从 R 中的 Google 地理编码 API 中提取 JSON 数据

Question

我对 Json 数据结构很陌生，因此无法从中提取数据。

这是来自存储在 csv 文件中的 Json 数据的示例行

row1)   {"results":[{"address_components":[{"long_name":"16","short_name":"16","types":["street_number"]},{"long_name":"Bhagwan Tatyasaheb Kawade Road","short_name":"BT Kawde Road","types":["route"]},{"long_name":"Palmgrove Society","short_name":"Palmgrove Society","types":["neighborhood","political"]},{"long_name":"Uday Baug","short_name":"Uday Baug","types":["political","sublocality","sublocality_level_2"]},{"long_name":"Ghorpadi","short_name":"Ghorpadi","types":["political","sublocality","sublocality_level_1"]},{"long_name":"Pune","short_name":"Pune","types":["locality","political"]},{"long_name":"Pune","short_name":"Pune","types":["administrative_area_level_2","political"]},{"long_name":"Maharashtra","short_name":"MH","types":["administrative_area_level_1","political"]},{"long_name":"India","short_name":"IN","types":["country","political"]},{"long_name":"411001","short_name":"411001","types":["postal_code"]}],"formatted_address":"16, BT Kawade Road, Palmgrove Society, Uday Baug, Ghorpadi, Pune, Maharashtra 411001, India","geometry":{"location":{"lat":18.5132611,"lng":73.907346},"location_type":"ROOFTOP","viewport":{"northeast":{"lat":18.5146100802915,"lng":73.90869498029151},"southwest":{"lat":18.51191211970849,"lng":73.90599701970851}}},"place_id":"ChIJo1QsU7nBwjsRiewRdiNc2i4","types":["street_address"]},{"address_components":[{"long_name":"Jambhulkar Mala","short_name":"Jambhulkar Mala","types":["political","sublocality","sublocality_level_2"]},{"long_name":"Wanowrie","short_name":"Wanowrie","types":["political","sublocality","sublocality_level_1"]},{"long_name":"Pune","short_name":"Pune","types":["locality","political"]},{"long_name":"Pune","short_name":"Pune","types":["administrative_area_level_2","political"]},{"long_name":"Maharashtra","short_name":"MH","types":["administrative_area_level_1","political"]},{"long_name":"India","short_name":"IN","types":["country","political"]}],"formatted_address":"Jambhulkar Mala, Wanowrie, Pune, Maharashtra, India","geometry":{"bounds":{"northeast":{"lat":18.510584,"lng":73.9071699},"southwest":{"lat":18.5064841,"lng":73.89949709999999}},"location":{"lat":18.508659,"lng":73.9029138},"location_type":"APPROXIMATE","viewport":{"northeast":{"lat":18.510584,"lng":73.9071699},"southwest":{"lat":18.5064841,"lng":73.89949709999999}}},"place_id":"ChIJJ0lCDMfBwjsRrPmUq2ZOxd8","types":["political","sublocality","sublocality_level_2"]},{"address_components":[{"long_name":"Wanowrie","short_name":"Wanowrie","types":["political","sublocality","sublocality_level_1"]},{"long_name":"Pune","short_name":"Pune","types":["locality","political"]},{"long_name":"Pune","short_name":"Pune","types":["administrative_area_level_2","political"]},{"long_name":"Maharashtra","short_name":"MH","types":["administrative_area_level_1","political"]},{"long_name":"India","short_name":"IN","types":["country","political"]}],"formatted_address":"Wanowrie, Pune, Maharashtra, India","geometry":{"bounds":{"northeast":{"lat":18.512962,"lng":73.9174169},"southwest":{"lat":18.480897,"lng":73.8890401}},"location":{"lat":18.4828904,"lng":73.9016832},"location_type":"APPROXIMATE","viewport":{"northeast":{"lat":18.512962,"lng":73.9174169},"southwest":{"lat":18.480897,"lng":73.8890401}}},"place_id":"ChIJ8TQ7l8LBwjsROUGpMh25HMw","types":["political","sublocality","sublocality_level_1"]},{"address_components":[{"long_name":"Pune","short_name":"Pune","types":["locality","political"]},{"long_name":"Prabhag 36","short_name":"Prabhag 36","types":["administrative_area_level_3","political"]},{"long_name":"Pune","short_name":"Pune","types":["administrative_area_level_2","political"]},{"long_name":"Maharashtra","short_name":"MH","types":["administrative_area_level_1","political"]},{"long_name":"India","short_name":"IN","types":["country","political"]}],"formatted_address":"Pune, Maharashtra, 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India","geometry":{"bounds":{"northeast":{"lat":18.5105018,"lng":73.9287002},"southwest":{"lat":18.4785059,"lng":73.87890030000001}},"location":{"lat":18.492095,"lng":73.90017759999999},"location_type":"APPROXIMATE","viewport":{"northeast":{"lat":18.5105018,"lng":73.9287002},"southwest":{"lat":18.4785059,"lng":73.87890030000001}}},"place_id":"ChIJ-64DzdvBwjsRKzv08MBrn18","types":["postal_code"]},{"address_components":[{"long_name":"Pune","short_name":"Pune","types":["administrative_area_level_2","political"]},{"long_name":"Maharashtra","short_name":"MH","types":["administrative_area_level_1","political"]},{"long_name":"India","short_name":"IN","types":["country","political"]}],"formatted_address":"Pune, Maharashtra, 