Adverse events for population health and its economic consequences
Rafael Resendiz Ramirez
September 20, 2014
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Introduction
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
The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. You can download the file from the course web site:
Storm Data [47Mb] There is also some documentation of the database available. Here you will find how some of the variables are constructed/defined.
National Weather Service Storm Data Documentation
National Climatic Data Center Storm Events FAQ
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.
Assignment
The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. You must use the database to answer the questions below and show the code for your entire analysis. Your analysis can consist of tables, figures, or other summaries. You may use any R package you want to support your analysis.
Questions
Your data analysis must 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?
Consider writing your report as if it were to be read by a government or municipal manager who might be responsible for preparing for severe weather events and will need to prioritize resources for different types of events. However, there is no need to make any specific recommendations in your report.
Sinopsis
This report is about of the damage caused by weather events in the U.S. The fundations are based on the data provided by U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. The NOAA database tracks characteristics of major storms and weather events in the United States, including date, time and place where they occur, that allow estimates fatalities, injuries, and property damage.
This writing analyses what events caused the fatals damage about popuulation health and its economic consequences. The damages to publics health and economic consequences caused by this events are exponentially distributed. The most damages in injuries and fatalities were caused by tornados, the principal property damages were caused by floods, and most crop damages were caused by droughts and floods.
Data Processing
Change o set the working directory
setwd("/Users/administrador/Specialization/Reproducible Research") The data are download, unziped and placed in the working directory “Reproducible Research” Download file
fileUrl <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2?accessType=DOWNLOAD"
download.file(fileUrl, destfile = "/Users/administrador/Specialization/Reproducible Research/stormData.csv.bz2",
method ="curl") Unzip the file
library(R.utils)## Loading required package: R.oo
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## cat, commandArgs, getOption, inherits, isOpen, parse, warnings bunzip2("/Users/administrador/Specialization/Reproducible Research/stormData.csv.bz2")The data are readed using read.csv()
data <- read.csv("/Users/administrador/Specialization/Reproducible Research/stormData.csv", header = T) Overview of the data file
head(data) ## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## 3 TORNADO 0 0
## 4 TORNADO 0 0
## 5 TORNADO 0 0
## 6 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14.0 100 3 0 0
## 2 NA 0 2.0 150 2 0 0
## 3 NA 0 0.1 123 2 0 0
## 4 NA 0 0.0 100 2 0 0
## 5 NA 0 0.0 150 2 0 0
## 6 NA 0 1.5 177 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## 3 2 25.0 K 0
## 4 2 2.5 K 0
## 5 2 2.5 K 0
## 6 6 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3
## 4 3458 8626 0 0 4
## 5 3412 8642 0 0 5
## 6 3450 8748 0 0 6 str(data) ## '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 ""," Christiansburg",..: 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 ""," CANTON"," TULIA",..: 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","%SD",..: 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 "","\t","\t\t",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...The values for event type variable “evtype” are cleaned by: * Remove irregular symbols, values shorter and values started with “summary” * Consolidate values for the same weather event. * Label moinor weather events as “other” * The values for damages are calculated and stored as new variable “propertyDamage” and “cropDamage”
Simplify the variable name
names(data) <- tolower(names(data))
names(data) <- gsub("_","",names(data)) Simplify the values
data$evtype <- tolower(as.character(data$evtype))
data$evtype <- gsub("^(([^:]+)://)?([^:/]+)(:([0-9]+))?(/.*)","",data$evtype) Removed to be valid values are very short
data <- subset(data,nchar(data$evtype)>=2) Remove values starts with “summary”
data$evtype[grep("summary", data$evtype)] <- "tbm"
data <- subset(data,data$evtype != "tbm") consolidate the events weather values
data$evtype[grep("hail", data$evtype)] <- "hail"
data$evtype[grep("wind", data$evtype)] <- "wind"
data$evtype[grep("tornado", data$evtype)] <- "tornado"
data$evtype[grep("flood", data$evtype)] <- "flood"
data$evtype[grep("lightning", data$evtype)] <- "lightning"
data$evtype[grep("snow", data$evtype)] <- "snow"
data$evtype[grep("rain", data$evtype)] <- "rain"
data$evtype[grep("winter", data$evtype)] <- "winter"
