Title: Storm Data Analysis using Data from NOAA
Shree
February 23, 2016
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Synopsis
This is the R Markdown file documenting a process to be followed to analyze the “Storm Data” from U.S. National Oceanic and Atmospheric Administration’s (NOAA). This analysis will try to find answer for following:
1. Across the United States, which types of events (as indicated in the 𝙴𝚅𝚃𝚈𝙿𝙴 variable) are most harmful with respect to population health?
2. Across the United States, which types of events have the greatest economic consequences?
It will provide details of what steps need to be taken to do data processing and also provide the results of the analysis. Anyone wants to do their own analysis can follow these steps to generate their own results and compare against these results.
Data Processing
Step 1 Downloading the data file: For this analysis it will download “StormData.csv.bz2”, a comma-separated file compressed via the biz2 algorithm. Then it will extract StormData.csv file.
Step 2 Loading up the data: The extracted file will be used as the raw data for analysis. Data from this file will be loaded in ‘data’ variable.
Step 3 Processing the data: The data will be examined to be used for the analysis to get the answers for the above two questions.
Analysis
Step 4 Analyzing the data:
- ‘EVTYPE’ variable and time variable will be used to see which event is the most harmful respect to population health.
- Again, ‘EVTYPE’ variable and varaibles related to economic consequences will be used to see which event is the influential to economic of USA.
Step 5 Report: Results from the analysis will be posted in here and to Rpubs.
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Question 1: Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Data Processing
Step 1 Downloading
From the URL “http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2”, the raw data will be downloaded as “StormData.csv.bz2”
## assumption is that the data file will be downloaded in the same folder where Rmd fils is
if(!file.exists("StormData.csv.bz2")) {
dataURL = "http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(dataURL, destfile="StormData.csv.bz2")
}Step 2 Loading
Will load the downloaded zip file now.
setwd("/Users/skhairnar/Documents/My/Coursera_Reproducible_research/week2")
## Uncompress the downloaded
## StormData = bzfile("StormData.csv.bz2")
stormData <- read.csv("StormData.csv.bz2", stringsAsFactors = FALSE)Step 3 Data Processing
Now that we loaded the data we will explore it and process for analysis.
# Check the data
str(stormData)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : chr "4/18/1950 0:00:00" "4/18/1950 0:00:00" "2/20/1951 0:00:00" "6/8/1951 0:00:00" ...
## $ BGN_TIME : chr "0130" "0145" "1600" "0900" ...
## $ TIME_ZONE : chr "CST" "CST" "CST" "CST" ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: chr "MOBILE" "BALDWIN" "FAYETTE" "MADISON" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : chr "" "" "" "" ...