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Baug","types":["bus_station","establishment","point_of_interest","transit_station"]},{"long_name":"Uday Baug","short_name":"Uday Baug","types":["political","sublocality","sublocality_level_2"]},{"long_name":"Ghorpadi","short_name":"Ghorpadi","types":["political","sublocality","sublocality_level_1"]},{"long_name":"Pune","short_name":"Pune","types":["locality","political"]},{"long_name":"Pune","short_name":"Pune","types":["administrative_area_level_2","political"]},{"long_name":"Maharashtra","short_name":"MH","types":["administrative_area_level_1","political"]},{"long_name":"India","short_name":"IN","types":["country","political"]},{"long_name":"411001","short_name":"411001","types":["postal_code"]}],"formatted_address":"Uday Baug, Ghorpadi, Pune, Maharashtra 411001, India","geometry":{"location":{"lat":18.510892,"lng":73.906956},"location_type":"GEOMETRIC_CENTER","viewport":{"northeast":{"lat":18.5122409802915,"lng":73.9083049802915},"southwest":{"lat":18.5095430197085,"lng":73.90560701970848}}},"place_id":"ChIJ5YdfqcDBwjsR5VJutWLCsfA","types":["bus_station","establishment","point_of_interest","transit_station"]}],"status":"OK"}
row2) {"results":[{"address_components":[{"long_name":"Canal Road","short_name":"Canal Rd","types":["route"]},{"long_name":"Empress Garden View Society","short_name":"Empress Garden View Society","types":["political","sublocality","sublocality_level_3"]},{"long_name":"Uday Baug","short_name":"Uday Baug","types":["political","sublocality","sublocality_level_2"]},{"long_name":"Ghorpadi","short_name":"Ghorpadi","types":["political","sublocality","sublocality_level_1"]},{"long_name":"Pune","short_name":"Pune","types":["locality","political"]},{"long_name":"Pune","short_name":"Pune","types":["administrative_area_level_2","political"]},{"long_name":"Maharashtra","short_name":"MH","types":["administrative_area_level_1","political"]},{"long_name":"India","short_name":"IN","types":["country","political"]},{"long_name":"411001","short_name":"411001","types":["postal_code"]}],"formatted_address":"Canal Rd, Empress Garden View Society, Uday Baug, Ghorpadi, Pune, Maharashtra 411001, India","geometry":{"bounds":{"northeast":{"lat":18.510637,"lng":73.9070137},"southwest":{"lat":18.5102785,"lng":73.9016839}},"location":{"lat":18.510445,"lng":73.90438309999999},"location_type":"GEOMETRIC_CENTER","viewport":{"northeast":{"lat":18.5118067302915,"lng":73.9070137},"southwest":{"lat":18.50910876970849,"lng":73.9016839}}},"place_id":"ChIJ88lKe8fBwjsRCRKukkS6-nM","types":["route"]},{"address_components":[{"long_name":"Jambhulkar Mala","short_name":"Jambhulkar Mala","types":["political","sublocality","sublocality_level_2"]},{"long_name":"Wanowrie","short_name":"Wanowrie","types":["political","sublocality","sublocality_level_1"]},{"long_name":"Pune","short_name":"Pune","types":["locality","political"]},{"long_name":"Pune","short_name":"Pune","types":["administrative_area_level_2","political"]},{"long_name":"Maharashtra","short_name":"MH","types":["administrative_area_level_1","political"]},{"long_name":"India","short_name":"IN","types":["country","political"]}],"formatted_address":"Jambhulkar Mala, Wanowrie, Pune, Maharashtra, 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row3) {"results":[{"address_components":[{"long_name":"Canal Road","short_name":"Canal Rd","types":["route"]},{"long_name":"Empress Garden View Society","short_name":"Empress Garden View Society","types":["political","sublocality","sublocality_level_3"]},{"long_name":"Uday Baug","short_name":"Uday Baug","types":["political","sublocality","sublocality_level_2"]},{"long_name":"Ghorpadi","short_name":"Ghorpadi","types":["political","sublocality","sublocality_level_1"]},{"long_name":"Pune","short_name":"Pune","types":["locality","political"]},{"long_name":"Pune","short_name":"Pune","types":["administrative_area_level_2","political"]},{"long_name":"Maharashtra","short_name":"MH","types":["administrative_area_level_1","political"]},{"long_name":"India","short_name":"IN","types":["country","political"]},{"long_name":"411001","short_name":"411001","types":["postal_code"]}],"formatted_address":"Canal Rd, Empress Garden View Society, Uday Baug, Ghorpadi, Pune, Maharashtra 411001, 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我正在尝试从第一次出现的地址组件中提取值

