This report aims to demonstrate the damage caused by climate events between 1950 to 2011 to people, their properties and crops in United States. This research was based on data from NOAA contained in the file “repdata_data_StormData.csv.bz2”. Between people the Tornado event was caused more deaths. The Flood was caused more financial loss. However, the hurricanes were that reached more properties and crops in terms of quantity. The weather events do not show an increase or decrease over time they are seasonal. If we observed during decades of 60s to 80s there was little loss (people, properties and crops) in relation the decade 90s into 2011. This investigation can be monitored through the grouping of the years in decades where it proves the data’s seasonality. This report also does not exclude the variables contained with NA’s because they represent only 0.1673921% of data. We can confirm that the states located Central US are the hardest hit with more devastating events.
The base of our research is file StormData where we get the all data wheather events occurred from 1950 to 2011.
We read the StormData from data raw file br2 where the all data variables were separated by ,(commas) and we trated the missing values as blank field. Our Data will call “StormData”.
#Loading the libraries
library(maps)
library(rMaps)
library(plotly)
library(knitr)
library(tidyr)
library(lattice)
library(xtable)
library(magrittr)
library(lubridate)
library(rmarkdown)
library(markdown)
library(data.table)
library(plyr)
library(dplyr)
library(dbplyr)
library(sqldf)
library(DBI)
library(choroplethr)
library(usmap)
library(ggplot2)
library(RSQLite)
StormData <- read.csv( "repdata_data_StormData.csv.bz2", header = T, sep = ",", na.strings = "")Although the database has many variables, we need to create others that will be important to our research.All variables created from now on will be incorporated into our main database so that the information processed does not emit false positive results. The first will be to create the YEAR variable that will only store the years each event.
StormData <- mutate(StormData, YEAR = as.integer(format(as.Date(StormData$BGN_DATE, "%m/%d/%Y 0:00:00"), "%Y")))The database the EVTYPE variable did not come according to Storm Data Event Table - (NWSI 10-1605 AUGUST 17, 2007) Documentation. We will classify them and with this the database will naturally decrease. The new variable will call EVENTS.
EVTYPE1 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("ASTRONOMICAL LOW TIDE")), EVENTS = "Astronomical Low Tide")
EVTYPE2 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("AVALANCHE","AVALANCE")), EVENTS = "Avalanche")
EVTYPE3 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("BLIZZARD","BLIZZARD AND EXTREME WIND CHI","BLIZZARD AND HEAVY SNOW", "Blizzard Summary", "BLIZZARD WEATHER", "COASTAL FLOODING/EROSION", "COASTAL EROSION", "BLIZZARD/HIGH WIND", "BLIZZARD/WINTER STORM")), EVENTS = "Blizzard")
EVTYPE4 <- data.frame(subset(StormData, StormData$EVTYPE %in% c(" COASTAL FLOOD","ASTRONOMICAL HIGH TIDE","BEACH EROSIN", "BEACH EROSION/COASTAL FLOOD", "BEACH FLOOD", "Coastal Flood", "COASTAL FLOOD", "coastal flooding", "Coastal Flooding",
"COASTAL FLOODING", "COASTAL FLOODING/EROSION", "COASTAL/TIDAL FLOOD", "COASTALFLOOD", "CSTL FLOODING/EROSION", "Erosion/Cstl Flood", "Landslump", "ROGUE WAVE", "ROUGH SEAS", "ROUGH SURF")), EVENTS = "Coastal Flood")
EVTYPE5 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("BITTER WIND CHILL", "BITTER WIND CHILL TEMPERATURES", "Cold", "COLD", "Cold and Frost", "COLD AND FROST", "COLD AND SNOW", "COLD AND WET CONDITIONS", "Cold Temperature", "COLD TEMPERATURES",
"COLD WAVE", "COLD WEATHER", "COLD WIND CHILL TEMPERATURES", "COLD/WIND CHILL", "COLD/WINDS", "COOL AND WET", "COOL SPELL", "MONTHLY TEMPERATURE")), EVENTS = "Cold/Wind Chill")
EVTYPE6 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("LANDSLIDE", "LANDSLIDE/URBAN FLOOD", "LANDSLIDES", "MUD SLIDE", "MUD SLIDES", "MUD SLIDES URBAN FLOODING", "MUD SLIDES URBAN FLOODING", "Mudslide", "MUDSLIDE", "MUDSLIDE/LANDSLIDE", "Mudslides", "MUDSLIDES", "ROCK SLIDE")), EVENTS = "Debris Flow")
EVTYPE7 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("DENSE FOG ", "FOG", "PATCHY DENSE FOG")), EVENTS = "Dense Fog")
EVTYPE8 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("DENSE SMOKE","SMOKE")), EVENTS = "Dense Smoke")
EVTYPE9 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("ABNORMALLY DRY","BELOW NORMAL PRECIPITATION", "DRIEST MONTH", "DROUGHT", "DROUGHT/EXCESSIVE HEAT", "DROWNING", "DRY", "DRY CONDITIONS", "DRY HOT WEATHER", "DRY PATTERN","DRY SPELL", "DRY WEATHER", "DRYNESS", "EXCESSIVELY DRY",
"Record dry month", "RECORD DRYNESS", "RECORD LOW RAINFALL", "VERY DRY", "WARM DRY CONDITIONS")), EVENTS = "Drought")
EVTYPE10 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("DUST DEVEL", "Dust Devil", "DUST DEVIL", "DUST DEVIL WATERSPOUT", "LANDSPOUT", "Whirlwind", "WHIRLWIND")), EVENTS = "Dust Devil")
