Health and Economic Assessment of the Severe Weather Events in the US
Marce
11/21/2019
1. Synopsis
This document was developed as part of the course Reproducible Research by Johns Hopkins Bloomberg University via Coursera. In this analysis we aimed to identify the types of severe weather events most harmful to the population health as well as those with the greatest economic consequences accross the United States of America. This assessment was performed using the NOAA Storm Database, which collects information from 1950-2011.
We used exploratory analysis, and our findings are: The events with higher economic consequences are Hurricane/Typhoon and Storm Surge, and the events more harmful to humans are Excesive heat and Tornado
2. Data Processing
Initially, the data was dowloaded from the link provided by the course professor and store it in a folder call data.
if(!file.exists("data")){
dir.create("data")
}
fileurl<- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(fileurl, destfile ="data/NOAA.csv.bz2" )
library(readr)
dt <- read_csv("data/NOAA.csv.bz2")2.1 General Information
Now, we need to do some basic exploratory analysis in the data to undestand our dataset.
As first step, we had to read the document explaining how the variables were constructed and defined.
From this document I extracted important information to help understanding the type the data: 1. The events recorded in this dataset are the following:
- According to the document the variable:
- Fatalities and Injuries: Direct and indirect causes of weather-related fatalities or injuries. I will use fatalities only because the the worst case scenario.
- Damage (DMG): Damage in usd. Types = Prop (Properties), Crop(Crops), WFO(flood related). Next to each damage there is another colum containing alphabetical characters used to signify magnitude include “K” for thousands, “M” for millions, and “B” for billions.
2.2 Exploratory analysis
We need to get a glipmse of our data.
