rsconnect::setAccountInfo(name = 'bdq9mo-allison-hallowell',
token = 'FB0A0CE55C982B81F3A3AEBBF20F79CD',
secret = '9MNvc7Kc0Lf1vAJgxNw7KNQDESazQR34Iwc4T2o1',
server = 'posit.cloud')
install.packages("tidyverse")
##
## The downloaded binary packages are in
## /var/folders/2l/27g2p3vs4cj8t70v145f6w8w0000gn/T//Rtmp4wTFcM/downloaded_packages
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(readr)
fema_data_2023_2024 <- read_csv("Desktop/LSC/fema_data_2023_2024.csv")
## New names:
## Rows: 1761473 Columns: 72
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (17): incidentType, county, damagedStateAbbreviation, damagedCity, dama... dbl
## (52): ...1, disasterNumber, occupantsUnderTwo, occupants2to5, occupants... lgl
## (1): sbaEligible dttm (1): lastRefresh date (1): declarationDate
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
# Assuming your dataset is already loaded and named fema_data_2023_2024
# Remove "(county)" from the county column
fema_data_2023_2024$county <- gsub("\\(county\\)", "", fema_data_2023_2024$county, ignore.case = TRUE)
# Trim any leading or trailing whitespace
fema_data_2023_2024$county <- trimws(fema_data_2023_2024$county)
# Install required packages if not already installed
if (!requireNamespace("dplyr", quietly = TRUE)) install.packages("dplyr")
if (!requireNamespace("leaflet", quietly = TRUE)) install.packages("leaflet")
if (!requireNamespace("sf", quietly = TRUE)) install.packages("sf")
if (!requireNamespace("maps", quietly = TRUE)) install.packages("maps")
if (!requireNamespace("DT", quietly = TRUE)) install.packages("DT")
if (!requireNamespace("htmltools", quietly = TRUE)) install.packages("htmltools")
# Load required libraries
library(dplyr)
library(leaflet)
library(sf)
## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE
library(maps)
##
## Attaching package: 'maps'
## The following object is masked from 'package:purrr':
##
## map
library(DT)
library(htmltools)
# Assuming disaster_summary is the dataframe we created earlier
disaster_summary <- fema_data_2023_2024 %>%
group_by(incidentType, declarationDate, disasterNumber, damagedStateAbbreviation) %>%
summarise(applications = n(), .groups = 'drop') %>%
arrange(desc(applications))
# Calculate total applications per state and keep disaster breakdown
state_data <- disaster_summary %>%
group_by(damagedStateAbbreviation) %>%
summarise(
total_applications = sum(applications),
disaster_breakdown = list(tibble(disasterNumber, declarationDate, applications))
) %>%
ungroup()
# Get US states map data
us_states <- st_as_sf(maps::map("state", plot = FALSE, fill = TRUE))
us_states$name <- tolower(us_states$ID)
# Create a data frame with state names and abbreviations
state_info <- data.frame(
name = tolower(state.name),
abbreviation = state.abb
)
# Join the state info and application data with the map data
us_states <- us_states %>%
left_join(state_info, by = "name") %>%
left_join(state_data, by = c("abbreviation" = "damagedStateAbbreviation"))
# Replace NA counts with 0 and empty list
us_states$total_applications[is.na(us_states$total_applications)] <- 0
us_states$disaster_breakdown[is.na(us_states$disaster_breakdown)] <- list(tibble(disasterNumber = character(), declarationDate = as.Date(character()), applications = numeric()))
# Create logarithmic color palette
max_applications <- max(us_states$total_applications, na.rm = TRUE)
log_breaks <- c(1, 10, 100, 1000, 10000, 100000, 1000000, max_applications)
pal <- colorBin(
palette = "YlOrRd",
domain = us_states$total_applications,
bins = log_breaks,
na.color = "white"
)
# Function to create popup content
create_popup_content <- function(state, total, breakdown) {
if (is.null(breakdown) || nrow(breakdown) == 0) {
return(paste0("<strong>", state, "</strong><br>No application data available"))
}
breakdown_table <- breakdown %>%
arrange(desc(applications)) %>%
slice_head(n = 10) %>% # Show top 10 disasters
mutate(
applications = format(applications, big.mark = ","),
declarationDate = format(declarationDate, "%Y-%m-%d"),
disasterNumber = sprintf('<span style="float:left;">%s</span>', disasterNumber)
) %>%
knitr::kable(format = "html", col.names = c("Disaster Number", "Declaration Date", "Applications"), escape = FALSE)
paste0(
"<strong>", state, "</strong><br>",
"Total Applications: ", format(total, big.mark = ","), "<br><br>",
breakdown_table
)
}
# Create the map
m <- leaflet(us_states) %>%
addTiles() %>%
setView(lng = -98, lat = 39, zoom = 4) %>%
addPolygons(
fillColor = ~pal(total_applications),
weight = 2,
opacity = 1,
color = "black",
dashArray = "3",
fillOpacity = 0.7,
highlightOptions = highlightOptions(
weight = 5,
color = "#666",
dashArray = "",
fillOpacity = 0.7,
bringToFront = TRUE
),
popup = ~lapply(seq_len(nrow(us_states)), function(i) {
create_popup_content(us_states$abbreviation[i], us_states$total_applications[i], us_states$disaster_breakdown[[i]])
}),
label = ~paste0(
abbreviation, ": ",
format(total_applications, big.mark = ","), " applications"
),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto"
)
) %>%
addLegend(
pal = pal,
values = ~total_applications,
opacity = 0.7,
title = "Total Applications",
position = "bottomright",
labFormat = labelFormat(big.mark = ",", digits = 0)
)
## Warning: sf layer has inconsistent datum (+proj=longlat +ellps=clrk66 +no_defs).
## Need '+proj=longlat +datum=WGS84'
## Warning in pal(total_applications): Some values were outside the color scale
## and will be treated as NA
# Create a data table for total applications per disaster number, including declaration date
disaster_table <- disaster_summary %>%
group_by(disasterNumber, declarationDate) %>%
summarise(total_applications = sum(applications), .groups = "drop") %>%
arrange(desc(total_applications))
dt <- datatable(
disaster_table,
options = list(pageLength = 10, scrollY = "300px"),
rownames = FALSE,
colnames = c("Disaster Number", "Declaration Date", "Total Applications")
) %>%
formatDate("declarationDate", method = "toLocaleDateString")
# Combine map and table in a single output
output <- tagList(
tags$h2("FEMA Data Visualization"),
tags$div(style = "display: flex; flex-direction: column; height: 800px;",
tags$div(style = "flex: 2;", m),
tags$div(style = "flex: 1; overflow-y: auto;",
tags$h3("Total Applications per Disaster Number"),
dt
)
)
)
# Display the combined output
output
FEMA Data Visualization
Total Applications per Disaster Number
# Install required packages if not already installed
if (!requireNamespace("dplyr", quietly = TRUE)) install.packages("dplyr")
if (!requireNamespace("leaflet", quietly = TRUE)) install.packages("leaflet")
if (!requireNamespace("sf", quietly = TRUE)) install.packages("sf")
if (!requireNamespace("tigris", quietly = TRUE)) install.packages("tigris")
if (!requireNamespace("htmltools", quietly = TRUE)) install.packages("htmltools")
# Load required libraries
library(dplyr)
library(leaflet)
library(sf)
library(tigris)
## To enable caching of data, set `options(tigris_use_cache = TRUE)`
## in your R script or .Rprofile.
library(htmltools)
# Assuming fema_data_2023_2024 is your original dataset
# Summarize data by county
county_summary <- fema_data_2023_2024 %>%
group_by(county, damagedStateAbbreviation, incidentType, declarationDate, disasterNumber) %>%
summarise(applications = n(), .groups = 'drop') %>%
arrange(desc(applications))
# Calculate total applications per county and keep disaster breakdown
county_data <- county_summary %>%
group_by(county, damagedStateAbbreviation) %>%
summarise(
total_applications = sum(applications),
disaster_breakdown = list(tibble(disasterNumber, declarationDate, incidentType, applications))
) %>%
ungroup()
