NOAA Storm Data Analysis

Synopsis

This analysis explores the NOAA Storm Database to identify severe weather events that are most harmful to population health and have the greatest economic consequences. The dataset includes information on event types, fatalities, injuries, and property and crop damages. After processing the data, it was found that tornadoes are the leading cause of fatalities and injuries, making them the most harmful to population health. Floods and hurricanes contribute significantly to economic damage. The findings highlight the importance of prioritizing disaster preparedness and response strategies for high-impact weather events.

Data Processing

The data was downloaded from the NOAA Storm Database and loaded into R for analysis.

url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url, destfile = "stormdata.csv.bz2")

data <- read.csv("stormdata.csv.bz2")

The relevant variables were selected and prepared for analysis.

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

storm <- data %>%
  select(EVTYPE, FATALITIES, INJURIES, PROPDMG, CROPDMG)

Data was grouped by event type to calculate total impact on population health.

health <- storm %>%
  group_by(EVTYPE) %>%
  summarise(total_fatalities = sum(FATALITIES),
            total_injuries = sum(INJURIES)) %>%
  arrange(desc(total_fatalities + total_injuries))

top_health <- head(health, 10)

Results

The following plot shows the most harmful events to population health based on fatalities.

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.5.3

ggplot(top_health, aes(x = reorder(EVTYPE, -total_fatalities), 
                       y = total_fatalities)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(title = "Top Events Harmful to Population Health",
       x = "Event Type",
       y = "Fatalities")

The following plot shows the events with the greatest economic consequences.

economic <- storm %>%
  group_by(EVTYPE) %>%
  summarise(total_damage = sum(PROPDMG + CROPDMG)) %>%
  arrange(desc(total_damage))

top_economic <- head(economic, 10)

ggplot(top_economic, aes(x = reorder(EVTYPE, -total_damage), 
                         y = total_damage)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(title = "Top Events with Economic Consequences",
       x = "Event Type",
       y = "Damage")