tidyjson (version 0.2.4)

spread_values: Spreads specific scalar values of a JSON object into new columns

Description

The spread_values function lets you extract extract specific values from (potentiall nested) JSON objects. spread_values takes jstring, jnumber or jlogical named function calls as arguments in order to specify the type of the data that should be captured at each desired name-value pair location. These values can be of varying types at varying depths.

Usage

spread_values(.x, ...)

Arguments

.x

a json string or tbl_json object

...

column = value pairs where column will be the column name created and value must be a call to jstring, jnumber or jlogical specifying the path to get the value (and the type implicit in the function name)

Value

a tbl_json object

Details

Note that jstring, jnumber and jlogical will fail if they encounter the incorrect type in any document.

The advantage of spread_values over spread_all is that you are guaranteed to get a consistent data frame structure (columns and types) out of any spread_values call. spread_all requires less typing, but because it infers the columns and their types from the JSON, it is less suitable when programming.

See Also

spread_all for spreading all values, spread for spreading data frames, jstring, jnumber, jlogical for accessing specific names

Examples

# NOT RUN {
# A simple example
json <- '{"name": {"first": "Bob", "last": "Jones"}, "age": 32}'

# Using spread_values
json %>%
  spread_values(
    first.name = jstring(name, first),
    last.name  = jstring(name, last),
    age        = jnumber(age)
  )

# Another document, this time with a middle name (and no age)
json2 <- '{"name": {"first": "Ann", "middle": "A", "last": "Smith"}}'

# spread_values still gives the same column structure
c(json, json2) %>%
  spread_values(
    first.name = jstring(name, first),
    last.name  = jstring(name, last),
    age        = jnumber(age)
  )

# whereas spread_all adds a new column
json %>% spread_all
c(json, json2) %>% spread_all
# }