## `summarise()` has grouped output by 'county'. You can override using the
## `.groups` argument.
# Get US counties map data
us_counties <- counties(cb = TRUE, resolution = "20m")
## Retrieving data for the year 2022
## | | | 0% | |= | 2% | |==== | 5% | |===== | 7% | |====== | 9% | |======== | 11% | |========== | 14% | |=========== | 16% | |============= | 18% | |============== | 20% | |=============== | 22% | |================ | 23% | |================== | 25% | |=================== | 27% | |==================== | 28% | |===================== | 30% | |====================== | 32% | |======================== | 34% | |========================= | 36% | |========================== | 37% | |============================ | 39% | |============================= | 41% | |============================== | 43% | |=============================== | 45% | |================================= | 47% | |================================== | 48% | |=================================== | 50% | |==================================== | 52% | |====================================== | 54% | |======================================= | 56% | |======================================== | 57% | |========================================= | 59% | |=========================================== | 61% | |============================================ | 63% | |============================================= | 64% | |============================================== | 66% | |================================================ | 68% | |================================================= | 70% | |================================================== | 72% | |=================================================== | 73% | |===================================================== | 75% | |====================================================== | 77% | |======================================================= | 79% | |======================================================== | 81% | |========================================================== | 82% | |=========================================================== | 84% | |============================================================ | 86% | |============================================================= | 88% | |=============================================================== | 89% | |================================================================ | 91% | |================================================================= | 93% | |================================================================== | 95% | |==================================================================== | 97% | |===================================================================== | 99% | |======================================================================| 100%
# Join the county application data with the map data
us_counties <- us_counties %>%
left_join(county_data, by = c("NAME" = "county", "STUSPS" = "damagedStateAbbreviation"))
# Replace NA counts with 0 and empty list
us_counties$total_applications[is.na(us_counties$total_applications)] <- 0
us_counties$disaster_breakdown[is.na(us_counties$disaster_breakdown)] <- list(tibble(disasterNumber = character(), declarationDate = as.Date(character()), incidentType = character(), applications = numeric()))
# Create logarithmic color palette
max_applications <- max(us_counties$total_applications, na.rm = TRUE)
log_breaks <- c(1, 10, 100, 1000, 10000, 100000, max_applications)
pal <- colorBin(
palette = "YlOrRd",
domain = us_counties$total_applications,
bins = log_breaks,
na.color = "white"
)
# Function to create popup content
create_popup_content <- function(county, state, total, breakdown) {
if (is.null(breakdown) || nrow(breakdown) == 0) {
return(paste0("<strong>", county, ", ", state, "</strong><br>No application data available"))
}
breakdown_table <- breakdown %>%
arrange(desc(applications)) %>%
slice_head(n = 5) %>% # Show top 5 disasters
mutate(
applications = format(applications, big.mark = ","),
declarationDate = format(declarationDate, "%Y-%m-%d"),
disasterNumber = sprintf('<span style="float:left;">%s</span>', disasterNumber)
) %>%
knitr::kable(format = "html", col.names = c("Disaster Number", "Declaration Date", "Incident Type", "Applications"), escape = FALSE)
paste0(
"<strong>", county, ", ", state, "</strong><br>",
"Total Applications: ", format(total, big.mark = ","), "<br><br>",
breakdown_table
)
}
# Create the map
m <- leaflet(us_counties) %>%
addTiles() %>%
setView(lng = -98, lat = 39, zoom = 4) %>%
addPolygons(
fillColor = ~pal(total_applications),
weight = 1,
opacity = 1,
color = "white",
dashArray = "3",
fillOpacity = 0.7,
highlightOptions = highlightOptions(
weight = 5,
color = "#666",
dashArray = "",
fillOpacity = 0.7,
bringToFront = TRUE
),
popup = ~lapply(seq_len(nrow(us_counties)), function(i) {
create_popup_content(us_counties$NAME[i], us_counties$STUSPS[i], us_counties$total_applications[i], us_counties$disaster_breakdown[[i]])
}),
label = ~paste0(
NAME, ", ", STUSPS, ": ",
format(total_applications, big.mark = ","), " applications"
),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "12px",
direction = "auto"
)
) %>%
addLegend(
pal = pal,
values = ~total_applications,
opacity = 0.7,
title = "Total Applications",
position = "bottomright",
labFormat = labelFormat(big.mark = ",", digits = 0)
)
## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'
## Warning in pal(total_applications): Some values were outside the color scale
## and will be treated as NA
# Display the map
m
# Load required libraries
library(httr)
library(jsonlite)
##
## Attaching package: 'jsonlite'
## The following object is masked from 'package:purrr':
##
## flatten
library(dplyr)
library(lubridate)
library(ggplot2)
library(maps)
library(scales) # For pretty_breaks function
##
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
##
## discard
## The following object is masked from 'package:readr':
##
## col_factor
# Function to fetch data from the API
fetch_fema_data <- function(url, declarationDate, limit = 1000, offset = 0) {
query_params <- list(
"$filter" = paste0("declarationDate ge '", declarationDate,
"' and (disasterNumber le 1999 or (disasterNumber ge 4000 and disasterNumber le 4999))"),
"$orderby" = "declarationDate desc",
"$top" = limit,
"$skip" = offset
)
response <- GET(url, query = query_params)
if (status_code(response) != 200) {
stop("Failed to fetch data from the API. Status code: ", status_code(response))
}
content <- content(response, "text", encoding = "UTF-8")
data <- fromJSON(content)
return(data$DisasterDeclarationsSummaries)
}
# Main script
main <- function() {
base_url <- "https://www.fema.gov/api/open/v2/DisasterDeclarationsSummaries"
all_data <- data.frame()
start_date <- "2023-01-01"
chunk_size <- 1000
cat("Fetching data from FEMA API...\n")
offset <- 0
repeat {
cat(sprintf("Fetching records starting from offset %d\n", offset))
chunk <- fetch_fema_data(base_url, start_date, chunk_size, offset)
all_data <- bind_rows(all_data, chunk)
if (nrow(chunk) < chunk_size) break
offset <- offset + chunk_size
}
cat(sprintf("Total records fetched: %d\n", nrow(all_data)))
# Convert declarationDate to Date type and disasterNumber to numeric
all_data$declarationDate <- as.Date(all_data$declarationDate)
all_data$disasterNumber <- as.numeric(all_data$disasterNumber)
# Keep only unique disaster declarations
filtered_data <- all_data %>%
distinct(disasterNumber, .keep_all = TRUE)
cat(sprintf("\nUnique major disaster declarations from %s to %s: %d\n",
start_date, format(Sys.Date(), "%Y-%m-%d"), nrow(filtered_data)))
# Analyze incident types
incident_types <- filtered_data %>%
group_by(incidentType) %>%
summarise(count = n()) %>%
arrange(desc(count))
cat("\nIncident types for major disasters from 2023-01-01 onwards:\n")
print(incident_types)
# Count disasters by state
state_counts <- filtered_data %>%
group_by(state) %>%
summarise(count = n()) %>%
arrange(desc(count))
# Get US state map data
us_states <- map_data("state")
# Convert state abbreviations to full names for joining
state_counts$region <- tolower(state.name[match(state_counts$state, state.abb)])
# Join map data with disaster counts
map_data <- left_join(us_states, state_counts, by = "region")
# Create the map
p <- ggplot(data = map_data, aes(x = long, y = lat, group = group, fill = count)) +
geom_polygon(color = "white") +
scale_fill_gradient(name = "Number of\nMajor Disasters",
low = "lightblue", high = "darkblue",
na.value = "grey90",
breaks = pretty_breaks(n = 5), # Use pretty breaks for whole numbers
labels = function(x) sprintf("%d", x)) + # Format labels as integers
coord_fixed(1.3) +
theme_minimal() +
labs(title = "FEMA Major Disaster Declarations by State (2023-01-01 onwards)",
subtitle = paste("Total declarations:", sum(state_counts$count, na.rm = TRUE)),
caption = "Data source: FEMA Disaster Declarations Summaries") +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# Display the map
print(p)
cat("Map has been displayed in R\n")
# Display summary of state counts
cat("\nStates by number of major disaster declarations (2023-01-01 onwards):\n")
print(state_counts)
# Additional analysis: Average number of days between incident begin and declaration
filtered_data$incidentBeginDate <- as.Date(filtered_data$incidentBeginDate)
avg_days_to_declaration <- filtered_data %>%
mutate(days_to_declaration = as.numeric(declarationDate - incidentBeginDate)) %>%
summarise(avg_days = mean(days_to_declaration, na.rm = TRUE))
cat(sprintf("\nAverage number of days between incident begin and declaration: %.2f\n", avg_days_to_declaration$avg_days))
# ... (previous code remains the same)
# List of disaster declarations
cat("\nList of major disaster declarations from 2023-01-01 onwards:\n")
disaster_list <- filtered_data %>%
select(disasterNumber, declarationDate, state, incidentType, declarationTitle) %>%
arrange(desc(declarationDate))
# Modified print statement
print(disaster_list, n = nrow(disaster_list), na.print = "NA")
}
# Run the main function
main()
