tidyr: pivot_wider_spec – R documentation

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pivot_wider_spec

Pivot data from long to wide using a spec

Description

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

Usage

pivot_wider_spec(
  data,
  spec,
  names_repair = "check_unique",
  id_cols = NULL,
  values_fill = NULL,
  values_fn = NULL
)

build_wider_spec(
  data,
  names_from = name,
  values_from = value,
  names_prefix = "",
  names_sep = "_",
  names_glue = NULL,
  names_sort = FALSE
)

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 pivotting.
`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 `names_from` and `values_from`. Typically used when you have redundant variables, i.e. variables whose values are perfectly correlated with existing variables.
`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.
`names_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.
`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.

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)

tidyr

Tidy Messy Data

v1.1.3

MIT + file LICENSE

Authors

Hadley Wickham [aut, cre], RStudio [cph]

Initial release