customer-heatmap.r

library(tidyverse)
library(tidygeocoder)
library(ggmap)
library(viridis)

# import a dataset that has addresses in this format: "123 Royal Drive, Mesa, AZ 85206."
# If the address values are in separate columns (address, city, state, zip) you can use 
# dplyr::mutate and paste to build the combined address: 
# mydata <- mydata %>% mutate(address = paste0(add1,", ",city,", ",state," ",zip))

# first geocode your addresses (get latitude and longitude)
lat_longs <- mydata %>%
  geocode(address, method = 'osm', lat = latitude , long = longitude)

# get the map--you'll want to adjust the coordinates (bbox) and zoom level
bbox <- c(left = -112.4, bottom = 33.15, right = -111.48, top = 33.8)
map_phx <- ggmap(get_stamenmap(bbox, zoom = 10))

# Overlay heatmap on top of the map
map_phx +
  stat_density2d(data = lat_longs, aes(x = longitude, y = latitude, fill = after_stat(density)), geom = 'tile', contour = F, alpha = .5)+
  scale_fill_viridis(option = 'inferno')