## Fetching data from FEMA API...
## Fetching records starting from offset 0
## Fetching records starting from offset 1000
## Total records fetched: 1592
##
## Unique major disaster declarations from 2023-01-01 to 2024-08-28: 132
##
## Incident types for major disasters from 2023-01-01 onwards:
## # A tibble: 11 × 2
## incidentType count
## <chr> <int>
## 1 Severe Storm 63
## 2 Flood 34
## 3 Winter Storm 11
## 4 Hurricane 6
## 5 Tornado 5
## 6 Fire 3
## 7 Mud/Landslide 3
## 8 Tropical Storm 3
## 9 Snowstorm 2
## 10 Earthquake 1
## 11 Straight-Line Winds 1
## Map has been displayed in R
##
## States by number of major disaster declarations (2023-01-01 onwards):
## # A tibble: 44 × 3
## state count region
## <chr> <int> <chr>
## 1 CA 13 california
## 2 TN 8 tennessee
## 3 ME 7 maine
## 4 NH 6 new hampshire
## 5 OK 6 oklahoma
## 6 VT 6 vermont
## 7 SD 5 south dakota
## 8 AR 4 arkansas
## 9 FL 4 florida
## 10 IA 4 iowa
## # ℹ 34 more rows
##
## Average number of days between incident begin and declaration: 64.49
##
## List of major disaster declarations from 2023-01-01 onwards:
## disasterNumber declarationDate state incidentType
## 1 4813 2024-08-23 MT Straight-Line Winds
## 2 4808 2024-08-20 NE Severe Storm
## 3 4812 2024-08-20 NH Severe Storm
## 4 4811 2024-08-20 KS Severe Storm
## 5 4810 2024-08-20 VT Severe Storm
## 6 4809 2024-08-20 NM Flood
## 7 4807 2024-08-15 SD Flood
## 8 4806 2024-08-10 FL Tropical Storm
## 9 4804 2024-07-23 KY Severe Storm
## 10 4803 2024-07-23 MO Severe Storm
## 11 4802 2024-07-23 OK Severe Storm
## 12 4805 2024-07-23 PR Flood
## 13 4801 2024-07-18 MT Severe Storm
## 14 4800 2024-07-15 KS Severe Storm
## 15 4799 2024-07-10 NH Winter Storm
## 16 4798 2024-07-09 TX Hurricane
## 17 4797 2024-06-28 MN Flood
## 18 4796 2024-06-24 IA Severe Storm
## 19 4795 2024-06-20 NM Fire
## 20 4792 2024-06-17 TN Severe Storm
## 21 4794 2024-06-17 FL Severe Storm
## 22 4793 2024-06-17 HI Flood
## 23 4791 2024-06-14 OK Severe Storm
## 24 4790 2024-06-10 MS Severe Storm
## 25 4789 2024-06-10 ID Flood
## 26 4788 2024-05-30 AR Severe Storm
## 27 4787 2024-05-24 WV Flood
## 28 4786 2024-05-24 NE Severe Storm
## 29 4785 2024-05-24 ME Winter Storm
## 30 4784 2024-05-24 IA Severe Storm
## 31 4783 2024-05-22 WV Severe Storm
## 32 4782 2024-05-22 KY Severe Storm
## 33 4781 2024-05-17 TX Flood
## 34 4780 2024-05-15 MA Flood
## 35 4779 2024-05-14 IA Tornado
## 36 4778 2024-05-03 NE Tornado
## 37 4777 2024-05-02 OH Tornado
## 38 4776 2024-04-30 OK Tornado
## 39 4775 2024-04-28 WA Severe Storm
## 40 4774 2024-04-28 KS Winter Storm
## 41 4773 2024-04-19 CA Severe Storm
## 42 4772 2024-04-19 CA Severe Storm
## 43 4771 2024-04-19 NH Severe Storm
## 44 4770 2024-04-19 VT Severe Storm
## 45 4769 2024-04-13 CA Severe Storm
## 46 4768 2024-04-13 OR Severe Storm
## 47 4767 2024-04-06 AK Mud/Landslide
## 48 4766 2024-03-20 RI Severe Storm
## 49 4765 2024-03-20 RI Severe Storm
## 50 4764 2024-03-20 ME Severe Storm
## 51 4763 2024-03-15 AK Mud/Landslide
## 52 4762 2024-03-02 VT Severe Storm
## 53 4761 2024-02-27 NH Severe Storm
## 54 4758 2024-02-19 CA Flood
## 55 4760 2024-02-15 ND Winter Storm
## 56 4759 2024-02-15 WA Fire
## 57 4757 2024-02-08 MI Flood
## 58 4756 2024-01-30 WV Flood
## 59 4755 2024-01-30 NY Flood
## 60 4754 2024-01-30 ME Severe Storm
## 61 4753 2024-01-07 RI Severe Storm
## 62 4752 2023-12-23 UT Flood
## 63 4751 2023-12-13 TN Severe Storm
## 64 4750 2023-11-21 CA Hurricane
## 65 4749 2023-11-20 IL Flood
## 66 4748 2023-11-14 AR Severe Storm
## 67 4747 2023-10-26 KS Severe Storm
## 68 4746 2023-10-18 CA Tropical Storm
## 69 4745 2023-10-11 MT Flood
## 70 4744 2023-10-06 VT Flood
## 71 4743 2023-09-27 CA Hurricane
## 72 4742 2023-09-27 TN Severe Storm
## 73 4741 2023-09-21 MO Severe Storm
## 74 4740 2023-09-14 NH Flood
## 75 4739 2023-09-11 WY Flood
## 76 4738 2023-09-07 GA Hurricane
## 77 4737 2023-09-06 ME Flood
## 78 4736 2023-09-05 ME Flood
## 79 4735 2023-09-04 TN Severe Storm
## 80 4734 2023-08-31 FL Hurricane
## 81 4733 2023-08-28 OR Mud/Landslide
## 82 4732 2023-08-25 IA Flood
## 83 4731 2023-08-25 CO Flood
## 84 4730 2023-08-23 AK Flood
## 85 4729 2023-08-17 TN Severe Storm
## 86 4728 2023-08-15 IL Severe Storm
## 87 4727 2023-08-12 MS Severe Storm
## 88 4726 2023-08-12 MT Flood
## 89 4725 2023-08-11 NJ Severe Storm
## 90 4724 2023-08-10 HI Fire
## 91 4723 2023-07-22 NY Severe Storm
## 92 4722 2023-07-19 MN Flood
## 93 4721 2023-07-19 OK Severe Storm
## 94 4720 2023-07-14 VT Flood
## 95 4719 2023-07-06 