Flatten a struct column within a Spark dataframe into one or more columns, similar what to tidyr::unnest_wider does to an R dataframe

sdf_unnest_wider(
  data,
  col,
  names_sep = 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

names_sep

If `NULL`, the default, the names will be left as is. If a string, the inner and outer names will be pasted together using `names_sep` as the delimiter.

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 to each component (e.g., list(`x = as.character`) to cast column `x` to String).

Examples

if (FALSE) { library(sparklyr) sc <- spark_connect(master = "local", version = "2.4.0") sdf <- copy_to( sc, tibble::tibble( x = 1:3, y = list(list(a = 1, b = 2), list(a = 3, b = 4), list(a = 5, b = 6)) ) ) # flatten struct column 'y' into two separate columns 'y_a' and 'y_b' unnested <- sdf %>% sdf_unnest_wider(y, names_sep = "_") }