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.

Data Processing

Reading dataset and Subseting dataset

    #Read data
    library(ggplot2)
## Registered S3 methods overwritten by 'ggplot2':
##   method         from 
##   [.quosures     rlang
##   c.quosures     rlang
##   print.quosures rlang
    library(grid)
    library(gridExtra)
    library(data.table)
    dataset <- read.csv('repdata-data-StormData.csv.bz2', stringsAsFactors=FALSE)    
    dataset.sub <- data.frame(dataset$STATE,
                              dataset$EVTYPE,
                              dataset$INJURIES,
                              dataset$FATALITIES,
                              dataset$PROPDMG,
                              dataset$PROPDMGEXP,
                              dataset$CROPDMG,
                              dataset$CROPDMGEXP)
    colnames(dataset.sub) <- c('STATE', 
                               'EVTYPE', 
                               'INJURIES', 
                               'FATALITIES', 
                               'PROPDMG', 
                               'PROPDMGEXP',
                               'CROPDMG',
                               'CROPDMGEXP')
    dataset.sub <- data.table(dataset.sub)
    dataset.sub <- dataset.sub[(EVTYPE != "?" & (INJURIES > 0 | FATALITIES > 0 | PROPDMG > 0 |CROPDMG > 0)), 
                               c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP" , "CROPDMG" , "CROPDMGEXP") ]

instead of (-,+, H, K, etc)

cols <- c("PROPDMGEXP", "CROPDMGEXP")
dataset.sub[,  (cols) := c(lapply(.SD, toupper)), .SDcols = cols]
propDmgKey <-  c("\"\"" = 10^0,"-" = 10^0,"+" = 10^0,"0" = 10^0,"1" = 10^1,"2" = 10^2,
                 "3" = 10^3,"4" = 10^4,"5" = 10^5,"6" = 10^6,"7" = 10^7,"8" = 10^8,"9" = 10^9,
                 "H" = 10^2,"K" = 10^3,"M" = 10^6,"B" = 10^9)
cropDmgKey <-  c("\"\"" = 10^0,"?" = 10^0, "0" = 10^0,"K" = 10^3,"M" = 10^6,"B" = 10^9)
dataset.sub[, PROPDMGEXP := propDmgKey[as.character(dataset.sub[,PROPDMGEXP])]]
dataset.sub[is.na(PROPDMGEXP), PROPDMGEXP := 10^0 ]

dataset.sub[, CROPDMGEXP := cropDmgKey[as.character(dataset.sub[,CROPDMGEXP])] ]
dataset.sub[is.na(CROPDMGEXP), CROPDMGEXP := 10^0 ]

Calculating Cost

dataset.sub <<- dataset.sub[, .(EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, propCost = PROPDMG * PROPDMGEXP, CROPDMG, CROPDMGEXP, cropCost = CROPDMG * CROPDMGEXP)]
totalCost <<- dataset.sub[, .(propCost = sum(propCost), cropCost = sum(cropCost), Total_Cost = sum(propCost) + sum(cropCost)), by = .(EVTYPE)]

totalCost <- totalCost[order(Total_Cost,decreasing = T), ]

totalCost <- totalCost[1:5, ]

head(totalCost, 3)
##               EVTYPE     propCost   cropCost   Total_Cost
## 1:             FLOOD 144657709807 5661968450 150319678257
## 2: HURRICANE/TYPHOON  69305840000 2607872800  71913712800
## 3:           TORNADO  56947380676  414953270  57362333946

Calcuating Injuries and Fatalities

totalInjuries <<- dataset.sub[, .(FATALITIES = sum(FATALITIES), INJURIES = sum(INJURIES), totals = sum(FATALITIES) + sum(INJURIES)), by = .(EVTYPE)]

totalInjuries <- totalInjuries[order(FATALITIES,decreasing = T), ]

totalInjuries <- totalInjuries[1:5, ]

head(totalInjuries, 3)
##            EVTYPE FATALITIES INJURIES totals
## 1:        TORNADO       5633    91346  96979
## 2: EXCESSIVE HEAT       1903     6525   8428
## 3:    FLASH FLOOD        978     1777   2755

Results

Most Harmful to Population Health

EVENTS_stuff <- melt(totalInjuries, id.vars="EVTYPE", variable.name = "Results")
head(EVENTS_stuff, 3)
##            EVTYPE    Results value
## 1:        TORNADO FATALITIES  5633
## 2: EXCESSIVE HEAT FATALITIES  1903
## 3:    FLASH FLOOD FATALITIES   978
p1 <- ggplot(EVENTS_stuff, aes(x=reorder(EVTYPE, -value), y=value)) +
                      geom_bar(stat="identity", aes(fill=Results), position="dodge") +
                      ylab("Frequency Count") +
                      xlab("Events") +
                      ggtitle("Top 5 Harmful to US Ppopulation Health") + theme(plot.title = element_text(hjust = 0.5)) +
                      theme(axis.text.x = element_text(angle=90, hjust=1))
p1

### Greatest Economic Consequences

E_consequences <<- melt(totalCost, id.vars="EVTYPE", variable.name = "Types")
head(E_consequences, 3)
##               EVTYPE    Types        value
## 1:             FLOOD propCost 144657709807
## 2: HURRICANE/TYPHOON propCost  69305840000
## 3:           TORNADO propCost  56947380676
# Create chart
p2 <- ggplot(E_consequences, aes(x=reorder(EVTYPE, -value), y=value)) + 
             geom_bar(stat="identity", aes(fill=Types), position="dodge") + 
             ylab("Cost (dollars)") + 
             xlab("Events") + 
             theme(axis.text.x = element_text(angle=90, hjust=1)) + 
             ggtitle("Top 5 US Storm Events causing Economic Consequences") + 
             theme(plot.title = element_text(hjust = 0.5))

p2