add metrics table and flatten vector graphics

app/storm_report.qmd

@@ -1,18 +1,23 @@
 ---
-title: "`r params$hurdat_id` `r params$storm_name` `r params$storm_year` STORM REPORT"
+title: "`r paste(params$hurdat_id, params$storm_name, params$storm_year)` STORM REPORT"
 subtitle: "Generated on: `r Sys.Date()`"
 format:
   pdf:
     theme: cosmo
+    keep-tex: false
+    dev: png
     geometry:
       - top=0.75in
       - bottom=0.75in
       - left=0.75in
       - right=0.75in
       - footskip=0.3in
+    embed-resourced: true
+    fig-format: png
 execute:
   warning: false
   message: false
+  dpi: 100
 params:
     storm_basin: AL
     storm_year: 1900
@@ -42,7 +47,7 @@ storm <- list(
     storm_name = params$storm_name
 )
 
-latest_normalization_year <- get_latest_normalization_year()
+latest_normalization_year <- get_latest_normalization_year()$latest_year
 
 unique_lfs <- get_unique_lf_ids(storm)
 hurdat_id <- get_hurdat_id(storm)
@@ -182,10 +187,8 @@ storm_track <- get_hurdat_track(storm)
       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 = 10)
-      legend.text = element_text(size = 8)
+    )
-    ) +
-    plot_theme
 
   if (nrow(storm_track) >= 2) {
     for (i in 1:(nrow(storm_track) - 1)) {
@@ -259,7 +262,8 @@ storm_track <- get_hurdat_track(storm)
       title = paste(params$storm_name, params$storm_year),
       x = "Longitude",
       y = "Latitude"
-    )
+    ) +
+    plot_theme
 
   print(p)
 ```
@@ -268,6 +272,7 @@ storm_track <- get_hurdat_track(storm)
 
 ```{r track_table, echo=FALSE}
 landfall_rows <- which(storm_track$record_identifier == "L")
+landfall_colors <- paste0(storm_track$line_color[landfall_rows], "A0")
 
 storm_track %>%
   select(
@@ -297,7 +302,7 @@ storm_track %>%
   kable_styling(
     #latex_options = c("striped"),
     font_size = 11,
-    position = "center"
+    position = "left"
   ) %>%
   column_spec(
     2,
@@ -308,7 +313,7 @@ storm_track %>%
   row_spec(
     landfall_rows,
     color = "white",
-    background = paste0(storm_track$line_color[landfall_rows], "A0")
+    background = landfall_colors
   ) %>% 
   column_spec(
     6,
@@ -339,6 +344,7 @@ storm_track %>%
 base_yearly_econ <- get_yearly_economics(storm$storm_year) 
 base_yearly_usa <- get_yearly_usa_pop_hu(storm$storm_year)
 
+# year, ccn, gdp, pop, housing
 base_yearly_metrics <- base_yearly_usa %>% 
   left_join(base_yearly_econ, by = "year") %>% 
   select(-aggregate_storm_loss) 
@@ -350,16 +356,41 @@ ref_yearly_metrics <- ref_yearly_usa %>%
   left_join(ref_yearly_econ, by = "year") %>%
   select(-aggregate_storm_loss)
 
-# year, ccn, gdp, ag loss
+metrics_table <- data.frame(
-# base...
+  Metric = c("CCNSFACDG", "GDP Deflator", "US Population", "US Housing"),
-# ref...
+  Base_Year = c(
+    base_yearly_metrics$ccnsfacdg,
+    base_yearly_metrics$gdp_deflator,
+    base_yearly_metrics$population,
+    base_yearly_metrics$housing
+  ),
+  Latest_Year = c(
+    ref_yearly_metrics$ccnsfacdg,
+    ref_yearly_metrics$gdp_deflator,
+    ref_yearly_metrics$population,
+    ref_yearly_metrics$housing
+  )
+) %>% 
+  mutate(
+    Ratio = Latest_Year / Base_Year
+  )
 
-# table cols metric, base year, ref year, ratio
+metrics_table %>%
-# ccn
+  kable(
-# gdp
+    col.names = c(
-# us pop
+      "Metric",
-# us hous
+      paste("Base Year (", storm$storm_year, ")", sep = ""),
-# rwpc/hu
+      paste("Ref Year (", latest_normalization_year, ")", sep = ""),
+      "Ratio"
+    ),
+    align = c("l", "r", "r", "r"),
+    booktabs = TRUE,
+    digits = 2
+  ) %>%
+  kable_styling(
+    font_size = 11,
+    position = "left"
+  )
 
 costs <- get_all_normalized_cost_index(storm)
 
