There are many ways to achieve this task. The general idea is to convert both the point data and polygon data to spatial objects. After that, count how many points fall within that polygon. I know we can do this using the sp package, which is widespread and well-known in the R community, but I decided to use the sf package because sf would be the next generation standard of spatial objects in R (https://cran.r-project.org/web/packages/sf/index.html). Knowing the usage and functionality of sf will probably be beneficial.
First, the OP provided an example point, but I decided to add more points so that we can see how to count the points and aggregate the data. To do so, I used the ggmap pakcage to geocode some cities that I selected as an example.
# Load package
library(tidyverse)
library(ggmap)
library(maps)
library(maptools)
library(sf)
# Create point data as a data frame
point_data <- data.frame(lat = 40.730610, lon = -73.935242)
# Geocode a series of cities
city <- c("Detroit", "Seattle", "Toranto", "Denver", "Mexico City", "Paris", "New Orleans",
"Tokyo", "Osaka", "Beijing", "Canberra", "New York", "Istanbul", "New Delhi",
"London", "Taipei", "Seoul", "Manila", "Bangkok", "Lagos", "Chicago", "Shanghai")
point_data2 <- geocode(city)
# Combine OP's example and the geocoding result
point_data3 <- bind_rows(point_data, point_data2)
Next, I converted the point_data3 data frame to the sf object. I will also get the polygon data of the world using the maps package and convert it to an sf object.
# Convert to simple feature object
point_sf <- st_as_sf(point_data3, coords = c("lon", "lat"), crs = 4326)
# Get world map data
worldmap <- maps::map("world", fill = TRUE, plot = FALSE)
# Convert world to sp class
IDs <- sapply(strsplit(worldmap$names, ":"), "[", 1L)
world_sp <- map2SpatialPolygons(worldmap, IDs = IDs,
proj4string = CRS("+proj=longlat +datum=WGS84"))
# Convert world_sp to simple feature object
world_sf <- st_as_sf(world_sp)
# Add country ID
world_sf <- world_sf %>%
mutate(region = map_chr(1:length(world_sp@polygons), function(i){
world_sp@polygons[[i]]@ID
}))
Now both point_sf and world_sf are sf objects. We can use the st_within function to examine which points are within which polygons.
# Use st_within
result <- st_within(point_sf, world_sf, sparse = FALSE)
# Calculate the total count of each polygon
# Store the result as a new column "Count" in world_sf
world_sf <- world_sf %>%
mutate(Count = apply(result, 2, sum))
The total count information is in the Count column of world_sf. We can get the world data frame as the OP did using the map_data function. We can then merge world_data and world_df.
# Convert world_sf to a data frame world_df
world_df <- world_sf
st_geometry(world_df) <- NULL
# Get world data frame
world_data <- map_data("world")
# Merge world_data and world_df
world_data2 <- world_data %>%
left_join(world_df, by = c("region"))
Now we are ready to plot the data. The following code is the same as the OP's ggplot code except that the input data is now world_data2 and fill = Count.
ggplot() +
geom_polygon(data = world_data2, aes(x = long, y = lat, group = group, fill = Count)) +
coord_fixed(1.3)