EVTYPE11 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("BLOWING DUST", "DUST STORM", "DUST STORM/HIGH WINDS", "DUSTSTORM", "Saharan Dust", "SAHARAN DUST")), EVENTS = "Dust Storm")
EVTYPE12 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("ABNORMAL WARMTH","EXCESSIVE","EXCESSIVE HEAT","EXTREME COLD/WIND CHILL","EXTREME HEAT", "HEAT DROUGHT", "Heat Wave", "HEAT WAVE", "HEAT WAVE DROUGHT", "HEAT WAVES", "HEAT/DROUGHT", "HIGH TEMPERATURE RECORD", "HYPERTHERMIA/EXPOSURE",
"PROLONG WARMTH", "Record Heat", "RECORD HEAT", "RECORD HEAT WAVE", "Record High", "RECORD HIGH", "RECORD HIGH TEMPERATURE", "RECORD HIGH TEMPERATURES", "RECORD TEMPERATURES","RECORD WARM", "RECORD WARM TEMPS.", "Record Warmth", "RECORD WARMTH",
"RECORD/EXCESSIVE HEAT", "UNSEASONABLY DRY", "UNSEASONABLY HOT", "UNSEASONABLY WARM", "UNSEASONABLY WARM & WET", "UNSEASONABLY WARM AND DRY", "UNSEASONABLY WARM YEAR", "UNSEASONABLY WARM/WET", "UNUSUAL WARMTH", "UNUSUAL/RECORD WARMTH", "UNUSUALLY WARM", "VERY WARM")), EVENTS = "Excessive Heat")
EVTYPE13 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("BLOWING SNOW- EXTREME WIND CHI","BLOWING SNOW & EXTREME WIND CH","BLOWING SNOW/EXTREME WIND CHIL","Excessive Cold", "EXCESSIVE HEAT/DROUGHT", "Extended Cold", "Extreme Cold", "EXTREME COLD", "EXTREME WIND CHILL", "EXTREME WIND CHILL/BLOWING SNO", "EXTREME WIND CHILLS",
"EXTREME WINDCHILL", "EXTREME WINDCHILL TEMPERATURES", "EXTREME/RECORD COLD", "HARD FREEZE", "LOW TEMPERATURE", "LOW TEMPERATURE RECORD", "LOW WIND CHILL", "Prolong Cold", "PROLONG COLD", "PROLONG COLD/SNOW", "RECORD COLD", "Record Cold", "RECORD COLD", "RECORD COLD AND HIGH WIND",
"RECORD COLD/FROST", "RECORD COOL", "RECORD LOW", "Record Temperatures", "Record Winter Snow", "SEVERE COLD", "Unseasonable Cold", "UNSEASONABLY COLD", "UNSEASONABLY COOL", "UNSEASONABLY COOL & WET", "UNSEASONAL LOW TEMP", "UNUSUALLY COLD")), EVENTS = "Extreme Cold/Wind Chill")
EVTYPE14 <- data.frame(subset(StormData, StormData$EVTYPE %in% c(" FLASH FLOOD","BREAKUP FLOODING","DAM BREAK","DAM FAILURE","FLASH FLOOD","FLASH FLOOD - HEAVY RAIN", "FLASH FLOOD FROM ICE JAMS", "FLASH FLOOD LANDSLIDES", "FLASH FLOOD WINDS", "FLASH FLOOD/", "FLASH FLOOD/ FLOOD", "FLASH FLOOD/ STREET", "FLASH FLOOD/FLOOD", "FLASH FLOOD/LANDSLIDE",
"FLASH FLOODING", "FLASH FLOODING/FLOOD", "FLASH FLOODING/THUNDERSTORM WI", "FLASH FLOODS", "FLASH FLOOODING", "FLOOD FLASH", "FLOOD FLOOD/FLASH", "FLOOD WATCH/", "HIGHWAY FLOODING", "LOCAL FLASH FLOOD", "TIDAL FLOOD", "Tidal Flooding", "TIDAL FLOODING")), EVENTS = "Flash Flood")
EVTYPE15 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("Flood","FLOOD","FLOOD & HEAVY RAIN", "FLOOD/FLASH", "Flood/Flash Flood", "FLOOD/FLASH FLOOD", "FLOOD/FLASH FLOODING", "FLOOD/FLASH/FLOOD", "FLOOD/FLASHFLOOD", "FLOOD/RAIN/WIND", "FLOOD/RAIN/WINDS", "FLOOD/RIVER FLOOD", "FLOODING", "FLOODING/HEAVY RAIN", "FLOODS",
"ICE JAM", "Ice jam flood (minor", "ICE JAM FLOODING", "LOCAL FLOOD", "MAJOR FLOOD", "MINOR FLOOD", "Minor Flooding", "MINOR FLOODING", "RIVER AND STREAM FLOOD", "RIVER FLOOD", "River Flooding", "RIVER FLOODING", "RURAL FLOOD", "SNOWMELT FLOODING")), EVENTS = "Flood")
EVTYPE16 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("AGRICULTURAL FREEZE","Black Ice","BLACK ICE","Damaging Freeze","DAMAGING FREEZE", "EARLY FREEZE", "Early Frost", "EARLY FROST", "FIRST FROST", "Freeze", "FREEZE", "Freezing drizzle", "Freezing Drizzle", "FREEZING DRIZZLE", "FREEZING DRIZZLE AND FREEZING", "Freezing rain",
"Freezing Rain", "FREEZING RAIN", "FREEZING RAIN AND SLEET", "FREEZING RAIN AND SNOW", "FREEZING RAIN SLEET AND", "FREEZING RAIN SLEET AND LIGHT", "FREEZING RAIN/SLEET", "FREEZING RAIN/SNOW", "Freezing Spray", "Frost", "FROST", "Frost/Freeze", "FROST/FREEZE",
"FUNNEL", "Glaze", "GLAZE", "GLAZE ICE", "GLAZE/ICE STORM", "HYPOTHERMIA", "Hypothermia/Exposure", "HYPOTHERMIA/EXPOSURE", "ICE", "ICE AND SNOW", "ICE FLOES", "ICE ON ROAD", "ICE PELLETS", "ICE ROADS", "Ice/Snow", "ICE/SNOW", "LATE FREEZE", "LIGHT FREEZING RAIN", "PATCHY ICE")), EVENTS = "Frost/Freeze")
EVTYPE17 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("COLD AIR FUNNEL","COLD AIR FUNNELS","Funnel Cloud", "FUNNEL CLOUD", "FUNNEL CLOUD.", "FUNNEL CLOUDS", "FUNNELS", "ROTATING WALL CLOUD", "WALL CLOUD/FUNNEL CLOUD")), EVENTS = "Funnel Cloud")
EVTYPE18 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("FOG AND COLD TEMPERATURES", "Freezing Fog", "FREEZING FOG", "Ice Fog")), EVENTS = "Freezing Fog")
EVTYPE19 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("DEEP HAIL", "FUNNEL CLOUD/HAIL", "HAIL", "HAIL 0.75", "HAIL 0.88", "HAIL 075", "HAIL 088", "HAIL 1.00", "HAIL 1.75", "HAIL 1.75)", "HAIL 100", "HAIL 125", "HAIL 150", "HAIL 175", "HAIL 200", "HAIL 225", "HAIL 275", "HAIL 450", "HAIL 75", "HAIL 80", "HAIL 88",
"HAIL ALOFT", "HAIL DAMAGE", "HAIL FLOODING", "HAIL STORM", "Hail(0.75)", "HAIL/ICY ROADS", "HAIL/WIND","HAIL/WINDS", "HAILSTORM", "HAILSTORMS", "LATE SEASON HAIL", "NON SEVERE HAIL", "small hail", "Small Hail", "SMALL HAIL")), EVENTS = "Hail")
