json_unnest_wider: Unnest a JSON object into columns
In jsontools: Working with JSON Vectors

Description Usage Arguments Value See Also Examples

Unnest a column of JSON objects in a data frame producing a wider data frame.

json_unnest_wider(
  data,
  col,
  ptype = list(),
  names_sort = FALSE,
  names_sep = NULL,
  names_repair = "check_unique",
  wrap_scalars = FALSE,
  bigint_as_char = bigint_default()
)

`data`	A data frame.
`col`	JSON-column of arrays to extract components from.
`ptype`	Output type. If `NULL`, the default, the output type is determined by computing the common type across all elements. Use `new_json_array()` resp. `new_json_object()` if you know every element is an array resp. object. Mind that the return type will only be `json2`.
`names_sort`	Should the extracted columns be sorted by name? If `FALSE`, the default, the columns are sorted by appearance.
`names_sep`	If `NULL`, the default, the keys of the objects in `col` are used as the column names. If a character it is used to join `col` and the object keys.
`names_repair`	What happens if the output has invalid column names?
`wrap_scalars`	A named list of `TRUE` or `FALSE` specifying for each field whether to wrap scalar values in a JSON array. Unspecified fields are not wrapped. This can also be a single value of `TRUE` or `FALSE` that is then used for every field. Note that scalars are only wrapped if either `ptype` is `new_json_array()` or `json2` vector. `ptype` is `NULL` and the elements are a mix of scalar values and arrays.
`bigint_as_char`	Convert big integers to character? The option `jsontools.bigint_as_char` is used as default.

A data frame, or subclass of data frame of the same length as data.

json_unnest_longer()

# turn all components of item into columns with json_unnest_wider()
tibble::tibble(
  id = 1:2,
  x = c(
    '{"name": "Peter", "age": 19}',
    '{"age": 37}'
  )
) %>%
  json_unnest_wider(x)

# sort names and specify proto types
tibble::tibble(
  id = 1:2,
  x = c(
    '{"name": "Peter", "age": 19, "purchase_ids": [1, 2]}',
    '{"age": 37, "purchase_ids": []}'
  )
) %>%
  json_unnest_wider(
    x,
    ptype = list(
      age = integer(),
      name = character(),
      purchase_id = new_json_array()
    ),
    names_sort = TRUE
  )