head(dt)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 NA NA NA NA 0
## 2 TORNADO 0 NA NA NA NA 0
## 3 TORNADO 0 NA NA NA NA 0
## 4 TORNADO 0 NA NA NA NA 0
## 5 TORNADO 0 NA NA NA NA 0
## 6 TORNADO 0 NA NA NA NA 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 NA NA 14.0 100 3 0 0
## 2 NA 0 NA NA 2.0 150 2 0 0
## 3 NA 0 NA NA 0.1 123 2 0 0
## 4 NA 0 NA NA 0.0 100 2 0 0
## 5 NA 0 NA NA 0.0 150 2 0 0
## 6 NA 0 NA NA 1.5 177 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0 NA NA NA NA
## 2 0 2.5 K 0 NA NA NA NA
## 3 2 25.0 K 0 NA NA NA NA
## 4 2 2.5 K 0 NA NA NA NA
## 5 2 2.5 K 0 NA NA NA NA
## 6 6 2.5 K 0 NA NA NA NA
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 NA 1
## 2 3042 8755 0 0 NA 2
## 3 3340 8742 0 0 NA 3
## 4 3458 8626 0 0 NA 4
## 5 3412 8642 0 0 NA 5
## 6 3450 8748 0 0 NA 6summary(dt)## STATE__ BGN_DATE BGN_TIME TIME_ZONE
## Min. : 1.0 Length:902297 Length:902297 Length:902297
## 1st Qu.:19.0 Class :character Class :character Class :character
## Median :30.0 Mode :character Mode :character Mode :character
## Mean :31.2
## 3rd Qu.:45.0
## Max. :95.0
##
## 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 Mode:logical Mode:logical Mode:logical
## 1st Qu.: 0.000 NA's:902297 TRUE:1 NA's:902297
## Median : 0.000 NA's:902296
## Mean : 1.484
## 3rd Qu.: 1.000
## Max. :3749.000
##
## END_TIME COUNTY_END COUNTYENDN END_RANGE
## Mode:logical Min. :0 Mode:logical Min. : 0.0000
## NA's:902297 1st Qu.:0 NA's:902297 1st Qu.: 0.0000
## 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
## Mode:logical Mode:logical Min. : 0.0000 Min. : 0.000
## NA's:902297 NA's:902297 1st Qu.: 0.0000 1st Qu.: 0.000
## 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 Mode :logical
## 1st Qu.: 0.00 Class :character 1st Qu.: 0.000 FALSE:19
## Median : 0.00 Mode :character Median : 0.000 NA's :902278
## Mean : 12.06 Mean : 1.527
## 3rd Qu.: 0.50 3rd Qu.: 0.000
## Max. :5000.00 Max. :990.000
##
## WFO STATEOFFIC ZONENAMES LATITUDE
## Mode:logical Mode:logical Mode:logical Min. : 0
## TRUE:7166 NA's:902297 NA's:902297 1st Qu.:2802
## NA's:895131 Median :3540
## Mean :2875
## 3rd Qu.:4019
## Max. :9706
## NA's :47
## LONGITUDE LATITUDE_E LONGITUDE_ REMARKS
## Min. :-14451 Min. : 0 Min. :-14455 Mode:logical
## 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0 NA's:902297
## Median : 8707 Median : 0 Median : 0
## 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
## Min. : 1
## 1st Qu.:225575
## Median :451149
## Mean :451149
## 3rd Qu.:676723
## Max. :902297
## table(dt$EVTYPE) # There's one event call ?##
## ? ABNORMAL WARMTH
## 1 4
## ABNORMALLY DRY ABNORMALLY WET
## 2 1
## ACCUMULATED SNOWFALL AGRICULTURAL FREEZE
## 4 6
## APACHE COUNTY ASTRONOMICAL HIGH TIDE
## 1 103
## ASTRONOMICAL LOW TIDE AVALANCE
## 174 1
## AVALANCHE BEACH EROSIN
## 386 1
## Beach Erosion BEACH EROSION
## 1 3
## BEACH EROSION/COASTAL FLOOD BEACH FLOOD
## 1 2
## BELOW NORMAL PRECIPITATION BITTER WIND CHILL
## 2 1
## BITTER WIND CHILL TEMPERATURES Black Ice
## 3 3
## BLACK ICE BLIZZARD
## 14 2719
## BLIZZARD AND EXTREME WIND CHIL BLIZZARD AND HEAVY SNOW
## 2 1
## Blizzard Summary BLIZZARD WEATHER
## 1 1
## BLIZZARD/FREEZING RAIN BLIZZARD/HEAVY SNOW
## 1 2
## BLIZZARD/HIGH WIND BLIZZARD/WINTER STORM
## 1 1
## BLOW-OUT TIDE BLOW-OUT TIDES
## 1 1
## BLOWING DUST blowing snow
## 4 2
## Blowing Snow BLOWING SNOW
## 3 12
## BLOWING SNOW & EXTREME WIND CH BLOWING SNOW- EXTREME WIND CHI
## 2 1
## BLOWING SNOW/EXTREME WIND CHIL BREAKUP FLOODING
## 1 1
## BRUSH FIRE BRUSH FIRES
## 3 1
## COASTAL FLOODING/EROSION COASTAL EROSION
## 1 1
## Coastal Flood COASTAL FLOOD
## 6 651
## coastal flooding Coastal Flooding
## 2 38
## COASTAL FLOODING COASTAL FLOODING/EROSION
## 143 5
## Coastal Storm COASTAL STORM
## 2 8
## COASTAL SURGE COASTAL/TIDAL FLOOD
## 2 2
## COASTALFLOOD COASTALSTORM
## 1 1
## Cold COLD
## 10 72
## COLD AIR FUNNEL COLD AIR FUNNELS
## 4 2
## COLD AIR TORNADO Cold and Frost
## 1 6
## COLD AND FROST COLD AND SNOW
## 1 1
## COLD AND WET CONDITIONS Cold Temperature
## 1 2
## COLD TEMPERATURES