Impact of Major Storms and Weather Events in the United States on Pubic Health and Economy

Synopsis

The main goal of this report is to analyze the United States National Oceanic and Atmospheric Adminstration’s (NOAA) storm database, which tracks major storm and severe weather events, to address the following questions:

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?

It is observed that high temperatures and tornado are most harmful with respect to population health, while flood, drought, and hurricane/typhoon have the greatest impacts on economy.

Loading and Processing the Raw Data

Download the file from internet

if(!file.exists('StormData.csv.bz2')){
  download.file("http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2",
                destfile='StormData.csv.bz2') 
}

Loading Libraries

library(R.utils)
library(plyr)
library(dplyr)
library(ggplot2)
library(graphics)

Uncompress the file and read the CSV file into a data file

if(file.exists('StormData.csv.bz2')){
  storm <- read.csv(bzfile('StormData.csv.bz2'), header = TRUE)
}
str(storm)

## 'data.frame':    902297 obs. of  37 variables:
##  $ STATE__   : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ BGN_DATE  : Factor w/ 16335 levels "1/1/1966 0:00:00",..: 6523 6523 4242 11116 2224 2224 2260 383 3980 3980 ...
##  $ BGN_TIME  : Factor w/ 3608 levels "00:00:00 AM",..: 272 287 2705 1683 2584 3186 242 1683 3186 3186 ...
##  $ TIME_ZONE : Factor w/ 22 levels "ADT","AKS","AST",..: 7 7 7 7 7 7 7 7 7 7 ...
##  $ COUNTY    : num  97 3 57 89 43 77 9 123 125 57 ...
##  $ COUNTYNAME: Factor w/ 29601 levels "","5NM E OF MACKINAC BRIDGE TO PRESQUE ISLE LT MI",..: 13513 1873 4598 10592 4372 10094 1973 23873 24418 4598 ...
##  $ STATE     : Factor w/ 72 levels "AK","AL","AM",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ EVTYPE    : Factor w/ 985 levels "   HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
##  $ BGN_RANGE : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BGN_AZI   : Factor w/ 35 levels "","  N"," NW",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ BGN_LOCATI: Factor w/ 54429 levels "","- 1 N Albion",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ END_DATE  : Factor w/ 6663 levels "","1/1/1993 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ END_TIME  : Factor w/ 3647 levels ""," 0900CST",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ COUNTY_END: num  0 0 0 0 0 0 0 0 0 0 ...
##  $ COUNTYENDN: logi  NA NA NA NA NA NA ...
##  $ END_RANGE : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ END_AZI   : Factor w/ 24 levels "","E","ENE","ESE",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ END_LOCATI: Factor w/ 34506 levels "","- .5 NNW",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ LENGTH    : num  14 2 0.1 0 0 1.5 1.5 0 3.3 2.3 ...
##  $ WIDTH     : num  100 150 123 100 150 177 33 33 100 100 ...
##  $ F         : int  3 2 2 2 2 2 2 1 3 3 ...
##  $ MAG       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ 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: Factor w/ 19 levels "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
##  $ CROPDMG   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ CROPDMGEXP: Factor w/ 9 levels "","?","0","2",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WFO       : Factor w/ 542 levels ""," CI","$AC",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ STATEOFFIC: Factor w/ 250 levels "","ALABAMA, Central",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ ZONENAMES : Factor w/ 25112 levels "","                                                                                                                               "| __truncated__,..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ LATITUDE  : num  3040 3042 3340 3458 3412 ...
##  $ LONGITUDE : num  8812 8755 8742 8626 8642 ...
##  $ LATITUDE_E: num  3051 0 0 0 0 ...
##  $ LONGITUDE_: num  8806 0 0 0 0 ...
##  $ REMARKS   : Factor w/ 436781 levels "","-2 at Deer Park\n",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ REFNUM    : num  1 2 3 4 5 6 7 8 9 10 ...

Extract data related to health and economy against severe weather.

