Reproducible Research Severe weather events across the United States
Rui Almeida
2024-05-14
Title
Severe weather events across the United States - Population health and economic consequences.
Synopsis
This study utilizes the NOAA Storm Database to investigate severe weather events across the United States, focusing on their impacts on population health and economic consequences. Through data analysis, we aim to identify the types of events that pose the greatest threat to population health and those with the most significant economic repercussions. The findings are intended to assist government or municipal managers in prioritizing resources for different types of severe weather events, aiding in better preparation and response strategies.
Data Processing
# Download the file
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", destfile = "StormData.csv.bz2")
# Check if the file exists
if (file.exists("StormData.csv")) {
# If the file exists, remove it
file.remove("StormData.csv")
}## [1] TRUE# Extract the contents of the BZIP2 file
bunzip2("StormData.csv.bz2", remove = FALSE)
# Load the data
storm_data <- read.csv("StormData.csv")
# Some data cleaning
# Convert countyname column to uppercase
storm_data$COUNTYNAME <- trimws(toupper(storm_data$COUNTYNAME))
# Convert 'evtype' column to uppercase
storm_data$EVTYPE <- trimws(toupper(storm_data$EVTYPE))
# List all unique values in 'countyname' column
unique_countynames <- unique(storm_data$COUNTYNAME)
#print(unique_countynames)
# List all unique values in 'evtype' column
unique_evtypes <- unique(storm_data$EVTYPE)
#print(unique_evtypes)
# List all unique values in 'State' column
unique_evtypes <- unique(storm_data$STATE)
#print(unique_evtypes)
# Create a vector of EVTYPE values from summary
summary_evtype <- c("YOUR", "SUMMARY", "VALUES", "HERE")
# Loop through each element of the 'evtype' column
for(i in 1:length(storm_data$EVTYPE)) {
# Check if the current value is one of the summary_evtype values
if(any(grepl(paste(summary_evtype, collapse = "|"), storm_data$EVTYPE[i], ignore.case = TRUE))) {
# If it is, replace it with "NOT DEFINED"
storm_data$EVTYPE[i] <- "NOT DEFINED"
}
}
# Convert the BGN_DATE column to Date format
storm_data$BGN_DATE <- as.Date(storm_data$BGN_DATE, format = "%m/%d/%Y")
# Extract year and month from BGN_DATE
storm_data$Year <- format(storm_data$BGN_DATE, "%Y")
storm_data$Month <- format(storm_data$BGN_DATE, "%m")
# Create a new column combining year and month
storm_data$YearMonth <- paste(storm_data$Year, storm_data$Month, sep = "-")
# Count severe weather events by year and month
events_by_year_month <- table(storm_data$YearMonth)Data Exploratory
List of variables:
# Summary of principal variables
summary(storm_data)## STATE__ BGN_DATE BGN_TIME TIME_ZONE
## Min. : 1.0 Min. :1950-01-03 Length:902297 Length:902297
## 1st Qu.:19.0 1st Qu.:1995-04-20 Class :character Class :character
## Median :30.0 Median :2002-03-18 Mode :character Mode :character
## Mean :31.2 Mean :1998-12-27
## 3rd Qu.:45.0 3rd Qu.:2007-07-28
## Max. :95.0 Max. :2011-11-30
##
## COUNTY COUNTYNAME STATE EVTYPE
## Min. : 0.0 Length:902297 Length:902297 Length:902297
## 1st Qu.: 31.0 Class :character Class :character Class :character
## Median : 75.0 Mode :character Mode :character Mode :character
## Mean :100.6
## 3rd Qu.:131.0
## Max. :873.0
##
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE
