Synopsis

This report analyzes the NOAA storm database to determine which types of events are most harmful to population health and which cause the greatest economic damage. The analysis focuses on fatalities, injuries, and property and crop damage.

Data Processing

library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.5.3
data <- read.csv("repdata_data_StormData.csv.bz2")

data2 <- data[, c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "CROPDMG")]

health <- aggregate(FATALITIES + INJURIES ~ EVTYPE, data = data2, sum)
economic <- aggregate(PROPDMG + CROPDMG ~ EVTYPE, data = data2, sum)

health <- health[order(-health$`FATALITIES + INJURIES`), ][1:10, ]
economic <- economic[order(-economic$`PROPDMG + CROPDMG`), ][1:10, ]

Results

Population Health Impact

ggplot(health, aes(x=reorder(EVTYPE, -`FATALITIES + INJURIES`), 
                   y=`FATALITIES + INJURIES`)) +
  geom_bar(stat="identity") +
  theme(axis.text.x = element_text(angle=45, hjust=1)) +
  labs(title="Top 10 Harmful Events", x="Event Type", y="Total Harm")

Economic Impact

ggplot(economic, aes(x=reorder(EVTYPE, -`PROPDMG + CROPDMG`), 
                     y=`PROPDMG + CROPDMG`)) +
  geom_bar(stat="identity") +
  theme(axis.text.x = element_text(angle=45, hjust=1)) +
  labs(title="Top 10 Economic Damage Events", x="Event Type", y="Damage")