# Reproducible Research: Course Project 2
Abstract
The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. The database was used to answer the questions: 1) Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health? 2) Across the United States, which types of events have the greatest economic consequences? The codes used for analyzing are shown below. To conclude, the weather event that causes most harm to public health is Tornadoes.Flood is the event that causes the highest economic loss.
load packages
library(knitr)
library(ggplot2)data processing
#reading the data
dsNOAA <- read.csv("repdata_data_StormData.csv.bz2", sep = ",", header = T)
head(dsNOAA)## 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
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE 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
## 6 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
## 6 0 1.5 177 2 0 0 6 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
## 6 K 0 3450 8748
## LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3051 8806 1
## 2 0 0 2
## 3 0 0 3
## 4 0 0 4
## 5 0 0 5
## 6 0 0 6#subset the data
tidyNOAA <- dsNOAA[,c("EVTYPE","FATALITIES","INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]
head(tidyNOAA)## 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 0str(tidyNOAA)## 'data.frame': 902297 obs. of 7 variables:
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ 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: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...To calculate the economic damage, PROPDMG and CROPDMG (Amonut of property and crop damage) should be converted to number with units.
# creat a new column to store the numbers
tidyNOAA$PROPDMGNUM = 0
# fill in the data with correct units
tidyNOAA[tidyNOAA$PROPDMGEXP == "H",]$PROPDMGNUM = tidyNOAA[tidyNOAA$PROPDMGEXP == "H",]$PROPDMG * 10 ^ 2
tidyNOAA[tidyNOAA$PROPDMGEXP == "K", ]$PROPDMGNUM = tidyNOAA[tidyNOAA$PROPDMGEXP == "K", ]$PROPDMG * 10 ^ 3
tidyNOAA[tidyNOAA$PROPDMGEXP == "M", ]$PROPDMGNUM = tidyNOAA[tidyNOAA$PROPDMGEXP == "M", ]$PROPDMG * 10 ^ 6
tidyNOAA[tidyNOAA$PROPDMGEXP == "B", ]$PROPDMGNUM = tidyNOAA[tidyNOAA$PROPDMGEXP == "B", ]$PROPDMG * 10 ^ 9
head(tidyNOAA)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP PROPDMGNUM
## 1 TORNADO 0 15 25.0 K 0 25000
## 2 TORNADO 0 0 2.5 K 0 2500
## 3 TORNADO 0 2 25.0 K 0 25000
## 4 TORNADO 0 2 2.5 K 0 2500
## 5 TORNADO 0 2 2.5 K 0 2500
## 6 TORNADO 0 6 2.5 K 0 2500# creat a new column to store the numbers
tidyNOAA$CROPDMGNUM = 0
# fill in the data with correct units
tidyNOAA[tidyNOAA$CROPDMGEXP == "H",]$CROPDMGNUM = tidyNOAA[tidyNOAA$CROPDMGEXP == "H",]$CROPDMG * 10 ^ 2
tidyNOAA[tidyNOAA$CROPDMGEXP == "K", ]$CROPDMGNUM = tidyNOAA[tidyNOAA$CROPDMGEXP == "K", ]$CROPDMG * 10 ^ 3
tidyNOAA[tidyNOAA$CROPDMGEXP == "M", ]$CROPDMGNUM = tidyNOAA[tidyNOAA$CROPDMGEXP == "M", ]$CROPDMG * 10 ^ 6
tidyNOAA[tidyNOAA$CROPDMGEXP == "B", ]$CROPDMGNUM = tidyNOAA[tidyNOAA$CROPDMGEXP == "B", ]$CROPDMG * 10 ^ 9
head(tidyNOAA)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP PROPDMGNUM
## 1 TORNADO 0 15 25.0 K 0 25000
## 2 TORNADO 0 0 2.5 K 0 2500
## 3 TORNADO 0 2 25.0 K 0 25000
## 4 TORNADO 0 2 2.5 K 0 2500
## 5 TORNADO 0 2 2.5 K 0 2500
## 6 TORNADO 0 6 2.5 K 0 2500
## CROPDMGNUM
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0Results
Question 1: Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health ?
#plot number of fatalities with the most harmful event type
fatalities <- aggregate(FATALITIES ~ EVTYPE, data = tidyNOAA, sum)
fatalities <- fatalities[order(-fatalities$FATALITIES),][1:10,]
ggplot(fatalities, aes(x = reorder(x = EVTYPE, -FATALITIES), y = FATALITIES))+
geom_col(fill = "red", las = 3)+
theme(axis.text = element_text(angle = 90, hjust = 1))+
xlab("Event type")+
ylab("Fatalities")+
ggtitle("Number of fatalities by top 10 Weather Events")## Warning in geom_col(fill = "red", las = 3): Ignoring unknown parameters: `las`
# plot the number of injuries with the most harmful event type
injuries <- aggregate(INJURIES ~ EVTYPE, data = tidyNOAA, sum)
injuries <- injuries[order(-injuries$INJURIES),][1:10,]
ggplot(injuries, aes(x = reorder(x = EVTYPE, -INJURIES), y = INJURIES))+
geom_col(fill = "blue", las = 3)+
theme(axis.text = element_text(angle = 90, hjust = 1))+
xlab("Event type")+
ylab("Injuries")+
ggtitle("Number of injuries by top 10 Weather Events")## Warning in geom_col(fill = "blue", las = 3): Ignoring unknown parameters: `las`
Conclusion: The weather event that causes most harm to public health is Tornadoes. They have shown in the largest cause of fatalities and injuries due to weather event in the US.
Question 2: Across the United States, which types of events hae the greatest economic consequences?
# plot the number of crop and property damages with the most harmful event type
damages <- aggregate(PROPDMGNUM + CROPDMGNUM ~ EVTYPE, data = tidyNOAA, sum)
names(damages) <- c("EVTYPE", "TOTALDAMAGE")
damages <- damages[order(-damages$TOTALDAMAGE),][1:10,]
ggplot(damages, aes(x = reorder(x = EVTYPE, -TOTALDAMAGE), y = TOTALDAMAGE))+
geom_col(fill = "yellow", las = 3)+
theme(axis.text = element_text(angle = 90, hjust = 1))+
xlab("Event type")+
ylab("Damages ($)")+
ggtitle("Property & Crop damages by top 10 Weather Events")## Warning in geom_col(fill = "yellow", las = 3): Ignoring unknown parameters:
## `las`
Conclusion: Flood is the event that causes the highest economic loss.