Tidygeocoder v1.0.0 is now live on CRAN. There are numerous new features and improvements such as batch geocoding (submitting multiple addresses per query), returning full results from geocoder services (not just latitude and longitude), address component arguments (city, country, etc.), query customization, and reduced package dependencies.

For a full list of new features and improvements refer to the release page on Github. For usage examples you can reference the Getting Started vignette.

To demonstrate a few of the new capabilities of this package, I decided to make a map of the stadiums for the UEFA Champions League Round of 16 clubs. To start, I looked up the addresses for the stadiums and put them in a dataframe.

library(dplyr)
library(tidygeocoder)
library(ggplot2)
require(maps)
library(ggrepel)

# https://www.uefa.com/uefachampionsleague/clubs/
stadiums <- tibble::tribble(
~Club,                ~Street,   ~City,   ~Country,
"Barcelona",          "Camp Nou", "Barcelona", "Spain",
"Bayern Munich",      "Allianz Arena", "Munich", "Germany",
"Chelsea",            "Stamford Bridge", "London", "UK",
"Borussia Dortmund",  "Signal Iduna Park", "Dortmund", "Germany",
"Juventus",           "Allianz Stadium", "Turin", "Italy",
"Liverpool",          "Anfield", "Liverpool", "UK",
"Olympique Lyonnais", "Groupama Stadium", "Lyon", "France",
"Man. City",          "Etihad Stadium", "Manchester", "UK",
"Napoli",             "San Paolo Stadium", "Naples", "Italy",
"Real Madrid",        "Santiago Bernabéu Stadium", "Madrid", "Spain",
"Tottenham",          "Tottenham Hotspur Stadium", "London", "UK",
"Valencia",           "Av. de Suècia, s/n, 46010", "Valencia", "Spain",
"Atalanta",           "Gewiss Stadium", "Bergamo", "Italy",
"Atlético Madrid",    "Estadio Metropolitano", "Madrid", "Spain",
"RB Leipzig",         "Red Bull Arena", "Leipzig", "Germany",
"PSG",                "Le Parc des Princes", "Paris", "France"
  )

To geocode these addresses, you can use the geocode function as shown below. New in v1.0.0, the street, city, and country arguments specify the address. The Nominatim (OSM) geocoder is selected with the method argument. Additionally, the full_results and custom_query arguments (also new in v1.0.0) are used to return the full geocoder results and set Nominatim’s “extratags” parameter which returns extra columns.

stadium_locations <- stadiums %>%
  geocode(street = Street, city = City, country = Country, method = 'osm', 
          full_results = TRUE, custom_query= list(extratags = 1))

This returns 40 columns including the longitude and latitude. A few of the columns returned due to the extratags argument are shown below.

stadium_locations %>%
  select(Club, City, Country, extratags.sport, extratags.capacity, extratags.operator, extratags.wikipedia) %>%
  rename_with(~gsub('extratags.', '', .)) %>%
  knitr::kable()
Club City Country sport capacity operator wikipedia
Barcelona Barcelona Spain soccer NA NA en:Camp Nou
Bayern Munich Munich Germany soccer 75021 NA de:Allianz Arena
Chelsea London UK soccer 41837 Chelsea Football Club en:Stamford Bridge (stadium)
Borussia Dortmund Dortmund Germany soccer NA NA de:Signal Iduna Park
Juventus Turin Italy soccer NA NA it:Allianz Stadium (Torino)
Liverpool Liverpool UK soccer 54074 Liverpool Football Club en:Anfield
Olympique Lyonnais Lyon France soccer 58000 Olympique Lyonnais fr:Parc Olympique lyonnais
Man. City Manchester UK soccer NA Manchester City Football Club en:City of Manchester Stadium
Napoli Naples Italy soccer NA NA en:Stadio San Paolo
Real Madrid Madrid Spain soccer 85454 NA es:Estadio Santiago Bernabéu
Tottenham London UK soccer;american_football 62062 Tottenham Hotspur en:Tottenham Hotspur Stadium
Valencia Valencia Spain NA NA NA NA
Atalanta Bergamo Italy soccer NA NA NA
Atlético Madrid Madrid Spain soccer NA NA es:Estadio Metropolitano (Madrid)
RB Leipzig Leipzig Germany NA NA NA de:Red Bull Arena (Leipzig)
PSG Paris France soccer 48527 Paris Saint-Germain fr:Parc des Princes

Below, the stadium locations are plotted on a map of Europe using the longitude and latitude coordinates and ggplot.

ggplot(stadium_locations, aes(x = long, y = lat)) +
  borders('world', xlim = c(-10, 10), ylim = c(40, 55)) +
  geom_label_repel(aes(label = Club), force = 2, segment.alpha = 0) + 
  geom_point() + theme_void() 

Alternatively, an interactive map can be created with the leaflet library:

library(leaflet)

stadium_locations %>% # Our dataset
  leaflet(width="100%", options = leafletOptions(attributionControl = FALSE)) %>%
  setView(lng = mean(stadium_locations$long), lat = mean(stadium_locations$lat), zoom = 5) %>%
  # Map Backgrounds
  addProviderTiles(providers$Stamen.Terrain, group='Terrain') %>%
  addProviderTiles(providers$NASAGIBS.ViirsEarthAtNight2012, group='Night') %>%
  addProviderTiles(providers$Stamen.Toner, group='Stamen') %>%
  addTiles(group = "OSM") %>%
  # Add Markers
  addMarkers(labelOptions = labelOptions(noHide = F), lng = ~long, lat = ~lat,
       clusterOptions = markerClusterOptions(maxClusterRadius = 10), label= ~Club,
       group="Stadiums") %>%
  # Map Control Options
  addLayersControl(baseGroups = c("OSM", "Stamen", "Terrain", "Night"),
       overlayGroups=c('Stadiums'),
       options = layersControlOptions(collapsed = TRUE))


If you find any issues with the package or have ideas on how to improve it, feel free to file an issue on Github. For reference, the RMarkdown file that generated this blog post can be found here.