## $ BGN_LOCATI: chr "" "" "" "" ...
## $ END_DATE : chr "" "" "" "" ...
## $ END_TIME : chr "" "" "" "" ...
## $ COUNTY_END: num 0 0 0 0 0 0 0 0 0 0 ...
## $ COUNTYENDN: logi NA NA NA NA NA NA ...
## $ END_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ END_AZI : chr "" "" "" "" ...
## $ END_LOCATI: chr "" "" "" "" ...
## $ LENGTH : num 14 2 0.1 0 0 1.5 1.5 0 3.3 2.3 ...
## $ WIDTH : num 100 150 123 100 150 177 33 33 100 100 ...
## $ F : int 3 2 2 2 2 2 2 1 3 3 ...
## $ MAG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ FATALITIES: num 0 0 0 0 0 0 0 0 1 0 ...
## $ INJURIES : num 15 0 2 2 2 6 1 0 14 0 ...
## $ PROPDMG : num 25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
## $ PROPDMGEXP: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...
## $ WFO : chr "" "" "" "" ...
## $ STATEOFFIC: chr "" "" "" "" ...
## $ ZONENAMES : chr "" "" "" "" ...
## $ LATITUDE : num 3040 3042 3340 3458 3412 ...
## $ LONGITUDE : num 8812 8755 8742 8626 8642 ...
## $ LATITUDE_E: num 3051 0 0 0 0 ...
## $ LONGITUDE_: num 8806 0 0 0 0 ...
## $ REMARKS : chr "" "" "" "" ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...# ==> 902297 obs. of 37 variables
head(stormData)## 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 0
## 2 TORNADO 0 0
## 3 TORNADO 0 0
## 4 TORNADO 0 0
## 5 TORNADO 0 0
## 6 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14.0 100 3 0 0
## 2 NA 0 2.0 150 2 0 0
## 3 NA 0 0.1 123 2 0 0
## 4 NA 0 0.0 100 2 0 0
## 5 NA 0 0.0 150 2 0 0
## 6 NA 0 1.5 177 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## 3 2 25.0 K 0
## 4 2 2.5 K 0
## 5 2 2.5 K 0
## 6 6 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3
## 4 3458 8626 0 0 4
## 5 3412 8642 0 0 5
## 6 3450 8748 0 0 6tail(stormData)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY
## 902292 47 11/28/2011 0:00:00 03:00:00 PM CST 21
## 902293 56 11/30/2011 0:00:00 10:30:00 PM MST 7
## 902294 30 11/10/2011 0:00:00 02:48:00 PM MST 9
## 902295 2 11/8/2011 0:00:00 02:58:00 PM AKS 213
## 902296 2 11/9/2011 0:00:00 10:21:00 AM AKS 202
## 902297 1 11/28/2011 0:00:00 08:00:00 PM CST 6
## COUNTYNAME STATE EVTYPE BGN_RANGE
## 902292 TNZ001>004 - 019>021 - 048>055 - 088 TN WINTER WEATHER 0
## 902293 WYZ007 - 017 WY HIGH WIND 0
## 902294 MTZ009 - 010 MT HIGH WIND 0
## 902295 AKZ213 AK HIGH WIND 0
## 902296 AKZ202 AK BLIZZARD 0
## 902297 ALZ006 AL HEAVY SNOW 0
## BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 902292 11/29/2011 0:00:00 12:00:00 PM 0
## 902293 11/30/2011 0:00:00 10:30:00 PM 0
## 902294 11/10/2011 0:00:00 02:48:00 PM 0
## 902295 11/9/2011 0:00:00 01:15:00 PM 0
## 902296 11/9/2011 0:00:00 05:00:00 PM 0
## 902297 11/29/2011 0:00:00 04:00:00 AM 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG
## 902292 NA 0 0 0 NA 0
## 902293 NA 0 0 0 NA 66
## 902294 NA 0 0 0 NA 52
## 902295 NA 0 0 0 NA 81
## 902296 NA 0 0 0 NA 0
## 902297 NA 0 0 0 NA 0
## FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO
## 902292 0 0 0 K 0 K MEG
## 902293 0 0 0 K 0 K RIW
## 902294 0 0 0 K 0 K TFX
## 902295 0 0 0 K 0 K AFG
## 902296 0 0 0 K 0 K AFG
## 902297 0 0 0 K 0 K HUN
## STATEOFFIC
## 902292 TENNESSEE, West
## 902293 WYOMING, Central and West
## 902294 MONTANA, Central
## 902295 ALASKA, Northern
## 902296 ALASKA, Northern
## 902297 ALABAMA, North
## ZONENAMES
## 902292 LAKE - LAKE - OBION - WEAKLEY - HENRY - DYER - GIBSON - CARROLL - LAUDERDALE - TIPTON - HAYWOOD - CROCKETT - MADISON - CHESTER - HENDERSON - DECATUR - SHELBY
## 902293 OWL CREEK & BRIDGER MOUNTAINS - OWL CREEK & BRIDGER MOUNTAINS - WIND RIVER BASIN
## 902294 NORTH ROCKY MOUNTAIN FRONT - NORTH ROCKY MOUNTAIN FRONT - EASTERN GLACIER
## 902295 ST LAWRENCE IS. BERING STRAIT - ST LAWRENCE IS. BERING STRAIT
## 902296 NORTHERN ARCTIC COAST - NORTHERN ARCTIC COAST
## 902297 MADISON - MADISON
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_
## 902292 0 0 0 0
## 902293 0 0 0 0
## 902294 0 0 0 0
## 902295 0 0 0 0
## 902296 0 0 0 0
## 902297 0 0 0 0
## REMARKS
## 902292 EPISODE NARRATIVE: A powerful upper level low pressure system brought snow to portions of Northeast Arkansas, the Missouri Bootheel, West Tennessee and extreme north Mississippi. Most areas picked up between 1 and 3 inches of with areas of Northeast Arkansas and the Missouri Bootheel receiving between 4 and 6 inches of snow.EVENT NARRATIVE: Around 1 inch of snow fell in Carroll County.