COLD WAVE
## 4 3
## COLD WEATHER COLD WIND CHILL TEMPERATURES
## 4 6
## COLD/WIND CHILL COLD/WINDS
## 539 1
## COOL AND WET COOL SPELL
## 1 1
## CSTL FLOODING/EROSION DAM BREAK
## 2 4
## DAM FAILURE Damaging Freeze
## 1 2
## DAMAGING FREEZE DEEP HAIL
## 6 1
## DENSE FOG DENSE SMOKE
## 1293 10
## DOWNBURST DOWNBURST WINDS
## 2 2
## DRIEST MONTH Drifting Snow
## 1 1
## DROUGHT DROUGHT/EXCESSIVE HEAT
## 2488 13
## DROWNING DRY
## 1 9
## DRY CONDITIONS DRY HOT WEATHER
## 6 1
## DRY MICROBURST DRY MICROBURST 50
## 186 1
## DRY MICROBURST 53 DRY MICROBURST 58
## 1 2
## DRY MICROBURST 61 DRY MICROBURST 84
## 1 1
## DRY MICROBURST WINDS DRY MIRCOBURST WINDS
## 5 1
## DRY PATTERN DRY SPELL
## 1 4
## DRY WEATHER DRYNESS
## 4 1
## DUST DEVEL Dust Devil
## 1 8
## DUST DEVIL DUST DEVIL WATERSPOUT
## 141 1
## DUST STORM DUST STORM/HIGH WINDS
## 427 1
## DUSTSTORM EARLY FREEZE
## 1 1
## Early Frost EARLY FROST
## 1 1
## EARLY RAIN EARLY SNOW
## 1 3
## Early snowfall EARLY SNOWFALL
## 2 5
## Erosion/Cstl Flood EXCESSIVE
## 2 1
## Excessive Cold EXCESSIVE HEAT
## 2 1678
## EXCESSIVE HEAT/DROUGHT EXCESSIVE PRECIPITATION
## 1 1
## EXCESSIVE RAIN EXCESSIVE RAINFALL
## 5 4
## EXCESSIVE SNOW EXCESSIVE WETNESS
## 25 1
## EXCESSIVELY DRY Extended Cold
## 1 1
## Extreme Cold EXTREME COLD
## 2 655
## EXTREME COLD/WIND CHILL EXTREME HEAT
## 1002 22
## EXTREME WIND CHILL EXTREME WIND CHILL/BLOWING SNO
## 6 1
## EXTREME WIND CHILLS EXTREME WINDCHILL
## 1 204
## EXTREME WINDCHILL TEMPERATURES EXTREME/RECORD COLD
## 19 4
## EXTREMELY WET FALLING SNOW/ICE
## 1 2
## FIRST FROST FIRST SNOW
## 1 9
## FLASH FLOOD FLASH FLOOD - HEAVY RAIN
## 54278 2
## FLASH FLOOD FROM ICE JAMS FLASH FLOOD LANDSLIDES
## 5 1
## FLASH FLOOD WINDS FLASH FLOOD/
## 1 1
## FLASH FLOOD/ FLOOD FLASH FLOOD/ STREET
## 2 1
## FLASH FLOOD/FLOOD FLASH FLOOD/HEAVY RAIN
## 22 1
## FLASH FLOOD/LANDSLIDE FLASH FLOODING
## 1 682
## FLASH FLOODING/FLOOD FLASH FLOODING/THUNDERSTORM WI
## 8 1
## FLASH FLOODS FLASH FLOOODING
## 32 1
## Flood FLOOD
## 1 25326
## FLOOD & HEAVY RAIN FLOOD FLASH
## 2 3
## FLOOD FLOOD/FLASH FLOOD WATCH/
## 1 1
## FLOOD/FLASH Flood/Flash Flood
## 2 1
## FLOOD/FLASH FLOOD FLOOD/FLASH FLOODING
## 624 2
## FLOOD/FLASH/FLOOD FLOOD/FLASHFLOOD
## 1 1
## FLOOD/RAIN/WIND FLOOD/RAIN/WINDS
## 1 6
## FLOOD/RIVER FLOOD Flood/Strong Wind
## 1 1
## FLOODING FLOODING/HEAVY RAIN
## 120 1
## FLOODS FOG
## 3 538
## FOG AND COLD TEMPERATURES FOREST FIRES
## 1 1
## Freeze FREEZE
## 2 74
## Freezing drizzle Freezing Drizzle
## 1 3
## FREEZING DRIZZLE FREEZING DRIZZLE AND FREEZING
## 20 1
## Freezing Fog FREEZING FOG
## 1 45
## Freezing rain Freezing Rain
## 3 7
## FREEZING RAIN FREEZING RAIN AND SLEET
## 250 6
## FREEZING RAIN AND SNOW FREEZING RAIN SLEET AND
## 1 1
## FREEZING RAIN SLEET AND LIGHT FREEZING RAIN/SLEET
## 1 9
## FREEZING RAIN/SNOW Freezing Spray
## 4 1
## Frost FROST
## 4 53
## Frost/Freeze FROST/FREEZE
## 1 1342
## FROST\\FREEZE FUNNEL
## 1 46
## Funnel Cloud FUNNEL CLOUD
## 5 6839
## FUNNEL CLOUD. FUNNEL CLOUD/HAIL
## 1 1
## FUNNEL CLOUDS FUNNELS
## 87 1
## Glaze GLAZE
## 11 32
## GLAZE ICE GLAZE/ICE STORM
## 2 1
## gradient wind Gradient wind
## 2 4
## GRADIENT WIND GRADIENT WINDS
## 3 8
## GRASS FIRES GROUND BLIZZARD
## 1 2
## GUSTNADO GUSTNADO AND
## 6 1
## GUSTY LAKE WIND GUSTY THUNDERSTORM WIND
## 1 3
## GUSTY THUNDERSTORM WINDS Gusty Wind
## 5 1
## GUSTY WIND GUSTY WIND/HAIL
## 23 1
## GUSTY WIND/HVY RAIN Gusty wind/rain
## 1 1
## Gusty winds Gusty Winds
## 2 10
## GUSTY WINDS HAIL
## 53 288661
## HAIL 0.75 HAIL 0.88
## 18 1
## HAIL 075 HAIL 088
## 1 1
## HAIL 1.00 HAIL 1.75
## 6 4
## HAIL 1.75) HAIL 100
## 1 13
## HAIL 125 HAIL 150
## 1 2
## HAIL 175 HAIL 200
## 13 1
## HAIL 225 HAIL 275
## 1 3
## HAIL 450 HAIL 75
## 1 29
## HAIL 80 HAIL 88
## 2 1
## HAIL ALOFT HAIL DAMAGE
## 1 2
## HAIL FLOODING HAIL STORM
## 1 1
## Hail(0.75) HAIL/ICY ROADS
## 1 1
## HAIL/WIND HAIL/WINDS
## 3 2
## HAILSTORM HAILSTORMS
## 3 1
## HARD FREEZE HAZARDOUS SURF
## 7 1
## HEAT HEAT DROUGHT
## 767 1
## Heat Wave HEAT WAVE
## 1 74
## HEAT