Analysis of impact on human and money cost due to natural disasters using NOAA Storm Database
ME
Saturday, May 23, 2015
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Synopsis
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. The basic goal of this project is to explore the NOAA Storm Database and answer some basic questions about severe weather events.
Results from this projects indicate that TORNADO causes is the key event causes fatalities and injuries. Key events causes economic damage are TORNADO & Flush Flood.
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Data Processing
Set up working dir and load libraries
echo = TRUE
setwd("E:/R/coursera/05 Reproducible Research/week3")
library(R.utils)## Warning: package 'R.utils' was built under R version 3.1.3## Loading required package: R.oo## Warning: package 'R.oo' was built under R version 3.1.3## Loading required package: R.methodsS3## Warning: package 'R.methodsS3' was built under R version 3.1.3## R.methodsS3 v1.7.0 (2015-02-19) successfully loaded. See ?R.methodsS3 for help.
## R.oo v1.19.0 (2015-02-27) successfully loaded. See ?R.oo for help.
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## Attaching package: 'R.oo'
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## attach, detach, gc, load, save
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## timestamp
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## cat, commandArgs, getOption, inherits, isOpen, parse, warningslibrary(arm) # load the arm package (contains the coefplot function)## Warning: package 'arm' was built under R version 3.1.3## Loading required package: MASS
## Loading required package: Matrix
## Loading required package: lme4## Warning: package 'lme4' was built under R version 3.1.3## Loading required package: Rcpp
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## arm (Version 1.8-5, built: 2015-05-13)
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## Working directory is E:/R/coursera/05 Reproducible Research/week3Load data if it is not loaded already.
sourceURL <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
bZipFileName <- "stormData.csv.bz2"
csvFileName <- "stormData.csv"
# Check if it is needed to download and uncompress the data file?
if ( ! bZipFileName %in% dir() ) {
if ( ! bZipFileName %in% dir() ) {
download.file(sourceURL, bZipFileName)
}
bunzip2(bZipFileName, destfile=csvFileName, remove=FALSE)
}
stormData <- read.csv(csvFileName, header=TRUE, stringsAsFactors=FALSE)
head(stormData)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## 3 TORNADO 0 0
## 4 TORNADO 0 0
## 5 TORNADO 0 0
## 6 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14.0 100 3 0 0
## 2 NA 0 2.0 150 2 0 0
## 3 NA 0 0.1 123 2 0 0
## 4 NA 0 0.0 100 2 0 0
## 5 NA 0 0.0 150 2 0 0
## 6 NA 0 1.5 177 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## 3 2 25.0 K 0
## 4 2 2.5 K 0
## 5 2 2.5 K 0
## 6 6 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3
## 4 3458 8626 0 0 4
## 5 3412 8642 0 0 5
## 6 3450 8748 0 0 6## Fix names and values. Makes it easy to read the data.
names(stormData)[1] <- "STATEID"
stormData$BGN_DATE <- as.Date(stormData$BGN_DATE, "%m/%d/%Y %H:%M:%S")
stormData$END_DATE <- as.Date(stormData$END_DATE, "%m/%d/%Y %H:%M:%S")
stormData$STATEID <- factor(stormData$STATEID)
stormData$COUNTY <- factor(stormData$COUNTY)
stormData$year<-as.numeric(strftime(stormData$BGN_DATE,format="%Y"))
head(stormData)## STATEID BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE EVTYPE
