如何从给定的经纬度信息中提取加州县的位置[已关闭]

看看 ggmap::revgeocode

密码

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

后果

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

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)

我不认为图书馆提供了一个选择,以获得该县的首都或最大的城市,但我认为你不会有太多的麻烦建立一个从网上信息查表。