The purpose of this lab is to get acquainted with using gghighlight and creating functions to further automate the processes we’re building.


To start out, I’ll be changing the data I’m using to North American Equidistant Conic. This will be helpful in preserving the distance between the cities and borders so my measurements are accurate. Additionally, since I’m only dealing with cities in the contiguous United States, a conic projection of North America will be more than adequate for my measurements.



Now it’s time to start measuring the distances to national, state, and Canade/Mexico borders.

National_Border <- st_cast(st_union(Conus_States), "MULTILINESTRING")
State_Border <- st_cast(st_combine(Conus_States), "MULTILINESTRING")
Mexico_Border <- st_cast(filter(Can_Mex_Borders, admin == "Mexico"),"MULTILINESTRING")
Canada_Border <- st_cast(filter(Can_Mex_Borders, admin == "Canada"),"MULTILINESTRING")

US_Conus_Cities <- US_Conus_Cities %>% 
  #add distance between the city and the nearest US national border in km
  mutate(dist_to_nat_border = set_units(st_distance(US_Conus_Cities, National_Border), "km")) %>% 
  #add distance to the nearest state border
  mutate(dist_to_state_border = set_units(st_distance(US_Conus_Cities, State_Border), "km")) %>% 
  #add the distance to mexico border
  mutate(dist_to_mex = set_units(st_distance(US_Conus_Cities, Mexico_Border), "km")) %>% 
  #add the distance from us cities to canada
  mutate(dist_to_can = set_units(st_distance(US_Conus_Cities, Canada_Border), "km")) %>% 
  #add the difference between the distance to mexico and canada as an absolute value in km
  mutate(dist_between_can_mex = set_units(abs(drop_units((st_distance(US_Conus_Cities, Canada_Border, "km") - st_distance(US_Conus_Cities, Mexico_Border, "km")))), "km")     )

Now table printing.

#first distance to national border
JCA_print_top_table(US_Conus_Cities, "dist_to_nat_border", sf_column_names = c("city", "state_name", "dist_to_nat_border"), new_column_names = c("City", "State", "Distance"), title = "Distance From National Border To City")
Distance From National Border To City
City State Distance
Dresden Kansas 1012.398 [km]
Herndon Kansas 1007.763 [km]
Hill City Kansas 1005.143 [km]
Atwood Kansas 1004.754 [km]
Jennings Kansas 1003.646 [km] 
#then distance to state border
JCA_print_top_table(US_Conus_Cities, "dist_to_state_border", sf_column_names = c("city", "state_name", "dist_to_state_border"), new_column_names = c("City", "State", "Distance"), title = "Distance from State Border to City")
Distance from State Border to City
City State Distance
Lampasas Texas 308.9216 [km]
Kempner Texas 302.5912 [km]
Bertram Texas 302.5703 [km]
Harker Heights Texas 298.8125 [km]
Florence Texas 298.6804 [km] 
#then dist to mexico
JCA_print_top_table(US_Conus_Cities, "dist_to_mex", sf_column_names = c("city", "state_name", "dist_to_mex"), new_column_names = c("City", "State", "Distance"), title = "Distance from City to Mexico Border")
Distance from City to Mexico Border
City State Distance
Caribou Maine 3250.334 [km]
Presque Isle Maine 3234.570 [km]
Calais Maine 3134.348 [km]
Passamaquoddy Pleasant Point Maine 3127.869 [km]
Eastport Maine 3125.624 [km] 
#dist to canada
JCA_print_top_table(US_Conus_Cities, "dist_to_can", sf_column_names = c("city", "state_name", "dist_to_can"), new_column_names = c("City", "State", "Distance"), title = "Distance from City to Canada Border")
Distance from City to Canada Border
City State Distance
Guadalupe Guerra Texas 2206.455 [km]
Fronton Texas 2204.790 [km]
Fronton Ranchettes Texas 2202.118 [km]
Evergreen Texas 2202.020 [km]
Ramos Texas 2201.882 [km]



Now it’s time to create the maps.

