This can be "vectorized". Here's an example:
library(sf)
library(tidyverse)
Singapore shapefile:
singapore <- st_read("~/data/master-plan-2014-subzone-boundary-no-sea-shp/MP14_SUBZONE_NO_SEA_PL.shp", quiet=TRUE, stringsAsFactors=FALSE)
singapore <- st_transform(singapore, 4326)
CSV of recycling centers:
centers <- read_csv("~/data/recycl.csv")
glimpse(centers)
## Observations: 407
## Variables: 10
## $ lng <dbl> 104.0055, 103.7677, 103.7456, 103.7361, 103.8106, 103.962...
## $ lat <dbl> 1.316764, 1.296245, 1.319204, 1.380412, 1.286512, 1.33355...
## $ inc_crc <chr> "F8907D68D7EB64A1", "ED1F74DC805CEC8B", "F48D575631DCFECB...
## $ name <chr> "RENEW (Recycling Nation's Electronic Waste)", "RENEW (Re...
## $ block_house_num <chr> "10", "84", "698", "3", "2", "1", "1", "1", "357", "50", ...
## $ bldg_name <chr> "Changi Water Reclamation Plant", "Clementi Woods", "Comm...
## $ floor <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ post_code <int> 498785, 126811, 608784, 689814, 159047, 486036, 39393, 55...
## $ street <chr> "Changi East Close", "West Coast Road , Clementi Woods Co...
## $ unit <chr> "(Lobby)", "#B1-01 (Management Office)", "(School foyer)"...
Turn ^^ into a simple features object:
map2(centers$lng, centers$lat, ~st_point(c(.x, .y))) %>%
st_sfc(crs = 4326) %>%
st_sf(centers[,-(1:2)], .) -> centers_sf
This is likely faster than the row-wise op but I'll let someone else have fun benchmarking:
bind_cols(
centers,
singapore[as.numeric(st_within(centers_sf, singapore)),]
) %>%
select(lng, lat, inc_crc, subzone_name=SUBZONE_N) %>%
mutate(subzone_name = str_to_title(subzone_name))
## # A tibble: 407 x 4
## lng lat inc_crc subzone_name
## <dbl> <dbl> <chr> <chr>
## 1 104.0055 1.316764 F8907D68D7EB64A1 Changi Airport
## 2 103.7677 1.296245 ED1F74DC805CEC8B Clementi Woods
## 3 103.7456 1.319204 F48D575631DCFECB Teban Gardens
## 4 103.7361 1.380412 1F910E0086FD4798 Peng Siang
## 5 103.8106 1.286512 55A0B9E7CBD34AFE Alexandra Hill
## 6 103.9624 1.333555 C664D09D9CD5325F Xilin
## 7 103.8542 1.292778 411F79EAAECFE609 City Hall
## 8 103.8712 1.375876 F4516742CFD4228E Serangoon North Ind Estate
## 9 103.8175 1.293319 B05B32DF52D922E7 Alexandra North
## 10 103.9199 1.335878 58E9EAF06206C772 Bedok Reservoir
## # ... with 397 more rows