Storm Joana - Assignment 2

Title: How do weather events impact the economy and health in USA habitants?

Synopsis: The all point of this study is to assess the impact of different weather events in areas such as health and economics in USA. We will focus our first stage at processing the data by filter the 4 most impact events in both this areas and then presenting our discoveries in the section Results using ggplot2 tool to enhance our findings.

Packages:

if (!require("ggplot2")) { install.packages("ggplot2"); library(ggplot2) }

## Loading required package: ggplot2

## Warning: package 'ggplot2' was built under R version 3.3.2

if (!require("dplyr")) { install.packages("dplyr"); library(dplyr) }

## Loading required package: dplyr

## Warning: package 'dplyr' was built under R version 3.3.2

## 
## 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 Processing

Extracting the raw data

if (!file.exists("storm")){
  dir.create("storm")
}
fileurl<-"https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(fileurl,destfile = "storm.csv",method = "auto")
storm1<-read.table("storm.csv",header = TRUE,sep = ",")

Question 1

Calculating the total number of fatalities/injuries of different events impact accross the USA

stormfatal<-aggregate(storm1$FATALITIES, by=list(storm1$EVTYPE),sum, na.rm=TRUE)
fatal<-rep(c("FATALITIES"),times=985)
stormfatal<-cbind(stormfatal,fatal)
colnames(stormfatal)<-c("EVTYPE","CASES","HEALTH")
stormfatalMAX<-stormfatal[order(stormfatal$CASES,decreasing=TRUE)[1:4],] 

storminj<-aggregate(storm1$INJURIES, by=list(storm1$EVTYPE),sum,na.rm=TRUE )
inju<-rep(c("INJURIES"),times=985)
storminj<-cbind(storminj,inju)
colnames(storminj)<-c("EVTYPE","CASES","HEALTH")

storminjMAX<-storminj[order(storminj$CASES,decreasing=TRUE)[1:4],] 

health<-rbind(stormfatalMAX,storminjMAX)
levels(health$EVTYPE) <- tolower(levels(health$EVTYPE))

Question 2

Calculating the economic impact of weather events impact accross the USA based on properties and crop damages.

## We will use agreggate so we can sum all the losses per event type.
propdmg<-aggregate(storm1$PROPDMG, by=list(storm1$EVTYPE),sum, na.rm=TRUE)
#this rep it will be used to create 985 character vector named "Properties" to be used later for panel plot
property<-rep(c("Properties"),times=985)
propdmg<-cbind(propdmg,property)
colnames(propdmg)<-c("EVTYPE","CASES","DAMAGE")
#this step will define the most 4th influential events
propdmgMAX<-propdmg[order(propdmg$CASES,decreasing=TRUE)[1:4],] 

cropdmg<-aggregate(storm1$CROPDMG, by=list(storm1$EVTYPE),sum,na.rm=TRUE )
crop<-rep(c("Crops"),times=985)
cropdmg<-cbind(cropdmg,crop)
colnames(cropdmg)<-c("EVTYPE","CASES","DAMAGE")
cropdmgMAX<-cropdmg[order(cropdmg$CASES,decreasing=TRUE)[1:4],] 

econom<-rbind(propdmgMAX,cropdmgMAX)
levels(econom$EVTYPE) <- tolower(levels(econom$EVTYPE))
econom$CASES<-round(econom$CASES,digits = 0)

Results

Question 1

Figure of weather events impact in Health in USA

h<-ggplot(health, aes(x = EVTYPE, y = CASES,label=CASES)) +
  theme_grey()+ 
  geom_bar(aes(fill=EVTYPE),stat = "identity")+ facet_grid(HEALTH~., scales="free")+
  geom_text(colour = "black", fontface = "italic", size= 4,hjust= 0.5,vjust=-0.25,angle=0)+
  theme(axis.text.x=element_text(angle=90, size=10, vjust=0.5))+
  theme(legend.title = element_blank())+
  theme(axis.ticks.y = element_blank(),axis.text.y = element_blank())+
  xlab("Event Types")+
  ylab("Count (units)")+
  ggtitle("The types of events most harmful to population health in USA")+
  theme(plot.title = element_text(size = 15,lineheight = 0.3,vjust=0.5))+
  coord_cartesian(ylim = c(0,95000))
print(h)

Question 2

Figure of weather events impact in Economy in USA

g<-ggplot(econom, aes(x = EVTYPE, y = CASES,label=round(CASES,0))) +
  theme_grey()+ 
  geom_bar(aes(fill=EVTYPE),stat = "identity")+ facet_grid(DAMAGE~.,scales = "free")+
  geom_text(colour = "black", fontface = "italic", size= 4,hjust=0.5,vjust=-0.25,angle=0)+
  theme(axis.text.x=element_text(angle=90, size=10, vjust=0.5))+
  theme(legend.title = element_blank())+
  theme(axis.ticks.y = element_blank(),axis.text.y = element_blank())+
  xlab("Event Types")+
  ylab("Economical Losses (USD)")+
  ggtitle("The types of events with the greatest economic consequences in USA")+
  theme(plot.title = element_text(size = 15,lineheight = 0.3,vjust=0.5))+
  coord_cartesian(ylim = c(0,3500000))
print(g)

End of analysis