Geocode your data using, R, JSON and Google Maps’ Geocoding APIs

January 24, 2012

(This article was first published on All Things R, and kindly contributed to R-bloggers)

Over the last year and half, I have faced numerous challenges with geocoding the data that I have used to showcase my passion for location analytics. In 2012, I decided to take thing in my control and turned to R. Here, I am sharing a simple R script that I wrote to geo-code my data whenever I needed it, even BIG Data.

To geocode my data, I use Google's Geocoding service which returns the geocoded data in a JSON. I will recommend that you register with Google Maps API and get a key if you have large amount of data and would do repeated geo coding.

Here is function that can be called repeatedly by other functions:

getGeoCode <- function(gcStr)

{

library("RJSONIO") #Load Library

gcStr <- gsub(' ','%20',gcStr) #Encode URL Parameters

#Open Connection

connectStr <- paste('http://maps.google.com/maps/api/geocode/json?sensor=false&address=',gcStr, sep="")

con <- url(connectStr)

data.json <- fromJSON(paste(readLines(con), collapse=""))

close(con)

#Flatten the received JSON

data.json <- unlist(data.json)

lat <- data.json["results.geometry.location.lat"]

lng <- data.json["results.geometry.location.lng"]

gcodes <- c(lat, lng)

names(gcodes) <- c("Lat", "Lng")

return (gcodes)

}

Let's put this function to test:

geoCodes <- getGeoCode("Palo Alto,California")

> geoCodes
Lat Lng
"37.4418834" "-122.1430195"

You can run this on the entire column of a data frame or a data table:

Here is my sample data frame with three columns - Opposition, Ground.Country and Toss. Two of the columns, you guessed it right, need geocoding.

> head(shortDS,10)

Opposition Ground.Country Toss

1 Pakistan Karachi,Pakistan won

2 Pakistan Faisalabad,Pakistan lost

3 Pakistan Lahore,Pakistan won

4 Pakistan Sialkot,Pakistan lost

5 New Zealand Christchurch,New Zealand lost

6 New Zealand Napier,New Zealand won

7 New Zealand Auckland,New Zealand won

8 England Lord's,England won

9 England Manchester,England lost

10 England The Oval,England won

To geo code this, here is a simple one liner I execute:

shortDS <- with(shortDS, data.frame(Opposition, Ground.Country, Toss,

laply(Ground.Country, function(val){getGeoCode(val)})))

> head(shortDS, 10)
Opposition Ground.Country Toss Ground.Lat Ground.Lng
1 Pakistan Karachi,Pakistan won 24.893379 67.028061
2 Pakistan Faisalabad,Pakistan lost 31.408951 73.083458
3 Pakistan Lahore,Pakistan won 31.54505 74.340683
4 Pakistan Sialkot,Pakistan lost 32.4972222 74.5361111
5 New Zealand Christchurch,New Zealand lost -43.5320544 172.6362254
6 New Zealand Napier,New Zealand won -39.4928444 176.9120178
7 New Zealand Auckland,New Zealand won -36.8484597 174.7633315
8 England Lord's,England won 51.5294 -0.1727
9 England Manchester,England lost 53.479251 -2.247926
10 England The Oval,England won 51.369037 -2.378269

Happy Coding!

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