Tidygeocoder makes getting data from geocoding services easy. A unified high-level interface is provided for a selection of supported geocoding services and results are returned in tibble (dataframe) format.


  • Forward geocoding (addresses ⮕ coordinates)
  • Reverse geocoding (coordinates ⮕ addresses)
  • Batch geocoding (geocoding multiple addresses or coordinates in a single query) is automatically used if applicable.
  • Duplicate, NA, and blank input data is handled elegantly; only unique inputs are submitted in queries, but the rows in the original data are preserved by default.
  • The maximum rate of querying is automatically set according to the usage policies of the selected geocoding service.

In addition to the usage examples below, see the Getting Started Vignette and blog posts on tidygeocoder.


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


Alternatively, you can install the latest development version from GitHub:



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

library(dplyr, warn.conflicts = FALSE)

# create a dataframe with addresses
some_addresses <- tibble::tribble(
~name,                  ~addr,
"White House",          "1600 Pennsylvania Ave NW, 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 = 'osm', lat = latitude , long = longitude)
#> Passing 3 addresses to the Nominatim single address geocoder
#> Query completed in: 3 seconds

The geocode() function geocodes addresses contained in a dataframe. The Nominatim (“osm”) geocoding service is used here, but other services can be specified with the method argument. Only latitude and longitude are returned from the geocoding service in this example, but full_results = TRUE can be used to return all of the data from the geocoding service. See the geo() function documentation for details.

name addr latitude longitude
White House 1600 Pennsylvania Ave NW, Washington, DC 38.89770 -77.03655
Transamerica Pyramid 600 Montgomery St, San Francisco, CA 94111 37.79520 -122.40279
Willis Tower 233 S Wacker Dr, Chicago, IL 60606 41.87887 -87.63591

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


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

To perform reverse geocoding (obtaining addresses from geographic coordinates), we can use the reverse_geocode() function. The arguments are similar to the geocode() function, but now we specify the input data columns with the lat and long arguments. The input dataset used here is the results of the geocoding query above.

The single line address is returned in a column named by the address argument and all columns from the geocoding service results are returned because full_results = TRUE. See the reverse_geo() function documentation for more details.

reverse <- lat_longs %>%
  reverse_geocode(lat = latitude, long = longitude, method = 'osm',
                  address = address_found, full_results = TRUE) %>%
  select(-addr, -licence)
#> Passing 3 coordinates to the Nominatim single coordinate geocoder
#> Query completed in: 3 seconds
name latitude longitude address_found place_id osm_type osm_id osm_lat osm_lon office house_number road city state postcode country country_code boundingbox tourism neighbourhood building suburb county
White House 38.89770 -77.03655 White House, 1600, Pennsylvania Avenue Northwest, Washington, District of Columbia, 20500, United States 159983331 way 238241022 38.897699700000004 -77.03655315 White House 1600 Pennsylvania Avenue Northwest Washington District of Columbia 20500 United States us 38.8974908 , 38.897911 , -77.0368537, -77.0362519 NA NA NA NA NA
Transamerica Pyramid 37.79520 -122.40279 Transamerica Pyramid, 600, Montgomery Street, Chinatown, San Francisco, California, 94111, United States 106008002 way 24222973 37.795200550000004 -122.40279267840137 NA 600 Montgomery Street San Francisco California 94111 United States us 37.7948854 , 37.7954472 , -122.4031399, -122.4024317 Transamerica Pyramid Chinatown NA NA NA
Willis Tower 41.87887 -87.63591 Willis Tower, 233, South Wacker Drive, Printer’s Row, Loop, Chicago, Cook County, Illinois, 60606, United States 116070994 way 58528804 41.878871700000005 -87.63590784114558 NA 233 South Wacker Drive Chicago Illinois 60606 United States us 41.8785389 , 41.8791932 , -87.6363362, -87.6354746 NA Printer’s Row Willis Tower Loop Cook County

In the Wild

For inspiration, here are a few articles (with code) that leverage tidygeocoder:


Contributions to the tidygeocoder package are welcome. File an issue for bug fixes or suggested features. If you would like to contribute code such as adding support for a new geocoding service, reference the developer notes for instructions and documentation.

Citing tidygeocoder

Use the citation() function:


To cite tidygeocoder use:

  Cambon J, Hernangómez D, Belanger C, Possenriede D (2021).
  tidygeocoder: An R package for geocoding. Journal of Open Source
  Software, 6(65), 3544, https://doi.org/10.21105/joss.03544 (R package
  version 1.0.5)

A BibTeX entry for LaTeX users is

    title = {tidygeocoder: An R package for geocoding},
    author = {Jesse Cambon and Diego Hernangómez and Christopher Belanger and Daniel Possenriede},
    year = {2021},
    journal = {Journal of Open Source Software},
    publisher = {The Open Journal},
    doi = {10.21105/joss.03544},
    url = {https://doi.org/10.21105/joss.03544},
    volume = {6},
    number = {65},
    pages = {3544},
    note = {R package version 1.0.5},

Or refer to the citation page.