Title: “Peer Grade Assignment: Course Project 2 Impact of Natural Disasters on Population Health and Economy”
Synopsis
This study analyzes the NOAA storm database (between January 1950 and March 2016) comprises severe weather events on damage to property and lives. The purpose of this study to findout total fatalities, injuries, damages by event type and impact of events on people’s lives and economy.
Data Processing
1.Downloading the datafile
setwd("/Users/innugantii/Desktop/Indu/Coursera/Course 5")
if (!"StormData.csv.bz2" %in% dir("./")) {
print("Downloading File.....")
download.file("http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", destfile = "StormData.csv.bz2")
}Reading CSV file
if (!"storm" %in% ls()) {
storm <- read.csv(bzfile("StormData.csv.bz2"), sep = ",", header = TRUE, stringsAsFactors = FALSE)
}
dim(storm)## [1] 902297 37head(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 6- Across the United States, which types of events are most harmful with respect to population health Compute combined economic damage (property damage + crops damage)
# Changing invalid Property Damage EXP and CROP Damage EXP to 0
storm$PROPDMGEXP <- ifelse(storm$PROPDMGEXP %in% c("B", "h", "H", "K", "m", "M"), as.character(storm$PROPDMGEXP),"NONE")
storm$CROPDMGEXP <- ifelse(storm$CROPDMGEXP %in% c("B", "k", "K", "m", "M"), as.character(storm$CROPDMGEXP), "NONE")
# Multiplying property and crops damage by 10 raised to the power of the exponent
storm$PROPDMG <- storm$PROPDMG * (10^9 * (storm$PROPDMGEXP == "B") + 10^6 *(storm$PROPDMGEXP %in% c("m", "M")) + 10^3 * (storm$PROPDMGEXP %in% c("k", "K")) + 100 * (storm$PROPDMGEXP %in% c("h", "H")))
# Compute combined economic damage (property damage + crops damage)
storm$CROPDMG <- storm$CROPDMG * (10^9 * (storm$CROPDMGEXP == "B") + 10^6 *(storm$CROPDMGEXP %in% c("m", "M")) + 10^3 * (storm$CROPDMGEXP %in% c("k", "K")) + 100 * (storm$CROPDMGEXP %in% c("h", "H")))
## Combination of PROP and CROP damage
storm$PROPandCROP <- storm$PROPDMG + storm$CROPDMGTotal Number of Fatalities (by Event Type)
# Total number of fatalites by event type
total_fatalities <- by(storm$FATALITIES, storm$EVTYPE, sum)
# Plots
layout(matrix(c(1,2,1,2), 2, 2, byrow=T))
# Top 10 Total fatalities by event type
par(mar=c(5, 12, 4, 2))
par(bg = 'lemonchiffon')
barplot(sort(by(storm$FATALITIES, storm$EVTYPE, sum), decreasing=T)[10:1], horiz=T, las=1,cex.names=0.7,xlab="Fatalities Per Event Type")
mtext("Top 10 Fatalities\n by Event Type", side=3, line=1, cex=1, at=1500, font=2)
# Top 10 Average number of fatalities by event type
mean_fatalities <- by(storm$FATALITIES, storm$EVTYPE, mean)
# Top 10 Injuries by event
par(mar=c(5, 12, 4, 2))
par(bg = 'lemonchiffon')
barplot(sort(by(storm$FATALITIES, storm$EVTYPE, mean), decreasing=T)[10:1], horiz=T,
las=1, cex.names=0.7, xlab="Ave Number of Injuries\n by Event")
mtext("Top 10 Injuries\n by Event Type ", side=3, line=1, cex=1, at=8, font=2)
Total Property Damage
layout(matrix(c(1,2,1,2), 2, 2, byrow=T))
# Plotting Total damage
total_damage <- by(storm$PROPandCROP/10e9, storm$EVTYPE, sum)
par(mar=c(5, 12, 4, 2))
par(bg = 'aliceblue')
barplot(sort(by(storm$PROPandCROP/10e9, storm$EVTYPE, sum), decreasing=T)[10:1], horiz=T, las=1, cex.names=0.7, xlab="Total Cost of Damage(Billions)\n By Event Type")
mtext("Top 10 Damages\n By Event Type", side=3, line=1, cex=1, at=4, font=2)
# Plotting Damage by Event
mean_damage <- by(storm$PROPandCROP/10e6, storm$EVTYPE, mean)
par(mar=c(5, 12, 4, 2))
par(bg = 'aliceblue')
barplot(sort(by(storm$PROPandCROP/10e6, storm$EVTYPE, mean), decreasing=T)[10:1], horiz=T, las=1, cex.names=0.7, xlab="Cost of Damage(Millions)\n by Event")
mtext("Top 10 Damages\n by Event", side=3, line=1, cex=1, at=50, font=2)
Conclusion
From this analysis, we can infer that Excessive Heat and Tornadoes are most harmful to people lives and Flood, Drought and Hurricane/Typhoon caused the damage to properties.