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数据中的示例行
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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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我正在尝试从地址组件的第一次出现中提取值
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")
这是试用代码
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 存在 Json 对象的列的名称
有没有人知道我该怎么去做。
任何帮助将不胜感激。
谢谢。
我认为您首先必须在fromJSON函数中使用simplifyDataFrame = FALSE，以便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(考虑到您的最后一条评论，我修改了lapply(( 函数(，
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"
.....
如果你想修改 lapply(( 函数或 ifelse 语句，那么可以在这里找到一个很好的教程。
有一些地理编码包可以让您的生活更轻松，例如我的googleway软件包，它可以查询各种Google API并将结果"简化"为data.frames/列表。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