Storm Data Analysis: Event Types Causing Greatest Damage, Injuries, and Fatalities 1996 - 2011
Len Greski
October 22, 2015
Synopsis
The National Weather Service StormData data set describes the location, event type, and damage information for over 900,000 extreme weather events from 1950 to 2011. This study identifies the event types that are associated with the greatest amount of economic damage, as well as the greatest impact to population health. Because the database only tracked a small proportion of event types between 1950 and 1995, we restricted our analysis to January 1, 1996 through November 30, 2011.
Our analysis found the following across the 48 individual event types tracked between 1996 and 2011:
- Tornadoes had the greatest impact on human health, causing the injury or death of 22,178 people (33% of total people harmed), and
- Floods had the greatest economic consequences, causing a total of $149.1 billion in total damage (37% of total damage caused by all event types) over the time period in our analysis.
Understanding the Data
Per the NOAA website, the StormData database only has data for all 48 event types starting in 1996, even though the data set starts in 1950. See https://www.ncdc.noaa.gov/stormevents/details.jsp for a listing of the event types collected over time. Between 1950 and 1954 only Tornado event types were recorded. Between 1955 and 1995, data was collected on Tornado, Thunderstorm Wind, and Hail event types. Starting in 1996, data has been collected for all 48 event types listed in the National Weather Service Directive 10-1605 document.
The unit of analysis that defines an “observation” in the database is a storm event. Each event is assigned a unique reference number. An event has a start date and time, and an end date and time.
Research Questions
For the purposes of the course project, we must answer two questions:
- Across the United States, which types of events are most harmful to human health?
- Across the United States, which types of events have the greatest economic consequences?
To answer the research questions, we’ll need to complete the following steps.
- Decide whether to use the pre-1996 data, which contains only a small subset of total event types
- Establish the evaluation criteria for most harmful to health and greatest economic consequences
- Clean the event types, which do not exactly match the 48 event types described in Directive 10-1605
- Clean the property and crop damage values by applying the exponent factor
- Analyze the data and report the results
Consideration 1: Data Included in the Analysis
Given that the purpose of this study is to compare impact across event types, it is important that we use time period when all 48 event types were being tracked. Therefore, we will restrict our analysis to data collected as of January 1996.
Also, for each research question, we will restrict our analysis to the storms that have non-zero values for the evaluation criteria. That is, for most harmful to health, we will evaluate storms that had at least one injury or fatality, and for greatest economic consequences we will evaluate storms that had some property or crop damage reported.
Finally, we will include data for all state Federal Information Processing Standard (FIPS) codes provided in the input data set, because the U.S. territories are under the jurisdiction of the United States of America. American emergency responders and tax dollars are used to respond to emergencies and repair damage after storms within the territories. Since these storms impact the American people and economy, we include them in the analysis.
Consideration 2: Evaluation Criteria
For the harmful to human health criterion, we will analyze the number of people injured or killed by a storm, summing the injuries and fatalities as total number of people harmed. We will not attempt to assess relative severity of deaths and injuries for the following reasons.
- Severity of injury varies widely, and the StormData data set does not include objective information about injuries by severity, and
- The actuarial valuation of a fatality is beyond the scope of this course.
For the greatest economic consequences criterion, we will analyze total economic impact, as measured by the sum of property damage and crop damage.
Data Processing
Data for this project assignment is stored on cloudfront.net, and the following section of code downloads and unzips the file if it is not already present in the R Working Directory.
# download bz2 version of input data file if needed, and unzip
if(!file.exists("repdata-data-StormData.csv")){
library(R.utils)
dlMethod <- "curl"
if(substr(Sys.getenv("OS"),1,7) == "Windows") dlMethod <- "wininet"
url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url,destfile='repdata-data-StormData.csv.bz2',method=dlMethod,mode="wb")
# unzip first because it improves performance of read_csv()
bunzip2("repdata-data-StormData.csv.bz2",
"repdata-data-StormData.csv")
}Next, we load the data using readr. We specify the total number of rows: 902,297 so the function allocates memory efficiently. Since the National Weather Service Directive 10-1605 codebook does not concisely describe the data types for all the columns in the data set, we will read everything as character and convert individual columns to numeric or date as needed. We refer the reader to the codebook for a detailed explanation of each variable contained in the StormData database.
The property and crop damage values are stored as floating point numbers with 3 digits of precision plus an exponent. We standardize the numeric values by inflating them by one thousand, one million, or one billion based on the value of the PROPDMGEXP or CROPDMGEXP variables.
library(readr)
stormData <- read_csv("repdata-data-StormData.csv",
n_max = 902297,
col_names = TRUE,
col_types = "ccccccccccccccccccccccccccccccccccccc")##
|================================================================================| 100% 535 MB# subset data to 1996 or newer, and print min and max dates
stormData$BGN_DATE <- as.Date(stormData$BGN_DATE,"%m/%d/%Y %T")
stormData <- stormData[stormData$BGN_DATE > as.Date("12/31/1995","%m/%d/%Y"),]
min(stormData$BGN_DATE)## [1] "1996-01-01"max(stormData$BGN_DATE)## [1] "2011-11-30"message("Property Damage Exponent Values")## Property Damage Exponent Valuestable(stormData$PROPDMGEXP)##
## 0 B K M
## 276185 1 32 369938 7374message("Crop Damage Exponent Values")## Crop Damage Exponent Valuestable(stormData$CROPDMGEXP)##
## B K M
## 373069 4 278686 1771# convert required variables to numeric
stormData$FATALITIES <- as.numeric(stormData$FATALITIES)
stormData$INJURIES <- as.numeric(stormData$INJURIES)
stormData$PROPDMG <- as.numeric(stormData$PROPDMG)
stormData$CROPDMG <- as.numeric(stormData$CROPDMG)
stormData$REFNUM <- as.numeric(stormData$REFNUM)
# adjust by exponents
stormData[stormData$PROPDMGEXP == "K","PROPDMG"] <- stormData[stormData$PROPDMGEXP == "K","PROPDMG"] * 1000
stormData[stormData$PROPDMGEXP == "M","PROPDMG"] <- stormData[stormData$PROPDMGEXP == "M","PROPDMG"] * 1000000
stormData[stormData$PROPDMGEXP == "B","PROPDMG"] <- stormData[stormData$PROPDMGEXP == "B","PROPDMG"] * 1000000000
stormData[stormData$CROPDMGEXP == "K","CROPDMG"] <- stormData[stormData$CROPDMGEXP == "K","CROPDMG"] * 1000
stormData[stormData$CROPDMGEXP == "M","CROPDMG"] <- stormData[stormData$CROPDMGEXP == "M","CROPDMG"] * 1000000
stormData[stormData$CROPDMGEXP == "B","CROPDMG"] <- stormData[stormData$CROPDMGEXP == "B","CROPDMG"] * 1000000000
# uppercase EVTYPE and remove leading / trailing blanks
stormData$EVTYPE <- trimws(toupper(stormData$EVTYPE))
# calculate total humans harmed
stormData$humansHarmed <- stormData$FATALITIES + stormData$INJURIES
# calculate economic impact
stormData$econImpact <- stormData$PROPDMG + stormData$CROPDMGPreparing the data for analysis
To prepare the data for analysis, we must complete two steps, including:
- Subset the data to events containing the variable of interest (fatalities / injuries, or economic impact), and
- For events in the subset, standardize them so they match the list of 48 event types, and no “non-standard” event types are included in the analysis reports.
