NOAA Storm Databse Analysis

Synopsis

This report analyzes the U.S. National Oceanic and Atmospheric Administration (NOAA) Storm Database to determine:

1. Which event types are most harmful to population health across the United States

2. Which events cause the greatest economic consequences in the United States.

The analysis uses the original NOAA Storm Database, provided as a comma-separated values (CSV) file compressed with the bzip2 algorithm to reduce its size. The dataset covers weather events in the United States from 1950 to 2011 and includes detailed records of fatalities, injuries, and property and crop damages. The data were read directly from the compressed file, cleaned, and fully processed within R. All data cleaning steps and underlying assumptions are clearly documented. The code identifies the most significant event types contributing to human and economic impacts and visualizes them through bar plots. The Results section presents two figures—one for population health impacts and another for economic consequences followed by a brief interpretation and conclusion. All R code used in the analysis is included for full transparency and reproducibility.

Loading and processing the data

Loading libraries needed for the analysis:

library(readr)     # For reading CSV files quickly
library(dplyr)     # For data manipulation

## 
## Adjuntando el paquete: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(stringr)   # For string handling
library(forcats)   # For factor reordering

## Warning: package 'forcats' was built under R version 4.5.2

library(ggplot2)   # For visualization
library(scales)    # For formatting numbers 

## 
## Adjuntando el paquete: 'scales'

## The following object is masked from 'package:readr':
## 
##     col_factor

library(knitr)     # For nicer tables

Reading the file:

setwd("C:\\Users\\ladym\\Downloads") #set the path to your directory

storm_raw <- read_csv("repdata_data_StormData.csv.bz2", progress = TRUE, show_col_types = FALSE)

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    indexed 2.15GB in 31s, 2.15GB/s                                                                              

glimpse(storm_raw)

## Rows: 902,297
## Columns: 37
## $ STATE__    <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ BGN_DATE   <chr> "4/18/1950 0:00:00", "4/18/1950 0:00:00", "2/20/1951 0:00:0…
## $ BGN_TIME   <chr> "0130", "0145", "1600", "0900", "1500", "2000", "0100", "09…
## $ TIME_ZONE  <chr> "CST", "CST", "CST", "CST", "CST", "CST", "CST", "CST", "CS…
## $ COUNTY     <dbl> 97, 3, 57, 89, 43, 77, 9, 123, 125, 57, 43, 9, 73, 49, 107,…
## $ COUNTYNAME <chr> "MOBILE", "BALDWIN", "FAYETTE", "MADISON", "CULLMAN", "LAUD…
## $ STATE      <chr> "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "AL",…
## $ EVTYPE     <chr> "TORNADO", "TORNADO", "TORNADO", "TORNADO", "TORNADO", "TOR…
## $ BGN_RANGE  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ BGN_AZI    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ BGN_LOCATI <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ END_DATE   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ END_TIME   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ COUNTY_END <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ COUNTYENDN <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ END_RANGE  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ END_AZI    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ END_LOCATI <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ LENGTH     <dbl> 14.0, 2.0, 0.1, 0.0, 0.0, 1.5, 1.5, 0.0, 3.3, 2.3, 1.3, 4.7…
## $ WIDTH      <dbl> 100, 150, 123, 100, 150, 177, 33, 33, 100, 100, 400, 400, 2…
## $ F          <dbl> 3, 2, 2, 2, 2, 2, 2, 1, 3, 3, 1, 1, 3, 3, 3, 4, 1, 1, 1, 1,…
## $ MAG        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ FATALITIES <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 4, 0, 0, 0, 0,…
## $ INJURIES   <dbl> 15, 0, 2, 2, 2, 6, 1, 0, 14, 0, 3, 3, 26, 12, 6, 50, 2, 0, …
## $ PROPDMG    <dbl> 25.0, 2.5, 25.0, 2.5, 2.5, 2.5, 2.5, 2.5, 25.0, 25.0, 2.5, …
## $ PROPDMGEXP <chr> "K", "K", "K", "K", "K", "K", "K", "K", "K", "K", "M", "M",…
## $ CROPDMG    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ CROPDMGEXP <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ WFO        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ STATEOFFIC <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ZONENAMES  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ LATITUDE   <dbl> 3040, 3042, 3340, 3458, 3412, 3450, 3405, 3255, 3334, 3336,…
## $ LONGITUDE  <dbl> 8812, 8755, 8742, 8626, 8642, 8748, 8631, 8558, 8740, 8738,…
## $ LATITUDE_E <dbl> 3051, 0, 0, 0, 0, 0, 0, 0, 3336, 3337, 3402, 3404, 0, 3432,…
## $ LONGITUDE_ <dbl> 8806, 0, 0, 0, 0, 0, 0, 0, 8738, 8737, 8644, 8640, 0, 8540,…
## $ REMARKS    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ REFNUM     <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, …

Cleaning and tidying the data

The dataset includes property/crop damage in two parts:

• PROPDMG / CROPDMG: numeric value

• PROPDMGEXP / CROPDMGEXP: an exponent indicator (K= thousand, M= million and B= billion)

We need to convert these into actual dollar values:

# Function to convert exponent symbols into numeric multipliers
exp_to_multiplier <- function(e) {
  e2 <- ifelse(is.na(e), "", as.character(e))
  sapply(e2, function(x) {
    x <- str_trim(tolower(x))
    if (x %in% c("k")) return(1e3)   
    if (x %in% c("m")) return(1e6)    
    if (x %in% c("b")) return(1e9)     
    if (x %in% as.character(0:9)) return(10^(as.numeric(x)))  # numeric exponent
    return(1)  # Default if blank or unknown
  })
}

# Clean and transform the data
storm <- storm_raw %>%
  mutate(
    # Standardize event type text
    EVTYPE = str_to_upper(str_trim(EVTYPE)),               # normalize event names
    
    # Compute numeric multipliers 
    PROP_MULT = exp_to_multiplier(PROPDMGEXP),             # property multiplier
    CROP_MULT = exp_to_multiplier(CROPDMGEXP),             # crop multiplier
    
    PROPDMG_DOLLARS = as.numeric(PROPDMG) * PROP_MULT,     # property damage ($)
    CROPDMG_DOLLARS = as.numeric(CROPDMG) * CROP_MULT,     # crop damage ($)
    
    # Combine health and economic impacts (fatalities + injuries)
    HEALTH_IMPACT = as.numeric(FATALITIES) + as.numeric(INJURIES),
    ECONOMIC_IMPACT = PROPDMG_DOLLARS + CROPDMG_DOLLARS    # Total damage
  ) %>%
  select(EVTYPE, FATALITIES, INJURIES, HEALTH_IMPACT,
         PROPDMG_DOLLARS, CROPDMG_DOLLARS, ECONOMIC_IMPACT)

Once the data is clean and tidy, we will arrange the events by type to summarize and identify the wanted results:

# Aggregate total impacts by event type
impact_by_event <- storm %>%
  group_by(EVTYPE) %>%
  summarise(
    total_fatalities = sum(FATALITIES, na.rm = TRUE),
    total_injuries = sum(INJURIES, na.rm = TRUE),
    total_health = sum(HEALTH_IMPACT, na.rm = TRUE),
    total_prop_dmg = sum(PROPDMG_DOLLARS, na.rm = TRUE),
    total_crop_dmg = sum(CROPDMG_DOLLARS, na.rm = TRUE),
    total_economic = sum(ECONOMIC_IMPACT, na.rm = TRUE),
    events= n()
  ) %>%
  ungroup() %>%
  arrange(desc(total_health))


top_health <- impact_by_event %>%
  arrange(desc(total_health)) %>%
  slice_head(n = 10) # Top 10 events causing most harm to population health

top_economic <- impact_by_event %>%
  arrange(desc(total_economic)) %>%
  slice_head(n = 10) # Top 10 events causing greatest economic damage

In this case, for the visualization of the results we will display just the first 10 events since they are the most significant ones.

#Top 10 events by population health impact
kable(top_health)   

EVTYPE	total_fatalities	total_injuries	total_health	total_prop_dmg	total_crop_dmg	total_economic	events
TORNADO	5633	91346	96979	56947380677	414953270	57362333947	60652
EXCESSIVE HEAT	1903	6525	8428	7753700	492402000	500155700	1678
TSTM WIND	504	6957	7461	4493058495	554007350	5047065845	219946
FLOOD	470	6789	7259	144657709807	5661968450	150319678257	25327
LIGHTNING	816	5230	6046	930379430	12092090	942471520	15755
HEAT	937	2100	3037	1797000	401461500	403258500	767
FLASH FLOOD	978	1777	2755	16822723979	1421317100	18244041079	54278
ICE STORM	89	1975	2064	3944927860	5022113500	8967041360	2006
THUNDERSTORM WIND	133	1488	1621	3483122472	414843050	3897965522	82564
WINTER STORM	206	1321	1527	6688497251	26944000	6715441251	11433

#Top 10 events by economic impact
kable(top_economic) 

EVTYPE	total_fatalities	total_injuries	total_health	total_prop_dmg	total_crop_dmg	total_economic	events
FLOOD	470	6789	7259	144657709807	5661968450	150319678257	25327
HURRICANE/TYPHOON	64	1275	1339	69305840000	2607872800	71913712800	88
TORNADO	5633	91346	96979	56947380677	414953270	57362333947	60652
STORM SURGE	13	38	51	43323536000	5000	43323541000	261
HAIL	15	1361	1376	15735267018	3025954473	18761221491	288661
FLASH FLOOD	978	1777	2755	16822723979	1421317100	18244041079	54278
DROUGHT	0	4	4	1046106000	13972566000	15018672000	2488
HURRICANE	61	46	107	11868319010	2741910000	14610229010	174
RIVER FLOOD	2	2	4	5118945500	5029459000	10148404500	173
ICE STORM	89	1975	2064	3944927860	5022113500	8967041360	2006

For a further research,we will analyze property and crop damages separately to identify which event types harm infrastructure versus agriculture. And we will do the same with fatalities and injuries with the populations health.

