Reproducible Research Course Project 2
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 Download
The following code is used to download and read the data:
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2",
dest="tmp.bz2",
method="curl")
data <- read.csv(bzfile("tmp.bz2"),
header=TRUE,
sep=",",
stringsAsFactors=FALSE)str(data)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : chr "4/18/1950 0:00:00" "4/18/1950 0:00:00" "2/20/1951 0:00:00" "6/8/1951 0:00:00" ...
## $ BGN_TIME : chr "0130" "0145" "1600" "0900" ...
## $ TIME_ZONE : chr "CST" "CST" "CST" "CST" ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: chr "MOBILE" "BALDWIN" "FAYETTE" "MADISON" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : chr "" "" "" "" ...
## $ BGN_LOCATI: chr "" "" "" "" ...
## $ END_DATE : chr "" "" "" "" ...
## $ END_TIME : chr "" "" "" "" ...
## $ COUNTY_END: num 0 0 0 0 0 0 0 0 0 0 ...
## $ COUNTYENDN: logi NA NA NA NA NA NA ...
## $ END_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ END_AZI : chr "" "" "" "" ...
## $ END_LOCATI: chr "" "" "" "" ...
## $ LENGTH : num 14 2 0.1 0 0 1.5 1.5 0 3.3 2.3 ...
## $ WIDTH : num 100 150 123 100 150 177 33 33 100 100 ...
## $ F : int 3 2 2 2 2 2 2 1 3 3 ...
## $ MAG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ FATALITIES: num 0 0 0 0 0 0 0 0 1 0 ...
## $ INJURIES : num 15 0 2 2 2 6 1 0 14 0 ...
## $ PROPDMG : num 25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
## $ PROPDMGEXP: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...
## $ WFO : chr "" "" "" "" ...
## $ STATEOFFIC: chr "" "" "" "" ...
## $ ZONENAMES : chr "" "" "" "" ...
## $ LATITUDE : num 3040 3042 3340 3458 3412 ...
## $ LONGITUDE : num 8812 8755 8742 8626 8642 ...
## $ LATITUDE_E: num 3051 0 0 0 0 ...
## $ LONGITUDE_: num 8806 0 0 0 0 ...
## $ REMARKS : chr "" "" "" "" ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...Data Subsetting:
Subset the dataset on the parameters of interest.
# Change parameter names to lowercase.
colnames(data) <- tolower(colnames(data))
# Subset on the parameters of interest.
data <- subset(x=data,
subset=(evtype != "?" &
(injuries > 0 | fatalities > 0 | propdmg > 0 | cropdmg > 0)),
select=c("evtype",
"fatalities",
"injuries",
"propdmg",
"propdmgexp",
"cropdmg",
"cropdmgexp")) Data Cleansing:
Map the property and crop damage exponent alphabetic multipliers to numeric values.
# Change all damage exponents to uppercase.
data$propdmgexp <- toupper(data$propdmgexp)
data$cropdmgexp <- toupper(data$cropdmgexp)
# Map property damage alphanumeric exponents to numeric values.
propDmgKey <- c("\"\"" = 10^0,
"-" = 10^0,
"+" = 10^0,
"0" = 10^0,
"1" = 10^1,
"2" = 10^2,
"3" = 10^3,
"4" = 10^4,
"5" = 10^5,
"6" = 10^6,
"7" = 10^7,
"8" = 10^8,
"9" = 10^9,
"H" = 10^2,
"K" = 10^3,
"M" = 10^6,
"B" = 10^9)
data$propdmgexp <- propDmgKey[as.character(data$propdmgexp)]
data$propdmgexp[is.na(data$propdmgexp)] <- 10^0
# Map crop damage alphanumeric exponents to numeric values
cropDmgKey <- c("\"\"" = 10^0,
"?" = 10^0,
"0" = 10^0,
"K" = 10^3,
"M" = 10^6,
"B" = 10^9)
data$cropdmgexp <- cropDmgKey[as.character(data$cropdmgexp)]
data$cropdmgexp[is.na(data$cropdmgexp)] <- 10^0Human Heath Data Processing:
Select the applicable health columns from the dataset, then calculate the total number of fatalities and injuries per event type.
