Storm Data Analysis

Synopsis

This analysis explores the NOAA Storm Database to identify which weather events are most harmful to population health and cause the greatest economic damage.

Data Processing

data <- read.csv("C:\\Users\\rshru\\Downloads\\repdata_data_StormData.csv\\repdata_data_StormData.csv")

storm <- data[, c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP")]

storm$PROPDMGEXP <- toupper(storm$PROPDMGEXP)

storm$multiplier <- ifelse(storm$PROPDMGEXP == "K", 1e3,
                    ifelse(storm$PROPDMGEXP == "M", 1e6,
                    ifelse(storm$PROPDMGEXP == "B", 1e9, 1)))

storm$PROPDMG_TOTAL <- storm$PROPDMG * storm$multiplier

Results

Health Impact

health <- aggregate(cbind(FATALITIES, INJURIES) ~ EVTYPE, data = storm, sum)
health$total <- health$FATALITIES + health$INJURIES
health <- health[order(-health$total), ]
top_health <- head(health, 10)

barplot(top_health$total,
        names.arg = top_health$EVTYPE,
        las = 2,
        col = "red",
        main = "Top 10 Harmful Weather Events",
        ylab = "Fatalities + Injuries")

Tornadoes and heat events cause the most harm to population health.

Economic Impact

economic <- aggregate(PROPDMG_TOTAL ~ EVTYPE, data = storm, sum)
economic <- economic[order(-economic$PROPDMG_TOTAL), ]
top_economic <- head(economic, 10)

barplot(top_economic$PROPDMG_TOTAL,
        names.arg = top_economic$EVTYPE,
        las = 2,
        col = "blue",
        main = "Top 10 Economic Damage Events",
        ylab = "Damage ($)")

Floods and hurricanes cause the greatest economic damage.