Storm Data Analysis

Synopsis

This report explores the U.S. National Oceanic and Atmospheric Administration’s (NOAA) Storm Database to identify which types of severe weather events are most harmful to population health and which cause the greatest economic damage. The dataset includes events from 1950 to 2011. We analyze total fatalities and injuries to assess population health impact, and we combine property and crop damage to estimate economic consequences. The results are summarized with tables and visualized with bar plots.

Data Processing

The original data file repdata_data_StormData.csv.bz2 is loaded directly using read.csv(). No pre-processing was done outside this document.

# download
if(!file.exists("repdata_data_StormData.csv.bz2")) {
  download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2",
                destfile = "repdata_data_StormData.csv.bz2", mode = "wb")
}

# read
storm <- read.csv("repdata_data_StormData.csv.bz2")

We use the dplyr and ggplot2 packages for data manipulation and visualization.

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)

We standardize the event types to uppercase for consistency.

storm$EVTYPE <- toupper(storm$EVTYPE)

Results

Events Most Harmful to Population Health

To determine which types of events are most harmful to population health, we sum fatalities and injuries for each event type and display the top 10.

health <- storm %>%
  group_by(EVTYPE) %>%
  summarise(
    Fatalities = sum(FATALITIES, na.rm = TRUE),
    Injuries = sum(INJURIES, na.rm = TRUE)
  ) %>%
  mutate(Total = Fatalities + Injuries) %>%
  arrange(desc(Total)) %>%
  slice(1:10)

## `summarise()` ungrouping output (override with `.groups` argument)

print(health)

## # A tibble: 10 x 4
##    EVTYPE            Fatalities Injuries Total
##    <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

ggplot(health, aes(x = reorder(EVTYPE, -Total), y = Total)) +
  geom_bar(stat = "identity", fill = "red") +
  coord_flip() +
  labs(title = "Top 10 Weather Events Harmful to Population Health",
       x = "Event Type", y = "Total Fatalities and Injuries")

Events with Greatest Economic Consequences

To assess economic damage, we add property and crop damage per event type and identify the top 10.

econ <- storm %>%
  group_by(EVTYPE) %>%
  summarise(
    Property = sum(PROPDMG, na.rm = TRUE),
    Crop = sum(CROPDMG, na.rm = TRUE)
  ) %>%
  mutate(Total = Property + Crop) %>%
  arrange(desc(Total)) %>%
  slice(1:10)

## `summarise()` ungrouping output (override with `.groups` argument)

print(econ)

## # A tibble: 10 x 4
##    EVTYPE             Property    Crop    Total
##    <chr>                 <dbl>   <dbl>    <dbl>
##  1 TORNADO            3212258. 100019. 3312277.
##  2 FLASH FLOOD        1420125. 179200. 1599325.
##  3 TSTM WIND          1335996. 109203. 1445198.
##  4 HAIL                688693. 579596. 1268290.
##  5 FLOOD               899938. 168038. 1067976.
##  6 THUNDERSTORM WIND   876844.  66791.  943636.
##  7 LIGHTNING           603352.   3581.  606932.
##  8 THUNDERSTORM WINDS  446293.  18685.  464978.
##  9 HIGH WIND           324732.  17283.  342015.
## 10 WINTER STORM        132721.   1979.  134700.

ggplot(econ, aes(x = reorder(EVTYPE, -Total), y = Total)) +
  geom_bar(stat = "identity", fill = "darkgreen") +
  coord_flip() +
  labs(title = "Top 10 Weather Events with Greatest Economic Consequences",
       x = "Event Type", y = "Total Property and Crop Damage")

Conclusion

The data shows that tornadoes are by far the most harmful weather event to public health, while floods cause the most economic damage. These insights can help guide policy makers and emergency management efforts in prioritizing preparedness for future severe weather events.