Storm Data Analysis

Synopsis

The basic goal of this assignment is to explore the U.S. NOAA Storm Database to address two key questions:

1. Across the United States, which types of events are most harmful with respect to population health?
2. Across the United States, which types of events have the greatest economic consequences?

---

Data Processing

Loading Required Libraries

library(dplyr)

## Warning: package 'dplyr' was built under R version 4.3.3

## 
## 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)

Loading the Data (Local File)

# Load dataset (file should be in same folder as .Rmd)
storm_data <- read.csv("C:/Users/jmorris/Documents/RR project2/repdata_data_StormData (1).csv/repdata_data_StormData (1).csv", stringsAsFactors = FALSE)

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

Data Transformation

# Function to convert damage exponent values
convert_exp <- function(exp) {
  ifelse(exp == "K", 1e3,
  ifelse(exp == "M", 1e6,
  ifelse(exp == "B", 1e9, 1)))
}

# Apply transformations
data <- data %>%
  mutate(
    PROP_MULT = convert_exp(PROPDMGEXP),
    CROP_MULT = convert_exp(CROPDMGEXP),
    PROP_DAMAGE = PROPDMG * PROP_MULT,
    CROP_DAMAGE = CROPDMG * CROP_MULT,
    TOTAL_DAMAGE = PROP_DAMAGE + CROP_DAMAGE,
    HEALTH_IMPACT = FATALITIES + INJURIES
  )

Aggregating Data

# Health impact
health_summary <- data %>%
  group_by(EVTYPE) %>%
  summarise(TOTAL_HEALTH = sum(HEALTH_IMPACT, na.rm = TRUE)) %>%
  arrange(desc(TOTAL_HEALTH)) %>%
  slice(1:10)

# Economic impact
economic_summary <- data %>%
  group_by(EVTYPE) %>%
  summarise(TOTAL_DAMAGE = sum(TOTAL_DAMAGE, na.rm = TRUE)) %>%
  arrange(desc(TOTAL_DAMAGE)) %>%
  slice(1:10)

---

Results

Most Harmful Events to Population Health

ggplot(health_summary, aes(x = reorder(EVTYPE, TOTAL_HEALTH), y = TOTAL_HEALTH)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(
    title = "Top 10 Weather Events Harmful to Population Health",
    x = "Event Type",
    y = "Total Fatalities and Injuries"
  )

Figure 1: Tornadoes are the leading cause of harm to population health, with excessive heat and floods also contributing significantly.

---

Events with Greatest Economic Consequences

ggplot(economic_summary, aes(x = reorder(EVTYPE, TOTAL_DAMAGE), y = TOTAL_DAMAGE)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(
    title = "Top 10 Weather Events by Economic Damage",
    x = "Event Type",
    y = "Total Damage (USD)"
  )

Figure 2: Floods, hurricanes/typhoons, and storm surges are responsible for the highest levels of economic loss.

---

Summary Tables

health_summary

## # A tibble: 10 × 2
##    EVTYPE            TOTAL_HEALTH
##    <chr>                    <dbl>
##  1 TORNADO                  96979
##  2 EXCESSIVE HEAT            8428
##  3 TSTM WIND                 7461
##  4 FLOOD                     7259
##  5 LIGHTNING                 6046
##  6 HEAT                      3037
##  7 FLASH FLOOD               2755
##  8 ICE STORM                 2064
##  9 THUNDERSTORM WIND         1621
## 10 WINTER STORM              1527

economic_summary

## # A tibble: 10 × 2
##    EVTYPE             TOTAL_DAMAGE
##    <chr>                     <dbl>
##  1 FLOOD             150319678257 
##  2 HURRICANE/TYPHOON  71913712800 
##  3 TORNADO            57340614060.
##  4 STORM SURGE        43323541000 
##  5 HAIL               18752904943.
##  6 FLASH FLOOD        17562129167.
##  7 DROUGHT            15018672000 
##  8 HURRICANE          14610229010 
##  9 RIVER FLOOD        10148404500 
## 10 ICE STORM           8967041360

---

Conclusion

The results indicate that severe weather events pose significant risks to both human health and economic stability. Events such as tornadoes, floods, and hurricanes consistently contribute to the highest levels of injuries, fatalities, and financial losses. These findings emphasize the need for improved early warning systems and effective disaster management strategies.