Reproducible Research Course Project 2
KC Toh
12th August 2017
Title
Tornadoes inflicted the most human fatalities while Floods caused the most financial damage.
Synopsis
From the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database, it can be concluded that Tornadoes caused the most human fatalies. This includes 5633 fatalities and 91346 injuries. Floods have the greatest economic impact, costing $150 Billion.The Top 10 events with high human harm and economic impact have been included in this report.
Data Processing
The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. Data can be downloaded from https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2
Library load
library(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(ggplot2)Steps
Data is downloaded to local working directory Read the csv file
stormdatabz <- bzfile("repdata_data_StormData.csv.bz2", open = "r")
stormdata<-read.table(stormdatabz, sep = ",", header = TRUE)
close(stormdatabz)Check and prepare data
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 6str(stormdata)## '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 ...stormdata$EVTYPE<-toupper(stormdata$EVTYPE)Calculate harmful impact
harmful<-summarize(group_by(stormdata,EVTYPE), Deaths=sum(FATALITIES,na.rm=TRUE),Injuries=sum(INJURIES,na.rm=TRUE))
harmful## # A tibble: 898 x 3
## EVTYPE Deaths Injuries
## <chr> <dbl> <dbl>
## 1 HIGH SURF ADVISORY 0 0
## 2 COASTAL FLOOD 0 0
## 3 FLASH FLOOD 0 0
## 4 LIGHTNING 0 0
## 5 TSTM WIND 0 0
## 6 TSTM WIND (G45) 0 0
## 7 WATERSPOUT 0 0
## 8 WIND 0 0
## 9 ? 0 0
## 10 ABNORMAL WARMTH 0 0
## # ... with 888 more rowsRemove events with no data
harmful<-harmful[harmful$Deaths>0|harmful$Injuries>0,]Save and sort data for deaths
death<-harmful[order(-harmful$Deaths),]
death$EVTYPE<-factor(death$EVTYPE, levels=death$EVTYPE[order(-death$Deaths)])Save and sort data for injuries
injury<-harmful[order(-harmful$Injuries),]
injury$EVTYPE<-factor(injury$EVTYPE,levels=injury$EVTYPE[order(-injury$Injuries)])Convert cost data into dollars
conversion <- function(x) { if (toupper(x) == "H") {return(100);}
else if (toupper(x) == "K") { return(1000)}
else if (toupper(x) == "M") { return(1000000)}
else if (toupper(x) == "B") {return(1000000000)}
else return(1)
}
property<-sapply(stormdata$PROPDMGEXP, function(x) conversion(x))
crop<-sapply(stormdata$CROPDMGEXP, function(x) conversion(x))
stormdata$TOTPROPDMG <- stormdata$PROPDMG*property
stormdata$TOTCROPDMG <- stormdata$CROPDMG*cropCalculate total cost
stormdata$TOTCOST<-stormdata$TOTPROPDMG+stormdata$TOTCROPDMG
costly<-summarize(group_by(stormdata, EVTYPE), Cost=sum(TOTCOST/1000000,na.rm=TRUE))Remove events with no cost data, sort and round off cost data
costly<-costly[costly$Cost>0,]
costly<-costly[order(-costly$Cost),]
costly$EVTYPE<-factor(costly$EVTYPE, levels=costly$EVTYPE[order(-costly$Cost)])
costly$Cost<-round(costly$Cost, digits=2)Results
Tornadoes caused the most deaths with 5633 recorded. The other events in the Top 10 are Excessive Heat, Flash Flood, Heat, Lightning, TSTM Wind, Flood, Rip Current, High Wind and Avalanche.
df<-as.data.frame(death)
plot1<-ggplot(df[1:10,],aes(x=df[1:10,"EVTYPE"],y=df[1:10,"Deaths"]))
plot1<-plot1 + ggtitle("Top 10 Event Type with Deaths")
plot1<-plot1 + labs(x="Event", y="Deaths")
plot1<-plot1 + geom_bar(stat="identity", fill="red")
plot1<-plot1 + geom_text(aes(label=df[1:10,"Deaths"]), size=2, vjust = -0.5)
plot1<-plot1 + theme(axis.text.x=element_text(angle=90, hjust=1))
print(plot1)
Tornadoes caused the most injuries with 91346 recorded. The other events in the Top 10 are TSTM Wind, Flood, Excessive Heat, Lightning, Heat, Ice Storm,Flash Flood, Thunderstorm Wind and Hail.
df2<-as.data.frame(injury)
plot2<-ggplot(df2[1:10,],aes(x=df2[1:10,"EVTYPE"],y=df2[1:10,"Injuries"]))
plot2<-plot2 + ggtitle("Top 10 Event Type with Injuries")
plot2<-plot2 + labs(x="Event", y="Injuries")
plot2<-plot2 + geom_bar(stat="identity", fill="orange")
plot2<-plot2 + geom_text(aes(label=df2[1:10,"Injuries"]), size=2, vjust = -0.5)
plot2<-plot2 + theme(axis.text.x=element_text(angle=90, hjust=1))
print(plot2)
Floods has the greatest economic impact with 150billion in damages. The other events in the Top 10 are Hurricane/Typhoon, Tornado, Storm Surge, Hail, Flash Flood, Drought, Hurricane, River Flood and Ice Storm.
df3<-as.data.frame(costly)
plot3<-ggplot(df3[1:10,],aes(x=df3[1:10,"EVTYPE"],y=df3[1:10,"Cost"]))
plot3<-plot3 + ggtitle("Top 10 Event Type with High Cost")
plot3<-plot3 + labs(x="Event", y="Cost")
plot3<-plot3 + geom_bar(stat="identity", fill="lightblue")
plot3<-plot3 + geom_text(aes(label=df3[1:10,"Cost"]), size=2, vjust = -0.5)
plot3<-plot3 + theme(axis.text.x=element_text(angle=90, hjust=1))
print(plot3)