r - how can i extract California county locations from given latitude and longitude information -
i have following dataset 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 have latitude & longitude information in datasets, extract corresponding county given geo information using r. possible getting capital city(or largest city) each of extracted counties .these make stratified analysis more insightful;intend clustering/mapping exercise.
take @ ggmap::revgeocode
code
library(ggmap) revgeocode(c(-122.47,38.48)) # longitude 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 info need result
df12 %>% select(longitude,latitude,address,county) # 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 library offers option capital or largest city in county think won't have trouble building lookup table online info.
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