我有一个从嵌套列表转换而来的嵌套莫斯科街道地址列表。但是,我从中进行地理编码的数据框只有没有邮政编码的地址,并且在几百(33k)的情况下,该地址为具有不同邮政编码的同一街道地址返回了多个结果。这会在列表中创建额外的嵌套,在转换为数据框时会导致与初始数据帧的观察数量不同。具有可变嵌套层次的展平列表创建更多观察
只有一个地址A结果具有以下结构: (忽略乱码,R控制台将不呈现西里尔正确)
structure(list(results = structure(list(address_components = list(
structure(list(long_name = c("4", "óëèöà Áîëüøàÿ Àêàäåìè÷åñêàÿ",
"Ñåâåðíûé àäìèíèñòðàòèâíûé îêðóã", "Ìîñêâà", "Ìîñêâà", "Ðîññèÿ",
"127299"), short_name = c("4", "óë. Áîëüøàÿ Àêàäåìè÷åñêàÿ",
"Ñåâåðíûé àäìèíèñòðàòèâíûé îêðóã", "Ìîñêâà", "Ìîñêâà", "RU",
"127299"), types = list("street_number", "route", c("political",
"sublocality", "sublocality_level_1"), c("locality", "political"
), c("administrative_area_level_2", "political"), c("country",
"political"), "postal_code")), .Names = c("long_name", "short_name",
"types"), class = "data.frame", row.names = c(NA, 7L))),
formatted_address = "óë. Áîëüøàÿ Àêàäåìè÷åñêàÿ, 4, Ìîñêâà, Ðîññèÿ, 127299",
geometry = structure(list(location = structure(list(lat = 55.8176896,
lng = 37.522891), .Names = c("lat", "lng"), class = "data.frame", row.names = 1L),
location_type = "ROOFTOP", viewport = structure(list(
northeast = structure(list(lat = 55.8190385802915,
lng = 37.5242399802915), .Names = c("lat", "lng"
), class = "data.frame", row.names = 1L), southwest = structure(list(
lat = 55.8163406197085, lng = 37.5215420197085), .Names = c("lat",
"lng"), class = "data.frame", row.names = 1L)), .Names = c("northeast",
"southwest"), class = "data.frame", row.names = 1L)), .Names = c("location",
"location_type", "viewport"), class = "data.frame", row.names = 1L),
partial_match = TRUE, place_id = "ChIJ59yLsy1ItUYR5EEBFbFJoSA",
types = list("street_address")), .Names = c("address_components",
"formatted_address", "geometry", "partial_match", "place_id",
"types"), class = "data.frame", row.names = 1L), status = "OK"), .Names = c("results",
"status"))
而具有多个可能的地址的结果如下所示:
structure(list(results = structure(list(address_components = list(
structure(list(long_name = c("23", "óëèöà Áîëüøàÿ Àêàäåìè÷åñêàÿ",
"Ñåâåðíûé àäìèíèñòðàòèâíûé îêðóã", "Ìîñêâà", "Ìîñêâà", "Ðîññèÿ",
"127299"), short_name = c("23", "óë. Áîëüøàÿ Àêàäåìè÷åñêàÿ",
"Ñåâåðíûé àäìèíèñòðàòèâíûé îêðóã", "Ìîñêâà", "Ìîñêâà", "RU",
"127299"), types = list("street_number", "route", c("political",
"sublocality", "sublocality_level_1"), c("locality", "political"
), c("administrative_area_level_2", "political"), c("country",
"political"), "postal_code")), .Names = c("long_name", "short_name",
"types"), class = "data.frame", row.names = c(NA, 7L)), structure(list(
long_name = c("23", "óëèöà Áîëüøàÿ Àêàäåìè÷åñêàÿ", "Ñåâåðíûé àäìèíèñòðàòèâíûé îêðóã",
"Ìîñêâà", "Ìîñêâà", "Ðîññèÿ", "125008"), short_name = c("23",
"óë. Áîëüøàÿ Àêàäåìè÷åñêàÿ", "Ñåâåðíûé àäìèíèñòðàòèâíûé îêðóã",
"Ìîñêâà", "Ìîñêâà", "RU", "125008"), types = list("street_number",
"route", c("political", "sublocality", "sublocality_level_1"
), c("locality", "political"), c("administrative_area_level_2",
"political"), c("country", "political"), "postal_code")), .Names = c("long_name",
"short_name", "types"), class = "data.frame", row.names = c(NA,
7L))), formatted_address = c("óë. Áîëüøàÿ Àêàäåìè÷åñêàÿ, 23, Ìîñêâà, Ðîññèÿ, 127299",
"óë. Áîëüøàÿ Àêàäåìè÷åñêàÿ, 23, Ìîñêâà, Ðîññèÿ, 125008"), geometry = structure(list(
location = structure(list(lat = c(55.8169112, 55.826859),
lng = c(37.5202899, 37.529427)), .Names = c("lat", "lng"
), class = "data.frame", row.names = 1:2), location_type = c("ROOFTOP",
"ROOFTOP"), viewport = structure(list(northeast = structure(list(
lat = c(55.8182601802915, 55.8282079802915), lng = c(37.5216388802915,
37.5307759802915)), .Names = c("lat", "lng"), class = "data.frame", row.names = 1:2),
southwest = structure(list(lat = c(55.8155622197085,
55.8255100197085), lng = c(37.5189409197085, 37.5280780197085
)), .Names = c("lat", "lng"), class = "data.frame", row.names = 1:2)), .Names = c("northeast",
"southwest"), class = "data.frame", row.names = 1:2)), .Names = c("location",
"location_type", "viewport"), class = "data.frame", row.names = 1:2),
partial_match = c(TRUE, TRUE), place_id = c("ChIJnVMw7C1ItUYRdfeWEQrXuAk",
"ChIJnbnwOdY3tUYR1_D9pHTqCsI"), types = list("street_address",
"street_address")), .Names = c("address_components",
"formatted_address", "geometry", "partial_match", "place_id",
"types"), class = "data.frame", row.names = 1:2), status = "OK"), .Names = c("results",
"status"))
在第二个列表中的results元素中,每个可能的地址都有一个额外的嵌套级别,当这个地址变扁时会为该地址创建一个“额外”观察值,从而使得不可能对将地理编码结果返回到地址列表。我正在使用以下功能将我的嵌套列表平铺到数据框架。如何在额外的嵌套发生时修改它们以仅占用第一个地址?如果地址不正确,那么当我稍后与另一个数据帧合并时,建筑物将简单地从样本中丢弃,因此我只关心将每个地理编码观察匹配到原始数据框(地址的来源)中的相应行。
flatten_googleway <- function(df) {
require(jsonlite)
res <- jsonlite::flatten(df)
res[, names(res) %in% c("geometry.location_type", "geometry.location.lat",
"geometry.location.lng", "formatted_address")]
}
moscowhousegeo.df <- do.call(rbind, lapply(moscowhouse.list, function(x) {
if (length(x$results) == 0) template_res[1, ] else flatten_googleway(x$results)
}))
##template for NA results
structure(list(formatted_address = character(0), geometry.location_type = character(0),
geometry.location.lat = numeric(0), geometry.location.lng = numeric(0)), .Names = c("formatted_address",
"geometry.location_type", "geometry.location.lat", "geometry.location.lng"
), row.names = integer(0), class = "data.frame")