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Pivot data from wide to long

Source: R/pivot_longer.R
pivot_longer.Rd

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

Usage

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

Arguments

data

data.frame. The data to pivot.

cols

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

names_to

character(n). The name of the new column(s) that will contain the column names.

names_prefix

character(1). A regular expression used to remove matching text from the start of each variable name.

names_sep, names_pattern

character(1). If names_to contains multiple values, this argument controls how the column name is broken up. names_pattern takes a regular expression containing matching groups (()).

values_to

character(n). The name of the new column(s) that will contain the values of the pivoted variables.

values_drop_na

logical(1). If TRUE, will drop rows that contain only NA in the values_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.

Value

A data.frame.

Examples

wide_data <- data.frame(replicate(5, rnorm(10)))
# Customizing the names
pivot_longer(
  data = wide_data,
  cols = c(1, 2),
  names_to = "Column",
  values_to = "Numbers"
)
#>             X3         X4          X5 Column     Numbers
#> 1   1.78899235  0.9843769  0.52705132     X1  0.98154448
#> 2   1.78899235  0.9843769  0.52705132     X2 -0.19356013
#> 3   1.51424621  0.2540864 -0.31225835     X1 -1.87268571
#> 4   1.51424621  0.2540864 -0.31225835     X2 -0.87699220
#> 5   0.30775935 -1.2383525 -0.22312119     X1 -1.28509875
#> 6   0.30775935 -1.2383525 -0.22312119     X2  0.03921099
#> 7   1.09908832 -1.2077549  1.28323417     X1 -0.10970363
#> 8   1.09908832 -1.2077549  1.28323417     X2  0.32376765
#> 9  -1.68567376 -1.0256935 -0.33836661     X1 -0.95695466
#> 10 -1.68567376 -1.0256935 -0.33836661     X2  0.93877579
#> 11 -1.83765201 -1.5889537 -1.26325951     X1 -0.62069332
#> 12 -1.83765201 -1.5889537 -1.26325951     X2  0.15594255
#> 13 -0.05881348 -0.6766464 -0.07780695     X1  0.76654548
#> 14 -0.05881348 -0.6766464 -0.07780695     X2 -1.62802827
#> 15  0.17695824 -0.5129396  2.12530048     X1 -0.38745731
#> 16  0.17695824 -0.5129396  2.12530048     X2  0.71722359
#> 17  0.05000912 -0.9373457  0.40654532     X1 -1.54494665
#> 18  0.05000912 -0.9373457  0.40654532     X2 -1.25528683
#> 19  1.27183323 -0.2986913  1.12212716     X1  3.13227103
#> 20  1.27183323 -0.2986913  1.12212716     X2 -0.06333574