STORM DISASTER AND DAMAGE

Synopsis

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.

Goal

The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. We must use the database to answer the questions below and show the code for your entire analysis. Our analysis can consist of tables, figures, or other summaries and filters.

Questions Solved

EVTYPE: Events like Tornados, floods, cyclones.
FATALITIES: Comparing fatalities during disaster types. INJURIES: Injuries Ocurred during events type. PROPDMG : Property Damage.
CROPDMG : Crop Damage.

Downloading the Data and Reading

The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size.

if(!file.exists("./repdata_data_StormData.csv.bz2")){
    file.create("./repdata_data_StormData.csv.bz2")
     download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2",
                   "./repdata_data_StormData.csv.bz2")
}
storm<-read.csv("./repdata_data_StormData.csv.bz2", header=T)

Data Transformation and Wrangling

Specify certain attributes for futher processing

storm_data_needed <- storm[, c(8,23,24,25,27)]
names(storm_data_needed)

## [1] "EVTYPE"     "FATALITIES" "INJURIES"   "PROPDMG"    "CROPDMG"

dim(storm_data_needed)

## [1] 902297      5

Fatalities: It is grouped by event type(like Tornados, Cyclone) and Summarized. Moreover it is filtered greater than 300 fatalities

#loading the required packages
library(ggplot2)
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

#Converting as.character to numberic Value
storm_data_needed$FATALITIES <- as.numeric(storm_data_needed$FATALITIES )

fatal <- storm_data_needed %>%
        group_by(EVTYPE) %>%
        summarize(FATALITIES = sum(FATALITIES)) %>%
        filter(FATALITIES>300)
#Plot the fatalities with point presentation
ggplot(fatal, aes(EVTYPE, FATALITIES)) + geom_point(size=3)

INJURIES:It is grouped by event type(like Tornados, Cyclone) and Summarized. Moreover it is filtered greater than 8000 injuries

#loading the required packages
library(ggplot2)
library(dplyr)
#Converting as.character to numberic Value
storm_data_needed$INJURIES <- as.numeric( storm_data_needed$INJURIES )

injury <- storm_data_needed %>%
        group_by(EVTYPE) %>%
        summarize(INJURIES = sum(INJURIES)) %>%
        filter(INJURIES>8000)
#Plot the injuries with point presentation
ggplot(injury, aes(EVTYPE, INJURIES)) + geom_point(size=3)

PROPERTY DAMAGE: property damage ocurred during event types and Group the data by property damage type and summarizes it and filter the property damage greater than $25000 value.

#loading the required packages
library(ggplot2)
library(dplyr)
#Converting as.character to numberic Value
storm_data_needed$PROPDMG <- as.numeric( storm_data_needed$PROPDMG )
property_damage_plot <- storm_data_needed %>%
        group_by(EVTYPE) %>%
        summarize(PROPDMG = sum(PROPDMG)) %>%
        filter(PROPDMG > 25000)
#Plot the property damage with point presentation
ggplot(property_damage_plot, aes(EVTYPE, PROPDMG)) + geom_point(size=3)

CROP DAMAGE:Crop damage ocurred during event types and Group the data by crop damage type and summarizes it and filter the crop damage greater than $50000 value.

#loading the required packages
library(ggplot2)
library(dplyr)
#Converting as.character to numberic Value
storm_data_needed$CROPDMG <- as.numeric(storm_data_needed$CROPDMG)
crop_damage_plot <- storm_data_needed %>%
        group_by(EVTYPE) %>%
        summarize(CROPDMG = sum(CROPDMG)) %>%
        filter(CROPDMG > 50000)
#Plot the crop damage with point presentation
ggplot(crop_damage_plot, aes(EVTYPE, CROPDMG)) + geom_point(size=3)