hurricane_normalization_app/dashboard.Rmd

---
title: "Hurricane Normalization App"
runtime: shiny
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    theme: 
      version: 4
      bootswatch: litera
---

```{r setup, include=FALSE}
# TODO:
# - update normalization to new 2024 data
# - 

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)
library(dygraphs)
library(tidyverse)
library(sf)
library(shinyBS)
library(xts)
library(tigris)

linuxdir <- "/home/dylan/Personal/Projects/Hurricane Normalization/"
macdir <- "~/Desktop/Personal/Projects/Hurricane Normalization/"
#baseDir <- macdir
baseDir <- linuxdir

config <- config::get(file = paste0(baseDir, "R/dataScripts/restructured/app/config.yml"))

# SUPABASE CON
con <- dbConnect(
  RPostgres::Postgres(),
  host = config$db_host,
  port = config$db_port,
  dbname = config$db_dbname,
  user = config$db_user,
  password = config$db_password
)

selected_storm_name <- "KATRINA"
selected_storm_year <- 2005
selected_storm_basin <- "AL"
selected_lf_type = "LF"
selected_lf_id = "2"

xlallLandfallsNormalized <- read.csv(paste0(baseDir, "R/dataScripts/restructured/normalization/all-landfalls-normalized.csv"), header = T) %>% 
  select(-X)

  

normalized2024 <- read.csv(paste0(baseDir, "R/dataScripts/restructured/normalization/2024-normalized.csv"), header = T) %>% 
  select(-X, -hurdatId, -MMH23, -MMP23) %>% 
  filter(MMH24 > 0) %>% 
  mutate(
    lf_date = trimws(lf_date)
  )

normalized2024 <- normalized2024 %>% 
  mutate(
    lf_date = mdy(lf_date)
  ) %>% 
  rename(
    Storm = storm_name,
    Landfall = lf_date
  )

pop <- read.csv(paste0(baseDir, "Data/population_with_projections.csv"), stringsAsFactors = F) %>% 
  pivot_longer(
    cols = starts_with("X"),
    names_to = "year",
    values_to = "pop",
  ) %>% 
  rename(population = pop) %>% 
  mutate(
    year = parse_number(year),
    FIPS = ifelse(nchar(FIPS) == 4, paste0("0", FIPS), FIPS),
    state_fips = substr(FIPS, 1, 2),
    county_fips = substr(FIPS, 3, 5)
  ) %>% 
  select(!full_county_and_state:County & !county_state & !FIPS)

housing <- read.csv(paste0(baseDir, "Data/housing_units.csv"), stringsAsFactors = F) %>%
  pivot_longer(
    cols = starts_with("X"),
    names_to = "year",
    values_to = "housing"
  ) %>% 
  mutate(
    year = parse_number(year),
    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"))

weightedCounties <- read.csv(paste0(baseDir, "R/dataScripts/weighted-counties.csv"), header = T) %>% 
  mutate(
    weight = (PERCENTAGE/100),
    FIPS = ifelse(nchar(FIPS) == 4, paste0("0", FIPS), FIPS),
    state_fips = substr(FIPS, 1, 2),
    county_fips = substr(FIPS, 3, 5)
  ) %>% 
  select(
    state_fips,
    county_fips,
    hurdatId = HURDAT_Cod,
    storm_name = Name_1,
    lfId = LF_ID_Mull,
    lf_date = LF_Date,
    year = Year,
    rmw = RMW,
    lat = Lat,
    lon = Long,
    rmw_2x = RMW_x2,
    area = AREA,
    weight
  ) 

katrinaAffectedCounties <- weightedCounties %>% 
  filter(hurdatId == "AL122005" & lfId == "LF2") %>% 
  mutate(
    fips = paste0(state_fips, county_fips)
  )

katrinaLfTwoCountyMetrics <- metrics.pop_and_housing %>% 
  filter(year >= 2005) %>% 
  pivot_longer(
    cols = c("population", "housing_units"),
    names_to = "metric",
    values_to = "value"
  ) %>% 
  pivot_wider(
    names_from = year,
    values_from = value,
  ) %>% 
  mutate(
    fips = paste0(state_fips, county_fips)
  ) %>% 
  filter(fips %in% katrinaAffectedCounties$fips) %>% 
  select(fips, !state_fips & !county_fips)

###### REACTIVE VALUES 

storm_selection <- reactiveValues(
  storm_year = NULL,
  storm_name = NULL,
  storm_basin = NULL,
  lf_id = NULL,
  is_selected = FALSE
)

######

# LAZY LOAD DB TABLES

econ.normalized_landfalls <- tbl(con, I("econ.normalized_landfalls"))
