Unnest wider


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


  names_sep = NULL,
  names_repair = "check_unique",
  ptype = list(),
  transform = list()


Argument Description
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).


sc <- spark_connect(master = "local", version = "2.4.0")

sdf <- copy_to(
    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 = "_")