mirror of
https://github.com/dylanbenzi/hurricane_normalization_app.git
synced 2026-07-30 05:08:57 +00:00
init commit
This commit is contained in:
+895
@@ -0,0 +1,895 @@
|
||||
---
|
||||
title: "Hurricane Normalization App"
|
||||
runtime: shiny
|
||||
output:
|
||||
flexdashboard::flex_dashboard:
|
||||
orientation: columns
|
||||
vertical_layout: fill
|
||||
theme:
|
||||
version: 4
|
||||
bootswatch: litera
|
||||
---
|
||||
|
||||
```{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)
|
||||
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"))
|
||||
|
||||
|
||||
|
||||
# LOCAL CON
|
||||
#con <- dbConnect(
|
||||
# RPostgres::Postgres(),
|
||||
# user = "postgres",
|
||||
# password = "oiuqBub8s9n65sgan09",
|
||||
# host = "192.168.0.6",
|
||||
# port = 5432,
|
||||
# dbname = "hurricanedb"
|
||||
#)
|
||||
|
||||
# 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"
|
||||
|
||||
|
||||
|
||||
bestTrackQry <- paste0("SELECT * FROM hurdat.best_track WHERE storm_basin = '", selected_storm_basin,
|
||||
"' AND storm_name = '", selected_storm_name,
|
||||
"' AND storm_year = ", selected_storm_year
|
||||
)
|
||||
|
||||
qry <- "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 = 'AL' AND storm_name = 'KATRINA' AND storm_year = 2005"
|
||||
|
||||
xlallLandfallsNormalized <- read.csv(paste0(baseDir, "R/dataScripts/restructured/normalization/all-landfalls-normalized.csv"), header = T) %>%
|
||||
select(-X)
|
||||
|
||||
katrinaTrack <- dbGetQuery(con, qry)
|
||||
katrinaTrack <- katrinaTrack %>%
|
||||
mutate(
|
||||
date = make_datetime(datetime)
|
||||
) %>%
|
||||
select(
|
||||
Date = date,
|
||||
Latitude = lat,
|
||||
Longitude = lon,
|
||||
Pressure = pressure,
|
||||
Windspeed = windspeed,
|
||||
RMW = rmw,
|
||||
record_identifier
|
||||
)
|
||||
|
||||
katrinaLandfalls <- katrinaTrack %>%
|
||||
filter(record_identifier == 'L') %>%
|
||||
select(-record_identifier)
|
||||
|
||||
|
||||
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"))
|
||||
|
||||
#counties <- counties(state = "LA", cb = T)
|
||||
|
||||
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
|
||||
)
|
||||
|
||||
growth_trends <- reactiveValues(
|
||||
lf_id = NULL
|
||||
)
|
||||
|
||||
######
|
||||
|
||||
###### SUPABASE PORT
|
||||
|
||||
hurdat.hurdat_storms <- dbGetQuery(con, "SELECT *, CONCAT(storm_basin, storm_number, storm_year) AS hurdatId FROM hurdat.hurdat_storms")
|
||||
|
||||
#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")
|
||||
|
||||
econ.normalized_landfalls <- dbGetQuery(con, "SELECT * FROM econ.normalized_landfalls")
|
||||
|
||||
econ.storm_base_loss <- reactive({
|
||||
req(storm_selection$is_selected)
|
||||
|
||||
query <- paste0("
|
||||
SELECT DISTINCT ON (storm_basin, storm_year, storm_name, lf_type, lf_id)
|
||||
storm_basin,
|
||||
storm_year,
|
||||
storm_name,
|
||||
lf_type,
|
||||
lf_id,
|
||||
base_loss_source,
|
||||
base_loss
|
||||
FROM econ.storm_base_loss
|
||||
WHERE base_loss IS NOT NULL
|
||||
ORDER BY
|
||||
storm_basin, storm_year, storm_name, lf_type, lf_id,
|
||||
CASE
|
||||
WHEN base_loss_source LIKE '%ncei%' THEN 1
|
||||
WHEN base_loss_source LIKE '%mwr%' THEN 2
|
||||
ELSE 3
|
||||
END
|
||||
")
|
||||
|
||||
dbGetQuery(con, query)
|
||||
})
|
||||
|
||||
gis.affected_area_landfalls <- tbl(con, I("gis.affected_area_landfalls"))
|
||||
public.counties <- tbl(con, I("public.counties"))
|
||||
|
||||
|
||||
#allLandfallsNormalized <- econ.normalized_landfalls %>%
|
||||
