Author:Darwin Lajoie

1. Description of Assignment

The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. You must use the database to answer the questions below and show the code for your entire analysis. Your analysis can consist of tables, figures, or other summaries. You may use any R package you want to support your analysis. Source: Coursera Assignment Description.

2. Synopsis

In this report we wish to show which types of weather events have caused the most financial and bodily harm in the United States over the last 60 years. As global climent change makes the weather more volatile, our plans for protecting ourselves against the variability of future weather depends on having knowledge about costs of weather in the past. For this report we have used a data base of weather events from NOAA, the National Oceanic & Atmospheric Administration. This data provides descriptions of weather events including number of deaths and injuries as well as estimates of the costs of property and crop damage. The data covers the years 1950 through 2011.

3. Data Processing

Set-up

Set working directory and load necessary packages for downstream analysis.

3.1 Load file and look at data

setwd("C:\\Users\\Dcollin\\Desktop\\RepData_PeerAssessment1\\PA2")

# Dowloading data if it's not already done
if(!file.exists("stormData.csv.bz2")) {
  download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", destfile = "stormData.csv.bz2", mode = "wb")
}

# Loading data
library(R.utils)
## Warning: package 'R.utils' was built under R version 3.4.2
## 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 v1.21.0 (2016-10-30) 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 v2.5.0 (2016-11-07) 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
dataSTORM <- read.csv(bunzip2("stormData.csv.bz2"), sep=",", header=T)

3.2. Narrow data down by subsetting and reducing

# Subset (NOAA) storm database
tidyNOAA <- dataSTORM[,c('EVTYPE','FATALITIES','INJURIES', 'PROPDMG', 'PROPDMGEXP', 'CROPDMG', 'CROPDMGEXP')]

3.3. Clean up data set and prepare data for analysis

Prepare property damage data and crop data to allow exponents to show correctly in your tidy data set and figures.

Property Exponents

# Convert H, K, M, B units to calculate Property Damage 
tidyNOAA$PROPDMGNUM = 0
tidyNOAA[tidyNOAA$PROPDMGEXP == "H", ]$PROPDMGNUM = tidyNOAA[tidyNOAA$PROPDMGEXP == "H", ]$PROPDMG * 10^2
tidyNOAA[tidyNOAA$PROPDMGEXP == "K", ]$PROPDMGNUM = tidyNOAA[tidyNOAA$PROPDMGEXP == "K", ]$PROPDMG * 10^3
tidyNOAA[tidyNOAA$PROPDMGEXP == "M", ]$PROPDMGNUM = tidyNOAA[tidyNOAA$PROPDMGEXP == "M", ]$PROPDMG * 10^6
tidyNOAA[tidyNOAA$PROPDMGEXP == "B", ]$PROPDMGNUM = tidyNOAA[tidyNOAA$PROPDMGEXP == "B", ]$PROPDMG * 10^9

Crop Exponents

# Convert H, K, M, B units to calculate Crop Damage
tidyNOAA$CROPDMGNUM = 0
tidyNOAA[tidyNOAA$CROPDMGEXP == "H", ]$CROPDMGNUM = tidyNOAA[tidyNOAA$CROPDMGEXP == "H", ]$CROPDMG * 10^2
tidyNOAA[tidyNOAA$CROPDMGEXP == "K", ]$CROPDMGNUM = tidyNOAA[tidyNOAA$CROPDMGEXP == "K", ]$CROPDMG * 10^3
tidyNOAA[tidyNOAA$CROPDMGEXP == "M", ]$CROPDMGNUM = tidyNOAA[tidyNOAA$CROPDMGEXP == "M", ]$CROPDMG * 10^6
tidyNOAA[tidyNOAA$CROPDMGEXP == "B", ]$CROPDMGNUM = tidyNOAA[tidyNOAA$CROPDMGEXP == "B", ]$CROPDMG * 10^9

4. Results

4.1. Question 1

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

Injuries versus Fatalities -> create the plot

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

ggplot(fatalities, aes(x = EVTYPE, y = FATALITIES)) + 
    geom_bar(stat = "identity", fill = "red", 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

# import ggplot2 library to plot the result
library(ggplot2)
# plot number of injuries with the most harmful event type
injuries <- aggregate(INJURIES ~ EVTYPE, data=tidyNOAA, 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 = "red", las = 3) + 
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
    xlab("Event Type") + ylab("Injuries") + ggtitle("Number of injuries by top 10 Weather Events")
## Warning: Ignoring unknown parameters: las

Conclusion: The weather event that causes the most harm to public health is Tornadoes. They have shown in the graphs above to be the largest cause of fatalities and injuries due to weather events in the United States.

4.2. 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=tidyNOAA, 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 = "red", las = 3) + 
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
    xlab("Event Type") + ylab("Damages ($)") + ggtitle("Property & Crop Damages by top 10 Weather Events")
## Warning: Ignoring unknown parameters: las

Conclusion: The events that have caused the most damage in the United states from an economic stand point are Flood, Drought and Hurricane, but for different reasons. For example the biggest risk to crops is a drougth event, whereas the biggest threat to properties are floods.