Calculating harm to population health
The data set includes two variables related to population health: FATALITIES and INJURIES. We will assess the harm by analyzing event types relative to the total proportion of fatalities and injuries. We will use a Pareto chart to identify the event types with the greatest impact on humans. The Pareto chart combines a frequency bar chart with a cumulative percentage line graph, sorted by descending frequency. Since the qcc package includes a Pareto chart, we’ll use the package instead of designing a custom Pareto chart.
Calculating economic damage
We will combine information from the damage amounts and damage EXP columns to calculate the damage in a consistent scale across events, and sum across property and crops. We will assess the economic damage by analyzing event types relative to the total amount of damage across all event types. We will also use a Pareto chart to assess the event types, displaying total damage as the bar chart variable, and combining it with a cumulative percentage line graph.
Cleaning the event types: coding strategy
The data suffers from numerous data entry errors that cause the event types to fail to match the list of 48 types described in National Weather Service Directive 10-1605. We use a combination of regular expressions and direct assignment statements to recode the data to ensure all event types in our data match the event type names in the NWS Directive document.
We clean the event types after subsetting for our two questions: events harmful to human health, and events causing economic damage. We subset before cleaning to reduce the number of event types that must be cleaned prior to analysis.
To ensure all event types in the analysis conform to the NWS Directive list, the following code builds a data frame containing the event types and designators from The Storm Data Event Table listed on page 6 of National Weather Service Directive 10-1605. A description of the technique used below to read the data inline as is frequently done with SAS or SPSS is Bob Muenchen’s R for SAS and SPSS Users, page 14.
#
# read text data in R
#
theStormClasses <-
"eventType|designator
Astronomical Low Tide |Z
Avalanche |Z
Blizzard |Z
Coastal Flood |Z
Cold/Wind Chill |Z
Debris Flow |C
Dense Fog |Z
Dense Smoke |Z
Drought |Z
Dust Devil |C
Dust Storm |Z
Excessive Heat |Z
Extreme Cold/Wind Chill |Z
Flash Flood |C
Flood |C
Frost/Freeze |Z
Funnel Cloud |C
Freezing Fog |Z
Hail |C
Heat |Z
Heavy Rain |C
Heavy Snow |Z
High Surf |Z
High Wind |Z
Hurricane (Typhoon) |Z
Ice Storm |Z
Lake-Effect Snow |Z
Lakeshore Flood |Z
Lightning |C
Marine Hail |M
Marine High Wind |M
Marine Strong Wind |M
Marine Thunderstorm Wind |M
Rip Current |Z
Seiche |Z
Sleet |Z
Storm Surge/Tide |Z
Strong Wind |Z
Thunderstorm Wind |C
Tornado |C
Tropical Depression |Z
Tropical Storm |Z
Tsunami |Z
Volcanic Ash |Z
Waterspout |M
Wildfire |Z
Winter Storm |Z
Winter Weather |Z"
stormClasses <- read.table(textConnection(theStormClasses),
header=TRUE,sep="|",stringsAsFactors=FALSE)
stormClasses$eventType <- toupper(trimws(stormClasses$eventType))
stormClasses$designator <- trimws(stormClasses$designator)Event Types Most Harmful to Population Health
Here we reduce the data set to the events that killed or injured at least one person, then clean the event types so they conform to the list of 48 event types that are listed in the codebook provided by the National Weather Service. There are 12,764 events that caused at least one injury or death between January 1, 1996 and November 30, 2011.
