Is there a fast way to convert latitude and longitude coordinates to State codes in R? I've been using the zipcode package as a look up table but it's too slow when I'm querying lots of lat/long values
If not in R is there any way to do this using google geocoder or any other type of fast querying service?
Thanks!
Here are two options, one using sf and one using sp package functions. sf is the more modern (and, here in 2020, recommended) package for analyzing spatial data, but in case it's still useful, I am leaving my original 2012 answer showing how to do this with sp-related functions.
library(sf)
library(spData)
## pointsDF: A data.frame whose first column contains longitudes and
## whose second column contains latitudes.
##
## states: An sf MULTIPOLYGON object with 50 states plus DC.
##
## name_col: Name of a column in `states` that supplies the states'
## names.
lonlat_to_state <- function(pointsDF,
states = spData::us_states,
name_col = "NAME") {
## Convert points data.frame to an sf POINTS object
pts <- st_as_sf(pointsDF, coords = 1:2, crs = 4326)
## Transform spatial data to some planar coordinate system
## (e.g. Web Mercator) as required for geometric operations
states <- st_transform(states, crs = 3857)
pts <- st_transform(pts, crs = 3857)
## Find names of state (if any) intersected by each point
state_names <- states[[name_col]]
ii <- as.integer(st_intersects(pts, states))
state_names[ii]
}
## Test the function with points in Wisconsin, Oregon, and France
testPoints <- data.frame(x = c(-90, -120, 0), y = c(44, 44, 44))
lonlat_to_state(testPoints)
## [1] "Wisconsin" "Oregon" NA
If you need higher resolution state boundaries, read in your own vector data as an sf object using sf::st_read() or by some other means. One nice option is to install the rnaturalearth package and use it to load a state vector layer from rnaturalearthhires. Then use the lonlat_to_state() function we just defined as shown here:
library(rnaturalearth)
us_states_ne <- ne_states(country = "United States of America",
returnclass = "sf")
lonlat_to_state(testPoints, states = us_states_ne, name_col = "name")
## [1] "Wisconsin" "Oregon" NA
For very accurate results, you can download a geopackage containing GADM-maintained administrative borders for the United States from this page. Then, load the state boundary data and use them like this:
USA_gadm <- st_read(dsn = "gadm36_USA.gpkg", layer = "gadm36_USA_1")
lonlat_to_state(testPoints, states = USA_gadm, name_col = "NAME_1")
## [1] "Wisconsin" "Oregon" NA
Here is a function that takes a data.frame of lat-longs within the lower 48 states, and for each point, returns the state in which it is located.
Most of the function simply prepares the SpatialPoints and SpatialPolygons objects needed by the over() function in the sp package, which does the real heavy lifting of calculating the 'intersection' of points and polygons:
library(sp)
library(maps)
library(maptools)
# The single argument to this function, pointsDF, is a data.frame in which:
# - column 1 contains the longitude in degrees (negative in the US)
# - column 2 contains the latitude in degrees
lonlat_to_state_sp <- function(pointsDF) {
# Prepare SpatialPolygons object with one SpatialPolygon
# per state (plus DC, minus HI & AK)
states <- map('state', fill=TRUE, col="transparent", plot=FALSE)
IDs <- sapply(strsplit(states$names, ":"), function(x) x[1])
states_sp <- map2SpatialPolygons(states, IDs=IDs,
proj4string=CRS("+proj=longlat +datum=WGS84"))
# Convert pointsDF to a SpatialPoints object
pointsSP <- SpatialPoints(pointsDF,
proj4string=CRS("+proj=longlat +datum=WGS84"))
# Use 'over' to get _indices_ of the Polygons object containing each point
indices <- over(pointsSP, states_sp)
# Return the state names of the Polygons object containing each point
stateNames <- sapply(states_sp@polygons, function(x) x@ID)
stateNames[indices]
}
# Test the function using points in Wisconsin and Oregon.
testPoints <- data.frame(x = c(-90, -120), y = c(44, 44))
lonlat_to_state_sp(testPoints)
[1] "wisconsin" "oregon" # IT WORKS
You can do it in a few lines of R.
library(sp)
library(rgdal)
#lat and long
Lat <- 57.25
Lon <- -9.41
#make a data frame
coords <- as.data.frame(cbind(Lon,Lat))
#and into Spatial
points <- SpatialPoints(coords)
#SpatialPolygonDataFrame - I'm using a shapefile of UK counties
counties <- readOGR(".", "uk_counties")
#assume same proj as shapefile!
proj4string(points) <- proj4string(counties)
#get county polygon point is in
result <- as.character(over(points, counties)$County_Name)
See ?over in the sp package.
You'll need to have the state boundaries as a SpatialPolygonsDataFrame.
Example data (polygons and points)
library(raster)
pols <- shapefile(system.file("external/lux.shp", package="raster"))
xy <- coordinates(p)
Use raster::extract
extract(p, xy)
# point.ID poly.ID ID_1 NAME_1 ID_2 NAME_2 AREA
#1 1 1 1 Diekirch 1 Clervaux 312
#2 2 2 1 Diekirch 2 Diekirch 218
#3 3 3 1 Diekirch 3 Redange 259
#4 4 4 1 Diekirch 4 Vianden 76
#5 5 5 1 Diekirch 5 Wiltz 263
#6 6 6 2 Grevenmacher 6 Echternach 188
#7 7 7 2 Grevenmacher 7 Remich 129
#8 8 8 2 Grevenmacher 12 Grevenmacher 210
#9 9 9 3 Luxembourg 8 Capellen 185
#10 10 10 3 Luxembourg 9 Esch-sur-Alzette 251
#11 11 11 3 Luxembourg 10 Luxembourg 237
#12 12 12 3 Luxembourg 11 Mersch 233
It's very straightforward using sf:
library(maps)
library(sf)
## Get the states map, turn into sf object
US <- st_as_sf(map("state", plot = FALSE, fill = TRUE))
## Test the function using points in Wisconsin and Oregon
testPoints <- data.frame(x = c(-90, -120), y = c(44, 44))
# Make it a spatial dataframe, using the same coordinate system as the US spatial dataframe
testPoints <- st_as_sf(testPoints, coords = c("x", "y"), crs = st_crs(US))
#.. and perform a spatial join!
st_join(testPoints, US)
ID geometry
1 wisconsin POINT (-90 44)
2 oregon POINT (-120 44)
