Storm_data_projectweek4

Synopsis

This report analyzes data from the U.S. NOAA Storm Database to identify the most harmful weather events according to population health and economic impact. Tornadoes are shown to be the leading cause of injuries and fatalities, while floods and hurricanes contribute to the greatest economic damage.

Data processing

library(dplyr)

## Warning: package 'dplyr' was built under R version 4.4.3

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)
library(data.table)

## Warning: package 'data.table' was built under R version 4.4.3

## 
## Attaching package: 'data.table'

## The following objects are masked from 'package:dplyr':
## 
##     between, first, last

storm <- read.csv("repdata_data_StormData.csv")
storm <- as.data.table(storm)

storm_data <- storm[, .(EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP)]

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

storm_data[, PROPDMGVAL := PROPDMG * convert_exp(PROPDMGEXP)]
storm_data[, CROPDMGVAL := CROPDMG * convert_exp(CROPDMGEXP)]
storm_data[, TOTALDMG := PROPDMGVAL + CROPDMGVAL]

Results

Most Harmful Events to Population Health

health_impact <- storm_data[, .(Fatalities = sum(FATALITIES),
                                Injuries = sum(INJURIES)),
                            by = EVTYPE][order(-Fatalities - Injuries)]
top_health <- head(health_impact, 10)
top_health

##                EVTYPE Fatalities Injuries
##                <char>      <num>    <num>
##  1:           TORNADO       5633    91346
##  2:    EXCESSIVE HEAT       1903     6525
##  3:         TSTM WIND        504     6957
##  4:             FLOOD        470     6789
##  5:         LIGHTNING        816     5230
##  6:              HEAT        937     2100
##  7:       FLASH FLOOD        978     1777
##  8:         ICE STORM         89     1975
##  9: THUNDERSTORM WIND        133     1488
## 10:      WINTER STORM        206     1321

ggplot(top_health, aes(reorder(EVTYPE, -(Fatalities + Injuries)), Injuries + Fatalities)) +
  geom_bar(stat = "identity", fill = "tomato") +
  labs(title = "Top 10 Most Harmful Events to Population Health",
       x = "Event Type", y = "Total Casualties (Fatalities + Injuries)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Events with Greatest Economic Impact

econ_impact <- storm_data[, .(Total_Damage = sum(TOTALDMG, na.rm = TRUE)),
                          by = EVTYPE][order(-Total_Damage)]
top_econ <- head(econ_impact, 10)
top_econ

##                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

ggplot(top_econ, aes(reorder(EVTYPE, -Total_Damage), Total_Damage / 1e9)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  labs(title = "Top 10 Events with Greatest Economic Impact",
       x = "Event Type", y = "Total Damage (Billion USD)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

## Conclusion Tornadoes appear to be the most harmful weather event to public health, while floods and hurricanes cause the most significant economic damages.