我怎样才能从给定的经度和纬度信息提取加利福尼亚县的位置 [英] how can i extract California county locations from given latitude and longitude information
问题描述
我有以下加利福尼亚州住房数据数据集：
pre $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $中位数房屋价值中位数收入中位数房屋年龄总计房间总数卧室人口
1 190300 4.20510 16 2697.00 490.00 1462
2 150800 2.54810 33 2821.00 652.00 1206
3 252600 6.08290 17 6213.20 1276.05 3288
4 269700 4.03680 52 919.00 213.00 413
5 91200 1.63680 28 3072.00 790.00 1375
6 66200 2.18980 30 744.00 156.00 410
7 148800 2.63640 39 620.95 136.00 348
8 384800 4.46150 20 2270.00 498.00 1070
9 153200 2.75000 22 1931.00 445.00 1009
10 66200 1.60057 36 973.00 219.00 613
11 461500 3.78130 43 3070.00 668.00 1240
12 144600 2.85000 22 5175.00 1213.00 2804
13 143700 5.09410 8 6213.20 1276.05 3288
14 195500 5.30620 16 2918.00 444.00 1697
15 268800 2.42110 22 620.95 136.00 348
家庭纬度经度cluster_kmeans gender_dom婚姻竞赛edu_level出租
1 515 38.48 -122.47 1 M其他黑人jrcollege租用
2 640 38.00 -122.13 1 F其他西班牙裔博士拥有
3 1162 33.88 -117.79 3 M其他白人jrcollege拥有
4 193 37.85 -122.25 1 M单其他jrcollege拥有
5 705 38.13 -122.26 1 F单身白人博士学位出租
6 165 38.96 -122.21 1 F单身其他人jrcollege拥有
7 125 34.01 -118.18 2 M已婚其他人postgrad拥有
8 521 33.83 -118.38 2 F单身白人毕业生租用
9 407 38.95 -121.04 1 M已婚其他毕业后租赁
10 187 35.34 -119.01 2 M单身西班牙裔博士拥有
11 646 33.76 -118.12 2 F其他人highschl租赁
12 1091 37.95 -122.05 3 M其他白人毕业租用
13 1162 36.87 -119.75 3 M其他其他人postgrad租赁
14 444 32.93 -117.13 2 M其他亚洲jrcollege o wned
15 125 37.71 -120.98 1 F单身亚洲postgrad租借
由于我有纬度;数据集中的经度信息，我想使用R为给定的地理信息提取相应的县。还有可能获得每个城市（或最大的城市）这些可以使我的分层分析更具洞察力;打算做一些聚类/映射练习。 看看 ggmap :: revgeocode
code $ b
library（ggmap）
revgeocode（c（-122.47,38.48））＃经度然后纬度
＃[1]2233 Sulphur Springs Ave，St Helena，CA 94574，USA
library（dplyr）
library（magrittr）
df12％<>％rowwise％>％mutate（address = （地址，c（street_address，city，％）％县，国家），删除= F，sep =，）＃结构所有你需要的信息
结果
df12％>％select（经度，纬度，地址，县）
＃一个tibble：15 x 4
＃经度纬度地址县
＃*< dbl> < DBL> < CHR> < CHR>
＃1 -122.47 38.48 2233 Sulphur Springs Ave，St Helena，CA 94574，USA CA 94574
＃2 -122.13 38.00 3400-3410 Brookside Dr，Martinez，CA 94553，USA CA 94553
＃3 -117.79 33.88 19721 Bluefield Plaza，Yorba Linda，CA 92886，USA CA 92886
＃4 -122.25 37.85 6365 Florio St，Oakland，CA 94618，USA CA 94618
＃5 -122.26 38.13 119含羞草Ct，Vallejo，CA 94589，USA CA 94589
＃6 -122.21 38.96 Unnamed Road，Arbuckle，CA 95912，USA CA 95912
＃7 -118.18 34.01 4360-4414 Noakes St，Los Angeles，CA 90023 ，CA 90023
＃8 -118.38 33.83 903 Serpentine St，Redondo Beach，CA 90277，USA CA 90277
＃9 -121.04 38.95 14666-14690 Musso Rd，Auburn，CA 95603，USA CA 95603
＃10 -119.01 35.34 800 Ming Ave，Bakersfield，CA 93307，USA CA 93307
＃11 -118.12 33.76 6211-6295 E Marina Dr，Long Beach，CA 90803，USA CA 90803
＃ 12 -122.05 37.95 1120 Carey Dr，Concord，CA 94520，USA CA 94520
＃13 -119.75 36.87 1815-1899 E Pryor Dr，Fresno，CA 93720，USA CA 93720
＃14 -117.13 32.93 9010- 9016 Danube Ln，San Diego，CA 92126，USA CA 92126
＃15 -120.98 37.71 748-1298 Claribel Rd，Modesto，CA 95356，USA CA 95356
$ b data
df1 < - read.table（text =MedianHouseValue MedianIncome MedianHouseAge TotalRooms TotalBedrooms Population
1 190300 4.20510 16 2697.00 490.00 1462
2 150800 2.54810 33 2821.00 652.00 1206
3 252600 6.08290 17 6213.20 1276.05 3288
4 269700 4.03680 52 919.00 213.00 413
5 91200 1.63680 28 3072.00 790.00 1375