这样我的虚拟代码看起来像

我正在检查这些字符向量

 chck_list=c("street_address","street_number","route","intersection","political","country","administrative_area_level_1","administrative_area_level_2","administrative_area_level_3","administrative_area_level_4","administrative_area_level_5","colloquial_area","locality","ward","sublocality","neighborhood","premise","subpremise","postal_code","natural_feature","airport","park","point_of_interest")

这是试用码

 js <- fromJSON(as.character(json_data_df1[1:nrow(json_data_df1), 'Json_obj']))
    count_numb=list()
    Type=list()
    long_name=list()
    short_name=list()

    for(i in 1:nrows(js)){
    if(js$status=="ok"){
    count_numb[i] <- length(js[1:nrows(js)][grep("type",js$results[[1]]$address_components[[1]])]) #Counting number of times the word "type" occurs so that the loop can be iterated that many number of times.
    if(js$results[[1]]$address_components[[1]] %in% chck_list) {
    Type[i] = #print the word from the object chck_list that is present in data
    long_name[i] = #print the value of long_name from the data that corresponds to Type[i]
    short_name[i] = #print the value of short_name from the data that corresponds to Type[i]
    }
}

所以我的 o/p 看起来像

      Street_number  route                                neighborhood          sublocality_level_2     sublocality_level_1    Locality     Sub_locality_level_3             ....
1)      16            Bhagwan Tatyasaheb Kawade Road       Palmgrove Society     Uday Baug               Ghorpadi               Pune           NA                             ....
2)      NA            Canal Road                           NA                    Uday Baug               Ghorpadi               Pune         Empress Garden View Society                        
.       .                .
.       .                . 
.       .                .

P.S= json_data_df1 是我的数据框的名称；； Json_obj 是 Json 对象所在列的名称

有没有人知道我该怎么做。

任何帮助将不胜感激。

谢谢。

Answer 1

我认为您必须首先在 fromJSON 函数中使用 simplifyDataFrame = FALSE 才能将数据作为列表获取：

dat = jsonlite::fromJSON("your_example_data.json", simplifyDataFrame = FALSE)

