sparklyr (version 1.8.5)

sdf_unnest_longer: Unnest longer

Description

Expand a struct column or an array column within a Spark dataframe into one or more rows, similar what to tidyr::unnest_longer does to an R dataframe. An index column, if included, will be 1-based if `col` is an array column.

Usage

sdf_unnest_longer(
  data,
  col,
  values_to = NULL,
  indices_to = NULL,
  include_indices = NULL,
  names_repair = "check_unique",
  ptype = list(),
  transform = list()
)

Arguments

data

The Spark dataframe to be unnested

col

The struct column to extract components from

values_to

Name of column to store vector values. Defaults to `col`.

indices_to

A string giving the name of column which will contain the inner names or position (if not named) of the values. Defaults to `col` with `_id` suffix

include_indices

Whether to include an index column. An index column will be included by default if `col` is a struct column. It will also be included if `indices_to` is not `NULL`.

names_repair

Strategy for fixing duplicate column names (the semantic will be exactly identical to that of `.name_repair` option in tibble)

ptype

Optionally, supply an R data frame prototype for the output. Each column of the unnested result will be casted based on the Spark equivalent of the type of the column with the same name within `ptype`, e.g., if `ptype` has a column `x` of type `character`, then column `x` of the unnested result will be casted from its original SQL type to StringType.

transform

Optionally, a named list of transformation functions applied

Examples

if (FALSE) {
library(sparklyr)
sc <- spark_connect(master = "local", version = "2.4.0")

# unnesting a struct column
sdf <- copy_to(
  sc,
  dplyr::tibble(
    x = 1:3,
    y = list(list(a = 1, b = 2), list(a = 3, b = 4), list(a = 5, b = 6))
  )
)

unnested <- sdf %>% sdf_unnest_longer(y, indices_to = "attr")

# unnesting an array column
sdf <- copy_to(
  sc,
  dplyr::tibble(
    x = 1:3,
    y = list(1:10, 1:5, 1:2)
  )
)

unnested <- sdf %>% sdf_unnest_longer(y, indices_to = "array_idx")
}