The Types of Events across the United States

Synopsis

The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. I use the database to answer the questions below and show the code for my entire analysis. My analysis can consist of tables, figures, or other summaries. I also use any R package to support my analysis.

Data Processing

data <- read.csv("~/Desktop/repdata-data-StormData.csv")

Results

Questions

Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?

data.injuries<-aggregate(data$INJURIES,by=list(data$EVTYPE),sum)
data.fatalities<-aggregate(data$FATALITIES,by=list(data$EVTYPE),sum)
data.injuries.high <- data.injuries[head(order(data.injuries$x, decreasing=TRUE), 10),]
data.fatalities.high <- data.fatalities[head(order(data.fatalities$x, decreasing=TRUE), 10),]
data.harm<-cbind(data.fatalities.high,data.injuries.high)
names(data.harm)<-c("Event Types","Fatalities","Event Types","Injuries")
data.harm

##        Event Types Fatalities       Event Types Injuries
## 834        TORNADO       5633           TORNADO    91346
## 130 EXCESSIVE HEAT       1903         TSTM WIND     6957
## 153    FLASH FLOOD        978             FLOOD     6789
## 275           HEAT        937    EXCESSIVE HEAT     6525
## 464      LIGHTNING        816         LIGHTNING     5230
## 856      TSTM WIND        504              HEAT     2100
## 170          FLOOD        470         ICE STORM     1975
## 585    RIP CURRENT        368       FLASH FLOOD     1777
## 359      HIGH WIND        248 THUNDERSTORM WIND     1488
## 19       AVALANCHE        224              HAIL     1361

Conclusin: From the dataset, we could see that the TORNADO is the most harmful event with the respect to population health.

Across the United States, which types of events have the greatest economic consequences?

data.prop<-aggregate(data$PROPDMG,by=list(data$EVTYPE),sum)
data.crop<-aggregate(data$CROPDMG,by=list(data$EVTYPE),sum)
data.cost<-data.frame(data.prop[,1],data.prop[,2]+data.crop[,2])
names(data.cost)<-c("Event Types","Economics Cost")

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, union

data.cost <- arrange(data.cost, desc(data.cost[,2]))
data.cost.high<- head(data.cost, 10)
#data.cost.high[,1] <- as.character(data.cost.high[,1])
data.cost.high[,1] <- factor(data.cost.high[,1])

plot(data.cost.high[,1],data.cost.high[,2]/100000,xlab="Event Types",ylab="Economic Cost (10^5 US Dollars)",main="Event Types with Greatest Economic Consequences")

Conclusin: From the dataset and the plot, we could see that the TORNADO costs the greatest economic consequence.