然后使用嵌套的 lapply 接收每个子列表的三个项目（我修改 lapply() 函数考虑到您的最后评论），

res = lapply(dat, function(x) {

  lapply(x[["results"]], function(y) {

    do.call(rbind, lapply(y[['address_components']], function(z) {

      if (is.null(z)) {                            # if array is empty return NA's for all 3 output items

        c(rep(NA, 3))}

      else {

        tmp_array_type = z[['types']]               # temporarily get the json-array including "street_number", "route", "neighborhood" etc.

        if (length(tmp_array_type) == 0) {          # if array is of length 0 then return NA

          out_type = NA}

        else if (length(tmp_array_type) == 3) {     # it array is of length 3 return the 3rd item

          out_type = z[['types']][3]}

        else if (("political" %in% tmp_array_type) && length(tmp_array_type) > 1) {    # if array includes political and it's length is greater than 1 then remove political and then receive the 1st item

          tmp_array_type = tmp_array_type[-which(tmp_array_type == "political")]

          out_type = tmp_array_type[1]}

        else {

          out_type = tmp_array_type[1]                 # for all other cases return the 1st item of the array
        }

        c(out_type, z[['long_name']], z[['short_name']])
      }
    }))
  })
})

示例输出

[[1]]
[[1]][[1]]
      [,1]                          [,2]                             [,3]               
 [1,] "street_number"               "16"                             "16"               
 [2,] "route"                       "Bhagwan Tatyasaheb Kawade Road" "BT Kawde Road"    
 [3,] "neighborhood"                "Palmgrove Society"              "Palmgrove Society"
 [4,] "sublocality_level_2"         "Uday Baug"                      "Uday Baug"        
 [5,] "sublocality_level_1"         "Ghorpadi"                       "Ghorpadi"         
 [6,] "locality"                    "Pune"                           "Pune"             
 [7,] "administrative_area_level_2" "Pune"                           "Pune"             
 [8,] "administrative_area_level_1" "Maharashtra"                    "MH"               
 [9,] "country"                     "India"                          "IN"               
[10,] "postal_code"                 "411001"                         "411001"           

[[1]][[2]]
     [,1]                          [,2]              [,3]             
[1,] "sublocality_level_2"         "Jambhulkar Mala" "Jambhulkar Mala"
[2,] "sublocality_level_1"         "Wanowrie"        "Wanowrie"       
[3,] "locality"                    "Pune"            "Pune"           
[4,] "administrative_area_level_2" "Pune"            "Pune"           
[5,] "administrative_area_level_1" "Maharashtra"     "MH"             
[6,] "country"                

.....

如果您想修改 lapply() 函数或 ifelse 语句，那么可以在 here 找到一个很好的教程。

Answer 2

有一些可用的地理编码包可以让您的生活更轻松，例如我的 googleway 包，它可以查询各种 Google API 并将结果“简化”到 data.frames/lists 中。从那里您可以使用标准的 list/data.frame 子集方法提取所需的元素。

library(googleway)

addresses <- c("Bhagwan Tatyasaheb Kawade Road", "Empress Garden View Society")
apiKey <- 'your_api_key'

lst_geocode <- lapply(addresses, function(x){
    google_geocode(address = x,
                                 key = apiKey)
})

lapply(lst_geocode, function(x){
    x[['results']][['address_components']]
})

# [[1]]
# [[1]][[1]]
# long_name    short_name                                  types
# 1 Bhagwan Tatyasaheb Kawade Road BT Kawde Road                                  route
# 2                           Pune          Pune                    locality, political
# 3                           Pune          Pune administrative_area_level_2, political
# 4                    Maharashtra            MH administrative_area_level_1, political
# 5                          India            IN                     country, political
# 
# 
# [[2]]
# [[2]][[1]]
# long_name         short_name                                       types
# 1    Sopan Baug Road      Sopan Baug Rd                                       route
# 2 Sopan Baug Society Sopan Baug Society political, sublocality, sublocality_level_3
# 3        Kavade Mala        Kavade Mala political, sublocality, sublocality_level_2
# 4           Ghorpadi           Ghorpadi political, sublocality, sublocality_level_1
# 5               Pune               Pune                         locality, political
# 6               Pune               Pune      administrative_area_level_2, political
# 7        Maharashtra                 MH      administrative_area_level_1, political
# 8              India                 IN                          country, political
# 9             411001             411001                                 postal_code