SwimmingTheDataLake Geocoding with R and mapZen | Swimming the Data Lake

Geocoding with R and mapZen



The most widely used service for Geocoding is Google Maps, but you’d hit the limits early in your project if you don’t have a paid account. I often place data on a map, so I need precise geocoding. It’s not unlikely you had to test some other geocodig options ‘til you get the budget for a Google Maps paid account. I’ve been in that situation myself a couple of times.

Here, I’m going to describe my approach using the free MapZen geocoding service. We’ll use the R package rmapzen, available on CRAN, but first of all you’ll need to get a unique mapzen key for you project, so go there and create your own: https://mapzen.com/documentation/overview/api-keys/

First importing data to geocode. Your source can be different.


# import data to geocode

df <- rio::import("locations.xlsx") 

I recommend to paste country name for better results if it’s not in data

df$complete_address<- paste0(df$address,", France")

mapzen_df <- data.frame()

Sometimes you have a lot of repeated adresses so, let’s geocode just unique adresses. MapZen is free, but don’t be rude. Then enter te loop to geocode all data.

unique_adresses <- unique(df$complete_adress)

i <- 0

for (address in unique_adresses) {
    mapzen_df <- rbind(mapzen_df, mz_geocode(address, api_key = "insert_your_mapzen_key_here" ))
    message <- paste0(i,": ", address)
  }, error=function(e){cat("ERROR :",conditionMessage(e), "\n")})

Join the original df with the geocoded one

final_df <-  inner_join(df, mapzen_df)

And all locations geocoded!