Reproducible Research: Peer Assessment 2
Health and Economic Impact of Weather Events in the US
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.
Synopsis
The analysis on the storm event database revealed that tornadoes are the most dangerous weather event to the population health.
Data Processing
- The data is from a compression types of comma-separated-value file available here.
- The data is downloaded , extracted to the folder and read via CSV reader.
- Some of the data also converted into lower case , converted into dataframe via ddplay , combine via plyr and data preview .
New basic function introduced to convert into exponential transformation
Basic command line using KnitR ( manual )
library(markdown) library(knitr) setwd("D:/Google Drive/Coursera/Assignment 5.1/R/coursera-repdata/project2") knitr::opts_chunk$set(echo=FALSE, fig.path='D:/Google Drive/Coursera/Assignment 5.1/R/coursera-repdata/project2/figure/', cache=TRUE) knitr::knit2html("storm.analysis.Rmd", options=c("use_xhtml","smartypants","mathjax","highlight_code", "base64_images"))Load all the required library
options( warn = -1 )
is.installed <- function(mypkg) is.element(mypkg, installed.packages()[,1])
if (is.installed('dplyr') == 'FALSE') {install.packages("dplyr");library(dplyr)} else{suppressMessages(library(dplyr))}
if (is.installed('ggthemes') == 'FALSE') {install.packages("ggthemes");library(ggthemes)} else{suppressMessages(library(ggthemes))}
if (is.installed('scales') == 'FALSE') {install.packages("scales");library(scales)} else{suppressMessages(library(scales))}
if (is.installed('RColorBrewer') == 'FALSE') {install.packages("RColorBrewer");suppressMessages(library(RColorBrewer))} else{library(RColorBrewer)}
if (is.installed('lubridate') == 'FALSE') {install.packages("lubridate");library(lubridate)} else{suppressMessages(library(lubridate))}
if (is.installed('ggplot2') == 'FALSE') {install.packages("ggplot2");library(ggplot2)} else{suppressMessages(library(ggplot2))}
if (is.installed('plyr') == 'FALSE') {install.packages("plyr");library(plyr)} else{suppressMessages(library(plyr))}
if (is.installed('knitr') == 'FALSE') {install.packages("knitr");library(knitr)} else{suppressMessages(library(knitr))}
if (is.installed('lattice') == 'FALSE') {install.packages("lattice");library(lattice)} else{suppressMessages(library(lattice))}
if (is.installed('RCurl') == 'FALSE') {install.packages("RCurl");library(RCurl)} else{suppressMessages(library(RCurl))}
if (is.installed('reshape') == 'FALSE') {install.packages("reshape");library(reshape)} else{suppressMessages(library(reshape))}
if (is.installed('car') == 'FALSE') {install.packages("car");library(car)} else{suppressMessages(library(car))}
if (is.installed('gridExtra') == 'FALSE') {install.packages("gridExtra");library(gridExtra)} else{suppressMessages(library(gridExtra))}
if (is.installed('grid') == 'FALSE') {install.packages("grid");library(grid)} else{suppressMessages(library(grid))}
if (is.installed('xtable') == 'FALSE') {install.packages("xtable");library(xtable)} else{suppressMessages(library(xtable))}- The first step is to read the data into a data frame.
options( warn = -1 )
curdir <-getwd()
file.url<-'http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2'
download.file(file.url,destfile=paste(curdir,'/repdata%2Fdata%2FStormData.csv.bz2',sep=""))
storm <- read.csv(bzfile(paste(curdir,'/repdata%2Fdata%2FStormData.csv.bz2',sep="")))
#storm <- read.csv(bzfile("c://repdata%2Fdata%2FStormData.csv.bz2"))
length(unique(storm$EVTYPE))## [1] 985- Get the no. of event types
length(unique(storm$EVTYPE))## [1] 985- Converting letters to lower casing
event_types <- tolower(storm$EVTYPE)- Replace punctation characters with a space
event_types <- gsub("[[:blank:][:punct:]+]", " ", event_types)- Get the unique of event types
length(unique(event_types))## [1] 874- Get the casualities
library(plyr)
casualties <- ddply(storm, .(EVTYPE), summarize,
fatalities = sum(FATALITIES),
injuries = sum(INJURIES))- Find events which causing most injury and death
fatal_events <- head(casualties[order(casualties$fatalities, decreasing = T), ], 10)
injury_events <- head(casualties[order(casualties$injuries, decreasing = T), ], 10)- Top 10 Fatal Events List
fatal_events[, c("EVTYPE", "fatalities")]## EVTYPE fatalities
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504
## 170 FLOOD 470
## 585 RIP CURRENT 368
## 359 HIGH WIND 248
## 19 AVALANCHE 224- Top 10 Injury Events List
injury_events[, c("EVTYPE", "injuries")]## EVTYPE injuries
## 834 TORNADO 91346
## 856 TSTM WIND 6957
## 170 FLOOD 6789
## 130 EXCESSIVE HEAT 6525
## 464 LIGHTNING 5230
## 275 HEAT 2100
## 427 ICE STORM 1975
## 153 FLASH FLOOD 1777
## 760 THUNDERSTORM WIND 1488
## 244 HAIL 1361- Define function for exponent transformation to convert into numeric values for ranking purposes.
- The property damage is represented with two fields, a number PROPDMG in dollars and the exponent PROPDMGEXP
- The crop damage is represented using two fields, CROPDMG and CROPDMGEXP.