6 66200 2.18980 30 744.00 156.00 410
7 148800 2.63640 39 620.95 136.00 348
8 384800 4.46150 20 2270.00 498.00 1070
9 153200 2.75000 22 1931.00 445.00 1009
10 66200 1.60057 36 973.00 219.00 613
11 461500 3.78130 43 3070.00 668.00 1240
12 144600 2.85000 22 5175.00 1213.00 2804
13 143700 5.09410 8 6213.20 1276.05 3288
14 195500 5.30620 16 2918.00 444.00 1697
15 268800 2.42110 22 620.95 136.00 348，header = T，stringsAsFactors = F）
df2< - read.table（text =家庭纬度经度cluster_kmeans gender_dom婚姻竞赛edu_level出租
1 515 38.48 -122.47 1 M其他黑人jrcollege租用
2 640 38.00 -122.13 1 F其他西班牙裔博士拥有
3 1162 33.88 -117.79 3 M其他白人jrcollege拥有
4 193 37.85 -122.25 1 M单身其他人jrcollege拥有
5 705 38.13 -122.26 1 F单身白人博士出租
6 165 38.96 -122.21 1 F单身其他jrcollege拥有
7 125 34.01 -118.18 2 M已婚其他人postgrad拥有
8 521 33.83 -118.38 2 F单身白人研究生租借
9 407 38.95 -121.04 1 M已婚其他人postgrad租用
10 187 35.34 -119.01 2 M单身西班牙裔博士拥有
11 646 33.76 -118.12 2 F其他人highschl租
12 1091 37.95 -122.05 3 M其他白人毕业生租
13 1162 36.87 -119.75 3 M其他其他毕业后租赁
14 444 32.93 -117.13 2 M其他亚洲jrcollege拥有
15 125 37.71 -120.98 1 F单一亚洲postgrad leased，header = T，stringsAsFactors = F）
df12 < - cbind（df1，df2）
我不认为图书馆提供了一个选择去县里的首都或最大的城市，但我认为你不会有太多麻烦建设来自在线信息的查询表。
I have the following dataset for California housing data:
head(calif_cluster,15)
MedianHouseValue MedianIncome MedianHouseAge TotalRooms TotalBedrooms Population
1 190300 4.20510 16 2697.00 490.00 1462
2 150800 2.54810 33 2821.00 652.00 1206
3 252600 6.08290 17 6213.20 1276.05 3288
4 269700 4.03680 52 919.00 213.00 413
5 91200 1.63680 28 3072.00 790.00 1375
6 66200 2.18980 30 744.00 156.00 410
7 148800 2.63640 39 620.95 136.00 348
8 384800 4.46150 20 2270.00 498.00 1070
9 153200 2.75000 22 1931.00 445.00 1009
10 66200 1.60057 36 973.00 219.00 613
11 461500 3.78130 43 3070.00 668.00 1240
12 144600 2.85000 22 5175.00 1213.00 2804
13 143700 5.09410 8 6213.20 1276.05 3288
14 195500 5.30620 16 2918.00 444.00 1697
15 268800 2.42110 22 620.95 136.00 348
Households Latitude Longitude cluster_kmeans gender_dom marital race edu_level rental
1 515 38.48 -122.47 1 M other black jrcollege rented
2 640 38.00 -122.13 1 F other hispanic doctorate owned
3 1162 33.88 -117.79 3 M other white jrcollege owned
4 193 37.85 -122.25 1 M single others jrcollege owned
5 705 38.13 -122.26 1 F single white doctorate rented
6 165 38.96 -122.21 1 F single others jrcollege owned
7 125 34.01 -118.18 2 M married others postgrad owned
8 521 33.83 -118.38 2 F single white graduate rented
9 407 38.95 -121.04 1 M married others postgrad leased
10 187 35.34 -119.01 2 M single hispanic doctorate owned
11 646 33.76 -118.12 2 F other others highschl leased
12 1091 37.95 -122.05 3 M other white graduate rented
13 1162 36.87 -119.75 3 M other others postgrad leased
14 444 32.93 -117.13 2 M other asian jrcollege owned
15 125 37.71 -120.98 1 F single asian postgrad leased
As i have latitude & longitude information in the datasets, i would like to extract corresponding county for the given geo information using R. Also is it possible to getting the capital city(or largest city) for each of the extracted counties .These could make my stratified analysis more insightful;intend to do some clustering/mapping exercise.
