Synopsis

Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events can result in fatalities, injuries, and property damage, and preventing such outcomes to the extent possible is a key concern.

This project involves exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. This database tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage.

Data

The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size.

The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete.

Assignment

The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. You must use the database to answer the questions below and show the code for your entire analysis. Your analysis can consist of tables, figures, or other summaries. You may use any R package you want to support your analysis.

Questions

This data analysis must address the following questions:

1 Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health? Across the United States, which types of events have the greatest economic consequences?
2 Consider writing your report as if it were to be read by a government or municipal manager who might be responsible for preparing for severe weather events and will need to prioritize resources for different types of events. However, there is no need to make any specific recommendations in your report.

Data Processing

Data Load

storm.data = read.csv(bzfile("repdata-data-StormData.csv.bz2"), header = TRUE)

Selecting Columns based on the need of questions

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

Modifying Event Names to maintain uniformity.

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 WIND", reduced.storm.data$EVTYPE)

Top 10 Causes of fatalities with respect to Event Type.

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)

Top 10 Causes of Injuries with respect to Event Type.

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)

Top 10 Causes of Property Damage with respect to Event Type.

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

As you can see in following graph, you need to prioritize your actions on following Top 10 reasons to reduce the fatalities.

library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.3.2
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))

As you can see in following graph, you need to prioritize your actions on following Top 10 reasons to reduce the 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))

As you can see in following graph, you need to prioritize your actions on following Top 10 reasons to reduce the 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))