add pop and housing growth

app/storm_report.qmd

@@ -23,12 +23,14 @@ params:
 ```{r setup, echo=FALSE}
 library(ggplot2)
 library(sf)
+sf_use_s2(FALSE)
 library(rnaturalearth)
 library(dplyr)
 library(scales)
 library(knitr)
 library(kableExtra)
 library(patchwork)
+library(lubridate)
 
 source(file = "queries.R")
 
@@ -38,6 +40,8 @@ storm <- list(
     storm_name = params$storm_name
 )
 
+latest_normalization_year <- get_latest_normalization_year()
+
 unique_lfs <- get_unique_lf_ids(storm)
 hurdat_id <- get_hurdat_id(storm)
 
@@ -330,6 +334,31 @@ storm_track %>%
 ```{r normalization, echo=FALSE}
 #| out-width: "100%"
 
+base_yearly_econ <- get_yearly_economics(storm$storm_year) 
+base_yearly_usa <- get_yearly_usa_pop_hu(storm$storm_year)
+
+base_yearly_metrics <- base_yearly_usa %>% 
+  left_join(base_yearly_econ, by = "year") %>% 
+  select(-aggregate_storm_loss)
+
+ref_yearly_econ <- get_yearly_economics(latest_normalization_year)
+ref_yearly_usa <- get_yearly_usa_pop_hu(latest_normalization_year)
+
+ref_yearly_metrics <- ref_yearly_usa %>% 
+  left_join(ref_yearly_econ, by = "year") %>% 
+  select(-aggregate_storm_loss)
+
+# year, ccn, gdp, ag loss
+# base...
+# ref...
+
+# table cols metric, base year, ref year, ratio
+# ccn
+# gdp
+# us pop
+# us hous
+# rwpc/hu
+
 costs <- get_all_normalized_cost_index(storm)
 