Event Type
* EVTYPE as a measure of event type (e.g. tornado, flood, etc.)

Public Health
* FATALITIES as a measure of harm to human health
* INJURIES as a measure of harm to human health

Economy
* PROPDMG as a measure of property damage and hence economic damage in USD
* PROPDMGEXP as a measure of magnitude of property damage (e.g. thousands, millions USD, etc.)
* CROPDMG as a measure of crop damage and hence economic damage in USD
* CROPDMGEXP as a measure of magnitude of crop damage (e.g. thousands, millions USD, etc.)

To increase the speed of our computation, only above mentioned variables are extracted.

Extractcols <- c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")
storm <- storm[Extractcols]
str(storm)

## 'data.frame':    902297 obs. of  7 variables:
##  $ EVTYPE    : Factor w/ 985 levels "   HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
##  $ 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: Factor w/ 19 levels "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
##  $ CROPDMG   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ CROPDMGEXP: Factor w/ 9 levels "","?","0","2",..: 1 1 1 1 1 1 1 1 1 1 ...

Function to determine the top 10 severe weather events caused by each measure.

FieldTops <- function(Field, top = 20, dataset = storm){
  byField <- aggregate(Field ~ dataset$EVTYPE, data = dataset, FUN = "sum")
  Ftops <- byField[order(-byField$Field), ][1:top,]
  return(Ftops)
}

Top 10 severe events caused FATALITIES

fatal <- FieldTops(storm$FATALITIES,10,storm)
names(fatal) <- c("EVTYPE","FATALITIES")
fatal

##             EVTYPE FATALITIES
## 834        TORNADO       5633
## 130 EXCESSIVE HEAT       1903
## 153    FLASH FLOOD        978
## 275           HEAT        937
## 464      LIGHTNING        816
## 856      TSTM WIND        504
## 170          FLOOD        470
## 585    RIP CURRENT        368
## 359      HIGH WIND        248
## 19       AVALANCHE        224

Top 10 severe events caused INJURIES

injury <- FieldTops(storm$INJURIES,10,storm)
names(injury) <- c("EVTYPE","INJURIES")
injury

##                EVTYPE INJURIES
## 834           TORNADO    91346
## 856         TSTM WIND     6957
## 170             FLOOD     6789
## 130    EXCESSIVE HEAT     6525
## 464         LIGHTNING     5230
## 275              HEAT     2100
## 427         ICE STORM     1975
## 153       FLASH FLOOD     1777
## 760 THUNDERSTORM WIND     1488
## 244              HAIL     1361

Property and Crop damages impact the US economy. PROPDMGEXP and CROPDMGEXP are factor variables, has to be converted into comparable numerical forms using the definition of the units described in the Code Book.

unique(storm$PROPDMGEXP)

##  [1] K M   B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels:  - ? + 0 1 2 3 4 5 6 7 8 B h H K m M

storm$PROPDMGEXP <- as.character(storm$PROPDMGEXP)
storm$PROPDMGEXP = gsub("\\-|\\+|\\?","0",storm$PROPDMGEXP)
storm$PROPDMGEXP = gsub("B|b", "9", storm$PROPDMGEXP)
storm$PROPDMGEXP = gsub("M|m", "6", storm$PROPDMGEXP)
storm$PROPDMGEXP = gsub("K|k", "3", storm$PROPDMGEXP)
storm$PROPDMGEXP = gsub("H|h", "2", storm$PROPDMGEXP)
storm$PROPDMGEXP <- as.numeric(storm$PROPDMGEXP)
storm$PROPDMGEXP[is.na(storm$PROPDMGEXP)] = 0
storm$ACTPROPDMG <- storm$PROPDMG * 10^storm$PROPDMGEXP

Top 10 severe events caused PROPERTY DAMAGE

propertydamage <- FieldTops(storm$ACTPROPDMG,10,storm)
names(propertydamage) <- c("EVTYPE","PROPERTYDAMAGE")
propertydamage