econ.storm_base_loss <- tbl(con, I("econ.storm_base_loss"))
econ.usa_yearly <- tbl(con, I("econ.usa_yearly"))

fatal.storm_fatalities_type <- tbl(con, I("fatal.storm_fatalities_type"))
fatal.storm_total_fatalities <- tbl(con, I("fatal.storm_total_fatalities"))

fips.counties <- tbl(con, I("fips.counties"))
fips.states <- tbl(con, I("fips.states"))

gis.affected_area_landfalls <- tbl(con, I("gis.affected_area_landfalls"))

hurdat.best_track <- tbl(con, I("hurdat.best_track"))
hurdat.hurdat_storms <- tbl(con, I("hurdat.hurdat_storms"))

metrics.geo_attributes <- tbl(con, I("metrics.geo_attributes"))
#metrics.pop_and_housing <- tbl(con, I("metrics.pop_and_housing"))

public.counties <- tbl(con, I("public.counties"))

### COMMONLY USED DATA

loss_storms <- econ.storm_base_loss %>% 
  select(
    storm_basin, storm_year, storm_name
  ) %>% 
  distinct(
    storm_basin, storm_year, storm_name
  ) %>% 
  left_join(
    hurdat.hurdat_storms %>%
    mutate(hurdatId = paste0(storm_basin, storm_number, storm_year)), 
    by = c("storm_basin", "storm_year", "storm_name")) %>% 
  select(
    hurdatId, storm_name, storm_year
  ) %>% 
  collect()

base_losses_by_lf <- econ.storm_base_loss %>% 
  filter(!is.na(base_loss)) %>% 
  mutate(
    ncei_priority = case_when(
      str_like(base_loss_source, "%ncei%") ~ 1,
      str_like(base_loss_source, "%ncei%") ~ 2,
      TRUE ~ 3
    )
  ) %>% 
  group_by(storm_basin, storm_year, storm_name, lf_type, lf_id) %>% 
  slice_min(ncei_priority, n = 1, with_ties = F) %>% 
  ungroup() %>% 
  collect()

total_base_losses <- base_losses_by_lf %>% 
  group_by(storm_basin, storm_year, storm_name) %>% 
  summarize(
    total_base_loss = sum(base_loss),
    .groups = "drop"
  ) %>% 
  collect()

normalized_losses_2024 <- econ.normalized_landfalls %>% 
  filter(normalization_year == 2024) %>% 
  left_join(econ.storm_base_loss %>% 
              filter(!is.na(base_loss)) %>% 
              mutate(
                ncei_priority = case_when(
                  str_like(base_loss_source, "%ncei%") ~ 1,
                  str_like(base_loss_source, "%ncei%") ~ 2,
                  TRUE ~ 3
                )
              ) %>% 
              group_by(storm_basin, storm_year, storm_name, lf_type, lf_id) %>% 
              slice_min(ncei_priority, n = 1, with_ties = F) %>% 
              ungroup(),
            by = c("storm_basin", "storm_year", "storm_name", "lf_type", "lf_id")) %>% 
  mutate(
    mmh_lf = (base_loss * gdp_deflator * rwhu * affected_housing),
    mmp_lf = (base_loss * gdp_deflator * rwpc * affected_population)
  ) %>% 
  group_by(storm_basin, storm_year, storm_name) %>% 
  summarize(
    mmh = sum(mmh_lf, na.rm = T),
    mmp = sum(mmp_lf, na.rm = T),
    .groups = "drop"
  ) %>% 
  filter(!is.na(mmh) & !is.na(mmp)) %>% 
  collect()
```

```{r}
###### REACTIVE SETUP

#REACTIVES USE LAZY LOADING

#hurdat.best_track <- dbGetQuery(con, "SELECT *, ST_X(ST_Transform(location::geometry, 4326)) as lon, ST_Y(ST_Transform(location::geometry, 4326)) as lat FROM hurdat.best_track")


selected.best_track <- reactive({
  req(storm_selection$is_selected)
  
  query <- paste0("
                  SELECT *,
                  ST_X(ST_Transform(location::geometry, 4326)) as lon,
                  ST_Y(ST_Transform(location::geometry, 4326)) as lat
                  FROM hurdat.best_track
                  WHERE storm_basin = '", storm_selection$storm_basin,
                  "' AND storm_year = ", storm_selection$storm_year,
                  " AND storm_name = '", storm_selection$storm_name,
                  "'")
  
  result <- dbGetQuery(con, query)
  
  cat(paste0("DEBUG: selected.best_track returned ", nrow(result)))
  
  return(result)
})


