~a, ~b, ~c,
  1, "2000-01-01", "1.5"
) %>%
  dplyr::mutate(
    dplyr::across(a, as.integer),
    dplyr::across(b, as.Date)
  )

tribble(
  ~colA, ~colB,
  "a",   1,
  "b",   2,
  "c",   3
)

# tribble will create a list column if the value in any cell is
# not a scalar
tribble(
  ~x,  ~y,
  "a", 1:3,
  "b", 4:6
)


# Use dplyr::mutate(dplyr::across(...)) to assign an explicit type
tribble(
  ~a, ~b, ~c,
  1, "2000-01-01", "1.5"
) %>%
  dplyr::mutate(
    dplyr::across(a, as.integer),
    dplyr::across(b, as.Date)
  )

View an object

Description

Calls utils::View() on the input and returns it, invisibly. If the input is not a data frame, it is processed using a variant of as.data.frame(head(x, n)). A message is printed if the number of rows exceeds n. This function has no effect in noninteractive sessions.

Usage

view(x, title = NULL, ..., n = NULL)
view(x, title = NULL, ..., n = NULL)

Arguments

`x`	The object to display.
`title`	The title to use for the display, by default the deparsed expression is used.
`...`	Unused, must be empty.
`n`	Maximum number of rows to display. Only used if `x` is not a data frame. Uses the `view_max` option by default.

Details

The RStudio IDE overrides utils::View(), this is picked up correctly.

Package 'tibble'

Help Index

tibble: Simple Data Frames

Description

Details

Author(s)

See Also

Add columns to a data frame

Description

Usage

Arguments

See Also

Examples

Add rows to a data frame

Description

Usage

Arguments

See Also

Examples

Coerce lists, matrices, and more to data frames

Description

Usage

Arguments

Row names

Life cycle

See Also

Examples

Format a character vector

Description

Usage

Arguments

See Also

Examples

Converting vectors to data frames, and vice versa

Description

Usage

Arguments

Value

Examples

Printing tibbles

Description

Usage

Arguments

Examples

Row-wise matrix creation

Description

Usage

Arguments

Value

See Also

Examples

Test if the object is a tibble

Description

Usage

Arguments

Value

Build a list

Description

Usage

Arguments

Value

Examples

Tibble constructor and validator

Description

Usage

Arguments

Construction

Validation

See Also

Examples

Format a numeric vector

Description

Usage

Arguments

See Also

Examples

Tools for working with row names

Description

Usage

Arguments

`tbl_df` class

Properties of `tbl_df`

Behavior of `tbl_df`