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TITLE : “Most Harmful Weather Events” author: “Sachin Vianney” date : “April 4, 2018”

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Synopsis

Across the United States, tornadoes, excessive heat, and flash floods are most harmful with respect to population health. Across the United States, tornadoes, thunderstorm winds, and flash floods have the greatest economic consequences.

Data Processing

We load our data.

setwd("C:\\Users\\mahe\\Desktop")
storm.data =  read.csv("data.csv" , header = TRUE)

We don’t need all the columns.

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

Normalize event names.

reduced.storm.data$EVTYPE <- 
    gsub("^HEAT$", "EXCESSIVE HEAT", reduced.storm.data$EVTYPE)
reduced.storm.data$EVTYPE <- 
    gsub("^TSTM WIND$", "THUNDERSTORM WIND", reduced.storm.data$EVTYPE)
reduced.storm.data$EVTYPE <- 
    gsub("^THUNDERSTORM WIND$", "THUNDERSTORM WINDS", reduced.storm.data$EVTYPE)

First we aggregate data on fatalities and find which events are the top 10 causes of fatalities.

agg.fatalities.data <-
        aggregate(
                reduced.storm.data$FATALITIES, 
                by=list(reduced.storm.data$EVTYPE), FUN=sum, na.rm=TRUE)
colnames(agg.fatalities.data) = c("event.type", "fatality.total")
fatalities.sorted <- 
    agg.fatalities.data[order(-agg.fatalities.data$fatality.total),] 
top.fatalities <- fatalities.sorted[1:10,]
top.fatalities$event.type <- 
    factor(
        top.fatalities$event.type, levels=top.fatalities$event.type, 
        ordered=TRUE)

We next do the same for injuries.

agg.injuries.data <-
        aggregate(
                reduced.storm.data$INJURIES, 
                by=list(reduced.storm.data$EVTYPE), FUN=sum, na.rm=TRUE)
colnames(agg.injuries.data) = c("event.type", "injury.total")
injuries.sorted <- agg.injuries.data[order(-agg.injuries.data$injury.total),] 
top.injuries <- injuries.sorted[1:10,]
top.injuries$event.type <- 
    factor(
        top.injuries$event.type, levels=top.injuries$event.type, 
        ordered=TRUE)

Finally we do the same for property damage.

agg.prop.dmg.data <-
        aggregate(
                reduced.storm.data$PROPDMG, 
                by=list(reduced.storm.data$EVTYPE), FUN=sum, na.rm=TRUE)
colnames(agg.prop.dmg.data) = c("event.type", "prop.dmg.total")
prop.dmg.sorted <- agg.prop.dmg.data[order(-agg.prop.dmg.data$prop.dmg.total),] 
top.prop.dmg <- prop.dmg.sorted[1:10,]
top.prop.dmg$event.type <- 
    factor(
        top.prop.dmg$event.type, levels=top.prop.dmg$event.type, 
        ordered=TRUE)

Results

Top 10 causes of :-

1. Fatalities

library(ggplot2)
ggplot(data=top.fatalities, aes(x=event.type, y=fatality.total)) + 
    geom_bar(stat="identity") + xlab("Event type") + ylab("Total fatalities") + 
    ggtitle("Fatalities By Event Type") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))

2. Injuries

ggplot(data=top.injuries, aes(x=event.type, y=injury.total)) + 
    geom_bar(stat="identity") + xlab("Event type") + ylab("Total injuries") + 
    ggtitle("Injuries By Event Type") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))

3. Property damage

ggplot(data=top.prop.dmg, aes(x=event.type, y=prop.dmg.total)) + 
    geom_bar(stat="identity") + xlab("Event type") + 
    ylab("Total property damage") +  ggtitle("Property Damage By Event Type") + 
    theme(axis.text.x = element_text(angle = 45, hjust = 1))