## 902293 EPISODE NARRATIVE: A strong cold front moved south through north central Wyoming bringing high wind to the Meeteetse area and along the south slopes of the western Owl Creek Range. Wind gusts to 76 mph were recorded at Madden Reservoir.EVENT NARRATIVE:
## 902294 EPISODE NARRATIVE: A strong westerly flow aloft produced gusty winds at the surface along the Rocky Mountain front and over the plains of Central Montana. Wind gusts in excess of 60 mph were reported.EVENT NARRATIVE: A wind gust to 60 mph was reported at East Glacier Park 1ENE (the Two Medicine DOT site).
## 902295 EPISODE NARRATIVE: A 960 mb low over the southern Aleutians at 0300AKST on the 8th intensified to 945 mb near the Gulf of Anadyr by 2100AKST on the 8th. The low crossed the Chukotsk Peninsula as a 956 mb low at 0900AKST on the 9th, and moved into the southern Chukchi Sea as a 958 mb low by 2100AKST on the 9th. The low then tracked to the northwest and weakened to 975 mb about 150 miles north of Wrangel Island by 1500AKST on the 10th. The storm was one of the strongest storms to impact the west coast of Alaska since November 1974. \n\nZone 201: Blizzard conditions were observed at Wainwright from approximately 1153AKST through 1611AKST on the 9th. The visibility was frequently reduced to one quarter mile in snow and blowing snow. There was a peak wind gust to 43kt (50 mph) at the Wainwright ASOS. During this event, there was also a peak wind gust to \n68 kt (78 mph) at the Cape Lisburne AWOS. \n\nZone 202: Blizzard conditions were observed at Barrow from approximately 1021AKST through 1700AKST on the 9th. The visibility was frequently reduced to one quarter mile or less in blowing snow. There was a peak wind gust to 46 kt (53 mph) at the Barrow ASOS. \n\nZone 207: Blizzard conditions were observed at Kivalina from approximately 0400AKST through 1230AKST on the 9th. The visibility was frequently reduced to one quarter of a mile in snow and blowing snow. There was a peak wind gust to 61 kt (70 mph) at the Kivalina ASOS. The doors to the village transportation shed were blown out to sea. Many homes lost portions of their tin roofing, and satellite dishes were ripped off of roofs. One home had its door blown off. At Point Hope, severe blizzard conditions were observed. There was a peak wind gust of 68 kt (78 mph) at the Point Hope AWOS before power was lost to the AWOS. It was estimated that the wind gusted as high as 85 mph in the village during the height of the storm during the morning and early afternoon hours on the 9th. Five power poles were knocked down in the storm EVENT NARRATIVE:
## 902296 EPISODE NARRATIVE: A 960 mb low over the southern Aleutians at 0300AKST on the 8th intensified to 945 mb near the Gulf of Anadyr by 2100AKST on the 8th. The low crossed the Chukotsk Peninsula as a 956 mb low at 0900AKST on the 9th, and moved into the southern Chukchi Sea as a 958 mb low by 2100AKST on the 9th. The low then tracked to the northwest and weakened to 975 mb about 150 miles north of Wrangel Island by 1500AKST on the 10th. The storm was one of the strongest storms to impact the west coast of Alaska since November 1974. \n\nZone 201: Blizzard conditions were observed at Wainwright from approximately 1153AKST through 1611AKST on the 9th. The visibility was frequently reduced to