Synopsis: 1. Across the United States, Tornado is most harmful to population health. Due to National Weather Service Storm Database, it caused over 9378 fatalities, 1064 injuries, 11960 casualities.

1.1 In Central Standard Time Flood cause biggest impact on population health on 1998, but among all year tornado is most harmful.

  1. Across the United States, HURRICANE/TYPHOON has greatest economic consequences. Due to National Weather Service Storm Database, it caused over 26,740,295,000 property damage, 2,607,872,800 crop damage, 29,348,167,800 total damage.
    2.1. In Eastern Standard Time, HURRICANE/TYPHOON casuse biggest damage on 2004, following by Flood on 2011.
    2.2. In Central Standard Time, HURRICANE/TYPHOON cause biggest damage on 2005, following by Tornado on 2011.
    2.3. In Pacific Standard Time, and Mountain Standard Time, Flood and Tornado cause bigger damage.

Data Processing

set working directory and load require libraries
setwd("/Users/hadoop/RepData_PeerAssessment2")
suppressMessages(source("installPackage.R"))
suppressMessages(require(ggplot2))
suppressMessages(require(data.table))
suppressMessages(require(grid))
suppressMessages(require(dplyr))
suppressMessages(require(lubridate))
suppressMessages(require(tidyr))
suppressMessages(source("multiplot.R"))
Read raw data by Fread@data.table
#If you want to use fread, please install the dev version  
data.file <-"repdata-data-StormData.csv"
if(packageVersion("data.table") >= "1.9.5" ) {
        topData <- read.table(data.file, sep=",", nrows = 50, header=TRUE)
        col.classes <- sapply(topData, class)
        raw.data <- fread(data.file, header= TRUE, sep=",", colClasses = col.classes)
} else {
        raw.data <- read.table(data.file, sep=",", header = TRUE)
}
## 
Read 0.0% of 967216 rows
Read 32.1% of 967216 rows
Read 52.7% of 967216 rows
Read 69.3% of 967216 rows
Read 79.6% of 967216 rows
Read 92.0% of 967216 rows
Read 902297 rows and 37 (of 37) columns from 0.523 GB file in 00:00:08
## Warning in fread(data.file, header = TRUE, sep = ",", colClasses =
## col.classes): Read less rows (902297) than were allocated (967216). Run
## again with verbose=TRUE and please report.
define function to transform damage by Unit
  • H = 100
  • K = 1,000
  • M = 1,000,000
  • B = 1,000,000,000
transferDamage <- function(Dmg, Exp) {
        value <- Dmg
        if(value > 1000000) {
                value <- Dmb    
        } else if(Exp == "K" || Exp == "k") {
                value <- Dmg * 1000    
        } else if(Exp == "M" || Exp == "m") {
                value <- Dmg * 1e+6   
        } else if(Exp == "B" || Exp == "b") {
                value <- Dmg * 1e+9    
        } else if(Exp == "H" || Exp == "h") {
                value <- Dmg * 100    
        }
        return (value)
}