WAVE DROUGHT HEAT WAVES
## 1 2
## HEAT/DROUGHT Heatburst
## 1 1
## HEAVY LAKE SNOW HEAVY MIX
## 25 8
## HEAVY PRECIPATATION Heavy Precipitation
## 1 2
## HEAVY PRECIPITATION Heavy rain
## 1 3
## Heavy Rain HEAVY RAIN
## 16 11723
## HEAVY RAIN AND FLOOD Heavy Rain and Wind
## 1 4
## HEAVY RAIN EFFECTS HEAVY RAIN; URBAN FLOOD WINDS;
## 1 1
## HEAVY RAIN/FLOODING Heavy Rain/High Surf
## 2 1
## HEAVY RAIN/LIGHTNING HEAVY RAIN/MUDSLIDES/FLOOD
## 1 1
## HEAVY RAIN/SEVERE WEATHER HEAVY RAIN/SMALL STREAM URBAN
## 2 1
## HEAVY RAIN/SNOW HEAVY RAIN/URBAN FLOOD
## 1 1
## HEAVY RAIN/WIND HEAVY RAINFALL
## 4 3
## HEAVY RAINS HEAVY RAINS/FLOODING
## 26 9
## HEAVY SEAS HEAVY SHOWER
## 2 2
## HEAVY SHOWERS HEAVY SNOW
## 1 15708
## HEAVY SNOW FREEZING RAIN HEAVY SNOW & ICE
## 1 1
## HEAVY SNOW AND HEAVY SNOW AND HIGH WINDS
## 1 2
## HEAVY SNOW AND ICE HEAVY SNOW AND ICE STORM
## 2 2
## HEAVY SNOW AND STRONG WINDS HEAVY SNOW ANDBLOWING SNOW
## 1 1
## Heavy snow shower HEAVY SNOW SQUALLS
## 1 32
## HEAVY SNOW-SQUALLS HEAVY SNOW/BLIZZARD
## 15 3
## HEAVY SNOW/BLIZZARD/AVALANCHE HEAVY SNOW/BLOWING SNOW
## 1 1
## HEAVY SNOW/FREEZING RAIN HEAVY SNOW/HIGH
## 2 1
## HEAVY SNOW/HIGH WIND HEAVY SNOW/HIGH WINDS
## 1 1
## HEAVY SNOW/HIGH WINDS & FLOOD HEAVY SNOW/HIGH WINDS/FREEZING
## 1 1
## HEAVY SNOW/ICE HEAVY SNOW/ICE STORM
## 5 2
## HEAVY SNOW/SLEET HEAVY SNOW/SQUALLS
## 1 2
## HEAVY SNOW/WIND HEAVY SNOW/WINTER STORM
## 1 1
## HEAVY SNOWPACK Heavy Surf
## 1 3
## HEAVY SURF Heavy surf and wind
## 84 1
## HEAVY SURF COASTAL FLOODING HEAVY SURF/HIGH SURF
## 1 228
## HEAVY SWELLS HEAVY WET SNOW
## 1 1
## HIGH HIGH SWELLS
## 1 1
## HIGH WINDS HIGH SEAS
## 1 8
## High Surf HIGH SURF
## 9 725
## HIGH SURF ADVISORIES HIGH SURF ADVISORY
## 1 5
## HIGH SWELLS HIGH TEMPERATURE RECORD
## 5 3
## HIGH TIDES HIGH WATER
## 2 6
## HIGH WAVES High Wind
## 3 2
## HIGH WIND HIGH WIND (G40)
## 20212 2
## HIGH WIND 48 HIGH WIND 63
## 1 1
## HIGH WIND 70 HIGH WIND AND HEAVY SNOW
## 1 1
## HIGH WIND AND HIGH TIDES HIGH WIND AND SEAS
## 2 1
## HIGH WIND DAMAGE HIGH WIND/ BLIZZARD
## 2 1
## HIGH WIND/BLIZZARD HIGH WIND/BLIZZARD/FREEZING RA
## 6 1
## HIGH WIND/HEAVY SNOW HIGH WIND/LOW WIND CHILL
## 3 1
## HIGH WIND/SEAS HIGH WIND/WIND CHILL
## 1 1
## HIGH WIND/WIND CHILL/BLIZZARD HIGH WINDS
## 1 1533
## HIGH WINDS 55 HIGH WINDS 57
## 1 1
## HIGH WINDS 58 HIGH WINDS 63
## 1 2
## HIGH WINDS 66 HIGH WINDS 67
## 2 1
## HIGH WINDS 73 HIGH WINDS 76
## 1 1
## HIGH WINDS 80 HIGH WINDS 82
## 2 1
## HIGH WINDS AND WIND CHILL HIGH WINDS DUST STORM
## 1 1
## HIGH WINDS HEAVY RAINS HIGH WINDS/
## 1 1
## HIGH WINDS/COASTAL FLOOD HIGH WINDS/COLD
## 1 5
## HIGH WINDS/FLOODING HIGH WINDS/HEAVY RAIN
## 1 1
## HIGH WINDS/SNOW HIGHWAY FLOODING
## 3 1
## Hot and Dry HOT PATTERN
## 2 1
## HOT SPELL HOT WEATHER
## 2 1
## HOT/DRY PATTERN HURRICANE
## 1 174
## Hurricane Edouard HURRICANE EMILY
## 2 1
## HURRICANE ERIN HURRICANE FELIX
## 7 2
## HURRICANE GORDON HURRICANE OPAL
## 1 9
## HURRICANE OPAL/HIGH WINDS HURRICANE-GENERATED SWELLS
## 1 3
## HURRICANE/TYPHOON HVY RAIN
## 88 2
## HYPERTHERMIA/EXPOSURE HYPOTHERMIA
## 1 1
## Hypothermia/Exposure HYPOTHERMIA/EXPOSURE
## 3 3
## ICE ICE AND SNOW
## 61 1
## ICE FLOES Ice Fog
## 2 2
## ICE JAM Ice jam flood (minor
## 4 1
## ICE JAM FLOODING ICE ON ROAD
## 5 1
## ICE PELLETS ICE ROADS
## 1 1
## ICE STORM ICE STORM AND SNOW
## 2006 1
## ICE STORM/FLASH FLOOD Ice/Snow
## 1 2
## ICE/SNOW ICE/STRONG WINDS
## 3 1
## Icestorm/Blizzard Icy Roads
## 1 4
## ICY ROADS LACK OF SNOW
## 28 1
## Lake Effect Snow LAKE EFFECT SNOW
## 2 21
## LAKE FLOOD LAKE-EFFECT SNOW
## 1 636
## LAKESHORE FLOOD LANDSLIDE
## 23 600
## LANDSLIDE/URBAN FLOOD LANDSLIDES
## 1 8
## Landslump LANDSLUMP
## 1 1
## LANDSPOUT LARGE WALL CLOUD
## 2 1
## LATE FREEZE LATE SEASON HAIL
## 1 1
## LATE SEASON SNOW Late Season Snowfall
## 1 2
## LATE SNOW Late-season Snowfall
## 2 1
## LIGHT FREEZING RAIN Light snow
## 23 1
## Light Snow LIGHT SNOW
## 21 154
## LIGHT SNOW AND SLEET Light Snow/Flurries
## 2 3
## LIGHT SNOW/FREEZING PRECIP Light Snowfall
## 1 1
## LIGHTING LIGHTNING
## 3 15755
## LIGHTNING WAUSEON LIGHTNING AND HEAVY RAIN
## 1 1
## LIGHTNING AND THUNDERSTORM WIN LIGHTNING AND WINDS
## 1 1
## LIGHTNING DAMAGE LIGHTNING FIRE
## 1 1
## LIGHTNING INJURY LIGHTNING THUNDERSTORM WINDS
## 1 1
## LIGHTNING THUNDERSTORM WINDSS LIGHTNING.