ME Flood
## 96 4718 2023-07-06 SD Flood
## 97 4717 2023-07-05 ND Flood
## 98 4716 2023-06-02 MP Tropical Storm
## 99 4715 2023-05-25 GU Hurricane
## 100 4714 2023-05-25 CA Severe Storm
## 101 4713 2023-05-18 CA Severe Storm
## 102 4712 2023-05-17 TN Severe Storm
## 103 4711 2023-05-09 KY Flood
## 104 4710 2023-05-05 AL Severe Storm
## 105 4709 2023-04-27 FL Flood
## 106 4708 2023-04-27 NV Flood
## 107 4707 2023-04-25 CA Winter Storm
## 108 4706 2023-04-24 OK Tornado
## 109 4705 2023-04-21 TX Winter Storm
## 110 4704 2023-04-15 IN Severe Storm
## 111 4703 2023-04-11 AZ Severe Storm
## 112 4702 2023-04-10 KY Severe Storm
## 113 4701 2023-04-07 TN Severe Storm
## 114 4700 2023-04-04 AR Winter Storm
## 115 4699 2023-04-03 CA Severe Storm
## 116 4698 2023-04-02 AR Severe Storm
## 117 4697 2023-03-26 MS Severe Storm
## 118 4696 2023-03-22 ME Severe Storm
## 119 4695 2023-03-20 VT Severe Storm
## 120 4694 2023-03-15 NY Snowstorm
## 121 4693 2023-03-15 NH Severe Storm
## 122 4692 2023-03-08 CA Earthquake
## 123 4691 2023-03-08 TN Winter Storm
## 124 4690 2023-03-03 OK Severe Storm
## 125 4689 2023-02-27 SD Winter Storm
## 126 4688 2023-02-20 SD Winter Storm
## 127 4687 2023-02-20 SD Winter Storm
## 128 4686 2023-02-05 ND Snowstorm
## 129 4685 2023-01-16 GA Severe Storm
## 130 4684 2023-01-15 AL Severe Storm
## 131 4683 2023-01-14 CA Flood
## 132 4682 2023-01-12 WA Severe Storm
## declarationTitle
## 1 STRAIGHT-LINE WINDS
## 2 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## 3 SEVERE STORM AND FLOODING
## 4 SEVERE STORM, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## 5 SEVERE STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 6 SEVERE STORMS AND FLOODING
## 7 SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 8 HURRICANE DEBBY
## 9 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, LANDSLIDES, AND MUDSLIDES
## 10 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## 11 SEVERE STORMS
## 12 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 13 SEVERE WINTER STORM AND FLOODING
## 14 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## 15 SEVERE WINTER STORM AND FLOODING
## 16 HURRICANE BERYL
## 17 SEVERE STORMS AND FLOODING
## 18 SEVERE STORMS, FLOODING, STRAIGHT-LINE WINDS, AND TORNADOES
## 19 SOUTH FORK FIRE, SALT FIRE, AND FLOODING
## 20 SEVERE STORMS, TORNADOES, AND FLOODING
## 21 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 22 SEVERE STORMS, FLOODING, AND LANDSLIDES
## 23 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## 24 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## 25 SEVERE STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 26 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## 27 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 28 SEVERE WINTER STORM AND STRAIGHT-LINE WINDS
## 29 SEVERE WINTER STORM
## 30 SEVERE STORMS, TORNADOES, AND FLOODING
## 31 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, FLOODING, LANDSLIDES, AND MUDSLIDES
## 32 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, LANDSLIDES, AND MUDSLIDES
## 33 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## 34 SEVERE STORMS AND FLOODING
## 35 SEVERE STORMS AND TORNADOES
## 36 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 37 TORNADOES
## 38 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## 39 SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 40 SEVERE WINTER STORM
## 41 SEVERE WINTER STORM
## 42 SEVERE STORMS AND FLOODING
## 43 SEVERE STORMS AND FLOODING
## 44 SEVERE WINTER STORM
## 45 SEVERE WINTER STORMS, TORNADOES, FLOODING, LANDSLIDES, AND MUDSLIDES
## 46 SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, LANDSLIDES, AND MUDSLIDES
## 47 SEVERE STORM, FLOODING, AND LANDSLIDES
## 48 SEVERE STORMS AND FLOODING
## 49 SEVERE STORM AND FLOODING
## 50 SEVERE STORMS AND FLOODING
## 51 SEVERE STORM, LANDSLIDES, AND MUDSLIDES
## 52 SEVERE STORM AND FLOODING
## 53 SEVERE STORM AND FLOODING
## 54 SEVERE STORM AND FLOODING
## 55 SEVERE WINTER STORM AND STRAIGHT-LINE WINDS
## 56 WILDFIRES
## 57 SEVERE STORMS, TORNADOES, AND FLOODING
## 58 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 59 SEVERE STORM AND FLOODING
## 60 SEVERE STORM AND FLOODING
## 61 SEVERE STORMS, FLOODING, AND TORNADOES
## 62 FLOODING
## 63 SEVERE STORMS AND TORNADOES
## 64 TROPICAL STORM HILARY
## 65 SEVERE STORMS AND FLOODING
## 66 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 67 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## 68 TROPICAL STORM HILARY
