knitr::opts_chunk$set(echo = TRUE)

Synopsis

This analysis investigates the impacts of different storm event types in terms of human casualties and economic damage. The dataset includes both fatalities, injuries, and property/crop damage for various event types. We start from the raw CSV data and process it entirely within R. The goal is to identify which events are most harmful to people and the economy. We summarize total fatalities, injuries, and casualties by event type. We also calculate total economic damage (property + crop) and highlight the top 10 events in terms of economic losses. Finally, visualizations show the relative impact of storm types on human and economic losses. The results provide insights for emergency planning and resource allocation. Overall, tornadoes, floods, and hurricanes appear among the most impactful events. This document contains all code used for data processing and visualization, with at least one figure included. ##

DATA PROCESSING

Load required libraries

library(dplyr) library(ggplot2) library(stringr) library(lubridate)

Set working directory

setwd(“/Users/isnawatihidayah/Documents/Graduation Course”)

Load raw data

storm_data <- read.csv(“repdata-data-StormData.csv”, stringsAsFactors = FALSE)

Preprocess data

storm_data <- storm_data %>% mutate( casualties = FATALITIES + INJURIES, EVTYPE = toupper(trimws(EVTYPE)), PROPDMG = as.numeric(PROPDMG), CROPDMG = as.numeric(CROPDMG) )

Convert damage exponents to multipliers

exp_to_mult <- function(e) { if (is.na(e) || e == ““) return(1) e <- toupper(e) if (e ==”H”) return(100) if (e == “K”) return(1e3) if (e == “M”) return(1e6) if (e == “B”) return(1e9) if (grepl(“1+$”, e)) return(10^as.numeric(e)) return(1) }

storm_data\(PROP_MULT <- sapply(storm_data\)PROPDMGEXP, exp_to_mult) storm_data\(CROP_MULT <- sapply(storm_data\)CROPDMGEXP, exp_to_mult)

storm_data <- storm_data %>% mutate( prop_dmg_dollars = PROPDMG * PROP_MULT, crop_dmg_dollars = CROPDMG * CROP_MULT, total_econ_dmg = prop_dmg_dollars + crop_dmg_dollars )

RESULT

econ_summary <- storm_data %>% group_by(EVTYPE) %>% summarise( total_prop_dmg = sum(prop_dmg_dollars, na.rm = TRUE), total_crop_dmg = sum(crop_dmg_dollars, na.rm = TRUE), total_econ = sum(total_econ_dmg, na.rm = TRUE), events = n() ) %>% arrange(desc(total_econ))

top10_econ <- head(econ_summary, 10) top10_econ <- top10_econ %>% mutate(total_econ_million = total_econ / 1e6) print(top10_econ)

health_summary <- storm_data %>% group_by(EVTYPE) %>% summarise( total_fatalities = sum(FATALITIES, na.rm = TRUE), total_injuries = sum(INJURIES, na.rm = TRUE), total_casualties = sum(casualties, na.rm = TRUE), events = n() ) %>% arrange(desc(total_casualties))

top10_health <- head(health_summary, 10) print(top10_health)

Combine figure : Prepare combined dataset

health_top10 <- top10_health %>% select(EVTYPE, total_casualties) %>% mutate(type = “Casualties”, value = total_casualties) %>% select(EVTYPE, type, value)

econ_top10 <- top10_econ %>% select(EVTYPE, total_econ_million) %>% mutate(type = “Economic Damage (Million USD)”, value = total_econ_million) %>% select(EVTYPE, type, value)

combined_top10 <- bind_rows(health_top10, econ_top10)

Plot

p_combined <- ggplot(combined_top10, aes(x = reorder(EVTYPE, value), y = value, fill = type)) + geom_bar(stat = “identity”) + facet_wrap(~type, scales = “free”) + coord_flip() + labs( title = “Top 10 Event Types by Human and Economic Impacts”, x = “Event Type”, y = “Value” ) + theme_minimal() + scale_fill_manual(values = c(“steelblue”, “darkorange”)) + theme(legend.position = “none”)

print(p_combined)

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