## 1 1
## LIGHTNING/HEAVY RAIN LIGNTNING
## 1 1
## LOCAL FLASH FLOOD LOCAL FLOOD
## 1 1
## LOCALLY HEAVY RAIN LOW TEMPERATURE
## 1 7
## LOW TEMPERATURE RECORD LOW WIND CHILL
## 1 1
## MAJOR FLOOD Marine Accident
## 3 1
## MARINE HAIL MARINE HIGH WIND
## 442 135
## MARINE MISHAP MARINE STRONG WIND
## 2 48
## MARINE THUNDERSTORM WIND MARINE TSTM WIND
## 5812 6175
## Metro Storm, May 26 Microburst
## 1 4
## MICROBURST MICROBURST WINDS
## 5 5
## Mild and Dry Pattern MILD PATTERN
## 1 1
## MILD/DRY PATTERN MINOR FLOOD
## 1 1
## Minor Flooding MINOR FLOODING
## 1 3
## MIXED PRECIP Mixed Precipitation
## 10 3
## MIXED PRECIPITATION MODERATE SNOW
## 34 1
## MODERATE SNOWFALL MONTHLY PRECIPITATION
## 101 36
## Monthly Rainfall MONTHLY RAINFALL
## 2 11
## Monthly Snowfall MONTHLY SNOWFALL
## 1 1
## MONTHLY TEMPERATURE Mountain Snows
## 4 1
## MUD SLIDE MUD SLIDES
## 7 1
## MUD SLIDES URBAN FLOODING MUD/ROCK SLIDE
## 1 1
## Mudslide MUDSLIDE
## 8 9
## MUDSLIDE/LANDSLIDE Mudslides
## 1 5
## MUDSLIDES NEAR RECORD SNOW
## 4 1
## No Severe Weather NON SEVERE HAIL
## 1 7
## NON TSTM WIND NON-SEVERE WIND DAMAGE
## 2 1
## NON-TSTM WIND NONE
## 1 2
## NORMAL PRECIPITATION NORTHERN LIGHTS
## 3 1
## Other OTHER
## 4 48
## PATCHY DENSE FOG PATCHY ICE
## 3 1
## Prolong Cold PROLONG COLD
## 5 17
## PROLONG COLD/SNOW PROLONG WARMTH
## 1 4
## PROLONGED RAIN RAIN
## 4 16
## RAIN (HEAVY) RAIN AND WIND
## 1 1
## Rain Damage RAIN/SNOW
## 1 5
## RAIN/WIND RAINSTORM
## 1 1
## RAPIDLY RISING WATER RECORD COLD
## 1 1
## Record Cold RECORD COLD
## 3 64
## RECORD COLD AND HIGH WIND RECORD COLD/FROST
## 1 2
## RECORD COOL Record dry month
## 5 1
## RECORD DRYNESS Record Heat
## 2 1
## RECORD HEAT RECORD HEAT WAVE
## 81 1
## Record High RECORD HIGH
## 2 5
## RECORD HIGH TEMPERATURE RECORD HIGH TEMPERATURES
## 3 1
## RECORD LOW RECORD LOW RAINFALL
## 4 2
## Record May Snow RECORD PRECIPITATION
## 1 1
## RECORD RAINFALL RECORD SNOW
## 14 8
## RECORD SNOW/COLD RECORD SNOWFALL
## 1 6
## Record temperature RECORD TEMPERATURE
## 11 5
## Record Temperatures RECORD TEMPERATURES
## 2 3
## RECORD WARM RECORD WARM TEMPS.
## 1 1
## Record Warmth RECORD WARMTH
## 8 146
## Record Winter Snow RECORD/EXCESSIVE HEAT
## 3 3
## RECORD/EXCESSIVE RAINFALL RED FLAG CRITERIA
## 1 2
## RED FLAG FIRE WX REMNANTS OF FLOYD
## 2 2
## RIP CURRENT RIP CURRENTS
## 470 304
## RIP CURRENTS HEAVY SURF RIP CURRENTS/HEAVY SURF
## 1 2
## RIVER AND STREAM FLOOD RIVER FLOOD
## 2 173
## River Flooding RIVER FLOODING
## 5 24
## ROCK SLIDE ROGUE WAVE
## 2 1
## ROTATING WALL CLOUD ROUGH SEAS
## 5 3
## ROUGH SURF RURAL FLOOD
## 4 2
## Saharan Dust SAHARAN DUST
## 2 2
## Seasonal Snowfall SEICHE
## 1 21
## SEVERE COLD SEVERE THUNDERSTORM
## 1 13
## SEVERE THUNDERSTORM WINDS SEVERE THUNDERSTORMS
## 5 23
## SEVERE TURBULENCE SLEET
## 1 59
## SLEET & FREEZING RAIN SLEET STORM
## 1 12
## SLEET/FREEZING RAIN SLEET/ICE STORM
## 2 1
## SLEET/RAIN/SNOW SLEET/SNOW
## 1 2
## small hail Small Hail
## 5 1
## SMALL HAIL SMALL STREAM
## 47 1
## SMALL STREAM AND SMALL STREAM AND URBAN FLOOD
## 1 2
## SMALL STREAM AND URBAN FLOODIN SMALL STREAM FLOOD
## 1 7
## SMALL STREAM FLOODING SMALL STREAM URBAN FLOOD
## 4 1
## SMALL STREAM/URBAN FLOOD Sml Stream Fld
## 5 2
## SMOKE Snow
## 11 30
## SNOW Snow Accumulation
## 587 1
## SNOW ACCUMULATION SNOW ADVISORY
## 1 1
## SNOW AND COLD SNOW AND HEAVY SNOW
## 2 2
## Snow and Ice SNOW AND ICE
## 1 33
## SNOW AND ICE STORM Snow and sleet
## 1 1
## SNOW AND SLEET SNOW AND WIND
## 4 1
## SNOW DROUGHT SNOW FREEZING RAIN
## 7 11
## SNOW SHOWERS SNOW SLEET
## 6 1
## SNOW SQUALL Snow squalls
## 19 1
## Snow Squalls SNOW SQUALLS
## 4 17
## SNOW- HIGH WIND- WIND CHILL SNOW/ BITTER COLD
## 1 1
## SNOW/ ICE SNOW/BLOWING SNOW
## 1 7
## SNOW/COLD SNOW/FREEZING