Impact of Storms and Severe Weather Events to Human Society
Reproducible Research - Project Asssignment 2"
July 13, 2016
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."
*As explained on the assignment
Sinopsys
The purpose of this project is to provide answers to the following questions:
1.Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
2.Across the United States, which types of events have the greatest economic consequences?
The dataset was downloaded from NOAA site and it has been segregated on a new data set with variables required to answer these questions. From this dataset the variables related to population health( injuries and fatalities) have been tallied up to produce a summary by event type. Consequently, the casualties (injuries+fatalities) were sorted in descending order to find out the top offending events.
A similar approach was applied to the events that have the greatest economic consequences (property damage and crop damage). These variables were expanded to derive the total damage cost per event type. The property and crop damage cost were added up to produce the total damage cost and aggregated per event type. The total cost was sorted in descending order to determine the top events with the greatest economic impact.
Data Processing
Load Libraries
# Load libraries
library(lattice)
library(ggplot2)
library(car)## Warning: package 'car' was built under R version 3.2.5library(gridExtra)## Warning: package 'gridExtra' was built under R version 3.2.5Download and open the compressed file from NOAA site
setwd("~/Coursera/DataScience/Reproducible Research/Week 4 Assignment")
zipFile <- "./repdata%2Fdata%2FStormData.csv.bz2"
if (!file.exists(zipFile)){
fileURL <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(fileURL, zipFile, mode="wb")
}
if (file.exists(zipFile)) {
storm <- read.csv(zipFile)
}
# Verify data was loaded properly
head(storm)## 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 6dim(storm)## [1] 902297 37str(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 ...Analysis
1.Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
#Create a dataset with the relevant variables to answers the questions
stormData <- subset(storm,select=c(EVTYPE,FATALITIES,INJURIES, PROPDMG, CROPDMG, PROPDMGEXP, CROPDMGEXP))
# Add variable for population health issues and include as new variable in the data set.
stormData$HUMCAS <- stormData$INJURIES + stormData$FATALITIES
# Aggregate injuries, fatalities and casualties by event type
sumCasualties <- aggregate(x = list(injuries = stormData$INJURIES,
fatalities=stormData$FATALITIES,
casualties=stormData$HUMCAS),
by = list(eventType = stormData$EVTYPE),
FUN = sum, na.rm = TRUE)
# Provide summary per variable to have an idea of the data distribution
summary(sumCasualties)## eventType injuries fatalities
## HIGH SURF ADVISORY: 1 Min. : 0.0 Min. : 0.00
## COASTAL FLOOD : 1 1st Qu.: 0.0 1st Qu.: 0.00
## FLASH FLOOD : 1 Median : 0.0 Median : 0.00
## LIGHTNING : 1 Mean : 142.7 Mean : 15.38
## TSTM WIND : 1 3rd Qu.: 0.0 3rd Qu.: 0.00
## TSTM WIND (G45) : 1 Max. :91346.0 Max. :5633.00
## (Other) :979
## casualties
## Min. : 0
## 1st Qu.: 0
## Median : 0
## Mean : 158
## 3rd Qu.: 0
## Max. :96979
## #calculate top events based on injuries
top10Inj <- head(sumCasualties[order(sumCasualties[2],decreasing="TRUE"),],10)
#calculate top events based on fatalities
top10Fatal <- head(sumCasualties[order(sumCasualties[3],decreasing="TRUE"),],10)
# Plot with events based on injuries and fatalities
injPlot <- xyplot(injuries~eventType,data=top10Inj,type="h",lw=5,scales=list(x=list(rot=60)),
main ="Injuries per Event Type")
fatalPlot <- xyplot(fatalities~eventType,data=top10Fatal,type="h",lw=5,scales=list(x=list(rot=60)),main ="Fatalities per Event Type")
grid.arrange(injPlot,fatalPlot,ncol=2)
#calculate and plot top events based on casualties =injuries + fatalities
top10Casualties <- head(sumCasualties[order(sumCasualties[4],decreasing="TRUE"),],10)
casualtyChart <-xyplot(casualties~eventType,data=top10Casualties,type="h",lw=5,scales=list(x=list(rot=60)),main ="Total Casualties caused by Storm and Weather Events")
