Reproducible Research Week 4
Nor Badrul Anuar
29 June 2016
Introduction
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.
Data
The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. You can download the file from the course web site:
- Storm Data [47Mb]
There is also some documentation of the database available. Here you will find how some of the variables are constructed/defined.
- National Weather Service Storm Data Documentation
- National Climatic Data Center Storm Events FAQ
The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete.
In order to execute this project, some of the library required.
library(R.utils)
library(dplyr)
library(ggplot2)
library(gridExtra)Data Processing
In order to answer this question, there are several steps need to be followed.
- Download the dataset which can be found directly from the URL given; or simply use the following command using R.
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", destfile = "repdata_data_StormData.csv.bz2")- Once downloaded, extract the file manually in the working directory; or simply use the following command using R. Check there is a file named
repdata_data_StormData.csvextracted from thebunzip2command.
bunzip2("repdata_data_StormData.csv.bz2")- Read the data from the csv file and run
headandnamesfor the overview of the data.
storm<-read.csv("repdata_data_StormData.csv")head(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 6names(storm)## [1] "STATE__" "BGN_DATE" "BGN_TIME" "TIME_ZONE" "COUNTY"
## [6] "COUNTYNAME" "STATE" "EVTYPE" "BGN_RANGE" "BGN_AZI"
## [11] "BGN_LOCATI" "END_DATE" "END_TIME" "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE" "END_AZI" "END_LOCATI" "LENGTH" "WIDTH"
## [21] "F" "MAG" "FATALITIES" "INJURIES" "PROPDMG"
## [26] "PROPDMGEXP" "CROPDMG" "CROPDMGEXP" "WFO" "STATEOFFIC"
## [31] "ZONENAMES" "LATITUDE" "LONGITUDE" "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS" "REFNUM"- Subset the data by selecting interesting variables.
stormSelect <- subset(storm,select = c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP"))
head(stormSelect)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25.0 K 0
## 2 TORNADO 0 0 2.5 K 0
## 3 TORNADO 0 2 25.0 K 0
## 4 TORNADO 0 2 2.5 K 0
## 5 TORNADO 0 2 2.5 K 0
## 6 TORNADO 0 6 2.5 K 01st Question (Results)
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
- Sum the
FatalitiesandInjuriesbased on theEvtypeand show the top 10 of the most harmfull events.
sortFatalities <- stormSelect %>%
group_by(EVTYPE) %>%
summarize(FATALITIES = sum(FATALITIES)) %>%
arrange(desc(FATALITIES))
sortFatalities[1:10,]## Source: local data frame [10 x 2]
##
## EVTYPE FATALITIES
## (fctr) (dbl)
## 1 TORNADO 5633
## 2 EXCESSIVE HEAT 1903
## 3 FLASH FLOOD 978
## 4 HEAT 937
## 5 LIGHTNING 816
## 6 TSTM WIND 504
## 7 FLOOD 470
## 8 RIP CURRENT 368
## 9 HIGH WIND 248
## 10 AVALANCHE 224sortInjuries <- stormSelect %>%
group_by(EVTYPE) %>%
summarize(INJURIES =sum(INJURIES)) %>%
arrange(desc(INJURIES))
sortInjuries[1:10,]## Source: local data frame [10 x 2]
##
## EVTYPE INJURIES
## (fctr) (dbl)
## 1 TORNADO 91346
## 2 TSTM WIND 6957
## 3 FLOOD 6789
## 4 EXCESSIVE HEAT 6525
## 5 LIGHTNING 5230
## 6 HEAT 2100
## 7 ICE STORM 1975
## 8 FLASH FLOOD 1777
## 9 THUNDERSTORM WIND 1488
## 10 HAIL 1361- Design and draw the graph of the most harmfull event in the US
sortFatalities$EVTYPE <- factor(sortFatalities$EVTYPE, levels = sortFatalities$EVTYPE[order(sortFatalities$FATALITIES)])
ggplot(data=sortFatalities[1:10,], aes(x=EVTYPE,y=FATALITIES)) +
geom_bar(stat="identity", fill="red") +
ylab("Fatalities") +
xlab("Event Type") +
coord_flip() +
ggtitle("Fatalities vs. Event Type across the U.S (Top Ten)")
sortInjuries$EVTYPE <- factor(sortInjuries$EVTYPE, levels = sortInjuries$EVTYPE[order(sortInjuries$INJURIES)])
ggplot(data=sortInjuries[1:10,], aes(x=EVTYPE,y=INJURIES)) +
geom_bar(stat="identity", fill="red") +
ylab("Injuries") +
xlab("Event Type") +
coord_flip() +
ggtitle("Injuries vs. Event Type across the U.S (Top Ten)")
As we can see in the graph, Tonado is the most harmful event.
2nd Question (Results)
Across the United States, which types of events have the greatest economic consequences?
- Convert the Prop and Crop exponential to real numbers, and add these values as TotalFinal.
stormSelect$pexp=0
stormSelect$pexp[stormSelect$PROPDMGEXP=='K']<-1000
stormSelect$pexp[stormSelect$PROPDMGEXP=='M']<-1000000
stormSelect$pexp[stormSelect$PROPDMGEXP=='B']<-1000000000
stormSelect$cexp=0
stormSelect$cexp[stormSelect$CROPDMGEXP=='K']<-1000
stormSelect$cexp[stormSelect$CROPDMGEXP=='M']<-1000000
stormSelect$cexp[stormSelect$CROPDMGEXP=='B']<-1000000000
stormSelect$propFinal <- stormSelect$PROPDMG*stormSelect$pexp
stormSelect$cropFinal <- stormSelect$CROPDMG*stormSelect$cexp
stormSelect$TotalFinal <- stormSelect$propFinal+stormSelect$cropFinal- Sum the
TotalFinalbased on theEvtypeand show the top 10 of the greatest economic impact (in $million).
sortTotalFinal <- stormSelect %>%
group_by(EVTYPE) %>%
summarize(TotalFinal = sum(TotalFinal)/1e6) %>%
arrange(desc(TotalFinal))
sortTotalFinal[1:10,]## Source: local data frame [10 x 2]
##
## EVTYPE TotalFinal
## (fctr) (dbl)
## 1 FLOOD 150319.678
## 2 HURRICANE/TYPHOON 71913.713
## 3 TORNADO 57340.614
## 4 STORM SURGE 43323.541
## 5 HAIL 18752.904
## 6 FLASH FLOOD 17562.129
## 7 DROUGHT 15018.672
## 8 HURRICANE 14610.229
## 9 RIVER FLOOD 10148.405
## 10 ICE STORM 8967.041- Design and draw the graph of the greatest economic impact
sortTotalFinal$EVTYPE <- factor(sortTotalFinal$EVTYPE, levels = sortTotalFinal$EVTYPE[order(sortTotalFinal$TotalFinal)])
ggplot(data=sortTotalFinal[1:10,], aes(x=EVTYPE,y=TotalFinal)) +
geom_bar(stat="identity", fill="red") +
ylab("Total Damage ($ in Million)") +
xlab("Event Type") +
coord_flip() +
ggtitle("Economic Impact (Total Damage) across the U.S (Top Ten)")
In the conclusion, flood is the event that has the greatest impact on economic impact in the US