EVTYPE20 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("HEAT","Hot and Dry", "HOT PATTERN", "HOT SPELL", "HOT WEATHER", "HOT/DRY PATTERN", "Mild and Dry Pattern", "MILD PATTERN", "MILD/DRY PATTERN", "WARM WEATHER")), EVENTS = "Heat")
EVTYPE21 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("ABNORMALLY WET", "EARLY RAIN", "EXCESSIVE PRECIPITATION", "EXCESSIVE RAIN", "EXCESSIVE RAINFALL", "EXCESSIVE WETNESS", "EXTREMELY WET", "FLASH FLOOD/HEAVY RAIN", "HEAVY MIX", "HEAVY PRECIPATATION", "Heavy Precipitation", "HEAVY PRECIPITATION",
"Heavy rain", "Heavy Rain", "HEAVY RAIN", "HEAVY RAIN AND FLOOD", "Heavy Rain and Wind", "HEAVY RAIN EFFECTS", "HEAVY RAIN/FLOODING", "Heavy Rain/High Surf", "HEAVY RAIN/LIGHTNING", "HEAVY RAIN/MUDSLIDES/FLOOD", "HEAVY RAIN/SEVERE WEATHER",
"HEAVY RAIN/SMALL STREAM URBAN", "HEAVY RAIN/SNOW", "HEAVY RAIN/URBAN FLOOD", "HEAVY RAIN/WIND", "HEAVY RAIN; URBAN FLOOD WINDS;", "HEAVY RAINFALL", "HEAVY RAINS", "HEAVY RAINS/FLOODING", "HEAVY SHOWER", "HEAVY SHOWERS", "HVY RAIN", "LANDSLUMP",
"LOCALLY HEAVY RAIN", "MIXED PRECIP", "Mixed Precipitation", "MIXED PRECIPITATION", "MONTHLY PRECIPITATION", "Monthly Rainfall", "MONTHLY RAINFALL", "NORMAL PRECIPITATION", "PROLONGED RAIN", "RAIN", "RAIN (HEAVY)", "RAIN AND WIND", "Rain Damage",
"RAIN/SNOW", "RAIN/WIND", "RAINSTORM", "RAPIDLY RISING WATER", "RECORD PRECIPITATION", "RECORD RAINFALL", "RECORD/EXCESSIVE RAINFALL", "SMALL STREAM", "SMALL STREAM AND", "SMALL STREAM AND URBAN FLOOD", "SMALL STREAM AND URBAN FLOODIN", "SMALL STREAM FLOOD",
"SMALL STREAM FLOODING", "SMALL STREAM URBAN FLOOD", "SMALL STREAM/URBAN FLOOD", "Sml Stream Fld", "STREAM FLOODING", "STREET FLOOD", "STREET FLOODING", "UNSEASONABLY WET", "UNSEASONAL RAIN", "URBAN AND SMALL", "URBAN AND SMALL STREAM", "URBAN AND SMALL STREAM FLOOD",
"URBAN AND SMALL STREAM FLOODIN", "Urban flood", "Urban Flood", "URBAN FLOOD", "URBAN FLOOD LANDSLIDE", "Urban Flooding", "URBAN FLOODING", "URBAN FLOODS", "URBAN SMALL", "URBAN SMALL STREAM FLOOD", "URBAN/SMALL", "URBAN/SMALL FLOODING", "URBAN/SMALL STREAM",
"URBAN/SMALL STREAM FLOOD", "URBAN/SMALL STREAM FLOOD", "URBAN/SMALL STREAM FLOODING", "URBAN/SMALL STRM FLDG", "URBAN/SML STREAM FLD", "URBAN/SML STREAM FLDG", "URBAN/STREET FLOODING", "Wet Month", "WET WEATHER", "Wet Year")), EVENTS = "Heavy Rain")
EVTYPE22 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("ACCUMULATED SNOWFALL", "blowing snow", "Blowing Snow", "BLOWING SNOW", "Drifting Snow", "EARLY SNOW", "Early snowfall", "EARLY SNOWFALL", "EXCESSIVE SNOW", "FIRST SNOW", "HEAVY SNOW", "HEAVY SNOW-SQUALLS", "HEAVY SNOW FREEZING RAIN",
"HEAVY SNOW & ICE", "HEAVY SNOW AND", "HEAVY SNOW AND HIGH WINDS", "HEAVY SNOW AND ICE", "HEAVY SNOW AND ICE STORM", "HEAVY SNOW AND STRONG WINDS", "HEAVY SNOW ANDBLOWING SNOW", "Heavy snow shower", "HEAVY SNOW SQUALLS", "HEAVY SNOW/BLIZZARD",
"HEAVY SNOW/BLIZZARD/AVALANCHE", "HEAVY SNOW/BLOWING SNOW", "HEAVY SNOW/FREEZING RAIN", "HEAVY SNOW/HIGH", "HEAVY SNOW/HIGH WIND", "HEAVY SNOW/HIGH WINDS", "HEAVY SNOW/HIGH WINDS & FLOOD", "HEAVY SNOW/HIGH WINDS/FREEZING", "HEAVY SNOW/ICE", "HEAVY SNOW/ICE STORM",
"HEAVY SNOW/SLEET", "HEAVY SNOW/SQUALLS", "HEAVY SNOW/WIND", "HEAVY SNOW/WINTER STORM", "HEAVY SNOWPACK", "HEAVY WET SNOW", "LACK OF SNOW", "Late-season Snowfall", "LATE SEASON SNOW", "Late Season Snowfall", "LATE SNOW", "Light snow", "Light Snow",
"LIGHT SNOW", "LIGHT SNOW AND SLEET", "Light Snow/Flurries", "LIGHT SNOW/FREEZING PRECIP", "Light Snowfall", "MODERATE SNOW", "MODERATE SNOWFALL", "Monthly Snowfall", "MONTHLY SNOWFALL", "NEAR RECORD SNOW", "Record May Snow", "RECORD SNOW", "RECORD SNOW/COLD",
"RECORD SNOWFALL", "Seasonal Snowfall", "Snow", "SNOW", "SNOW- HIGH WIND- WIND CHILL", "Snow Accumulation", "SNOW ACCUMULATION", "SNOW ADVISORY", "SNOW AND COLD", "SNOW AND HEAVY SNOW", "Snow and Ice", "SNOW AND ICE", "SNOW AND ICE STORM", "Snow and sleet",
"SNOW AND SLEET", "SNOW AND WIND", "SNOW DROUGHT", "SNOW FREEZING RAIN", "SNOW SHOWERS", "SNOW SLEET", "SNOW SQUALL", "Snow squalls", "Snow Squalls", "SNOW SQUALLS", "SNOW/ BITTER COLD", "SNOW/ ICE", "SNOW/BLOWING SNOW", "SNOW/COLD", "SNOW/FREEZING RAIN",
"SNOW/HEAVY SNOW", "SNOW/HIGH WINDS", "SNOW/ICE", "SNOW/ICE STORM", "SNOW/RAIN", "SNOW/RAIN/SLEET", "SNOW/SLEET", "SNOW/SLEET/FREEZING RAIN", "SNOW/SLEET/RAIN", "SNOW\\COLD", "SNOWFALL RECORD", "SNOWSTORM", "UNUSUALLY LATE SNOW", "WET SNOW")),EVENTS = "Heavy Snow")
EVTYPE23 <- data.frame(subset(StormData, StormData$EVTYPE %in% c(" HIGH SURF ADVISORY","BEACH EROSION", "HAZARDOUS SURF", "HEAVY SEAS", "Heavy Surf", "HEAVY SURF", "Heavy surf and wind", "HEAVY SURF COASTAL FLOODING", "HEAVY SURF/HIGH SURF", "HIGH SEAS", "High Surf", "HIGH SURF", "HIGH SURF ADVISORIES", "HIGH SURF ADVISORY", "HIGH TIDES", "HIGH WATER", "HIGH WAVES", "Marine Accident")), EVENTS = "High Surf")
EVTYPE24 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("Beach Erosion","DOWNBURST","DOWNBURST WINDS","gradient wind","Gradient wind", "GRADIENT WIND", "GRADIENT WINDS", "HIGH", "HIGH WINDS", "High Wind", "HIGH WIND", "HIGH WIND (G40)", "HIGH WIND 48", "HIGH WIND 63", "HIGH WIND 70", "HIGH WIND AND HEAVY SNOW",