unifyEventType <- function(EVTYPE) {
        EventType <- EVTYPE
        
        if(grepl('WILD', EVTYPE) &  grepl('FIRE', EVTYPE)) {
                EventType <- 'WILDFIRE'
        } else if(grepl('/', EVTYPE) ) {
                EventType <- EVTYPE
        } else if(grepl('.AND.', EVTYPE) ) {
                EventType <- EVTYPE
        } else if(grepl('TSTM.WIND', EVTYPE) ) {
                EventType <- 'TSTM.WIND'    
        } else if(grepl('VOLCANIC', EVTYPE)) {
                EventType <- 'VOLCANIC.ASH'
        } else if(grepl('FLASH.FLOOD', EVTYPE)) {
                EventType <- 'FLASH.FLOOD'
        } else if(grepl('FLOOD', EVTYPE)) {
                EventType <- 'FLOOD'
        } else if(grepl('TORNADO', EVTYPE)) {
                EventType <- 'TORNADO'
        } else if(grepl('THUNDERSTORM', EVTYPE)) {
                EventType <- 'THUNDERSTORM'
        } else if(grepl('LIGHTNING', EVTYPE)) {
                EventType <- 'LIGHTNING'
        } else if(grepl('HIGH.WINDS', EVTYPE)){
                EventType <- 'HIGH.WINDS'
        }else if(grepl('HAIL', EVTYPE)){
                EventType <- 'HAIL'
        }
        return (EventType)
}
Cleaning data
  • uppercase EVTYPE, TIME_ZONE, PROPDMGEXP, CROPDMGEXP
  • transform PROPDMG by PROPDMGEXP
  • transform CROPDMG by CROPDMGEXP
  • get Event Year by BGN_DATE
cleanData <- function(data) {
        clean.data <- data %>%
                mutate(EVTYPE = gsub("[^(a-zA-Z0-9/,)]",".", toupper(EVTYPE)) ) %>%
                mutate(TIME_ZONE = toupper(TIME_ZONE)) %>%
                mutate(PROPDMGEXP = toupper(PROPDMGEXP)) %>%
                mutate(CROPDMGEXP = toupper(CROPDMGEXP)) %>%
                mutate(BGN_DATE = as.Date(BGN_DATE, format="%m/%d/%Y")) %>%
                mutate(BGN_YR = year(BGN_DATE)) %>%
                mutate(BGN_DECADE = cut(BGN_YR, breaks = c(1949, 1959, 1969, 1979, 1989, 1999,2012) ) )%>%
                rowwise() %>%
                mutate(EVTYPE = unifyEventType(EVTYPE)) %>%
                mutate(PROPDMG = transferDamage(PROPDMG, PROPDMGEXP) ) %>%
                mutate(CROPDMG = transferDamage(CROPDMG, CROPDMGEXP) ) 
        levels(clean.data$BGN_DECADE) <- c('1950~', '1960~','1970~','1980~','1990~','2000~')
        return (clean.data)
}

clean.data <- cleanData(raw.data)
Which Event cause most Properties Damage
max.PROPDMG <-  clean.data[which(clean.data$PROPDMG == max(clean.data$PROPDMG, na.rm = TRUE) ), ]
max.PROPDMG[,c("EVTYPE","BGN_DATE","STATE","PROPDMG")]
## Source: local data frame [1 x 4]
## 
##   EVTYPE   BGN_DATE STATE  PROPDMG
## 1  FLOOD 2006-01-01    CA 1.15e+11
max.PROPDMG$REMARKS
## [1] "Major flooding continued into the early hours of January 1st, before the Napa River finally fell below flood stage and the water receeded. Flooding was severe in Downtown Napa from the Napa Creek and the City and Parks Department was hit with $6 million in damage alone. The City of Napa had 600 homes with moderate damage, 150 damaged businesses with costs of at least $70 million."

I saw discussion about this event in the course forum, the damage mentioned for this event is far below the PROPDMG, so I decided to remove this record in the following steps

Which Event cause most Crop Damage
max.CROPDMG <- clean.data[which(clean.data$CROPDMG == max(clean.data$CROPDMG, na.rm = TRUE) ),]
max.CROPDMG[,c("EVTYPE","BGN_DATE","STATE","CROPDMG")]
## Source: local data frame [2 x 4]
## 
##      EVTYPE   BGN_DATE STATE CROPDMG
## 1     FLOOD 1993-08-31    IL   5e+09
## 2 ICE.STORM 1994-02-09    MS   5e+09
#max.CROPDMG$REMARKS
Which Event cause most FATALITIES
max.FATALITIES <- clean.data[which(clean.data$FATALITIES == max(clean.data$FATALITIES, na.rm = TRUE) ),]
max.FATALITIES[,c("EVTYPE","BGN_DATE","STATE","FATALITIES")]
## Source: local data frame [1 x 4]
## 
##   EVTYPE   BGN_DATE STATE FATALITIES
## 1   HEAT 1995-07-12    IL        583
#max.FATALITIES$REMARKS
Which Event cause most INJURIES
max.INJURIES <- clean.data[which(clean.data$INJURIES == max(clean.data$INJURIES, na.rm = TRUE) ),]
max.INJURIES[,c("EVTYPE","BGN_DATE","STATE","INJURIES")]
## Source: local data frame [1 x 4]
## 
##    EVTYPE   BGN_DATE STATE INJURIES
## 1 TORNADO 1979-04-10    TX     1700
#max.INJURIES$REMARKS
Remove data without proper damage infomation
  • check ratio for removing data
  • data without proper damage unit [B/M/K/H or empty]
  • Nappa River Flood on 2006-01-01
noExpCnt <- sum(!raw.data$PROPDMGEXP %in% c("B","M","K","H","") | 
            !raw.data$CROPDMGEXP %in% c("B","M","K","H","")) 