#first is the map of distance from the us border
ggplot()+
  geom_sf(data = Conus_States, fill = "#454742", col = "#8d8c8b", size = .5)+
  geom_sf(data = US_Conus_Cities, size=.001, aes(color = drop_units(dist_to_nat_border), alpha = .8))+
  scale_color_gradient(low = "#099bc4", high = "#47e88c")+
  geom_sf(data = slice_max(US_Conus_Cities, dist_to_nat_border, n = 5),size = .5, color = "#eef7f2")+
  ggrepel::geom_label_repel(data = slice_max(US_Conus_Cities, dist_to_nat_border, n = 5),
                            aes(label = city, geometry = geometry),
                            stat = "sf_coordinates",
                            size = 3,
                            color = "white",
                            fill = "#454742")+
  theme(panel.background = element_rect(fill = "#272727",
                                        colour = "#272727",
                                        size = .5,
                                        linetype = "dashed"),
        panel.grid.major = element_line(size = .5,
                                        color = "#3d3d3d",
                                        linetype = "solid"))+
   labs(title = "Distance from US National Border to US Cities",
         x = "",
         y ="")+
  theme(legend.position = "hide")

#turn this into a function

Figuring out how to make the Canada Mexico difference work.

ggplot()+
  geom_sf(data = Conus_States, fill = "#454742", col = "#8d8c8b", size = .5)+
  geom_sf(data = US_Conus_Cities, size=2, aes(color = drop_units(dist_between_can_mex), alpha = .8))+
  gghighlight(drop_units(dist_between_can_mex) < 100, unhighlighted_params = aes(alpha = .1, size = .001))+
  scale_color_gradient(low = "#099bc4", high = "#47e88c")+
  geom_sf(data = slice_max(US_Conus_Cities, dist_between_can_mex, n = 5), size = .5, color = "#eef7f2")+
  ggrepel::geom_label_repel(data = slice_min(US_Conus_Cities, drop_units(dist_between_can_mex), n = 5),
                            aes(label = city, geometry = geometry),
                            stat = "sf_coordinates",
                            size = 3,
                            color = "white",
                            fill = "#454742")+
  theme(panel.background = element_rect(fill = "#272727",
                                        colour = "#272727",
                                        size = .5,
                                        linetype = "dashed"),
        panel.grid.major = element_line(size = .5,
                                        color = "#3d3d3d",
                                        linetype = "solid"))+
     labs(title = "Equidistant from Canada and Mexico Border",
         x = "",
         y ="")+
  theme(legend.position = "hide")

#distance from city to us border
JCA_plot_sf_point_var_firefly(sf_base_poly = Conus_States, sf_point_data = US_Conus_Cities, slice_var_name = "dist_to_nat_border", "city", "Distance to US Border", "Top 5 States Farthest from US Border")

#distance from city to state border
JCA_plot_sf_point_var_firefly(sf_base_poly = Conus_States, sf_point_data = US_Conus_Cities, slice_var_name = "dist_to_state_border", "city", "Distance to US State Border", "Top 5 States Farthest from US State Border")

#equidistant between Mexico and Canada

#first we get a new sf with mutate between 
#JCA_plot_sf_point_var_firefly(sf_base_poly = Conus_States, sf_point_data = US_Conus_Cities, slice_var_name = #"dist_between_can_mex", "city", "Distance between Canada and Mexico", "Within 100 km", screen_points = "TRUE", #highlight_value = 100)
JCA_one_row_table <- function(sf_data, sf_max_col, sf_column_names, new_column_names, title = "Table"){
  sf_data %>% 
     
    kable(col.names = new_column_names) %>% 
    kable_material_dark(c("striped", "hover")) %>% 
    kable_styling(repeat_header_text = "Title") %>% 
    add_header_above(c(title ,"", ""))
}

