NOAA Storm Database Analysis

Synopsis

The United States (US) National Oceanic and Atmospheric Administration’s (NOAA) NOAA Storm Database is analyzed to provide information about severe weather events. Information about which types of weather events across the United States are most harmful to population health and which types of events across the United States are most costly for the economy is found. Using the data provided, more characteristics of these storms concerning when and where they occur and estimates of any fatalities, injuries, and property damage can be explored to prevent such outcomes to the extent possible. The analysis shows that tornados have the most impact on the population’s health and the nation’s economy.

Data Processing

To process the data, certain libraries have to be loaded to ensure that a proper analysis is done with the information provided by the database.

library(dplyr)

## Warning: package 'dplyr' was built under R version 4.2.3

## 
## 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)

## Warning: package 'ggplot2' was built under R version 4.2.3

library(readr)

## Warning: package 'readr' was built under R version 4.2.3

storm_data <- read.csv("repdata-data-StormData.csv")

Then the data can be filtered to remove and rows that may contain missing data.

storm_data <- storm_data %>% 
  filter(!is.na(EVTYPE) & !is.na(FATALITIES) & !is.na(INJURIES) & !is.na(PROPDMG) & !is.na(CROPDMG))

The total impact for each event type (including information such as sum of fatalities, injuries, property damage, and crop damage) can be calculated. After this, the events can be sorted by total impact.

storm_data_health <- storm_data %>% 
  group_by(EVTYPE) %>% 
  summarise(total_impact = sum(FATALITIES, INJURIES))
storm_data_economics <- storm_data %>% 
  group_by(EVTYPE) %>% 
  summarise(total_impact = sum(PROPDMG, CROPDMG))

storm_data_health <- storm_data_health %>% 
  arrange(desc(storm_data_health$total_impact))
storm_data_economics <- storm_data_economics %>% 
  arrange(desc(storm_data_economics$total_impact))

Results

Sorting through this data will reveal the events with the highest total impact on population health.

top_events_health <- storm_data_health[1:10, ]
print(top_events_health)

## # A tibble: 10 × 2
##    EVTYPE            total_impact
##    <chr>                    <dbl>
##  1 TORNADO                  96979
##  2 EXCESSIVE HEAT            8428
##  3 TSTM WIND                 7461
##  4 FLOOD                     7259
##  5 LIGHTNING                 6046
##  6 HEAT                      3037
##  7 FLASH FLOOD               2755
##  8 ICE STORM                 2064
##  9 THUNDERSTORM WIND         1621
## 10 WINTER STORM              1527

A bar plot can be created to better visualize this data.

ggplot(top_events_health, aes(x = reorder(EVTYPE, -total_impact), y = total_impact)) +
  geom_bar(stat = "identity", fill = "skyblue") +
  labs(title = "Top 10 Events with Highest Total Impact on Population Health",
       x = "Event Type",
       y = "Total Impact on Health (Fatalities and Injuries)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

This same approach can be used to find the top events with the highest economic consequences.

top_economic_impacts <- storm_data_economics[1:10, ]
print(top_economic_impacts)

## # A tibble: 10 × 2
##    EVTYPE             total_impact
##    <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.

A plot can then be considered in the same way.

ggplot(top_economic_impacts, aes(x = reorder(EVTYPE, -total_impact), y = total_impact)) +
  geom_bar(stat = "identity", fill = "skyblue") +
  labs(title = "Top 10 Events with Highest Total Impact on Economy",
       x = "Event Type",
       y = "Total Impact on Economy (Property and Crop Damage)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))