humanHarm <- stormData[stormData$humansHarmed > 0, ]
nrow(humanHarm)## [1] 12764humanHarm$incidents <- 1
#
# Recoded event types here
#
humanHarm$EVTYPE <- gsub("AVALANCE","AVALANCHE",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("BLOWING SNOW","BLIZZARD",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("COASTAL FLOODING","COASTAL FLOOD",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("COASTAL FLOOD/EROSION","COASTAL FLOOD",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("COASTALSTORM","WINTER STORM",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("COASTAL STORM","WINTER STORM",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("BLACK ICE","FROST/FREEZE",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("BRUSH FIRE","WILDFIRE",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("COLD TEMPERATURE","cold/wind CHILL",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("COLD WEATHER$","cold/wind CHILL",humanHarm$EVTYPE,fixed=TRUE)
humanHarm$EVTYPE <- gsub("COLD AND SNOW","WINTER STORM",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("DROWNING","FLASH FLOOD",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("COLD WEATHER$","cold/wind CHILL",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("DRY MICROBURST","THUNDERSTORM WIND",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("^FOG","DENSE FOG",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("EXCESSIVE SNOW","HEAVY SNOW",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("EXTREME COLD$","EXTREME COLD/WIND CHILL",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("EXTENDED COLD$","cold/wind CHILL",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("EXTREME WINDCHILL","EXTREME COLD/WIND CHILL",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("^FREEZING","WINTER STORM",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("FROST$","FROST/FREEZE",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("GLAZE","ICE STORM",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("GUSTY WINDS","HIGH WIND",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("GUSTY WIND","HIGH WIND",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("^HAZARDOUS SURF","HIGH SURF",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("HEAT WAVE","HEAT",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("^HEAVY SURF AND WIND","HIGH SURF",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("^HEAVY SURF$","HIGH SURF",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("^HEAVY SURF/HIGH SURF","HIGH SURF",humanHarm$EVTYPE)
# NOTE there is no reasonable equivalent to high seas other than high surf
humanHarm$EVTYPE <- gsub("HEAVY SEAS","HIGH SURF",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("HIGH SEAS","HIGH SURF",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("HIGH SWELLS","HIGH SURF",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("HIGH WATER","FLOOD",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("HURRICANE$","HURRICANE (TYPHOON)",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("HURRICANE EDOUARD","HURRICANE (TYPHOON)",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("HURRICANE/TYPHOON$","HURRICANE (TYPHOON)",humanHarm$EVTYPE)
# Next item miscoded hypothermia as hyperthermia
humanHarm$EVTYPE <- gsub("^HYPERTHERMIA/EXPOSURE","cold/wind CHILL",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("^HYPOTHERMIA/EXPOSURE","cold/wind CHILL",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("ICE ON ROAD","FROST/FREEZE",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("^ICE ROADS","FROST/FREEZE",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("^ICY ROADS","FROST/FREEZE",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("FALLING SNOW/ICE","AVALANCHE",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("HEAVY SNOW SHOWER$","HEAVY SNOW",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("LANDSLIDES","HEAVY RAIN",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("LANDSLIDE$","HEAVY RAIN",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("LIGHT SNOW","FROST/FREEZE",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("MARINE ACCIDENT","HIGH SURF",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("MARINE TSTM WIND","MARINE THUNDERSTORM WIND",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("MIXED PRECIP","FROST/FREEZE",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("MUDSLIDE$","HEAVY RAIN",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("MUDSLIDES$","HEAVY RAIN",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("NON TSTM WIND$","HIGH WIND",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("NON-SEVERE WIND DAMAGE$","HIGH WIND",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("OTHER","DUST DEVIL",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("RAIN/SNOW","WINTER STORM",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("RECORD HEAT","EXCESSIVE HEAT",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("RIP CURRENTS","RIP CURRENT",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("RIVER FLOODING$","FLOOD",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("RIVER FLOOD$","FLOOD",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("ROGUE WAVE","HIGH SURF",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("ROUGH SEAS","HIGH SURF",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("ROUGH SURF","HIGH SURF",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("SMALL HAIL","HAIL",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("SNOW AND ICE","WINTER STORM",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("SNOW SQUALLS$","BLIZZARD",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("SNOW SQUALL$","BLIZZARD",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("STORM SURGE$","STORM SURGE/TIDE",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("STRONG WINDS","HIGH WIND",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("TIDAL FLOODING$","STORM SURGE/TIDE",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("TORRENTIAL RAINFALL","HEAVY RAIN",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("THUNDERSTORM WIND (.*)","THUNDERSTORM WIND",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("THUNDERSTORM$","THUNDERSTORM WIND",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("^TSTM WIND (.*)","THUNDERSTORM WIND",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("^TSTM WIND (G45)","THUNDERSTORM WIND",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("^TSTM WIND (G40)","THUNDERSTORM WIND",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("TSTM WIND/HAIL$","THUNDERSTORM WIND",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("TSTM WIND$","THUNDERSTORM WIND",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("^TYPHOON","HURRICANE (TYPHOON)",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("UNSEASONABLY WARM","HEAT",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("URBAN/SML STREAM FLD","FLOOD",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("WARM WEATHER","HEAT",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("WHIRLWIND","DUST DEVIL",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("WILD/FOREST FIRE$","WILDFIRE",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("WINDS$","HIGH WIND",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("^WIND","HIGH WIND",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("WINTER STORM AND ICE","WINTER STORM",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("WINTER STORM DRIZZLE","WINTER STORM",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("WINTER STORM RAIN","WINTER STORM",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("WINTER STORM SPRAY","WINTER STORM",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("WINTER STORM SQUALLS$","WINTER STORM",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("WINTER STORM SQUALL$","WINTER STORM",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("WINTER WEATHER MIX","WINTER STORM",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("WINTER WEATHER/MIX","WINTER STORM",humanHarm$EVTYPE)
humanHarm$EVTYPE <- gsub("WINTRY MIX","WINTER STORM",humanHarm$EVTYPE)
humanHarm[humanHarm$EVTYPE == "COLD","EVTYPE"] <- "COLD/WIND CHILL"
humanHarm[humanHarm$EVTYPE == "SNOW","EVTYPE"] <- "WINTER STORM"
#
# end recoded event types
#
humanHarm$EVTYPE <- toupper(humanHarm$EVTYPE)
#
# compare to NWS Storm Event Table, mismatches should be zero
#
library(sqldf)## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBIsqldf("select EVTYPE from humanHarm where EVTYPE not
in(select eventType from stormClasses)")## Loading required package: tcltk## [1] EVTYPE
## <0 rows> (or 0-length row.names)harmByEvtype <- aggregate(cbind(humansHarmed,incidents) ~ EVTYPE,
data = humanHarm, FUN="sum")
# calculate percentages so we can conduct Pareto analysis
harmByEvtype$pctHarm <- harmByEvtype$humansHarmed / sum(harmByEvtype$humansHarmed)
harmByEvtype$pctOfIncidents <- harmByEvtype$incidents / sum(harmByEvtype$incidents)
# order by descending percentage of people harmed
harmByEvtype <- harmByEvtype[order(-harmByEvtype$pctHarm),]Event Types with Greatest Economic Consequences
Here we reduce the data set to the events that caused property or crop damage, then clean the event types so they conform to the list of 48 event types that are listed in the codebook provided by the National Weather Service. There are 194,525 events that caused at least $1 of economic impact between January 1, 1996 and November 30, 2011.