## 69 FLOODING
## 70 SEVERE STORMS AND FLOODING
## 71 TROPICAL STORM HILARY
## 72 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADO
## 73 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## 74 SEVERE STORMS AND FLOODING
## 75 FLOODING
## 76 HURRICANE IDALIA
## 77 SEVERE STORM AND FLOODING
## 78 SEVERE STORM AND FLOODING
## 79 SEVERE STORMS AND STRAIGHT-LINE WINDS
## 80 HURRICANE IDALIA
## 81 SEVERE STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 82 FLOODING
## 83 SEVERE STORMS, FLOODING, AND TORNADOES
## 84 FLOODING
## 85 SEVERE STORMS AND STRAIGHT-LINE WINDS
## 86 SEVERE STORMS AND FLOODING
## 87 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES.
## 88 FLOODING
## 89 SEVERE STORM AND FLOODING
## 90 WILDFIRES AND HIGH WINDS
## 91 SEVERE STORMS AND FLOODING
## 92 SEVERE STORMS AND FLOODING
## 93 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 94 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 95 SEVERE STORM AND FLOODING
## 96 FLOODING
## 97 FLOODING
## 98 TYPHOON MAWAR
## 99 TYPHOON MAWAR
## 100 SEVERE STORM AND FLOODING
## 101 SEVERE WINTER STORM AND FLOODING
## 102 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADO
## 103 SEVERE STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 104 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 105 SEVERE STORMS, TORNADOES, AND FLOODING
## 106 SEVERE WINTER STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 107 SEVERE WINTER STORMS AND MUDSLIDES
## 108 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 109 SEVERE WINTER STORM
## 110 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 111 SEVERE WINTER STORMS AND FLOODING
## 112 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, FLOODING, LANDSLIDES, AND MUDSLIDES
## 113 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 114 SEVERE WINTER STORM
## 115 SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 116 SEVERE STORMS AND TORNADOES
## 117 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 118 SEVERE STORM AND FLOODING
## 119 SEVERE STORM AND FLOODING
## 120 SEVERE WINTER STORM AND SNOWSTORM
## 121 SEVERE STORM AND FLOODING
## 122 EARTHQUAKE
## 123 SEVERE WINTER STORM
## 124 SEVERE WINTER STORM
## 125 SEVERE WINTER STORMS AND SNOWSTORM
## 126 SEVERE WINTER STORMS AND SNOWSTORM
## 127 SEVERE WINTER STORMS AND SNOWSTORM
## 128 SEVERE WINTER STORM, SNOWSTORM, AND STRAIGHT-LINE WINDS
## 129 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 130 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 131 SEVERE WINTER STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 132 SEVERE WINTER STORM, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
# Load required libraries
library(httr)
library(jsonlite)
library(dplyr)
library(lubridate)
library(ggplot2)
library(maps)
library(scales)
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:httr':
##
## config
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
# Function to fetch data from the API
fetch_fema_data <- function(url, declarationDate, limit = 1000, offset = 0) {
query_params <- list(
"$filter" = paste0("declarationDate ge '", declarationDate,
"' and (disasterNumber le 1999 or (disasterNumber ge 4000 and disasterNumber le 4999))"),
"$orderby" = "declarationDate desc",
"$top" = limit,
"$skip" = offset
)
response <- GET(url, query = query_params)
if (status_code(response) != 200) {
stop("Failed to fetch data from the API. Status code: ", status_code(response))
}
content <- content(response, "text", encoding = "UTF-8")
data <- fromJSON(content)
return(data$DisasterDeclarationsSummaries)
}
# Main script
main <- function() {
base_url <- "https://www.fema.gov/api/open/v2/DisasterDeclarationsSummaries"
all_data <- data.frame()
start_date <- "2023-01-01"
chunk_size <- 1000
cat("Fetching data from FEMA API...\n")
offset <- 0
repeat {
cat(sprintf("Fetching records starting from offset %d\n", offset))
chunk <- fetch_fema_data(base_url, start_date, chunk_size, offset)
all_data <- bind_rows(all_data, chunk)
if (nrow(chunk) < chunk_size) break
offset <- offset + chunk_size
}
cat(sprintf("Total records fetched: %d\n", nrow(all_data)))
# Convert declarationDate to Date type and disasterNumber to numeric
all_data$declarationDate <- as.Date(all_data$declarationDate)
all_data$disasterNumber <- as.numeric(all_data$disasterNumber)
# Keep only unique disaster declarations
filtered_data <- all_data %>%
distinct(disasterNumber, .keep_all = TRUE)
cat(sprintf("\nUnique major disaster declarations from %s to %s: %d\n",
start_date, format(Sys.Date(), "%Y-%m-%d"), nrow(filtered_data)))
# Analyze incident types
incident_types <- filtered_data %>%
group_by(incidentType) %>%
summarise(count = n()) %>%
arrange(desc(count))
cat("\nIncident types for major disasters from 2023-01-01 onwards:\n")
print(incident_types)
# Count disasters by state and get additional information
state_data <- filtered_data %>%
group_by(state) %>%
summarise(
count = n(),
most_common_incident = names(which.max(table(incidentType))),
avg_days_to_declare = mean(as.numeric(declarationDate - as.Date(incidentBeginDate)), na.rm = TRUE),
latest_disaster_date = max(declarationDate),
latest_disaster_title = declarationTitle[which.max(declarationDate)]
) %>%
arrange(desc(count))
# Get US state map data
us_states <- map_data("state")
# Convert state abbreviations to full names for joining
state_data$region <- tolower(state.name[match(state_data$state, state.abb)])
# Join map data with state data
map_data <- left_join(us_states, state_data, by = "region")
# Create the base ggplot map
p <- ggplot(data = map_data, aes(x = long, y = lat, group = group, fill = count,
text = paste0("State: ", state, "<br>",
"Disaster Count: ", count, "<br>",
"Most Common Incident: ", most_common_incident, "<br>",
"Avg Days to Declare: ", round(avg_days_to_declare, 1), "<br>",
"Latest Disaster Date: ", latest_disaster_date, "<br>",
"Latest Disaster: ", latest_disaster_title))) +
geom_polygon(color = "white") +
scale_fill_gradient(name = "Number of\nMajor Disasters",
low = "lightblue", high = "darkblue",
na.value = "grey90",
breaks = pretty_breaks(n = 5)) +
coord_fixed(1.3) +
theme_minimal() +
labs(title = "FEMA Major Disaster Declarations by State (2023-01-01 onwards)",
subtitle = paste("Total declarations:", sum(state_data$count, na.rm = TRUE)),
caption = "Data source: FEMA Disaster Declarations Summaries") +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# Convert ggplot to plotly
interactive_map <- ggplotly(p, tooltip = "text")
# Display the interactive map
print(interactive_map)
cat("Interactive map has been displayed in R\n")
# Display summary of state counts
cat("\nStates by number of major disaster declarations (2023-01-01 onwards):\n")
print(state_data)
# Additional analysis: Average number of days between incident begin and declaration
avg_days_to_declaration <- mean(state_data$avg_days_to_declare, na.rm = TRUE)
cat(sprintf("\nOverall average number of days between incident begin and declaration: %.2f\n", avg_days_to_declaration))
}
# Run the main function
main()
