Synopsis

This report analyzes the U.S. National Oceanic and Atmospheric Administration’s (NOAA) Storm Database to determine which types of severe weather events are most harmful to public health and which cause the greatest economic damage. Data from 1950 to November 2011 are examined, with focus on total fatalities, injuries, and property/crop damages across event types. Tornadoes are identified as the most harmful to population health, while floods and hurricanes cause the most economic losses. This information can be useful for government agencies to better prepare for and mitigate the impacts of severe weather.

Data Processing

# Load required packages
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.4.3
## 
## 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
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.4.3
library(readr)
## Warning: package 'readr' was built under R version 4.4.3
library(knitr)
## Warning: package 'knitr' was built under R version 4.4.3
# Load the dataset
data <- read.csv("repdata_data_StormData.csv.bz2")

# Select relevant columns
storm_data <- data %>%
  select(EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP)

# Function to convert exponent values
exp_convert <- function(exp) {
  exp <- toupper(as.character(exp))
  ifelse(exp == "H", 1e2,
         ifelse(exp == "K", 1e3,
                ifelse(exp == "M", 1e6,
                       ifelse(exp == "B", 1e9, 1))))
}

# Convert exponents and calculate total damage
storm_data <- storm_data %>%
  mutate(PROPDMGEXP = exp_convert(PROPDMGEXP),
         CROPDMGEXP = exp_convert(CROPDMGEXP),
         PROPDMGVAL = PROPDMG * PROPDMGEXP,
         CROPDMGVAL = CROPDMG * CROPDMGEXP)

Results

Table 1: Top 10 Most Harmful Weather Events to Population Health

# Summarize total injuries and fatalities by event type
health_impact <- storm_data %>%
  group_by(EVTYPE) %>%
  summarise(Fatalities = sum(FATALITIES, na.rm = TRUE),
            Injuries = sum(INJURIES, na.rm = TRUE),
            TotalHealthImpact = Fatalities + Injuries) %>%
  arrange(desc(TotalHealthImpact)) %>%
  slice(1:10)

# Print table
kable(health_impact, caption = "Top 10 Most Harmful Weather Events to Population Health")
Top 10 Most Harmful Weather Events to Population Health
EVTYPE Fatalities Injuries TotalHealthImpact
TORNADO 5633 91346 96979
EXCESSIVE HEAT 1903 6525 8428
TSTM WIND 504 6957 7461
FLOOD 470 6789 7259
LIGHTNING 816 5230 6046
HEAT 937 2100 3037
FLASH FLOOD 978 1777 2755
ICE STORM 89 1975 2064
THUNDERSTORM WIND 133 1488 1621
WINTER STORM 206 1321 1527 
# Plot: Health Impact
ggplot(health_impact, aes(x = reorder(EVTYPE, -TotalHealthImpact), y = TotalHealthImpact)) +
  geom_bar(stat = "identity", fill = "firebrick") +
  labs(title = "Top 10 Most Harmful Weather Events to Population Health",
       x = "Event Type", y = "Total Fatalities + Injuries") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Table 2: Top 10 Weather Events with the Greatest Economic Impact

# Summarize economic damage by event type
economic_impact <- storm_data %>%
  group_by(EVTYPE) %>%
  summarise(TotalPropertyDamage = sum(PROPDMGVAL, na.rm = TRUE),
            TotalCropDamage = sum(CROPDMGVAL, na.rm = TRUE),
            TotalEconomicDamage = TotalPropertyDamage + TotalCropDamage) %>%
  arrange(desc(TotalEconomicDamage)) %>%
  slice(1:10)

# Print table
kable(economic_impact, caption = "Top 10 Weather Events with Greatest Economic Impact")
Top 10 Weather Events with Greatest Economic Impact
EVTYPE TotalPropertyDamage TotalCropDamage TotalEconomicDamage
FLOOD 144657709807 5661968450 150319678257
HURRICANE/TYPHOON 69305840000 2607872800 71913712800
TORNADO 56937160779 414953270 57352114049
STORM SURGE 43323536000 5000 43323541000
HAIL 15732267543 3025954473 18758222016
FLASH FLOOD 16140812067 1421317100 17562129167
DROUGHT 1046106000 13972566000 15018672000
HURRICANE 11868319010 2741910000 14610229010
RIVER FLOOD 5118945500 5029459000 10148404500
ICE STORM 3944927860 5022113500 8967041360 
# Plot: Economic Impact
ggplot(economic_impact, aes(x = reorder(EVTYPE, -TotalEconomicDamage), y = TotalEconomicDamage / 1e9)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  labs(title = "Top 10 Weather Events with Greatest Economic Impact",
       x = "Event Type", y = "Total Damage (in Billions USD)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))