# Property Damage
top_prop <- impact_by_event %>%
  arrange(desc(total_prop_dmg)) %>%
  slice_head(n = 10)

# Crop Damage
top_crop <- impact_by_event %>%
  arrange(desc(total_crop_dmg)) %>%
  slice_head(n = 10)

Total health impact over time:

storm$year <- as.numeric(format(as.Date(storm_raw$BGN_DATE, "%m/%d/%Y"), "%Y"))

Results

# Plot: Top 10 Population Health Impact
ggplot(top_health, aes(x = reorder(EVTYPE, total_health), y = total_health)) +
  geom_col(fill = "steelblue") +
  coord_flip() +
  labs(
    title = "Top 10 Event Types by Population Health Impact",
    x = " ",
    y = "Total fatalities + injuries"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold"),
    axis.text.y = element_text(size = 9)
  )

# Plot: Top 10 Economic Impact
ggplot(top_economic, aes(x = reorder(EVTYPE, total_economic), y = total_economic)) +
  geom_col(fill = "firebrick") +
  coord_flip() +
  scale_y_continuous(labels = dollar_format(scale = 1e-9, suffix = "B")) +
  labs(
    title = "Top 10 Event Types by Economic Impact",
    x = " ",
    y = "Property + crop damages in billions of USD"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold"),
    axis.text.y = element_text(size = 9)
  )

Interpretation

1. Population Health: Tornadoes stand out as the leading cause of both fatalities and injuries, with thousands of casualties,far exceeding any other event type. Other major contributors include excessive heat, flash floods, and lightning, though their impacts are considerably smaller than tornadoes.

2. Economic Damage: Floods produce the largest overall financial damage, followed by hurricanes/typhoons, tornadoes, and storm surges. These events cause billions of dollars in losses, largely due to widespread infrastructure and agricultural destruction.

This information will help prioritize emergency preparedness and mitigation resources.The findings suggest that strategies should particularly prioritize tornadoes for human safety and flood-related events for economic resilience.

Health Impact breakdown

In order to get a better insight of the deadliest events versus the nonfatal ones we will represent separately the injures and the fatalities.

ggplot(top_health) +
  geom_col(aes(x = reorder(EVTYPE, total_health), y = total_fatalities, fill = "Fatalities")) +
  geom_col(aes(x = reorder(EVTYPE, total_health), y = total_injuries, fill = "Injuries"), alpha = 0.6) +
  coord_flip() +
  scale_fill_manual(
    values = c("Fatalities" = "orange", "Injuries" = "yellow2"),
    name = ""  # legend title (blank for cleaner look)
  ) +
  labs(
    title = "Top 10 Event Types by Fatalities and Injuries",
    x = "",
    y = "Number of People Affected"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold"),
    axis.text.y = element_text(size = 9),
    legend.position = "bottom")

Conclusions

Tornadoes stand out as the leading cause of both fatalities and injuries in the United States, indicating their extreme danger to human life. Excessive heat and flash floods causes a relatively higher number of fatalities compared to injuries, highlighting its deadly nature. In contrast, events like thunderstorms and lightning result in more injuries than deaths. Overall, while several weather events threaten public safety, tornadoes remain the most devastating in both categories. Therefore, mitigation resources should primary focus on tornadoes and heat.

Economic impact breakdown

And similarly, we will separate the property damage from the crop damage

ggplot(top_economic, aes(x = reorder(EVTYPE, total_economic))) +
  geom_col(aes(y = total_prop_dmg, fill = "Property Damage")) +
  geom_col(aes(y = total_crop_dmg, fill = "Crop Damage"), alpha = 0.6) +
  coord_flip() +
  scale_y_continuous(labels = scales::dollar_format(scale = 1e-9, suffix = "B")) +
  scale_fill_manual(values = c("Property Damage" = "royalblue", "Crop Damage" = "green3")) +
  labs(
    title = "Top 10 Events by Property and Crop Damage",
    x = "",
    y = "Total Damage in billions USD",
    fill = ""
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold"),
    axis.text.y = element_text(size = 9),
    legend.position = "bottom")

Conclusions

Floods and hurricanes cause the greatest property losses, reflecting their destructive impact on infrastructure and urban areas. In contrast, drought and hail produce the most severe crop damages, mainly affecting agricultural productivity. This distinction shows that property and crop damages are driven by different types of weather events, emphasizing the need for targeted mitigation strategies for each sector.

Total health impact over time

ggplot(storm, aes(x = year, y = HEALTH_IMPACT)) +
  geom_line(stat = "summary", fun = sum, color = "navy") +
  labs(title = "Total Health Impact Over Time", y = "Fatalities + Injuries")

The plot reveals whether extreme event impacts are increasing due to climate change or population growth.