Introduction

Tidygeocoder makes getting data from geocoder services easy. In addition to the usage example below you can find a post on making a map of European soccer club stadiums here, a post on mapping landmarks in Washington, DC here, and a vignette with more detailed usage examples here.

All results are returned in tibble format. Batch geocoding (geocoding multiple addresses per query) is used by default for the US Census and Geocodio services when multiple addresses are provided. Duplicate, missing/NA, and blank address data is handled elegantly - only unique addresses are passed to geocoder services, but the rows in the original data are preserved.

Geocoder Services

The currently supported services are the US Census geocoder, Nominatim (OSM), Geocodio, and Location IQ. The Census geocoder is restricted to street-level addresses in the United States, Geocodio covers the U.S. and Canada, while Location IQ and OSM have worldwide coverage. The Census and OSM services support batch geocoding (Location IQ and OSM do not).

The Census and OSM services are free; Geocodio and Location IQ are commercial services that require API keys, but also offer free usage tiers. OSM and Location IQ both have usage frequency limits. Refer to the documentation of each service for more details.

Installation

To install the stable version from CRAN (the official R package servers):

install.packages('tidygeocoder')

Alternatively you can install the development version from GitHub:

if(!require(devtools)) install.packages("devtools")
devtools::install_github("jessecambon/tidygeocoder")

Usage

In this example we will geocode a few addresses using the geocode() function and plot them on a map with ggplot.

library(dplyr)
library(tibble)
library(tidygeocoder)

# create a dataframe with addresses
some_addresses <- tribble(
~name,                  ~addr,
"White House",          "1600 Pennsylvania Ave, Washington, DC",
"Transamerica Pyramid", "600 Montgomery St, San Francisco, CA 94111",
"Willis Tower",         "233 S Wacker Dr, Chicago, IL 60606"
)

# geocode the addresses
lat_longs <- some_addresses %>%
  geocode(addr, method = 'census', lat = latitude , long = longitude)

The geocode() function attaches latitude and longitude columns to our input dataset of addresses. The US Census geocoder is used here, but other services can be specified with the method argument. See the geo() function documentation for details.

name addr latitude longitude
White House 1600 Pennsylvania Ave, Washington, DC 38.89875 -77.03535
Transamerica Pyramid 600 Montgomery St, San Francisco, CA 94111 37.79470 -122.40314
Willis Tower 233 S Wacker Dr, Chicago, IL 60606 41.87851 -87.63666

Now that we have the longitude and latitude coordinates, we can use ggplot to plot our addresses on a map.

library(ggplot2)
library(maps)
library(ggrepel)

ggplot(lat_longs, aes(longitude, latitude), color="grey99") +
  borders("state") + geom_point() +
  geom_label_repel(aes(label = name)) +
  theme_void()

To return the full results from a geocoder service (not just latitude and longitude) you can use full_results = TRUE. Additionally, for the Census geocoder you can use return_type = 'geographies' to return geography columns (state, county, Census tract, and Census block).

full <- some_addresses %>%
  geocode(addr, method = 'census', full_results = TRUE, return_type = 'geographies')

glimpse(full)
#> Rows: 3
#> Columns: 15
#> $ name            <chr> "White House", "Transamerica Pyramid", "Willis Tower"
#> $ addr            <chr> "1600 Pennsylvania Ave, Washington, DC", "600 Montgom…
#> $ lat             <dbl> 38.89875, 37.79470, 41.87851
#> $ long            <dbl> -77.03535, -122.40314, -87.63666
#> $ id              <int> 1, 2, 3
#> $ input_address   <chr> "1600 Pennsylvania Ave, Washington, DC, , , ", "600 M…
#> $ match_indicator <chr> "Match", "Match", "Match"
#> $ match_type      <chr> "Non_Exact", "Exact", "Exact"
#> $ matched_address <chr> "1600 PENNSYLVANIA AVE NW, WASHINGTON, DC, 20006", "6…
#> $ tiger_line_id   <int> 76225813, 192281262, 112050003
#> $ tiger_side      <chr> "L", "R", "L"
#> $ state_fips      <int> 11, 6, 17
#> $ county_fips     <int> 1, 75, 31
#> $ census_tract    <int> 6202, 61100, 839100
#> $ census_block    <int> 1031, 1013, 2006

For further documentation, refer to the Getting Started Vignette and the function documentation.