NOAA Storm Data Analysis
Muhammad Azeem
2026-07-30
Synopsis
Severe weather events can have significant impacts on public health and the economy. This analysis explores the U.S. National Oceanic and Atmospheric Administration (NOAA) Storm Database to identify which types of weather events are most harmful to population health and which result in the greatest economic damage. The analysis begins with the original compressed dataset, performs the required data processing in R, summarizes the results, and presents visualizations that help identify the most impactful event types.
Data Processing
Load Required Packages
library(dplyr)
library(ggplot2)
library(knitr)Load the Raw Storm Data
storm <- read.csv(
bzfile("repdata_data_StormData.csv.bz2"),
stringsAsFactors = FALSE
)
dim(storm)## [1] 902297 37str(storm)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 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 0:00:00" "6/8/1951 0:00:00" ...
## $ BGN_TIME : chr "0130" "0145" "1600" "0900" ...
## $ TIME_ZONE : chr "CST" "CST" "CST" "CST" ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: chr "MOBILE" "BALDWIN" "FAYETTE" "MADISON" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : chr "" "" "" "" ...
## $ BGN_LOCATI: chr "" "" "" "" ...
## $ END_DATE : chr "" "" "" "" ...
## $ END_TIME : chr "" "" "" "" ...
## $ COUNTY_END: num 0 0 0 0 0 0 0 0 0 0 ...
## $ COUNTYENDN: logi NA NA NA NA NA NA ...
## $ END_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ END_AZI : chr "" "" "" "" ...
## $ END_LOCATI: chr "" "" "" "" ...
## $ LENGTH : num 14 2 0.1 0 0 1.5 1.5 0 3.3 2.3 ...
## $ WIDTH : num 100 150 123 100 150 177 33 33 100 100 ...
## $ F : int 3 2 2 2 2 2 2 1 3 3 ...
## $ MAG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ FATALITIES: num 0 0 0 0 0 0 0 0 1 0 ...
## $ INJURIES : num 15 0 2 2 2 6 1 0 14 0 ...
## $ PROPDMG : num 25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
## $ PROPDMGEXP: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...
## $ WFO : chr "" "" "" "" ...
## $ STATEOFFIC: chr "" "" "" "" ...
## $ ZONENAMES : chr "" "" "" "" ...
## $ LATITUDE : num 3040 3042 3340 3458 3412 ...
## $ LONGITUDE : num 8812 8755 8742 8626 8642 ...
## $ LATITUDE_E: num 3051 0 0 0 0 ...
## $ LONGITUDE_: num 8806 0 0 0 0 ...
## $ REMARKS : chr "" "" "" "" ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...summary(storm)## Warning in grep("^[ \t\r\n]*$", object, perl = TRUE): input string 192565 is
## invalid UTF-8## Warning in grep("^[ \t\r\n]*$", object, perl = TRUE): input string 194345 is
## invalid UTF-8## Warning in grep("^[ \t\r\n]*$", object, perl = TRUE): input string 199735 is
## invalid UTF-8## Warning in grep("^[ \t\r\n]*$", object, perl = TRUE): input string 199745 is
## invalid UTF-8## Warning in grep("^[ \t\r\n]*$", object, perl = TRUE): input string 200467 is
## invalid UTF-8## STATE__ BGN_DATE BGN_TIME TIME_ZONE
## Min. : 1.0 Length :902297 Length :902297 Length :902297
## 1st Qu.:19.0 N.unique : 16335 N.unique : 3608 N.unique : 22
## Median :30.0 N.blank : 0 N.blank : 0 N.blank : 0
## Mean :31.2 Min.nchar: 16 Min.nchar: 3 Min.nchar: 3
## 3rd Qu.:45.0 Max.nchar: 18 Max.nchar: 11 Max.nchar: 3
## Max. :95.0
##
## COUNTY COUNTYNAME STATE EVTYPE
## Min. : 0.0 Length :902297 Length :902297 Length :902297
## 1st Qu.: 31.0 N.unique : 29601 N.unique : 72 N.unique : 985
## Median : 75.0 N.blank : 1589 N.blank : 0 N.blank : 0
## Mean :100.6 Min.nchar: 0 Min.nchar: 2 Min.nchar: 1
## 3rd Qu.:131.0 Max.nchar: 200 Max.nchar: 2 Max.nchar: 30
## Max. :873.0
##
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE
