我正在从Salesforce中读取JSON对象。从某种意义上说，该对象是不规则的，因为有些嵌套数组是空的，有些不是。如何在tidyjson中处理此问题？
我正在使用R中的Salesforce设置API。目标是从Salesforce中获取有意义的数据以在R中进行处理。
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": "SYD",
"RMA_Number__c": "548402",
"Part_Number__c": "01t1N00000JNeAQQA1",
"Marketing_Part__c": "10WSLU09FLL_R6",
"Family__c": "WSS-DWP50",
"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": "SYD",
"RMA_Number__c": "962816",
"Part_Number__c": "01t1N00000JNc3qQAD",
"Marketing_Part__c": "10WSHW09FLL",
"Family__c": "WSS-DWP50",
"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": "HUAWEI",
"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"))
不规则之处在[记录] [Serial_Numbers__r] [记录]中。在此示例中，第一次出现为空{}，第二次出现不为空。 代码在执行collect_keys或collect _array时会产生以下错误： collect_keys（。）中的错误：1个记录是值而不是对象 collect_array（。）中的错误：1个记录是值而不是数组
我认为这是由空数组[records]引起的。 Salesforce输出中存在很多此类不规则性：某些记录具有详细的嵌套数据，而有些则没有。 我该如何处理？
答案 0 :(得分：1)
这是一个奇妙的问题，我们应该真正采用一种更清洁的方式来处理。在这类情况下，enter_object()会造成问题，因为您会根据不规则的JSON做法丢失记录。
我在这里提交了一个问题来跟踪改进情况：https://github.com/colearendt/tidyjson/issues/121
同时，我通常这样做的方法是根据描绘记录的特征来分割记录。在这种情况下，可以在父对象上使用gather_object()来获得与enter_object()相同的效果，然后使用filter / bind_rows来区别行。
理想情况下，bind_rows()在这里的管道中会更好...这是我想作为dplyr (Issue here)的改进！我很好奇这是否能解决您的问题！ （此外，请记住spread_all()，以简化某些指定的列，但要花一些“猜测”的代价！）。
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
由reprex package（v0.3.0）于2020-03-15创建