#synopsis
This report analysis th NOAA storm database to determine:
The analysis includes data processing, cleaning, and visualization to provide insights into the impact of different storm events across the United States.
The dataset contains various columns related to storm events,
including event type (EVTYPE
), fatalities, injuries, and
economic damage. We will filter and clean the data before analysis.
# Select relevant columns
storm_data <- storm_data %>% select(EVTYPE, FATALITIES, INJURIES, PROPDMG, CROPDMG)
# Aggregate health impact (fatalities + injuries)
storm_data$TOTAL_IMPACT <- storm_data$FATALITIES + storm_data$INJURIES
# Aggregate economic impact (property + crop damage)
storm_data$TOTAL_ECONOMIC_DAMAGE <- storm_data$PROPDMG + storm_data$CROPDMG
# Aggregate by event type
health_impact <- storm_data %>% group_by(EVTYPE) %>% summarise(TOTAL_IMPACT = sum(TOTAL_IMPACT)) %>% arrange(desc(TOTAL_IMPACT))
# Top 10 event affecting health
top_health_events <- head(health_impact, 10)
# Plot
ggplot(top_health_events, aes(x=reorder(EVTYPE, -TOTAL_IMPACT), y=TOTAL_IMPACT)) +
geom_bar(stat="identity", fill="red") +
coord_flip() +
labs(title="Top 10 Events Affecting Population Health", x="Event Type", y="Total Fatalities & Injuries") +
theme_minimal()
# Aggregate by event type
economic_impact <- storm_data %>% group_by(EVTYPE) %>% summarise(TOTAL_ECONOMIC_DAMAGE = sum(TOTAL_ECONOMIC_DAMAGE)) %>% arrange(desc(TOTAL_ECONOMIC_DAMAGE))
# Top 10 events affecting economy
top_economic_events <- head(economic_impact, 10)
# Plot
ggplot(top_economic_events, aes(x=reorder (EVTYPE, -TOTAL_ECONOMIC_DAMAGE), y=TOTAL_ECONOMIC_DAMAGE)) +
geom_bar(stat="identity", fill="blue") +
coord_flip() +
labs(title="Top 10 Events with Greatest Economi Impact", x="Event Type" , y="Total Damage (Property + Crop)") +
theme_minimal()