Trouble spreading values using tidyjson - json

Question

I am trying to convert the following multi-document JSON file into a data.frame.
x = '[
{"name": "Bob","groupIds": ["kwt6x61", "yiahf43"]},
{"name": "Sally","groupIds": "yiahf43"}
]'
I'm almost there by using
y = x %>% gather_array() %>%
spread_values(
name = jstring("name"),
groupIds = jstring("groupIds")
)
print(y)
Which returns:
document.id array.index name groupIds
1 1 1 Bob list("kwt6x61", "yiahf43")
2 1 2 Sally yiahf43
Can someone help spread the groupsIds into addtional rows?

Answer 1

This is an interesting problem. The issue stems from the fact that an array of 1 is stored as a string. Otherwise, enter_object('groupIds') %>% gather_array %>% append_values_string would work nicely. tidyjson does not seem to handle this situation nicely. I wonder whether this would even be considered valid JSON, since in one case groupIds is a string, and in another it is an array.
In any case, although this is not an ideal solution, you can use json_types() to illustrate the difference and then conditionally treat each. I converted to a tbl_df (i.e. dropped JSON component) for future processing when done parsing.
library(tidyjson)
library(dplyr)
library(tidyr)
x = '[
{"name": "Bob","groupIds": ["kwt6x61", "yiahf43"]},
{"name": "Sally","groupIds": "yiahf43"}
]'
## Show the different types
z <- x %>% gather_array() %>% spread_values(
name=jstring('name')
) %>% enter_object('groupIds') %>% json_types()
## Conditionally treat each
final <- bind_rows(
z[z$type=='array',] %>% gather_array('id') %>% append_values_string('groupId')
, z[z$type=='string',] %>% append_values_string('groupId') %>% mutate(id=1)
) %>% tbl_df
## Spread them out, maybe? Depends on what you're looking for
final %>% spread('id','groupId')