Analysis of NOAA Storm Database: Understanding Severe Weather Events in the United States
2023-12-05
Synopsis
This data analysis aims to explore the NOAA Storm Database and provide insights into severe weather events in the United States. The analysis addresses two key questions: 1) Which types of events are most harmful to population health? 2) Which types of events have the greatest economic consequences? The results are presented in a clear and concise manner to assist government or municipal managers in prioritizing resources for different types of severe weather events.
Data Processing
# Load required libraries
library(ggplot2)
# Load the NOAA Storm Database
storm_data <- read.csv("repdata_data_StormData.csv")
# Display the first few rows of the data
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 6Most Harmful Events to Population Health
# Summarize the data to find harmful events to population health
harmful_events <- aggregate(cbind(FATALITIES, INJURIES) ~ EVTYPE, storm_data, sum)
harmful_events <- harmful_events[order(rowSums(harmful_events[, 2:3]), decreasing = TRUE), ][1:10, ]
# Display the top harmful events
harmful_events## EVTYPE FATALITIES INJURIES
## 834 TORNADO 5633 91346
## 130 EXCESSIVE HEAT 1903 6525
## 856 TSTM WIND 504 6957
## 170 FLOOD 470 6789
## 464 LIGHTNING 816 5230
## 275 HEAT 937 2100
## 153 FLASH FLOOD 978 1777
## 427 ICE STORM 89 1975
## 760 THUNDERSTORM WIND 133 1488
## 972 WINTER STORM 206 1321# Load ggplot2
library(ggplot2)
harmful_events$EVTYPE <- factor(harmful_events$EVTYPE, levels = harmful_events$EVTYPE)
# Reshape the data
harmful_events_long <- tidyr::gather(harmful_events, key = "Category", value = "Count", -EVTYPE)
# Create a ggplot bar plot with facets and color
ggplot(harmful_events_long, aes(x = EVTYPE, y = Count, fill = Category)) +
geom_bar(stat = "identity", position = "dodge", color = "black") +
labs(title = "Total Fatalities and Injuries by Event Type",
x = "Event Type",
y = "Count") +
facet_grid(Category ~ ., scales = "free_y") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8)) # Rotate axis labels by 45 degrees
Events with the Greatest Economic Consequences
# Summarize the data to find events with the greatest economic consequences
economic_consequences <- aggregate(cbind(PROPDMG, CROPDMG) ~ EVTYPE, storm_data, sum)
economic_consequences <- economic_consequences[order(rowSums(economic_consequences[, 2:3]), decreasing = TRUE), ][1:10, ]
# Display the events with greatest economic consequences
economic_consequences## EVTYPE PROPDMG CROPDMG
## 834 TORNADO 3212258.2 100018.52
## 153 FLASH FLOOD 1420124.6 179200.46
## 856 TSTM WIND 1335965.6 109202.60
## 244 HAIL 688693.4 579596.28
## 170 FLOOD 899938.5 168037.88
## 760 THUNDERSTORM WIND 876844.2 66791.45
## 464 LIGHTNING 603351.8 3580.61
## 786 THUNDERSTORM WINDS 446293.2 18684.93
## 359 HIGH WIND 324731.6 17283.21
## 972 WINTER STORM 132720.6 1978.99economic_consequences$EVTYPE <- factor(economic_consequences$EVTYPE, levels = economic_consequences$EVTYPE)
# Reshape the data
economic_consequences_long <- tidyr::gather(economic_consequences, key = "Category", value = "Count", -EVTYPE)
# Create a ggplot bar plot with facets and color
ggplot(economic_consequences_long, aes(x = EVTYPE, y = Count, fill = Category)) +
geom_bar(stat = "identity", position = "dodge", color = "black") +
labs(title = "Total Property and Crop Damage by Event Type",
x = "Event Type",
y = "Count") +
facet_grid(Category ~ ., scales = "free_y") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))
Results
From the first plot, we can see that the most harmful event for both fatalities and injuries are Tornados. From the second plot, we can see that the event with the greatest economic consequence is Tornados for Poperty damage, but Hail for Crop Damage.