Reproducible data on weather events in the United States
ETSulato
29/09/2020
Synopsis
The damage from severe weather events was based on the analysis of data available in the United States Oceanic and Atmospheric Administration (NOAA) storm database. For that, fatality, personal injury, property damage and damage to crops resulting from the events were considered as variables. From the interpretation of the graphs, tornado and heat showed the greatest health damage (fatalities and injury variables), being the most impactful for the population. In turn, thunderstorms, rains and storms presented the greatest damage to properties and crops, having the greatest economic consequences.
Data Processing
Setting the directory and data loading
setwd("~/Coursera_directory/RepData_project2")
if (!file.exists("repdata-data-StormData.csv.bz2")){
URL<- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url=URL, destfile = "repdata-data-StormData.csv.bz2")
}
data<- read.csv("repdata-data-StormData.csv.bz2", header = T)
data[1:5,]## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE EVTYPE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL TORNADO
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL TORNADO
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL TORNADO
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL TORNADO
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL TORNADO
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1 0 0 NA
## 2 0 0 NA
## 3 0 0 NA
## 4 0 0 NA
## 5 0 0 NA
## END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES INJURIES PROPDMG
## 1 0 14.0 100 3 0 0 15 25.0
## 2 0 2.0 150 2 0 0 0 2.5
## 3 0 0.1 123 2 0 0 2 25.0
## 4 0 0.0 100 2 0 0 2 2.5
## 5 0 0.0 150 2 0 0 2 2.5
## PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES LATITUDE LONGITUDE
## 1 K 0 3040 8812
## 2 K 0 3042 8755
## 3 K 0 3340 8742
## 4 K 0 3458 8626
## 5 K 0 3412 8642
## LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3051 8806 1
## 2 0 0 2
## 3 0 0 3
## 4 0 0 4
## 5 0 0 5Packages
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(reshape2)
library(ggplot2)
library(ggthemes)Target variables
data<- mutate(data, year=year(mdy_hms(data$BGN_DATE)))
health_total <- aggregate(cbind(FATALITIES,INJURIES) ~ EVTYPE + year, data, sum)
economic_total <- aggregate(cbind(PROPDMG,CROPDMG) ~ EVTYPE + year, data, sum)Variables of fatality and injury
health_total_arranged<- arrange(health_total, desc(FATALITIES), desc(INJURIES))
TopRank<-health_total_arranged[1:5,]
TopRank## EVTYPE year FATALITIES INJURIES
## 1 HEAT 1995 687 808
## 2 TORNADO 2011 587 6163
## 3 TORNADO 1953 519 5131
## 4 EXCESSIVE HEAT 1999 500 1461
## 5 TORNADO 1974 366 6824health_total_arranged$EVTYPE<-gsub("EXCESSIVE HEAT", "HEAT", health_total_arranged$EVTYPE)Unification of property and crop variables in a single column
economic_total_long <- melt(economic_total, id=c("year", "EVTYPE"))
economic_total_long[1:5,]## year EVTYPE variable value
## 1 1950 TORNADO PROPDMG 16999.15
## 2 1951 TORNADO PROPDMG 10560.99
## 3 1952 TORNADO PROPDMG 16679.74
## 4 1953 TORNADO PROPDMG 19182.20
## 5 1954 TORNADO PROPDMG 23367.82Reassigning the event names to enable the grouping of the events (EVTYPE) for comparison
Grouping each event by total sum
economic_total_plot <- aggregate(value~year+EVTYPE+variable, economic_total_long, sum)
economic_total_plot[1:5,]## year EVTYPE variable value
## 1 1950 COASTAL ISSUES PROPDMG 16999.15
## 2 1951 COASTAL ISSUES PROPDMG 10560.99
## 3 1952 COASTAL ISSUES PROPDMG 16679.74
## 4 1953 COASTAL ISSUES PROPDMG 19182.20
## 5 1954 COASTAL ISSUES PROPDMG 23367.82The economic damage variables were renamed
economic_total_plot$variable<-as.character(economic_total_plot$variable)
economic_total_plot$variable[grepl("PROPDMG", economic_total_plot$variable, ignore.case=T)]<-"PROPERTY DAMAGE"
economic_total_plot$variable[grepl("CROPDMG", economic_total_plot$variable, ignore.case=T)]<-"CROP DAMAGE"
names(economic_total_plot)[2]="Type of event"Results
Types of harmful events to the population
g1<- ggplot(aes(x = year, y= FATALITIES), data=health_total_arranged[1:10,])
g1+geom_col(color = "black",size = 1)+
facet_grid(.~EVTYPE)+
labs(y="Occurrences", x="Year", title= "Fatalities per event")+
theme_igray()
g2<- ggplot(aes(x = year, y= INJURIES), data=health_total_arranged[1:10,])
g2+geom_col(color = "black",size = 1)+
facet_grid(.~EVTYPE)+
labs(y="Occurrences", x="Year", title= "Injuries per event")+
theme_igray()
Considering the variables fatalities and injuries, tornado and heat have shown the greatest occurrences. Thus, it can be concluded that tornado and heat presented the greatest damage to population among the types of events.
Types of events with greatest economic consequences
g3<- ggplot(data=economic_total_plot, aes(x=year, y=value, color=`Type of event`))+
geom_line(lwd=1)+
facet_grid(.~variable)+
labs(y="Occurrences", x="Year", title= "Economical damage per event")+
theme_igray()
g3
As noted, thunderstorms and rain and storms were the main causes of damage to property and crops. Thus, it can be concluded that storms have greater economic consequences.