RAIN
## 2 6
## SNOW/HEAVY SNOW SNOW/HIGH WINDS
## 1 2
## SNOW/ICE SNOW/ICE STORM
## 7 17
## SNOW/RAIN SNOW/RAIN/SLEET
## 1 1
## SNOW/SLEET SNOW/SLEET/FREEZING RAIN
## 10 6
## SNOW/SLEET/RAIN SNOW\\COLD
## 1 1
## SNOWFALL RECORD SNOWMELT FLOODING
## 1 5
## SNOWSTORM SOUTHEAST
## 1 1
## STORM FORCE WINDS STORM SURGE
## 1 261
## STORM SURGE/TIDE STREAM FLOODING
## 148 1
## STREET FLOOD STREET FLOODING
## 3 3
## Strong Wind STRONG WIND
## 3 3566
## STRONG WIND GUST Strong winds
## 2 1
## Strong Winds STRONG WINDS
## 7 196
## Summary August 10 Summary August 11
## 2 2
## Summary August 17 Summary August 2-3
## 1 1
## Summary August 21 Summary August 28
## 1 1
## Summary August 4 Summary August 7
## 1 1
## Summary August 9 Summary Jan 17
## 1 1
## Summary July 23-24 Summary June 18-19
## 1 1
## Summary June 5-6 Summary June 6
## 1 1
## Summary of April 12 Summary of April 13
## 2 1
## Summary of April 21 Summary of April 27
## 2 1
## Summary of April 3rd Summary of August 1
## 1 1
## Summary of July 11 Summary of July 2
## 1 1
## Summary of July 22 Summary of July 26
## 1 1
## Summary of July 29 Summary of July 3
## 1 1
## Summary of June 10 Summary of June 11
## 1 1
## Summary of June 12 Summary of June 13
## 1 2
## Summary of June 15 Summary of June 16
## 1 1
## Summary of June 18 Summary of June 23
## 1 1
## Summary of June 24 Summary of June 3
## 1 2
## Summary of June 30 Summary of June 4
## 1 1
## Summary of June 6 Summary of March 14
## 1 1
## Summary of March 23 Summary of March 24
## 2 1
## SUMMARY OF MARCH 24-25 SUMMARY OF MARCH 27
## 1 1
## SUMMARY OF MARCH 29 Summary of May 10
## 1 1
## Summary of May 13 Summary of May 14
## 1 1
## Summary of May 22 Summary of May 22 am
## 1 1
## Summary of May 22 pm Summary of May 26 am
## 1 1
## Summary of May 26 pm Summary of May 31 am
## 1 1
## Summary of May 31 pm Summary of May 9-10
## 1 1
## Summary Sept. 25-26 Summary September 20
## 1 1
## Summary September 23 Summary September 3
## 2 1
## Summary September 4 Summary: Nov. 16
## 1 2
## Summary: Nov. 6-7 Summary: Oct. 20-21
## 1 1
## Summary: October 31 Summary: Sept. 18
## 1 1
## Temperature record THUDERSTORM WINDS
## 43 2
## THUNDEERSTORM WINDS THUNDERESTORM WINDS
## 2 1
## THUNDERSNOW Thundersnow shower
## 1 1
## THUNDERSTORM THUNDERSTORM WINDS
## 45 7
## THUNDERSTORM DAMAGE THUNDERSTORM DAMAGE TO
## 2 1
## THUNDERSTORM HAIL THUNDERSTORM W INDS
## 1 1
## Thunderstorm Wind THUNDERSTORM WIND
## 1 82563
## THUNDERSTORM WIND (G40) THUNDERSTORM WIND 50
## 1 2
## THUNDERSTORM WIND 52 THUNDERSTORM WIND 56
## 1 1
## THUNDERSTORM WIND 59 THUNDERSTORM WIND 59 MPH
## 1 1
## THUNDERSTORM WIND 59 MPH. THUNDERSTORM WIND 60 MPH
## 1 4
## THUNDERSTORM WIND 65 MPH THUNDERSTORM WIND 65MPH
## 1 1
## THUNDERSTORM WIND 69 THUNDERSTORM WIND 98 MPH
## 1 1
## THUNDERSTORM WIND G50 THUNDERSTORM WIND G51
## 4 1
## THUNDERSTORM WIND G52 THUNDERSTORM WIND G55
## 2 1
## THUNDERSTORM WIND G60 THUNDERSTORM WIND G61
## 2 1
## THUNDERSTORM WIND TREES THUNDERSTORM WIND.
## 1 1
## THUNDERSTORM WIND/ TREE THUNDERSTORM WIND/ TREES
## 1 4
## THUNDERSTORM WIND/AWNING THUNDERSTORM WIND/HAIL
## 1 1
## THUNDERSTORM WIND/LIGHTNING THUNDERSTORM WINDS
## 1 20843
## THUNDERSTORM WINDS LE CEN THUNDERSTORM WINDS 13
## 1 1
## THUNDERSTORM WINDS 2 THUNDERSTORM WINDS 50
## 1 1
## THUNDERSTORM WINDS 52 THUNDERSTORM WINDS 53
## 1 1
## THUNDERSTORM WINDS 60 THUNDERSTORM WINDS 61
## 1 1
## THUNDERSTORM WINDS 62 THUNDERSTORM WINDS 63 MPH
## 1 1
## THUNDERSTORM WINDS AND THUNDERSTORM WINDS FUNNEL CLOU
## 2 2
## THUNDERSTORM WINDS G THUNDERSTORM WINDS G60
## 2 1
## THUNDERSTORM WINDS HAIL THUNDERSTORM WINDS HEAVY RAIN
## 61 1
## THUNDERSTORM WINDS LIGHTNING THUNDERSTORM WINDS SMALL STREA
## 7 1
## THUNDERSTORM WINDS URBAN FLOOD THUNDERSTORM WINDS.