## Fetching data from FEMA API...
## Fetching records starting from offset 0
## Fetching records starting from offset 1000
## Total records fetched: 1592
##
## Unique major disaster declarations from 2023-01-01 to 2024-08-28: 132
##
## Incident types for major disasters from 2023-01-01 onwards:
## # A tibble: 11 × 2
## incidentType count
## <chr> <int>
## 1 Severe Storm 63
## 2 Flood 34
## 3 Winter Storm 11
## 4 Hurricane 6
## 5 Tornado 5
## 6 Fire 3
## 7 Mud/Landslide 3
## 8 Tropical Storm 3
## 9 Snowstorm 2
## 10 Earthquake 1
## 11 Straight-Line Winds 1
## Interactive map has been displayed in R
##
## States by number of major disaster declarations (2023-01-01 onwards):
## # A tibble: 44 × 7
## state count most_common_incident avg_days_to_declare latest_disaster_date
## <chr> <int> <chr> <dbl> <date>
## 1 CA 13 Severe Storm 63.1 2024-04-19
## 2 TN 8 Severe Storm 44.5 2024-06-17
## 3 ME 7 Flood 66 2024-05-24
## 4 NH 6 Severe Storm 77 2024-08-20
## 5 OK 6 Severe Storm 45.7 2024-07-23
## 6 VT 6 Severe Storm 62.8 2024-08-20
## 7 SD 5 Winter Storm 73 2024-08-15
## 8 AR 4 Severe Storm 53.5 2024-05-30
## 9 FL 4 Flood 16.5 2024-08-10
## 10 IA 4 Severe Storm 38.2 2024-06-24
## # ℹ 34 more rows
## # ℹ 2 more variables: latest_disaster_title <chr>, region <chr>
##
## Overall average number of days between incident begin and declaration: 68.90
# Load required libraries
library(httr)
library(jsonlite)
library(dplyr)
library(lubridate)
library(leaflet)
library(sf)
library(maps)
# Function to fetch data from the API
fetch_fema_data <- function(url, declarationDate, limit = 1000, offset = 0) {
query_params <- list(
"$filter" = paste0("declarationDate ge '", declarationDate,
"' and (disasterNumber le 1999 or (disasterNumber ge 4000 and disasterNumber le 4999))"),
"$orderby" = "declarationDate desc",
"$top" = limit,
"$skip" = offset
)
response <- GET(url, query = query_params)
if (status_code(response) != 200) {
stop("Failed to fetch data from the API. Status code: ", status_code(response))
}
content <- content(response, "text", encoding = "UTF-8")
data <- fromJSON(content)
return(data$DisasterDeclarationsSummaries)
}
# Main script
main <- function() {
base_url <- "https://www.fema.gov/api/open/v2/DisasterDeclarationsSummaries"
all_data <- data.frame()
start_date <- "2023-01-01"
chunk_size <- 1000
cat("Fetching data from FEMA API...\n")
offset <- 0
repeat {
cat(sprintf("Fetching records starting from offset %d\n", offset))
chunk <- fetch_fema_data(base_url, start_date, chunk_size, offset)
all_data <- bind_rows(all_data, chunk)
if (nrow(chunk) < chunk_size) break
offset <- offset + chunk_size
}
cat(sprintf("Total records fetched: %d\n", nrow(all_data)))
if (nrow(all_data) == 0) {
stop("No data fetched from the API. Please check the API endpoint and parameters.")
}
# Convert date fields to Date type and numeric fields to appropriate types
all_data$declarationDate <- as.Date(all_data$declarationDate)
all_data$incidentBeginDate <- as.Date(all_data$incidentBeginDate)
all_data$incidentEndDate <- as.Date(all_data$incidentEndDate)
all_data$disasterNumber <- as.integer(all_data$disasterNumber)
all_data$fyDeclared <- as.integer(all_data$fyDeclared)
# Keep only unique disaster declarations
filtered_data <- all_data %>%
distinct(disasterNumber, .keep_all = TRUE)
cat(sprintf("\nUnique major disaster declarations from %s to %s: %d\n",
start_date, format(Sys.Date(), "%Y-%m-%d"), nrow(filtered_data)))
# Analyze incident types
incident_types <- filtered_data %>%
group_by(incidentType) %>%
summarise(count = n()) %>%
arrange(desc(count))
cat("\nIncident types for major disasters from 2023-01-01 onwards:\n")
print(incident_types)
# Count disasters by state and get additional information
state_data <- filtered_data %>%
group_by(state) %>%
summarise(
count = n(),
most_common_incident = names(which.max(table(incidentType))),
avg_days_to_declare = mean(as.numeric(declarationDate - incidentBeginDate), na.rm = TRUE),
latest_disaster_date = max(declarationDate),
latest_disaster_title = declarationTitle[which.max(declarationDate)],
ia_program_count = sum(iaProgramDeclared, na.rm = TRUE),
pa_program_count = sum(paProgramDeclared, na.rm = TRUE),
hm_program_count = sum(hmProgramDeclared, na.rm = TRUE)
) %>%
arrange(desc(count))
cat("\nState data summary:\n")
print(state_data)
# Get US state map data
us_states <- st_as_sf(maps::map("state", plot = FALSE, fill = TRUE))
us_states$state_name <- toupper(us_states$ID)
# Convert state abbreviations to full names for joining
state_data$state_name <- tolower(state.name[match(state_data$state, state.abb)])
# Join state data with map data
map_data <- left_join(us_states, state_data, by = c("ID" = "state_name"))
cat("\nMap data summary:\n")
print(summary(map_data))
if (all(is.na(map_data$count))) {
stop("All count values are NA. Check the join between us_states and state_data.")
}
# Create color palette
pal <- colorNumeric(
palette = "YlOrRd",
domain = map_data$count,
na.color = "grey"
)
# Create Leaflet map
m <- leaflet(map_data) %>%
addTiles() %>%
addPolygons(
fillColor = ~pal(count),
weight = 2,
opacity = 1,
color = "white",
dashArray = "3",
fillOpacity = 0.7,
highlightOptions = highlightOptions(
weight = 5,
color = "#666",
dashArray = "",
fillOpacity = 0.7,
bringToFront = TRUE),
label = ~ID,
popup = ~paste0(
"<strong>", ID, "</strong><br>",
"Disaster Count: ", count, "<br>",
"Most Common Incident: ", most_common_incident, "<br>",
"Avg Days to Declare: ", round(avg_days_to_declare, 1), "<br>",
"Latest Disaster Date: ", latest_disaster_date, "<br>",
"Latest Disaster: ", latest_disaster_title, "<br>",
"IA Program Declared: ", ia_program_count, "<br>",
"PA Program Declared: ", pa_program_count, "<br>",
"HM Program Declared: ", hm_program_count
)
) %>%
addLegend(pal = pal,
values = ~count,
opacity = 0.7,
title = "Number of Major Disasters",
position = "bottomright")
cat("Interactive Leaflet map has been created\n")
# Display summary of state counts
cat("\nStates by number of major disaster declarations (2023-01-01 onwards):\n")
print(state_data)
# Additional analysis: Average number of days between incident begin and declaration
avg_days_to_declaration <- mean(state_data$avg_days_to_declare, na.rm = TRUE)
cat(sprintf("\nOverall average number of days between incident begin and declaration: %.2f\n", avg_days_to_declaration))
# List of disaster declarations
cat("\nList of major disaster declarations from 2023-01-01 onwards:\n")
disaster_list <- filtered_data %>%
select(disasterNumber, declarationDate, state, incidentType, declarationTitle,
iaProgramDeclared, paProgramDeclared, hmProgramDeclared) %>%
arrange(desc(declarationDate))
print(disaster_list, na.print = "NA", row.names = FALSE)
# Return the Leaflet map object
return(m)
}
# Run the main function and store the result
result <- main()
