Question 1

#continental US States
USAboundaries::us_states(resolution = "low") %>%
  st_as_sf(coords = c("lng", "lat"), crs = 4326) %>%
  filter(!state_name %in% c("Puerto Rico",
                            "Alaska",
                            "Hawaii")) ->
  conus

#set projected coordinate system to 'eqdc'
eqdc = '+proj=eqdc +lat_0=40 +lon_0=-96 +lat_1=20 +lat_2=60 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs'

st_transform(conus, eqdc) -> conus
#country boundaries for Mexico, the United States of America, and Canada
rnaturalearthdata::countries110 -> countries110

st_as_sf(countries110, crs = 4326) -> worldboundaries

st_transform(worldboundaries, eqdc) -> worldboundaries
#loading cities from CSV file 
readr::read_csv("~/github/geog-176A-labs/data/uscities.csv") %>%
  st_as_sf(coords = c("lng", "lat"), crs = 4326) %>%
  filter(!state_name %in% c("Hawaii", "Alaska", "Puerto Rico")) %>%
         st_transform(., eqdc) ->
  uscities

Question 2

#distance to US borders (2.1)

st_union(conus) %>%
  st_cast("MULTILINESTRING") ->
  USboundaries

uscities %>%
  select(city, state_name) %>%
  mutate(dist = st_distance(uscities, USboundaries),
  dist = units::set_units(dist, "km"),
  dist = units::drop_units(dist)) ->
  border_dist

border_dist %>%
  slice_max(dist, n = 5) %>%
  st_drop_geometry() ->
  top5_to_border

knitr::kable(top5_to_border,
             caption = "Distance of US Cities to US Borders",
             col.names = c("City", "State", "Distance to Border")) %>%
    kable_styling(bootstrap_options = "striped")
Distance of US Cities to US Borders
City State Distance to Border
Dresden Kansas 1012.317
Herndon Kansas 1007.750
Hill City Kansas 1005.147
Atwood Kansas 1004.734
Jennings Kansas 1003.646 
#2.2 calculating the distance of each city to the nearest state boundary
st_combine(conus) %>%
  st_cast("MULTILINESTRING") ->
  USboundaries2

uscities %>%
  select(city, state_name) %>%
  mutate(dist2 = st_distance(uscities, USboundaries2),
         dist2 = units::set_units(dist2, "km"),
         dist2 = units::drop_units(dist2)) ->
  border_dist2

border_dist2 %>%
  slice_max(dist2, n = 5) %>%
  st_drop_geometry() ->
  top5_to_stateborder

knitr::kable(top5_to_stateborder,
             caption = "Distance of US Cities to US State Borders",
             col.names = c("City", "State", "Distance to State Border")) %>%
    kable_styling(bootstrap_options = "striped")
Distance of US Cities to US State Borders
City State Distance to State Border
Lampasas Texas 308.9216
Bertram Texas 302.8190
Kempner Texas 302.5912
Harker Heights Texas 298.8125
Florence Texas 298.6804 
#2.3 calculating the distance of each city to the Mexican Border
worldboundaries %>%
  st_cast("MULTILINESTRING") %>%
  filter(admin == "Mexico") ->
  mexico_border

uscities %>%
  select(city, state_name) %>%
  mutate(dist3 = st_distance(uscities, mexico_border),
         dist3 = units::set_units(dist3, "km"),
         dist3 = units::drop_units(dist3)) ->
  border_dist3

border_dist3 %>%
  slice_max(dist3, n = 5) %>%
  st_drop_geometry() ->
  top5_to_mexico

knitr::kable(top5_to_mexico,
             caption = "Distance of US Cities to Mexican Border",
             col.names = c("City", "State", "Distance to Mexican Border")) %>%
    kable_styling(bootstrap_options = "striped")
Distance of US Cities to Mexican Border
City State Distance to Mexican Border
Caribou Maine 3250.334
Presque Isle Maine 3234.570
Calais Maine 3134.348
Eastport Maine 3125.624
Old Town Maine 3048.366 
#2.4 calculating the distance of each city to the Canadian border
worldboundaries %>%
  st_cast("MULTILINESTRING") %>%
  filter(admin == "Canada") ->
  canada_border