"HIGH WIND AND HIGH TIDES", "HIGH WIND AND SEAS", "HIGH WIND DAMAGE", "HIGH WIND/ BLIZZARD", "HIGH WIND/BLIZZARD", "HIGH WIND/BLIZZARD/FREEZING RA", "HIGH WIND/HEAVY SNOW", "HIGH WIND/LOW WIND CHILL", "HIGH WIND/SEAS", "HIGH WIND/WIND CHILL", "HIGH WIND/WIND CHILL/BLIZZARD",
"HIGH WINDS", "HIGH WINDS 55", "HIGH WINDS 57", "HIGH WINDS 58", "HIGH WINDS 63", "HIGH WINDS 66", "HIGH WINDS 67", "HIGH WINDS 73", "HIGH WINDS 76", "HIGH WINDS 80", "HIGH WINDS 82", "HIGH WINDS AND WIND CHILL", "HIGH WINDS DUST STORM", "HIGH WINDS HEAVY RAINS",
"HIGH WINDS/", "HIGH WINDS/COASTAL FLOOD", "HIGH WINDS/COLD", "HIGH WINDS/FLOODING", "HIGH WINDS/HEAVY RAIN", "HIGH WINDS/SNOW", "NON-SEVERE WIND DAMAGE", "NON-TSTM WIND", "Wind", "WIND", "WIND ADVISORY", "WIND AND WAVE", "WIND CHILL", "WIND CHILL/HIGH WIND", "Wind Damage",
"WIND DAMAGE", "WIND GUSTS", "WIND STORM", "WIND/HAIL")), EVENTS = "High Wind")
EVTYPE25 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("HURRICANE","HURRICANE-GENERATED SWELLS", "Hurricane Edouard", "HURRICANE EMILY", "HURRICANE ERIN", "HURRICANE FELIX", "HURRICANE GORDON", "HURRICANE OPAL", "HURRICANE OPAL/HIGH WINDS", "HURRICANE/TYPHOON", "TYPHOON")), EVENTS = "Hurricane (Typhoon)")
EVTYPE26 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("FALLING SNOW/ICE","ICE STORM","ICE STORM AND SNOW","ICE STORM/FLASH FLOOD", "ICE/STRONG WINDS", "Icestorm/Blizzard", "Icy Roads", "ICY ROADS", "Mountain Snows", "HEAVY LAKE SNOW", "LAKE-EFFECT SNOW", "Lake Effect Snow", "LAKE EFFECT SNOW")), EVENTS = "Ice Storm")
EVTYPE27 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("HEAVY LAKE SNOW", "LAKE-EFFECT SNOW", "Lake Effect Snow", "LAKE EFFECT SNOW")), EVENTS = "Lake-Effect Snow")
EVTYPE28 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("Flood/Strong Wind", "LAKE FLOOD", "LAKESHORE FLOOD")), EVENTS = "Lakeshore Flood")
EVTYPE29 <- data.frame(subset(StormData, StormData$EVTYPE %in% c(" LIGHTNING", "LIGHTING", "LIGHTNING", "LIGHTNING WAUSEON", "LIGHTNING AND HEAVY RAIN", "LIGHTNING AND THUNDERSTORM WIN", "LIGHTNING AND WINDS", "LIGHTNING DAMAGE", "LIGHTNING FIRE", "LIGHTNING INJURY", "LIGHTNING THUNDERSTORM WINDS", "LIGHTNING THUNDERSTORM WINDSS", "LIGHTNING.", "LIGHTNING/HEAVY RAIN", "LIGNTNING")), EVENTS = "Lightning")
EVTYPE30 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("MARINE HAIL")), EVENTS = "Marine Hail")
EVTYPE31 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("MARINE HIGH WIND")), EVENTS = "Marine High Wind")
EVTYPE32 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("MARINE MISHAP", "MARINE STRONG WIND")), EVENTS = "Marine Strong Wind")
EVTYPE33 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("MARINE THUNDERSTORM WIND","MARINE TSTM WIND")), EVENTS = "Marine Thunderstorm Wind")
EVTYPE34 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("RIP CURRENT","RIP CURRENTS", "RIP CURRENTS HEAVY SURF", "RIP CURRENTS/HEAVY SURF")), EVENTS = "Rip Current")
EVTYPE35 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("GUSTY LAKE WIND", "HEAVY SWELLS", "HIGH SWELLS", "HIGH SWELLS", "SEICHE")), EVENTS = "Seiche")
EVTYPE36 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("SLEET", "SLEET & FREEZING RAIN", "SLEET STORM", "SLEET/FREEZING RAIN", "SLEET/ICE STORM", "SLEET/RAIN/SNOW", "SLEET/SNOW", "WINTERY MIX", "Wintry mix", "Wintry Mix", "WINTRY MIX")), EVENTS = "Sleet")
EVTYPE37 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("Coastal Storm", "COASTAL STORM", "COASTAL SURGE", "COASTALSTORM", "STORM SURGE", "STORM SURGE/TIDE")), EVENTS = "Storm Surge/Tide")
EVTYPE38 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("APACHE COUNTY","GROUND BLIZZARD", "GUSTNADO", "GUSTNADO AND", "GUSTY THUNDERSTORM WIND", "GUSTY THUNDERSTORM WINDS", "Gusty Wind", "GUSTY WIND", "GUSTY WIND/HAIL", "GUSTY WIND/HVY RAIN", "Gusty wind/rain", "Gusty winds", "Gusty Winds", "GUSTY WINDS", "Heatburst", "NON TSTM WIND", "SEVERE TURBULENCE", "Strong Wind", "STRONG WIND",
"STRONG WIND GUST", "Strong winds", "Strong Winds", "STRONG WINDS", "WND")), EVENTS = "Strong Wind")
EVTYPE39 <- data.frame(subset(StormData, StormData$EVTYPE %in% c(" TSTM WIND"," TSTM WIND (G45)","BLOW-OUT TIDE","BLOW-OUT TIDES","DRY MICROBURST", "DRY MICROBURST 50","DRY MICROBURST 53","DRY MICROBURST 58","DRY MICROBURST 61","DRY MICROBURST 84", "DRY MICROBURST WINDS", "DRY MIRCOBURST WINDS", "LARGE WALL CLOUD", "Metro Storm, May 26", "Microburst", "MICROBURST", "MICROBURST WINDS",
"SEVERE THUNDERSTORM", "SEVERE THUNDERSTORM WINDS", "SEVERE THUNDERSTORMS", "Summary August 10", "Summary August 11", "Summary August 17", "Summary August 2-3", "Summary August 21", "Summary August 28", "Summary August 4", "Summary August 7", "Summary August 9", "Summary Jan 17", "Summary July 23-24", "Summary June 18-19",
"Summary June 5-6", "Summary June 6", "Summary of April 12", "Summary of April 13", "Summary of April 21", "Summary of April 27", "Summary of April 3rd", "Summary of August 1", "Summary of July 11", "Summary of July 2", "Summary of July 22", "Summary of July 26", "Summary of July 29", "Summary of July 3", "Summary of June 10",
