GoogleジオコーディングAPIからのJSONデータの抽出R

Question

Jsonのデータ構造が新しく、データを抽出できません。

この

はcsvファイルに格納されたJSONデータからのサンプル行です

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私はアドレスコンポーネントの最初の発生から値を抽出しようとしている

ように私のダミーコードは

のように見える私は、これらの文字ベクトルのため

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") 

をチェックしています

これは、誰もが、私はそれのために行く方法の任意のアイデアを持っていますので、私のO/pが

 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 is the name of my data frame;; Json_obj is the name of the column in which Json object is present

ようになりトライアルコード

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] 
    } 
} 

です。

ご協力いただければ幸いです。

ありがとうございました。

Answer 1

私はあなたがリストとしてデータを取得するためにfromJSON機能にsimplifyDataFrame = FALSEを使用する最初のを持っていると思う：

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

その後の3つの項目を受け取るために、ネストされたlapplyを使用各サブリストは、（私ははアカウントに最後のコメントを取っlapply（）関数の修正）

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']]) 
     } 
    })) 
    }) 
}) 

あなたはlapply（）関数またはifelse文を変更したい場合は

出力例

[[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"     

..... 

は、良いチュートリアルではhere見つけることができます。

Answer 2

さまざまなGoogle APIをクエリして結果をdata.frames/listsにクエリするgooglewayパッケージなど、あなたの生活が楽になるジオコーディングパッケージがいくつかあります。そこから、標準のlist/data.frameサブセット化メソッドを使用して、必要な要素を抽出できます。

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