## Fetching data from FEMA API...
## Fetching records starting from offset 0
## Fetching records starting from offset 1000
## Total records fetched: 1592
##
## Unique major disaster declarations from 2023-01-01 to 2024-08-28: 132
##
## Incident types for major disasters from 2023-01-01 onwards:
## # A tibble: 11 × 2
## incidentType count
## <chr> <int>
## 1 Severe Storm 63
## 2 Flood 34
## 3 Winter Storm 11
## 4 Hurricane 6
## 5 Tornado 5
## 6 Fire 3
## 7 Mud/Landslide 3
## 8 Tropical Storm 3
## 9 Snowstorm 2
## 10 Earthquake 1
## 11 Straight-Line Winds 1
##
## State data summary:
## # A tibble: 44 × 9
## state count most_common_incident avg_days_to_declare latest_disaster_date
## <chr> <int> <chr> <dbl> <date>
## 1 CA 13 Severe Storm 63.1 2024-04-19
## 2 TN 8 Severe Storm 44.5 2024-06-17
## 3 ME 7 Flood 66 2024-05-24
## 4 NH 6 Severe Storm 77 2024-08-20
## 5 OK 6 Severe Storm 45.7 2024-07-23
## 6 VT 6 Severe Storm 62.8 2024-08-20
## 7 SD 5 Winter Storm 73 2024-08-15
## 8 AR 4 Severe Storm 53.5 2024-05-30
## 9 FL 4 Flood 16.5 2024-08-10
## 10 IA 4 Severe Storm 38.2 2024-06-24
## # ℹ 34 more rows
## # ℹ 4 more variables: latest_disaster_title <chr>, ia_program_count <int>,
## # pa_program_count <int>, hm_program_count <int>
##
## Map data summary:
## ID state_name state count
## Length:49 Length:49 Length:49 Min. : 1.000
## Class :character Class :character Class :character 1st Qu.: 1.000
## Mode :character Mode :character Mode :character Median : 3.000
## Mean : 3.179
## 3rd Qu.: 4.000
## Max. :13.000
## NA's :10
## most_common_incident avg_days_to_declare latest_disaster_date
## Length:49 Min. : 6.00 Min. :2023-04-11
## Class :character 1st Qu.: 45.08 1st Qu.:2024-01-11
## Mode :character Median : 59.50 Median :2024-05-24
## Mean : 71.26 Mean :2024-03-21
## 3rd Qu.: 84.33 3rd Qu.:2024-07-23
## Max. :247.00 Max. :2024-08-23
## NA's :10 NA's :10
## latest_disaster_title ia_program_count pa_program_count hm_program_count
## Length:49 Min. :0 Min. : 0.000 Min. : 1.000
## Class :character 1st Qu.:0 1st Qu.: 1.000 1st Qu.: 1.000
## Mode :character Median :0 Median : 2.000 Median : 3.000
## Mean :0 Mean : 2.923 Mean : 3.154
## 3rd Qu.:0 3rd Qu.: 4.000 3rd Qu.: 4.000
## Max. :0 Max. :12.000 Max. :13.000
## NA's :10 NA's :10 NA's :10
## geom
## MULTIPOLYGON :49
## epsg:NA : 0
## +proj=long...: 0
##
##
##
##
## Interactive Leaflet map has been created
##
## States by number of major disaster declarations (2023-01-01 onwards):
## # A tibble: 44 × 10
## state count most_common_incident avg_days_to_declare latest_disaster_date
## <chr> <int> <chr> <dbl> <date>
## 1 CA 13 Severe Storm 63.1 2024-04-19
## 2 TN 8 Severe Storm 44.5 2024-06-17
## 3 ME 7 Flood 66 2024-05-24
## 4 NH 6 Severe Storm 77 2024-08-20
## 5 OK 6 Severe Storm 45.7 2024-07-23
## 6 VT 6 Severe Storm 62.8 2024-08-20
## 7 SD 5 Winter Storm 73 2024-08-15
## 8 AR 4 Severe Storm 53.5 2024-05-30
## 9 FL 4 Flood 16.5 2024-08-10
## 10 IA 4 Severe Storm 38.2 2024-06-24
## # ℹ 34 more rows
## # ℹ 5 more variables: latest_disaster_title <chr>, ia_program_count <int>,
## # pa_program_count <int>, hm_program_count <int>, state_name <chr>
##
## Overall average number of days between incident begin and declaration: 68.90
##
## List of major disaster declarations from 2023-01-01 onwards:
## disasterNumber declarationDate state incidentType
## 4813 2024-08-23 MT Straight-Line Winds
## 4808 2024-08-20 NE Severe Storm
## 4812 2024-08-20 NH Severe Storm
## 4811 2024-08-20 KS Severe Storm
## 4810 2024-08-20 VT Severe Storm
## 4809 2024-08-20 NM Flood
## 4807 2024-08-15 SD Flood
## 4806 2024-08-10 FL Tropical Storm
## 4804 2024-07-23 KY Severe Storm
## 4803 2024-07-23 MO Severe Storm
## 4802 2024-07-23 OK Severe Storm
## 4805 2024-07-23 PR Flood
## 4801 2024-07-18 MT Severe Storm
## 4800 2024-07-15 KS Severe Storm
## 4799 2024-07-10 NH Winter Storm
## 4798 2024-07-09 TX Hurricane
## 4797 2024-06-28 MN Flood
## 4796 2024-06-24 IA Severe Storm
## 4795 2024-06-20 NM Fire
## 4792 2024-06-17 TN Severe Storm
## 4794 2024-06-17 FL Severe Storm
## 4793 2024-06-17 HI Flood
## 4791 2024-06-14 OK Severe Storm
## 4790 2024-06-10 MS Severe Storm
## 4789 2024-06-10 ID Flood
## 4788 2024-05-30 AR Severe Storm
## 4787 2024-05-24 WV Flood
## 4786 2024-05-24 NE Severe Storm
## 4785 2024-05-24 ME Winter Storm
## 4784 2024-05-24 IA Severe Storm
## 4783 2024-05-22 WV Severe Storm
## 4782 2024-05-22 KY Severe Storm
## 4781 2024-05-17 TX Flood
## 4780 2024-05-15 MA Flood
## 4779 2024-05-14 IA Tornado
## 4778 2024-05-03 NE Tornado
## 4777 2024-05-02 OH Tornado
## 4776 2024-04-30 OK Tornado
## 4775 2024-04-28 WA Severe Storm
## 4774 2024-04-28 KS Winter Storm
## 4773 2024-04-19 CA Severe Storm
## 4772 2024-04-19 CA Severe Storm
## 4771 2024-04-19 NH Severe Storm
## 4770 2024-04-19 VT Severe Storm
## 4769 2024-04-13 CA Severe Storm
## 4768 2024-04-13 OR Severe Storm
## 4767 2024-04-06 AK Mud/Landslide
## 4766 2024-03-20 RI Severe Storm
## 4765 2024-03-20 RI Severe Storm
## 4764 2024-03-20 ME Severe Storm
## 4763 2024-03-15 AK Mud/Landslide
## 4762 2024-03-02 VT Severe Storm
## 4761 2024-02-27 NH Severe Storm
## 4758 2024-02-19 CA Flood
## 4760 2024-02-15 ND Winter Storm
## 4759 2024-02-15 WA Fire
## 4757 2024-02-08 MI Flood
## 4756 2024-01-30 WV Flood
## 4755 2024-01-30 NY Flood
## 4754 2024-01-30 ME Severe Storm
## 4753 2024-01-07 RI Severe Storm
## 4752 2023-12-23 UT Flood
## 4751 2023-12-13 TN Severe Storm
## 4750 2023-11-21 CA Hurricane
## 4749 2023-11-20 IL Flood
## 4748 2023-11-14 AR Severe Storm
## 4747 2023-10-26 KS Severe Storm
## 4746 2023-10-18 CA Tropical Storm
## 4745 2023-10-11 MT Flood
## 4744 2023-10-06 VT Flood
## 4743 2023-09-27 CA Hurricane
## 4742 2023-09-27 TN Severe Storm
## 4741 2023-09-21 MO Severe Storm
## 4740 2023-09-14 NH Flood
## 4739 2023-09-11 WY Flood
## 4738 2023-09-07 GA Hurricane
## 4737 2023-09-06 ME Flood
## 4736 2023-09-05 ME Flood
## 4735 2023-09-04 TN Severe Storm
## 4734 2023-08-31 FL Hurricane
## 4733 2023-08-28 OR Mud/Landslide
## 4732 2023-08-25 IA Flood
## 4731 2023-08-25 CO Flood
## 4730 2023-08-23 AK Flood
## 4729 2023-08-17 TN Severe Storm
## 4728 2023-08-15 IL Severe Storm
## 4727 2023-08-12 MS Severe Storm
## 4726 2023-08-12 MT Flood
## 4725 2023-08-11 NJ Severe Storm
## 4724 2023-08-10 HI Fire
## 4723 2023-07-22 NY Severe Storm
## 4722 2023-07-19 MN Flood
## 4721 2023-07-19 OK Severe Storm
## 4720 2023-07-14 