Density of zipcodes in US. Data from https://public.opendatasoft.com/explore/dataset/us-zip-code-latitude-and-longitude/export/ copying tutorial at https://unconj.ca/blog/not-all-population-maps-are-boring.html

zipcodes.r

#load the libraries
library(ggplot2)
library(grid)
library(dplyr)

#load the data
#https://public.opendatasoft.com/explore/dataset/us-zip-code-latitude-and-longitude/export/ 
zips <-
    read.csv("us-zip-code-latitude-and-longitude.csv",
             header = TRUE, stringsAsFactors = TRUE,sep=";")
             
             
#add in id row
zips$ID <- seq.int(nrow(zips))

#just take data we want for now. Theres cool stuff in this dataset though
zips<-zips%>%
  select(ID, Latitude, Longitude)

#filter out very far south places that do not look right
zips<-zips %>%
  filter(Latitude>10)

#copy method in this Australian public toilet tutorial 
#https://unconj.ca/blog/not-all-population-maps-are-boring.html
#make a map
p <-
    ggplot(zips, aes(x = Longitude, y = Latitude)) +
    stat_binhex(
        colour = NA,
        aes(fill = cut(..count.., c(0, 5, 10, 50, 100,
                                    500, 1000, Inf)))
    ) +
    coord_equal() +
    labs(fill = NULL) +
    scale_fill_brewer(
        palette = "OrRd",
        labels = c("<5 ", "5-9 ", "10-49 ", "50-99 ",
                   "100-499 ", "500-999 ", "1000+  ")
    )

print(p)
#clean it up a bit
require(grid, quiet = TRUE) # For the unit() function.
p <-p + labs(fill = "Number zipcodes")
p <- p +
    #geom_text(aes(x = long, y = lat, label = name, vjust = vjust,
     #             hjust = hjust), data = poi, size = 4) +
    theme(panel.background = element_rect(fill = "gray90", colour = NA),
          plot.background = element_rect(fill = "gray90", colour = NA),
          # Remove titles, ticks, gridlines, and borders.
          axis.text = element_blank(),
          axis.title = element_blank(),
          axis.ticks = element_blank(),
          panel.grid = element_blank(),
          panel.border = element_blank(),
          # Set the legend background.
          legend.background = element_rect(fill = NA, colour = NA),
          legend.key = element_rect(fill = NA, colour = NA),
    ) 

print(p)
#save image
p<-p+ggtitle("Zip code Density in the US")+theme(plot.title = element_text(hjust = 0.5,size=20, face="bold"))
ggsave("zipcodes.png", height=5, width=10)