one quarter mile in snow and blowing snow. There was a peak wind gust to 43kt (50 mph) at the Wainwright ASOS. During this event, there was also a peak wind gust to \n68 kt (78 mph) at the Cape Lisburne AWOS. \n\nZone 202: Blizzard conditions were observed at Barrow from approximately 1021AKST through 1700AKST on the 9th. The visibility was frequently reduced to one quarter mile or less in blowing snow. There was a peak wind gust to 46 kt (53 mph) at the Barrow ASOS. \n\nZone 207: Blizzard conditions were observed at Kivalina from approximately 0400AKST through 1230AKST on the 9th. The visibility was frequently reduced to one quarter of a mile in snow and blowing snow. There was a peak wind gust to 61 kt (70 mph) at the Kivalina ASOS. The doors to the village transportation shed were blown out to sea. Many homes lost portions of their tin roofing, and satellite dishes were ripped off of roofs. One home had its door blown off. At Point Hope, severe blizzard conditions were observed. There was a peak wind gust of 68 kt (78 mph) at the Point Hope AWOS before power was lost to the AWOS. It was estimated that the wind gusted as high as 85 mph in the village during the height of the storm during the morning and early afternoon hours on the 9th. Five power poles were knocked down in the storm EVENT NARRATIVE:
## 902297 EPISODE NARRATIVE: An intense upper level low developed on the 28th at the base of a highly amplified upper trough across the Great Lakes and Mississippi Valley. The upper low closed off over the mid South and tracked northeast across the Tennessee Valley during the morning of the 29th. A warm conveyor belt of heavy rainfall developed in advance of the low which dumped from around 2 to over 5 inches of rain across the eastern two thirds of north Alabama and middle Tennessee. The highest rain amounts were recorded in Jackson and DeKalb Counties with 3 to 5 inches. The rain fell over 24 to 36 hour period, with rainfall remaining light to moderate during most its duration. The rainfall resulted in minor river flooding along the Little River, Big Wills Creek and Paint Rock. A landslide occurred on Highway 35 just north of Section in Jackson County. A driver was trapped in his vehicle, but was rescued unharmed. Trees, boulders and debris blocked 100 to 250 yards of Highway 35.\n\nThe rain mixed with and changed to snow across north Alabama during the afternoon and evening hours of the 28th, and lasted into the 29th. The heaviest bursts of snow occurred in northwest Alabama during the afternoon and evening hours, and in north central and northeast Alabama during the overnight and morning hours. Since ground temperatures were in the 50s, and air temperatures in valley areas only dropped into the mid 30s, most of the snowfall melted on impact with mostly trace amounts reported in valley locations. However, above 1500 foot elevation, snow accumulations of 1 to 2 inches were reported. The heaviest amount was 2.3 inches on Monte Sano Mountain, about 5 miles northeast of Huntsville.EVENT NARRATIVE: Snowfall accumulations of up to 2.3 inches were reported on the higher elevations of eastern Madison County. A snow accumulation of 1.5 inches was reported 2.7 miles south of Gurley, while 2.3 inches was reported 3 miles east of Huntsville atop Monte Sano Mountain.
