Tornados and Floods are the most damaging weather events in the USA

Based on the data collected by the NOAA, priorities for budget allocation for disaster prevention and recovery can be set. In this analysis the weather events that are most harmful based on human and economic impact are outlined. Human impact is based on number of injuries and fatalities, while economic impact is determined based on the property damage inflicted by the weather type.

1. Data Processing

Reading the csv

setwd("C:\\Users\\maknickel\\Desktop")
download.file(url = 'https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2', destfile = 'stormdata.csv.bz2')
data <- read.csv("repdata_data_StormData.csv.bz2", header = TRUE)
dframe <- data.frame(data) 

1b. Preprocessing

load needed libraries

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.4.1

library(dplyr)

## Warning: package 'dplyr' was built under R version 4.4.1

## 
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library(tidyverse)

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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(chron)

## Warning: package 'chron' was built under R version 4.4.1

## 
## Attaching package: 'chron'
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library(ggpubr)

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To determine the Event type that causes the most harm first a count of the number of fatalities and injuries per event type will be calculated.

colnames <- c("evtype", "fatalities", "injuries")
evtype <- unique(dframe$EVTYPE)
eventtypes <- length(evtype)
mylist.names <- NULL
fatalities <- vector(mode = "integer", length = eventtypes)
injuries <- vector(mode = "integer", length = eventtypes)
simpframe <- data.frame(evtype, fatalities, injuries)
for(i in 1:nrow(dframe))
{
  evtyperow <- which(simpframe$evtype == dframe$EVTYPE[i])
  simpframe$fatalities[evtyperow] <- dframe$FATALITIES[i] + simpframe$fatalities[evtyperow]
  simpframe$injuries[evtyperow] <- dframe$INJURIES[i] + simpframe$injuries[evtyperow]
}

In order to determine the most harmful event on economic factors, the impact of each event type (in $) will be calculated

colnames <- c("evtype", "cost")
cost <- vector(mode = "integer", length = eventtypes)
simpframe2 <- data.frame(evtype, cost)
number <- 1
for(i in 1:nrow(dframe))
{
  if(dframe$PROPDMGEXP[i] == "K")
  {
    number <- dframe$PROPDMG[i]*1000
  }
  if(dframe$PROPDMGEXP[i] == "M")
  {
    number <- dframe$PROPDMG[i]*1000000
  }
  if(dframe$PROPDMGEXP[i] == "B")
  {
    number <- dframe$PROPDMG[i]*1000000000
  }
  else
  {
    number <- dframe$PROPDMG[i]
  }
  evtyperow <- which(simpframe2$evtype == dframe$EVTYPE[i])
  simpframe2$cost[evtyperow] <- number + simpframe2$cost[evtyperow]
}

3. Results

The most harmful weather event is:

simpframe$evtype[which.max(simpframe$fatalities)]

## [1] "TORNADO"

tophits <- slice_max(simpframe, fatalities, n=10)
barplot(height = tophits$fatalities, name = tophits$evtype)

This barplot shows the top 10 most damaging weather events in human cost and their respective fatalities.

The most economically damaging weather event is:

simpframe2$evtype[which.max(simpframe2$cost)]

## [1] "FLOOD"

tophits <- slice_max(simpframe2, cost, n=10)
barplot(height = tophits$cost, name = tophits$evtype)

This barplot shows the top 10 most damaging weather events economically and their respective property damgage in USD.