# left_join(econ.storm_base_loss, by = c("storm_basin", "storm_year", "storm_name", "lf_type", "lf_id")) %>%
|
||||
# mutate(
|
||||
# mmh = gdp_deflator * rwhu * affected_housing,
|
||||
# mmp = gdp_deflator * rwpc * affected_population
|
||||
# )
|
||||
```
|
||||
|
||||
```{r}
|
||||
###### REACTIVE SETUP
|
||||
|
||||
#REACTIVES USE LAZY LOADING
|
||||
|
||||
#hurdat.best_track <- dbGetQuery(con, "SELECT *, ST_X(ST_Transform(location::geometry, 4326)) as lon, ST_Y(ST_Transform(location::geometry, 4326)) as lat FROM hurdat.best_track")
|
||||
|
||||
|
||||
selected.best_track <- reactive({
|
||||
req(storm_selection$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}
|
||||
h5("Select Storm Name and Year")
|
||||
|
||||
dbStormYears <- dbGetQuery(con, "SELECT DISTINCT storm_year, storm_name FROM econ.storm_base_loss ORDER BY storm_year")
|
||||
|
||||
fluidRow(
|
||||
column(6,
|
||||
selectInput("stormYear", "Select Year", choices = dbStormYears$storm_year)
|
||||
),
|
||||
column(6,
|
||||
selectInput("stormName", "Select Storm", choices = NULL)
|
||||
)
|
||||
)
|
||||
|
||||
fluidRow(
|
||||
column(12,
|
||||
actionButton("selectStorm", "Submit", class = "btn-primary"))
|
||||
)
|
||||
|
||||
#h5("Search")
|
||||
|
||||
#textInput("stormSearch", "Search by hurricane name, year")
|
||||
|
||||
observeEvent(input$stormYear, {
|
||||
stormsByChosenYear <- dbStormYears[dbStormYears$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(
|
||||
normalized2024,
|
||||
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"}
|
||||
====================================
|
||||
|
||||
Col {data-width=500}
|
||||
------------------------------------
|
||||
|
||||
### Storm Details {data-height=200}
|
||||
```{r}
|
||||
HTML('
|
||||
<h6>Katrina</h6>
|
||||
<p>Hurricane Katrina was a powerful, devestating and historic tropical cyclone that caused 1,392 fatalities and damages estimated at $125 billion in late August 2005.</p>
|
||||
')
|
||||
```
|
||||
|
||||
### Normalization Cost Index {data-height=800}
|
||||
```{r}
|
||||
|
||||
##BY LANDFALL###
|
||||
##MULTIPLE LINES###
|
||||
|
||||
dbNormalizedLandfalls <- reactive({
|
||||
req(storm_selection$is_selected)
|
||||
|
||||
query <- paste0("
|
||||
SELECT *, CONCAT(lf_type, lf_id) as lfid FROM econ.normalized_landfalls
|
||||
WHERE storm_basin = '", storm_selection$storm_basin,
|
||||
"' AND storm_year = ", storm_selection$storm_year,
|
||||
" AND storm_name = '", storm_selection$storm_name,
|
||||
"'")
|
||||
|
||||
result <- dbGetQuery(con, query)
|
||||
|
||||
#cat(str(result))
|
||||
|
||||
showNotification(nrow(result))
|
||||
|
||||
return(result)
|
||||
})
|
||||
|
||||
dbUniq <- reactive({
|
||||
req(dbNormalizedLandfalls(), nrow(dbNormalizedLandfalls()) > 0)
|
||||
|
||||
dbNormalizedLandfalls() %>%
|
||||
distinct(lfid)
|
||||
|
||||
#re <- unique(res$lfid)
|
||||
|
||||
#cat(str(res))
|
||||
#cat(str(re))
|
||||
|
||||
#cat("hi2")
|
||||
|
||||
#cat(str(res))
|
||||
|
||||
#return(res)
|
||||
})
|
||||
|
||||
lfNormalized <- reactive({
|
||||
req(storm_selection$lf_id)
|
||||
|
||||
lf <- dbNormalizedLandfalls() %>%
|
||||
filter(lfid == storm_selection$lf_id) %>%
|
||||
mutate(
|
||||
mmh = gdp_deflator * rwhu * affected_housing,
|
||||
mmp = gdp_deflator * rwpc * affected_population,
|
||||
years = as.Date(paste0(normalization_year, "-01-01"))
|
||||
) %>%
|
||||
select(
|
||||
years, mmh, mmp
|
||||
)
|
||||
|
||||
lf_ts <- lf %>%
|
||||
select(-years) %>%
|
||||
xts(order.by = lf$years)
|
||||
|
||||
cat(storm_selection$lf_id)
|
||||
cat(str(lf_ts))