noExpCnt/ dim(raw.data)[1]
## [1] 0.0004100645
clean.data <- clean.data %>%
            filter(PROPDMGEXP %in% c("B","M","K","H","")) %>%
            filter(CROPDMGEXP %in% c("B","M","K","H","")) %>%
            filter(PROPDMG != 115000000000)
dim(clean.data)
## [1] 901955     39
summary clean data by Event Type
  • sum.FATALITIES
  • sum.INJURIES
  • sum.PROPDMG
  • sum.CROPDMG
  • sum.DMG = sum.PROPDMG + sum.CROPDMG
  • sum.casualties = sum.FATALITIES + sum.INJURIES
  • index.casualties = sum.FATALITIES * 3 + sum.INJURIES I gave FATALITIES a factor of 3 to calculate index.casualties
summaryEventData <- function(cleanData) {
        summary.data <- cleanData %>%
                group_by(EVTYPE) %>%
                summarise(event.count=n() ,
                          sum.FATALITIES = sum(FATALITIES, na.rm= TRUE),
                          sum.INJURIES = sum(INJURIES, na.rm= TRUE),
                          sum.PROPDMG = sum(PROPDMG, na.rm= TRUE),
                          sum.CROPDMG = sum(CROPDMG, na.rm= TRUE)) %>%
                mutate(sum.DMG = sum.PROPDMG + sum.CROPDMG)  %>%
                mutate(sum.casualties = sum.FATALITIES  + sum.INJURIES) %>%
                mutate(index.casualties = sum.FATALITIES * 3 + sum.INJURIES)
        return (summary.data)
}

Result

Summary Damage by Event Type
  • get top 10 events caused largest damage
summary.event.data <- summaryEventData(clean.data)
summary.dmg.data <- summary.event.data %>%
        ungroup() %>%
        arrange(desc(sum.DMG)) %>%
        top_n(10) %>%
        select(EVTYPE, c(1,5,6,7))
## Selecting by index.casualties
dmg.data <- summary.dmg.data %>%
        gather(damageType,  damageValue, sum.PROPDMG:sum.CROPDMG)

p1 <- ggplot(dmg.data, aes(x=EVTYPE, y=damageValue/1e+9, fill=damageType, order=desc(damageType))) + #works
        geom_bar(stat="identity") +
        theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
        #geom_text(aes(label=totalDeathInjury)) +
        ggtitle(paste("Weather Events Casuse Most Damage ")) +
        xlab("Weather Event") + 
        ylab("Damage(billion)") +
        labs(fill="Damage Type")  +
        theme(axis.text.x= element_text(size=5)) +
        theme(axis.text.y= element_text(size=7)) +
        theme(strip.text.x= element_text(size=7)) +
        theme(legend.text= element_text(size=7)) +
        theme(title= element_text(size=7))
Event Cause lagest damage in all years
max.dmg <- summary.dmg.data[which(summary.dmg.data$sum.DMG == max(summary.dmg.data$sum.DMG)), ]
max.dmg
## Source: local data frame [1 x 4]
## 
##    EVTYPE sum.PROPDMG sum.CROPDMG     sum.DMG
## 1 TORNADO 56941811733   364958360 57306770093
Summary Casualties by Event Type
  • index.casualties = FATALITIES * 3 + INJURIES to measure population health impact.
  • get top 10 events has largest population health impact
summary.casualties.data <- summary.event.data %>%
        ungroup() %>%
        arrange(desc(index.casualties)) %>%
        top_n(10) %>%
        select(EVTYPE, c(1,3,4,8,9))
## Selecting by index.casualties
casualties.data <- summary.casualties.data %>%
        gather(harmType,  harmValue, sum.FATALITIES:sum.INJURIES)

p2 <- ggplot(casualties.data, aes(x=EVTYPE, y=harmValue/1000, fill=harmType, order=(harmType))) + #works
        geom_bar(stat="identity") +
        theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
        #geom_text(aes(label=totalDeathInjury)) +
        ggtitle(paste("Weather Events Casuse Most Casualties ")) +
        xlab("Weather Event") + 
        ylab("Casualties(thousand)") +
        labs(fill="Harm Type") +
        theme(axis.text.x= element_text( size=5)) +
        theme(axis.text.y= element_text( size=7)) +
        theme(strip.text.x= element_text( size=7)) +
        theme(legend.text= element_text( size=5)) +
        theme(title= element_text( size=7))
Event has largest population health impact in all years
max.casualties <- summary.casualties.data[which(summary.casualties.data$sum.casualties == max(summary.casualties.data$sum.casualties)), ]
max.casualties
## Source: local data frame [1 x 5]
## 
##    EVTYPE sum.FATALITIES sum.INJURIES sum.casualties index.casualties
## 1 TORNADO           5630        91340          96970           108230
create figures to summarize damage and casualties by event
setwd("/Users/hadoop/RepData_PeerAssessment2") 

multiplot(p1, p2,  cols=2)

