add better map rendering

app/storm_report.qmd

@@ -31,6 +31,8 @@ library(knitr)
 library(kableExtra)
 library(patchwork)
 library(lubridate)
+library(ggrepel)
+library(ggtext)
 
 source(file = "queries.R")
 
@@ -294,7 +296,7 @@ storm_track %>%
   ) %>%
   kable_styling(
     #latex_options = c("striped"),
-    font_size = 9,
+    font_size = 11,
     position = "center"
   ) %>%
   column_spec(
@@ -372,7 +374,7 @@ for(lf in unique_lf_ids) {
     )
   
   mmh_plot <- ggplot(filtered_costs, aes(x = normalization_year, y = mmh_loss)) +
-    geom_area(fill = "blue", alpha = 0.7) +
+    geom_line(color = "blue") +
     scale_y_continuous(labels = scales::label_dollar(scale_cut = cut_short_scale())) +
     labs(
       title = paste(lf, "MMH Loss"),
@@ -383,7 +385,7 @@ for(lf in unique_lf_ids) {
     plot_theme
 
   mmp_plot <- ggplot(filtered_costs, aes(x = normalization_year, y = mmp_loss)) +
-    geom_area(fill = "red", alpha = 0.7) +
+    geom_line(color = "red") +
     scale_y_continuous(labels = scales::label_dollar(scale_cut = cut_short_scale())) +
     labs(
       title = paste(lf, "MMP Loss"),
@@ -402,6 +404,7 @@ for(lf in unique_lf_ids) {
 
 ```{r growth, echo=FALSE}
 #| out-width: "100%"
+#| fig-align: "left"
 
 for(lf in unique_lf_ids) {
   growth <- get_normalized_metric_growth(storm, lf) %>%
@@ -431,15 +434,32 @@ for(lf in unique_lf_ids) {
   lon_range <- c(bbox["xmin"], bbox["xmax"])
   lat_range <- c(bbox["ymin"], bbox["ymax"])
 
+  lon_width <- diff(lon_range)
+  lat_height <- diff(lat_range)
+
+  target_ratio <- 16/9
+  current_ratio <- lon_width / lat_height
+
+  if (current_ratio < target_ratio) {
+    needed_width <- lat_height * target_ratio
+    extra_width <- (needed_width - lon_width) / 2
+    lon_range <- c(lon_range[1] - extra_width, lon_range[2] + extra_width)
+  }
+
+  if (current_ratio > target_ratio) {
+    needed_height <- lon_width / target_ratio
+    extra_height <- (needed_height - lat_height) / 2
+    lat_range <- c(lat_range[1] - extra_height, lat_range[2] + extra_height)
+  }
+
   padding_percent <- 0.1
 
   lon_padding <- diff(lon_range) * padding_percent
   lat_padding <- diff(lat_range) * padding_percent
 
-  h_growth <- ggplot() +
+  base_growth_map <- ggplot() +
-    geom_sf(data = world, fill = "#E5E5E5", color = "#999999", size = 0.3) +
+    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 = 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),
@@ -449,17 +469,22 @@ for(lf in unique_lf_ids) {
     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)
     ) +
+    theme_minimal() +
     plot_theme
 
+  h_growth_map <- base_growth_map +
+    geom_sf(data = growth_sf, fill = "blue", color = "#e5e5e5", size = 0.5, alpha = growth_sf$housing_opacity) +
+    geom_sf_label(data = growth_sf, aes(label = growth_sf$name)) +
+    labs(
+      title = paste(lf, "Housing Growth Map By County"),
+    ) +
+    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 
+    ) 
+
   latest_opacity <- growth %>%
     filter(year == max(year)) %>%
     select(county_fips, name, housing_opacity, population_opacity)
@@ -480,28 +505,47 @@ for(lf in unique_lf_ids) {
   growth_colored <- growth_colored %>%
     mutate(name = factor(name, levels = housing_colors$name))
 
-  mmh_plot <- ggplot(growth_colored, aes(x = year, y = normalized_housing,
+  h_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))) +
+    geom_text(
+      data = growth_colored %>% filter(year == max(year)),
+      aes(label = name, x = year + 1),
+      hjust = 0,
+      size = 3
+    ) +
+    coord_cartesian(clip = "off") +
+    guides(color = "none") +
     labs(
       title = paste(lf, "Housing Growth"),
       x = "Year",
-      y = "Normalized Growth",
+      y = "Normalized Growth"
-      color = "County"
     ) +
     theme_minimal() +
-    plot_theme
+    plot_theme +
+    theme(plot.margin = margin(5.5, 40, 5.5, 5.5))
 
-  print(h_growth)
+  print(h_growth_map)
-  print(mmh_plot)
+  print(h_plot)
+
+  p_growth_map <- base_growth_map +
+    geom_sf(data = growth_sf, fill = "red", color = "#e5e5e5", size = 0.5, alpha = growth_sf$population_opacity) +
+    geom_sf_label(data = growth_sf, aes(label = growth_sf$name)) +
+    labs(
+      title = paste(lf, "Population Growth Map By County"),
+    ) + 
+    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 
+    ) 
 
   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$population_opacity) +
+    geom_sf(data = growth_sf, fill = "red", color = "#E5E5E5", size = 0.5, alpha = growth_sf$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),
@@ -509,6 +553,7 @@ for(lf in unique_lf_ids) {
       expand = FALSE
     ) +
     labs(
+      title = paste(lf, "Housing Growth Map By County"),
       x = "",
       y = ""
     )
@@ -516,9 +561,6 @@ for(lf in unique_lf_ids) {
     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
 
@@ -535,22 +577,30 @@ for(lf in unique_lf_ids) {
   growth_colored <- growth_colored %>%
     mutate(name = factor(name, levels = population_colors$name))
 
-  mmp_plot <- ggplot(growth_colored, aes(x = year, y = normalized_population,
+  p_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))) +
+    geom_text(
+      data = growth_colored %>% filter(year == max(year)),
+      aes(label = name, x = year + 1),
+      hjust = 0,
+      size = 3
+    ) +
+    coord_cartesian(clip = "off") +
+    guides(color = "none") +
     labs(
       title = paste(lf, "Population Growth"),
       x = "Year",
-      y = "Normalized Growth",
+      y = "Normalized Growth"
-      color = "County"
     ) +
     theme_minimal() +
-    plot_theme
+    plot_theme +
+    theme(plot.margin = margin(5.5, 40, 5.5, 5.5))
 
-  print(p_growth)
+
-  print(mmp_plot)
+  print(p_growth_map)
+  print(p_plot)
 }
 ```