## Min. : 0.000 Length :902297 Length :902297 Length :902297
## 1st Qu.: 0.000 N.unique : 35 N.unique : 54429 N.unique : 6663
## Median : 0.000 N.blank :547332 N.blank :287743 N.blank :243411
## Mean : 1.484 Min.nchar: 0 Min.nchar: 0 Min.nchar: 0
## 3rd Qu.: 1.000 Max.nchar: 3 Max.nchar: 21 Max.nchar: 18
## Max. :3749.000
##
## END_TIME COUNTY_END COUNTYENDN END_RANGE
## Length :902297 Min. :0 Mode:logical Min. : 0.0000
## N.unique : 3647 1st Qu.:0 NAs :902297 1st Qu.: 0.0000
## N.blank :238978 Median :0 Median : 0.0000
## Min.nchar: 0 Mean :0 Mean : 0.9862
## Max.nchar: 12 3rd Qu.:0 3rd Qu.: 0.0000
## Max. :0 Max. :925.0000
##
## END_AZI END_LOCATI LENGTH WIDTH
## Length :902297 Length :902297 Min. : 0.0000 Min. : 0.000
## N.unique : 24 N.unique : 34506 1st Qu.: 0.0000 1st Qu.: 0.000
## N.blank :724837 N.blank :499225 Median : 0.0000 Median : 0.000
## Min.nchar: 0 Min.nchar: 0 Mean : 0.2301 Mean : 7.503
## Max.nchar: 3 Max.nchar: 21 3rd Qu.: 0.0000 3rd Qu.: 0.000
## Max. :2315.0000 Max. :4400.000
##
## F MAG FATALITIES INJURIES
## Min. :0.000 Min. : 0.0 Min. : 0.00000 Min. : 0.0000
## 1st Qu.:0.000 1st Qu.: 0.0 1st Qu.: 0.00000 1st Qu.: 0.0000
## Median :1.000 Median : 50.0 Median : 0.00000 Median : 0.0000
## Mean :0.915 Mean : 46.9 Mean : 0.01678 Mean : 0.1557
## 3rd Qu.:1.000 3rd Qu.: 75.0 3rd Qu.: 0.00000 3rd Qu.: 0.0000
## Max. :5.000 Max. :22000.0 Max. :583.00000 Max. :1700.0000
## NAs :843563
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 Length :902297 Min. : 0.000 Length :902297
## 1st Qu.: 0.00 N.unique : 19 1st Qu.: 0.000 N.unique : 9
## Median : 0.00 N.blank :465934 Median : 0.000 N.blank :618413
## Mean : 12.06 Min.nchar: 0 Mean : 1.527 Min.nchar: 0
## 3rd Qu.: 0.50 Max.nchar: 1 3rd Qu.: 0.000 Max.nchar: 1
## Max. :5000.00 Max. :990.000
##
## WFO STATEOFFIC ZONENAMES LATITUDE
## Length :902297 Length :902297 Length :902297 Min. : 0
## N.unique : 542 N.unique : 250 N.unique : 25112 1st Qu.:2802
## N.blank :142069 N.blank :248769 N.blank :800017 Median :3540
## Min.nchar: 0 Min.nchar: 0 Min.nchar: 0 Mean :2875
## Max.nchar: 3 Max.nchar: 45 Max.nchar: 7226 3rd Qu.:4019
## Max. :9706
## NAs :47
## LONGITUDE LATITUDE_E LONGITUDE_ REMARKS
## Min. :-14451 Min. : 0 Min. :-14455 Length :902297
## 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0 N.unique :436781
## Median : 8707 Median : 0 Median : 0 N.blank :312091
## Mean : 6940 Mean :1452 Mean : 3509 Min.nchar: NA
## 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735 Max.nchar: NA
## Max. : 17124 Max. :9706 Max. :106220
## NAs :40
## REFNUM
## Min. : 1
## 1st Qu.:225575
## Median :451149
## Mean :451149
## 3rd Qu.:676723
## Max. :902297
## Results
Across the United States, Which Types of Events Are Most Harmful to Population Health?
