Add exit_obeject() functionality

[bogdanCsn]

In a nested json, it would be useful to be able to exit an object you have entered - a function that does the opposite of enter_object(). Or perhaps change the functionality of enter_object() so that full / relative paths are supported, for example "." would refer to the top level while ".." would refer to the previous level. Here is some code example:

library(tidyjson)
library(dplyr)


input <- '{
      "name": "Bob",
      "age": 30,
      "social": {
            "married": "yes",
            "kids": "no"
      },
      "work": {
            "title": "engineer",
            "salary": 5000
      } 
}'


output <- input %>% as.tbl_json() %>%
      spread_values(name = jstring("name"),
                    age = jnumber("age")) %>%
      enter_object("social") %>% 
      spread_values(married = jstring("married"),
                    kids = jstring("kids")) %>%
      #### I would need an exit_obeject() here
      enter_object("work") %>%
      spread_values(title = jstring("title"),
                    salary = jnumber("salary"))

[jeffwong-nflx]

Bump on this feature!

[arochettesteinberg]

It's not even a feature it's a real problem if nobody can tell me how to actually get around this? How to access several nested objects within a json object, this happens often it's not something exotic.

[timwilliate]

+1 on this feature. It would make parsing multiple nested objects possible from a single pipeline

[rpalloni]

+1 function absolutely needed. Or parallel pipes to get data from multiple objects. Json is harder than tidyjson examples...

[pgensler]

+1 this is definitely needed. @arochettesteinberg the only way I know how to use the enter object is to use it as the last argument when piping, especially when working with json lite.

[colearendt]

Please see discussion here. If you can provide a REPREX there, that would be fantastic. The jstring('social','married') solution using a more complex path, as discussed there, solves the example above as well.

Explicitly:

output <- input %>% as.tbl_json() %>%
      spread_values(name = jstring("name"),
                    age = jnumber("age")) %>%
      spread_values(married = jstring("social","married"),
                    kids = jstring("social","kids")) %>%
      spread_values(title = jstring("work","title"),
                    salary = jnumber("work","salary"))

[pgensler]

@robpalgit Is this example a good example of some of the data you run into? This is jsonlite, but I think an example like this might help to better reflect how to use tidyjson appropriate to parse this data. This work to parse out most of the data, but the spread_values for each element makes sense to me, as it is a separate object. Otherwise, you would have to do something like THIS approach, where you put every single command into a massive pipeline, which seems a bit unwieldy to me:

colearendt/tidyjson#95

#One interesting thing to note is that I am using the spread_values command at the end of the chain sequence, which diden't seem like it was possible based on the vignette

pacman::p_load("tidyjson","magrittr")
###Something to note is that the \\t must be escaped for R, AND the string must be in '' to parse properly
##data is jsonlite format
poop1 <- '{"review/appearance": 2.5,"beer/style": "Hefeweizen", "review/palate": 1.5, "review/taste": 1.5, "beer/name": "Sausa Weizen", "review/timeUnix": 1234817823, "beer/ABV": 5.0, "beer/beerId": "47986", "beer/brewerId": "10325", "review/timeStruct": {"isdst": 0, "mday": 16, "hour": 20, "min": 57, "sec": 3, "mon": 2, "year": 2009, "yday": 47, "wday": 0}, "review/overall": 1.5, "review/text": "A lot of foam. But a lot In the smell some banana, and then lactic and tart. Not a good start.\\tQuite dark orange in color, with a lively carbonation (now visible, under the foam).\\tAgain tending to lactic sourness.\\tSame for the taste. With some yeast and banana.", "user/profileName": "stcules", "review/aroma": 2.0}'
poop2 <- '{"review/appearance": 3.0, "beer/style": "English Strong Ale", "review/palate": 3.0, "review/taste": 3.0, "beer/name": "Red Moon", "review/timeUnix": 1235915097, "beer/ABV": 6.2, "beer/beerId": "48213", "beer/brewerId": "10325", "review/timeStruct": {"isdst": 0, "mday": 1, "hour": 13, "min": 44, "sec": 57, "mon": 3, "year": 2009, "yday": 60, "wday": 6}, "review/overall": 3.0, "review/text": "Dark red color, light beige foam, average.\\tIn the smell malt and caramel, not really light.\\tAgain malt and caramel in the taste, not bad in the end.\\tMaybe a note of honey in teh back, and a light fruitiness.\\tAverage body.\\tIn the aftertaste a light bitterness, with the malt and red fruit.\\tNothing exceptional, but not bad, drinkable beer.", "user/profileName": "stcules", "review/aroma": 2.5}'

clean  <- poop2 %>%
  spread_values(
    review_appearance = jnumber("review/appearance"),
    beer_style = jstring("beer/style"),
    review_palate = jnumber("review/palate"),
    review_taste = jnumber("review/taste"),
    beer_name = jstring("beer/name"),
    review_time = jstring("review/timeUnix"),
    beer_ABV = jstring("beer/ABV"),
    beer_beerid = jnumber("beer/beerId"),
    beer_breweryid = jstring("beer/brewerId"),
    review_overall = jnumber("review/overall"),
    review_text = jstring("review/text"),
    profile_name = jstring("user/profileName"),
    review_aroma = jnumber("review/aroma")
  ) %>% 
  spread_values(
    isdst = jnumber("isdst"),
    mday = jnumber("mday"),
    hour = jnumber("hour"),
    min = jnumber("min"),
    sec = jnumber("sec"),
    mon = jnumber("mon"),
    year = jnumber("year"),
    yday = jnumber("yday"),
    wday = jnumber("wday")
  )

[ghost]

An exit_object() feature would be especially valuable, particularly if one needs to enter a nested repeating structure, then exit out and enter another one to obtain all the data. Currently this is not possible without workarounds.

[colearendt]

@morebento If you can come up with a minimal use case / reprex, please post it here. At present, most of our use cases seem to revolve around better articulating the use of complex paths like json %>% spread_values(test=jnumber('one','two')) to pull information out of nested objects. The other piece of functionality worth looking at are ways to reduce the amount of typing, which can presently be done using the development version function spread_all:

devtools::install_github('jeremystan/tidyjson')
library(tidyjson)

json <- '{"a":1,"b":"test","c":3}'
json %>% spread_all()

Thanks!

[rpalloni]

@pgensler Attached an example of the deeply nested json object used.
flights.txt

Here the "parsing story"...
https://www.linkedin.com/pulse/web-data-acquisition-understanding-rcurl-from-command-roberto-palloni
https://www.linkedin.com/pulse/web-data-acquisition-structure-rcurl-request-part-2-roberto-palloni
https://www.linkedin.com/pulse/web-data-acquisition-parsing-json-objects-tidyjson-part-palloni
https://www.linkedin.com/pulse/web-data-acquisition-from-database-dataframe-analysis-roberto-palloni