Assessment of Severe Weather Events

This report seeks to analyse the impact of severe weather events on the economic stability and population health of the United States. Through comprehensive analysis of historical storm data provided by NOAA, the findings reveal tornadoes as the most significant threat, causing the highest economic damage and posing the greatest risk to population health.

Data Processing

#loading the original dataset into RStudio 
file_path <- "/Users/klaramunteanu/Desktop/PERSONAL/Hopkins/Reproducible Research - project 2/Storm Data.csv"
data <- read.csv(file_path)

class(data$EVTYPE)

## [1] "character"

data$EVTYPE <- as.factor(data$EVTYPE)
library(dplyr)

## 
## 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)

Data Analysis

Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?

harmful_events <- data %>%
  group_by(EVTYPE) %>%
  summarise(total_fatalities = sum(FATALITIES, na.rm = TRUE),
            total_injuries = sum(INJURIES, na.rm = TRUE)) %>%
  ungroup()

harmful_events <- mutate(harmful_events, total_harm = total_fatalities + total_injuries)

top_harmful_events <- harmful_events %>%
  arrange(desc(total_harm))
head(top_harmful_events)

## # A tibble: 6 × 4
##   EVTYPE         total_fatalities total_injuries total_harm
##   <fct>                     <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

#Creating a plot with the top 10 most influential types of weather events in the case of health damage 
top_10_events_2 <- head(top_harmful_events, 10)

ggplot(top_10_events_2, aes(x = reorder(EVTYPE, -total_harm), y = total_harm)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  labs(title = "Top 10 Weather Events by Total Health Damage",
       x = "Event Type",
       y = "Total Health Damage") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

After having conducted the analysis, it turns out that tornadoes represent the event which has the most harmful consequences on the population health across the United States.

Across the United States, which types of events have the greatest economic consequences?

economic_consequences <- data %>%
  group_by(EVTYPE) %>%
  summarise(total_property_damage = sum(PROPDMG, na.rm = TRUE),
            total_crop_damage = sum(CROPDMG, na.rm = TRUE)) %>%
  ungroup()

economic_consequences <- mutate(economic_consequences, total_damage = total_property_damage + total_crop_damage)


top_economic_events <- economic_consequences %>%
  arrange(desc(total_damage))
head(top_economic_events)

## # A tibble: 6 × 4
##   EVTYPE            total_property_damage total_crop_damage total_damage
##   <fct>                             <dbl>             <dbl>        <dbl>
## 1 TORNADO                        3212258.           100019.     3312277.
## 2 FLASH FLOOD                    1420125.           179200.     1599325.
## 3 TSTM WIND                      1335966.           109203.     1445168.
## 4 HAIL                            688693.           579596.     1268290.
## 5 FLOOD                           899938.           168038.     1067976.
## 6 THUNDERSTORM WIND               876844.            66791.      943636.

#Creating a plot with the top 10 most influential types of weather events in the case of economic damage 
top_10_events <- head(top_economic_events, 10)


ggplot(top_10_events, aes(x = reorder(EVTYPE, -total_damage), y = total_damage)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  labs(title = "Top 10 Weather Events by Total Economic Damage",
       x = "Event Type",
       y = "Total Economic Damage") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

After having conducted the analysis, it turns out that tornadoes are the type of events which cause the most economic consequences across the United States, with a total damage twice as high as the runner up (flash flood).

Results

A comprehensive analysis of historical storm data (provided by the National Oceanic and Atmospheric Administration - NOAA) has allowed to enhance the authorities’ capability of mitigating potential disastrous events. The objective was to identify the types of events that pose the greatest threats to both population health and economic well-being across the United States.

The data spun from 1950 to 2011, and it comprised a wide range of severe weather events. However, the analysis only focused on population health and economic consequences. For population health, we considered the total number of fatalities and injuries associated with each event type. For economic consequences, we examined the total property and crop damage caused by each event.

The analysis has revealed that tornadoes are the most dangerous events in both instances. The analysis revealed that tornadoes incurred total damage twice as high as the runner-up, flash floods. This underlines the severe financial burden that such meteorological events pose on the affected area. The inquiry also highlighted tornadoes as the most harmful to population health. Tornadoes consistently ranked highest in total harm score, surpassing other event types in their combined impact on fatalities and injuries.

To conclude, the findings of this analysis carry important implications for the relevant authorities. Given that tornadoes are the common threat in both instances, allocating resources and implementing targeted preparedness measures against this type of event should be of utmost priority.

Assessment of Severe Weather Events

Klara Munteanu

2024-04-22

Data Processing

Data Analysis

Results