Population health impact is measured using the total number of fatalities and injuries associated with each weather event type.
health <- storm %>%
group_by(EVTYPE) %>%
summarise(
Fatalities = sum(FATALITIES, na.rm = TRUE),
Injuries = sum(INJURIES, na.rm = TRUE),
HealthImpact = Fatalities + Injuries
) %>%
arrange(desc(HealthImpact))
head(health, 10)## # A tibble: 10 × 4
## EVTYPE Fatalities Injuries HealthImpact
## <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 1527Top 10 Most Harmful Weather Events
top_health <- head(health, 10)
ggplot(
top_health,
aes(
x = reorder(EVTYPE, HealthImpact),
y = HealthImpact
)
) +
geom_col(fill = "firebrick") +
coord_flip() +
labs(
title = "Top 10 Weather Events Most Harmful to Population Health",
x = "Event Type",
y = "Fatalities + Injuries"
) +
theme_minimal()
Interpretation
The plot shows the ten weather event types responsible for the greatest combined number of fatalities and injuries across the United States. These events represent the highest risk to public health and are important priorities for emergency preparedness and disaster management. ## Across the United States, Which Types of Events Have the Greatest Economic Consequences?
Economic damage is calculated by combining property damage and crop damage. The damage exponent values (K, M, B) are converted into their numeric multipliers before calculating the total damage.
storm$PROP_MULT <- 1
storm$PROP_MULT[storm$PROPDMGEXP %in% c("K","k")] <- 1e3
storm$PROP_MULT[storm$PROPDMGEXP %in% c("M","m")] <- 1e6
storm$PROP_MULT[storm$PROPDMGEXP %in% c("B","b")] <- 1e9
storm$CROP_MULT <- 1
storm$CROP_MULT[storm$CROPDMGEXP %in% c("K","k")] <- 1e3
storm$CROP_MULT[storm$CROPDMGEXP %in% c("M","m")] <- 1e6
storm$CROP_MULT[storm$CROPDMGEXP %in% c("B","b")] <- 1e9
storm$PropertyDamage <- storm$PROPDMG * storm$PROP_MULT
storm$CropDamage <- storm$CROPDMG * storm$CROP_MULT
storm$TotalDamage <- storm$PropertyDamage + storm$CropDamage
economic <- storm %>%
group_by(EVTYPE) %>%
summarise(
TotalDamage = sum(TotalDamage, na.rm = TRUE)
) %>%
arrange(desc(TotalDamage))
head(economic,10)## # A tibble: 10 × 2
## EVTYPE TotalDamage
## <chr> <dbl>
## 1 FLOOD 150319678257
## 2 HURRICANE/TYPHOON 71913712800
## 3 TORNADO 57352114049.
## 4 STORM SURGE 43323541000
## 5 HAIL 18758221521.
## 6 FLASH FLOOD 17562129167.
## 7 DROUGHT 15018672000
## 8 HURRICANE 14610229010
## 9 RIVER FLOOD 10148404500
## 10 ICE STORM 8967041360Top 10 Weather Events Causing the Greatest Economic Damage
top_economic <- head(economic,10)
ggplot(
top_economic,
aes(
x = reorder(EVTYPE, TotalDamage),
y = TotalDamage
)
) +
geom_col(fill="steelblue") +
coord_flip() +
labs(
title="Top 10 Weather Events by Economic Damage",
x="Event Type",
y="Total Damage (USD)"
) +
theme_minimal()
Interpretation
The figure presents the weather event types responsible for the greatest economic losses across the United States based on the combined value of property and crop damage.
Conclusion
This analysis explored the NOAA Storm Database to identify the weather events with the greatest impact on public health and the economy in the United States. The findings indicate that a small number of event types account for the majority of fatalities, injuries, and economic losses. These results can help government agencies and emergency management organizations better understand the risks associated with severe weather and support future planning and preparedness efforts.