Summarize total Damage and Casualties by Event Year
summaryYeadData <- function(cleanData) {
        summary.data <- cleanData %>%
                group_by(BGN_DECADE) %>%
                summarise(event.count=n() ,
                          sum.FATALITIES = sum(FATALITIES, na.rm= TRUE),
                          sum.INJURIES = sum(INJURIES, na.rm= TRUE),
                          sum.PROPDMG = sum(PROPDMG, na.rm= TRUE),
                          sum.CROPDMG = sum(CROPDMG, na.rm= TRUE)) %>%
                mutate(sum.DMG = sum.PROPDMG + sum.CROPDMG)  %>%
                mutate(sum.casualties = sum.FATALITIES  + sum.INJURIES) %>%
                mutate(index.casualties = sum.FATALITIES * 3 + sum.INJURIES)
        return (summary.data)
}  

summary.year.data <- summaryYeadData(clean.data)

year.dmg.data <- summary.year.data %>%
        select(c(1,3,4,5,6)) %>%
        ungroup() %>%
        arrange(desc(sum.PROPDMG), desc(sum.INJURIES))
head(year.dmg.data)
## Source: local data frame [6 x 5]
## 
##   BGN_DECADE sum.FATALITIES sum.INJURIES  sum.PROPDMG sum.CROPDMG
## 1      2000~           5994        35133 215797717610 23563649040
## 2      1990~           5083        38512  70041019047 25432443881
## 3      1980~            699        13454  13180060840           0
## 4      1970~            998        21640   8024638050           0
## 5      1960~            942        17265   3757536860           0
## 6      1950~           1419        14470   1516409420           0
summary Damage and Casualties by Different Timezone
summaryTimezoneData <- function(cleanData) {
        summary.data <- cleanData %>%
                group_by(TIME_ZONE) %>%
                summarise(event.count=n() ,
                          sum.FATALITIES = sum(FATALITIES, na.rm= TRUE),
                          sum.INJURIES = sum(INJURIES, na.rm= TRUE),
                          sum.PROPDMG = sum(PROPDMG, na.rm= TRUE),
                          sum.CROPDMG = sum(CROPDMG, na.rm= TRUE)) %>%
                mutate(sum.DMG = sum.PROPDMG + sum.CROPDMG)  %>%
                mutate(sum.casualties = sum.FATALITIES  + sum.INJURIES) %>%
                mutate(index.casualties = sum.FATALITIES * 3 + sum.INJURIES)
        return (summary.data)
}
sum.tz.data <- summaryTimezoneData(clean.data)

tz.dmg.data <- sum.tz.data %>%
        select(c(1,3,4,5,6)) %>%
        ungroup() %>%
        arrange(desc(sum.PROPDMG), desc(sum.INJURIES))
head(tz.dmg.data)
## Source: local data frame [6 x 5]
## 
##   TIME_ZONE sum.FATALITIES sum.INJURIES  sum.PROPDMG sum.CROPDMG
## 1       CST           9246       105684 211243390715 33046623911
## 2       EST           3888        25386  74902879232 10276063080
## 3       PST            902         4421  11148174040  4073395200
## 4       MST            736         3826  10504919740   636586550
## 5       AST            189          164   2467821280   616858000
## 6       HST             46           95    843077250     7876630