"Summary of June 11", "Summary of June 12", "Summary of June 13", "Summary of June 15", "Summary of June 16", "Summary of June 18", "Summary of June 23", "Summary of June 24", "Summary of June 3", "Summary of June 30", "Summary of June 4", "Summary of June 6", "Summary of March 14", "Summary of March 23", "Summary of March 24",
"SUMMARY OF MARCH 24-25", "SUMMARY OF MARCH 27", "SUMMARY OF MARCH 29", "Summary of May 10", "Summary of May 13", "Summary of May 14", "Summary of May 22", "Summary of May 22 am", "Summary of May 22 pm", "Summary of May 26 am", "Summary of May 26 pm", "Summary of May 31 am", "Summary of May 31 pm", "Summary of May 9-10",
"Summary Sept. 25-26", "Summary September 20", "Summary September 20", "Summary September 23", "Summary September 3", "Summary September 4", "Summary: Nov. 16", "Summary: Nov. 6-7", "Summary: Oct. 20-21", "Summary: October 31", "Summary: Sept. 18", "THUDERSTORM WINDS", "THUNDEERSTORM WINDS", "THUNDERESTORM WINDS",
"THUNDERSNOW", "Thundersnow shower", "THUNDERSTORM", "THUNDERSTORM WINDS", "THUNDERSTORM DAMAGE", "THUNDERSTORM DAMAGE TO", "THUNDERSTORM HAIL", "THUNDERSTORM W INDS", "Thunderstorm Wind", "THUNDERSTORM WIND", "THUNDERSTORM WIND (G40)", "THUNDERSTORM WIND 50", "THUNDERSTORM WIND 52", "THUNDERSTORM WIND 56", "THUNDERSTORM WIND 59",
"THUNDERSTORM WIND 59 MPH", "THUNDERSTORM WIND 59 MPH.", "THUNDERSTORM WIND 60 MPH", "THUNDERSTORM WIND 65 MPH", "THUNDERSTORM WIND 65MPH", "THUNDERSTORM WIND 69", "THUNDERSTORM WIND 98 MPH", "THUNDERSTORM WIND G50", "THUNDERSTORM WIND G51", "THUNDERSTORM WIND G52", "THUNDERSTORM WIND G55", "THUNDERSTORM WIND G60","THUNDERSTORM WIND G61",
"THUNDERSTORM WIND TREES", "THUNDERSTORM WIND.", "THUNDERSTORM WIND/ TREE", "THUNDERSTORM WIND/ TREES", "THUNDERSTORM WIND/AWNING", "THUNDERSTORM WIND/HAIL", "THUNDERSTORM WIND/LIGHTNING", "THUNDERSTORM WINDS", "THUNDERSTORM WINDS LE CEN", "THUNDERSTORM WINDS 13", "THUNDERSTORM WINDS 2", "THUNDERSTORM WINDS 50", "THUNDERSTORM WINDS 52",
"THUNDERSTORM WINDS 53", "THUNDERSTORM WINDS 60", "THUNDERSTORM WINDS 61", "THUNDERSTORM WINDS 62", "THUNDERSTORM WINDS 63 MPH", "THUNDERSTORM WINDS AND", "THUNDERSTORM WINDS FUNNEL CLOU", "THUNDERSTORM WINDS G", "THUNDERSTORM WINDS G60", "THUNDERSTORM WINDS HAIL", "THUNDERSTORM WINDS HEAVY RAIN","THUNDERSTORM WINDS LIGHTNING",
"THUNDERSTORM WINDS SMALL STREA", "THUNDERSTORM WINDS URBAN FLOOD", "THUNDERSTORM WINDS.", "THUNDERSTORM WINDS/ FLOOD", "THUNDERSTORM WINDS/ HAIL", "THUNDERSTORM WINDS/FLASH FLOOD", "THUNDERSTORM WINDS/FLOODING", "THUNDERSTORM WINDS/FUNNEL CLOU", "THUNDERSTORM WINDS/HAIL", "THUNDERSTORM WINDS/HEAVY RAIN", "THUNDERSTORM WINDS53",
"THUNDERSTORM WINDSHAIL", "THUNDERSTORM WINDSS", "THUNDERSTORM WINS", "THUNDERSTORMS", "THUNDERSTORMS WIND", "THUNDERSTORMS WINDS", "THUNDERSTORMW", "THUNDERSTORMW 50", "THUNDERSTORMW WINDS", "THUNDERSTORMWINDS", "THUNDERSTROM WIND", "THUNDERSTROM WINDS", "THUNDERTORM WINDS", "THUNDERTSORM WIND", "THUNDESTORM WINDS", "THUNERSTORM WINDS",
"TSTM", "TSTM HEAVY RAIN", "Tstm Wind", "TSTM WIND", "TSTM WIND (G45)", "TSTM WIND (41)", "TSTM WIND (G35)", "TSTM WIND (G40)", "TSTM WIND (G45)", "TSTM WIND 40", "TSTM WIND 45", "TSTM WIND 50", "TSTM WIND 51", "TSTM WIND 52", "TSTM WIND 55", "TSTM WIND 65)", "TSTM WIND AND LIGHTNING", "TSTM WIND DAMAGE", "TSTM WIND G45", "TSTM WIND G58",
"TSTM WIND/HAIL", "TSTM WINDS", "TSTM WND", "TSTMW", "TUNDERSTORM WIND", "WALL CLOUD", "wet micoburst", "WET MICROBURST")), EVENTS = "Thunderstorm Wind")
EVTYPE40 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("COLD AIR TORNADO", "TORNADO", "TORNADO DEBRIS", "TORNADO F0", "TORNADO F1", "TORNADO F2", "TORNADO F3", "TORNADO/WATERSPOUT", "TORNADOES", "TORNADOES, TSTM WIND, HAIL", "TORNADOS", "TORNDAO")), EVENTS = "Tornado")
EVTYPE41 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("TROPICAL DEPRESSION")), EVENTS = "Tropical Depression")
EVTYPE42 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("REMNANTS OF FLOYD", "STORM FORCE WINDS", "TORRENTIAL RAIN", "Torrential Rainfall", "TROPICAL STORM", "TROPICAL STORM ALBERTO", "TROPICAL STORM DEAN", "TROPICAL STORM GORDON", "TROPICAL STORM JERRY")), EVENTS = "Tropical Storm")
EVTYPE43 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("TSUNAMI")), EVENTS = "Tsunami")
EVTYPE44 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("VOG", "Volcanic Ash", "VOLCANIC ASH", "Volcanic Ash Plume", "VOLCANIC ASHFALL", "VOLCANIC ERUPTION")), EVENTS = "Volcanic Ash")
EVTYPE45 <- data.frame(subset(StormData, StormData$EVTYPE %in% c(" WATERSPOUT"," WIND", "WATER SPOUT", "WATERSPOUT", "WATERSPOUT-", "WATERSPOUT-TORNADO", "WATERSPOUT FUNNEL CLOUD", "WATERSPOUT TORNADO", "WATERSPOUT/", "WATERSPOUT/ TORNADO", "WATERSPOUT/TORNADO", "WATERSPOUTS", "WAYTERSPOUT")), EVENTS = "Waterspout")
EVTYPE46 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("BRUSH FIRE", "BRUSH FIRES", "FOREST FIRES", "GRASS FIRES", "WILD FIRES", "WILD/FOREST FIRE", "WILD/FOREST FIRES", "WILD/FOREST FIRES", "WILDFIRE", "WILDFIRES")), EVENTS = "Wildfire")