# Aggregate number of fatalities and injuries per evtype into healthData dataframe
healthData <- aggregate(cbind(fatalities, injuries) ~ evtype, data=data, FUN=sum)
# Add total column to healthData
healthData$total <- healthData$fatalities + healthData$injuriesFind the event types corresponding with the the highest health impacts.
# Remove rows with zero health impact
healthData <- healthData[healthData$total > 0, ]
# Sort health data in descending order
healthData <- healthData[order(healthData$total, decreasing=TRUE), ]
# Re-label the rows
rownames(healthData) <- 1:nrow(healthData)
# Create dataframe of highest health impacting event types and append an "other" event type as a catchall
# for everything else
healthDataTop <- healthData[1:10, ]Combine the damage and damage exponent multiplier parameters into the single parameters propertyloss and croploss.
# Combine propdmg and propdmgexp parameters into a single parameter called propertyloss.
data$propertyloss <- data$propdmg * data$propdmgexp
# Combine cropdmg and cropdmgexp parameters into a single parameter called croploss.
data$croploss <- data$cropdmg * data$cropdmgexpSelect the applicable economic columns from the dataset, then calculate the total amount of property loss and crop loss per event type.
# Aggregate amount of proploss and croploss per evtype into economicData dataframe
economicData <- aggregate(cbind(propertyloss, croploss) ~ evtype, data=data, FUN=sum)
# Add total loss column to economicData
economicData$total <- economicData$propertyloss + economicData$croplossFind the event types corresponding with the highest economic impacts.
# Remove rows with zero economic impact
economicData <- economicData[economicData$total > 0, ]
# Sort the economy data in descending order
economicData <- economicData[order(economicData$total, decreasing=TRUE), ]
# Re-label the rows
rownames(economicData) <- tolower(rownames(economicData))
# Create dataframe of highest economy impacting event types
economicDataTop <- economicData[1:10, ]Analysis Results:
Plot of the ten event types with the highest fatality counts plus an eleventh catchall event type that combines the total fatality counts of all other event types.
# Load necessary libraries
library(reshape2)
library(ggplot2)
# Melt the data
healthDataTopMelt <- melt(healthDataTop, id.vars="evtype")
# Create chart
healthChart <- ggplot(healthDataTopMelt, aes(x=reorder(evtype, -value), y=value))
# Plot data as bar chart
healthChart = healthChart + geom_bar(stat="identity", aes(fill=variable), position="dodge")
# Format y-axis scale and set y-axis label
healthChart = healthChart + scale_y_sqrt("Frequency Count")
# Set x-axis label
healthChart = healthChart + xlab("Event Type")
# Rotate x-axis tick labels
healthChart = healthChart + theme(axis.text.x = element_text(angle=45, hjust=1))
# Set chart title
healthChart = healthChart + ggtitle("Pareto Chart of Top 10 US Storm Health Impacts")
# Display the chart
print(healthChart)
Figure: Top 10 US Storm Health Impact
Top 10 Economic Impact Event Types
Plot of the ten event types with the highest economic impacts.
# Load necessary libraries
library(reshape2)
library(ggplot2)
# Melt the data
economicDataTopMelt <- melt(economicDataTop, id.vars="evtype")
# Create chart
economicChart <- ggplot(economicDataTopMelt, aes(x=reorder(evtype, -value), y=value))
# Add bars
economicChart <- economicChart + geom_bar(stat="identity", aes(fill=variable), position="dodge")
# Format y-axis scale and set y-axis label
economicChart <- economicChart + scale_y_sqrt("Damage Impact [$]")
# Set x-axis label
economicChart <- economicChart + xlab("Event Type")
# Rotate x-axis tick labels
economicChart <- economicChart + theme(axis.text.x = element_text(angle=45, hjust=1))
# Set chart title
economicChart <- economicChart + ggtitle("Pareto Chart of Top 10 US Storm Economic Impacts")
# Display the chart
print(economicChart)
Figure: Top 10 US Storm Economic Impact
Conclusions
1.Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Tornadoes are responsible for the largest proportion of both deaths and injuries out of all event types.
2.Across the United States, which types of events have the greatest economic consequences?
Flooding is responsible for the largeset proportion of total economic impact out of all event types.