This post is an update from the previous post, “How to geocode a CSV of addresses in R”. We will be using the
ggmap package again, and be sure to investigate the usage and billing policy for Google’s Geocoding API. The API now has a pay-as-you-go model, and every account gets a certain number of requests free per month,
For each billing account, for qualifying Google Maps Platform SKUs, a $200 USD Google Maps Platform credit is available each month, and automatically applied to the qualifying SKUs.Read more: https://developers.google.com/maps/documentation/geocoding/start
The ggmap package in R Studio
The code below will install and load the
tidyverse packages. We’ve covered the
tidyverse in previous posts, but all you need to know right now is that we’ll be using the
purrr package for iteration.
install.packages(c("tidyverse", "ggmap")) library(tidyverse) library(ggmap)
Import the data
In the previous post, we used a dataset of breweries in Boston, MA. In this post, we’ll be using a subset of the data from the Open Beer Database, which we’ve downloaded as a CSV.
The code below imports these data into RStudio as
BeerDataUS. We’ll use the
dplyr::glimpse() function to take a look at these data.
BeerDataUS <- readr::read_csv(file = "data/BeerDataUS.csv") dplyr::glimpse(BeerDataUS) #> Rows: 823 #> Columns: 6 #> $ brewer <chr> "McMenamins Mill Creek", "Diamond Knot Brewery & Alehouse",… #> $ address <chr> "13300 Bothell-Everett Highway #304", "621 Front Street", "… #> $ city <chr> "Mill Creek", "Mukilteo", "Everett", "Mount Vernon", "Phila… #> $ state <chr> "Washington", "Washington", "Washington", "Washington", "Pe… #> $ country <chr> "United States", "United States", "United States", "United … #> $ website <chr> NA, "http://www.diamondknot.com/", NA, NA, "http://www.inde…
We have a dataset with all the components of a physical address, but no latitude and longitude values.
Setting up your GeoCoding API key
Your private API key needs to be registered with
ggmap::register_google(key = "[your key]"). More detailed instructions have been pasted below from the package Github page:
Inside R, after loading the new version of
ggmap, you’ll need provide
ggmapwith your API key, a hash value (think string of jibberish) that authenticates you to Google’s servers. This can be done on a temporary basis with
register_google(key = "[your key]")or permanently using
register_google(key = "[your key]", write = TRUE)(note: this will overwrite your
~/.Renvironfile by replacing/adding the relevant line). If you use the former, know that you’ll need to re-do it every time you reset R.
After registering your API key, you’ll have access to the two functions for getting geocodes in
Create a location column
We need the address items into a new
BeerDataUS <- BeerDataUS %>% tidyr::unite(data = ., # combine all the location elements address, city, state, country, # new name for variable col = "location", # separated by comma sep = ", ", # keep the old columns remove = FALSE) # check the new variable BeerDataUS %>% dplyr::pull(location) %>% utils::head() #>  "13300 Bothell-Everett Highway #304, Mill Creek, Washington, United States" #>  "621 Front Street, Mukilteo, Washington, United States" #>  "1524 West Marine View Drive, Everett, Washington, United States" #>  "404 South Third Street, Mount Vernon, Washington, United States" #>  "1150 Filbert Street, Philadelphia, Pennsylvania, United States" #>  "1812 North 15th Street, Tampa, Florida, United States"
We’ll follow the iteration strategy from Charlotte Wickham’s tutorial on purrr.
Get a geocode for a single element
This will produce the following output:
single_element <- base::sample(x = BeerDataUS$location, size = 1, replace = TRUE) single_element ggmap::geocode(location = single_element)
> ggmap::geocode(location = single_element) Source : https://maps.googleapis.com/maps/api/geocode/json?address=NA,+Stillwater,+Minnesota,+United+States&key=xxx
Turn it into a recipe
Now we turn this into a
purrr::recipe. We’ll use the
purrr::map_df() variant to get the result in a
GeoCoded <- purrr::map_df(.x = BeerDataUS$location, .f = ggmap::geocode)
This will take some time, and you should see the following in the console:
When it’s done, you’ll have the following new dataset:
utils::head(x = GeoCoded) # A tibble: 6 x 2 lon lat <dbl> <dbl> 1 -122. 47.9 2 -122. 47.9 3 -122. 48.0 4 -122. 48.4 5 -75.2 40.0 6 -82.4 28.0
Now we just need to column bind these to the
BeerDataUS dataset and create a new
popuptext column. We’ll also rename the
lon column as
lng because it’s more common in the graphics we’ll be building.
GeoCodedBeerData <- dplyr::bind_cols(BeerDataUS, GeoCoded) %>% dplyr::select( brewer, lng = lon, lat, dplyr::everything()) # create a popuptext column GeoCodedBeerData <- GeoCodedBeerData %>% dplyr::mutate(popuptext = base::paste0("<b>", GeoCodedBeerData$brewer, "</b><br />", "<i>", GeoCodedBeerData$address, ", ", GeoCodedBeerData$city, "</i><br />", "<i>", GeoCodedBeerData$state, "</i>")) utils::head(GeoCodedBeerData)
Now that we have a dataset with latitude and longitude columns, we can start mapping the data!
Create a map!
We will use the
leaflet package to generate a quick map of brewery locations in the US.
# get a random lat and lng for the setView() # GeoCodedBeerData %>% # dplyr::sample_n(size = 1) %>% # dplyr::select(lat, lng) leaflet::leaflet(data = GeoCodedBeerData) %>% leaflet::addTiles() %>% leaflet::setView(lng = -96.50923, # random location lat = 39.19729, zoom = 4) %>% # zoom in on US leaflet::addCircles(color = "red", lng = ~lng, lat = ~lat, weight = 1.5, popup = ~popuptext)