damageData <- stormData[stormData$econImpact > 0, ]
nrow(damageData)## [1] 194525damageData$incidents <- 1
#
# recode EVTYPE to match code book for 48 event types
#
damageData$EVTYPE <- gsub("AVALANCE","AVALANCHE",damageData$EVTYPE)
damageData$EVTYPE <- gsub("BLOWING SNOW","BLIZZARD",damageData$EVTYPE)
damageData$EVTYPE <- gsub("COASTAL FLOODING","COASTAL FLOOD",damageData$EVTYPE)
damageData$EVTYPE <- gsub("COASTAL FLOOD/EROSION","COASTAL FLOOD",damageData$EVTYPE)
damageData$EVTYPE <- gsub("COASTALSTORM","WINTER STORM",damageData$EVTYPE)
damageData$EVTYPE <- gsub("COASTAL STORM","WINTER STORM",damageData$EVTYPE)
damageData$EVTYPE <- gsub("BLACK ICE","FROST/FREEZE",damageData$EVTYPE)
damageData$EVTYPE <- gsub("BRUSH FIRE","WILDFIRE",damageData$EVTYPE)
damageData$EVTYPE <- gsub("COLD TEMPERATURE","cold/wind CHILL",damageData$EVTYPE)
damageData$EVTYPE <- gsub("COLD WEATHER$","cold/wind CHILL",damageData$EVTYPE,fixed=TRUE)
damageData$EVTYPE <- gsub("COLD AND SNOW","WINTER STORM",damageData$EVTYPE)
damageData$EVTYPE <- gsub("DROWNING","FLASH FLOOD",damageData$EVTYPE)
damageData$EVTYPE <- gsub("COLD WEATHER$","cold/wind CHILL",damageData$EVTYPE)
damageData$EVTYPE <- gsub("DRY MICROBURST","THUNDERSTORM WIND",damageData$EVTYPE)
damageData$EVTYPE <- gsub("^FOG","DENSE FOG",damageData$EVTYPE)
damageData$EVTYPE <- gsub("EXCESSIVE SNOW","HEAVY SNOW",damageData$EVTYPE)
damageData$EVTYPE <- gsub("EXTREME COLD$","EXTREME COLD/WIND CHILL",damageData$EVTYPE)
damageData$EVTYPE <- gsub("EXTENDED COLD$","cold/wind CHILL",damageData$EVTYPE)
damageData$EVTYPE <- gsub("EXTREME WINDCHILL","EXTREME COLD/WIND CHILL",damageData$EVTYPE)
damageData$EVTYPE <- gsub("^FREEZING","WINTER STORM",damageData$EVTYPE)
damageData$EVTYPE <- gsub("FROST$","FROST/FREEZE",damageData$EVTYPE)
damageData$EVTYPE <- gsub("GLAZE","ICE STORM",damageData$EVTYPE)
damageData$EVTYPE <- gsub("GUSTY WINDS","HIGH WIND",damageData$EVTYPE)
damageData$EVTYPE <- gsub("GUSTY WIND","HIGH WIND",damageData$EVTYPE)
damageData$EVTYPE <- gsub("^HAZARDOUS SURF","HIGH SURF",damageData$EVTYPE)
damageData$EVTYPE <- gsub("HEAT WAVE","HEAT",damageData$EVTYPE)
damageData$EVTYPE <- gsub("^HEAVY SURF AND WIND","HIGH SURF",damageData$EVTYPE)
damageData$EVTYPE <- gsub("^HEAVY SURF$","HIGH SURF",damageData$EVTYPE)
damageData$EVTYPE <- gsub("^HEAVY SURF/HIGH SURF","HIGH SURF",damageData$EVTYPE)
# NOTE there is no reasonable equivalent to high seas other than high surf
damageData$EVTYPE <- gsub("HEAVY SEAS","HIGH SURF",damageData$EVTYPE)
damageData$EVTYPE <- gsub("HIGH SEAS","HIGH SURF",damageData$EVTYPE)
damageData$EVTYPE <- gsub("HIGH SWELLS","HIGH SURF",damageData$EVTYPE)
damageData$EVTYPE <- gsub("HIGH WATER","FLOOD",damageData$EVTYPE)
damageData$EVTYPE <- gsub("HURRICANE$","HURRICANE (TYPHOON)",damageData$EVTYPE)
damageData$EVTYPE <- gsub("HURRICANE EDOUARD","HURRICANE (TYPHOON)",damageData$EVTYPE)
damageData$EVTYPE <- gsub("HURRICANE/TYPHOON$","HURRICANE (TYPHOON)",damageData$EVTYPE)
# Next item miscoded hypothermia as hyperthermia
damageData$EVTYPE <- gsub("^HYPERTHERMIA/EXPOSURE","cold/wind CHILL",damageData$EVTYPE)
damageData$EVTYPE <- gsub("^HYPOTHERMIA/EXPOSURE","cold/wind CHILL",damageData$EVTYPE)
damageData$EVTYPE <- gsub("ICE ON ROAD","FROST/FREEZE",damageData$EVTYPE)
damageData$EVTYPE <- gsub("^ICE ROADS","FROST/FREEZE",damageData$EVTYPE)
damageData$EVTYPE <- gsub("^ICY ROADS","FROST/FREEZE",damageData$EVTYPE)
damageData$EVTYPE <- gsub("FALLING SNOW/ICE","AVALANCHE",damageData$EVTYPE)
damageData$EVTYPE <- gsub("HEAVY SNOW SHOWER$","HEAVY SNOW",damageData$EVTYPE)
damageData$EVTYPE <- gsub("LANDSLIDES","HEAVY RAIN",damageData$EVTYPE)
damageData$EVTYPE <- gsub("LANDSLIDE$","HEAVY RAIN",damageData$EVTYPE)
damageData$EVTYPE <- gsub("LIGHT SNOW","FROST/FREEZE",damageData$EVTYPE)
damageData$EVTYPE <- gsub("MARINE ACCIDENT","HIGH SURF",damageData$EVTYPE)
