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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,
  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

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. If of length > 1, one of names_sep or names_pattern must be supplied. Two values have special meaning: NA discards the corresponding piece of the column name and ".value" indicates that the corresponding piece of the column name defines the name of the output column containing the values, overriding values_to.

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_sep is a fixed string on which to split the column names. names_pattern takes a regular expression containing matching groups (()); with a single value of names_to, the first matching group is used.

names_ptypes, values_ptypes

list(). Optionally, a named list of column name-prototype pairs, e.g. list(week = factor()), used to cast the newly created columns. Casts that lose information are an error.

names_transform, values_transform

list(). Optionally, a named list of column name-function pairs, e.g. list(week = as.integer), applied to the newly created columns. Functions may be given as formulas, e.g. ~ as.integer(.x).

names_repair

character(1) or function. 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, "unique" to de-duplicate by adding numeric suffixes or "universal" to also make the names syntactic. A function is applied to the names.

values_to

character(1). The name of the new column that will contain the values of the pivoted variables. The pivoted columns are combined by these rules: logical, integer and double columns are combined into the richest of those types, factors are combined into a factor with the union of their levels, factors and characters are combined into a character and columns of the same class keep that class. Other combinations are an error.

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