```


Home
=============================================

Col {data-width=500}
----------------------------------------------

### {}
```{r eval=FALSE, include=FALSE}
HTML(
  '
  <h4>Welcome to the Hurricane Cost Normalization Web App!</h4>
  
  <p>Our platform provides access to normalized hurricane dama data spanning from 1900 to 2024, allowing researchers, policymakers, insurance professional, and the public to better understand how hurricane costs have changed over time.</p>
  
  <h6>Our Data</h6>
  
  <p>The core datasets used in this app are based on research by Muller et al. (2025) and the expanded analysis by Mooney et al. (2025), published in the <i>Bulletin of the American Meteorlogical Society</i> and <i>****JOURNAL****</i> respectively. These studies update and refine hurricane damage normalization methodologies to provide a more accurate picture of how historical hurricanes would impact today\'s society.</p>
  
  <ul>
    <li><b>PL22:</b> The Pielke-Landsea (2022) normalization that adjusts for inflation, wealth per capita, and population changes. This data has been updated to 2022.</li>
    
    <li><b>CL22:</b> The Collins-Lowe (2022) normalization that adjusts for inflation, wealth per housing unit, and housing unit changes. This data has been updated to 2022.</li>
    
    <li><b>MMP24:</b> The Muller-Mooney Population (2024) normalization with RMW weighting on affected population.</li>
    
    <li><b>MMH24:</b> The Muller-Mooney Housing (2024) normalization with RMW weighting on affected housing units.</li>
  </ul>
  
  <h6>Methodology Innovations</h6>
  
  <p>Our platform incoroprates several methodological innovations over previously used cost normalization formulas:
  
  <ul>
    <li><b>Radius of Maximum Wind (RMW) Data:</b> Using landfalling RMWs to identify impacted coastal counties.</li>
    
    <li><b>RMW Affected Area Weighting:</b> Determining affected population and housing unit figures based on RMW.</li>
    
    <li><b>Expanded Storm Coverage:</b> Inlcuding over 200 storms analyzed with interactive data.</li>
    
    <li><b>Up-to-date Data:</b> Using the latest population, housing unit, and economic data through 2024.</li>
  </ul>
  
  <h6>Questions?</h6>
  
  <p>CONTACT INFO?</p>
  '
)

```


```{r}
HTML(
  '
  <h4>Welcome to the Hurricane Cost Normalization Web App!</h4>
  
  <p>Our platform provides access to normalized hurricane dama data spanning from 1900 to 2024, allowing researchers, policymakers, insurance professional, and the public to better understand how hurricane costs have changed over time.</p>
  
  <h6>Our Data</h6>
  
  <p>The core datasets used in this app are based on research by Muller et al. (2025) published in the <i>Bulletin of the American Meteorlogical Society</i>. This study updates and refines hurricane damage normalization methodologies to provide a more accurate picture of how historical hurricanes would impact today\'s society.</p>
  
  <ul>
    <li><b>PL22:</b> The Pielke-Landsea (2022) normalization that adjusts for inflation, wealth per capita, and population changes. This data has been updated to 2022.</li>
    
    <li><b>CL22:</b> The Collins-Lowe (2022) normalization that adjusts for inflation, wealth per housing unit, and housing unit changes. This data has been updated to 2022.</li>
    
    <li><b>MMP24:</b> The Muller-Mooney Population (2024) normalization with RMW weighting on affected population.</li>
    
    <li><b>MMH24:</b> The Muller-Mooney Housing (2024) normalization with RMW weighting on affected housing units.</li>
  </ul>
  
  <h6>Methodology Innovations</h6>
  
  <p>Our platform incoroprates several methodological innovations over previously used cost normalization formulas:
  
  <ul>
    <li><b>Radius of Maximum Wind (RMW) Data:</b> Using landfalling RMWs to identify impacted coastal counties.</li>
    
    <li><b>RMW Affected Area Weighting:</b> Determining affected population and housing unit figures based on RMW.</li>
    
    <li><b>Expanded Storm Coverage:</b> Inlcuding over 200 storms analyzed with interactive data.</li>
    
    <li><b>Up-to-date Data:</b> Using the latest population, housing unit, and economic data through 2024.</li>
  </ul>
  
  <h6>Questions?</h6>
  
  <p>CONTACT INFO?</p>
  '
)