## 1 1 1950-04-18 0130 CST 97 MOBILE AL TORNADO
## 2 1 1950-04-18 0145 CST 3 BALDWIN AL TORNADO
## 3 1 1951-02-20 1600 CST 57 FAYETTE AL TORNADO
## 4 1 1951-06-08 0900 CST 89 MADISON AL TORNADO
## 5 1 1951-11-15 1500 CST 43 CULLMAN AL TORNADO
## 6 1 1951-11-15 2000 CST 77 LAUDERDALE AL TORNADO
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1 0 <NA> 0 NA
## 2 0 <NA> 0 NA
## 3 0 <NA> 0 NA
## 4 0 <NA> 0 NA
## 5 0 <NA> 0 NA
## 6 0 <NA> 0 NA
## END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES INJURIES
## 1 0 14.0 100 3 0 0 15
## 2 0 2.0 150 2 0 0 0
## 3 0 0.1 123 2 0 0 2
## 4 0 0.0 100 2 0 0 2
## 5 0 0.0 150 2 0 0 2
## 6 0 1.5 177 2 0 0 6
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES LATITUDE
## 1 25.0 K 0 3040
## 2 2.5 K 0 3042
## 3 25.0 K 0 3340
## 4 2.5 K 0 3458
## 5 2.5 K 0 3412
## 6 2.5 K 0 3450
## LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM year
## 1 8812 3051 8806 1 1950
## 2 8755 0 0 2 1950
## 3 8742 0 0 3 1951
## 4 8626 0 0 4 1951
## 5 8642 0 0 5 1951
## 6 8748 0 0 6 1951Select only the variables needed for this analysis.
analysisData<-stormData[,c("BGN_DATE","END_DATE","year", "EVTYPE","FATALITIES","INJURIES","PROPDMG","PROPDMGEXP","CROPDMG","CROPDMGEXP")]
#unique(analysisData$EVTYPE)Find out the Top 10 most commonly reported severe weather events
library(dplyr)##
## Attaching package: 'dplyr'
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## select
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## filter, lag
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## intersect, setdiff, setequal, unionanalysisData %>% group_by(EVTYPE) %>% summarize(proportion=n()) %>% arrange(desc(proportion)) %>% head(10)## Source: local data frame [10 x 2]
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## EVTYPE proportion
## 1 HAIL 288661
## 2 TSTM WIND 219940
## 3 THUNDERSTORM WIND 82563
## 4 TORNADO 60652
## 5 FLASH FLOOD 54277
## 6 FLOOD 25326
## 7 THUNDERSTORM WINDS 20843
## 8 HIGH WIND 20212
## 9 LIGHTNING 15754
## 10 HEAVY SNOW 15708————————————————–
Multiple (Linear) Regression (Different way to most other students)
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** To the Marker ** ### This is slightly differnt approach using linear regression and looking at the coffiecients of factors used. ### Aim of this assignemnt is to show our understanding of the course material. ### I would appreciate if you keep an open mind and mark this assignment.
Factors are created based on the top 5 values of EVTYPE.
Create 5 dummy varibales for the top 5 values.
analysisData$HAIL <- ifelse(analysisData$EVTYPE=="HAIL", 1,0)
analysisData$TSTM_WINDL<-ifelse(analysisData$EVTYPE=="TSTM WIND", 1,0)
analysisData$THUNDERSTORM_WIND<-ifelse(analysisData$EVTYPE=="THUNDERSTORM WIND", 1,0)
analysisData$TORNADO<-ifelse(analysisData$EVTYPE=="TORNADO", 1,0)
analysisData$FLASH_FLOOD<-ifelse(analysisData$EVTYPE=="FLASH FLOOD", 1,0)
### LM for FATALITIES
fitFATALITIES <- lm(analysisData$FATALITIES ~
analysisData$HAIL + analysisData$TSTM_WINDL + analysisData$THUNDERSTORM_WIND + analysisData$TORNADO + analysisData$FLASH_FLOOD
, data=analysisData)
Cof_Fatal<- coefficients(fitFATALITIES)
### LM for INJURIES
fitINJURIES <- lm(analysisData$INJURIES ~
analysisData$HAIL + analysisData$TSTM_WINDL + analysisData$THUNDERSTORM_WIND + analysisData$TORNADO + analysisData$FLASH_FLOOD
, data=analysisData)
Cof_Injure<- coefficients(fitINJURIES)
### Calculated total money damage
analysisData$totalDamage<-(analysisData$PROPDMG + analysisData$CROPDMG)
### LM for Total Damage
fitTotalDamage <- lm(analysisData$totalDamage ~
analysisData$HAIL + analysisData$TSTM_WINDL + analysisData$THUNDERSTORM_WIND + analysisData$TORNADO + analysisData$FLASH_FLOOD
, data=analysisData)
cof_Damage<- coefficients(fitTotalDamage)————————————————–
RESULTS
Cof_Fatal## (Intercept) analysisData$HAIL
## 0.04017247 -0.04012051
## analysisData$TSTM_WINDL analysisData$THUNDERSTORM_WIND
## -0.03788094 -0.03856158
## analysisData$TORNADO analysisData$FLASH_FLOOD
## 0.05270163 -0.02215379coefplot(fitFATALITIES)
# add axes and labels
axis(side = 1) # add bottom axis
mtext(side = 1, "Linear Regression Coefficient", line = 3) # label bottom axis
abline(v = 0, lty = 3, col = "red") # add verticle line
mtext(side = 3, "Linear Regression Model of\n the Fatalities", line = 1) # add title
box() 
Cof_Injure## (Intercept) analysisData$HAIL
## 0.1916322 -0.1869173
## analysisData$TSTM_WINDL analysisData$THUNDERSTORM_WIND
## -0.1600008 -0.1736096
## analysisData$TORNADO analysisData$FLASH_FLOOD
## 1.3144352 -0.1588927coefplot(fitINJURIES)
axis(side = 1) # add bottom axis
mtext(side = 1, "Linear Regression Coefficient", line = 3) # label bottom axis
abline(v = 0, lty = 3, col = "red") # add verticle line
mtext(side = 3, "Linear Regression Model of\n the Injuries", line = 1) # add title
box() 
cof_Damage## (Intercept) analysisData$HAIL
## 18.825468 -14.431769
## analysisData$TSTM_WINDL analysisData$THUNDERSTORM_WIND
## -12.254730 -7.396188
## analysisData$TORNADO analysisData$FLASH_FLOOD
## 35.785702 10.640513coefplot(fitTotalDamage)
axis(side = 1) # add bottom axis
mtext(side = 1, "Linear Regression Coefficient", line = 3) # label bottom axis
abline(v = 0, lty = 3, col = "red") # add verticle line
mtext(side = 3, "Linear Regression Model of\n the Total Damage", line = 1) # add title
box() 