tidyr::pivot_longer()

Updated April 19, 2026 · Tidyverse

r tidyr data-wrangling tidy-data reshape

Overview

pivot_longer() transforms wide data into long format. It takes two or more columns and gathers them into key-value pairs: column names land in a new column, and their values land in another. This is the inverse of pivot_wider().

The function is useful when your data has columns that represent values rather than variables — a common structure in survey data, repeated measurements, and any dataset where the column header encodes information.

Signature

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

Parameters

Parameter	Type	Default	Description
`data`	tibble / data frame	—	Input data.
`cols`	tidy-select	—	Columns to pivot into longer format.
`names_to`	character	`"name"`	Name of the column that will hold the old column names.
`values_to`	character	`"value"`	Name of the column that will hold the cell values.
`names_prefix`	character	`NULL`	Strip this prefix from column names before pivoting.
`names_sep`	character	`NULL`	Separator between multiple `names_to` columns.
`names_pattern`	character	`NULL`	Regex pattern to extract column name parts for multiple `names_to`.
`names_ptypes`	named list	`NULL`	Types for the new name columns.
`values_ptypes`	named list	`NULL`	Types for the value columns.
`values_drop_na`	logical	`FALSE`	If `TRUE`, drop rows where all value columns are `NA`.

Basic Usage

Simple pivot

The most common case: you have columns that each represent a category, and you want to stack them.

library(tidyr)

df <- tibble(
  id = c("A", "B"),
  Jan = c(100, 200),
  Feb = c(150, 250),
  Mar = c(120, 220)
)

pivot_longer(df, c(Jan, Feb, Mar), names_to = "month", values_to = "sales")
# # A tibble: 6 × 3
#   id    month sales
#   <chr> <chr> <dbl>
# 1 A     Jan    100
# 2 A     Feb    150
# 3 A     Mar    120
# 4 B     Jan    200
# 5 B     Feb    250
# 6 B     Mar    220

Pivot all columns except identifier

Use the ! operator to pivot all columns except the identifier:

df <- tibble(
  id = c("A", "B"),
  Jan = c(100, 200),
  Feb = c(150, 250)
)

pivot_longer(df, !id, names_to = "month", values_to = "sales")
# # A tibble: 4 × 3
#   id    month sales
#   <chr> <chr> <dbl>
# 1 A     Jan    100
# 2 A     Feb    150
# 3 B     Jan    200
# 4 B     Feb    250

Strip prefixes from column names

Use names_prefix to remove a common prefix from column names before pivoting:

df <- tibble(
  id = c(1, 2),
  product_2023 = c(300, 450),
  product_2024 = c(320, 480)
)

pivot_longer(
  df,
  c(product_2023, product_2024),
  names_to = "year",
  values_to = "revenue",
  names_prefix = "product_"
)
# # A tibble: 4 × 3
#   id    year  revenue
#   <dbl> <chr>   <dbl>
# 1     1 2023      300
# 2     1 2024      320
# 3     2 2023      450
# 4     2 2024      480

Multiple Name Columns

When column names encode multiple variables, split them into separate columns with names_sep or names_pattern.

Using names_sep

When column names follow a consistent separator, names_sep splits on that character:

df <- tibble(
  id = c(1, 2),
  temp_2021 = c(22.1, 18.5),
  temp_2022 = c(23.4, 19.2)
)

pivot_longer(
  df,
  c(temp_2021, temp_2022),
  names_to = c("var", "year"),
  values_to = "reading",
  names_sep = "_"
)
# # A tibble: 4 × 4
#   id    var   year  reading
#   <dbl> <chr> <chr>   <dbl>
# 1     1 temp  2021     22.1
# 2     1 temp  2022     23.4
# 3     2 temp  2021     18.5
# 4     2 temp  2022     19.2

Using names_pattern

For irregular column name structures, use a regex with capture groups:

df <- tibble(
  id = c(1, 2),
  Alice_score = c(85, 92),
  Bob_score = c(78, 88)
)

pivot_longer(
  df,
  c(Alice_score, Bob_score),
  names_to = "student",
  values_to = "score",
  names_pattern = "(.+)_.+"
)
# # A tibble: 4 × 3
#   id    student score
#   <dbl> <chr>   <dbl>
# 1     1 Alice      85
# 2     1 Bob        78
# 3     2 Alice      92
# 4     2 Bob        88

For two capture groups:

df <- tibble(
  site = c("A", "B"),
  alpha_2021 = c(10, 20),
  beta_2021 = c(15, 25)
)

pivot_longer(
  df,
  c(alpha_2021, beta_2021),
  names_to = c("site_code", "year"),
  values_to = "measurement",
  names_pattern = "(.+)_(.+)"
)
# # A tibble: 4 × 4
#   site  site_code year  measurement
#   <chr> <chr>     <chr>        <dbl>
# 1 A     alpha     2021            10
# 2 A     beta      2021            15
# 3 B     alpha     2021            20
# 4 B     beta      2021            25

Multiple Value Columns

By default, all pivoted columns share the same values_to column. To split values across multiple columns, provide a named vector with .value as a placeholder:

df <- tibble(
  id = c(1, 2),
  group_low_n = c(10, 20),
  group_high_n = c(5, 8),
  group_low_mean = c(2.1, 3.4),
  group_high_mean = c(5.6, 6.2)
)

pivot_longer(
  df,
  c(group_low_n, group_high_n, group_low_mean, group_high_mean),
  names_to = c("group", ".value"),
  values_to = c("n", "mean"),
  names_pattern = "group_(.+)_(.+)"
)
# # A tibble: 4 × 4
#   id    group     n     mean
#   <dbl> <chr> <dbl>   <dbl>
# 1     1 low       10     2.1
# 2     1 high       5     5.6
# 3     2 low       20     3.4
# 4     2 high       8     6.2

The .value placeholder tells pivot_longer to create separate value columns from the matched groups.

Gotchas and Edge Cases

values_drop_na

Setting values_drop_na = TRUE removes rows where the pivoted values are all NA. This is useful after a join or for sparse data:

df <- tibble(
  id = c(1, 2),
  x = c(1, NA),
  y = c(NA, 2)
)

pivot_longer(df, c(x, y), names_to = "var", values_to = "val", values_drop_na = TRUE)
# # A tibble: 2 × 3
#   id    var     val
#   <dbl> <chr> <dbl>
# 1     1 x         1
# 2     2 y         2

NA in original data

If a cell in the original data is NA, that NA propagates to the pivoted row:

df <- tibble(id = c("A", "B"), x = c(1, NA), y = c(NA, 4))

pivot_longer(df, c(x, y), names_to = "col", values_to = "val")
# # A tibble: 4 × 3
#   id    col     val
#   <chr> <chr> <dbl>
# 1 A     x         1
# 2 A     y        NA
# 3 B     x        NA
# 4 B     y         4

Specifying column types

Use names_ptypes and values_ptypes to enforce types on the new columns:

df <- tibble(
  id = c(1, 2),
  x_2021 = c("1", "2"),
  x_2022 = c("3", "4")
)

pivot_longer(
  df,
  c(x_2021, x_2022),
  names_to = "year",
  values_to = "val",
  names_ptypes = list(year = factor()),
  values_ptypes = list(val = double())
)