```

Col {data-width=500}
----------------------------------------------

### Storm Selector {data-height=500}
```{r}
fluidRow(
  column(6, 
         h5("Select Storm Name and Year"),
         selectInput("stormYear", "Select Year", choices = loss_storms$storm_year),
         selectInput("stormName", "Select Storm", choices = NULL),
         actionButton("selectStorm", "Submit", class = "btn-primary")
  ),
  column(6,
         #TODO: ADD TABLE
      
         #DTOutput("storm_selector_table")
  )
)

output$storm_selector_table <- renderDT({
  datatable(
    loss_storms,
    rownames = F,
    options = list(
      pageLength = 1000,
      order = list(2, 'desc'),
      searching = F,
      paging = F,
      info = F,
      lengthChange = F,
      server = T
    )
  )
})

observeEvent(input$stormYear, {
  stormsByChosenYear <- loss_storms[loss_storms$storm_year == input$stormYear, ]
  
  updateSelectInput(session, "stormName", 
                    choices = stormsByChosenYear$storm_name,
                    selected = NULL)
})

observeEvent(input$selectStorm, {
  storm_selection$storm_year <- input$stormYear
  storm_selection$storm_name <- input$stormName
  storm_selection$storm_basin <- "AL"
  storm_selection$is_selected <- TRUE
  
  showNotification("Storm selection updated!", type = "message")
})

```

### All Storms {data-height=500 .no-padding}
```{r}
output$allStorms <- renderDT({
  datatable(
    normalized_losses_2024 %>% select(Storm = storm_name, Year = storm_year, MMH24 = mmh, MMP24 = mmp),
    rownames = F,
    options = list(
      pageLength = 1000,
      order = list(2, 'desc'),
      searching = F,
      paging = F,
      info = F,
      lengthChange = F,
      server = T
    )
  ) %>% 
    formatCurrency(c("MMH24", "MMP24"), "$", digits = 0)
})

DTOutput("allStorms")
```



Storm Overview {data-navmenu="Storm Details"}
====================================

```{r}
### REACTIVE VALUES FOR STORM OVERVIEW

storm_overview_reactive <- reactiveValues(
  lf_type = NULL,
  lf_id = NULL,
  full_lf_id = NULL,
  use_normalized_costs = FALSE
)

```

Col {data-width=500}
------------------------------------

### Storm Details {data-height=200}
```{r}
# TODO: add storm details section: name, base loss, base loss source, etc.
HTML('

')
```

