ANALYSING DATA FROM NOAA STROM DATABASE:ECONOMIC AND HEALTH DAMAGES BY SEVERE WHEATHER IN US

June 10,2017

K.S.S SIVA TEJA

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Goal

The goal of this assignment is to analyse the data and answer the required questions about severe weather events all the code that is used to analyse the database is given below

Synopsis

stroms and severe weather can cause a lot of damage which also includes health and economic problems for society

This project is to analyse the data from U.S Natinal oceanic and Atmospheric strom data base and understand the relations and effects of a strom including thier accurance and flatalities to property damage

The two main questions to be answered

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?

Data processing

#reading data into r
dsn<-read.csv("C:/Users/shanmukhasri/Documents/repdata%2Fdata%2FStormData.csv")

Analysis

#subseting data from data base
tidy<-dsn[,c('EVTYPE','FATALITIES','INJURIES', 'PROPDMG', 'PROPDMGEXP', 'CROPDMG', 'CROPDMGEXP')]

converting to calculate property damage hundred,thousands….

tidy$PROPDMGNUM = 0
tidy[tidy$PROPDMGEXP == "H", ]$PROPDMGNUM = tidy[tidy$PROPDMGEXP == "H", ]$PROPDMG * 10^2
tidy[tidy$PROPDMGEXP == "K", ]$PROPDMGNUM = tidy[tidy$PROPDMGEXP == "K", ]$PROPDMG * 10^3
tidy[tidy$PROPDMGEXP == "M", ]$PROPDMGNUM = tidy[tidy$PROPDMGEXP == "M", ]$PROPDMG * 10^6
tidy[tidy$PROPDMGEXP == "B", ]$PROPDMGNUM = tidy[tidy$PROPDMGEXP == "B", ]$PROPDMG * 10^9

doing same conversion to crop damage

tidy$CROPDMGNUM = 0
tidy[tidy$CROPDMGEXP == "H", ]$CROPDMGNUM = tidy[tidy$CROPDMGEXP == "H", ]$CROPDMG * 10^2
tidy[tidy$CROPDMGEXP == "K", ]$CROPDMGNUM = tidy[tidy$CROPDMGEXP == "K", ]$CROPDMG * 10^3
tidy[tidy$CROPDMGEXP == "M", ]$CROPDMGNUM = tidy[tidy$CROPDMGEXP == "M", ]$CROPDMG * 10^6
tidy[tidy$CROPDMGEXP == "B", ]$CROPDMGNUM = tidy[tidy$CROPDMGEXP == "B", ]$CROPDMG * 10^9

Results

Question 1:Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health ?

# import ggplot2 library to plot the result
library(ggplot2)
# plot number of fatalities with the most harmful event type
fatal <- aggregate(FATALITIES ~ EVTYPE, data=tidy, sum)
fatal <- fatal[order(-fatal$FATALITIES), ][1:10, ]
fatalEVTYPE <- factor(fatal$EVTYPE, levels = fatal$EVTYPE)

ggplot(fatal, aes(x = EVTYPE, y = FATALITIES)) + 
    geom_bar(stat = "identity", fill = "blue", las = 3) + 
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
    xlab("Event Type") + ylab("Fatalities") + ggtitle("Number of fatalities by top 10 Weather Events")

## Warning: Ignoring unknown parameters: las

top 10 weather events

# import ggplot2 library to plot the result
library(ggplot2)
# plot number of injuries with the most harmful event type
injuries <- aggregate(INJURIES ~ EVTYPE, data=tidy, sum)
injuries <- injuries[order(-injuries$INJURIES), ][1:10, ]
injuries$EVTYPE <- factor(injuries$EVTYPE, levels = injuries$EVTYPE)

ggplot(injuries, aes(x = EVTYPE, y = INJURIES)) + 
    geom_bar(stat = "identity", fill = "yellow", las = 3) + 
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
    xlab("Event") + ylab("Injuries") + ggtitle("Number of injuries by top 10")

## Warning: Ignoring unknown parameters: las

question 2:Across the United States, which types of events have the greatest economic consequences ?

# import ggplot2 library to plot the result
library(ggplot2)
# plot number of damages with the most harmful event type
damages <- aggregate(PROPDMGNUM + CROPDMGNUM ~ EVTYPE, data=tidy, sum)
names(damages) = c("EVTYPE", "TOTALDAMAGE")
damages <- damages[order(-damages$TOTALDAMAGE), ][1:10, ]
damages$EVTYPE <- factor(damages$EVTYPE, levels = damages$EVTYPE)

ggplot(damages, aes(x = EVTYPE, y = TOTALDAMAGE)) + 
    geom_bar(stat = "identity", fill = "blue", las = 3) + 
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
    xlab("Event") + ylab("Damages") + ggtitle("Property & Crop Damages by top 10 Weather Events")

## Warning: Ignoring unknown parameters: las