Pivot data from wide to long — pivot

pivot_longer() "lengthens" the data, increasing the number of rows and decreasing the number of columns.

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

Arguments

.df: A data.table or data.frame
cols: Columns to pivot. tidyselect compatible.
names_to: Name of the new "names" column. Must be a string.
values_to: Name of the new "values" column. Must be a string.
names_prefix: Remove matching text from the start of selected columns using regex.
names_sep: If names_to contains multiple values, names_sep takes the same specification as separate().
names_pattern: If names_to contains multiple values, names_pattern takes the same specification as extract(), a regular expression containing matching groups.
names_ptypes, values_ptypes: A list of column name-prototype pairs. See ``?vctrs::`theory-faq-coercion``` for more info on vctrs coercion.
names_transform, values_transform: A list of column name-function pairs. Use these arguments if you need to change the types of specific columns.
names_repair: Treatment of duplicate names. See ?vctrs::vec_as_names for options/details.
values_drop_na: If TRUE, rows will be dropped that contain NAs.
fast_pivot: experimental: Fast pivoting. If TRUE, the names_to column will be returned as a factor, otherwise it will be a character column. Defaults to FALSE to match tidyverse semantics.
...: Additional arguments to passed on to methods.

Examples

df <- data.table(
  x = 1:3,
  y = 4:6,
  z = c("a", "b", "c")
)

df %>%
  pivot_longer(cols = c(x, y))
#> # A tidytable: 6 × 3
#>   z     name  value
#>   <chr> <chr> <int>
#> 1 a     x         1
#> 2 b     x         2
#> 3 c     x         3
#> 4 a     y         4
#> 5 b     y         5
#> 6 c     y         6

df %>%
  pivot_longer(cols = -z, names_to = "stuff", values_to = "things")
#> # A tidytable: 6 × 3
#>   z     stuff things
#>   <chr> <chr>  <int>
#> 1 a     x          1
#> 2 b     x          2
#> 3 c     x          3
#> 4 a     y          4
#> 5 b     y          5
#> 6 c     y          6