data$evtype[grep("heat", data$evtype)] <- "heat"
data$evtype[grep("fog", data$evtype)] <- "fog"
data$evtype[grep("surf", data$evtype)] <- "surf"
data$evtype[grep("ice storm", data$evtype)] <- "ice storm"
data$evtype[grep("fire", data$evtype)] <- "wild fire"
data$evtype[grep("storm surge", data$evtype)] <- "storm surge"
data$evtype[grep("hurricane", data$evtype)] <- "hurricane"
data$evtype[grep("drought", data$evtype)] <- "drought"
data$evtype[grep("thunderstorm", data$evtype)] <- "thunderstorm"Review climatic events that concentrate most of the observations
sum(data$evtype %in% c("flood","wind","snow","tornado","hail","rain","lightning","winter","fog","heat","surf","ice storm","wild fire","storm surge","hurricane","drought","thunderstorm"))/nrow(data)## [1] 0.9781Label those weather events that concentrate the minority of observations in a category of weather events called “other”
tbc <- data$evtype %in% c("flood","wind","snow","tornado","hail","rain","lightning","winter","heat","surf","fog","ice storm","wild fire","storm surge","hurricane","drought","thunderstorm") == F
data$evtype[tbc == T] <- "other"Summary of the number of observations with the values by type of event
sort(table(data$evtype))##
## thunderstorm hurricane storm surge surf fog
## 92 199 261 833 1883
## ice storm drought heat wild fire rain
## 2006 2488 2630 2781 12136
## lightning snow winter other tornado
## 15762 17569 18492 19552 60688
## flood hail wind
## 81967 289338 362164Calculating Damages Simplify the units of the values of damage
data$propdmgexp <- as.character(data$propdmgexp)
data$propdmgexp[grep("K", data$propdmgexp)] <- "1000"
data$propdmgexp[grep("M", data$propdmgexp)] <- "1000000"
data$propdmgexp[grep("m", data$propdmgexp)] <- "1000000"
data$propdmgexp[grep("B", data$propdmgexp)] <- "1000000000"
tbc <- data$propdmgexp %in% c("1000","1000000","1000000000") == F
data$propdmgexp[tbc == T] <- "1"
data$propdmgexp <- as.numeric(data$propdmgexp)
# do the same thing to cropDamage
data$cropdmgexp <- as.character(data$cropdmgexp)
data$cropdmgexp[grep("K", data$cropdmgexp)] <- "1000"
data$cropdmgexp[grep("M", data$cropdmgexp)] <- "1000000"
data$cropdmgexp[grep("m", data$cropdmgexp)] <- "1000000"
data$cropdmgexp[grep("B", data$cropdmgexp)] <- "1000000000"
tbc <- data$cropdmgexp %in% c("1000","1000000","1000000000") == F
data$cropdmgexp[tbc == T] <- "1"
data$cropdmgexp <- as.numeric(data$cropdmgexp)Calculating and stored damages as new variable
data$newPropdamage <- data$propdmg * data$propdmgexp
data$newCropdamage <- data$cropdmg * data$cropdmgexpResults
Damages to Population Health
statistics about climatic events that cause most damages
# Event that caused most injuries
totalInjuries <- tapply(data$injuries, data$evtype, sum)
sort(totalInjuries, decreasing = T)[1]## tornado
## 91365# Percentage of injuries caused by tornado
sum(sort(totalInjuries, decreasing = T)[1])/sum(totalInjuries)## [1] 0.661# The top 4 events causing most injuries
sort(totalInjuries, decreasing = T)[1:4]## tornado wind heat flood
## 91365 11319 9224 8582# Percentage of injuries caused by the top 4 weather events
sum(sort(totalInjuries, decreasing = T)[1:4])/sum(totalInjuries)## [1] 0.8717# Event that caused most fatalities
totalFatal <- tapply(data$fatalities, data$evtype, sum)
sort(totalFatal, decreasing = T)[1]## tornado
## 5633# Percentage of fatalities caused by the tornado
sum(sort(totalFatal, decreasing = T)[1])/sum(totalFatal)## [1] 0.3846# The top 4 events causing most fatalities
sort(totalFatal, decreasing = T)[1:4]## tornado heat flood other
## 5633 3119 1488 1398# Percentage of death caused by the top 4 weather events
sum(sort(totalFatal, decreasing = T)[1:4])/sum(totalFatal) ## [1] 0.7947Economic Consequences
statistics about climatic events that cause most property and crop damages
# The event that caused most property damages and its amounts
totalPropDamage <- tapply(data$newPropdamage, data$evtype, sum)
sort(totalPropDamage, decreasing = T)[1]## flood
## 1.67e+11# Percentage of property damages caused by flood
sort(totalPropDamage, decreasing = T)[3]/sum(totalPropDamage)## storm surge
## 0.1249# top 3 weather events causing the most property damages
sort(totalPropDamage, decreasing = T)[1:3]## flood tornado storm surge
## 1.670e+11 5.694e+10 4.332e+10# percentage of total property damages caused by thetop 3 weather events
sum(sort(totalPropDamage, decreasing = T)[1:3])/sum(totalPropDamage)## [1] 0.7705# The event that caused most crop damages
totalCropDamage <- tapply(data$newCropdamage, data$evtype, sum)
sort(totalCropDamage, decreasing = T)[1]## drought
## 1.397e+10# the percentage of crop damages caused by drought
sort(totalCropDamage, decreasing = T)[1]/sum(totalCropDamage)## drought
## 0.3106# Top 4 weather events causing the most crop damages
sort(totalCropDamage, decreasing = T)[1:3]## drought flood ice storm
## 1.397e+10 1.217e+10 5.022e+09# Percentage of total porperty damages caused by the top 4 weather events
sum(sort(totalCropDamage, decreasing = T)[1:3])/sum(totalCropDamage)## [1] 0.6927Figures
First Plot 
Second Plot
Disregard this last image for evaluation. 