VT Flood
## 4719 2023-07-06 ME Flood
## 4718 2023-07-06 SD Flood
## 4717 2023-07-05 ND Flood
## 4716 2023-06-02 MP Tropical Storm
## 4715 2023-05-25 GU Hurricane
## 4714 2023-05-25 CA Severe Storm
## 4713 2023-05-18 CA Severe Storm
## 4712 2023-05-17 TN Severe Storm
## 4711 2023-05-09 KY Flood
## 4710 2023-05-05 AL Severe Storm
## 4709 2023-04-27 FL Flood
## 4708 2023-04-27 NV Flood
## 4707 2023-04-25 CA Winter Storm
## 4706 2023-04-24 OK Tornado
## 4705 2023-04-21 TX Winter Storm
## 4704 2023-04-15 IN Severe Storm
## 4703 2023-04-11 AZ Severe Storm
## 4702 2023-04-10 KY Severe Storm
## 4701 2023-04-07 TN Severe Storm
## 4700 2023-04-04 AR Winter Storm
## 4699 2023-04-03 CA Severe Storm
## 4698 2023-04-02 AR Severe Storm
## 4697 2023-03-26 MS Severe Storm
## 4696 2023-03-22 ME Severe Storm
## 4695 2023-03-20 VT Severe Storm
## 4694 2023-03-15 NY Snowstorm
## 4693 2023-03-15 NH Severe Storm
## 4692 2023-03-08 CA Earthquake
## 4691 2023-03-08 TN Winter Storm
## 4690 2023-03-03 OK Severe Storm
## 4689 2023-02-27 SD Winter Storm
## 4688 2023-02-20 SD Winter Storm
## 4687 2023-02-20 SD Winter Storm
## 4686 2023-02-05 ND Snowstorm
## 4685 2023-01-16 GA Severe Storm
## 4684 2023-01-15 AL Severe Storm
## 4683 2023-01-14 CA Flood
## 4682 2023-01-12 WA Severe Storm
## declarationTitle
## STRAIGHT-LINE WINDS
## SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## SEVERE STORM AND FLOODING
## SEVERE STORM, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## SEVERE STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## SEVERE STORMS AND FLOODING
## SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## HURRICANE DEBBY
## SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, LANDSLIDES, AND MUDSLIDES
## SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## SEVERE STORMS
## SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## SEVERE WINTER STORM AND FLOODING
## SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## SEVERE WINTER STORM AND FLOODING
## HURRICANE BERYL
## SEVERE STORMS AND FLOODING
## SEVERE STORMS, FLOODING, STRAIGHT-LINE WINDS, AND TORNADOES
## SOUTH FORK FIRE, SALT FIRE, AND FLOODING
## SEVERE STORMS, TORNADOES, AND FLOODING
## SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## SEVERE STORMS, FLOODING, AND LANDSLIDES
## SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## SEVERE STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## SEVERE WINTER STORM AND STRAIGHT-LINE WINDS
## SEVERE WINTER STORM
## SEVERE STORMS, TORNADOES, AND FLOODING
## SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, FLOODING, LANDSLIDES, AND MUDSLIDES
## SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, LANDSLIDES, AND MUDSLIDES
## SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## SEVERE STORMS AND FLOODING
## SEVERE STORMS AND TORNADOES
## SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## TORNADOES
## SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## SEVERE WINTER STORM
## SEVERE WINTER STORM
## SEVERE STORMS AND FLOODING
## SEVERE STORMS AND FLOODING
## SEVERE WINTER STORM
## SEVERE WINTER STORMS, TORNADOES, FLOODING, LANDSLIDES, AND MUDSLIDES
## SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, LANDSLIDES, AND MUDSLIDES
## SEVERE STORM, FLOODING, AND LANDSLIDES
## SEVERE STORMS AND FLOODING
## SEVERE STORM AND FLOODING
## SEVERE STORMS AND FLOODING
## SEVERE STORM, LANDSLIDES, AND MUDSLIDES
## SEVERE STORM AND FLOODING
## SEVERE STORM AND FLOODING
## SEVERE STORM AND FLOODING
## SEVERE WINTER STORM AND STRAIGHT-LINE WINDS
## WILDFIRES
## SEVERE STORMS, TORNADOES, AND FLOODING
## SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## SEVERE STORM AND FLOODING
## SEVERE STORM AND FLOODING
## SEVERE STORMS, FLOODING, AND TORNADOES
## FLOODING
## SEVERE STORMS AND TORNADOES
## TROPICAL STORM HILARY
## SEVERE STORMS AND FLOODING
## SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## TROPICAL STORM HILARY
## FLOODING
## SEVERE STORMS AND FLOODING
## TROPICAL STORM HILARY
## SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADO
## SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## SEVERE STORMS AND FLOODING
## FLOODING
## HURRICANE IDALIA
## SEVERE STORM AND FLOODING
## SEVERE STORM AND FLOODING
## SEVERE STORMS AND STRAIGHT-LINE WINDS
## HURRICANE IDALIA
## SEVERE STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## FLOODING
## SEVERE STORMS, FLOODING, AND TORNADOES
## FLOODING
## SEVERE STORMS AND STRAIGHT-LINE WINDS
## SEVERE STORMS AND FLOODING
## SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES.
## FLOODING
## SEVERE STORM AND FLOODING
## WILDFIRES AND HIGH WINDS
## SEVERE STORMS AND FLOODING
## SEVERE STORMS AND FLOODING
## SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## SEVERE STORM AND FLOODING
## FLOODING
## FLOODING
## TYPHOON MAWAR
## TYPHOON MAWAR
## SEVERE STORM AND FLOODING
## SEVERE WINTER STORM AND FLOODING
## SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADO
## SEVERE STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## SEVERE STORMS, TORNADOES, AND FLOODING
## SEVERE WINTER STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## SEVERE WINTER STORMS AND MUDSLIDES
## SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## SEVERE WINTER STORM
## SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## SEVERE WINTER STORMS AND FLOODING
## SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, FLOODING, LANDSLIDES, AND MUDSLIDES
## SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## SEVERE WINTER STORM
## SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## SEVERE STORMS AND TORNADOES
## SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## SEVERE STORM AND FLOODING
## SEVERE STORM AND FLOODING
## SEVERE WINTER STORM AND SNOWSTORM
## SEVERE STORM AND FLOODING
## EARTHQUAKE
## SEVERE WINTER STORM
## SEVERE WINTER STORM
## SEVERE WINTER STORMS AND SNOWSTORM
## SEVERE WINTER STORMS AND SNOWSTORM
## SEVERE WINTER STORMS AND SNOWSTORM
## SEVERE WINTER STORM, SNOWSTORM, AND STRAIGHT-LINE WINDS
## SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## SEVERE WINTER STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## SEVERE WINTER STORM, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## iaProgramDeclared paProgramDeclared hmProgramDeclared
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# Display the Leaflet map
print(result)