### Normalization Cost Index {data-height=800}
```{r}
storm_unique_landfalls <- reactive({
  req(storm_selection$is_selected)
  
  result <- econ.storm_base_loss %>% 
    filter(
      storm_basin == storm_selection$storm_basin,
      storm_year == storm_selection$storm_year,
      storm_name == storm_selection$storm_name
    ) %>% 
    mutate(
      full_lf_id = paste0(lf_type, lf_id)
    ) %>% 
    distinct(
      full_lf_id, 
      .keep_all = T
    ) %>% 
    arrange(
      full_lf_id
    ) %>% 
    select(
      storm_basin, storm_year, storm_name, lf_type, lf_id, full_lf_id
    ) %>% 
    collect()
  
  return(result)
})

storm_normalized_landfall <- reactive({
  req(storm_overview_reactive$full_lf_id)
  
  result <- econ.normalized_landfalls %>% 
  filter(
    storm_basin == storm_selection$storm_basin,
    storm_year == storm_selection$storm_year,
    storm_name == storm_selection$storm_name,
    lf_type == storm_overview_reactive$lf_type,
    lf_id == storm_overview_reactive$lf_id
  ) %>% 
  left_join(econ.storm_base_loss %>% 
              filter(!is.na(base_loss)) %>% 
              mutate(
                ncei_priority = case_when(
                  str_like(base_loss_source, "%ncei%") ~ 1,
                  str_like(base_loss_source, "%ncei%") ~ 2,
                  TRUE ~ 3
                )
              ) %>% 
              group_by(storm_basin, storm_year, storm_name, lf_type, lf_id) %>% 
              slice_min(ncei_priority, n = 1, with_ties = F) %>% 
              ungroup(),
            by = c("storm_basin", "storm_year", "storm_name", "lf_type", "lf_id")) %>% 
  mutate(
    mmh_index = (gdp_deflator * rwhu * affected_housing),
    mmp_index = (gdp_deflator * rwpc * affected_population),
    mmh = (base_loss * mmh_index),
    mmp = (base_loss * mmp_index)
  ) %>% 
  select(
    -base_loss_source, -base_loss, -base_loss_citation, -doi, -notes, -ncei_priority
  ) %>% 
  collect()
  
  cat(str(result))
  
  return(result)
})

storm_cost_index_base_index_ts <- reactive({
  req(storm_normalized_landfall())
  
  cost_index <- storm_normalized_landfall() %>% 
    select(
      normalization_year, mmh_index, mmp_index
    ) %>% 
    mutate(normalization_year = as.Date(paste0(normalization_year, "-01-01")))
  
  cost_index_ts <- cost_index %>% 
    select(-normalization_year) %>% 
    xts(order.by = cost_index$normalization_year)
  
  cat(str(cost_index_ts))
  
  result <- cost_index_ts
  
  return(result)
})

storm_cost_index_normalized_costs_ts <- reactive({
  req(storm_normalized_landfall())
  
  cost_index <- storm_normalized_landfall() %>% 
    select(
      normalization_year, mmh, mmp
    ) %>% 
    mutate(normalization_year = as.Date(paste0(normalization_year, "-01-01")))
  
  cost_index_ts <- cost_index %>% 
    select(-normalization_year) %>% 
    xts(order.by = cost_index$normalization_year)
  
  cat(str(cost_index_ts))
  
  result <- cost_index_ts
  
  return(result)
})
  
observe({
  req(storm_unique_landfalls())

  storm_overview_reactive$lf_type = storm_unique_landfalls()$lf_type[1]
  storm_overview_reactive$lf_id = storm_unique_landfalls()$lf_id[1]
  storm_overview_reactive$full_lf_id = storm_unique_landfalls()$full_lf_id[1]
})

observe({
  req(storm_overview_reactive$full_lf_id)
  
  updateSelectInput(
    session,
    "storm_overview_cost_index_lf", 
    choices = storm_unique_landfalls()$full_lf_id,
    selected = storm_overview_reactive$full_lf_id
  )
})

observeEvent(input$storm_overview_select_base, {
  # TODO: add button to select normalized costs
})

output$costIndex <- renderDygraph({
  req(storm_normalized_landfall())
  
  if(storm_overview_reactive$use_normalized_costs == F) {
    dygraph(storm_cost_index_base_index_ts(), main = paste(storm_selection$storm_name, storm_selection$storm_year, "Cost Index")) %>% 
      dySeries("mmh_index", label = "MMH Index") %>% 
      dySeries("mmp_index", label = "MMP Index") %>% 
      dyRangeSelector(height = 30)
  }else{
    dygraph(storm_cost_index_normalized_costs_ts(), main = paste(storm_selection$storm_name, storm_selection$storm_year, "Cost Index")) %>% 
      dySeries("mmh", label = "MMH") %>% 
      dySeries("mmp", label = "MMP") %>% 
      dyRangeSelector(height = 30)
  }
  
})

fillCol(
  flex = c(.2, .8),
  fluidRow(
    column(4,
           selectInput("storm_overview_cost_index_lf", "Landfall Select", choices = NULL)
    ),
    column(4,
           # TODO: add button to select normalized costs
           #checkboxInput("storm_overview_select_base", "Include Normalized Losses", value = F)
    ),
    column(4,
           #checkboxInput("mmpSelect", "Display MMP", value = T)
    )
  ),
  dygraphOutput("costIndex")
)
```

