Synopsis

This document describes the weather events which are the most harmful to the population health and the economic consequences in the United States.

The source data for this project was downloaded from here: Storm Data

Some additional description of the data used in this project can be found here: Storm Data Documentation

Data Processing

Load required library and dataset.

library(ggplot2)
if(!file.exists("../original_data_set")){dir.create("../original_data_set")}
data_set_url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(data_set_url,
              destfile="../original_data_set/StormData.csv.bz2")
stormdata <- read.csv(bzfile("../original_data_set/StormData.csv.bz2"), sep=",", header=TRUE)

Observe the column names.

names(stormdata)
##  [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"

Population health impact

To observe the population health impact by the weather event types, focus on EVTYPE, FATALITIES, and INJURIES.

health_impact <- stormdata[, c('EVTYPE', 'FATALITIES', 'INJURIES')]

Caluculate the total population health impact for each weather event types.

health_impact_by_event <- 
  setNames(
    aggregate(health_impact$FATALITIES + 
                health_impact$INJURIES,
              by=list(health_impact$EVTYPE),FUN=sum),
    c("EventType", "HealthImpact")
  )

To observe the most harmful weather event with respect to population health, visualize the total population health impact for the top 10 weather events.

health_impact_by_event <- 
  head(health_impact_by_event[order(health_impact_by_event$HealthImpact, 
                                    decreasing= TRUE),], n = 10)

g<- ggplot(health_impact_by_event, 
           aes(x=reorder(EventType, HealthImpact), y=HealthImpact))

g + geom_bar(stat="identity")  + 
  labs(x="Weather event Type", 
       y="Population health impact", 
       title = "Fig.1: Population health impact by weather event type") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5),
        plot.title = element_text(hjust = 0.5))

Economic impact

To observe the economic consequences by the weather event types, focus on EVTYPE, PROPDMG, PROPDMGEXP, CROPDMG, and CROPDMGEXP

economic_impact <- 
  stormdata[, c('EVTYPE', 'PROPDMG', 'PROPDMGEXP', 'CROPDMG', 'CROPDMGEXP')]

Observe PROPDMGEXP and CROPDMGEXP.

levels(economic_impact$PROPDMGEXP)
##  [1] ""  "-" "?" "+" "0" "1" "2" "3" "4" "5" "6" "7" "8" "B" "h" "H" "K"
## [18] "m" "M"
levels(economic_impact$CROPDMGEXP)
## [1] ""  "?" "0" "2" "B" "k" "K" "m" "M"
summary(economic_impact)
##                EVTYPE          PROPDMG          PROPDMGEXP    
##  HAIL             :288661   Min.   :   0.00          :465934  
##  TSTM WIND        :219940   1st Qu.:   0.00   K      :424665  
##  THUNDERSTORM WIND: 82563   Median :   0.00   M      : 11330  
##  TORNADO          : 60652   Mean   :  12.06   0      :   216  
##  FLASH FLOOD      : 54277   3rd Qu.:   0.50   B      :    40  
##  FLOOD            : 25326   Max.   :5000.00   5      :    28  
##  (Other)          :170878                     (Other):    84  
##     CROPDMG          CROPDMGEXP    
##  Min.   :  0.000          :618413  
##  1st Qu.:  0.000   K      :281832  
##  Median :  0.000   M      :  1994  
##  Mean   :  1.527   k      :    21  
##  3rd Qu.:  0.000   0      :    19  
##  Max.   :990.000   B      :     9  
##                    (Other):     9

Based on the information from the 2.7 Damage section in Storm Data Documentation, calculate the estimation for property damages and corporation damages as follows:

economic_impact['CROPDMG_ESTIMATE']<-0
economic_impact['PROPDMG_ESTIMATE']<-0

economic_impact[economic_impact$PROPDMGEXP == "H", ]$PROPDMG_ESTIMATE <- 
  economic_impact[economic_impact$PROPDMGEXP == "H", ]$PROPDMG * 10^2

economic_impact[economic_impact$PROPDMGEXP == "K", ]$PROPDMG_ESTIMATE <- 
  economic_impact[economic_impact$PROPDMGEXP == "K", ]$PROPDMG * 10^3

economic_impact[economic_impact$PROPDMGEXP == "M", ]$PROPDMG_ESTIMATE <- 
  economic_impact[economic_impact$PROPDMGEXP == "M", ]$PROPDMG * 10^6

economic_impact[economic_impact$PROPDMGEXP == "B", ]$PROPDMG_ESTIMATE <- 
  economic_impact[economic_impact$PROPDMGEXP == "B", ]$PROPDMG * 10^9

economic_impact[economic_impact$PROPDMGEXP == "5", ]$PROPDMG_ESTIMATE <- 
  economic_impact[economic_impact$PROPDMGEXP == "5", ]$PROPDMG * 10^5


economic_impact[economic_impact$CROPDMGEXP == "K", ]$CROPDMG_ESTIMATE <- 
  economic_impact[economic_impact$CROPDMGEXP == "K", ]$CROPDMG * 10^3

economic_impact[economic_impact$CROPDMGEXP == "M", ]$CROPDMG_ESTIMATE <- 
  economic_impact[economic_impact$CROPDMGEXP == "M", ]$CROPDMG * 10^6

economic_impact[economic_impact$CROPDMGEXP == "k", ]$CROPDMG_ESTIMATE <- 
  economic_impact[economic_impact$CROPDMGEXP == "k", ]$CROPDMG * 10^3

economic_impact[economic_impact$CROPDMGEXP == "B", ]$CROPDMG_ESTIMATE <- 
  economic_impact[economic_impact$CROPDMGEXP == "B", ]$CROPDMG * 10^9

Calculate the total economic damage estimations for each weather event types.

economic_impact_by_event <- 
  setNames(
    aggregate(economic_impact$PROPDMG_ESTIMATE + 
                economic_impact$CROPDMG_ESTIMATE,
              by=list(economic_impact$EVTYPE), FUN=sum),
    c("EventType", "EconimicImpact")
  )

To observe the weather event which has the greatest economic consequences, visualize the total economic impact for the top 10 weather events.

economic_impact_by_event<-
  head(economic_impact_by_event[order(economic_impact_by_event$EconimicImpact, 
                                                   decreasing= TRUE),], n = 10)
g<- ggplot(economic_impact_by_event, 
           aes(x=reorder(EventType, EconimicImpact), y=EconimicImpact))

g + geom_bar(stat="identity")  + 
  labs(x="Weather event type", 
       y="Economic damage estimations", 
       title = "Fig.2: Economic damage estimations by weather event type") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5),
        plot.title = element_text(hjust = 0.5))

Results