rownames(top10Casualties)<-NULL
top10Casualties[c(1,4)]## eventType casualties
## 1 TORNADO 96979
## 2 EXCESSIVE HEAT 8428
## 3 TSTM WIND 7461
## 4 FLOOD 7259
## 5 LIGHTNING 6046
## 6 HEAT 3037
## 7 FLASH FLOOD 2755
## 8 ICE STORM 2064
## 9 THUNDERSTORM WIND 1621
## 10 WINTER STORM 15272.Across the United States, which types of events have the greatest economic consequences?
# Find out possible values for property/crop damage exp variables
unique(tolower(stormData$CROPDMGEXP))## [1] "" "m" "k" "b" "?" "0" "2"#[1] "" "m" "k" "b" "?" "0" "2"
unique(tolower(stormData$PROPDMGEXP))## [1] "k" "m" "" "b" "+" "0" "5" "6" "?" "4" "2" "3" "h" "7" "-" "1" "8"# [1] "k" "m" "" "b" "+" "0" "5" "6" "?" "4" "2" "3" "h" "7" "-" "1"
#[17] "8"
#replace prop/crop damage exp values with numbers
stormData$PROPEXP2 <- as.numeric(recode(tolower(stormData$PROPDMGEXP),
"'h'=10^2; 'k'=10^3; 'm'=10^6; 'b'=10^9;'+'=1;'-'=0;'?'=0;''=0; else=10"))
stormData$CROPEXP2 <- as.numeric(recode(tolower(stormData$CROPDMGEXP),
"'h'=10^2; 'k'=10^3; 'm'=10^6; 'b'=10^9;'+'=1;'-'=0;'?'=0;''=0; else=10"))
sumEconDmg <- aggregate(x =
list(propertyDamage = stormData$PROPDMG*stormData$PROPEXP2,
cropDamage =stormData$CROPDMG*stormData$CROPEXP2,
sumDamage =(stormData$PROPDMG*stormData$PROPEXP2)+ (stormData$CROPDMG*stormData$CROPEXP2)), by = list(eventType = stormData$EVTYPE), FUN = sum, na.rm = T)
summary(sumEconDmg) ## eventType propertyDamage cropDamage
## HIGH SURF ADVISORY: 1 Min. :0.000e+00 Min. :0.000e+00
## COASTAL FLOOD : 1 1st Qu.:0.000e+00 1st Qu.:0.000e+00
## FLASH FLOOD : 1 Median :0.000e+00 Median :0.000e+00
## LIGHTNING : 1 Mean :4.338e+08 Mean :4.985e+07
## TSTM WIND : 1 3rd Qu.:5.105e+04 3rd Qu.:0.000e+00
## TSTM WIND (G45) : 1 Max. :1.447e+11 Max. :1.397e+10
## (Other) :979
## sumDamage
## Min. :0.000e+00
## 1st Qu.:0.000e+00
## Median :0.000e+00
## Mean :4.837e+08
## 3rd Qu.:8.500e+04
## Max. :1.503e+11
## top10EconDamage <- head(sumEconDmg[order(sumEconDmg[,4],decreasing="TRUE"),],10)
econDamageChart <- xyplot(sumDamage/1000000~eventType,data=top10EconDamage,type="h",lw="7",
main ="Cost of Damage caused by Storm and Weather Events",
xlab="Event Type", bw=10,
ylab="Total Damage Cost (in Millions $)",
scales=list(x=list(rot=60)))
rownames(top10EconDamage)<-NULL
top10EconDamage[c(1,4)]## eventType sumDamage
## 1 FLOOD 150319678250
## 2 HURRICANE/TYPHOON 71913712800
## 3 TORNADO 57352117607
## 4 STORM SURGE 43323541000
## 5 HAIL 18758224527
## 6 FLASH FLOOD 17562132111
## 7 DROUGHT 15018672000
## 8 HURRICANE 14610229010
## 9 RIVER FLOOD 10148404500
## 10 ICE STORM 8967041810Results
- Based on the data analysis the top Storm and Severe Weather Events that caused must of the casualties on the human health are: TORNADOS, EXCESIVE HEAT and TUNDERSTORM WIND.
- Based on the data analysis the top Storm and Severe Weather Events that caused must of the damages to properties and crops are: FLOOD, HURRICANE/TYPHOON and TORNADOS.