Col {data-width=500}
------------------------------------

### Fatalities {data-height=200}
```{r}
# TODO: fatalities dashboard

fluidRow(
  column(6,
         HTML('
         <h6>Katrina recorded 1,392 fatalities</h6>
         <p>Surge: 387</p>
         ')
  ),
  column(6,
         actionLink("fatalitiesLink", tagList(icon("arrow-right"), "Fatalities dashboard"), class = "btn btn-outline")
  )
)
```

### Landfalls {data-height=300 .no-padding}
```{r}
storm_landfalls <- reactive({
  req(storm_selection$is_selected)
  
  result <- hurdat.best_track %>% 
    filter(
      storm_basin == storm_selection$storm_basin,
      storm_year == storm_selection$storm_year,
      storm_name == storm_selection$storm_name,
      record_identifier == "L"
    ) %>% 
    mutate(
      lon = sql("ST_X(ST_Transform(location::geometry, 4326))"),
      lat = sql("ST_Y(ST_Transform(location::geometry, 4326))")
    ) %>% 
    select(
      Date = datetime,
      Longitude = lon,
      Latitude = lat,
      RMW = rmw,
      Pressure = pressure,
      Windspeed = windspeed
    ) %>% 
    collect()
  
  cat(str(result))
  
  return(result)
})

output$landfalls_table <- renderDT({
  datatable(
    storm_landfalls(),
    rownames = F,
    options = list(
      order = list(0, 'asc'),
      paging = F,
      searching = F,
      info = F,
      lengthChange = F,
      server = T
    )
  ) %>% 
    formatDate(columns = "Date", method = "toUTCString")
})

DTOutput("landfalls_table")

```

### Storm Track {data-height=500 .no-padding}
```{r}
output$trackMap <- renderLeaflet({
  leaflet() %>% 
    addProviderTiles("CartoDB.Positron", option = providerTileOptions(minZoom = 2, maxZoom = 18)) %>% 
    setView(lng = -80, lat = 32, zoom = 4) %>% 
    addCircleMarkers(
      #data = katrinaTrack,
      #lng = ~Longitude,
      #lat = ~Latitude,
      data = selected.best_track(),
      lng = ~lon,
      lat = ~lat,
      radius = 5,
      color = ~ifelse(record_identifier == "L", "red", "blue"),
      fillColor = ~ifelse(record_identifier == "L", "red", "blue")
    )
})

leafletOutput("trackMap", height="100%")
```

Growth Trends {data-navmenu="Storm Details"}
================================

Column {data-width=550}
-------------------------------

### {data-height=1000 .no-padding}

```{r}
growth_trends <- reactiveValues(
  lf_id = NULL
)



dbStormCounties <- reactive({
  req(storm_selection$is_selected)
  
  sel_storm_basin <- storm_selection$storm_basin
  sel_storm_year <- storm_selection$storm_year
  sel_storm_name <- storm_selection$storm_name
  sel_lfid <- growth_trends$lf_id
  
  affectedCounties <- gis.affected_area_landfalls %>% 
  mutate(
    fips = paste0(state_fips, county_fips),
    lfid = paste0(lf_type, lf_id)
  ) %>% 
  filter(
    storm_basin == sel_storm_basin,
    storm_year == sel_storm_year,
    storm_name == sel_storm_name,
    lfid == sel_lfid
  ) %>% 
  left_join(
    public.counties %>% 
      mutate(geom_wkt = sql("ST_AsText(ST_Transform(geom, 4326))")) %>% 
      rename(state_fips = statefp, county_fips = countyfp), 
    by = c("state_fips", "county_fips")
  ) %>% 
  collect()
  
  affectedCounties_sf <- affectedCounties %>% 
    st_as_sf(wkt = "geom_wkt")
  
  cat(str(affectedCounties_sf))
  
  return(affectedCounties_sf)
})

output$popMapPoly <- renderLeaflet({
  leaflet(dbStormCounties()) %>% 
    addProviderTiles("CartoDB.Positron", option = providerTileOptions(minZoom = 2, maxZoom = 18)) %>% 
    setView(lng = -89.8, lat = 29.6, zoom = 8) %>% 
    addPolygons(
      fillColor = "red",
      fillOpacity = 0.3,
      color = "black", 
      weight = 2
    )
})

output$housingMapPoly <- renderLeaflet({
  leaflet() %>% 
    addProviderTiles("CartoDB.Positron", option = providerTileOptions(minZoom = 2, maxZoom = 18)) %>% 
    setView(lng = -89.8, lat = 29.6, zoom = 8) %>% 
    addPolygons(
      data = dbStormCounties(),
      fillColor = "blue",
      fillOpacity = 0.3,
      color = "black", 
      weight = 2
    )
})

fillCol(
  flex = c(1, 1),
  leafletOutput("popMapPoly"),
  leafletOutput("housingMapPoly")
)

