Synopsis

This report analyzes storm data to determine which weather events are most harmful to population health and which cause the greatest economic damage in the United States.

Data Processing

data <- read.csv(“repdata-data-StormData.csv”)

library(dplyr)

Select relevant columns

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

Convert damage exponent to multiplier

convert_exp <- function(exp) { if (exp %in% c(“K”, “k”)) return(1e3) else if (exp %in% c(“M”, “m”)) return(1e6) else if (exp %in% c(“B”, “b”)) return(1e9) else return(1) }

storm_data\(PROP_MULT <- sapply(storm_data\)PROPDMGEXP, convert_exp) storm_data\(CROP_MULT <- sapply(storm_data\)CROPDMGEXP, convert_exp)

storm_data\(PROP_TOTAL <- storm_data\)PROPDMG * storm_data\(PROP_MULT storm_data\)CROP_TOTAL <- storm_data\(CROPDMG * storm_data\)CROP_MULT

storm_data\(TOTAL_DAMAGE <- storm_data\)PROP_TOTAL + storm_data$CROP_TOTAL

health_impact <- storm_data %>% group_by(EVTYPE) %>% summarise(total_health = sum(FATALITIES + INJURIES, na.rm = TRUE)) %>% arrange(desc(total_health)) %>% head(10)

health_impact

barplot(health_impact\(total_health, names.arg = health_impact\)EVTYPE, las = 2, col = “red”, main = “Top 10 Most Harmful Weather Events”, ylab = “Total Injuries + Fatalities”)

    economic_impact <- storm_data %>%

group_by(EVTYPE) %>% summarise(total_damage = sum(TOTAL_DAMAGE, na.rm = TRUE)) %>% arrange(desc(total_damage)) %>% head(10)

economic_impact

barplot(economic_impact\(total_damage, names.arg = economic_impact\)EVTYPE, las = 2, col = “blue”, main = “Top 10 Events by Economic Damage”, ylab = “Damage (USD)”)