Reproducible Research Project 2
Gabriela Ochoa
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
The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. You can download the file from the course web site:
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.
Questions
1. Across the United States, which types of events (as indicated in the **EVTYPE** variable) are most harmful with respect to population health?
2. Across the United States, which types of events have the greatest economic consequences?Data Processing
if (!file.exists("StormData.csv.bz2")) {
fileURL <- 'https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2'
download.file(fileURL, destfile='StormData.csv.bz2', method = 'curl')
}
noaaDF <- read.csv(bzfile('StormData.csv.bz2'),header=TRUE, stringsAsFactors = FALSE)Load Packages
exploring data
We can select the variables that will help us to analyze the damage based on EVTYPE (event type), FATALITIES, INJURIES, PROPDMG (property damage), PROPDMGEXP (property damage expense), CROPDMG (crop damage), and CROPDMGEXP (crop damage expense).
NOAA <- noaaDF[,c('EVTYPE','FATALITIES','INJURIES', 'PROPDMG', 'PROPDMGEXP', 'CROPDMG', 'CROPDMGEXP')]
## View headers in revised data set
head(NOAA)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25.0 K 0
## 2 TORNADO 0 0 2.5 K 0
## 3 TORNADO 0 2 25.0 K 0
## 4 TORNADO 0 2 2.5 K 0
## 5 TORNADO 0 2 2.5 K 0
## 6 TORNADO 0 6 2.5 K 0## Organize type of event ~ fatalities and store in object "fatalities" & same for "injuries"
fatalities <- aggregate(FATALITIES ~ EVTYPE, data=NOAA, sum)
injuries <- aggregate(INJURIES ~ EVTYPE, data = NOAA, sum)
## Sort fatalities
fatalities <- fatalities[order(-fatalities$FATALITIES), ][1:20, ]
fatalities$EVTYPE <- factor(fatalities$EVTYPE, levels = fatalities$EVTYPE)
head(fatalities)## EVTYPE FATALITIES
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504## Sort Injuries
injuries <- injuries[order(-injuries$INJURIES), ][1:20, ]
injuries$EVTYPE <- factor(injuries$EVTYPE, levels = injuries$EVTYPE)
head(injuries)## EVTYPE INJURIES
## 834 TORNADO 91346
## 856 TSTM WIND 6957
## 170 FLOOD 6789
## 130 EXCESSIVE HEAT 6525
## 464 LIGHTNING 5230
## 275 HEAT 2100Results
Question 1
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
p1 = ggplot(fatalities, aes(x = EVTYPE, y = FATALITIES, theme_set(theme_bw()))) +
geom_bar(stat = "identity", fill = "steelblue") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 6)) +
xlab("Event Type") + ylab("Fatalities") + ggtitle("Fatalities by top 20 Weather Event Types") +
theme(plot.title = element_text(size = 10))
p2 = ggplot(injuries, aes(x = EVTYPE, y = INJURIES, theme_set(theme_bw()))) +
geom_bar(stat = "identity", fill = "plum4") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 6)) +
xlab("Event Type") + ylab("Injuries") + ggtitle("Injuries by top 20 Weather Event Types") +
theme(plot.title = element_text(size = 10))
## Plot both side by side using gridExtra package
grid.arrange(p1, p2, ncol = 2, top = "Most Harmful Events with Respect to Population Health")
As show in the plot Tornado is the event type that has the highest level of Fatalities and Injuries.
Question 2
Across the United States, which types of events have the greatest economic consequences?
We proceed to calculate the costo of property and crop damages.
## property
prop <- aggregate(NOAA$PROPDMG, by = list(NOAA$EVTYPE), "sum")
names(prop) <- c("Event", "Property")
prop_sorted <- prop[order(-prop$Property), ][1:20, ]
head(prop_sorted)## Event Property
## 834 TORNADO 3212258.2
## 153 FLASH FLOOD 1420124.6
## 856 TSTM WIND 1335965.6
## 170 FLOOD 899938.5
## 760 THUNDERSTORM WIND 876844.2
## 244 HAIL 688693.4Crop
crop <- aggregate(NOAA$CROPDMG, by = list(NOAA$EVTYPE), "sum")
names(crop) <- c("Event", "Crop")
crop_sorted <- crop[order(-crop$Crop), ][1:20, ]
head(crop_sorted)## Event Crop
## 244 HAIL 579596.28
## 153 FLASH FLOOD 179200.46
## 170 FLOOD 168037.88
## 856 TSTM WIND 109202.60
## 834 TORNADO 100018.52
## 760 THUNDERSTORM WIND 66791.45Plot
par(mfrow = c(1, 2), mar = c(10, 4, 2, 2), las = 3, cex = 0.7, cex.main = 1.4, cex.lab = 1.2)
barplot(prop_sorted$Property, names.arg = prop_sorted$Event, col= "lightseagreen", main="Top Weather Events for Poperty Damage", ylab="Amout of Property Damage", ylim=c(0,3500000))
barplot(crop_sorted$Crop, names.arg = crop_sorted$Event, col= "hotpink", main="Top Weather Events for Crop Damage", ylab="Amout of Crop Damage", ylim=c(0,3500000))
## Considering both, property and crop
TotalCost <- aggregate(NOAA$CROPDMG+NOAA$PROPDMG, by = list(NOAA$EVTYPE), "sum")
names(TotalCost) <- c("Event", "TotalCost")
total_sorted <- TotalCost[order(-TotalCost$TotalCost), ][1:20, ]
head(total_sorted)## Event TotalCost
## 834 TORNADO 3312276.7
## 153 FLASH FLOOD 1599325.1
## 856 TSTM WIND 1445168.2
## 244 HAIL 1268289.7
## 170 FLOOD 1067976.4
## 760 THUNDERSTORM WIND 943635.6barplot(total_sorted$TotalCost, names.arg = total_sorted$Event, col = 'palevioletred4',
main = 'Top Weather Events for total Damage ', ylab = 'Amount of total Damage', ylim = c(0, 3500000))
We can confirm that Tornadoes are the main cause for damage.