I am reading a JSON object from Salesforce. The object is irregular in the sense that some nested arrays are empty and some are not. How to deal with this in tidyjson?
I am setting up an API with Salesforce in R. The objective is to get meaningful data out of Salesforce to process in R.
json <- '
{
"totalSize": [
355710
],
"done": [
false
],
"nextRecordsUrl": [
"/services/data/v45.0/query/01gc000001L8zdkAAB-749"
],
"records": [
{
"attributes": {
"type": "Order_Line__c",
"url": "/services/data/v45.0/sobjects/Order_Line__c/a0T1N000009aZ9lUAE"
},
"Id": "a0T1N000009aZ9lUAE",
"Name": "OrderLine-1099369",
"SO_Number_Formula__c": "548402-2.3",
"Ship_From_Inventory__c": "XXX",
"RMA_Number__c": "548402",
"Part_Number__c": "01t1N00000JNeAQQA1",
"Marketing_Part__c": "XXXXXXXXXXX",
"Family__c": "XXXXXXXX",
"Serial_Numbers__r": {
"records": {}
}
},
{
"attributes": {
"type": "Order_Line__c",
"url": "/services/data/v45.0/sobjects/Order_Line__c/a0T1N000009aZ9mUAE"
},
"Id": "a0T1N000009aZ9mUAE",
"Name": "OrderLine-1099370",
"SO_Number_Formula__c": "962816-1.1",
"Ship_From_Inventory__c": "XXX",
"RMA_Number__c": "962816",
"Part_Number__c": "01t1N00000JNc3qQAD",
"Marketing_Part__c": "XXXXXXXXXX",
"Family__c": "XXXXXXX",
"RMA_Received_Date__c": "2019-02-18",
"Serial_Numbers__r": {
"totalSize": 1,
"done": true,
"records": [
{
"attributes": {
"type": "Serial_Number__c",
"url": "/services/data/v45.0/sobjects/Serial_Number__c/a0X1N00000NoyAjUAJ"
},
"Id": "a0X1N00000NoyAjUAJ",
"Name": "SN217426",
"Legacy_Line_Id__c": "962816SN217426",
"Customer_Name__c": "XXXXXX",
"Original_Shipment_Date__c": "2018-06-26",
"Disposition__c": "Pending",
"Status__c": "FailureVerification"
}
]
}
}
]
}
'
mydata <- json %>%
as.tbl_json %>%
enter_object("records") %>%
gather_array() %>%
spread_values(
Id = jstring("Id"),
Name = jstring("Name"),
SO_Number_Formula = jstring("SO_Number_Formula__c"),
Ship_From_Inventory = jstring("Ship_From_Inventory__c"),
RMA_Number = jstring("RMA_Number__c"),
Part_Number = jstring("Part_Number__c"),
Marketing_Part = jstring("Marketing_Part__c"),
Family = jstring("Family__c")) %>%
enter_object("Serial_Numbers__r") %>%
enter_object("records") %>%
gather_ %>%
spread_values(
Id = jstring("Id"))
The irregularity is in the [records][Serial_Numbers__r][records]. In this example, the first occurrence is empty {} and the second occurrence is not empty. The code generates the following error when executing gather_keys or gather _array: Error in gather_keys(.) : 1 records are values not objects Error in gather_array(.) : 1 records are values not arrays
I am thinking that this is caused by the empty array [records]. There is plenty of such irregularity in the Salesforce output: some records have detailed nested data, some do not. How can I deal with this?
This is a fantastic question, and is something that we should really have a cleaner way to handle. enter_object() proves problematic in these types of cases where you lose records based on irregular JSON practices.
I submitted an issue to track improvements here: https://github.com/colearendt/tidyjson/issues/121
In the meantime, the way I typically do this is by splitting records based on the characteristic that delineates them. In this case, you can use gather_object() on the parent object to get the same effect as enter_object(), and then use filter / bind_rows to treat the rows differently.