damageData$EVTYPE <- gsub("MARINE TSTM WIND","MARINE THUNDERSTORM WIND",damageData$EVTYPE)
damageData$EVTYPE <- gsub("MIXED PRECIP","FROST/FREEZE",damageData$EVTYPE)
damageData$EVTYPE <- gsub("MUDSLIDE$","HEAVY RAIN",damageData$EVTYPE)
damageData$EVTYPE <- gsub("MUDSLIDES$","HEAVY RAIN",damageData$EVTYPE)
damageData$EVTYPE <- gsub("NON TSTM WIND$","HIGH WIND",damageData$EVTYPE)
damageData$EVTYPE <- gsub("NON-SEVERE WIND DAMAGE$","HIGH WIND",damageData$EVTYPE)
damageData$EVTYPE <- gsub("OTHER","DUST DEVIL",damageData$EVTYPE)
damageData$EVTYPE <- gsub("RAIN/SNOW","WINTER STORM",damageData$EVTYPE)
damageData$EVTYPE <- gsub("RECORD HEAT","EXCESSIVE HEAT",damageData$EVTYPE)
damageData$EVTYPE <- gsub("RIP CURRENTS","RIP CURRENT",damageData$EVTYPE)
damageData$EVTYPE <- gsub("RIVER FLOODING$","FLOOD",damageData$EVTYPE)
damageData$EVTYPE <- gsub("RIVER FLOOD$","FLOOD",damageData$EVTYPE)
damageData$EVTYPE <- gsub("ROGUE WAVE","HIGH SURF",damageData$EVTYPE)
damageData$EVTYPE <- gsub("ROUGH SEAS","HIGH SURF",damageData$EVTYPE)
damageData$EVTYPE <- gsub("ROUGH SURF","HIGH SURF",damageData$EVTYPE)
damageData$EVTYPE <- gsub("SMALL HAIL","HAIL",damageData$EVTYPE)
damageData$EVTYPE <- gsub("SNOW AND ICE","WINTER STORM",damageData$EVTYPE)
damageData$EVTYPE <- gsub("SNOW SQUALLS$","BLIZZARD",damageData$EVTYPE)
damageData$EVTYPE <- gsub("SNOW SQUALL$","BLIZZARD",damageData$EVTYPE)
damageData$EVTYPE <- gsub("STORM SURGE$","STORM SURGE/TIDE",damageData$EVTYPE)
damageData$EVTYPE <- gsub("STRONG WINDS","HIGH WIND",damageData$EVTYPE)
damageData$EVTYPE <- gsub("TIDAL FLOODING$","STORM SURGE/TIDE",damageData$EVTYPE)
damageData$EVTYPE <- gsub("TORRENTIAL RAINFALL","HEAVY RAIN",damageData$EVTYPE)
damageData$EVTYPE <- gsub("THUNDERSTORM WIND (.*)","THUNDERSTORM WIND",damageData$EVTYPE)
damageData$EVTYPE <- gsub("THUNDERSTORM$","THUNDERSTORM WIND",damageData$EVTYPE)
damageData$EVTYPE <- gsub("^TSTM WIND (.*)","THUNDERSTORM WIND",damageData$EVTYPE)
damageData$EVTYPE <- gsub("^TSTM WIND (G45)","THUNDERSTORM WIND",damageData$EVTYPE)
damageData$EVTYPE <- gsub("^TSTM WIND (G40)","THUNDERSTORM WIND",damageData$EVTYPE)
damageData$EVTYPE <- gsub("TSTM WIND/HAIL$","THUNDERSTORM WIND",damageData$EVTYPE)
damageData$EVTYPE <- gsub("TSTM WIND$","THUNDERSTORM WIND",damageData$EVTYPE)
damageData$EVTYPE <- gsub("^TYPHOON","HURRICANE (TYPHOON)",damageData$EVTYPE)
damageData$EVTYPE <- gsub("UNSEASONABLY WARM","HEAT",damageData$EVTYPE)
damageData$EVTYPE <- gsub("URBAN/SML STREAM FLD","FLOOD",damageData$EVTYPE)
damageData$EVTYPE <- gsub("WARM WEATHER","HEAT",damageData$EVTYPE)
damageData$EVTYPE <- gsub("WHIRLWIND","DUST DEVIL",damageData$EVTYPE)
damageData$EVTYPE <- gsub("WILD/FOREST FIRE$","WILDFIRE",damageData$EVTYPE)
damageData$EVTYPE <- gsub("WINDS$","HIGH WIND",damageData$EVTYPE)
damageData$EVTYPE <- gsub("^WIND","HIGH WIND",damageData$EVTYPE)
damageData$EVTYPE <- gsub("WINTER STORM AND ICE","WINTER STORM",damageData$EVTYPE)
damageData$EVTYPE <- gsub("WINTER STORM DRIZZLE","WINTER STORM",damageData$EVTYPE)
damageData$EVTYPE <- gsub("WINTER STORM RAIN","WINTER STORM",damageData$EVTYPE)
damageData$EVTYPE <- gsub("WINTER STORM SPRAY","WINTER STORM",damageData$EVTYPE)
damageData$EVTYPE <- gsub("WINTER STORM SQUALLS$","WINTER STORM",damageData$EVTYPE)
damageData$EVTYPE <- gsub("WINTER STORM SQUALL$","WINTER STORM",damageData$EVTYPE)
damageData$EVTYPE <- gsub("WINTER WEATHER MIX","WINTER STORM",damageData$EVTYPE)
damageData$EVTYPE <- gsub("WINTER WEATHER/MIX","WINTER STORM",damageData$EVTYPE)
damageData$EVTYPE <- gsub("WINTRY MIX","WINTER STORM",damageData$EVTYPE)
damageData[damageData$EVTYPE == "COLD","EVTYPE"] <- "COLD/WIND CHILL"
damageData[damageData$EVTYPE == "SNOW","EVTYPE"] <- "WINTER STORM"
damageData[damageData$EVTYPE == "AGRICULTURAL FREEZE","EVTYPE"] <- "FROST/FREEZE"
