Introduction

This document provides a brief analysis of data provided by the U.S National Oceanic and Atmospheric Administrations (NOAA) storm database.

We will analyze which types of events cause the most fatalities, the most injuries and the most physical/crop damage in the United States.

Results and conclusions are provided at the bottom of the document.

Data Processing

The data was recieved in a raw csv file, using read.csv we read the data into R so that we can work with it. To start off, we form a vector called ‘health’, which contains EVTYPE, INJURIES and FATALITIES.

We will use this vector to subset our data in R to focus on the issues of interest.

storm<-read.csv("repdata_data_StormData.csv",header=TRUE) #Read in data

health<-c("EVTYPE","INJURIES","FATALITIES")


storm.health<-storm[ ,health]


a<-aggregate(FATALITIES~EVTYPE,storm.health,sum)

a.sub<-a$FATALITIES>20

new.a<-a[a.sub, ]

#Some have zero fatalities at all, should remove those.

#droplevels(new.a$EVTYPE)

H=new.a$FATALITIES
L=new.a$EVTYPE

barplot(H,names.arg=L,space=1.5,las=2,main="Fatalities for event types with more than 20 deaths total",ylab="Fatalities",cex.names=0.6,col='red')

injuries<-aggregate(INJURIES~EVTYPE,storm.health,sum)

injuries.sub<-injuries$INJURIES>50

new.injuries<-injuries[injuries.sub,]

#droplevels(new.injuries$EVTYPE)

H2=new.injuries$INJURIES
L2=new.injuries$EVTYPE

barplot(H2,names.arg=L2,las=2,main="Injuries for event types with more than 50 total injuries",cex.names=0.5,col='blue',space=1.5)

#Now we are interested in PROPDMG and CROPDMG

damage<-c("EVTYPE","PROPDMG","CROPDMG")

storm.damage<-storm[,damage]

property.damage<-aggregate(PROPDMG~EVTYPE,storm.damage,sum)

prop<-property.damage$PROPDMG >50000

property.damage<-property.damage[prop, ]

#droplevels(property.damage$EVTYPE)

crop.damage<-aggregate(CROPDMG~EVTYPE,storm.damage,sum)

crop<-crop.damage$CROPDMG >2000

crop.damage<-crop.damage[crop,]

#barplot(property.damage$PROPDMG,names.arg=property.damage$EVTYPE,las=2,size=1.5,cex.names=0.8,col="purple",main="Property Damage by event type for damage greater than 50000")

property.damage
##                 EVTYPE    PROPDMG
## 153        FLASH FLOOD 1420124.59
## 170              FLOOD  899938.48
## 244               HAIL  688693.38
## 290         HEAVY RAIN   50842.14
## 310         HEAVY SNOW  122251.99
## 359          HIGH WIND  324731.56
## 376         HIGH WINDS   55625.00
## 427          ICE STORM   66000.67
## 464          LIGHTNING  603351.78
## 676        STRONG WIND   62993.81
## 760  THUNDERSTORM WIND  876844.17
## 786 THUNDERSTORM WINDS  446293.18
## 834            TORNADO 3212258.16
## 856          TSTM WIND 1335965.61
## 957           WILDFIRE   84459.34
## 972       WINTER STORM  132720.59
#

options(scipen = 999)
barplot(crop.damage$CROPDMG,names.arg=crop.damage$EVTYPE,las=2,size=1.5,cex.names=0.8,col="green",main="Crop Damage by event type for damage greater than 2000",ylim=c(2000,600000))

Results

We include three total barplots and one chart. The barplots can be used to visualize which event types are the most serious for a given topic, and the chart can be read in a similar way.

For population health we considered fatalities and Injuries. For fatalities, we can clearly see that Tornados cause the most number of deaths. Excessive heat also cause many fatalities, as well as flash floods and heat.

For injuries, again Tornados are the most devestating by far. Excessive heat, floods, lighting and TSTM wind also cause injuries at higher rates than other event types.

For property damage we include a chart comparing the amount of damage for certian extreme event types. Toranados cause the most major property damage, followed by Flash floods.

Hail causes major crop damage, as well as flash floods and floods. TSTM Wind, storms and tropical storms also cause major crop damage.