@@ -373,30 +404,59 @@ for(lf in unique_lf_ids) {
       full_lf_id == lf
     )
 
-  mmh_plot <- ggplot(filtered_costs, aes(x = normalization_year, y = mmh_loss)) +
+  last_year <- filtered_costs %>% filter(normalization_year == max(normalization_year))
-    geom_line(color = "blue") +
+
+  cost_plot <- ggplot(filtered_costs, aes(x = normalization_year)) +
+    geom_line(aes(y = mmh_loss), color = "blue") +
+    geom_line(aes(y = mmp_loss), color = "red") +
+    geom_text(
+      data = last_year,
+      aes(x = normalization_year + 1, y = mmh_loss, label = "MMH"),
+      hjust = 0,
+      size = 3,
+      color = "blue"
+    ) +
+    geom_text_repel(
+      data = last_year,
+      aes(x = normalization_year + 1, y = mmp_loss, label = "MMP"),
+      hjust = 0,
+      size = 3,
+      color = "red"
+    ) +
     scale_y_continuous(labels = scales::label_dollar(scale_cut = cut_short_scale())) +
+    coord_cartesian(clip = "off") +
     labs(
-      title = paste(lf, "MMH Loss"),
+      title = paste(lf, "MMH and MMP Loss"),
       x = "Year",
       y = "Aggregate Loss (USD)"
     ) +
     theme_minimal() +
-    plot_theme
+    plot_theme +
+    theme(plot.margin = margin(5.5, 40, 5.5, 5.5))
   
-  mmp_plot <- ggplot(filtered_costs, aes(x = normalization_year, y = mmp_loss)) +
+  #mmh_plot <- ggplot(filtered_costs, aes(x = normalization_year, y = mmh_loss)) +
-    geom_line(color = "red") +
+  #  geom_line(color = "blue") +
-    scale_y_continuous(labels = scales::label_dollar(scale_cut = cut_short_scale())) +
+  #  scale_y_continuous(labels = scales::label_dollar(scale_cut = cut_short_scale())) +
-    labs(
+  #  labs(
-      title = paste(lf, "MMP Loss"),
+  #    title = paste(lf, "MMH Loss"),
-      x = "Year",
+  #    x = "Year",
-      y = "Aggregate Loss (USD)"
+  #    y = "Aggregate Loss (USD)"
-    ) +
+  #  ) +
-    theme_minimal() +
+  #  theme_minimal() +
-    plot_theme
+  #  plot_theme
 
-  print(mmh_plot)
+  #mmp_plot <- ggplot(filtered_costs, aes(x = normalization_year, y = mmp_loss)) +
-  print(mmp_plot)
+  #  geom_line(color = "red") +
+  #  scale_y_continuous(labels = scales::label_dollar(scale_cut = cut_short_scale())) +
+  #  labs(
+  #    title = paste(lf, "MMP Loss"),
+  #    x = "Year",
+  #    y = "Aggregate Loss (USD)"
+  #  ) +
+  #  theme_minimal() +
+  #  plot_theme
+
+  print(cost_plot)
 }
 ```
 
@@ -510,9 +570,10 @@ for(lf in unique_lf_ids) {
                                           group = county_fips)) +
     geom_line() +
     scale_color_manual(values = housing_color_values) +
-    geom_text(
+    geom_text_repel(
       data = growth_colored %>% filter(year == max(year)),
       aes(label = name, x = year + 1),
+      direction = "y",
       hjust = 0,
       size = 3
     ) +
@@ -542,28 +603,6 @@ for(lf in unique_lf_ids) {
       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 = "#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),
-      ylim = c(lat_range[1] - lat_padding, lat_range[2] + lat_padding),
-      expand = FALSE
-    ) +
-    labs(
-      title = paste(lf, "Housing Growth Map By County"),
-      x = "",
-      y = ""
-    )
-    theme_minimal() +
-    theme(
-      panel.background = element_rect(fill = "#D4E6F1"),
-      panel.grid.major = element_line(color = "#BBBBBB", size = 0.2),
-    ) +
-    plot_theme
-
   population_colors <- latest_opacity %>%
     distinct(name, population_opacity) %>%
     arrange(desc(population_opacity)) %>%
@@ -582,9 +621,10 @@ for(lf in unique_lf_ids) {
                                           group = county_fips)) +
     geom_line() +
     scale_color_manual(values = population_color_values) +
-    geom_text(
+    geom_text_repel(
       data = growth_colored %>% filter(year == max(year)),
       aes(label = name, x = year + 1),
+      direction = "y",
       hjust = 0,
       size = 3
     ) +