damageData[damageData$EVTYPE == "ASTRONOMICAL HIGH TIDE","EVTYPE"] <- "STORM SURGE/TIDE"
damageData[damageData$EVTYPE == "BEACH EROSION","EVTYPE"] <- "STORM SURGE/TIDE"
damageData[damageData$EVTYPE == "BLOWING DUST","EVTYPE"] <- "DUST STORM"
damageData[damageData$EVTYPE == "COASTAL FLOODING/EROSION","EVTYPE"] <- "COASTAL FLOOD"
damageData[damageData$EVTYPE == "COASTAL EROSION","EVTYPE"] <- "COASTAL FLOOD"
damageData[damageData$EVTYPE == "DAM BREAK","EVTYPE"] <- "HEAVY RAIN"
damageData[damageData$EVTYPE == "DAMAGING FREEZE","EVTYPE"] <- "FROST/FREEZE"
damageData[damageData$EVTYPE == "DOWNBURST","EVTYPE"] <- "THUNDERSTORM WIND"
damageData[damageData$EVTYPE == "EARLY FROST/FREEZE","EVTYPE"] <- "FROST/FREEZE"
damageData[damageData$EVTYPE == "EROSION/CSTL FLOOD","EVTYPE"] <- "STORM SURGE/TIDE"
damageData[damageData$EVTYPE == "FLOOD/FLASH/FLOOD","EVTYPE"] <- "FLASH FLOOD"
damageData[damageData$EVTYPE == "FLASH FLOOD/FLOOD","EVTYPE"] <- "FLASH FLOOD"
damageData[damageData$EVTYPE == "FREEZE","EVTYPE"] <- "FROST/FREEZE"
damageData[damageData$EVTYPE == "FROST/FREEZEFALL","EVTYPE"] <- "FROST/FREEZE"
damageData[damageData$EVTYPE == "FROST/FREEZEITATION","EVTYPE"] <- "FROST/FREEZE"
damageData[damageData$EVTYPE == "GRADIENT WIND","EVTYPE"] <- "HIGH WIND"
damageData[damageData$EVTYPE == "HARD FREEZE","EVTYPE"] <- "FROST/FREEZE"
damageData[damageData$EVTYPE == "HEAVY RAIN/HIGH SURF","EVTYPE"] <- "STORM SURGE/TIDE"
damageData[damageData$EVTYPE == "HIGH HIGH WIND","EVTYPE"] <- "HIGH WIND"
damageData$EVTYPE <- gsub("^HIGH WIND(.*)","HIGH WIND",damageData$EVTYPE)
damageData[damageData$EVTYPE == "HIGH SURF ADVISORY","EVTYPE"] <- "HIGH SURF"
damageData[damageData$EVTYPE == "ICE JAM FLOOD (MINOR","EVTYPE"] <- "FLOOD"
damageData[damageData$EVTYPE == "LAKE EFFECT SNOW","EVTYPE"] <- "LAKE-EFFECT SNOW"
damageData[damageData$EVTYPE == "LANDSLUMP","EVTYPE"] <- "COASTAL FLOOD"
damageData[damageData$EVTYPE == "LANDSPOUT","EVTYPE"] <- "DUST DEVIL"
damageData[damageData$EVTYPE == "LATE SEASON SNOW","EVTYPE"] <- "WINTER STORM"
damageData[damageData$EVTYPE == "LIGHT FREEZING RAIN","EVTYPE"] <- "WINTER STORM"
damageData[damageData$EVTYPE == "MICROBURST","EVTYPE"] <- "THUNDERSTORM WIND"
damageData[damageData$EVTYPE == "MUD SLIDE","EVTYPE"] <- "HEAVY RAIN"
damageData[damageData$EVTYPE == "NON-THUNDERSTORM WIND","EVTYPE"] <- "HIGH WIND"
damageData[damageData$EVTYPE == "RAIN","EVTYPE"] <- "HEAVY RAIN"
damageData[damageData$EVTYPE == "ROCK SLIDE","EVTYPE"] <- "HEAVY RAIN"
damageData[damageData$EVTYPE == "UNSEASONABLE COLD","EVTYPE"] <- "COLD/WIND CHILL"
damageData[damageData$EVTYPE == "UNSEASONABLY COLD","EVTYPE"] <- "COLD/WIND CHILL"
damageData[damageData$EVTYPE == "UNSEASONAL RAIN","EVTYPE"] <- "HEAVY RAIN"
damageData[damageData$EVTYPE == "WET MICROBURST","EVTYPE"] <- "THUNDERSTORM WIND"
damageData[damageData$EVTYPE == "WINTER STORM FOG","EVTYPE"] <- "DENSE FOG"
#
#
# end recoding
damageData$EVTYPE <- toupper(damageData$EVTYPE)
#
# compare to NWS Storm Event Table, mismatches should be zero
#
sqldf("select EVTYPE from damageData where EVTYPE not
in(select eventType from stormClasses)")## [1] EVTYPE
## <0 rows> (or 0-length row.names)damageByEvtype <- aggregate(cbind(econImpact,incidents) ~ EVTYPE,
data = damageData,FUN="sum")
# calculate percentages so we can conduct Pareto analysis
damageByEvtype$pctDamage <-
damageByEvtype$econImpact / sum(damageByEvtype$econImpact)
damageByEvtype$pctOfIncidents <-
damageByEvtype$incidents / sum(damageByEvtype$incidents)
damageByEvtype <- damageByEvtype[order(-damageByEvtype$pctDamage),]Results
Event types causing most harm to humans
Tornadoes cause the most harm to humans, with 1,968 incidents that killed or injured 22,178 people over the almost 15 years within our analysis. Tornadoes accounted for 33% of the total number of people killed or injured by storms.