EVTYPE47 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("WINDS", "WINTER MIX", "WINTER STORM", "WINTER STORM HIGH WINDS", "WINTER STORM/HIGH WIND", "WINTER STORM/HIGH WINDS", "WINTER STORMS")), EVENTS = "Winter Storm")
EVTYPE48 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("Winter Weather", "WINTER WEATHER", "WINTER WEATHER MIX", "WINTER WEATHER/MIX")), EVENTS = "Winter Weather")
EVTYPE49 <- data.frame(subset(StormData, StormData$EVTYPE %in% c("?", "No Severe Weather", "NONE", "NORTHERN LIGHTS", "Other", "OTHER", "Record temperature", "RECORD TEMPERATURE", "RED FLAG CRITERIA", "RED FLAG FIRE WX", "SOUTHEAST", "Temperature record", "WAKE LOW WIND")), EVENTS = "No Information Available")
StormData <- rbind(EVTYPE1, EVTYPE2, EVTYPE3, EVTYPE4, EVTYPE5, EVTYPE6, EVTYPE7, EVTYPE8, EVTYPE9, EVTYPE10, EVTYPE11, EVTYPE12, EVTYPE13,
EVTYPE14, EVTYPE15, EVTYPE16, EVTYPE17, EVTYPE18, EVTYPE19, EVTYPE20, EVTYPE21, EVTYPE22, EVTYPE23, EVTYPE24, EVTYPE25,
EVTYPE26, EVTYPE27, EVTYPE28, EVTYPE29, EVTYPE30, EVTYPE31 ,EVTYPE32, EVTYPE33, EVTYPE34, EVTYPE35, EVTYPE36, EVTYPE37,
EVTYPE38, EVTYPE39, EVTYPE40, EVTYPE41, EVTYPE42, EVTYPE43, EVTYPE44, EVTYPE45, EVTYPE46, EVTYPE47, EVTYPE48, EVTYPE49)The result of the New Variable incorporated into the StormData database.
Events <- as.data.frame(sort(table(StormData$EVENTS), decreasing = TRUE))
names(Events)[1]<-"Events"
knitr::kable(Events)| Events | Freq |
|---|---|
| Thunderstorm Wind | 325126 |
| Hail | 288839 |
| Tornado | 60688 |
| Flash Flood | 55077 |
| Flood | 26322 |
| High Wind | 22241 |
| Heavy Snow | 16971 |
| Heavy Rain | 15857 |
| Lightning | 15770 |
| Marine Thunderstorm Wind | 11987 |
| Winter Storm | 11478 |
| Winter Weather | 8155 |
| Funnel Cloud | 6945 |
| Wildfire | 4237 |
| Strong Wind | 3890 |
| Waterspout | 3862 |
| Excessive Heat | 3278 |
| Ice Storm | 2729 |
| Blizzard | 2726 |
| Drought | 2542 |
| Frost/Freeze | 2015 |
| High Surf | 1083 |
| Extreme Cold/Wind Chill | 1073 |
| Coastal Flood | 968 |
| Heat | 778 |
| Rip Current | 777 |
| Tropical Storm | 702 |
| Lake-Effect Snow | 684 |
| Cold/Wind Chill | 660 |
| Debris Flow | 647 |
| Dense Fog | 541 |
| Marine Hail | 442 |
| Dust Storm | 437 |
| Storm Surge/Tide | 422 |
| Avalanche | 387 |
| Hurricane (Typhoon) | 299 |
| Astronomical Low Tide | 174 |
| Sleet | 174 |
| Dust Devil | 156 |
| Marine High Wind | 135 |
| No Information Available | 123 |
| Tropical Depression | 60 |
| Marine Strong Wind | 50 |
| Freezing Fog | 49 |
| Volcanic Ash | 30 |
| Seiche | 29 |
| Lakeshore Flood | 25 |
| Dense Smoke | 21 |
| Tsunami | 20 |
By Filtering the PROPDMGEXP and CROPDMGEXP according the (NWSI 10-1605 AUGUST 17, 2007)
StormData$MULTIPLIE_FACTOR_PROPDMG=1
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "-", ]$MULTIPLIE_FACTOR_PROPDMG <- 1
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "+", ]$MULTIPLIE_FACTOR_PROPDMG <- 1
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "?", ]$MULTIPLIE_FACTOR_PROPDMG <- 0
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "0", ]$MULTIPLIE_FACTOR_PROPDMG <- 10
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "1", ]$MULTIPLIE_FACTOR_PROPDMG <- 10
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "2", ]$MULTIPLIE_FACTOR_PROPDMG <- 10
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "3", ]$MULTIPLIE_FACTOR_PROPDMG <- 10
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "4", ]$MULTIPLIE_FACTOR_PROPDMG <- 10
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "5", ]$MULTIPLIE_FACTOR_PROPDMG <- 10
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "6", ]$MULTIPLIE_FACTOR_PROPDMG <- 10
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "7", ]$MULTIPLIE_FACTOR_PROPDMG <- 10
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "8", ]$MULTIPLIE_FACTOR_PROPDMG <- 10
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "h", ]$MULTIPLIE_FACTOR_PROPDMG <- as.numeric(format(10^2))
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "H", ]$MULTIPLIE_FACTOR_PROPDMG <- as.numeric(format(10^2))
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "K", ]$MULTIPLIE_FACTOR_PROPDMG <- as.numeric(format(10^3))
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "m", ]$MULTIPLIE_FACTOR_PROPDMG <- as.numeric(format(10^6))
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "M", ]$MULTIPLIE_FACTOR_PROPDMG <- as.numeric(format(10^6))
StormData[!is.na(StormData$PROPDMGEXP) & StormData$PROPDMGEXP == "B", ]$MULTIPLIE_FACTOR_PROPDMG <- as.numeric(format(10^9))
StormData$MULTIPLIE_FACTOR_CROPDMG=1
StormData[!is.na(StormData$CROPDMGEXP) & StormData$CROPDMGEXP == "?", ]$MULTIPLIE_FACTOR_CROPDMG <- 0
StormData[!is.na(StormData$CROPDMGEXP) & StormData$CROPDMGEXP == "2", ]$MULTIPLIE_FACTOR_CROPDMG <- 0
StormData[!is.na(StormData$CROPDMGEXP) & StormData$CROPDMGEXP == "0", ]$MULTIPLIE_FACTOR_CROPDMG <- 1
StormData[!is.na(StormData$CROPDMGEXP) & StormData$CROPDMGEXP == "k", ]$MULTIPLIE_FACTOR_CROPDMG <- as.numeric(format(10^3))
StormData[!is.na(StormData$CROPDMGEXP) & StormData$CROPDMGEXP == "K", ]$MULTIPLIE_FACTOR_CROPDMG <- as.numeric(format(10^3))