take a look at ggmap::revgeocode
code
library(ggmap)
revgeocode(c(-122.47,38.48)) # longitude then latitude
# [1] "2233 Sulphur Springs Ave, St Helena, CA 94574, USA"
library(dplyr)
library(magrittr)
df12 %<>% rowwise %>% mutate(address = revgeocode(c(Longitude,Latitude))) %>% ungroup # add full address using google api through ggmap
df12 %<>% separate(address,c("street_address", "city","county","country"),remove=F,sep=",") # structure all the info you need
result
df12 %>% select(Longitude,Latitude,address,county)
# A tibble: 15 x 4
# Longitude Latitude address county
# * <dbl> <dbl> <chr> <chr>
# 1 -122.47 38.48 2233 Sulphur Springs Ave, St Helena, CA 94574, USA CA 94574
# 2 -122.13 38.00 3400-3410 Brookside Dr, Martinez, CA 94553, USA CA 94553
# 3 -117.79 33.88 19721 Bluefield Plaza, Yorba Linda, CA 92886, USA CA 92886
# 4 -122.25 37.85 6365 Florio St, Oakland, CA 94618, USA CA 94618
# 5 -122.26 38.13 119 Mimosa Ct, Vallejo, CA 94589, USA CA 94589
# 6 -122.21 38.96 Unnamed Road, Arbuckle, CA 95912, USA CA 95912
# 7 -118.18 34.01 4360-4414 Noakes St, Los Angeles, CA 90023, USA CA 90023
# 8 -118.38 33.83 903 Serpentine St, Redondo Beach, CA 90277, USA CA 90277
# 9 -121.04 38.95 14666-14690 Musso Rd, Auburn, CA 95603, USA CA 95603
# 10 -119.01 35.34 800 Ming Ave, Bakersfield, CA 93307, USA CA 93307
# 11 -118.12 33.76 6211-6295 E Marina Dr, Long Beach, CA 90803, USA CA 90803
# 12 -122.05 37.95 1120 Carey Dr, Concord, CA 94520, USA CA 94520
# 13 -119.75 36.87 1815-1899 E Pryor Dr, Fresno, CA 93720, USA CA 93720
# 14 -117.13 32.93 9010-9016 Danube Ln, San Diego, CA 92126, USA CA 92126
# 15 -120.98 37.71 748-1298 Claribel Rd, Modesto, CA 95356, USA CA 95356
data
df1 <- read.table(text = "MedianHouseValue MedianIncome MedianHouseAge TotalRooms TotalBedrooms Population
1 190300 4.20510 16 2697.00 490.00 1462
2 150800 2.54810 33 2821.00 652.00 1206
3 252600 6.08290 17 6213.20 1276.05 3288
4 269700 4.03680 52 919.00 213.00 413
5 91200 1.63680 28 3072.00 790.00 1375
6 66200 2.18980 30 744.00 156.00 410
7 148800 2.63640 39 620.95 136.00 348
8 384800 4.46150 20 2270.00 498.00 1070
9 153200 2.75000 22 1931.00 445.00 1009
10 66200 1.60057 36 973.00 219.00 613
11 461500 3.78130 43 3070.00 668.00 1240
12 144600 2.85000 22 5175.00 1213.00 2804
13 143700 5.09410 8 6213.20 1276.05 3288
14 195500 5.30620 16 2918.00 444.00 1697
15 268800 2.42110 22 620.95 136.00 348",header=T,stringsAsFactors=F)
df2 <- read.table(text = "Households Latitude Longitude cluster_kmeans gender_dom marital race edu_level rental
1 515 38.48 -122.47 1 M other black jrcollege rented
2 640 38.00 -122.13 1 F other hispanic doctorate owned
3 1162 33.88 -117.79 3 M other white jrcollege owned
4 193 37.85 -122.25 1 M single others jrcollege owned
5 705 38.13 -122.26 1 F single white doctorate rented
6 165 38.96 -122.21 1 F single others jrcollege owned
7 125 34.01 -118.18 2 M married others postgrad owned
8 521 33.83 -118.38 2 F single white graduate rented
9 407 38.95 -121.04 1 M married others postgrad leased
10 187 35.34 -119.01 2 M single hispanic doctorate owned
11 646 33.76 -118.12 2 F other others highschl leased
12 1091 37.95 -122.05 3 M other white graduate rented
13 1162 36.87 -119.75 3 M other others postgrad leased
14 444 32.93 -117.13 2 M other asian jrcollege owned
15 125 37.71 -120.98 1 F single asian postgrad leased",header=T,stringsAsFactors=F)
df12 <- cbind(df1,df2)
I don't think the library offers an option to get the capital or largest city in the county but I think you won't have too much trouble building a lookup table from online info.
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