The top 5 event types account for 72% of total injuries and deaths. Of the top five event types, only Excessive Heat and Flood are of the nature that public safety officials can prepare the public well in advance to minimize the impact to humans. For Tornadoes, thunderstorm wind, and lightning, rapid warning systems are required to alert people quickly to take cover when these types of events are imminent.
harmByEvtype[1:5,]## EVTYPE humansHarmed incidents pctHarm pctOfIncidents
## 31 TORNADO 22178 1968 0.33246886 0.15418364
## 9 EXCESSIVE HEAT 8190 666 0.12277572 0.05217800
## 12 FLOOD 7285 420 0.10920893 0.03290505
## 30 THUNDERSTORM WIND 5536 2330 0.08298979 0.18254466
## 23 LIGHTNING 4792 2649 0.07183654 0.20753682harm <- harmByEvtype$humansHarmed
names(harm) <- harmByEvtype$EVTYPE
library(qcc)## Package 'qcc', version 2.6
## Type 'citation("qcc")' for citing this R package in publications.par(mar=c(3,1,1,1))
par(las=2)
pareto.chart(harm,
ylab="Total Injuries & Deaths",
main="Injuries and Deaths - All Event Types, 1996 - 2011",
ylab2 = "Cum. Percentage",
cex.names=0.5)
##
## Pareto chart analysis for harm
## Frequency Cum.Freq. Percentage Cum.Percent.
## TORNADO 22178 22178 33.246885634 33.24689
## EXCESSIVE HEAT 8190 30368 12.277572069 45.52446
## FLOOD 7285 37653 10.920892860 56.44535
## THUNDERSTORM WIND 5536 43189 8.298979118 64.74433
## LIGHTNING 4792 47981 7.183653889 71.92798
## FLASH FLOOD 2562 50543 3.840676391 75.76866
## WINTER STORM 1788 52331 2.680378371 78.44904
## HEAT 1548 53879 2.320596039 80.76963
## WILDFIRE 1545 55424 2.316098760 83.08573
## HIGH WIND 1473 56897 2.208164061 85.29390
## HURRICANE (TYPHOON) 1453 58350 2.178182200 87.47208
## RIP CURRENT 1045 59395 1.566552236 89.03863
## DENSE FOG 924 60319 1.385161977 90.42379
## HEAVY SNOW 809 61128 1.212766276 91.63656
## HAIL 730 61858 1.094337926 92.73090
## ICE STORM 613 62471 0.918944039 93.64984
## BLIZZARD 495 62966 0.742051059 94.39189
## HEAVY RAIN 426 63392 0.638613639 95.03051
## HIGH SURF 415 63807 0.622123615 95.65263
## TROPICAL STORM 395 64202 0.592141754 96.24477
## DUST STORM 387 64589 0.580149010 96.82492
## AVALANCHE 381 64970 0.571154452 97.39608
## STRONG WIND 381 65351 0.571154452 97.96723
## WINTER WEATHER 376 65727 0.563658986 98.53089
## EXTREME COLD/WIND CHILL 365 66092 0.547168963 99.07806
## TSUNAMI 162 66254 0.242853074 99.32091
## COLD/WIND CHILL 150 66404 0.224863957 99.54577
## FROST/FREEZE 88 66492 0.131920188 99.67769
## STORM SURGE/TIDE 56 66548 0.083949211 99.76164
## MARINE THUNDERSTORM WIND 53 66601 0.079451932 99.84110
## DUST DEVIL 46 66647 0.068958280 99.91005
## MARINE STRONG WIND 36 66683 0.053967350 99.96402
## COASTAL FLOOD 13 66696 0.019488210 99.98351
## DROUGHT 4 66700 0.005996372 99.98951
## WATERSPOUT 4 66704 0.005996372 99.99550
## MARINE HIGH WIND 2 66706 0.002998186 99.99850
## FUNNEL CLOUD 1 66707 0.001499093 100.00000Event types causing most economic damage
Floods cause the greatest economic damage, with 10,170 incidents causing a total of $149.1 billion in damage over the 15 years included within our analysis. Hurricanes and Storm Surges / Tides were second and third in terms of total damage. Together these three event types account for 71% of the total $401.5 billion in property and/or crop damage from storms in the United States between January 1996 and November 2011, or $284 billion.
Given that the event types causing the greatest economic impact are water-driven events, mitigation of their impact would require large investments in sea walls, dams, and other types of water management infrastructure. Further analysis of the geographic distribution of these types of events would be required to prioritize the needed infrastructure investments.
damageByEvtype[1:5,]## EVTYPE econImpact incidents pctDamage pctOfIncidents
## 14 FLOOD 149142742700 10170 0.37144505 0.052281198
## 23 HURRICANE (TYPHOON) 87068996810 199 0.21684828 0.001023005
## 34 STORM SURGE/TIDE 47876317000 227 0.11923759 0.001166945
## 37 TORNADO 24900370720 12147 0.06201522 0.062444416
## 17 HAIL 17092035870 22621 0.04256829 0.116288395sum(damageByEvtype$econImpact / 10^9)## [1] 401.5203sum(damageByEvtype$econImpact[1:3] / 10^9)## [1] 284.0881damage <- damageByEvtype$econImpact / 10^9
names(damage) <- damageByEvtype$EVTYPE
par(mar=c(3,1,1,1))
par(las=2)
pareto.chart(damage,
ylab="Damage (in billions USD)",
main="Economic Impact - All Event Types, 1996 - 2011",
cex.names=0.5,
ylab2 = "Cum. Percentage")