```

Column {data-width=450}
----------------------------------

### Landfall Selection {data-height=150}
```{r}


fluidRow(
  column(4,
          selectInput("growthTrendLfSelect", "Landfall", choices = NULL)
  ), 
  column(4,
         
  ),
  column(4,
         
  )
)

observeEvent(input$growthTrendLfSelect, {
  growth_trends$lf_id = input$growthTrendLfSelect
  
  showNotification("Landfall updated", type = "message")
})
```

### Time Series {data-height=400 .no-padding}
```{r}


katrinaLfTwoLong <- katrinaLfTwoCountyMetrics %>% 
  pivot_longer(
    cols = -c(fips, metric),
    names_to = "year",
    values_to = "value"
  ) %>% 
  group_by(
    metric, year
  ) %>% 
  summarize(
    aggregateValue = sum(value, na.rm = T),
    .groups = "drop"
  ) %>% 
  pivot_wider(
    names_from = metric,
    values_from = aggregateValue
  ) 

normalizedKatrinaLfTwo <- xlallLandfallsNormalized %>% 
  filter(hurdatId == "AL122005" & lfId == "LF2") %>% 
  select(normalizationYear, affectedPop, affectedHousing)

normalizedKatrinaLfTwoTs <- normalizedKatrinaLfTwo %>% 
  select(-normalizationYear) %>% 
  as.matrix() %>% 
  xts(order.by = as.Date(paste0(normalizedKatrinaLfTwo$normalizationYear, "-01-01")))

katrinaLfTwoLongTs <- katrinaLfTwoLong %>% 
  select(-year) %>% 
  as.matrix() %>% 
  xts(order.by = as.Date(paste0(katrinaLfTwoLong$year, "-01-01")))


output$popHu <- renderDygraph({
  dygraph(normalizedKatrinaLfTwoTs, main = "Normalized LF2 Aggregate Growth") %>% 
    dySeries("affectedPop", label = "Population") %>% 
    dySeries("affectedHousing", label = "Housing Units") %>% 
    dyRangeSelector()
})


#dygraphOutput("popHu")
```

### County Data {data-height=450 .no-padding}
```{r}


output$katrinaCounties <- renderDT({
  datatable(
    katrinaLfTwoCountyMetrics,
    rownames = F,
    #extensions = "RowGroup",
    options = list(
      pageLength = 1000,
      #rowGroup = list(dataSrc = 1),
      paging = F,
      searching = F,
      info = F,
      lengthChange = F,
      server = T
    )
  ) 
})

#DTOutput("katrinaCounties")

```



Fatalities {data-navmenu="Fatalities"}
===

Column {data-width=500}
---

### {data-height=500}

### {data-height=500}

Column {data-width=500}
---

### {data-height=500}
```{r}
```

### {data-height=500}
```{r}
```

Storm Comparison {data-navmenu="Compute"}
===

Sandbox {data-navmenu="Compute"}
===

Data {data-navmenu="Compute"}
===

Top 50 Storms 
===
```{r}

#DT with storm, hurdatid, base damage, mmh, mmp, maybe multipliers?, sparkline?



```

About 
===