uscities %>%
  select(city, state_name) %>%
  mutate(dist4 = st_distance(uscities, canada_border),
         dist4 = units::set_units(dist4, "km"),
         dist4 = units::drop_units(dist4)) ->
  border_dist4

border_dist4 %>%
  slice_max(dist4, n = 5) %>%
  st_drop_geometry() ->
  top5_to_canada

knitr::kable(top5_to_canada,
             caption = "Distance of US Cities to Canadian Border",
             col.names = c("City", "State", "Distance to Canadian Border")) %>%
    kable_styling(bootstrap_options = "striped")
Distance of US Cities to Canadian Border
City State Distance to Canadian Border
Guadalupe Guerra Texas 2206.455
Sandoval Texas 2205.641
Fronton Texas 2204.784
Fronton Ranchettes Texas 2202.118
Evergreen Texas 2202.020

Question 3

#3.1 10 largest USA cities (by population)
uscities %>%
  slice_max(population, n = 10) ->
  top10_pop

ggplot() +
  geom_sf(data = conus) +
  geom_sf(data = top10_pop$geometry, add = TRUE, size= 2, color = "red") +
  ggrepel::geom_label_repel(
    data = top10_pop,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 3) +
  labs(title = "10 Largest US Cities by Population") +
  ggthemes::theme_map() 

#3.2 City Distance from the National Border
border_dist %>%
  slice_max(dist, n = 5) ->
  top5_to_bordermap

ggplot() +
  geom_sf(data = USboundaries) +
  geom_sf(data = border_dist, aes(col = dist), size = .1) +
  geom_sf(data = top5_to_bordermap, col = "red", size = 1) +
  scale_color_gradient(low = "grey", high = "blue") +
  ggrepel::geom_label_repel(
    data = top5_to_bordermap,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 3) +
  labs(title = "US Cities' Distance from National Border") +
  ggthemes::theme_map()

#3.3 City Distance from the Nearest State Border
border_dist2 %>%
  slice_max(dist2, n = 5) ->
  top5_to_statebordermap

ggplot() +
  geom_sf(data = USboundaries2) +
  geom_sf(data = border_dist2, aes(col = dist2), size = .1) +
  geom_sf(data = top5_to_statebordermap, col = "red", size = 1) +
  scale_color_gradient(low = "grey", high = "blue") +
  ggrepel::geom_label_repel(
    data = top5_to_statebordermap,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 3) + 
  labs(title = "US Cities' Distance from State Borders") +
  ggthemes::theme_map()

#3.4
uscities %>%
  select(city, state_name, population) %>%
  mutate(dist3 = st_distance(uscities, mexico_border),
         dist3 = units::set_units(dist3, "km"),
         dist3 = units::drop_units(dist3)) %>%
  mutate(dist4 = st_distance(uscities, canada_border),
         dist4 = units::set_units(dist4, "km"),
         dist4 = units::drop_units(dist4)) %>%
  mutate(diffdist = abs(dist3 - dist4)) ->
  equaldistance

equaldistance %>% 
  filter(diffdist <= 100) %>%
  slice_max(population, n = 5) ->
  ED_pop

ggplot() +
  geom_sf(data = USboundaries2) +
  geom_sf(data = equaldistance, aes(col = diffdist), size = .1) +
  scale_color_gradient(low = "grey", high = "blue") +
  gghighlight::gghighlight(diffdist <= 100) +
  ggrepel::geom_label_repel(
    data = ED_pop,
    aes(label = city, geometry = geometry),
    stat = "sf_coordinates",
    size = 3) +
  labs(title = "Cities that are equal distance from the Canadian and Mexican border ± 100 km") +
  ggthemes::theme_map()

#4.1 Cities Within 100 Miles from Borders
uscities %>%
  select(city, state_name, population) %>%
  mutate(dist = st_distance(uscities, USboundaries),
         dist = units::set_units(dist, "km"),
         dist = units::drop_units(dist)) %>%
  filter(dist <= 160) %>%
  st_drop_geometry() ->
  bordercities

bordercities %>%
  summarize(population) %>%
  sum()
## [1] 259935815
data.frame(number_cities = "12283",
           pop = "259935815") -> table

knitr::kable(table,
             caption = "Cities Near National Border",
             col.names = c("Number of Cities", "Population")) %>%
  kable_styling(bootstrap_options = "striped")
Cities Near National Border
Number of Cities Population
12283 259935815