 # for each lf, create charts of mmh/mmp
@@ -367,4 +396,161 @@ for(lf in unique_lf_ids) {
   print(mmh_plot)
   print(mmp_plot)
 }
+```
+
+## Population and Housing Growth
+
+```{r growth, echo=FALSE}
+#| out-width: "100%"
+
+for(lf in unique_lf_ids) {
+  growth <- get_normalized_metric_growth(storm, lf) %>%
+    mutate(
+      population_opacity = rescale(
+        normalized_population,
+        to = c(0.2, 0.8),
+        from = range(normalized_population, na.rm = T)
+      ),
+      housing_opacity = rescale(
+        normalized_housing,
+        to = c(0.2, 0.8),
+        from = range(normalized_housing, na.rm = T)
+      )
+    ) 
+
+  growth <- get_county_and_state(growth)
+
+  growth_sf <- growth %>%
+    st_as_sf(wkt = "geom_wkt", crs = 4326) %>% 
+    filter(year == max(year))
+
+  combined_geom <- st_union(growth_sf)
+
+  bbox <- st_bbox(combined_geom)
+
+  lon_range <- c(bbox["xmin"], bbox["xmax"])
+  lat_range <- c(bbox["ymin"], bbox["ymax"])
+
+  padding_percent <- 0.1
+
+  lon_padding <- diff(lon_range) * padding_percent
+  lat_padding <- diff(lat_range) * padding_percent
+
+  h_growth <- ggplot() +
+    geom_sf(data = world, fill = "#E5E5E5", color = "#999999", size = 0.3) +
+    geom_sf(data = states, fill = NA, color = "#CCCCCC", size = 0.2) +
+    geom_sf(data = growth_sf, fill = "blue", color = "blue", alpha = growth_sf$housing_opacity) +
+    geom_sf_label(data = growth_sf, aes(label = growth_sf$name)) +
+    coord_sf(
+      xlim = c(lon_range[1] - lon_padding, lon_range[2] + lon_padding),
+      ylim = c(lat_range[1] - lat_padding, lat_range[2] + lat_padding),
+      expand = FALSE
+    ) +
+    labs(
+      x = "",
+      y = ""
+    )
+    theme_minimal() +
+    theme(
+      panel.background = element_rect(fill = "#D4E6F1"),
+      panel.grid.major = element_line(color = "#BBBBBB", size = 0.2),
+      legend.position = "right",
+      legend.key.size = unit(0.4, "cm"),
+      legend.text = element_text(size = 8)
+    ) +
+    plot_theme
+
+  latest_opacity <- growth %>%
+    filter(year == max(year)) %>%
+    select(county_fips, name, housing_opacity, population_opacity)
+
+  growth_colored <- growth %>%
+    left_join(latest_opacity, by = c("county_fips", "name"), suffix = c("", "_latest"))
+
+  housing_colors <- latest_opacity %>%
+    distinct(name, housing_opacity) %>%
+    arrange(desc(housing_opacity)) %>%
+    mutate(
+      alpha_hex = sprintf("%02X", round(housing_opacity * 255)),
+      color_with_alpha = paste0("#0000FF", alpha_hex)
+    )
+
+  housing_color_values <- setNames(housing_colors$color_with_alpha, housing_colors$name)
+
+  growth_colored <- growth_colored %>%
+    mutate(name = factor(name, levels = housing_colors$name))
+
+  mmh_plot <- ggplot(growth_colored, aes(x = year, y = normalized_housing,
+                                          color = name,
+                                          group = county_fips)) +
+    geom_line() +
+    scale_color_manual(values = housing_color_values) +
+    guides(color = guide_legend(override.aes = list(linewidth = 3))) +
+    labs(
+      title = paste(lf, "Housing Growth"),
+      x = "Year",
+      y = "Normalized Growth",
+      color = "County"
+    ) +
+    theme_minimal() +
+    plot_theme
+
+  print(h_growth)
+  print(mmh_plot)
+
+  p_growth <- ggplot() +
+    geom_sf(data = world, fill = "#E5E5E5", color = "#999999", size = 0.3) +
+    geom_sf(data = states, fill = NA, color = "#CCCCCC", size = 0.2) +
+    geom_sf(data = growth_sf, fill = "red", color = "red", alpha = growth_sf$population_opacity) +
+    geom_sf_label(data = growth_sf, aes(label = growth_sf$name)) +
+    coord_sf(
+      xlim = c(lon_range[1] - lon_padding, lon_range[2] + lon_padding),
+      ylim = c(lat_range[1] - lat_padding, lat_range[2] + lat_padding),
+      expand = FALSE
+    ) +
+    labs(
+      x = "",
+      y = ""
+    )
+    theme_minimal() +
+    theme(
+      panel.background = element_rect(fill = "#D4E6F1"),
+      panel.grid.major = element_line(color = "#BBBBBB", size = 0.2),
+      legend.position = "right",
+      legend.key.size = unit(0.4, "cm"),
+      legend.text = element_text(size = 8)
+    ) +
+    plot_theme
+
+  population_colors <- latest_opacity %>%
+    distinct(name, population_opacity) %>%
+    arrange(desc(population_opacity)) %>%
+    mutate(
+      alpha_hex = sprintf("%02X", round(population_opacity * 255)),
+      color_with_alpha = paste0("#FF0000", alpha_hex)
+    )
+
+  population_color_values <- setNames(population_colors$color_with_alpha, population_colors$name)
+
+  growth_colored <- growth_colored %>%
+    mutate(name = factor(name, levels = population_colors$name))
+
+  mmp_plot <- ggplot(growth_colored, aes(x = year, y = normalized_population,
+                                          color = name,
+                                          group = county_fips)) +
+    geom_line() +
+    scale_color_manual(values = population_color_values) +
+    guides(color = guide_legend(override.aes = list(linewidth = 3))) +
+    labs(
+      title = paste(lf, "Population Growth"),
+      x = "Year",
+      y = "Normalized Growth",
+      color = "County"
+    ) +
+    theme_minimal() +
+    plot_theme
+
+  print(p_growth)
+  print(mmp_plot)
+}
 ```