dtplyr (version 1.3.1)

pivot_wider.dtplyr_step: Pivot data from long to wide

Description

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

Usage

# S3 method for dtplyr_step
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 lazy_dt().

id_cols

<tidy-select> A set of columns that uniquely identify each observation. Typically used when you have redundant variables, i.e. variables whose values are perfectly correlated with existing variables.

Defaults to all columns in data except for the columns specified through names_from and values_from. If a tidyselect expression is supplied, it will be evaluated on data after removing the columns specified through names_from and values_from.

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 fill values to different value columns.

values_fn

A function, the default is length(). Note this is different behavior than tidyr::pivot_wider(), which returns a list column by default.

...

Additional arguments passed on to methods.

Examples

library(tidyr)

fish_encounters_dt <- lazy_dt(fish_encounters)
fish_encounters_dt
fish_encounters_dt %>%
  pivot_wider(names_from = station, values_from = seen)
# Fill in missing values
fish_encounters_dt %>%
  pivot_wider(names_from = station, values_from = seen, values_fill = 0)

# Generate column names from multiple variables
us_rent_income_dt <- lazy_dt(us_rent_income)
us_rent_income_dt
us_rent_income_dt %>%
  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_dt %>%
  pivot_wider(
    names_from = variable,
    names_sep = ".",
    values_from = c(estimate, moe)
  )

# Can perform aggregation with values_fn
warpbreaks_dt <- lazy_dt(as_tibble(warpbreaks[c("wool", "tension", "breaks")]))
warpbreaks_dt
warpbreaks_dt %>%
  pivot_wider(
    names_from = wool,
    values_from = breaks,
    values_fn = mean
  )