tidyr (version 1.1.3)

pivot_longer: Pivot data from wide to long

Description

pivot_longer() "lengthens" data, increasing the number of rows and decreasing the number of columns. The inverse transformation is pivot_wider()

Learn more in vignette("pivot").

Usage

pivot_longer(
  data,
  cols,
  names_to = "name",
  names_prefix = NULL,
  names_sep = NULL,
  names_pattern = NULL,
  names_ptypes = list(),
  names_transform = list(),
  names_repair = "check_unique",
  values_to = "value",
  values_drop_na = FALSE,
  values_ptypes = list(),
  values_transform = list(),
  ...
)

Arguments

data

A data frame to pivot.

cols

<tidy-select> Columns to pivot into longer format.

names_to

A string specifying the name of the column to create from the data stored in the column names of data.

Can be a character vector, creating multiple columns, if names_sep or names_pattern is provided. In this case, there are two special values you can take advantage of:

  • NA will discard that component of the name.

  • .value indicates that component of the name defines the name of the column containing the cell values, overriding values_to.

names_prefix

A regular expression used to remove matching text from the start of each variable name.

names_sep, names_pattern

If names_to contains multiple values, these arguments control how the column name is broken up.

names_sep takes the same specification as separate(), and can either be a numeric vector (specifying positions to break on), or a single string (specifying a regular expression to split on).

names_pattern takes the same specification as extract(), a regular expression containing matching groups (()).

If these arguments do not give you enough control, use pivot_longer_spec() to create a spec object and process manually as needed.

names_ptypes, values_ptypes

A list of column name-prototype pairs. A prototype (or ptype for short) is a zero-length vector (like integer() or numeric()) that defines the type, class, and attributes of a vector. Use these arguments if you want to confirm that the created columns are the types that you expect. Note that if you want to change (instead of confirm) the types of specific columns, you should use names_transform or values_transform instead.

names_transform, values_transform

A list of column name-function pairs. Use these arguments if you need to change the types of specific columns. For example, names_transform = list(week = as.integer) would convert a character variable called week to an integer.

If not specified, the type of the columns generated from names_to will be character, and the type of the variables generated from values_to will be the common type of the input columns used to generate them.

names_repair

What happens if the output has invalid column names? The default, "check_unique" is to error if the columns are duplicated. Use "minimal" to allow duplicates in the output, or "unique" to de-duplicated by adding numeric suffixes. See vctrs::vec_as_names() for more options.

values_to

A string specifying the name of the column to create from the data stored in cell values. If names_to is a character containing the special .value sentinel, this value will be ignored, and the name of the value column will be derived from part of the existing column names.

values_drop_na

If TRUE, will drop rows that contain only NAs in the value_to column. This effectively converts explicit missing values to implicit missing values, and should generally be used only when missing values in data were created by its structure.

...

Additional arguments passed on to methods.

Details

pivot_longer() is an updated approach to gather(), designed to be both simpler to use and to handle more use cases. We recommend you use pivot_longer() for new code; gather() isn't going away but is no longer under active development.

Examples

# NOT RUN {
# See vignette("pivot") for examples and explanation

# Simplest case where column names are character data
relig_income
relig_income %>%
  pivot_longer(!religion, names_to = "income", values_to = "count")

# Slightly more complex case where columns have common prefix,
# and missing missings are structural so should be dropped.
billboard
billboard %>%
 pivot_longer(
   cols = starts_with("wk"),
   names_to = "week",
   names_prefix = "wk",
   values_to = "rank",
   values_drop_na = TRUE
 )

# Multiple variables stored in column names
who %>% pivot_longer(
  cols = new_sp_m014:newrel_f65,
  names_to = c("diagnosis", "gender", "age"),
  names_pattern = "new_?(.*)_(.)(.*)",
  values_to = "count"
)

# Multiple observations per row
anscombe
anscombe %>%
 pivot_longer(everything(),
   names_to = c(".value", "set"),
   names_pattern = "(.)(.)"
 )
# }