Reproducible Research Course Project 2
Report of Health and Economic Impact by Severe Weather Events - Based on NOAA Storm Database
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.
This report contains the exploratory analysis results on the health and economic impact by the severe weather events based on the data from NOAA database.
Load needed packages and set defaults
Setting Global options default to echo code, set working directory and load needed packages
knitr::opts_chunk$set(echo=TRUE)
knitr::opts_knit$set(root.dir = "~/R_Programs")
library(dplyr)
library(ggplot2)Create Project’s folder in working directory
if(!file.exists("./RR_Course_Project_2")){
dir.create("./RR_Course_Project_2")
}Data Processing
1.Load the data
Download file if not exists and Load data
if(!file.exists("./RR_Course_Project_2/StormData.csv.bz2")){
fileUrl <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(fileUrl, destfile = "./RR_Course_Project_2/StormData.csv.bz2")
Stormdata<-read.csv("./RR_Course_Project_2/StormData.csv.bz2")
}else{
Stormdata<-read.csv("./RR_Course_Project_2/StormData.csv.bz2")
}
head(Stormdata)## 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 62.View NOAA’s complete Event records per Year
Create a barplot with complete event records per year and keep only years with sufficient data.We keep only Years with more than 20.000 records
Stormdata$BGN_DATE<-as.Date(Stormdata$BGN_DATE,format = "%m/%d/%Y %H:%M:%S")
Stormdata$Year<-as.integer(format(Stormdata$BGN_DATE, "%Y"))
datarecords<-Stormdata %>% group_by(Year)%>% summarize(Records=n())
p<-ggplot(data=datarecords, aes(x=Year, y=Records)) +
geom_bar(stat="identity", fill="steelblue")+
ggtitle("Complete Records per Year - (Figure. 1)") +
ylab("No.of records")+
geom_vline(xintercept = 1993.5)+
geom_text(aes(x=1994, label="Years with >20.000 records (1994-2011)"),y=0, colour="black", angle=90, vjust = -1,hjust=0)
p
Stormdata<-filter(Stormdata,Year>1993)3.Prepare Crop And Property damage data and summarize per Weather Event
Replacing CROPDMGEXP and PROPDMGEXP fields with numerics and summarize per weather event and per examined impact
explevels<-levels(Stormdata$CROPDMGEXP)
explevels=append(explevels,levels(Stormdata$PROPDMGEXP))
explevels<-as.factor(explevels)
explevels<-levels(explevels)
explevelsfixed<-vector()
explevelsfixed[grepl("[kK]",explevels)]<-1000
explevelsfixed[grepl("[Mm]",explevels)]<-1000000
explevelsfixed[grepl("[Hh]",explevels)]<-100
explevelsfixed[grepl("[bB]",explevels)]<-1000000000
explevelsfixed[grepl("[0-9]",explevels)]<-10^as.integer(explevels[grepl("[0-9]",explevels)])
explevelsfixed[is.na(explevelsfixed)]<-0
mapper<-data.frame(explevels,explevelsfixed)
colnames(mapper)<-c("CROPDMGEXP","CROPDMGEXPFIXED")
mapper2<-mapper
colnames(mapper2)<-c("PROPDMGEXP","PROPDMGEXPFIXED")
Stormdata<-select(Stormdata,"EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")
Stormdata<-merge(Stormdata,mapper,all.x = TRUE)
Stormdata<-merge(Stormdata,mapper2,all.x = TRUE)
Stormdata<-mutate(Stormdata,CROPDMGTOTALS=as.numeric(Stormdata$CROPDMGEXPFIXED)*as.numeric(Stormdata$CROPDMG)/1000000)
Stormdata<-mutate(Stormdata,PROPDMGTOTALS=as.numeric(Stormdata$PROPDMGEXPFIXED)*as.numeric(Stormdata$PROPDMG)/1000000)
Stormdatafinal<-as.data.frame(Stormdata %>% group_by(EVTYPE)%>% summarize(FATALITIES=sum(FATALITIES),INJURIES=sum(INJURIES),CROPDMG=sum(CROPDMGTOTALS),PROPDMG=sum(PROPDMGTOTALS)))
head(Stormdatafinal[order(-Stormdatafinal$FATALITIES), ][1:15, ])## EVTYPE FATALITIES INJURIES CROPDMG PROPDMG
## 122 EXCESSIVE HEAT 1903 6525 492.40200 7.7537
## 785 TORNADO 1593 22571 361.82447 25630.5883
## 145 FLASH FLOOD 951 1754 1402.66150 16398.2557
## 260 HEAT 930 2095 401.46150 1.7970
## 437 LIGHTNING 794 5116 11.98709 873.4378
## 160 FLOOD 450 6778 5506.94245 144179.6088Results
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
par(mfrow = c(1, 2), mar = c(10, 4, 4, 2), mgp = c(3, 1, 0), cex = 0.8,oma=c(0,0,2,0))
barplot(Stormdatafinal[order(-Stormdatafinal$FATALITIES), ][1:15, ]$FATALITIES, las = 3, names.arg = Stormdatafinal[order(-Stormdatafinal$FATALITIES), ][1:15, ]$EVTYPE, main = "Top 15 Weather Events \n with Highest Fatalities (1994-2011)",
ylab = "No. of fatalities", col = "red")
barplot(Stormdatafinal[order(-Stormdatafinal$INJURIES), ][1:15, ]$INJURIES, las = 3, names.arg = Stormdatafinal[order(-Stormdatafinal$INJURIES), ][1:15, ]$EVTYPE, main = "Top 15 Weather Events \n with Highest Injuries (1994-2011)",
ylab = "No. of Injuries", col = "purple")
title("Weather Events with Major Health Impact (1994-2011) - (Figure. 2)", outer=TRUE)
The most harmful events recorded from 1994 to 2011 across all US on population health, were both Excessive Heat and Tornadoes. Excessive Heat was the most fatal Weather Event followed by Torando, but Tornado was also the event that has caused the most Injuries.
Across the United States, which types of events have the greatest economic consequences?
par(mfrow = c(1, 2), mar = c(10, 4, 4, 2), mgp = c(3, 1, 0), cex = 0.8,oma=c(0,0,2,0))
barplot(Stormdatafinal[order(-Stormdatafinal$CROPDMG), ][1:15, ]$CROPDMG,las=3, names.arg = Stormdatafinal[order(-Stormdatafinal$CROPDMG), ][1:15, ]$EVTYPE, main = "Top 15 Weather Events \n with Highest Crop Damage (1994-2011)",
ylab = "Millions U.S. Dollars", col = "green")
barplot(Stormdatafinal[order(-Stormdatafinal$PROPDMG), ][1:15, ]$PROPDMG, las = 3, names.arg = Stormdatafinal[order(-Stormdatafinal$PROPDMG), ][1:15, ]$EVTYPE, main = "Top 15 Weather Events \n with Highest Property Damage (1994-2011)",
ylab = "Millions U.S. Dollars", col = "blue")
title("Weather Events with Major Economic Impact (1994-2011) - (Figure. 3)", outer=TRUE)