Synopsis

This project has the objectiv to explore the NOAA Stom Database. Also answer some basic questions about sever weather events. This events ocurred across the United States that was documented according to NATIONAL WEATHER SERVICE INSTRUCTION. This institution informed at AUGUST 17, 2007. The priority is to study the events that have the greatest economic consequences and also that are most harmful with respect to population helth. In order to show the trend of the Storm. The Rstudio was used in order to present the result in Rmd format.

Data Processing

1.Load the data

data <- read.csv(bzfile("repdata-data-StormData.csv.bz2"))

2.Identify the most harmful with respect to population helth

fatalTYPE <- aggregate(FATALITIES ~ EVTYPE,data, sum)
fataltypetop <- fatalTYPE[fatalTYPE$FATALITIES>200,]

INJURTYPE <- aggregate(INJURIES ~ EVTYPE,data, sum)
INJURTYPEtop <- INJURTYPE[INJURTYPE$INJURIES>1000,]

3.Identify The greatest economic consequences

PROPTYPE <- aggregate(PROPDMG ~ EVTYPE,data, sum)
PROPTYPEtop <- PROPTYPE[PROPTYPE$PROPDMG>100000,]

CROPTYPE <- aggregate(CROPDMG ~ EVTYPE,data, sum)
CROPTYPEtop <- CROPTYPE[CROPTYPE$CROPDMG>10000,]

Result

1.The most harmful with respect to population helth

In this plot we can see the total of fatalities and injuries, that ocurred at Severe Weather Events in EEUU.

library(ggplot2)
library(grid)
library(gridExtra)

p1 <- qplot(fataltypetop$EVTYPE,fataltypetop$FATALITIES, col="black", xlab="Events Type", ylab="Total of fatalities", main="Total of fatalities by Events Type")



p2 <- qplot(INJURTYPEtop$EVTYPE,INJURTYPEtop$INJURIES, col="black", xlab="Events Type", ylab="Total of Injuries", main="Total of Injuries by Events Type")



grid.arrange(p1, p2, nrow=2 ,ncol = 1, top = "The most harmful to population helth")

2.The greatest economic consequences

In this plot we can see the total cost in miles of $ that damaged in properties and crops at Severe Weather Events in EEUU.

library(ggplot2)
library(grid)
library(gridExtra)
        

p1 <- qplot(PROPTYPEtop$EVTYPE,PROPTYPEtop$PROPDMG/1000,  col="black", xlab="Events Type", ylab="Total of properties (miles$) ", main="Total of properties by Events Type")



p2 <- qplot(CROPTYPEtop$EVTYPE,CROPTYPEtop$CROPDMG/1000,  col="black", xlab="Events Type", ylab="Total of crops (miles$)", main="Total of crops by Events Type")


grid.arrange(p1, p2, nrow=2 ,ncol = 1, top = "The greatest economic consequences")