## Min. : 0.000 Length:902297 Length:902297 Length:902297
## 1st Qu.: 0.000 Class :character Class :character Class :character
## Median : 0.000 Mode :character Mode :character Mode :character
## Mean : 1.484
## 3rd Qu.: 1.000
## Max. :3749.000
##
## END_TIME COUNTY_END COUNTYENDN END_RANGE
## Length:902297 Min. :0 Mode:logical Min. : 0.0000
## Class :character 1st Qu.:0 NA's:902297 1st Qu.: 0.0000
## Mode :character Median :0 Median : 0.0000
## Mean :0 Mean : 0.9862
## 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
## Class :character Class :character 1st Qu.: 0.0000 1st Qu.: 0.000
## Mode :character Mode :character Median : 0.0000 Median : 0.000
## Mean : 0.2301 Mean : 7.503
## 3rd Qu.: 0.0000 3rd Qu.: 0.000
## Max. :2315.0000 Max. :4400.000
##
## F MAG FATALITIES INJURIES
## Min. :0.0 Min. : 0.0 Min. : 0.0000 Min. : 0.0000
## 1st Qu.:0.0 1st Qu.: 0.0 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median :1.0 Median : 50.0 Median : 0.0000 Median : 0.0000
## Mean :0.9 Mean : 46.9 Mean : 0.0168 Mean : 0.1557
## 3rd Qu.:1.0 3rd Qu.: 75.0 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :5.0 Max. :22000.0 Max. :583.0000 Max. :1700.0000
## NA's :843563
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 Length:902297 Min. : 0.000 Length:902297
## 1st Qu.: 0.00 Class :character 1st Qu.: 0.000 Class :character
## Median : 0.00 Mode :character Median : 0.000 Mode :character
## Mean : 12.06 Mean : 1.527
## 3rd Qu.: 0.50 3rd Qu.: 0.000
## Max. :5000.00 Max. :990.000
##
## WFO STATEOFFIC ZONENAMES LATITUDE
## Length:902297 Length:902297 Length:902297 Min. : 0
## Class :character Class :character Class :character 1st Qu.:2802
## Mode :character Mode :character Mode :character Median :3540
## Mean :2875
## 3rd Qu.:4019
## Max. :9706
## NA's :47
## LONGITUDE LATITUDE_E LONGITUDE_ REMARKS
## Min. :-14451 Min. : 0 Min. :-14455 Length:902297
## 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0 Class :character
## Median : 8707 Median : 0 Median : 0 Mode :character
## Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. : 17124 Max. :9706 Max. :106220
## NA's :40
## REFNUM Year Month YearMonth
## Min. : 1 Length:902297 Length:902297 Length:902297
## 1st Qu.:225575 Class :character Class :character Class :character
## Median :451149 Mode :character Mode :character Mode :character
## Mean :451149
## 3rd Qu.:676723
## Max. :902297
## # Compute the sum of Fatalities and Injuries for each combination of STATE and EVTYPE
fatalities_injuries_tbl <- aggregate(cbind(FATALITIES, INJURIES) ~ STATE + EVTYPE, data = storm_data, sum)
# Display the resulting table
#print(fatalities_injuries_tbl)
# Aggregate the total Fatalities and Injuries for each event type
event_harmfulness <- aggregate(cbind(FATALITIES, INJURIES) ~ EVTYPE, data = storm_data, sum)
# Calculate a harm score for each event type (sum of Fatalities and Injuries)
event_harmfulness$HarmScore <- event_harmfulness$FATALITIES + event_harmfulness$INJURIES
# Sort the event types by their harm score in descending order
event_harmfulness <- event_harmfulness[order(event_harmfulness$HarmScore, decreasing = TRUE), ]
# Display the top harmful event types
#print(head(event_harmfulness, n = 10))
# Create a new column called DMG that represents the sum of PROPDMG and CROPDMG
storm_data$DMG <- storm_data$PROPDMG + storm_data$CROPDMG
# Aggregate the total Damage for each event type
event_damage <- aggregate(cbind(DMG) ~ EVTYPE, data = storm_data, sum)
# Sort the event types by their damage in descending order
event_damage <- event_damage[order(event_damage$DMG, decreasing = TRUE), ]
# Display the top damage event types
#print(head(event_damage, n = 10))Plots
This data represents severe weather events aggregated by year and month from 1950 to 2011. Here’s a summary of the data:
The table spans from January 1950 to November 2011. The frequency of severe weather events varies across different months and years. Over the years, there are fluctuations in the number of severe weather events, with some months experiencing higher frequencies than others. The highest frequencies of severe weather events are generally observed during spring and summer months. There are occasional peaks in severe weather events, likely corresponding to specific weather patterns or phenomena.