StormData[!is.na(StormData$CROPDMGEXP) & StormData$CROPDMGEXP == "m", ]$MULTIPLIE_FACTOR_CROPDMG <- as.numeric(format(10^6))
StormData[!is.na(StormData$CROPDMGEXP) & StormData$CROPDMGEXP == "M", ]$MULTIPLIE_FACTOR_CROPDMG <- as.numeric(format(10^6))
StormData[!is.na(StormData$CROPDMGEXP) & StormData$CROPDMGEXP == "B", ]$MULTIPLIE_FACTOR_CROPDMG <- as.numeric(format(10^9))By Calculating PROPDMG and CROPDMG in “US$”
StormData$USD_PROPDMG <- as.numeric(format(StormData$PROPDMG * StormData$MULTIPLIE_FACTOR_PROPDMG))
StormData$USD_CROPDMG <- as.numeric(format(StormData$CROPDMG * StormData$MULTIPLIE_FACTOR_CROPDMG))This new variable we will create is very important because it will grouping the YEARS in DECADES.
Dec1950 <- data.frame(subset(StormData, StormData$YEAR %in% c(1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959)), DECADE = 1950)
Dec1960 <- data.frame(subset(StormData, StormData$YEAR %in% c(1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969)), DECADE = 1960)
Dec1970 <- data.frame(subset(StormData, StormData$YEAR %in% c(1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979)), DECADE = 1970)
Dec1980 <- data.frame(subset(StormData, StormData$YEAR %in% c(1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989)), DECADE = 1980)
Dec1990 <- data.frame(subset(StormData, StormData$YEAR %in% c(1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999)), DECADE = 1990)
Dec2000 <- data.frame(subset(StormData, StormData$YEAR %in% c(2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009)), DECADE = 2000)
Dec2010 <- data.frame(subset(StormData, StormData$YEAR %in% c(2010, 2011)), DECADE = 2010)
StormData <- rbind(Dec1950, Dec1960, Dec1970, Dec1980, Dec1990, Dec2000, Dec2010)Query_Decade <- sqldf('select DECADE as Decade , sum(FATALITIES) as Fatalities, sum(INJURIES) as Injuries, sum(USD_PROPDMG) as Property_Damage_US$, sum(USD_CROPDMG) as Prejudice_Crops_US$ from StormData group by DECADE')
Query1 <- sqldf('select EVENTS as Events, sum(FATALITIES) as Total_Fatalities, sum(INJURIES) as Total_Injuries, sum(FATALITIES + INJURIES) as Total_Injuries_Fatalities from StormData group by EVENTS order by sum(FATALITIES) desc, sum(INJURIES) desc, sum(FATALITIES + INJURIES) desc')
Query2 <- sqldf('select EVENTS, sum(PROPDMG) as Total_Properties, sum(USD_PROPDMG) as Total_US$, sum(CROPDMG) as Total_Crops, sum(USD_CROPDMG) as Total_US$ from StormData group by EVENTS order by sum(PROPDMG) desc, sum(USD_PROPDMG) desc, sum(CROPDMG) desc, sum(USD_CROPDMG) desc')
MapResult <- sqldf('select STATE, sum(FATALITIES) as Tot_Fatalities, sum(INJURIES) as Tot_Injuries, sum(USD_PROPDMG) as Tot_Prop_US$, sum(USD_CROPDMG) as Tot_Crops_US$, sum(FATALITIES + INJURIES) as Fatalities_Injuries from StormData group by STATE')
MapResult$hover <- with(MapResult, paste(STATE,"<br>", "Tot_Fatalities", Tot_Fatalities,"Tot_Injuries",
Tot_Injuries,"<br>", "US$_Property", `Tot_Prop_US$`, "US$_Crops", `Tot_Crops_US$`))
MapResult$hover <- as.ordered(MapResult$hover)Following the order question we will shows the results with some comments about them.
This table presents all harmful events to the population. Highlighting the top three that killed more people in U.S.
knitr::kable(Query1)| Events | Total_Fatalities | Total_Injuries | Total_Injuries_Fatalities |
|---|---|---|---|
| Tornado | 5658 | 91364 | 97022 |
| Excessive Heat | 2365 | 7165 | 9530 |
| Flash Flood | 1018 | 1786 | 2804 |
| Heat | 937 | 2102 | 3039 |
| Lightning | 817 | 5232 | 6049 |
| Thunderstorm Wind | 715 | 9538 | 10253 |
| Rip Current | 577 | 529 | 1106 |
| Flood | 500 | 6809 | 7309 |
| High Wind | 317 | 1565 | 1882 |
| Avalanche | 225 | 170 | 395 |
| Winter Storm | 218 | 1354 | 1572 |
| Extreme Cold/Wind Chill | 190 | 236 | 426 |
| High Surf | 170 | 255 | 425 |
| Cold/Wind Chill | 158 | 60 | 218 |
| Heavy Snow | 145 | 1155 | 1300 |
| Heavy Rain | 137 | 383 | 520 |
| Hurricane (Typhoon) | 135 | 1333 | 1468 |
| Strong Wind | 116 | 314 | 430 |
| Blizzard | 101 | 805 | 906 |
| Ice Storm | 95 | 2009 | 2104 |
| Wildfire | 90 | 1608 | 1698 |
| Tropical Storm | 66 | 387 | 453 |
| Dense Fog | 62 | 734 | 796 |
| Winter Weather | 61 | 538 | 599 |
| Debris Flow | 44 | 55 | 99 |
| Frost/Freeze | 36 | 434 | 470 |
| Tsunami | 33 | 129 | 162 |
| Storm Surge/Tide | 28 | 45 | 73 |
| Dust Storm | 22 | 440 | 462 |
| Marine Strong Wind | 21 | 27 | 48 |
| Marine Thunderstorm Wind | 19 | 34 | 53 |
| Coastal Flood | 18 | 15 | 33 |
| Hail | 15 | 1371 | 1386 |
| Waterspout | 6 | 72 | 78 |
| Sleet | 3 | 77 | 80 |
| Dust Devil | 3 | 43 | 46 |
| Drought | 3 | 4 | 7 |
| Freezing Fog | 1 | 1 | 2 |
| Marine High Wind | 1 | 1 | 2 |
| Seiche | 1 | 0 | 1 |
| No Information Available | 0 | 4 | 4 |
| Funnel Cloud | 0 | 3 | 3 |
| Astronomical Low Tide | 0 | 0 | 0 |
| Dense Smoke | 0 | 0 | 0 |
| Lake-Effect Snow | 0 | 0 | 0 |
| Lakeshore Flood | 0 | 0 | 0 |
| Marine Hail | 0 | 0 | 0 |
| Tropical Depression | 0 | 0 | 0 |
| Volcanic Ash | 0 | 0 | 0 |
This graph is the summary of the previous table occurring every 10 years. Only the decade 2010 has only the data of the years 2010 and 2011.
