pivot_wider_spec: Pivot data from long to wide using a spec

In tidyverse/tidyr: Tidy Messy Data

Pivot data from long to wide using a spec

Description

This is a low level interface to pivoting, inspired by the cdata package, that allows you to describe pivoting with a data frame.

Usage

pivot_wider_spec(
  data,
  spec,
  ...,
  names_repair = "check_unique",
  id_cols = NULL,
  id_expand = FALSE,
  values_fill = NULL,
  values_fn = NULL,
  unused_fn = NULL,
  error_call = current_env()
)

build_wider_spec(
  data,
  ...,
  names_from = name,
  values_from = value,
  names_prefix = "",
  names_sep = "_",
  names_glue = NULL,
  names_sort = FALSE,
  names_vary = "fastest",
  names_expand = FALSE,
  error_call = current_env()
)

Arguments

data	A data frame to pivot.
spec	A specification data frame. This is useful for more complex pivots because it gives you greater control on how metadata stored in the columns become column names in the result. Must be a data frame containing character .name and .value columns. Additional columns in spec should be named to match columns in the long format of the dataset and contain values corresponding to columns pivoted from the wide format. The special .seq variable is used to disambiguate rows internally; it is automatically removed after pivoting.
...	These dots are for future extensions and must be empty.
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.
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 spec$.value and the columns of the spec that aren't named .name or .value. Typically used when you have redundant variables, i.e. variables whose values are perfectly correlated with existing variables.
id_expand	Should the values in the id_cols columns be expanded by expand() before pivoting? This results in more rows, the output will contain a complete expansion of all possible values in id_cols. Implicit factor levels that aren't represented in the data will become explicit. Additionally, the row values corresponding to the expanded id_cols will be sorted.
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 fill values 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 names_from columns does not uniquely identify an observation. This can be a named list if you want to apply different aggregations to different values_from columns.
unused_fn	Optionally, a function applied to summarize the values from the unused columns (i.e. columns not identified by id_cols, names_from, or values_from). The default drops all unused columns from the result. This can be a named list if you want to apply different aggregations to different unused columns. id_cols must be supplied for unused_fn to be useful, since otherwise all unspecified columns will be considered id_cols. This is similar to grouping by the id_cols then summarizing the unused columns using unused_fn.
error_call	The execution environment of a currently running function, e.g. caller_env(). The function will be mentioned in error messages as the source of the error. See the call argument of abort() for more information.
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_vary	When names_from identifies a column (or columns) with multiple unique values, and multiple values_from columns are provided, in what order should the resulting column names be combined? "fastest" varies names_from values fastest, resulting in a column naming scheme of the form: ⁠value1_name1, value1_name2, value2_name1, value2_name2⁠. This is the default. "slowest" varies names_from values slowest, resulting in a column naming scheme of the form: ⁠value1_name1, value2_name1, value1_name2, value2_name2⁠.
names_expand	Should the values in the names_from columns be expanded by expand() before pivoting? This results in more columns, the output will contain column names corresponding to a complete expansion of all possible values in names_from. Implicit factor levels that aren't represented in the data will become explicit. Additionally, the column names will be sorted, identical to what names_sort would produce.

Examples

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

us_rent_income
spec1 <- us_rent_income %>%
  build_wider_spec(names_from = variable, values_from = c(estimate, moe))
spec1

us_rent_income %>%
  pivot_wider_spec(spec1)

# Is equivalent to
us_rent_income %>%
  pivot_wider(names_from = variable, values_from = c(estimate, moe))

# `pivot_wider_spec()` provides more control over column names and output format
# instead of creating columns with estimate_ and moe_ prefixes,
# keep original variable name for estimates and attach _moe as suffix
spec2 <- tibble(
  .name = c("income", "rent", "income_moe", "rent_moe"),
  .value = c("estimate", "estimate", "moe", "moe"),
  variable = c("income", "rent", "income", "rent")
)

us_rent_income %>%
  pivot_wider_spec(spec2)

tidyverse/tidyr documentation built on April 13, 2025, 11:51 a.m.