(— title: “Impact of U.S. Weather Events on Health and Economy (1996–2011)” author: “Asmaa Hassan” date: “2024-12-25” output: html_document —
Introduction
“This analysis investigates the impact of weather events on population health and economic consequences in the U.S. from 1996–2011. Using NOAA storm data, the study identifies the most harmful events based on fatalities, injuries, property damage, and crop damage. Tornadoes and excessive heat are the leading causes of fatalities and injuries, while floods result in the greatest economic losses. The findings can inform disaster preparedness and mitigation strategies.”
Data Processing
1. Loading Necessary Packages
library(ggplot2)
library(dplyr)
install.packages("gdtools")
install.packages("plyr")
install.packages("flextable")
library(plyr)
library(flextable)2. Reading and Transforming the Data
We load the NOAA storm event dataset and focus on data from January 1996 onwards, as this is when all event types started being recorded.
stormfile <- "/home/asmae/Downloads/repdata_data_StormData.csv.bz2"
rawdata <- read.csv(file = stormfile, header = TRUE, sep = ",")
rawdata$BGN_DATE <- strptime(rawdata$BGN_DATE, "%m/%d/%Y %H:%M:%S")
maindata <- subset(rawdata, BGN_DATE > "1995-12-31")
rm(rawdata)
maindata <- subset(maindata, select = c(EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP))
maindata$EVTYPE <- toupper(maindata$EVTYPE)
maindata <- maindata[maindata$FATALITIES != 0 |
maindata$INJURIES != 0 |
maindata$PROPDMG != 0 |
maindata$CROPDMG != 0, ]Results
Q1: Which Events Are Most Harmful to Population Health? Summing Fatalities and Injuries
fatalities <- aggregate(FATALITIES ~ EVTYPE, data = maindata, sum)
injuries <- aggregate(INJURIES ~ EVTYPE, data = maindata, sum)
fatalities <- arrange(fatalities, desc(FATALITIES), EVTYPE)[1:10,]
injuries <- arrange(injuries, desc(INJURIES), EVTYPE)[1:10,]Top 10 Events by Fatalities&Top 10 Events by Injuries
fatalities## EVTYPE FATALITIES
## 1 EXCESSIVE HEAT 1797
## 2 TORNADO 1511
## 3 FLASH FLOOD 887
## 4 LIGHTNING 651
## 5 FLOOD 414
## 6 RIP CURRENT 340
## 7 TSTM WIND 241
## 8 HEAT 237
## 9 HIGH WIND 235
## 10 AVALANCHE 223injuries## EVTYPE INJURIES
## 1 TORNADO 20667
## 2 FLOOD 6758
## 3 EXCESSIVE HEAT 6391
## 4 LIGHTNING 4141
## 5 TSTM WIND 3629
## 6 FLASH FLOOD 1674
## 7 THUNDERSTORM WIND 1400
## 8 WINTER STORM 1292
## 9 HURRICANE/TYPHOON 1275
## 10 HEAT 1222Visualization: Fatalities and Injuries Fatalities
ggplot(fatalities, aes(x = EVTYPE, y = FATALITIES)) +
geom_bar(stat = "identity", fill = "red") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(
title = "Impact of Weather Events on Population Health (1996–2011)",
x = "Event Type",
y = "Number of Fatalities",
fill = "Metric",
caption = "Figure 1: Top 10 Weather Events by Fatalities,excessive heat resulted in the highest fatalities."
)
Injuries
ggplot(injuries, aes(x = EVTYPE, y = INJURIES)) +
geom_bar(stat = "identity", fill = "red") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(
title = "Impact of Weather Events on Population Health (1996–2011)",
x = "Event Type",
y = "Number of Injuries",
fill = "Metric",
caption = "Figure 1: Top 10 Weather Events by Injuries. Tornadoes caused the most injuries"
)
Q2: Which Events Have the Greatest Economic Consequences? Transforming Damage Data
maindata$PROPDMGEXP <- gsub("[Hh]", "2", maindata$PROPDMGEXP)
maindata$PROPDMGEXP <- gsub("[Kk]", "3", maindata$PROPDMGEXP)
maindata$PROPDMGEXP <- gsub("[Mm]", "6", maindata$PROPDMGEXP)
maindata$PROPDMGEXP <- gsub("[Bb]", "9", maindata$PROPDMGEXP)
maindata$PROPDMGEXP <- gsub("\\+", "1", maindata$PROPDMGEXP)
maindata$PROPDMGEXP <- gsub("\\?|\\-|\\ ", "0", maindata$PROPDMGEXP)
maindata$PROPDMGEXP <- as.numeric(maindata$PROPDMGEXP)
maindata$CROPDMGEXP <- gsub("[Hh]", "2", maindata$CROPDMGEXP)
maindata$CROPDMGEXP <- gsub("[Kk]", "3", maindata$CROPDMGEXP)
maindata$CROPDMGEXP <- gsub("[Mm]", "6", maindata$CROPDMGEXP)
maindata$CROPDMGEXP <- gsub("[Bb]", "9", maindata$CROPDMGEXP)
maindata$CROPDMGEXP <- gsub("\\+", "1", maindata$CROPDMGEXP)
maindata$CROPDMGEXP <- gsub("\\-|\\?|\\ ", "0", maindata$CROPDMGEXP)
maindata$CROPDMGEXP <- as.numeric(maindata$CROPDMGEXP)
maindata$PROPDMGEXP[is.na(maindata$PROPDMGEXP)] <- 0
maindata$CROPDMGEXP[is.na(maindata$CROPDMGEXP)] <- 0
maindata <- mutate(maindata,
PROPDMGTOTAL = PROPDMG * (10 ^ PROPDMGEXP),
CROPDMGTOTAL = CROPDMG * (10 ^ CROPDMGEXP))Summring Economic Losses
Economic_data <- aggregate(cbind(PROPDMGTOTAL, CROPDMGTOTAL) ~ EVTYPE, data = maindata, FUN = sum)
Economic_data$ECONOMIC_LOSS <- Economic_data$PROPDMGTOTAL + Economic_data$CROPDMGTOTAL
Economic_data <- Economic_data[order(Economic_data$ECONOMIC_LOSS, decreasing = TRUE), ]
worsteconomicevents <- Economic_data[1:10, c(1, 4)]
worsteconomicevents## EVTYPE ECONOMIC_LOSS
## 48 FLOOD 148919611950
## 88 HURRICANE/TYPHOON 71913712800
## 141 STORM SURGE 43193541000
## 149 TORNADO 24900370720
## 66 HAIL 17071172870
## 46 FLASH FLOOD 16557105610
## 86 HURRICANE 14554229010
## 32 DROUGHT 14413667000
## 152 TROPICAL STORM 8320186550
## 83 HIGH WIND 5881421660Visualization: Economic Loss
ggplot(worsteconomicevents, aes(x = EVTYPE, y = ECONOMIC_LOSS)) +
geom_bar(stat = "identity", fill = "blue") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Event Type") + ylab("Total Property & Crop Damages (USD)") +
ggtitle("Total Economic Loss in the US (1996 - 2011)")
## Conclusions For each of the two main questions, ensure your
conclusions are supported by evidence from the data: For population
health: Highlight the fatalities and injuries numbers for top events
(already included). For economic consequences: Use the total economic
loss numbers for top events to draw detailed conclusions. Example:
“Floods caused the highest economic losses, totaling approximately $148.9 billion, followed by hurricanes/typhoons ($71.9 billion). These events resulted in significant property and crop damages, highlighting their economic impact.”