|
||||
|
||||
return(lf_ts)
|
||||
})
|
||||
|
||||
fillCol(
|
||||
flex = c(.2, .8),
|
||||
fluidRow(
|
||||
column(4,
|
||||
selectInput("lfSelect", "Landfall Select", choices = NULL)
|
||||
),
|
||||
column(4,
|
||||
#checkboxInput("mmhSelect", "Display MMH", value = T)
|
||||
),
|
||||
column(4,
|
||||
#checkboxInput("mmpSelect", "Display MMP", value = T)
|
||||
)
|
||||
),
|
||||
#dygraphOutput("katrinaCostIndex")
|
||||
dygraphOutput("costIndex")
|
||||
)
|
||||
|
||||
|
||||
observe({
|
||||
req(dbUniq(), nrow(dbUniq()) > 0)
|
||||
|
||||
#cat("hi")
|
||||
#cat(nrow(dbUniq()))
|
||||
#cat(str(dbUniq()))
|
||||
|
||||
updateSelectInput(
|
||||
session,
|
||||
"lfSelect",
|
||||
choices = dbUniq()$lfid,
|
||||
selected = dbUniq()$lfid[1]
|
||||
)
|
||||
|
||||
updateSelectInput(
|
||||
session,
|
||||
"growthTrendLfSelect",
|
||||
choices = dbUniq()$lfid,
|
||||
selected = dbUniq()$lfid[1]
|
||||
)
|
||||
|
||||
storm_selection$lf_id = dbUniq()$lfid[1]
|
||||
})
|
||||
|
||||
observeEvent(input$lfSelect, {
|
||||
storm_selection$lf_id = input$lfSelect
|
||||
|
||||
showNotification("Landfall updated", type = "message")
|
||||
})
|
||||
|
||||
output$costIndex <- renderDygraph({
|
||||
req(lfNormalized())
|
||||
|
||||
dygraph(lfNormalized(), main = "Cost Index") %>%
|
||||
dySeries("mmh", label = "MMH") %>%
|
||||
dySeries("mmp", label = "MMP") %>%
|
||||
dyRangeSelector(height = 30)
|
||||
})
|
||||
|
||||
```
|
||||
|
||||
Col {data-width=500}
|
||||
------------------------------------
|
||||
|
||||
### Fatalities {data-height=200}
|
||||
```{r}
|
||||
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}
|
||||
#selectLandfalls <- hurdat.best_track %>%
|
||||
# filter(
|
||||
# storm_name == storm_selection$storm_name &
|
||||
# storm_year == storm_selection$storm_year &
|
||||
# record_identifier == "L"
|
||||
# )
|
||||
|
||||
landfallsdata <- reactive({
|
||||
req(storm_selection$is_selected)
|
||||
|
||||
df <- selected.best_track() %>%
|
||||
filter(record_identifier == "L") %>%
|
||||
select(
|
||||
Date = datetime,
|
||||
Latitude = lat,
|
||||
Longitude = lon,
|
||||
Pressure = pressure,
|
||||
Windspeed = windspeed,
|
||||
RMW = rmw
|
||||
)
|
||||
})
|
||||
|
||||
output$landfalls <- renderDT({
|
||||
datatable(
|
||||
landfallsdata(),
|
||||
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")
|
||||
|
||||
```
|
||||
|
||||
### Storm Track {data-height=500 .no-padding}
|
||||
```{r}
|
||||
output$trackMap <- renderLeaflet({
|
||||
leaflet() %>%
|
||||
addProviderTiles("CartoDB.Positron", option = providerTileOptions(minZoom = 2, maxZoom = 18)) %>%
|
||||
setView(lng = -80, lat = 32, zoom = 4) %>%
|
||||
addCircleMarkers(
|
||||
#data = katrinaTrack,
|
||||
#lng = ~Longitude,
|
||||
#lat = ~Latitude,
|
||||
data = selected.best_track(),
|
||||
lng = ~lon,
|
||||
lat = ~lat,
|
||||
radius = 5,
|
||||
color = ~ifelse(record_identifier == "L", "red", "blue"),
|
||||
fillColor = ~ifelse(record_identifier == "L", "red", "blue")
|
||||
)
|
||||
})
|
||||
|
||||
leafletOutput("trackMap", height="100%")
|
||||
```
|
||||
|
||||
Growth Trends {data-navmenu="Storm Details"}
|
||||
================================
|
||||
|
||||
Column {data-width=550}
|
||||
-------------------------------
|
||||
|
||||
### {data-height=1000 .no-padding}
|
||||
|
||||
```{r}
|
||||
dbStormCounties <- reactive({
|
||||
req(storm_selection$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
|
||||
===
|
||||
|
||||
About
|
||||
===
|
||||
Reference in New Issue
Block a user