This analysis uses the NOAA Storm Database to evaluate the impact of severe weather events in the U.S. Tornadoes are the most harmful to public health, causing the highest number of fatalities and injuries. Floods cause the greatest economic damage, followed by hurricanes and tornadoes. Data was processed to account for damage exponents and summarized by event type. Visualizations show the top 10 events for both health and economic impact. The analysis was conducted in R and is fully reproducible.

Data Processing

Load Data and Packages

repdata_data_StormData <- read_csv("C:/Users/ldincasarcikli/Desktop/ReproducibleResearch_Project2_LDA_files/repdata_data_StormData.csv")
## Rows: 902297 Columns: 37
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (18): BGN_DATE, BGN_TIME, TIME_ZONE, COUNTYNAME, STATE, EVTYPE, BGN_AZI,...
## dbl (18): STATE__, COUNTY, BGN_RANGE, COUNTY_END, END_RANGE, LENGTH, WIDTH, ...
## lgl  (1): COUNTYENDN
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

#Across the United States, which types of events are most harmful with respect to population health? The most harmful type of events for population health is TORNADO.

# Summarize total fatalities and injuries by event type
harmful_events <- repdata_data_StormData %>%
  group_by(EVTYPE) %>%
  summarise(
    total_fatalities = sum(FATALITIES, na.rm = TRUE),
    total_injuries = sum(INJURIES, na.rm = TRUE),
    total_harmed = total_fatalities + total_injuries
  ) %>%
  arrange(desc(total_harmed))

View top 10 harmful event types

head(harmful_events, 10)
## # A tibble: 10 × 4
##    EVTYPE            total_fatalities total_injuries total_harmed
##    <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

Plot top 10 harmful_events

top10 <- harmful_events %>% top_n(10, total_harmed)

ggplot(top10, aes(x = reorder(EVTYPE, total_harmed), y = total_harmed)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(
    title = "Top 10 Most Harmful Weather Events in the US (Population Health)",
    x = "Event Type", y = "Total Fatalities + Injuries"
  )

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

The most harmful type of events for economy is FLOOD. # Function to convert exponent codes

convert_exp <- function(e) {
  e <- toupper(as.character(e))
  val <- rep(1, length(e))
  val[e == "K"] <- 1e3
  val[e == "M"] <- 1e6
  val[e == "B"] <- 1e9
  val[e %in% c("H")] <- 1e2
  val[!e %in% c("", "K", "M", "B", "H", "0")] <- NA
  return(val)
}

Apply exponent conversion

repdata_data_StormData <- repdata_data_StormData %>%
  mutate(
    prop_dmg_exp = convert_exp(PROPDMGEXP),
    crop_dmg_exp = convert_exp(CROPDMGEXP),
    prop_dmg_val = PROPDMG * prop_dmg_exp,
    crop_dmg_val = CROPDMG * crop_dmg_exp,
    total_damage = prop_dmg_val + crop_dmg_val
  )

Summarize total economic damage by event type

econ_damage <- repdata_data_StormData %>%
  group_by(EVTYPE) %>%
  summarise(
    property_damage = sum(prop_dmg_val, na.rm = TRUE),
    crop_damage = sum(crop_dmg_val, na.rm = TRUE),
    total_damage = sum(total_damage, na.rm = TRUE)
  ) %>%
  arrange(desc(total_damage))

View top 10 economic damage

head(econ_damage, 10)
## # A tibble: 10 × 4
##    EVTYPE            property_damage crop_damage total_damage
##    <chr>                       <dbl>       <dbl>        <dbl>
##  1 FLOOD               144657709800   5661968450 138007444500
##  2 HURRICANE/TYPHOON    69305840000   2607872800  29348167800
##  3 TORNADO              56937160614.   414953270  16570326363
##  4 HURRICANE            11868319010   2741910000  12405268000
##  5 RIVER FLOOD           5118945500   5029459000  10108369000
##  6 HAIL                 15732267456.  3025954470  10020596590
##  7 FLASH FLOOD          16140861772.  1421317100   8715455177
##  8 ICE STORM             3944927860   5022113500   5925150850
##  9 STORM SURGE/TIDE      4641188000       850000   4641493000
## 10 THUNDERSTORM WIND     3483121270    414843050   3813647990

##Plot Top 10 Economically Damaging Events

top10_econ <- econ_damage %>% top_n(10, total_damage)

ggplot(top10_econ, aes(x = reorder(EVTYPE, total_damage), y = total_damage)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(
    title = "Top 10 Weather Events with Greatest Economic Damage in the US",
    x = "Event Type", y = "Total Economic Damage (USD)"
  )