从R中的Google地理编码API提取JSON数据 [英] Extracting JSON data from Google geocoding API in R
本文介绍了从R中的Google地理编码API提取JSON数据的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我对Json数据结构很陌生，因此无法从中提取数据.
I'm quite new to Json data structure and so unable to extract data from it.
这是存储在csv文件中的Json数据的示例行
This is the sample rows from Json data which is stored in csv file
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我正在尝试从首次出现的地址成分中提取值
使我的伪代码看起来像
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]
}
}
所以我的输出看起来像
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对象所在的列的名称
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
有人对我该怎么做有任何想法.
Does anyone have any idea of how do I go for it.
任何帮助将不胜感激.
谢谢.
推荐答案
我认为您必须首先在 fromJSON 函数中使用 simplifyDataFrame = FALSE 数据作为列表:
I think you have first to use simplifyDataFrame = FALSE in the fromJSON function in order to get the data as a list:
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项(我修改 lapply()函数，并考虑了您的最后评论)
Then use a nested lapply to receive the three items of each sublist (I modified the lapply() function taking into account your last comment),
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"
.....
如果要修改lapply()函数或ifelse语句，则可以找到一个很好的教程
If you want to modify the lapply() function or the ifelse statements then a good tutorial can be found here.
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