##
## Pareto chart analysis for damage
## Frequency Cum.Freq. Percentage
## FLOOD 149.14274270 149.1427 3.714450e+01
## HURRICANE (TYPHOON) 87.06899681 236.2117 2.168483e+01
## STORM SURGE/TIDE 47.87631700 284.0881 1.192376e+01
## TORNADO 24.90037072 308.9884 6.201522e+00
## HAIL 17.09203587 326.0805 4.256829e+00
## FLASH FLOOD 16.55717061 342.6376 4.123619e+00
## DROUGHT 14.41366700 357.0513 3.589773e+00
## THUNDERSTORM WIND 8.93230308 365.9836 2.224620e+00
## TROPICAL STORM 8.32018655 374.3038 2.072171e+00
## WILDFIRE 8.16270463 382.4665 2.032949e+00
## HIGH WIND 5.88972595 388.3562 1.466856e+00
## ICE STORM 3.65805881 392.0143 9.110519e-01
## HEAVY RAIN 1.67066134 393.6849 4.160839e-01
## WINTER STORM 1.55509775 395.2400 3.873024e-01
## FROST/FREEZE 1.39118720 396.6312 3.464799e-01
## EXTREME COLD/WIND CHILL 1.35518640 397.9864 3.375138e-01
## LIGHTNING 0.74997552 398.7364 1.867839e-01
## HEAVY SNOW 0.70748464 399.4439 1.762014e-01
## BLIZZARD 0.53283395 399.9767 1.327041e-01
## EXCESSIVE HEAT 0.50012570 400.4768 1.245580e-01
## COASTAL FLOOD 0.39157556 400.8684 9.752322e-02
## STRONG WIND 0.23971295 401.1081 5.970132e-02
## TSUNAMI 0.14408200 401.2522 3.588411e-02
## HIGH SURF 0.09544450 401.3476 2.377078e-02
## LAKE-EFFECT SNOW 0.04018200 401.3878 1.000746e-02
## WINTER WEATHER 0.03586600 401.4237 8.932549e-03
## COLD/WIND CHILL 0.03338650 401.4571 8.315021e-03
## DENSE FOG 0.02264650 401.4797 5.640187e-03
## DUST STORM 0.00859400 401.4883 2.140365e-03
## LAKESHORE FLOOD 0.00754000 401.4959 1.877863e-03
## MARINE THUNDERSTORM WIND 0.00590740 401.5018 1.471258e-03
## WATERSPOUT 0.00573020 401.5075 1.427126e-03
## AVALANCHE 0.00371180 401.5112 9.244363e-04
## DUST DEVIL 0.00177253 401.5130 4.414546e-04
## TROPICAL DEPRESSION 0.00173700 401.5147 4.326057e-04
## HEAT 0.00170650 401.5164 4.250096e-04
## MARINE HIGH WIND 0.00129701 401.5177 3.230247e-04
## SEICHE 0.00098000 401.5187 2.440723e-04
## VOLCANIC ASH 0.00050000 401.5192 1.245267e-04
## MARINE STRONG WIND 0.00041833 401.5196 1.041865e-04
## ASTRONOMICAL LOW TIDE 0.00032000 401.5199 7.969708e-05
## RIP CURRENT 0.00016300 401.5201 4.059570e-05
## FUNNEL CLOUD 0.00013410 401.5202 3.339806e-05
## DENSE SMOKE 0.00010000 401.5203 2.490534e-05
## MARINE HAIL 0.00000400 401.5203 9.962135e-07
##
## Pareto chart analysis for damage
## Cum.Percent.
## FLOOD 37.14450
## HURRICANE (TYPHOON) 58.82933
## STORM SURGE/TIDE 70.75309
## TORNADO 76.95461
## HAIL 81.21144
## FLASH FLOOD 85.33506
## DROUGHT 88.92483
## THUNDERSTORM WIND 91.14945
## TROPICAL STORM 93.22163
## WILDFIRE 95.25457
## HIGH WIND 96.72143
## ICE STORM 97.63248
## HEAVY RAIN 98.04857
## WINTER STORM 98.43587
## FROST/FREEZE 98.78235
## EXTREME COLD/WIND CHILL 99.11986
## LIGHTNING 99.30665
## HEAVY SNOW 99.48285
## BLIZZARD 99.61555
## EXCESSIVE HEAT 99.74011
## COASTAL FLOOD 99.83763
## STRONG WIND 99.89733
## TSUNAMI 99.93322
## HIGH SURF 99.95699
## LAKE-EFFECT SNOW 99.96700
## WINTER WEATHER 99.97593
## COLD/WIND CHILL 99.98424
## DENSE FOG 99.98988
## DUST STORM 99.99202
## LAKESHORE FLOOD 99.99390
## MARINE THUNDERSTORM WIND 99.99537
## WATERSPOUT 99.99680
## AVALANCHE 99.99773
## DUST DEVIL 99.99817
## TROPICAL DEPRESSION 99.99860
## HEAT 99.99902
## MARINE HIGH WIND 99.99935
## SEICHE 99.99959
## VOLCANIC ASH 99.99972
## MARINE STRONG WIND 99.99982
## ASTRONOMICAL LOW TIDE 99.99990
## RIP CURRENT 99.99994
## FUNNEL CLOUD 99.99997
## DENSE SMOKE 100.00000
## MARINE HAIL 100.00000References
Kabacoff, Robert (2015) – R in Action: Data Analysis and Graphics with R, Second Edition. Manning Publications, Shelter Island, NY 2015.
Muenchen, Robert (2006) – R for SAS and SPSS Users, PDF document retrieved from the internet on October 19, 2015.
National Oceanic and Atmospheric Administration of the United States – National Weather Service Directives, retrieved from the internet on October 15, 2015.
National Oceanic and Atmospheric Administration of the United States – National Weather Service Directive 10-1605, retrieved from the intenet on October 10, 2015.
Scrucca, L. (2004) – qcc: an R package for quality control charting and statistical process control. R News 4/1, 11-17.
citation("qcc")##
## To cite qcc in publications use:
##
## Scrucca, L. (2004). qcc: an R package for quality control
## charting and statistical process control. R News 4/1, 11-17.
##
## A BibTeX entry for LaTeX users is
##
## @Article{,
## title = {qcc: an R package for quality control charting and statistical process control},
## author = {Luca Scrucca},
## journal = {R News},
## year = {2004},
## pages = {11--17},
## volume = {4/1},
## url = {http://CRAN.R-project.org/doc/Rnews/},
## }sessionInfo()## R version 3.2.2 (2015-08-14)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.5 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] tcltk stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] qcc_2.6 sqldf_0.4-10 RSQLite_1.0.0 DBI_0.3.1 gsubfn_0.6-6
## [6] proto_0.3-10 readr_0.1.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.0 digest_0.6.8 MASS_7.3-43 chron_2.3-47
## [5] formatR_1.2 magrittr_1.5 evaluate_0.7.2 stringi_0.5-5
## [9] rmarkdown_0.7 tools_3.2.2 stringr_1.0.0 yaml_2.1.13
## [13] htmltools_0.2.6 knitr_1.11