p1 <- plot_ly(Query_Decade, x = ~Decade, y = ~Fatalities, name = "Fatalities")%>%
add_bars()
p2 <- Query_Decade%>%
dplyr::count(Decade)%>%
plot_ly(x = ~Query_Decade$Decade, y = ~Query_Decade$Injuries, name = "Injuries")%>%
add_bars()
subplot(p1, p2)%>% layout(title = "Quantitative Fatalities and Injuries by Decades", yaxis = list(title = "Count"), xaxis = list(title = "Decades"), barmode = "group")This table shows an interesting feature. Although the Tornado event had destroyed several properties and crops it was not caused major financial losses. The biggest one responsible for financial losses was the Flood event that appears on 4th.
knitr::kable(Query2)| EVENTS | Total_Properties | Total_US$ | Total_Crops | Total_US$..5 |
|---|---|---|---|---|
| Tornado | 3214534.01 | 58541934650 | 100029.27 | 417461520 |
| Thunderstorm Wind | 2677430.56 | 10977861649 | 199360.38 | 1271678988 |
| Flash Flood | 1455240.65 | 16733892348 | 185056.51 | 1437163150 |
| Flood | 942257.52 | 150309536661 | 177696.22 | 10941191950 |
| Hail | 689807.28 | 15974542434 | 581468.51 | 3046887623 |
| Lightning | 603439.78 | 933743920 | 3580.61 | 12092090 |
| High Wind | 385099.20 | 6013622763 | 21412.81 | 686656900 |
| Heavy Snow | 137029.56 | 981292767 | 2195.72 | 134683100 |
| Winter Storm | 133427.09 | 6749643260 | 2489.49 | 32949000 |
| Wildfire | 125218.29 | 8496628500 | 9565.74 | 403281630 |
| Heavy Rain | 102491.98 | 3321210040 | 17031.10 | 958288900 |
| Ice Storm | 81053.37 | 3989451510 | 1688.95 | 5022113500 |
| Strong Wind | 66149.85 | 179596290 | 1833.45 | 70165050 |
| Tropical Storm | 50132.68 | 7714590550 | 6465.12 | 694896000 |
| Storm Surge/Tide | 26720.54 | 47965274000 | 855.00 | 855000 |
| Blizzard | 26599.48 | 675479950 | 172.00 | 112060000 |
| Hurricane (Typhoon) | 25186.65 | 85356410010 | 11637.79 | 5516117800 |
| Debris Flow | 20198.04 | 326831100 | 37.00 | 20017000 |
| Coastal Flood | 19560.69 | 438771060 | 56.00 | 56000 |
| Winter Weather | 16908.40 | 27298000 | 15.00 | 15000000 |
| Lake-Effect Snow | 14708.00 | 40682000 | 0.00 | 0 |
| Frost/Freeze | 12982.69 | 46639000 | 9628.11 | 1983961800 |
| Waterspout | 10780.20 | 60730200 | 0.00 | 0 |
| Extreme Cold/Wind Chill | 9118.04 | 124642400 | 6243.84 | 1373215500 |
| Dense Fog | 8849.81 | 13155500 | 0.00 | 0 |
| High Surf | 7210.62 | 102087000 | 0.00 | 0 |
| Excessive Heat | 5588.36 | 27176750 | 864.70 | 503062000 |
| Dust Storm | 5119.50 | 5619000 | 2101.50 | 3600000 |
| Drought | 4099.05 | 1046106000 | 33904.40 | 13972571780 |
| Marine Thunderstorm Wind | 2860.40 | 5857400 | 50.00 | 50000 |
| Cold/Wind Chill | 2544.00 | 2544000 | 671.00 | 71600000 |
| Avalanche | 1623.90 | 3721800 | 0.00 | 0 |
| Seiche | 1000.00 | 1000000 | 0.00 | 0 |
| Tsunami | 905.30 | 144062000 | 20.00 | 20000 |
| Dust Devil | 738.13 | 738130 | 0.00 | 0 |
| Tropical Depression | 738.00 | 1737000 | 0.00 | 0 |
| Sleet | 512.50 | 512500 | 0.00 | 0 |
| Volcanic Ash | 500.00 | 500000 | 0.00 | 0 |
| Marine Strong Wind | 418.33 | 418330 | 0.00 | 0 |
| Astronomical Low Tide | 320.00 | 320000 | 0.00 | 0 |
| Heat | 298.50 | 1797000 | 662.70 | 401461500 |
| Marine High Wind | 298.01 | 1297010 | 0.00 | 0 |
| Funnel Cloud | 194.60 | 194600 | 0.00 | 0 |
| Freezing Fog | 184.00 | 2182000 | 0.00 | 0 |
| Rip Current | 163.00 | 163000 | 0.00 | 0 |
| Dense Smoke | 100.00 | 100000 | 0.00 | 0 |
| Lakeshore Flood | 77.50 | 7570000 | 0.00 | 0 |
| No Information Available | 60.50 | 60500 | 1034.40 | 1034400 |
| Marine Hail | 4.00 | 4000 | 0.00 | 0 |
We present a more detailed summary of our report through this dynamic graph where, when hovering over each state, the total of Fatalities, Injuries and Financial Losses (Properties and Crops)
l1 <- list(color = toRGB("white"), width = 2)
g1 <- list(scope = 'usa', projection = list(type = 'albers usa'), showlakes = T, lakecolor = toRGB('white'))
m <- plot_ly(MapResult, z = ~Fatalities_Injuries, text = ~hover, locations = ~STATE, type = "choropleth",
locationmode = "USA-states", color = ~Fatalities_Injuries, marker = list(line=l1),
colorbar = list(title = "Total Personal Injury and Fatalities"))%>%
layout(title = "Demonstration of Damage of Weather Events Occured From 1950 To 2011", geo = g1)
m