exp_transform <- function(e) {
# h -> hundred, k -> thousand, m -> million, b -> billion
if (e %in% c('h', 'H'))
return(2)
else if (e %in% c('k', 'K'))
return(3)
else if (e %in% c('m', 'M'))
return(6)
else if (e %in% c('b', 'B'))
return(9)
else if (!is.na(as.numeric(e))) # if a digit
return(as.numeric(e))
else if (e %in% c('', '-', '?', '+'))
return(0)
else {
stop("Invalid exponent value.")
}
}
prop_dmg_exp <- sapply(storm$PROPDMGEXP, FUN=exp_transform)
storm$prop_dmg <- storm$PROPDMG * (10 ** prop_dmg_exp)
crop_dmg_exp <- sapply(storm$CROPDMGEXP, FUN=exp_transform)
storm$crop_dmg <- storm$CROPDMG * (10 ** crop_dmg_exp)- Calculating loss by event type
library(plyr)
econ_loss <- ddply(storm, .(EVTYPE), summarize,
prop_dmg = sum(prop_dmg),
crop_dmg = sum(crop_dmg))- Removing events with no loss
econ_loss <- econ_loss[(econ_loss$prop_dmg > 0 | econ_loss$crop_dmg > 0), ]
prop_dmg_events <- head(econ_loss[order(econ_loss$prop_dmg, decreasing = T), ], 10)
crop_dmg_events <- head(econ_loss[order(econ_loss$crop_dmg, decreasing = T), ], 10)- Top 10 Property damage List
prop_dmg_events[, c("EVTYPE", "prop_dmg")]## EVTYPE prop_dmg
## 153 FLASH FLOOD 6.820237e+13
## 786 THUNDERSTORM WINDS 2.086532e+13
## 834 TORNADO 1.078951e+12
## 244 HAIL 3.157558e+11
## 464 LIGHTNING 1.729433e+11
## 170 FLOOD 1.446577e+11
## 411 HURRICANE/TYPHOON 6.930584e+10
## 185 FLOODING 5.920825e+10
## 670 STORM SURGE 4.332354e+10
## 310 HEAVY SNOW 1.793259e+10- Top 10 Crop damage List
crop_dmg_events[, c("EVTYPE", "crop_dmg")]## EVTYPE crop_dmg
## 95 DROUGHT 13972566000
## 170 FLOOD 5661968450
## 590 RIVER FLOOD 5029459000
## 427 ICE STORM 5022113500
## 244 HAIL 3025974480
## 402 HURRICANE 2741910000
## 411 HURRICANE/TYPHOON 2607872800
## 153 FLASH FLOOD 1421317100
## 140 EXTREME COLD 1292973000
## 212 FROST/FREEZE 1094086000Results
- Set the levels in order and produce 2 types of graph and combined as Top deadly weather
library(ggplot2)
library(gridExtra)
p1 <- ggplot(data=fatal_events,
aes(x=reorder(EVTYPE, fatalities), y=fatalities, fill=fatalities)) +
geom_bar(stat="identity" , fill="#FFBF00", colour="black") +
coord_flip() +
ylab("Total number of fatalities") +
xlab("Event type") +
theme(legend.position="none")
p2 <- ggplot(data=injury_events,
aes(x=reorder(EVTYPE, injuries), y=injuries, fill=injuries)) +
geom_bar(stat="identity" , fill="#FE2E2E", colour="black") +
coord_flip() +
ylab("Total number of injuries") +
xlab("Event type") +
theme(legend.position="none")
grid.arrange(p1, p2 , ncol=1, nrow=2, top = "Top deadly weather events in the US from 1950-2011") 
- Set the levels in order and produce 2 types of graph and combined as Weather costs
library(ggplot2)
library(gridExtra)
# Set the levels in order
p1 <- ggplot(data=prop_dmg_events,
aes(x=reorder(EVTYPE, prop_dmg), y=log10(prop_dmg), fill=prop_dmg )) +
geom_bar(stat="identity" , fill="#FFBF00", colour="black") +
coord_flip() +
xlab("Event type") +
ylab("Property damage (log-scale) in dollars") +
theme(legend.position="none")
p2 <- ggplot(data=crop_dmg_events,
aes(x=reorder(EVTYPE, crop_dmg), y=crop_dmg, fill=crop_dmg)) +
geom_bar(stat="identity" , fill="#FE2E2E", colour="black") +
coord_flip() +
xlab("Event type") +
ylab("Crop damage in dollars") +
theme(legend.position="none")
grid.arrange(p1, p2 , ncol=1, nrow=2, top = "Weather costs to the US economy from 1950-2011")
Summary
From the graph above what we can say that :-
- Drought is more significant to crop damage.
- Flash flood , ThunderStorm & Tornado seems caused more damage in property.