Reproducible Research-Project 2
Bikram Bhusal
12 August, 2019
Synopsis
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.
In this project, we explore the NOAA Storm Database and answer some basic questions about severe weather events.First, tornados are the most harmful with respect to population health. Second, excessive heat and flood have the greatest economic consequences. The largest damage to crops were caused by droughts,followed by floods and hailing.
Data Processing
Loading the data from the working directory(data is downloaded and saved in working directory).
Data<-read.csv("StormData.csv",header=TRUE)
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 "","- 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/ 436774 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 ...Extracting the requied data(data for health and economic impact analysis against weather):
Event<-c("EVTYPE","FATALITIES","EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP","CROPDMG", "CROPDMGEXP")
data <- Data[Event]
head(data)## EVTYPE FATALITIES EVTYPE.1 FATALITIES.1 INJURIES PROPDMG PROPDMGEXP
## 1 TORNADO 0 TORNADO 0 15 25.0 K
## 2 TORNADO 0 TORNADO 0 0 2.5 K
## 3 TORNADO 0 TORNADO 0 2 25.0 K
## 4 TORNADO 0 TORNADO 0 2 2.5 K
## 5 TORNADO 0 TORNADO 0 2 2.5 K
## 6 TORNADO 0 TORNADO 0 6 2.5 K
## CROPDMG CROPDMGEXP
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0attach(data)Assessing the Economical Damages
The events having the greatest property damages(in billion dollars) are:
# Assigning values for the Prop exponent data
Symbol <- sort(unique(as.character(PROPDMGEXP)))
Symbol## [1] "" "-" "?" "+" "0" "1" "2" "3" "4" "5" "6" "7" "8" "B" "h" "H" "K"
## [18] "m" "M"Multiplier <- c(0,0,0,1,10,10,10,10,10,10,10,10,10,10^9,10^2,10^2,10^3,10^6,10^6)
#assigning multipliar
m <- data.frame(Symbol, Multiplier)
Mult4_sym<- m$Multiplier[match(data$PROPDMGEXP, m$Symbol)]
PROPDMGVAL <- PROPDMG * Mult4_sym
propdmg <- aggregate(PROPDMGVAL ~ EVTYPE, data, FUN = sum)And,the events haing the greatest Crop damage data
# Assigning values for the Crop exponent data
Symbol1 <- sort(unique(as.character(CROPDMGEXP)))
Symbol1## [1] "" "?" "0" "2" "B" "k" "K" "m" "M"Multiplier1 <- c(0,0,0,10^2,10^9,10^3,10^3,10^6,10^6)
#assigning multipliar
m1 <- data.frame(Symbol1, Multiplier1)
Mult4_sym1<- m1$Multiplier1[match(data$CROPDMGEXP, m1$Symbol1)]
CROPDMGVAL <- CROPDMG * Mult4_sym1
cropdmg <- aggregate(CROPDMGVAL ~ EVTYPE, data, FUN = sum)Totalling the data by event
propdmg <- aggregate(PROPDMGVAL ~ EVTYPE, data, FUN = sum)
cropdmg <- aggregate(CROPDMGVAL ~ EVTYPE, data, FUN = sum)Also,
# Ten events with highest property damage
propdmg10 <- propdmg[order(-propdmg$PROPDMGVAL), ][1:10, ]
# Ten events with highest crop damage
cropdmg10 <- cropdmg[order(-cropdmg$CROPDMGVAL), ][1:10, ]Now plotting these damages in bar plota:
par(mfrow = c(1, 2), mar = c(12, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(propdmg10$PROPDMGVAL/(10^9), las = 2, names.arg = propdmg10$EVTYPE,
main = "Events with Greatest Property Damages", ylab = "Damage($ billions)",
col = "red")
barplot(cropdmg10$CROPDMGVAL/(10^9), las = 2, names.arg = cropdmg10$EVTYPE,
main = "Events With Greatest Crop Damages", ylab = "Damage($ billions)",
col = "yellow")
Assessing the Health Damages
The events having the highet fatalitis and injuries are:
# Totalling the data by event
fatal <- aggregate(FATALITIES ~ EVTYPE, data, FUN = sum)
injury <- aggregate(INJURIES ~ EVTYPE, data, FUN = sum)Here,
# Ten events with highest fatalities
fatal10 <- fatal[order(-fatal$FATALITIES), ][1:10, ]
# Ten events with highest crop damage
injury10 <- injury[order(-injury$INJURIES), ][1:10, ]Plotting the bar plots of events with highest fatalities and highest injuries.
par(mfrow = c(1, 2), mar = c(12, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(fatal10$FATALITIES, las = 2, names.arg = fatal10$EVTYPE, main = "Events with Highest Fatalities",
ylab = "Number of fatalities", col = "red")
barplot(injury10$INJURIES, las = 2, names.arg = injury10$EVTYPE, main = "Events with Highest Injuries",
ylab = "Number of injuries", col = "yellow")
Conclusion
From the data analysis and the bar plots we conclude that the flood has the greatest economic impact(or damages).One the other hand,Tornados caused the maximum number of fatalities and injuries.Other major events impacting economic and health damages are pretty clear form our data analysis.