Summary Damage by Top3 Event in different time zone by year

  • pickup TIME_ZONE(CST, EST, PST)
  • pickup BGN_YR >= 1990
  • pickup top 3 EVENT TYPE
  • show sum.PROPDMG, sum.CROPDMG by Top3 Event and Timezone
summaryDamageByTimeZone <- function(data, tz, topn = 3) {
        print(tz)
        sum.dmg.data <- data %>%
                filter(TIME_ZONE == tz) %>%
                group_by(EVTYPE) %>%
                summarise(sum.PROPDMG = sum(PROPDMG, na.rm= TRUE),
                          sum.CROPDMG = sum(CROPDMG, na.rm= TRUE)) %>%
                mutate(sum.DMG = sum.PROPDMG + sum.CROPDMG)  %>%
                ungroup() %>%
                arrange(desc(sum.DMG))
        
        topEvent <- as.data.frame(sum.dmg.data)[1:topn,1]
        summary.damage <- data %>%
                filter(EVTYPE %in% topEvent) %>%
                filter(TIME_ZONE == tz) %>%
                group_by(EVTYPE,BGN_YR, TIME_ZONE) %>%
                summarise(event.count=n() ,
                          sum.PROPDMG = sum(PROPDMG, na.rm= TRUE),
                          sum.CROPDMG = sum(CROPDMG, na.rm= TRUE)) %>%
                mutate(sum.Damage = sum.PROPDMG + sum.CROPDMG)  
        
        return (summary.damage)
}


plotDamageByTimeZone <- function(data, tz, topn = 3) {
        summary.dmg.data <- summaryDamageByTimeZone(data, tz, topn)
        head(summary.dmg.data)
        tzTitle <-  paste("Damages by Top",topn,"Event at Time Zone"  , tz)
        require(ggplot2)
        pd1 <-  ggplot(aes(x=BGN_YR), data=summary.dmg.data) +                       geom_line(aes(y= sum.PROPDMG/1e+09, colour="PROPDMG"))+
                geom_line(aes(y= sum.CROPDMG/1e+09,  colour="CROPDMG"))+
                facet_wrap(~ EVTYPE, ncol=topn) +
                
                labs(title = tzTitle) + 
                labs(y = "Damage(billion)") +
                labs(x = "Year of Event")  +
                theme(axis.text.x= element_text( size=7)) +
                theme(axis.text.y= element_text( size=7)) +
                theme(strip.text.x= element_text( size=6)) +
                theme(strip.text.y= element_text( size=6)) +
                theme(legend.text= element_text( size=5)) +
                theme(title= element_text( size=7))  
        
        damage <- summary.dmg.data
        max.dmg <- damage[which(damage$sum.Damage == max(damage$sum.Damage, na.rm = TRUE)),]
        print(paste("Top Damage at TimeZone", tz))
        print(max.dmg)
        return (pd1)
}

pd1  <- plotDamageByTimeZone(clean.data, "CST", 4)
## [1] "CST"
## [1] "Top Damage at TimeZone CST"
## Source: local data frame [1 x 7]
## Groups: EVTYPE, BGN_YR
## 
##        EVTYPE BGN_YR TIME_ZONE event.count sum.PROPDMG sum.CROPDMG
## 1 STORM.SURGE   2005       CST           7 42995540000           0
## Variables not shown: sum.Damage (dbl)
pd2  <- plotDamageByTimeZone(clean.data, "EST", 4)
## [1] "EST"
## [1] "Top Damage at TimeZone EST"
## Source: local data frame [1 x 7]
## Groups: EVTYPE, BGN_YR
## 
##              EVTYPE BGN_YR TIME_ZONE event.count sum.PROPDMG sum.CROPDMG
## 1 HURRICANE/TYPHOON   2004       EST          19 11750225000   512250000
## Variables not shown: sum.Damage (dbl)
multiplot( pd1, pd2, cols = 1)

summaryTimeZoneEvent <- function(data, tz, topn) {
        sum.data <- summaryDamageByTimeZone(data, tz, topn)
        head(sum.data)
        sum.tz.data <- sum.data %>%
                group_by(EVTYPE, TIME_ZONE) %>%
                summarise(sum.PROPDMG = sum(sum.PROPDMG, na.rm= TRUE),
                          sum.CROPDMG = sum(sum.CROPDMG, na.rm= TRUE),
                          sum.DMG = sum(sum.Damage, na.rm = TRUE)) %>%
                ungroup() %>%
                arrange(desc(sum.DMG))
        return (sum.tz.data)
}

