Impact of natural disasters in the United States

Synopsis

• This report analyzes the impact of natural disasters in United states.
• This data are obteined from data of the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database.
• This report indicates that hurricane was the most harmful event with respect to population health.
• This report indicates that hurricane was the largest impact on the prppaties.
• This report indicates that hail was the largest impact on the crops.

Installing from library

library(ggplot2)
library(reshape2)

Data Processing

RawData <- read.csv(bzfile("repdata-data-StormData.csv.bz2"))

Results

which types of events are most harmful with respect to population health?

First, we investigate the worst 5 most injured events. It shows that injuries due to hurricane were the largest across the US.

victim <- aggregate(RawData[c("INJURIES", "FATALITIES")], by=RawData[c("EVTYPE")], FUN=sum)
sortlist_INJ <- order(victim$INJURIES, decreasing = T)
TOP5_INJ <- victim[sortlist_INJ[1:5], ]
TOP5_INJ

##             EVTYPE INJURIES FATALITIES
## 834        TORNADO    91346       5633
## 856      TSTM WIND     6957        504
## 170          FLOOD     6789        470
## 130 EXCESSIVE HEAT     6525       1903
## 464      LIGHTNING     5230        816

The barplot is created from the tidy data.

melt_TOP5_INJ <- melt(TOP5_INJ, value.name = "value", variable.name = "variable", id.vars = "EVTYPE")
qplot(data = melt_TOP5_INJ, x = EVTYPE, y = value, geom="bar", stat = "identity", fill=variable)

We can also check the worst 5 event for fatalities number. It shows that fatalities due to hurricane were the largest across the US.

sortlist_FAT <- order(victim$FATALITIES, decreasing = T)
TOP5_FAT <- victim[sortlist_FAT[1:5], ]
TOP5_FAT

##             EVTYPE INJURIES FATALITIES
## 834        TORNADO    91346       5633
## 130 EXCESSIVE HEAT     6525       1903
## 153    FLASH FLOOD     1777        978
## 275           HEAT     2100        937
## 464      LIGHTNING     5230        816

Which types of events have the greatest economic consequences?

I investigate the impact of properties and crops respectively to find event for the greatest economic consequences. First, I investigate the worst 5 events of properties. Second, I investigate the worst 5 events of crops. It shows that the impact of hail was the largest to crops. However, the order of magnitude is smaller than properties’ one. Therefore, we can see hurricane had the greatest economic consequences.

• the worst 5 events to properties

data_PRPDMG <- aggregate(RawData["PROPDMG"], by=RawData[c("EVTYPE")], FUN=sum)
sortlist_PRPDMG <- order(data_PRPDMG$PROPDMG, decreasing = T)
TOP5_PRPDMG <- data_PRPDMG[sortlist_PRPDMG[1:5],]
TOP5_PRPDMG

##                EVTYPE   PROPDMG
## 834           TORNADO 3212258.2
## 153       FLASH FLOOD 1420124.6
## 856         TSTM WIND 1335965.6
## 170             FLOOD  899938.5
## 760 THUNDERSTORM WIND  876844.2

melt_TOP5_PRPDMG <- melt(TOP5_PRPDMG, value.name = "PROPDMG", id.vars = "EVTYPE")
qplot(data = melt_TOP5_PRPDMG, x = EVTYPE, y = PROPDMG, geom="bar", stat = "identity", main = "TOTAL ECONOMIC DAMAGES IN PROPERTIES")

• the worst 5 events to crops

data_CROPDMG <- aggregate(RawData["CROPDMG"], by=RawData[c("EVTYPE")], FUN=sum)
sortlist_CROPDMG <- order(data_CROPDMG$CROPDMG, decreasing = T)
TOP5_CROPDMG <- data_CROPDMG[sortlist_CROPDMG[1:5],]
TOP5_CROPDMG

##          EVTYPE  CROPDMG
## 244        HAIL 579596.3
## 153 FLASH FLOOD 179200.5
## 170       FLOOD 168037.9
## 856   TSTM WIND 109202.6
## 834     TORNADO 100018.5

melt_TOP5_CROPDMG <- melt(TOP5_CROPDMG, value.name = "CROPDMG", id.vars = "EVTYPE")
qplot(data = melt_TOP5_CROPDMG, x = EVTYPE, y = CROPDMG, geom="bar", stat = "identity", main = "TOTAL ECONOMIC DAMAGES IN CROPS")