## 1 3
## THUNDERSTORM WINDS/ FLOOD THUNDERSTORM WINDS/ HAIL
## 2 1
## THUNDERSTORM WINDS/FLASH FLOOD THUNDERSTORM WINDS/FLOODING
## 1 1
## THUNDERSTORM WINDS/FUNNEL CLOU THUNDERSTORM WINDS/HAIL
## 1 24
## THUNDERSTORM WINDS/HEAVY RAIN THUNDERSTORM WINDS53
## 1 1
## THUNDERSTORM WINDSHAIL THUNDERSTORM WINDSS
## 1 51
## THUNDERSTORM WINS THUNDERSTORMS
## 1 4
## THUNDERSTORMS WIND THUNDERSTORMS WINDS
## 6 14
## THUNDERSTORMW THUNDERSTORMW 50
## 1 1
## THUNDERSTORMW WINDS THUNDERSTORMWINDS
## 3 1
## THUNDERSTROM WIND THUNDERSTROM WINDS
## 1 2
## THUNDERTORM WINDS THUNDERTSORM WIND
## 3 1
## THUNDESTORM WINDS THUNERSTORM WINDS
## 2 1
## TIDAL FLOOD Tidal Flooding
## 1 5
## TIDAL FLOODING TORNADO
## 20 60652
## TORNADO DEBRIS TORNADO F0
## 1 19
## TORNADO F1 TORNADO F2
## 4 3
## TORNADO F3 TORNADO/WATERSPOUT
## 2 1
## TORNADOES TORNADOES, TSTM WIND, HAIL
## 2 1
## TORNADOS TORNDAO
## 1 1
## TORRENTIAL RAIN Torrential Rainfall
## 1 1
## TROPICAL DEPRESSION TROPICAL STORM
## 60 690
## TROPICAL STORM ALBERTO TROPICAL STORM DEAN
## 1 2
## TROPICAL STORM GORDON TROPICAL STORM JERRY
## 1 3
## TSTM TSTM HEAVY RAIN
## 1 3
## Tstm Wind TSTM WIND
## 2 219944
## TSTM WIND (G45) TSTM WIND (41)
## 1 1
## TSTM WIND (G35) TSTM WIND (G40)
## 1 10
## TSTM WIND (G45) TSTM WIND 40
## 40 1
## TSTM WIND 45 TSTM WIND 50
## 1 1
## TSTM WIND 51 TSTM WIND 52
## 2 5
## TSTM WIND 55 TSTM WIND 65)
## 3 1
## TSTM WIND AND LIGHTNING TSTM WIND DAMAGE
## 1 1
## TSTM WIND G45 TSTM WIND G58
## 1 1
## TSTM WIND/HAIL TSTM WINDS
## 1028 6
## TSTM WND TSTMW
## 1 1
## TSUNAMI TUNDERSTORM WIND
## 20 1
## TYPHOON Unseasonable Cold
## 11 1
## UNSEASONABLY COLD UNSEASONABLY COOL
## 23 12
## UNSEASONABLY COOL & WET UNSEASONABLY DRY
## 2 56
## UNSEASONABLY HOT UNSEASONABLY WARM
## 10 126
## UNSEASONABLY WARM & WET UNSEASONABLY WARM AND DRY
## 1 13
## UNSEASONABLY WARM YEAR UNSEASONABLY WARM/WET
## 2 2
## UNSEASONABLY WET UNSEASONAL LOW TEMP
## 19 2
## UNSEASONAL RAIN UNUSUAL WARMTH
## 2 10
## UNUSUAL/RECORD WARMTH UNUSUALLY COLD
## 2 8
## UNUSUALLY LATE SNOW UNUSUALLY WARM
## 1 4
## URBAN AND SMALL URBAN AND SMALL STREAM
## 2 3
## URBAN AND SMALL STREAM FLOOD URBAN AND SMALL STREAM FLOODIN
## 3 6
## Urban flood Urban Flood
## 1 1
## URBAN FLOOD URBAN FLOOD LANDSLIDE
## 249 1
## Urban Flooding URBAN FLOODING
## 1 98
## URBAN FLOODS URBAN SMALL
## 3 1
## URBAN SMALL STREAM FLOOD URBAN/SMALL
## 2 2
## URBAN/SMALL FLOODING URBAN/SMALL STREAM
## 1 8
## URBAN/SMALL STREAM FLOOD URBAN/SMALL STREAM FLOOD
## 2 30
## URBAN/SMALL STREAM FLOODING URBAN/SMALL STRM FLDG
## 4 1
## URBAN/SML STREAM FLD URBAN/SML STREAM FLDG
## 3392 1
## URBAN/STREET FLOODING VERY DRY
## 3 2
## VERY WARM VOG
## 1 1
## Volcanic Ash VOLCANIC ASH
## 1 22
## Volcanic Ash Plume VOLCANIC ASHFALL
## 1 3
## VOLCANIC ERUPTION WAKE LOW WIND
## 2 2
## WALL CLOUD WALL CLOUD/FUNNEL CLOUD
## 5 1
## WARM DRY CONDITIONS WARM WEATHER
## 1 1
## WATER SPOUT WATERSPOUT
## 1 3797
## WATERSPOUT FUNNEL CLOUD WATERSPOUT TORNADO
## 1 1
## WATERSPOUT- WATERSPOUT-TORNADO
## 10 2
## WATERSPOUT/ WATERSPOUT/ TORNADO
## 1 2
## WATERSPOUT/TORNADO WATERSPOUTS
## 8 37
## WAYTERSPOUT wet micoburst
## 1 1
## WET MICROBURST Wet Month
## 6 4
## WET SNOW WET WEATHER
## 1 1
## Wet Year Whirlwind
## 4 2
## WHIRLWIND WILD FIRES
## 1 4
## WILD/FOREST FIRE WILD/FOREST FIRES
## 1457 1
## WILDFIRE WILDFIRES
## 2761 8
## Wind WIND
## 6 341
## WIND ADVISORY WIND AND WAVE
## 12 1
## WIND CHILL WIND CHILL/HIGH WIND
## 18 1
## Wind Damage WIND DAMAGE
## 4 27
## WIND GUSTS WIND STORM
## 3 1
## WIND/HAIL WINDS
## 1 36
## WINTER MIX WINTER STORM
## 3 11433
## WINTER STORM HIGH WINDS WINTER STORM/HIGH WIND
## 1 1
## WINTER STORM/HIGH WINDS WINTER STORMS
## 1 3
## Winter Weather WINTER WEATHER
## 19 7026
## WINTER WEATHER MIX WINTER WEATHER/MIX
## 6 1104
## WINTERY MIX Wintry mix
## 2 3
## Wintry Mix WINTRY MIX
## 1 90
## WND
## 1table(dt$PROPDMGEXP) # More than the 3 described cathegories##
## - ? + 0 1 2 3 4 5 6
## 1 8 5 216 25 13 4 4 28 4
## 7 8 B h H K m M
## 5 1 40 1 6 424665 7 11330table(dt$CROPDMGEXP) # Logical?##
## FALSE
## 19Also as this is a very specific analysis we are going to select only the variables we will actually use in the analysis. We are going to use for this the package dplyr and the function select. I will create 2 datasets so I use each one for the each question.