head(summaryTimeZoneEvent(clean.data, "CST", 5))
## [1] "CST"
## Source: local data frame [5 x 5]
## 
##              EVTYPE TIME_ZONE sum.PROPDMG sum.CROPDMG     sum.DMG
## 1           TORNADO       CST 48070077350   313851410 48383928760
## 2 HURRICANE/TYPHOON       CST 45869775000  1627880800 47497655800
## 3       STORM.SURGE       CST 43007805000           0 43007805000
## 4             FLOOD       CST 20044169800  8328922000 28373091800
## 5           DROUGHT       CST  1027051000 11568273000 12595324000
head(summaryTimeZoneEvent(clean.data, "EST", 5))
## [1] "EST"
## Source: local data frame [5 x 5]
## 
##              EVTYPE TIME_ZONE sum.PROPDMG sum.CROPDMG     sum.DMG
## 1 HURRICANE/TYPHOON       EST 23163325000   975620000 24138945000
## 2             FLOOD       EST 11161732317  1686538450 12848270767
## 3       FLASH.FLOOD       EST  8029923072   667079050  8697002122
## 4           TORNADO       EST  7912645943    42505050  7955150993
## 5         HURRICANE       EST  5853741000  1844430000  7698171000
Top3 Event cause total Casualties in different time zone by decade
  • pickup TIME_ZONE(CST, EST)
  • pickup top 3 EVENT TYPE
  • show sum.FATALITIES, sum.INJURIES by Top3 Event and Timezone
plotCasualtiesByTimeZone <- function(clean.data, tz, topn =3) {
        summary.tz.data <- clean.data %>%
                filter(TIME_ZONE == tz) %>%
                group_by(EVTYPE) %>%
                summarise(sum.FATALITIES = sum(FATALITIES, na.rm= TRUE),
                          sum.INJURIES = sum(INJURIES, na.rm= TRUE)) %>%
                mutate(sum.casualties = sum.FATALITIES  + sum.INJURIES) %>%
                mutate(index.casualties = sum.FATALITIES * 3 + sum.INJURIES) %>%
                ungroup() %>%
                arrange(desc(index.casualties))
        
        summary.casualties <- clean.data %>%
                filter(EVTYPE %in% as.data.frame(summary.tz.data)[1:topn,1]) %>%
                filter(TIME_ZONE == tz) %>%
                group_by(EVTYPE,BGN_DECADE,TIME_ZONE) %>%
                summarise(sum.FATALITIES = sum(FATALITIES, na.rm= TRUE),
                          sum.INJURIES = sum(INJURIES, na.rm= TRUE)) %>%
                mutate(sum.Casualties = sum.FATALITIES + sum.INJURIES)  
        
        tzTitle <- paste("Total Casualties by Top",topn," Event at TimeZone", tz)
        
        
        casualties.data <- summary.casualties %>%
                gather(harmType,  harmValue, sum.FATALITIES:sum.Casualties)
        pc1 <- ggplot(casualties.data, aes(x=BGN_DECADE, y=harmValue/1000, fill=harmType)) + 
                geom_bar(stat="identity") +
                theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
                labs(title = tzTitle) + 
                facet_wrap(~ EVTYPE, ncol=topn) +
                xlab("Event Decade") + 
                ylab("Casualties(thousand)") +
                labs(fill="Harm Type") 
        theme(axis.text.x= element_text( size=7)) +
        theme(axis.text.y= element_text( size=7)) +
        theme(strip.text.x= element_text( size=6)) +
        theme(strip.text.y= element_text( size=6)) +
        theme(legend.text= element_text( size=7)) +
        theme(title= element_text( size=7))  
        
        casualties <- subset(summary.casualties, TIME_ZONE == tz )
        max.casualties <- casualties[which(casualties$sum.Casualties == max(casualties$sum.Casualties)),]
        print(paste("Top Casualties in TimeZone", tz))
        print(max.casualties)    
        return (pc1)
}

pc1  <- plotCasualtiesByTimeZone(clean.data, "EST", 4)
## [1] "Top Casualties in TimeZone EST"
## Source: local data frame [1 x 6]
## Groups: EVTYPE, BGN_DECADE
## 
##    EVTYPE BGN_DECADE TIME_ZONE sum.FATALITIES sum.INJURIES sum.Casualties
## 1 TORNADO      2000~       EST            259         4540           4799
pc2  <- plotCasualtiesByTimeZone(clean.data, "CST", 4)
## [1] "Top Casualties in TimeZone CST"
## Source: local data frame [1 x 6]
## Groups: EVTYPE, BGN_DECADE
## 
##    EVTYPE BGN_DECADE TIME_ZONE sum.FATALITIES sum.INJURIES sum.Casualties
## 1 TORNADO      1970~       CST            995        21436          22431
multiplot(pc1, pc2, cols = 1)