Pivot data from wide to long using a spec

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

pivot_longer_spec(data, spec, names_repair = "check_unique",
  values_drop_na = FALSE, values_ptypes = list())

build_longer_spec(data, cols, names_to = "name", values_to = "value",
  names_prefix = NULL, names_sep = NULL, names_pattern = NULL,
  names_ptypes = NULL)

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 column names turns into columns in the result. Must be a data frame containing character .name and .value columns.
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_drop_na	If TRUE, will drop rows that contain only NAs in the value_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.
values_ptypes	A list of of column name-prototype pairs. A prototype (or ptype for short) is a zero-length vector (like integer() or numeric()) that defines the type, class, and attributes of a vector. If not specified, the type of the columns generated from names_to will be character, and the type of the variables generated from values_to will be the common type of the input columns used to generate them.
cols	Columns to pivot into longer format. This takes a tidyselect specification.
names_to	A string specifying the name of the column to create from the data stored in the column names of data. Can be a character vector, creating multiple columns, if names_sep or names_pattern is provided.
values_to	A string specifying the name of the column to create from the data stored in cell values. If names_to is a character containing the special .value sentinel, this value will be ignored, and the name of the value column will be derived from part of the existing column names.
names_prefix	A regular expression used to remove matching text from the start of each variable name.
names_sep	If names_to contains multiple values, these arguments control how the column name is broken up. names_sep takes the same specification as separate(), and can either be a numeric vector (specifying positions to break on), or a single string (specifying a regular expression to split on). names_pattern takes the same specification as extract(), a regular expression containing matching groups (()). If these arguments does not give you enough control, use pivot_longer_spec() to create a spec object and process manually as needed.
names_pattern	If names_to contains multiple values, these arguments control how the column name is broken up. names_sep takes the same specification as separate(), and can either be a numeric vector (specifying positions to break on), or a single string (specifying a regular expression to split on). names_pattern takes the same specification as extract(), a regular expression containing matching groups (()). If these arguments does not give you enough control, use pivot_longer_spec() to create a spec object and process manually as needed.
names_ptypes	A list of of column name-prototype pairs. A prototype (or ptype for short) is a zero-length vector (like integer() or numeric()) that defines the type, class, and attributes of a vector. If not specified, the type of the columns generated from names_to will be character, and the type of the variables generated from values_to will be the common type of the input columns used to generate them.