Ideally bind_rows() would work better in the pipe here... that is something I would like to see as an improvement for dplyr (Issue here)! I'm curious to see if this solves your problem! (Also, keep spread_all() in mind to simplify some of the column specifying at a cost of some "guessing" on the package's part!).
json <- '{
"totalSize": [
355710
],
"done": [
false
],
"nextRecordsUrl": [
"/services/data/v45.0/query/01gc000001L8zdkAAB-749"
],
"records": [
{
"attributes": {
"type": "Order_Line__c",
"url": "/services/data/v45.0/sobjects/Order_Line__c/a0T1N000009aZ9lUAE"
},
"Id": "a0T1N000009aZ9lUAE",
"Name": "OrderLine-1099369",
"SO_Number_Formula__c": "548402-2.3",
"Ship_From_Inventory__c": "XXX",
"RMA_Number__c": "548402",
"Part_Number__c": "01t1N00000JNeAQQA1",
"Marketing_Part__c": "XXXXXXXXXXX",
"Family__c": "XXXXXXXX",
"Serial_Numbers__r": {
"records": {}
}
},
{
"attributes": {
"type": "Order_Line__c",
"url": "/services/data/v45.0/sobjects/Order_Line__c/a0T1N000009aZ9mUAE"
},
"Id": "a0T1N000009aZ9mUAE",
"Name": "OrderLine-1099370",
"SO_Number_Formula__c": "962816-1.1",
"Ship_From_Inventory__c": "XXX",
"RMA_Number__c": "962816",
"Part_Number__c": "01t1N00000JNc3qQAD",
"Marketing_Part__c": "XXXXXXXXXX",
"Family__c": "XXXXXXX",
"RMA_Received_Date__c": "2019-02-18",
"Serial_Numbers__r": {
"totalSize": 1,
"done": true,
"records": [
{
"attributes": {
"type": "Serial_Number__c",
"url": "/services/data/v45.0/sobjects/Serial_Number__c/a0X1N00000NoyAjUAJ"
},
"Id": "a0X1N00000NoyAjUAJ",
"Name": "SN217426",
"Legacy_Line_Id__c": "962816SN217426",
"Customer_Name__c": "XXXXXX",
"Original_Shipment_Date__c": "2018-06-26",
"Disposition__c": "Pending",
"Status__c": "FailureVerification"
}
]
}
}
]
}
'
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tidyr)
library(tidyjson)
#>
#> Attaching package: 'tidyjson'
#> The following object is masked from 'package:dplyr':
#>
#> bind_rows
#> The following object is masked from 'package:stats':
#>
#> filter
prep_data <- json %>%
as.tbl_json %>%
enter_object("records") %>%
gather_array() %>%
spread_values(
Id = jstring("Id"),
Name = jstring("Name"),
SO_Number_Formula = jstring("SO_Number_Formula__c"),
Ship_From_Inventory = jstring("Ship_From_Inventory__c"),
RMA_Number = jstring("RMA_Number__c"),
Part_Number = jstring("Part_Number__c"),
Marketing_Part = jstring("Marketing_Part__c"),
Family = jstring("Family__c")) %>%
enter_object("Serial_Numbers__r")
# show that types are different
prep_data %>%
gather_object("key") %>%
json_types() %>%
select(key, type) %>%
filter(key == "records")
#> # A tbl_json: 2 x 2 tibble with a "JSON" attribute
#> `attr(., "JSON")` key type
#> <chr> <chr> <fct>
#> 1 "{}" records object
#> 2 "[{\"attributes\":..." records array
# handle
taller <- prep_data %>%
gather_object("key") %>%
json_types("type") %>%
filter(key == "records")
final <- tidyjson::bind_rows(
taller %>% filter(type == "object"),
taller %>% filter(type == "array") %>%
gather_array("record_row") %>%
spread_values(
RecordId = jstring("Id")
)
)
final %>% select(key, type, record_row, RecordId)
#> # A tbl_json: 2 x 4 tibble with a "JSON" attribute
#> `attr(., "JSON")` key type record_row RecordId
#> <chr> <chr> <fct> <int> <chr>
#> 1 "{}" records object NA <NA>
#> 2 "{\"attributes\":{..." records array 1 a0X1N00000NoyAjUAJ
Created on 2020-03-15 by the reprex package (v0.3.0)