从 R 中的 Google 地理编码 API 中提取 JSON 数据
Extracting JSON data from Google geocoding API in R
我对 Json 数据结构很陌生，因此无法从中提取数据。
这是存储在 csv 文件
中的 Json 数据的示例行
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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, 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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 is the name of my data frame;; Json_obj is the
name of the column in which Json object is present
有谁知道我该怎么做。
如有任何帮助，我们将不胜感激。
谢谢。
我认为您必须先在 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 来接收每个子列表的三个项目（我 修改了 lapply() 函数，考虑到你的 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，可以找到一个很好的教程 here。
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，它查询各种 Google API 并将结果 'simplifies' 转换为 data.frames /列出。从那里您可以使用标准 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
我对 Json 数据结构很陌生，因此无法从中提取数据。
这是存储在 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, India","geometry":{"bounds":{"northeast":{"lat":18.6357545,"lng":73.9864569},"southwest":{"lat":18.4134784,"lng":73.7394779}},"location":{"lat":18.5204303,"lng":73.8567437},"location_type":"APPROXIMATE","viewport":{"northeast":{"lat":18.6357545,"lng":73.9864569},"southwest":{"lat":18.4134784,"lng":73.7394779}}},"place_id":"ChIJARFGZy6_wjsRQ-Oenb9DjYI","types":["locality","political"]},{"address_components":[{"long_name":"411040","short_name":"411040","types":["postal_code"]},{"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":"Pune, Maharashtra 411040, 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, India","geometry":{"bounds":{"northeast":{"lat":19.38404,"lng":75.16309},"southwest":{"lat":17.89324,"lng":73.32352}},"location":{"lat":18.6832564,"lng":74.0300122},"location_type":"APPROXIMATE","viewport":{"northeast":{"lat":19.38404,"lng":75.1447465},"southwest":{"lat":17.89324,"lng":73.32352}}},"place_id":"ChIJQ97RPE_AwjsR5zbDDbo3wHI","types":["administrative_area_level_2","political"]},{"address_components":[{"long_name":"Maharashtra","short_name":"MH","types":["administrative_area_level_1","political"]},{"long_name":"India","short_name":"IN","types":["country","political"]}],"formatted_address":"Maharashtra, India","geometry":{"bounds":{"northeast":{"lat":22.028441,"lng":80.890924},"southwest":{"lat":15.6024121,"lng":72.659363}},"location":{"lat":19.7514798,"lng":75.7138884},"location_type":"APPROXIMATE","viewport":{"northeast":{"lat":22.0279091,"lng":80.890924},"southwest":{"lat":15.6024121,"lng":72.659363}}},"place_id":"ChIJ-dacnB7EzzsRtk_gS5IiLxs","types":["administrative_area_level_1","political"]},{"address_components":[{"long_name":"India","short_name":"IN","types":["country","political"]}],"formatted_address":"India","geometry":{"bounds":{"northeast":{"lat":35.5087008,"lng":97.39535869999999},"southwest":{"lat":6.4626999,"lng":68.1097}},"location":{"lat":20.593684,"lng":78.96288},"location_type":"APPROXIMATE","viewport":{"northeast":{"lat":35.5087008,"lng":97.39498069999999},"southwest":{"lat":6.7535159,"lng":68.16288519999999}}},"place_id":"ChIJkbeSa_BfYzARphNChaFPjNc","types":["country","political"]},{"address_components":[{"long_name":"Uday Baug","short_name":"Uday 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, 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, 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India","geometry":{"bounds":{"northeast":{"lat":18.6357545,"lng":73.9864569},"southwest":{"lat":18.4134784,"lng":73.7394779}},"location":{"lat":18.5204303,"lng":73.8567437},"location_type":"APPROXIMATE","viewport":{"northeast":{"lat":18.6357545,"lng":73.9864569},"southwest":{"lat":18.4134784,"lng":73.7394779}}},"place_id":"ChIJARFGZy6_wjsRQ-Oenb9DjYI","types":["locality","political"]},{"address_components":[{"long_name":"411040","short_name":"411040","types":["postal_code"]},{"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":"Pune, Maharashtra 411040, 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India","geometry":{"bounds":{"northeast":{"lat":22.028441,"lng":80.890924},"southwest":{"lat":15.6024121,"lng":72.659363}},"location":{"lat":19.7514798,"lng":75.7138884},"location_type":"APPROXIMATE","viewport":{"northeast":{"lat":22.0279091,"lng":80.890924},"southwest":{"lat":15.6024121,"lng":72.659363}}},"place_id":"ChIJ-dacnB7EzzsRtk_gS5IiLxs","types":["administrative_area_level_1","political"]},{"address_components":[{"long_name":"India","short_name":"IN","types":["country","political"]}],"formatted_address":"India","geometry":{"bounds":{"northeast":{"lat":35.5087008,"lng":97.39535869999999},"southwest":{"lat":6.4626999,"lng":68.1097}},"location":{"lat":20.593684,"lng":78.96288},"location_type":"APPROXIMATE","viewport":{"northeast":{"lat":35.5087008,"lng":97.39498069999999},"southwest":{"lat":6.7535159,"lng":68.16288519999999}}},"place_id":"ChIJkbeSa_BfYzARphNChaFPjNc","types":["country","political"]}],"status":"OK"}
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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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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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 is the name of my data frame;; Json_obj is the name of the column in which Json object is present
有谁知道我该怎么做。
如有任何帮助，我们将不胜感激。
谢谢。
我认为您必须先在 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 来接收每个子列表的三个项目（我 修改了 lapply() 函数，考虑到你的 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，可以找到一个很好的教程 here。
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，它查询各种 Google API 并将结果 'simplifies' 转换为 data.frames /列出。从那里您可以使用标准 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