Reproducible Research

Project Introduction

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.

Data Processing

The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete. The data is downloaded directly from the url provided and is read using bunzip2 command to unzip the file. Finally read into a csv file.

url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url, "StormData.csv.bz2")
library(R.utils)

## Loading required package: R.oo

## Loading required package: R.methodsS3

## R.methodsS3 v1.7.1 (2016-02-15) successfully loaded. See ?R.methodsS3 for help.

## R.oo v1.22.0 (2018-04-21) successfully loaded. See ?R.oo for help.

## 
## Attaching package: 'R.oo'

## The following objects are masked from 'package:methods':
## 
##     getClasses, getMethods

## The following objects are masked from 'package:base':
## 
##     attach, detach, gc, load, save

## R.utils v2.8.0 successfully loaded. See ?R.utils for help.

## 
## Attaching package: 'R.utils'

## The following object is masked from 'package:utils':
## 
##     timestamp

## The following objects are masked from 'package:base':
## 
##     cat, commandArgs, getOption, inherits, isOpen, parse, warnings

bunzip2("StormData.csv.bz2", "StormData.csv")
storm <- read.csv("StormData.csv")

Codes

Health Impact

To evaluate the health impact, the total fatalities and the total injuries for each event type (EVTYPE) are calculated. The codes for this calculation are shown as follows.

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

storm.fatalities <- storm %>% select(EVTYPE, FATALITIES) %>% group_by(EVTYPE) %>% summarise(total.fatalities = sum(FATALITIES)) %>% arrange(-total.fatalities)
head(storm.fatalities, 10)

## # A tibble: 10 x 2
##    EVTYPE         total.fatalities
##    <fct>                     <dbl>
##  1 TORNADO                    5633
##  2 EXCESSIVE HEAT             1903
##  3 FLASH FLOOD                 978
##  4 HEAT                        937
##  5 LIGHTNING                   816
##  6 TSTM WIND                   504
##  7 FLOOD                       470
##  8 RIP CURRENT                 368
##  9 HIGH WIND                   248
## 10 AVALANCHE                   224

storm.injuries <- storm %>% select(EVTYPE, INJURIES) %>% group_by(EVTYPE) %>% summarise(total.injuries = sum(INJURIES)) %>% arrange(-total.injuries)
head(storm.injuries, 10)

## # A tibble: 10 x 2
##    EVTYPE            total.injuries
##    <fct>                      <dbl>
##  1 TORNADO                    91346
##  2 TSTM WIND                   6957
##  3 FLOOD                       6789
##  4 EXCESSIVE HEAT              6525
##  5 LIGHTNING                   5230
##  6 HEAT                        2100
##  7 ICE STORM                   1975
##  8 FLASH FLOOD                 1777
##  9 THUNDERSTORM WIND           1488
## 10 HAIL                        1361

storm.damage <- storm %>% select(EVTYPE, PROPDMG,PROPDMGEXP,CROPDMG,CROPDMGEXP)

Symbol <- sort(unique(as.character(storm.damage$PROPDMGEXP)))
Multiplier <- c(0,0,0,1,10,10,10,10,10,10,10,10,10,10^9,10^2,10^2,10^3,10^6,10^6)
convert.Multiplier <- data.frame(Symbol, Multiplier)

storm.damage$Prop.Multiplier <- convert.Multiplier$Multiplier[match(storm.damage$PROPDMGEXP, convert.Multiplier$Symbol)]
storm.damage$Crop.Multiplier <- convert.Multiplier$Multiplier[match(storm.damage$CROPDMGEXP, convert.Multiplier$Symbol)]

storm.damage <- storm.damage %>% mutate(PROPDMG = PROPDMG*Prop.Multiplier) %>% mutate(CROPDMG = CROPDMG*Crop.Multiplier) %>% mutate(TOTAL.DMG = PROPDMG+CROPDMG)

storm.damage.total <- storm.damage %>% group_by(EVTYPE) %>% summarize(TOTAL.DMG.EVTYPE = sum(TOTAL.DMG))%>% arrange(-TOTAL.DMG.EVTYPE) 

head(storm.damage.total,10)

## # A tibble: 10 x 2
##    EVTYPE            TOTAL.DMG.EVTYPE
##    <fct>                        <dbl>
##  1 FLOOD                 150319678250
##  2 HURRICANE/TYPHOON      71913712800
##  3 TORNADO                57352117607
##  4 STORM SURGE            43323541000
##  5 FLASH FLOOD            17562132111
##  6 DROUGHT                15018672000
##  7 HURRICANE              14610229010
##  8 RIVER FLOOD            10148404500
##  9 ICE STORM               8967041810
## 10 TROPICAL STORM          8382236550

Results

library(ggplot2)
ggplot(storm.fatalities[1:10,], aes(x=reorder(EVTYPE, -total.fatalities), y=total.fatalities)) + geom_bar(stat="identity")+ ggtitle("Top 10 Events with Highest Total Fatalities") +labs(x="EVENT TYPE", y="Total Fatalities")

ggplot(storm.injuries[1:10,], aes(x=reorder(EVTYPE, -total.injuries), y=total.injuries)) +labs(x="EVENT TYPE", y="Total Injuries")+geom_bar(stat="identity")

ggplot(storm.damage.total[1:10,], aes(x=reorder(EVTYPE, -TOTAL.DMG.EVTYPE), y=TOTAL.DMG.EVTYPE))+geom_bar(stat="identity")+ggtitle("Top 10 Events with Highest Economic Impact") +labs(x="EVENT TYPE", y="Total Economic Impact ($USD)")