Exploring the U.S. National Oceanic and Atmospheric Administration’s storm database

library(ggplot2)

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.

• 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.

• In my analysis I will adress the two question :

• 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?

Data processing:

#loading data:
storm<-read.csv("repdata_data_stormData.csv",sep=",",header = T,
                na.strings = c("","+","?","-"))

Exploratory data analysis

#data variables
names(storm)

##  [1] "STATE__"    "BGN_DATE"   "BGN_TIME"   "TIME_ZONE"  "COUNTY"    
##  [6] "COUNTYNAME" "STATE"      "EVTYPE"     "BGN_RANGE"  "BGN_AZI"   
## [11] "BGN_LOCATI" "END_DATE"   "END_TIME"   "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE"  "END_AZI"    "END_LOCATI" "LENGTH"     "WIDTH"     
## [21] "F"          "MAG"        "FATALITIES" "INJURIES"   "PROPDMG"   
## [26] "PROPDMGEXP" "CROPDMG"    "CROPDMGEXP" "WFO"        "STATEOFFIC"
## [31] "ZONENAMES"  "LATITUDE"   "LONGITUDE"  "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS"    "REFNUM"

#subsetting the data:

v_storm <- storm[,c('EVTYPE','FATALITIES','INJURIES', 'PROPDMG', 'PROPDMGEXP', 'CROPDMG', 'CROPDMGEXP')]

#data structur:
str(v_storm)

## 'data.frame':    902297 obs. of  7 variables:
##  $ EVTYPE    : chr  "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
##  $ FATALITIES: num  0 0 0 0 0 0 0 0 1 0 ...
##  $ INJURIES  : num  15 0 2 2 2 6 1 0 14 0 ...
##  $ PROPDMG   : num  25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
##  $ PROPDMGEXP: chr  "K" "K" "K" "K" ...
##  $ CROPDMG   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ CROPDMGEXP: chr  NA NA NA NA ...

#missing variables:
na=sum(is.na(v_storm))

paste("There are",na,"missing variables in my data set")

## [1] "There are 1084369 missing variables in my data set"

#transforming evtype to factor:
v_storm$EVTYPE<-as.factor(v_storm$EVTYPE)
v_storm$PROPDMGEXP<-as.factor(v_storm$PROPDMGEXP)
v_storm$CROPDMGEXP<-as.factor(v_storm$CROPDMGEXP)

Health perspective:

#aggregating:
fata<-aggregate(FATALITIES~EVTYPE, v_storm,sum)

fata <- fata[order(-fata$FATALITIES), ][1:20, ]

head(fata)

##             EVTYPE FATALITIES
## 833        TORNADO       5633
## 129 EXCESSIVE HEAT       1903
## 152    FLASH FLOOD        978
## 274           HEAT        937
## 463      LIGHTNING        816
## 855      TSTM WIND        504

#plot
p1=ggplot(data=fata,aes(x =reorder(EVTYPE,FATALITIES),y=FATALITIES))+
        geom_bar(stat="identity",fill="tomato")

p2=p1+ theme(axis.text.x = element_text(angle = 60, hjust = 1))

p3=p2+xlab("Event Type") + ylab("Fatalities") + ggtitle("Number of fatalities by top 20 Weather Events")

p3

#aggregating:
injur<-aggregate(INJURIES~EVTYPE, v_storm,sum)

injur <- injur[order(-injur$INJURIES), ][1:20, ]

head(injur)

##             EVTYPE INJURIES
## 833        TORNADO    91346
## 855      TSTM WIND     6957
## 169          FLOOD     6789
## 129 EXCESSIVE HEAT     6525
## 463      LIGHTNING     5230
## 274           HEAT     2100

#plot
g1=ggplot(data=injur,aes(x =reorder(EVTYPE,INJURIES),y=INJURIES))+
        geom_bar(stat="identity",fill="red")

g2=g1+ theme(axis.text.x = element_text(angle = 60, hjust = 1))

g3=g2+xlab("Event Type") + ylab("Injuries") + ggtitle("Number of injuries by top 20 Weather Events")

g3

Economic perspective:

eco<-aggregate(CROPDMG+PROPDMG~EVTYPE,v_storm,sum)

eco <- eco[order(-eco$`CROPDMG + PROPDMG`), ][1:20, ]

head(eco)

##                EVTYPE CROPDMG + PROPDMG
## 833           TORNADO         3312276.7
## 152       FLASH FLOOD         1599325.1
## 855         TSTM WIND         1445168.2
## 243              HAIL         1268289.7
## 169             FLOOD         1067976.4
## 759 THUNDERSTORM WIND          943635.6

#plot
e1=ggplot(data=eco,aes(x =reorder(EVTYPE,`CROPDMG + PROPDMG`),y=`CROPDMG + PROPDMG`))+
        geom_bar(stat="identity",fill="green")

e2=e1+ theme(axis.text.x = element_text(angle = 60, hjust = 1))

e3=e2+xlab("Event Type") + ylab("Damage($)") + ggtitle("Property & Crop Damages by top 20 Weather Events")

e3

Results:

• The data I have provides a strong evidence that TORNADOs have the worst consequents not only on the population health fatalities ans injuries but also economically.

• So preparing for severe weather events must take into consideration the need to prioritize resources for many different types of events but more importantly Tornados