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# Introduction

This analysis aims to assess the impact of different weather events in the United States from 1996 to 2011. We investigate two main questions: 1. Which weather events are most harmful to population health? 2. Which weather events have the greatest economic consequences?

Data Processing

1. Loading Necessary Packages

install.packages(c("dplyr", "ggplot2", "plyr", "Hmisc", "flextable"))
rmarkdown::render("C:/Users/hp/Desktop/project2.Rmd", encoding = "UTF-8")

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 <- "C:/Users/hp/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)
install.packages("dplyr")
library(dplyr)


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
injuries

Visualization: Fatalities and Injuries Fatalities

library(ggplot2)
ggplot(fatalities, aes(x = EVTYPE, y = FATALITIES)) + 
  geom_bar(stat = "identity", fill = "red") + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  xlab("Event Type") + ylab("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)) + xlab(“Event Type”) + ylab(“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))

Summing 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

Visualization: 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)”)

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