Assignment Introduction

This report analyzes the storm database of the U.S. National Oceanic and Atmospheric Administration (NOAA). The dataset documents key characteristics of severe storm events in the United States, as well as the associated deaths, injuries, and property damage. The analysis focuses on two questions: Which type of severe weather event has the greatest impact on public health, and which causes the greatest economic damage? The data analysis involves downloading and importing the CSV file, selecting and preparing the information relevant to the respective questions, and clearly presenting the results graphically. The results of this analysis show that, among the types of events examined, tornadoes cause by far the most severe health and economic consequences. These findings can help raise awareness among policymakers regarding the impacts of severe weather events. Furthermore, they provide a basis for setting priorities in disaster preparedness and for allocating resources more effectively to manage future events.

library(ggplot2)
library(dplyr)
## 
## Attache Paket: 'dplyr'
## Die folgenden Objekte sind maskiert von 'package:stats':
## 
##     filter, lag
## Die folgenden Objekte sind maskiert von 'package:base':
## 
##     intersect, setdiff, setequal, union
library(tidyr)

As a first step the data is loaded and imported into R Studio. The file used is in bz2- compressed format.

fileUrl <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
if (!file.exists("StormData.csv.bz2")) {
  download.file(fileUrl, "StormData.csv.bz2", method = "curl")
}
storm_data <- read.csv("StormData.csv.bz2")

Top 10 weather events with most harm to populations health

Across the United States, which types of events (as indicated in the EVTYPE) are most harmful with respect to population health? In the next step, the first question of the assignment is answered. To do this, the dataset is processed to isolate the data related to public health. This information is aggregated with data on fatalities and injuries by event type.

health_data <- storm_data %>% group_by(EVTYPE) %>% summarize(Total_Fatalities = sum(FATALITIES, na.rm = TRUE), Total_Injuries = sum(INJURIES, na.rm = TRUE), Total_Harm = Total_Fatalities + Total_Injuries) %>% arrange(desc(Total_Harm))
top_ten_health_events <- head(health_data, 10)
print(top_ten_health_events)
## # A tibble: 10 × 4
##    EVTYPE            Total_Fatalities Total_Injuries Total_Harm
##    <chr>                        <dbl>          <dbl>      <dbl>
##  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

Top 10 weather events with most harm to economy

Across the United States, which types of events have the greatest economic consequences? To answer this question, the dataset is processed to isolate the data on economic damage. To do this, property and crop damage are given priority.

economic_data <- storm_data %>% mutate(total_economic_damage = PROPDMG + CROPDMG) %>% group_by(EVTYPE) %>% summarize(Economic_Cost = sum(total_economic_damage, na.rm = TRUE)) %>% arrange(desc(Economic_Cost))
top_ten_economic_events <- head(economic_data, 10)
print(top_ten_economic_events)
## # A tibble: 10 × 2
##    EVTYPE             Economic_Cost
##    <chr>                      <dbl>
##  1 TORNADO                 3312277.
##  2 FLASH FLOOD             1599325.
##  3 TSTM WIND               1445168.
##  4 HAIL                    1268290.
##  5 FLOOD                   1067976.
##  6 THUNDERSTORM WIND        943636.
##  7 LIGHTNING                606932.
##  8 THUNDERSTORM WINDS       464978.
##  9 HIGH WIND                342015.
## 10 WINTER STORM             134700.

Results:

Top 10 weather events with most harm to populations health

To analyze which types of extreme weather events are most harmful to the population, the total number of fatalities and injuries was calculated for each type of weather event. The results are shown in the following graph. As clearly shown in this figure, Tornados cause by far rhe highest number of casualties (fatalities and injuries combined).

ggplot(top_ten_health_events, aes(x = reorder(EVTYPE, Total_Harm), y = Total_Harm)) + geom_bar(stat = "identity", fill = "darkblue") + coord_flip() + labs(title = "Top 10 most harmful weather events", x = "Event type", y = "Number of Fatalities and Injuries") + theme_classic()

Top 10 weather events with most harm to economic

To evaluate economic consequences, the sum of property and crop damages are calculated for each event type. To visualize the findings, the following barplot was created.

ggplot(top_ten_economic_events, aes(x = reorder(EVTYPE, Economic_Cost), y = Economic_Cost)) + geom_bar(stat = "identity", fill = "darkblue") + coord_flip() + labs(title = "Top 10 weather events with most economic consequences", x = "Event type", y = "Total damage cost (in USD)") + theme_classic()