Question

我在R中使用tidyjson将json转换为数据帧。我无法返回上一级/对象

"ShipmentID" : 0031632569, "ShipmentType" : Cross-border, "ShipmentStatus" : Final, "PartyInfo" : -[ -{ "Type" : Consignee, "Code" : 0590000001, "Name" : HP Inc. C/O XPOLogistics, "Address" : -{ "AddressLine" : -[ 4000 Technology Court ] }, "City" : -{ "CityName" : Sandston, "CityCode" : -[ -{ "value" : USSAX, "Qualifier" : UN } ], "State" : VA, "CountryCode" : US, "CountryName" : United States }

Answer 1

同意@Jaap，您的问题可以使用一些工作，使其对其他用户和帮助您的人更有帮助。无论如何，tidyjson这是一个经常出现的问题。请查看我们希望尽快提交给CRAN的更新文档here。特别是，关于this issue的讨论可能会有所帮助。这是一个肯定需要改进的问题（以及spread_values /等的详细程度。spread_all是新版本的替代品，但它被dplyr＆gt打破了; 0.7目前）。

如果您想使用tidyjson的开发版，请尝试使用devtools::install_github('jeremystan/tidyjson')。无论如何，我认为您提供的问题有一个可行的解决方案。你的JSON无效，所以我采取了一些自由来清理它。像JSON Pretty Print这样的网站可以帮助确保有效的JSON结构。

library(tidyjson)
library(dplyr)

raw_json <- "{\"ShipmentID\" : \"0031632569\", \"ShipmentType\" : \"Cross-border\", \"ShipmentStatus\" : \"Final\", \"PartyInfo\" : [{ \"Type\" : \"Consignee\", \"Code\" : \"0590000001\", \"Name\" : \"HP Inc. C/O XPOLogistics\", \"Address\": { \"AddressLine\" : [ \"4000 Technology Court\" ] }, \"City\" : { \"CityName\" : \"Sandston\", \"CityCode\" : [ { \"value\" : \"USSAX\", \"Qualifier\" : \"UN\" } ], \"State\" : \"VA\", \"CountryCode\" : \"US\", \"CountryName\" : \"United States\" }}]}"

prep <- raw_json %>% 
  spread_values(
     ShipmentID = jstring(ShipmentID)
     , ShipmentType = jstring(ShipmentType)
     , ShipmentStatus = jstring(ShipmentStatus)) %>% 
  enter_object("PartyInfo") %>% 
  gather_array("PartyInfoID") %>% 
  spread_values(
    Type = jstring(Type)
    , Code = jstring(Code)
    , Name = jstring(Name)
    , CityName = jstring(City, CityName)
    , CityState = jstring(City, State)
    , CityCountryCode = jstring(City, CountryCode)
    , CityCountryName = jstring(City, CountryName)
  )

addressline <- prep %>% 
   enter_object(Address, AddressLine) %>% 
   gather_array("AddressLineID") %>% 
   append_values_string("AddressLine") %>% 
   select(document.id, AddressLineID, AddressLine)

citycode <- prep %>% 
   enter_object(City, CityCode) %>% 
   gather_array("CityCodeID") %>% 
   spread_values(
      CityCode = jstring(value)
      , CityCodeQualifier = jstring(Qualifier)) %>% 
   select(document.id, CityCodeID, CityCode, CityCodeQualifier)

prep %>% 
     left_join(addressline, by = "document.id") %>% 
     left_join(citycode, by = "document.id")
#>   document.id ShipmentID ShipmentType ShipmentStatus PartyInfoID      Type
#> 1           1 0031632569 Cross-border          Final           1 Consignee
#>         Code                     Name CityName CityState CityCountryCode
#> 1 0590000001 HP Inc. C/O XPOLogistics Sandston        VA              US
#>   CityCountryName AddressLineID           AddressLine CityCodeID CityCode
#> 1   United States             1 4000 Technology Court          1    USSAX
#>   CityCodeQualifier
#> 1                UN

无法返回到tidyjson

1 个答案: