cty.info <- get_acs(geography = "county", 
                      variables = c(pop="B01003_001", 
                                    medincome = "B19013_001"),
                      survey="acs5",
                      state = c("WA", "OR", "ID", "MT", "WY"),
                      geometry = TRUE, key = censkey, progress_bar=FALSE) %>% 
  select(., -moe) %>% 
  pivot_wider(
    names_from = "variable",
    values_from = "estimate"
  )

p <- ggplot(data=cty.info) +
  geom_sf(mapping=aes(fill=medincome))

p <- ggplot(data=cty.info) +
  geom_sf(mapping=aes(fill=pop), color="white") +
  scale_fill_viridis()

st <- tigris::states(progress_bar=FALSE) %>% 
  filter(., STUSPS %in% c("WA", "OR", "ID", "MT", "WY"))

p <- ggplot(data=cty.info) +
  geom_sf(mapping=aes(fill=pop), color="white") +
  geom_sf(data=st, fill=NA, color="red") +
  scale_fill_viridis()

pt <- tm_shape(cty.info) + 
  tm_polygons(col = "pop",
              border.col = "white") + 
  tm_legend(outside = TRUE)

pt <- tm_shape(cty.info) + 
  tm_polygons(col = "pop", n=10,palette=viridis(10),
              border.col = "white") + 
  tm_legend(outside = TRUE)

pt <- tm_shape(cty.info) + 
  tm_polygons(col = "pop", n=10,palette=viridis(10),
              border.col = "white") + 
  tm_shape(st) +
  tm_borders("red") +
  tm_legend(outside = TRUE)

p

p + 
  theme_void()

p + 
  theme_void() +
  theme(panel.background = element_rect(fill="white", color="black"))

p +
  labs(title = "County Populations")

ggplot(data=cty.info) +
  geom_sf(mapping=aes(fill=pop), color="white") +
  geom_sf(data=st, fill=NA, color="red") +
  scale_fill_viridis(name = "Population\n(millions)",
                     breaks = seq(from=0, to=3e6, by=1e6),
                     labels = seq(from=0, to=3e6, by=1e6)/1e6)

ggplot(data=cty.info) +
  geom_sf(mapping=aes(fill=pop), color="white") +
  geom_sf(data=st, fill=NA, color="red") +
  scale_fill_viridis(name = "Population\n(millions)",
                     limits = c(0, 3e6),
                     breaks = seq(from=0, to=3e6, by=1e6),
                     labels = seq(from=0, to=3e6, by=1e6)/1e6)

fire.haz <- rast("/opt/data/data/assignment01/wildfire_hazard_agg.tif")

fire.haz_df <- as.data.frame(fire.haz)

str(fire.haz_df)

ggplot(data = fire.haz_df, aes(x=x, y=y, fill = WHP_ID)) +
  geom_raster()

ggplot(data = fire.haz_df, aes(x=x, y=y, fill = WHP_ID)) +
  geom_raster() +
  coord_sf(default_crs = crs(fire.haz))

# get data
states <- tigris::states(progress_bar=FALSE) %>% 
  filter(., STUSPS %in% c("WA", "OR", "ID")) %>%
  st_transform(crs = st_crs(fire.haz))

# method 1
# first dataset in ggplot() and use inherit.aes=FALSE
p2 <- ggplot(data = fire.haz_df, aes(x=x, y=y, fill = WHP_ID)) +
  geom_raster() +
  geom_sf(data = states, fill=NA, color="red", inherit.aes = FALSE) +
  coord_sf(default_crs = crs(fire.haz))

# method 2
# first dataset in geom so inherit.aes isn't necessary
p3 <- ggplot() +
  geom_raster(data = fire.haz_df, aes(x=x, y=y, fill = WHP_ID)) +
  geom_sf(data = states, fill=NA, color="red") +
  coord_sf(default_crs = crs(fire.haz))

p2+p3

p2+p3 + plot_layout(guides="collect")

p+p3 + plot_layout(nrow=2)

# get data (filter to continuous US)
conus <- tigris::states() %>%
  filter(!(DIVISION == 0 | STUSPS %in% c("HI", "AK"))) %>%
  st_transform(st_crs(fire.haz))

# create inset map
inset <- ggplot(data = conus) +
  geom_sf(fill="white") +
  # fill study area states by filtering to those states and setting color parameters
  geom_sf(data = filter(conus, STUSPS %in% c("ID", "OR", "WA")),
          fill = "gray70", color="red") +
  theme_void() +
  theme(panel.background = element_rect(fill="white", color="black")) +
  coord_sf()

layout <- c(
  patchwork::area(t = 1, l = 1, b = 5, r = 4),
  patchwork::area(t = 1, l = 4, b = 2, r = 5),
  patchwork::area(t = 3, l = 4, b = 5, r = 5)
)

p2 + inset + guide_area() + plot_layout(design=layout, guides='collect')