library(dplyr)
dt.hph <- dt %>% select(EVTYPE, FATALITIES)
dt.ec <- dt %>% select(EVTYPE, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP)Since we are looking for the events causing higher economic consequences or harm to public health I will delete those events <1.
dt.hph <- dt.hph %>% filter(!FATALITIES==0)
dt.ec <- dt.ec %>% filter(!PROPDMG==0 & CROPDMG==0)Theoretically I should do some cluster analysis or an SVD to identify relationships in the variables. But the question asked are very direct so I do not need to do this. Also since this analysis is so simple, this exploratory graphics would be basically the same than the results so I will not do it (and also the max is 3 figures).
2.3 Summarising Data
We already know there are several entries for each type of event (form table()). So, to unswer the identify the most harmful event and the economic cost I will create new variables using dplyr and tidyr:
In the dt.hph:
- Total Fatalities:Sumatory of all fatalities per event
As there are many events, and we are looking for the most dangerous.As we saw in the exploratory results the fatalities are very low for some of the events. Hence, we will select only those with fatalities higher than the 90 percentile.
library(tidyr)
dt.hph <- dt.hph %>%
group_by(EVTYPE)%>%
summarise(Total.Fatalities= sum(FATALITIES))%>%
filter(Total.Fatalities > quantile(Total.Fatalities, probs = 0.9))In the dt.ec:
-Total Damage: Sumatory of all the properties and crops damages per event. I do this because I do not need to know where the economic cost was, only which event costed higher amount of money. Here, I need to be carefull to do not mix the “K” with “B” or “M”.
Initially, according to the document describing the dataset, there are only 3 categories, “K”, “M” or “B”. However as we saw initially, there are other letters in the magnitud row. I will delete any other letter that is not this one. And remove the event call “?”
Furthermore, in regard to the costs every cost is associated to another column that contains the magnitud either thousands billios or millions. If this magnitud is missing then the value has no meaning to us because we have no way to know if it was a thousand or a million. So, we will delete them from our dataset
As there are many events, and we are looking for those with the highest economic consequences.As we saw in the exploratory results the costs are very low for some of the events. Hence, we will select only those with costs in the range of Billions
dt.ec <- dt.ec %>% # Reshape the dataset:
gather(source, value_in_dollars, - c(EVTYPE,PROPDMGEXP, CROPDMGEXP )) %>%
gather(source2, magnitud,-c(EVTYPE, source, value_in_dollars))%>%
select(-source, -source2) %>%
filter(magnitud == "M"|magnitud == "B"|magnitud == "K" )%>%
filter(!EVTYPE =="?") %>%
filter(!is.na(magnitud), value_in_dollars >0) %>%
filter(magnitud =="B" )%>%
select(-magnitud) %>%
group_by(EVTYPE)%>%
summarise(Total_cost= sum(value_in_dollars))3 Results
Our initial question was: Across the United States, which types of events are most harmful with respect to population health?
So, in order to unswer that question we prepared a graphic using ggplot.
library(ggplot2)
# Define the theme
marce_theme <- theme_light() + theme(legend.background = element_blank(),
legend.key = element_blank(),
legend.direction = "vertical",
legend.position= "right",
axis.line = element_line(colour = "black"),
plot.title = element_text(size = 12, hjust = 0.5))
ggplot(dt.hph, aes(Total.Fatalities,EVTYPE, col=Total.Fatalities))+
geom_point() +
ylab(" Type of Weather Event") +
xlab("Overall Number of fatalities 1950- 2011 ")+
scale_color_gradient(low="blue", high = "red")+
scale_x_continuous(breaks = seq(0,6000, by=1000))+
marce_theme
As we can see here, there are two major weather events causing the higher amount of fatalities, and those are:
Excesive heat (have caused 1903 fatalities since 1950) and Tornado (5633 fatalities since 1950)
The second question was:
Across the United States, which types of events have the greatest economic consequences?
ggplot(dt.ec, aes(Total_cost,EVTYPE, col=Total_cost))+
geom_point() +
ylab(" Type of Weather Event") +
xlab("Overall cost in billions of dollars from 1950- 2011 ") +
scale_color_gradient(low="blue", high = "red") +
scale_x_continuous(breaks = seq(0,50, by=10))+
marce_theme
According to the graphic, there are two major weather events causing the higher cost of dollars, and those are:
Hurricane/Typhoon (have costed 41.37 billions) and Storm Surge (have costed 42.56 billions)