pivot_longer.dtplyr_step {dtplyr}

Pivot data from wide to long

Description

This is a method for the tidyr pivot_longer() generic. It is translated to data.table::melt()

Usage

## S3 method for class 'dtplyr_step'
pivot_longer(
  data,
  cols,
  names_to = "name",
  names_prefix = NULL,
  names_sep = NULL,
  names_pattern = NULL,
  names_ptypes = NULL,
  names_transform = NULL,
  names_repair = "check_unique",
  values_to = "value",
  values_drop_na = FALSE,
  values_ptypes = NULL,
  values_transform = NULL,
  ...
)

Arguments

data

A lazy_dt().

cols

<tidy-select> Columns to pivot into longer format.

names_to

A character vector specifying the new column or columns to create from the information stored in the column names of data specified by cols.

• If length 0, or if NULL is supplied, no columns will be created.

• If length 1, a single column will be created which will contain the column names specified by cols.

• If length >1, multiple columns will be created. In this case, one of names_sep or names_pattern must be supplied to specify how the column names should be split. There are also two additional character values you can take advantage of:

  • NA will discard the corresponding component of the column name.

  • ".value" indicates that the corresponding component of the column name defines the name of the output column containing the cell values, overriding values_to entirely.

names_prefix

A regular expression used to remove matching text from the start of each variable name.

names_sep, 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 do not give you enough control, use pivot_longer_spec() to create a spec object and process manually as needed.

names_ptypes, names_transform, values_ptypes, values_transform

Not currently supported by dtplyr.

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_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.

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.

...

Additional arguments passed on to methods.

Examples

library(tidyr)

# Simplest case where column names are character data
relig_income_dt <- lazy_dt(relig_income)
relig_income_dt %>%
  pivot_longer(!religion, names_to = "income", values_to = "count")

# Slightly more complex case where columns have common prefix,
# and missing missings are structural so should be dropped.
billboard_dt <- lazy_dt(billboard)
billboard %>%
 pivot_longer(
   cols = starts_with("wk"),
   names_to = "week",
   names_prefix = "wk",
   values_to = "rank",
   values_drop_na = TRUE
 )

# Multiple variables stored in column names
lazy_dt(who) %>%
  pivot_longer(
    cols = new_sp_m014:newrel_f65,
    names_to = c("diagnosis", "gender", "age"),
    names_pattern = "new_?(.*)_(.)(.*)",
    values_to = "count"
  )

# Multiple observations per row
anscombe_dt <- lazy_dt(anscombe)
anscombe_dt %>%
 pivot_longer(
   everything(),
   names_to = c(".value", "set"),
   names_pattern = "(.)(.)"
 )