tidyr (version 1.1.3)

pivot_wider: 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_glue = NULL,
  names_sort = FALSE,
  names_repair = "check_unique",
  values_from = value,
  values_fill = NULL,
  values_fn = NULL,
  ...
)

Arguments

data

A data frame to pivot.

id_cols

<tidy-select> 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 redundant variables, i.e. variables whose values are perfectly correlated with existing variables.

names_from, values_from

<tidy-select> A pair of arguments describing which column (or columns) to get the name of the output column (names_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_glue

Instead of names_sep and names_prefix, you can supply a glue specification that uses the names_from columns (and special .value) to create custom column names.

names_sort

Should the column names be sorted? If FALSE, the default, column names are ordered by first appearance.

names_repair

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, or "unique" to de-duplicated by adding numeric suffixes. See vctrs::vec_as_names() for more options.

values_fill

Optionally, a (scalar) value that specifies what each value should be filled in with when missing.

This can be a named list if you want to apply different aggregations to different value columns.

values_fn

Optionally, a function 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.

This can be a named list if you want to apply different aggregations to different value columns.

...

Additional arguments passed on to methods.

Details

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

See Also

pivot_wider_spec() to pivot "by hand" with a data frame that defines a pivotting specification.

Examples

# NOT RUN {
# 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 = 0)

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

# When there are multiple `names_from` or `values_from`, you can use
# use `names_sep` or `names_glue` to control the output variable names
us_rent_income %>%
  pivot_wider(
    names_from = variable,
    names_sep = ".",
    values_from = c(estimate, moe)
  )
us_rent_income %>%
  pivot_wider(
    names_from = variable,
    names_glue = "{variable}_{.value}",
    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 = mean
  )
# }