Synopsis

This report analyzes storm event data from the U.S. National Oceanic and Atmospheric Administration (NOAA) to identify which types of events are most harmful to population health (fatalities and injuries) and which cause the greatest economic damage (property and crop damage). The analysis shows that tornadoes are the most dangerous to human life, while floods result in the highest economic losses.

Load Required Packages

library(tidyverse)
library(ggplot2)
library(dplyr)
library(tidyr)

Data Processing

First we will read in the data from our csv and take a look at the first five rows.

data <- read.csv("repdata_data_StormData.csv.bz2")
head(data)
##   STATE__           BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE  EVTYPE
## 1       1  4/18/1950 0:00:00     0130       CST     97     MOBILE    AL TORNADO
## 2       1  4/18/1950 0:00:00     0145       CST      3    BALDWIN    AL TORNADO
## 3       1  2/20/1951 0:00:00     1600       CST     57    FAYETTE    AL TORNADO
## 4       1   6/8/1951 0:00:00     0900       CST     89    MADISON    AL TORNADO
## 5       1 11/15/1951 0:00:00     1500       CST     43    CULLMAN    AL TORNADO
## 6       1 11/15/1951 0:00:00     2000       CST     77 LAUDERDALE    AL TORNADO
##   BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1         0                                               0         NA
## 2         0                                               0         NA
## 3         0                                               0         NA
## 4         0                                               0         NA
## 5         0                                               0         NA
## 6         0                                               0         NA
##   END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES INJURIES PROPDMG
## 1         0                      14.0   100 3   0          0       15    25.0
## 2         0                       2.0   150 2   0          0        0     2.5
## 3         0                       0.1   123 2   0          0        2    25.0
## 4         0                       0.0   100 2   0          0        2     2.5
## 5         0                       0.0   150 2   0          0        2     2.5
## 6         0                       1.5   177 2   0          0        6     2.5
##   PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES LATITUDE LONGITUDE
## 1          K       0                                         3040      8812
## 2          K       0                                         3042      8755
## 3          K       0                                         3340      8742
## 4          K       0                                         3458      8626
## 5          K       0                                         3412      8642
## 6          K       0                                         3450      8748
##   LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1       3051       8806              1
## 2          0          0              2
## 3          0          0              3
## 4          0          0              4
## 5          0          0              5
## 6          0          0              6

Next we will subset the data to extract the columns we’re interested in.

event_data <- data |>
  select(c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")) |>
  rename_all(tolower)

Public Health Impact

Now we will group the data by event type so that we can look at fatalities and injuries.

health <- event_data |>
  group_by(evtype) |>
  summarize(
    fatalities = sum(fatalities, na.rm=TRUE), 
    injuries = sum(injuries, na.rm=TRUE), 
    .groups = 'drop'
  ) |>
  arrange(desc(fatalities + injuries)) |>
  slice(1:10) |>
  pivot_longer(cols=c(fatalities, injuries), names_to="type", values_to="count")

Economic Consequences

First, inspect the exponent columns:

unique(event_data$propdmgexp)
##  [1] "K" "M" ""  "B" "m" "+" "0" "5" "6" "?" "4" "2" "3" "h" "7" "H" "-" "1" "8"
unique(event_data$cropdmgexp)
## [1] ""  "M" "K" "m" "B" "?" "0" "k" "2"

Next we will write a function to deal with these exponents.

convert_exp <- function(e) {
  e <- toupper(e)
  dplyr::case_when(
    e == "H" ~ 1e2,
    e == "K" ~ 1e3,
    e == "M" ~ 1e6,
    e == "B" ~ 1e9,
    e %in% c("", "+", "-", "?") ~ 1,
    grepl("^[0-8]$", e) ~ 10 ^ as.numeric(e),
    TRUE ~ 1
  )
}

Next we will use this function to calculate economic damage by event type.

economic <- event_data |>
  mutate(
    prop_exp = convert_exp(propdmgexp),
    crop_exp = convert_exp(cropdmgexp), 
    prop_dmg_total = propdmg * prop_exp,
    crop_dmg_total = cropdmg * crop_exp
  ) |>
  group_by(evtype) |>
  summarize(
    property = sum(prop_dmg_total, na.rm=TRUE), 
    crop=sum(crop_dmg_total, na.rm=TRUE), 
    .groups='drop'
  ) |>
  arrange(desc(property + crop)) |>
  slice(1:10) |>
  pivot_longer(cols=c(property, crop), names_to="type", values_to="damage")

Results

Impact on Population Health

Tornadoes are by far the leading cause of fatalities and injuries, suggesting a need for improved forecasting, infrastructure, and public education in tornado-prone areas.

ggplot(data=health, aes(x=reorder(evtype, -count), y=count, fill=type)) +
  geom_bar(position="dodge", stat="identity") +
  labs(title="Tope 10 Weather Events Causing Injuries and Fatalities", x="event Type", y="Number of People Affected") +
  scale_fill_manual(values=c("purple4", "mediumpurple1")) +
  theme_minimal() +
  theme(axis.text.x=element_text(angle=45, hjust=1))

Economic Consequences

Floods account for the greatest overall economic losses in the U.S., particularly through extensive property damage, although droughts account for the greatest amount of crop damage.

ggplot(data=economic, aes(x=reorder(evtype, -damage), y=damage / 1e9, fill=type)) +
  geom_bar(position="dodge", stat="identity") +
  labs(title="Top 10 Weather Events by Economic Damage", x="Event Type", y="Damage (Billion USD)") +
  scale_fill_manual(values=c("navy", "steelblue1")) +
  theme_minimal() +
  theme(axis.text.x=element_text(angle=45, hjust=1))