#number of cities within 100 miles of US border CONVERT UNITS!!!
Border_Cities <- US_Conus_Cities %>% 
  filter(drop_units(set_units(dist_to_nat_border, "miles")) <= 100) %>% 
  summarize(city_within = n(), population_within = sum(population, na.rm = TRUE)) %>% 
  st_drop_geometry() %>%
  mutate(percent_pop_within = population_within / 332540980 * 100) 


JCA_one_row_table(Border_Cities, "percent_pop_within", c("city_within", "population_within", "percent_pop_within"), c("Cities Within 100 Miles of US Border", "Total Population Within", "Percentage of Population Within"), title = "US Customs and Border Protection Jurisdiction")
US Customs and Border Protection Jurisdiction
Cities Within 100 Miles of US Border Total Population Within Percentage of Population Within
12008 261619550 78.67288

I’d like help setting up a census api key so that I can use tidycensus.

Now it’s time to map the areas in danger.

ggplot()+
  geom_sf(data = Conus_States, fill = "#454742", col = "#8d8c8b", size = .5)+
  geom_sf(data = US_Conus_Cities, size=1, aes(color = drop_units(dist_to_nat_border), alpha = .8))+
  gghighlight(drop_units(set_units(dist_to_nat_border, "miles")) < 100, unhighlighted_params = aes(alpha = .1, size = .001))+
  scale_color_gradient(low = "darkred", high = "orange")+
  geom_sf(data = slice_max(filter(US_Conus_Cities, drop_units(set_units(dist_to_nat_border, "miles")) <= 100), population, n = 10), size = .5, color = "#eef7f2")+
  ggrepel::geom_label_repel(data = slice_max(filter(US_Conus_Cities, drop_units(set_units(dist_to_nat_border, "miles")) <= 100), population, n = 10),
                            aes(label = city, geometry = geometry),
                            stat = "sf_coordinates",
                            size = 3,
                            color = "white",
                            fill = "#454742")+
  theme(panel.background = element_rect(fill = "#272727",
                                        colour = "#272727",
                                        size = .5,
                                        linetype = "dashed"),
        panel.grid.major = element_line(size = .5,
                                        color = "#3d3d3d",
                                        linetype = "solid"))+
     labs(title = "Cities at Risk for US Border and Customs Intervention",
          subtitle = "Top Five Most Populous, at Risk Cities",
         x = "",
         y ="")+
  theme(legend.position = "hide")

Now the most populous city in each state within 100 miles.

ggplot()+
  geom_sf(data = Conus_States, fill = "#454742", col = "#8d8c8b", size = .5)+
  geom_sf(data = US_Conus_Cities, size=1, aes(color = drop_units(dist_to_nat_border), alpha = .8))+
  gghighlight(drop_units(set_units(dist_to_nat_border, "miles")) < 100, unhighlighted_params = aes(alpha = .1, size = .001))+
  scale_color_gradient(low = "darkred", high = "orange")+
  geom_sf(data = slice_max(group_by(filter(US_Conus_Cities, drop_units(set_units(dist_to_nat_border, "miles")) <= 100), state_name), population, n = 1), size = .5, color = "#eef7f2")+
  ggrepel::geom_label_repel(data = slice_max(group_by(filter(US_Conus_Cities, drop_units(set_units(dist_to_nat_border, "miles")) <= 100), state_name), population, n = 1),
                            aes(label = city, geometry = geometry),
                            stat = "sf_coordinates",
                            size = 3,
                            color = "white",
                            fill = "#454742",
                            max.overlaps = 400)+
  theme(panel.background = element_rect(fill = "#272727",
                                        colour = "#272727",
                                        size = .5,
                                        linetype = "dashed"),
        panel.grid.major = element_line(size = .5,
                                        color = "#3d3d3d",
                                        linetype = "solid"))+
  labs(title = "Cities at Risk for US Border and Customs Intervention",
          subtitle = "Most Populous, at Risk City Per State",
         x = "",
         y ="")+
  theme(legend.position = "hide")