R: How to GeoCode a simple address using Data Science Toolbox

Question

I am fedup with Google\'s geocoding, and decided to try an alternative. The Data Science Toolkit (http://www.datasciencetoolkit.org) allows you to Geocode unlimited number o

Answer 1

Like this?

library(httr)
library(rjson)

data <- paste0("[",paste(paste0("\"",dff$address,"\""),collapse=","),"]")
url  <- "http://www.datasciencetoolkit.org/street2coordinates"
response <- POST(url,body=data)
json     <- fromJSON(content(response,type="text"))
geocode  <- do.call(rbind,sapply(json,
                                 function(x) c(long=x$longitude,lat=x$latitude)))
geocode
#                                                long      lat
# San Francisco, California, United States -117.88536 35.18713
# Mobile, Alabama, United States            -88.10318 30.70114
# La Jolla, California, United States      -117.87645 33.85751
# Duarte, California, United States        -118.29866 33.78659
# Little Rock, Arkansas, United States      -91.20736 33.60892
# Tucson, Arizona, United States           -110.97087 32.21798
# Redwood City, California, United States  -117.88536 35.18713
# New Haven, Connecticut, United States     -72.92751 41.36571
# Berkeley, California, United States      -122.29673 37.86058
# Hartford, Connecticut, United States      -72.76356 41.78516
# Sacramento, California, United States    -121.55541 38.38046
# Encinitas, California, United States     -116.84605 33.01693
# Birmingham, Alabama, United States        -86.80190 33.45641
# Stanford, California, United States      -122.16750 37.42509
# Orange, California, United States        -117.85311 33.78780
# Los Angeles, California, United States   -117.88536 35.18713

This takes advantage of the POST interface to the street2coordinates API (documented here), which returns all the results in 1 request, rather than using multiple GET requests.

EDIT (Response to OP's comment)

The absence of Phoenix seems to be a bug in the street2coordinates API. If you go the API demo page and try "Phoenix, Arizona, United States", you get a null response. However, as your example shows, using their "Google-style Geocoder" does give a result for Phoenix. So here's a solution using repeated GET requests. Note that this runs much slower.

geo.dsk <- function(addr){ # single address geocode with data sciences toolkit
  require(httr)
  require(rjson)
  url      <- "http://www.datasciencetoolkit.org/maps/api/geocode/json"
  response <- GET(url,query=list(sensor="FALSE",address=addr))
  json <- fromJSON(content(response,type="text"))
  loc  <- json['results'][[1]][[1]]$geometry$location
  return(c(address=addr,long=loc$lng, lat= loc$lat))
}
result <- do.call(rbind,lapply(as.character(dff$address),geo.dsk))
result <- data.frame(result)
result
#                                     address         long        lat
# 1        Birmingham, Alabama, United States   -86.801904  33.456412
# 2            Mobile, Alabama, United States   -88.103184  30.701142
# 3           Phoenix, Arizona, United States -112.0733333 33.4483333
# 4            Tucson, Arizona, United States  -110.970869  32.217975
# 5      Little Rock, Arkansas, United States   -91.207356  33.608922
# 6       Berkeley, California, United States   -122.29673  37.860576
# 7         Duarte, California, United States  -118.298662  33.786594
# 8      Encinitas, California, United States  -116.846046  33.016928
# 9       La Jolla, California, United States  -117.876447  33.857515
# 10   Los Angeles, California, United States  -117.885359  35.187133
# 11        Orange, California, United States  -117.853112  33.787795
# 12  Redwood City, California, United States  -117.885359  35.187133
# 13    Sacramento, California, United States  -121.555406  38.380456
# 14 San Francisco, California, United States  -117.885359  35.187133
# 15      Stanford, California, United States    -122.1675   37.42509
# 16     Hartford, Connecticut, United States   -72.763564   41.78516
# 17    New Haven, Connecticut, United States   -72.927507  41.365709

Answer 2

The ggmap package includes support for geocoding using either Google or Data Science Toolkit, the latter with their "Google-style geocoder". This is quite slow for multiple addresses, as noted in the earlier answer.

library(ggmap)
result <- geocode(as.character(dff[[1]]), source = "dsk")
print(cbind(dff, result))
#                                     address        lon      lat
# 1        Birmingham, Alabama, United States  -86.80190 33.45641
# 2            Mobile, Alabama, United States  -88.10318 30.70114
# 3           Phoenix, Arizona, United States -112.07404 33.44838
# 4            Tucson, Arizona, United States -110.97087 32.21798
# 5      Little Rock, Arkansas, United States  -91.20736 33.60892
# 6       Berkeley, California, United States -122.29673 37.86058
# 7         Duarte, California, United States -118.29866 33.78659
# 8      Encinitas, California, United States -116.84605 33.01693
# 9       La Jolla, California, United States -117.87645 33.85751
# 10   Los Angeles, California, United States -117.88536 35.18713
# 11        Orange, California, United States -117.85311 33.78780
# 12  Redwood City, California, United States -117.88536 35.18713
# 13    Sacramento, California, United States -121.55541 38.38046
# 14 San Francisco, California, United States -117.88536 35.18713
# 15      Stanford, California, United States -122.16750 37.42509
# 16     Hartford, Connecticut, United States  -72.76356 41.78516
# 17    New Haven, Connecticut, United States  -72.92751 41.36571