tidyr (version 1.3.0)

pivot_longer: Pivot data from wide to long


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").


  cols_vary = "fastest",
  names_to = "name",
  names_prefix = NULL,
  names_sep = NULL,
  names_pattern = NULL,
  names_ptypes = NULL,
  names_transform = NULL,
  names_repair = "check_unique",
  values_to = "value",
  values_drop_na = FALSE,
  values_ptypes = NULL,
  values_transform = NULL



A data frame to pivot.


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


Additional arguments passed on to methods.


When pivoting cols into longer format, how should the output rows be arranged relative to their original row number?

  • "fastest", the default, keeps individual rows from cols close together in the output. This often produces intuitively ordered output when you have at least one key column from data that is not involved in the pivoting process.

  • "slowest" keeps individual columns from cols close together in the output. This often produces intuitively ordered output when you utilize all of the columns from data in the pivoting process.


A character vector specifying the new column or columns to create from the information stored in the column names of data specified by cols.

  • If length 0, or if NULL is supplied, no columns will be created.

  • If length 1, a single column will be created which will contain the column names specified by cols.

  • If length >1, multiple columns will be created. In this case, one of names_sep or names_pattern must be supplied to specify how the column names should be split. There are also two additional character values you can take advantage of:

    • NA will discard the corresponding component of the column name.

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


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

Optionally, a list of column name-prototype pairs. Alternatively, a single empty prototype can be supplied, which will be applied to all columns. 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

Optionally, a list of column name-function pairs. Alternatively, a single function can be supplied, which will be applied to all columns. 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.


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.


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.


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.


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.


Run this code
# See vignette("pivot") for examples and explanation

# Simplest case where column names are character data
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 %>%
    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. Since all columns are used in the pivoting
# process, we'll use `cols_vary` to keep values from the original columns
# close together in the output.
anscombe %>%
    cols_vary = "slowest",
    names_to = c(".value", "set"),
    names_pattern = "(.)(.)"

Run the code above in your browser using DataCamp Workspace