tidyr: pivot_longer – R documentation

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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 `NA`s 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

# 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 = "(.)(.)"
 )

tidyr

Tidy Messy Data

v1.1.3

MIT + file LICENSE

Authors

Hadley Wickham [aut, cre], RStudio [cph]

Initial release