##                EVTYPE PROPERTYDAMAGE
## 170             FLOOD   144657709807
## 411 HURRICANE/TYPHOON    69305840000
## 834           TORNADO    56947380677
## 670       STORM SURGE    43323536000
## 153       FLASH FLOOD    16822673979
## 244              HAIL    15735267513
## 402         HURRICANE    11868319010
## 848    TROPICAL STORM     7703890550
## 972      WINTER STORM     6688497251
## 359         HIGH WIND     5270046295

unique(storm$CROPDMGEXP)

## [1]   M K m B ? 0 k 2
## Levels:  ? 0 2 B k K m M

storm$CROPDMGEXP <- as.character(storm$CROPDMGEXP)
storm$CROPDMGEXP = gsub("\\-|\\+|\\?","0",storm$CROPDMGEXP)
storm$CROPDMGEXP = gsub("B|b", "9", storm$CROPDMGEXP)
storm$CROPDMGEXP = gsub("M|m", "6", storm$CROPDMGEXP)
storm$CROPDMGEXP = gsub("K|k", "3", storm$CROPDMGEXP)
storm$CROPDMGEXP = gsub("H|h", "2", storm$CROPDMGEXP)
storm$CROPDMGEXP <- as.numeric(storm$CROPDMGEXP)
storm$CROPDMGEXP[is.na(storm$CROPDMGEXP)] = 0
storm$ACTCROPDMG <- storm$CROPDMG * 10^storm$CROPDMGEXP

Top 10 severe events caused CROP DAMAGE

cropdamage <- FieldTops(storm$ACTCROPDMG,10,storm)
names(cropdamage) <- c("EVTYPE","CROPDAMAGE")
cropdamage

##                EVTYPE  CROPDAMAGE
## 95            DROUGHT 13972566000
## 170             FLOOD  5661968450
## 590       RIVER FLOOD  5029459000
## 427         ICE STORM  5022113500
## 244              HAIL  3025954473
## 402         HURRICANE  2741910000
## 411 HURRICANE/TYPHOON  2607872800
## 153       FLASH FLOOD  1421317100
## 140      EXTREME COLD  1292973000
## 212      FROST/FREEZE  1094086000

Results

The following pair of plots show the top ten total fatalities and total injuries affected by severe weather events in the United States during 1950-2011.

par(mfrow = c(1,2), mar = c(12, 4, 3, 2), cex = 0.9, font = 2, las = 3)
barplot(fatal$FATALITIES, names.arg = fatal$EVTYPE, 
        main = "Top 10 Fatalities \n caused by severe weather", 
        ylab = "NUMBER OF FATALITIES", col = "blue")
barplot(injury$INJURIES, names.arg = injury$EVTYPE, 
        main = "Top 10 Injuries \n caused by severe weather", 
        ylab = "NUMBER OF INJURIES", col = "blue")

It is evident that TORNADO causes most fatalities and injuries in the United States. TORNADO is the most harmful event with respect to population health.

Top ten property and crop damages in billions ($) are plotted below. Interms of economic consequences, the following plot shows that flood and hurricane/typhoon caused most property damage, whereas, drought and flood are the reasons for crop damage.

par(mfrow = c(1,2), mar = c(12, 4, 3, 2), cex = 0.9, font = 2, las = 3)
barplot(propertydamage$PROPERTYDAMAGE/(10^9), names.arg = propertydamage$EVTYPE, 
        main = "Top 10 Property Damages \n caused by severe weather", 
        ylab = "PROPERTY DAMAGES IN BILLIONS ($)", col = "green")
barplot(cropdamage$CROPDAMAGE/(10^9), names.arg = cropdamage$EVTYPE, 
        main = "Top 10 Crop Damages \n caused by severe weather", 
        ylab = "CROP DAMAGES IN BILLIONS ($)", col = "green")

Reproducible Research Peer Assessment 2 – 12/22/2015