Storm Event Report

This is a report that determine the storm events that are most harmful to population health and the greatest economic consequences. It is based on the NOAA Storm Database repdata_data_StormData.csv.bz2 with data from 1950 to 2011.

Read in data from into storm data frame

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

home<-"C:/users/samlt/Documents"
setwd(home)

storm<-read.csv("repdata_data_StormData.csv.bz2")

Data Processing

Data Transformation

Definition of Function dmgEXP to convert the exponents PROPDMGEXP and CROPDMGEXP to the numeric multipliers for the corresponding numbers in PROPDMG and CROPDMG for Property and Crop Damages calculations. Sum of the two will be used as the total economical cost of damage. Conversions of PROPDMGEXP, CROPDMGEXP used are: b = 1e9,B=1e9,m=1e6,M =1e6,k=1e3,K=1e3,h=100,H=100,“+”=1,all other=0.

dmgEXP<- function(exp) {
  L<-length(exp)
  z<-rep(0,L)
  for (i in 1:L)
      {  z[i]<-switch((exp)[i],
            b = 1000000000,
            B=1000000000,
            m = 1000000,
            M = 1000000,
            k=1000,
            K=1000,
            h=100,
            H=100,
            "+"=1,
            0)
  }
  z
  }

Property and Crop Damage and Total Costs computation

Select only the revelant variables for computation from storm and store them in Data File storm2. Calculate the cost for p (Prorperty Damages), c (Crop Damages) and t (Total Cost of Damages, ie p+c), these are named PROP,CROP,ECONDMG respectively and included in storm2 and saved in data file storm3

storm2<-select(storm,EVTYPE,BGN_DATE,END_DATE,FATALITIES,INJURIES,PROPDMG,PROPDMGEXP,CROPDMG,CROPDMGEXP,REFNUM)
p<-as.numeric(dmgEXP(as.character(storm2$PROPDMGEXP)))*storm2$PROPDMG
c<-as.numeric(dmgEXP(as.character(storm2$CROPDMGEXP)))*storm2$CROPDMG
t<-p+c
storm3<-cbind(storm2,p,c,t)
names(storm3)<-c(names(storm2),"PROP","CROP","ECONDMG")

Top Economic Damage Events Plot

The maximum total cost of damage (ECONDMG) is summarised under different weather events EVTYPE. Top 12 weather events with maximum total cost (ECONDMG) are plotted as bar plot ## Top Casuality Events Plot Compute Casualties by summing up Fatalities and Injuries in data file storm3, sort them in descending order and plot the top 12 weather events that gives max casualties.

g<-group_by(storm3,EVTYPE)
pd<-summarize(g,maxpd=max(ECONDMG))
pd2<-arrange(pd,desc(maxpd))
pd2<-pd2[1:12,]
par(mfrow=c(1,1),mar=c(15,5,2,1))
barplot((pd2$maxpd/1200000000),names.arg=pd2$EVTYPE,ylim=c(1,120),font.main=1,main="Fig 1. Total Economic Cost (Property + Crop Damaged)\n NOAA storm database 1950-2011",ylab="Billions USD",log="y")

storm3$CASUALTIES<-storm3$FATALITIES+storm3$INJURIES

g<-group_by(storm3,EVTYPE)
cs<-summarize(g,maxpd=max(CASUALTIES))
cs2<-arrange(cs,desc(maxpd))
cs2<-cs2[1:12,]
par(mfrow=c(1,1),mar=c(11,5,2,1))
barplot(cs2$maxpd,names.arg=cs2$EVTYPE,main="Fig 2. Casualties (Death + Injured)\n NOAA storm database 1950-2011",font.main=4,ylim=c(1,5000),ylab="Number of Casualties",log="y")

Results

The list of Property Damage and Casualties for the top 12 Weather Events are calculated below. Their plots are as shown in Fig 1 and Fig 2.

dev.off

## function (which = dev.cur()) 
## {
##     if (which == 1) 
##         stop("cannot shut down device 1 (the null device)")
##     .External(C_devoff, as.integer(which))
##     dev.cur()
## }
## <bytecode: 0x0eea9f04>
## <environment: namespace:grDevices>

names(pd2)<-c("Weather Events","Property Damage (USD)")
print(head(pd2,12))

## # A tibble: 12 x 2
##    `Weather Events`          `Property Damage (USD)`
##    <fct>                                       <dbl>
##  1 FLOOD                               115032500000.
##  2 STORM SURGE                          31300000000.
##  3 HURRICANE/TYPHOON                    16930000000.
##  4 RIVER FLOOD                          10000000000.
##  5 TROPICAL STORM                        5150000000.
##  6 ICE STORM                             5000500000.
##  7 WINTER STORM                          5000000000.
##  8 STORM SURGE/TIDE                      4000000000.
##  9 HURRICANE                             3500000000.
## 10 TORNADO                               2800000000.
## 11 HEAVY RAIN/SEVERE WEATHER             2500000000.
## 12 HURRICANE OPAL                        2105000000.

names(cs2)<-c("Weather Events","Casualties")
print(head(cs2,12))

## # A tibble: 12 x 2
##    `Weather Events`  Casualties
##    <fct>                  <dbl>
##  1 TORNADO                1742.
##  2 ICE STORM              1569.
##  3 FLOOD                   802.
##  4 HURRICANE/TYPHOON       787.
##  5 HEAT                    583.
##  6 EXCESSIVE HEAT          521.
##  7 BLIZZARD                390.
##  8 HEAT WAVE               202.
##  9 TROPICAL STORM          201.
## 10 HEAVY SNOW              185.
## 11 WINTER STORM            170.
## 12 TSUNAMI                 161.