R: Pivot data from long to wide

pivot_wider {tidyr}

R Documentation

Pivot data from long to wide

Description

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

Learn more in vignette("pivot").

Usage

pivot_wider(
  data,
  id_cols = NULL,
  names_from = name,
  names_prefix = "",
  names_sep = "_",
  names_repair = "check_unique",
  values_from = value,
  values_fill = NULL,
  values_fn = NULL
)

Arguments

`data`	A data frame to pivot.
`id_cols`	A set of columns that uniquely identifies each observation. Defaults to all columns in `data` except for the columns specified in `names_from` and `values_from`. Typically used when you have additional variables that is directly related.
`names_from, values_from`	A pair of arguments describing which column (or columns) to get the name of the output column (`name_from`), and which column (or columns) to get the cell values from (`values_from`). If `values_from` contains multiple values, the value will be added to the front of the output column.
`names_prefix`	String added to the start of every variable name. This is particularly useful if `names_from` is a numeric vector and you want to create syntactic variable names.
`names_sep`	If `names_from` or `values_from` contains multiple variables, this will be used to join their values together into a single string to use as a column name.
`names_repair`	What happen 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.
`values_fill`	Optionally, a named list specifying what each `value` should be filled in with when missing.
`values_fn`	Optionally, a named list providing a function that will be applied to the `value` in each cell in the output. You will typically use this when the combination of `id_cols` and `value` column does not uniquely identify an observation.

Details

pivot_wider() is an updated approach to spread(), designed to be both simpler to use and to handle more use cases. We recomend you use pivot_wider() for new code; spread() isn't going away but is no longer under active development.

Examples

# See vignette("pivot") for examples and explanation

fish_encounters
fish_encounters %>%
  pivot_wider(names_from = station, values_from = seen)
# Fill in missing values
fish_encounters %>%
  pivot_wider(
    names_from = station,
    values_from = seen,
    values_fill = list(seen = 0)
  )

# Generate column names from multiple variables
us_rent_income %>%
  pivot_wider(names_from = variable, values_from = c(estimate, moe))

# Can perform aggregation with values_fn
warpbreaks <- as_tibble(warpbreaks[c("wool", "tension", "breaks")])
warpbreaks
warpbreaks %>%
  pivot_wider(
    names_from = wool,
    values_from = breaks,
    values_fn = list(breaks = mean)
  )