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2026-07-30 05:08:57 +00:00 · 2025-06-03 17:46:30 -04:00
commit 37a06c5f07
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+---
+title: "Hurricane Normalization App"
+runtime: shiny
+output: 
+  flexdashboard::flex_dashboard:
+    orientation: rows
+    vertical_layout: fill
+    navbar:
+      - {title: "Home", icon: "fa-home", href: "#"}
+      - {title: "About", icon: "fa-info-circle"}
+    theme: 
+      version: 4
+      bootswatch: journal
+---
+
+```{r setup, include=FALSE}
+library(flexdashboard)
+library(shiny)
+library(leaflet)
+library(DT)
+library(dplyr)
+library(DBI)
+library(tidyr)
+library(ggplot2)
+library(plotly)
+library(viridis)
+library(lubridate)
+library(scales)
+library(readr)
+library(stringr)
+library(kableExtra)
+library(bslib)
+
+setwd("/home/dylan/Personal/Projects/Hurricane Normalization/")
+
+pop <- read.csv("Data/population_with_projections.csv", stringsAsFactors = F) %>% 
+  pivot_longer(
+    cols = starts_with("X"),
+    names_to = "year",
+    values_to = "pop",
+  ) %>% 
+  mutate(
+    year = parse_number(year)
+  )
+
+housing <- read.csv("Data/housing_units.csv", stringsAsFactors = F) %>%
+  pivot_longer(
+    cols = starts_with("X"),
+    names_to = "year",
+    values_to = "housing"
+  ) %>% 
+  mutate(
+    year = parse_number(year)
+  )
+
+counties <- pop %>% 
+  left_join(housing %>% select(FIPS, year, housing), by = c("FIPS", "year")) %>% 
+  pivot_longer(
+    cols = c("pop", "housing"),
+    names_to = "metric",
+    values_to = "value"
+  )
+
+pop <- pop %>% 
+  mutate(
+    FIPS = ifelse(nchar(FIPS) == 4, paste0("0", FIPS), FIPS),
+    state_fips = substr(FIPS, 1, 2),
+    county_fips = substr(FIPS, 3, 5)
+  ) %>% 
+  rename(population = pop) %>% 
+  select(!full_county_and_state:County & !county_state & !FIPS)
+
+housing <- housing %>% 
+  mutate(
+    FIPS = ifelse(nchar(FIPS) == 4, paste0("0", FIPS), FIPS),
+    state_fips = substr(FIPS, 1, 2),
+    county_fips = substr(FIPS, 3, 5)
+  ) %>% 
+  rename(housing_units = housing) %>% 
+  select(!County.Full:County & !County.State & !FIPS)
+
+metrics.pop_and_housing <- pop %>% 
+  left_join(housing, by = c("state_fips", "county_fips", "year"))
+
+donna <- read.csv("R/dataScripts/restructured/hurdat2/gis_counties.csv") 
+
+donna <- donna %>% 
+  filter(HURDAT_C_1 == "AL051960") %>% 
+  mutate(
+    FIPS = as.character(FIPS)
+  )
+
+donna <- donna %>% 
+  mutate(
+    FIPS = case_when(
+      str_length(donna$FIPS) == 4 ~ paste0("0", donna$FIPS),
+      TRUE ~ donna$FIPS
+    ),
+    state_fips = substr(FIPS, 1, 2),
+    county_fips = substr(FIPS, 3, 5)
+  ) %>% 
+  rename(year = Year) 
+
+donna <- donna %>% 
+  left_join(metrics.pop_and_housing, by = c("state_fips", "county_fips", "year")) 
+
+donna <- donna %>% 
+  select(Storm_Name, STATE_NAME, County_Name, state_fips, county_fips, population, housing_units)
+
+donna <- donna %>% 
+  mutate(Storm_Name = substr(Storm_Name, 1, 5))
+
+donna <- donna %>% 
+  mutate(
+    FIPS = paste0(state_fips, county_fips)
+  ) %>% 
+  select(
+    -state_fips, 
+    -county_fips
+  )
+
+donna <- donna %>% 
+  select(
+    County = County_Name,
+    Population = population,
+    Housing = housing_units
+  )
+
+
+# 1-3 FL
+# 4-10 NC
+# 11-13 CT
+# 14-16 NY
+
+```
+
+
+Dashboard
+=============================================
+
+Row {data-height=100}
+----------------------------------------------
+### {data-width=103}
+```{r}
+h6("Hurricane")
+h3("Galveston 1900")
+```
+
+### {data-width=25}
+
+```{r}
+valueBox("#18", caption = "Rank by population")
+```
+
+### {data-width=25}
+
+```{r}
+valueBox("$58.11b", caption = "Loss by population", icon = "fa-user")
+```
+
+### {data-width=25}
+
+```{r}
+valueBox("#37", caption = "Rank by housing")
+```
+
+### {data-width=25}
+
+```{r}
+valueBox("$10.63b", caption = "Loss by housing", icon = "fa-house")
+```
+
+Row {data-height=400}
+----------------------------------------------
+
+### {data-width=50}
+
+```{r}
+donna %>% 
+  kbl() %>%
+  kable_styling(font_size = 16) %>% 
+  pack_rows("Florida", 1, 3, label_row_css = "color: #fff") %>% 
+  pack_rows("North Carolina", 4, 10, label_row_css = "color: #fff") %>% 
+  pack_rows("Connecticut", 11, 13, label_row_css = "color: #fff") %>% 
+  pack_rows("New York", 14, 16, label_row_css = "color: #fff") %>% 
+  row_spec(1:16, color = "white")
+```
+
+
+### {data-width=50}
+
+```{r}
+output$worldMap <- renderLeaflet({
+  leaflet() %>% 
+    addTiles() %>% 
+    setView(lng = 0, lat = 20, zoom = 2) %>% 
+    addScaleBar(position = "bottomleft") %>% 
+    addLayersControl(
+      baseGroups = c("Default", "Satellite", "Terrain"),
+      options = layersControlOptions(collapsed = FALSE)
+    ) %>% 
+    addProviderTiles(providers$Esri.WorldImagery, group = "Satellite") %>% 
+    addProviderTiles(providers$Stamen.Terrain, group = "Terrain") %>% 
+    hideGroup("Satellite") %>% 
+    hideGroup("Terrain")
+})
+
+output$map <- renderLeaflet({
+  leaflet() %>% 
+    addProviderTiles("Stadia.AlidadeSmoothDark", option = providerTileOptions(minZoom = 2, maxZoom = 18)) %>% 
+    setView(lng = 0, lat = 20, zoom = 2)
+})
+
+#leafletOutput("map", height="100%")
+
+leafletOutput("worldMap", height = "100%")
+```
+
+Row {data-height=400}
+---
+
+### {data-width=50}
+
+```{r}
+lines <- metrics.pop_and_housing %>% 
+  filter(state_fips == "01" & county_fips == "087" & year >= 1960)
+
+output$lineChart <- renderPlot({
+  ggplot(lines, aes())
+})
+```
+
+### {data-width=50}
+```{r eval=FALSE, include=FALSE}
+
+```