Overall, the data suggests variability in the occurrence of severe weather events over time, influenced by seasonal and possibly climate-related factors.
This second data represents the frequency of severe weather events by state. Here’s a breakdown of the information:
The table contains counts of severe weather events for each state of US, identified by their respective abbreviations. Each row represents a state , while each value in the row represents the count of severe weather events. The abbreviation “XX” may represent unspecified or unclassified events. States with larger populations or greater geographical areas might generally exhibit higher frequencies of severe weather events. Certain states prone to specific types of severe weather, such as hurricanes or tornadoes, may have higher counts compared to others.
## EVTYPE FATALITIES INJURIES HarmScore
## 684 TORNADO 5633 91346 96979
## 107 EXCESSIVE HEAT 1903 6525 8428
## 705 TSTM WIND 504 6957 7461
## 145 FLOOD 470 6789 7259
## 409 LIGHTNING 816 5230 6046
## 234 HEAT 937 2100 3037
## 129 FLASH FLOOD 978 1777 2755
## 378 ICE STORM 89 1975 2064
## 611 THUNDERSTORM WIND 133 1488 1621
## 814 WINTER STORM 206 1321 1527This data lists the top 10 severe weather event types based on their “HarmScore,” which is a composite measure calculated by adding the number of fatalities and injuries associated with each event type. Here’s a breakdown:
TORNADO: This event type has the highest HarmScore, with 5633 fatalities and 91346 injuries, totaling 96979.
EXCESSIVE HEAT: This event type ranks second in terms of HarmScore, with 1903 fatalities and 6525 injuries, totaling 8428.
TSTM WIND: Thunderstorm Wind events caused 504 fatalities and 6957 injuries, resulting in a HarmScore of 7461.
FLOOD: Floods resulted in 470 fatalities and 6789 injuries, with a HarmScore of 7259.
LIGHTNING: Lightning caused 816 fatalities and 5230 injuries, totaling 6046 in HarmScore.
HEAT: Heat events led to 937 fatalities and 2100 injuries, with a HarmScore of 3037.
FLASH FLOOD: Flash Floods resulted in 978 fatalities and 1777 injuries, totaling 2755 in HarmScore.
ICE STORM: Ice Storms caused 89 fatalities and 1975 injuries, with a HarmScore of 2064.
THUNDERSTORM WIND: Thunderstorm Wind events led to 133 fatalities and 1488 injuries, resulting in a HarmScore of 1621.
WINTER STORM: Winter Storms caused 206 fatalities and 1321 injuries, with a HarmScore of 1527.
## EVTYPE DMG
## 684 TORNADO 3312276.7
## 129 FLASH FLOOD 1599375.1
## 705 TSTM WIND 1445306.2
## 203 HAIL 1268289.7
## 145 FLOOD 1067976.4
## 611 THUNDERSTORM WIND 943635.6
## 409 LIGHTNING 606932.4
## 637 THUNDERSTORM WINDS 464978.1
## 311 HIGH WIND 342014.8
## 814 WINTER STORM 134699.6This data presents severe weather event types along with their associated damage (in dollars). Here’s a breakdown:
TORNADO: Tornadoes caused damage amounting to $3,312,276.7.
FLASH FLOOD: Flash Floods resulted in damage totaling $1,599,375.1.
TSTM WIND: Thunderstorm Winds caused damage amounting to $1,445,306.2.
HAIL: Hailstorms resulted in damage totaling $1,268,289.7.
FLOOD: Floods caused damage amounting to $1,067,976.4.
THUNDERSTORM WIND: Thunderstorm Winds led to damage totaling $943,635.6.
LIGHTNING: Lightning caused damage amounting to $606,932.4.
THUNDERSTORM WINDS: Similar to "Thunderstorm Wind," this event type caused damage totaling $464,978.1.
HIGH WIND: High Winds resulted in damage amounting to $342,014.8.
WINTER STORM: Winter Storms caused damage totaling $134,699.6.This data provides insights into the economic impact of various severe weather events, with tornadoes causing the highest damage followed by flash floods and thunderstorm winds.