什么是'；记录是值而不是对象'；tidyjson中的平均值

根据tidyjson对象，以下字符串中的json是正确的json：

library(dplyr)
library(tidyjson)

json <- '
    [{"country":"us","city":"Portland","topics":[{"urlkey":"videogame","name":"Video Games","id":4471},{"urlkey":"board-games","name":"Board Games","id":19585},{"urlkey":"computer-programming","name":"Computer programming","id":48471},{"urlkey":"opensource","name":"Open Source","id":563}],"joined":1416349237000,"link":"http://www.meetup.com/members/156440062","bio":"Analytics engineer.  Primarily work in the Hadoop space.","lon":-122.65,"other_services":{},"name":"Aaron Wirick","visited":1443078098000,"self":{"common":{}},"id":156440062,"state":"OR","lat":45.56,"status":"active"}]
    '
    json %>% as.tbl_json %>% gather_keys

---

按照我的想法，

as.tbl_json

生成的对象有点奇怪，单个项的名称为

document.id

，值为1。在它的属性中，有一个叫做

JSON

：

json <- '
     [{"country":"us","city":"Portland","topics":[{"urlkey":"videogame","name":"Video Games","id":4471},{"urlkey":"board-games","name":"Board Games","id":19585},{"urlkey":"computer-programming","name":"Computer programming","id":48471},{"urlkey":"opensource","name":"Open Source","id":563}],"joined":1416349237000,"link":"http://www.meetup.com/members/156440062","bio":"Analytics engineer.  Primarily work in the Hadoop space.","lon":-122.65,"other_services":{},"name":"Aaron Wirick","visited":1443078098000,"self":{"common":{}},"id":156440062,"state":"OR","lat":45.56,"status":"active"}]
     '
    obj <-  json %>% as.tbl_json

> dput(obj)
structure(list(document.id = 1L), .Names = "document.id", row.names = 1L, class = c("tbl_json", 
"tbl", "data.frame"), JSON = list(list(structure(list(country = "us", 
    city = "Portland", topics = list(structure(list(urlkey = "videogame", 
        name = "Video Games", id = 4471L), .Names = c("urlkey", 
    "name", "id")), structure(list(urlkey = "board-games", name = "Board Games", 
        id = 19585L), .Names = c("urlkey", "name", "id")), structure(list(
        urlkey = "computer-programming", name = "Computer programming", 
        id = 48471L), .Names = c("urlkey", "name", "id")), structure(list(
        urlkey = "opensource", name = "Open Source", id = 563L), .Names = c("urlkey", 
    "name", "id"))), joined = 1416349237000, link = "http://www.meetup.com/members/156440062", 
    bio = "Analytics engineer.  Primarily work in the Hadoop space.", 
    lon = -122.65, other_services = structure(list(), .Names = character(0)), 
    name = "Aaron Wirick", visited = 1443078098000, self = structure(list(
        common = structure(list(), .Names = character(0))), .Names = "common"), 
    id = 156440062L, state = "OR", lat = 45.56, status = "active"), .Names = c("country", 
"city", "topics", "joined", "link", "bio", "lon", "other_services", 
"name", "visited", "self", "id", "state", "lat", "status")))))

---

希望我能帮上更多的忙，但至少你知道错误是从哪里来的。（我也希望该软件包的帮助页面上有更多的示例。）

如其中一条评论中所述，

聚集键

正在查找具有数组的对象。您可能应该在这里使用的是

gather\u array

此外，另一个答案使用更为暴力的方法来解析tidyjson包创建的JSON属性。如果需要，tidyjson提供了在更干净的管道中处理此问题的方法：

library(dplyr)
library(tidyjson)

json <- '
[{"country":"us","city":"Portland"
,"topics":[
 {"urlkey":"videogame","name":"Video Games","id":4471}
 ,{"urlkey":"board-games","name":"Board Games","id":19585}
 ,{"urlkey":"computer-programming","name":"Computer programming","id":48471}
 ,{"urlkey":"opensource","name":"Open Source","id":563}
]
,"joined":1416349237000
,"link":"http://www.meetup.com/members/156440062"
,"bio":"Analytics engineer.  Primarily work in the Hadoop space."
,"lon":-122.65,"other_services":{}
,"name":"Aaron Wirick","visited":1443078098000
,"self":{"common":{}}
,"id":156440062,"state":"OR","lat":45.56,"status":"active"
}]
'

mydf <- json %>% as.tbl_json %>% gather_array %>% 
spread_values(
 country=jstring('country')
 , city=jstring('city')
 , joined=jnumber('joined')
 , bio=jstring('bio')
) %>% 
enter_object('topics') %>% 
gather_array %>%
spread_values(urlkey=jstring('urlkey'))

库（dplyr）
库（tidyjson）
json%gather_数组%>%
传播价值观(
country=jstring（'country'）
，city=jstring（'city'））
，joined=jnumber（'joined'）
，bio=jstring（'bio'）
) %>% 
输入\u对象（“主题”）%%>%
聚集数组%>%
排列值（urlkey=jstring（'urlkey'））

---

如果阵列中有多个这样的对象，则此管道确实会发光。希望这是有益的，即使很长时间后的事实

%%>%

？@pascal-当然是dplyr@Pascal-它在标题中-

tidyjson

package

%%>%

来自magrittr，可能由dplyr或tidyjson中的一个或两个导入。@42-是的，但在问题中指定使用的包时更容易。我问的时候不是这样的，谢谢！这给我的诊断工具包增加了一个窍门，让我看看引擎盖下面，尽管它不能解决我的问题

dput

提供了比仅使用

str

更多的信息。太好了，它很有帮助！

 names( attr(obj, "JSON")[[1]][[1]] )
#------------
 [1] "country"        "city"           "topics"         "joined"         "link"          
 [6] "bio"            "lon"            "other_services" "name"           "visited"       
[11] "self"           "id"             "state"          "lat"            "status"    

library(dplyr)
library(tidyjson)

json <- '
[{"country":"us","city":"Portland"
,"topics":[
 {"urlkey":"videogame","name":"Video Games","id":4471}
 ,{"urlkey":"board-games","name":"Board Games","id":19585}
 ,{"urlkey":"computer-programming","name":"Computer programming","id":48471}
 ,{"urlkey":"opensource","name":"Open Source","id":563}
]
,"joined":1416349237000
,"link":"http://www.meetup.com/members/156440062"
,"bio":"Analytics engineer.  Primarily work in the Hadoop space."
,"lon":-122.65,"other_services":{}
,"name":"Aaron Wirick","visited":1443078098000
,"self":{"common":{}}
,"id":156440062,"state":"OR","lat":45.56,"status":"active"
}]
'

mydf <- json %>% as.tbl_json %>% gather_array %>% 
spread_values(
 country=jstring('country')
 , city=jstring('city')
 , joined=jnumber('joined')
 , bio=jstring('bio')
) %>% 
enter_object('topics') %>% 
gather_array %>%
spread_values(urlkey=jstring('urlkey'))