Synopsis

This analysis explores the U.S. National Oceanic and Atmospheric Administration (NOAA) Storm Database to determine which types of weather events are the most harmful to public health and which cause the greatest economic damage.
We analyze fatalities and injuries to identify human health impact, and property and crop damages to assess economic consequences.
Tornadoes are shown to have the highest impact on both health and economic losses.
The goal is to help decision-makers prioritize resources in preparation for severe weather.
All analysis starts from the raw .csv.bz2 file.
Figures are limited to three, with clear labels and descriptions.
All results are reproducible and based on publicly available data.

Data Processing

# Load raw storm data
storm_data <- read.csv("repdata-data-StormData.csv.bz2")

# Quick preview
head(storm_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

Results

Events with the Greatest Impact on Public Health

health_impact <- storm_data %>%
  group_by(EVTYPE) %>%
  summarise(
    total_fatalities = sum(FATALITIES, na.rm = TRUE),
    total_injuries = sum(INJURIES, na.rm = TRUE),
    total_health_impact = total_fatalities + total_injuries
  ) %>%
  arrange(desc(total_health_impact))

top_health <- head(health_impact, 10)
top_health$EVTYPE <- factor(top_health$EVTYPE, levels = top_health$EVTYPE)

ggplot(top_health, aes(x = EVTYPE, y = total_health_impact)) +
  geom_col(fill = "tomato") +
  coord_flip() +
  labs(title = "Top 10 Events Most Harmful to Population Health",
       x = "Event Type",
       y = "Fatalities + Injuries")

Events with the Greatest Economic Consequences

eco_impact <- storm_data %>%
  group_by(EVTYPE) %>%
  summarise(
    total_property_damage = sum(PROPDMG, na.rm = TRUE),
    total_crop_damage = sum(CROPDMG, na.rm = TRUE),
    total_economic_damage = total_property_damage + total_crop_damage
  ) %>%
  arrange(desc(total_economic_damage))

top_eco <- head(eco_impact, 10)
top_eco$EVTYPE <- factor(top_eco$EVTYPE, levels = top_eco$EVTYPE)

ggplot(top_eco, aes(x = EVTYPE, y = total_economic_damage)) +
  geom_col(fill = "steelblue") +
  coord_flip() +
  labs(title = "Top 10 Events with Greatest Economic Impact",
       x = "Event Type",
       y = "Economic Damage (USD)")

Conclusion

This analysis shows that tornadoes are the most destructive weather events in the United States, both in terms of health impact and economic loss.
Such information is essential for emergency preparedness and resource planning at national and local levels.