Storm Data Analysis

Introduction

This report analyzes the impact of severe weather events on public health and the economy using the U.S. National Weather Service Storm Data.

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Data Loading

if (!require("data.table")) install.packages("data.table", repos="https://cloud.r-project.org")

## Loading required package: data.table

if (!require("R.utils")) install.packages("R.utils", repos="https://cloud.r-project.org")

## Loading required package: R.utils

## Loading required package: R.oo

## Loading required package: R.methodsS3

## R.methodsS3 v1.8.2 (2022-06-13 22:00:14 UTC) successfully loaded. See ?R.methodsS3 for help.

## R.oo v1.27.1 (2025-05-02 21:00:05 UTC) successfully loaded. See ?R.oo for help.

## 
## Attaching package: 'R.oo'

## The following object is masked from 'package:R.methodsS3':
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## The following objects are masked from 'package:methods':
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## The following objects are masked from 'package:base':
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## R.utils v2.13.0 (2025-02-24 21:20:02 UTC) successfully loaded. See ?R.utils for help.

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## Attaching package: 'R.utils'

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library(data.table)
library(R.utils)

if (!file.exists("storm.csv")) {
  bunzip2("repdata_data_StormData.csv.bz2", destname = "storm.csv", remove = FALSE)
}

storm_data <- fread("storm.csv")

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Health Impact Analysis

health_data <- storm_data[, .(
  FATALITIES = sum(FATALITIES, na.rm = TRUE),
  INJURIES = sum(INJURIES, na.rm = TRUE)
), by = EVTYPE]

health_data[, total_harm := FATALITIES + INJURIES]
setorder(health_data, -total_harm)

head(health_data, 10)

##                EVTYPE FATALITIES INJURIES total_harm
##                <char>      <num>    <num>      <num>
##  1:           TORNADO       5633    91346      96979
##  2:    EXCESSIVE HEAT       1903     6525       8428
##  3:         TSTM WIND        504     6957       7461
##  4:             FLOOD        470     6789       7259
##  5:         LIGHTNING        816     5230       6046
##  6:              HEAT        937     2100       3037
##  7:       FLASH FLOOD        978     1777       2755
##  8:         ICE STORM         89     1975       2064
##  9: THUNDERSTORM WIND        133     1488       1621
## 10:      WINTER STORM        206     1321       1527

Explanation

This analysis calculates total fatalities and injuries for each event type.
A combined metric total_harm is used to identify the most dangerous events.

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Health Impact Visualization

top_health <- head(health_data, 10)

barplot(top_health$total_harm,
        names.arg = top_health$EVTYPE,
        las = 2,
        col = "red",
        main = "Top 10 Most Harmful Events",
        ylab = "Total Harm")

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Economic Impact Analysis

convert_exp <- function(x) {
  ifelse(x == "K", 1e3,
  ifelse(x == "M", 1e6,
  ifelse(x == "B", 1e9, 1)))
}

storm_data[, PROPDMGEXP := toupper(PROPDMGEXP)]
storm_data[, CROPDMGEXP := toupper(CROPDMGEXP)]

storm_data[, prop_damage := PROPDMG * convert_exp(PROPDMGEXP)]
storm_data[, crop_damage := CROPDMG * convert_exp(CROPDMGEXP)]

storm_data[, total_damage := prop_damage + crop_damage]

econ_data <- storm_data[, .(
  total_damage = sum(total_damage, na.rm = TRUE)
), by = EVTYPE]

setorder(econ_data, -total_damage)

head(econ_data, 10)

##                EVTYPE total_damage
##                <char>        <num>
##  1:             FLOOD 150319678257
##  2: HURRICANE/TYPHOON  71913712800
##  3:           TORNADO  57352114049
##  4:       STORM SURGE  43323541000
##  5:              HAIL  18758221521
##  6:       FLASH FLOOD  17562129167
##  7:           DROUGHT  15018672000
##  8:         HURRICANE  14610229010
##  9:       RIVER FLOOD  10148404500
## 10:         ICE STORM   8967041360

Explanation

Economic damage is calculated by combining property and crop damage.
Damage multipliers (K, M, B) are converted into numeric values.

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Economic Impact Visualization

top_econ <- head(econ_data, 10)

barplot(top_econ$total_damage,
        names.arg = top_econ$EVTYPE,
        las = 2,
        col = "blue",
        main = "Top 10 Economic Damage Events",
        ylab = "Total Damage")

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Conclusion

• Tornadoes and extreme weather events have the highest health impact.
  
• Floods and hurricanes cause the highest economic damage.

This analysis helps identify the most severe weather events for better preparedness.

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