## REFNUM
## 902292 902292
## 902293 902293
## 902294 902294
## 902295 902295
## 902296 902296
## 902297 902297summary(stormData)## 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
## Min. : 0.000 Length:902297 Length:902297
## 1st Qu.: 0.000 Class :character Class :character
## Median : 0.000 Mode :character Mode :character
## Mean : 1.484
## 3rd Qu.: 1.000
## Max. :3749.000
##
## END_DATE END_TIME COUNTY_END COUNTYENDN
## Length:902297 Length:902297 Min. :0 Mode:logical
## Class :character Class :character 1st Qu.:0 NA's:902297
## Mode :character Mode :character Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
##
## END_RANGE END_AZI END_LOCATI
## Min. : 0.0000 Length:902297 Length:902297
## 1st Qu.: 0.0000 Class :character Class :character
## Median : 0.0000 Mode :character Mode :character
## Mean : 0.9862
## 3rd Qu.: 0.0000
## Max. :925.0000
##
## LENGTH WIDTH F MAG
## Min. : 0.0000 Min. : 0.000 Min. :0.0 Min. : 0.0
## 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.:0.0 1st Qu.: 0.0
## Median : 0.0000 Median : 0.000 Median :1.0 Median : 50.0
## Mean : 0.2301 Mean : 7.503 Mean :0.9 Mean : 46.9
## 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.:1.0 3rd Qu.: 75.0
## Max. :2315.0000 Max. :4400.000 Max. :5.0 Max. :22000.0
## NA's :843563
## FATALITIES INJURIES PROPDMG
## Min. : 0.0000 Min. : 0.0000 Min. : 0.00
## 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.00
## Median : 0.0000 Median : 0.0000 Median : 0.00
## Mean : 0.0168 Mean : 0.1557 Mean : 12.06
## 3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.: 0.50
## Max. :583.0000 Max. :1700.0000 Max. :5000.00
##
## PROPDMGEXP CROPDMG CROPDMGEXP
## Length:902297 Min. : 0.000 Length:902297
## Class :character 1st Qu.: 0.000 Class :character
## Mode :character Median : 0.000 Mode :character
## Mean : 1.527
## 3rd Qu.: 0.000
## Max. :990.000
##
## WFO STATEOFFIC ZONENAMES LATITUDE
## Length:902297 Length:902297 Length:902297 Min. : 0
## Class :character Class :character Class :character 1st Qu.:2802
## Mode :character Mode :character Mode :character Median :3540
## Mean :2875
## 3rd Qu.:4019
## Max. :9706
## NA's :47
## LONGITUDE LATITUDE_E LONGITUDE_ REMARKS
## Min. :-14451 Min. : 0 Min. :-14455 Length:902297
## 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0 Class :character
## Median : 8707 Median : 0 Median : 0 Mode :character
## Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. : 17124 Max. :9706 Max. :106220
## NA's :40
## REFNUM
## Min. : 1
## 1st Qu.:225575
## Median :451149
## Mean :451149
## 3rd Qu.:676723
## Max. :902297
## summary(stormData$EVTYPE)## Length Class Mode
## 902297 character character# EVTYPE
table(stormData$EVTYPE)##
## HIGH SURF ADVISORY COASTAL FLOOD
## 1 1
## FLASH FLOOD LIGHTNING
## 1 1
## TSTM WIND TSTM WIND (G45)
## 4 1
## WATERSPOUT WIND
## 1 1
## ? 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
## 6 650
## 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
## 54277 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 4
## 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 15754
## 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 219940
## TSTM WIND (G45) TSTM WIND (41)
## 1 1
## TSTM WIND (G35) TSTM WIND (G40)
## 1 10
## TSTM WIND (G45) TSTM WIND 40
## 39 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 3796
## 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
## 6 340
## 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
## 1uniqueEventType <- unique(stormData$EVTYPE)
summary(uniqueEventType)## Length Class Mode
## 985 character character# There are duplicate EVTYPE if considet the value as case insensitive so let's check for lowercase of EVTYPE
stormData$EVTYPE <- tolower(stormData$EVTYPE)
uniqueEventType <- unique(stormData$EVTYPE)
summary(uniqueEventType)## Length Class Mode
## 898 character characterhead(stormData$INJURIES)## [1] 15 0 2 2 2 6summary(stormData$INJURIES)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1557 0.0000 1700.0000head(stormData$FATALITIES)## [1] 0 0 0 0 0 0summary(stormData$FATALITIES)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.0168 0.0000 583.0000Analysis
Step 4 Analyzing data
# Installing ggplot2 and lattice packages
library(ggplot2)
library(lattice)
# Now for quesiton "Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?" I wiil calculate how many injuries and fatalities an event caused by using aggregate function.
casualties <- with(stormData, aggregate(INJURIES + FATALITIES ~ EVTYPE, stormData, FUN = "sum"))
# Changing the name
names(casualties)[2] <- "TotalCasualties"
# Order the number of casualties by decending method
orderedCasualties <- casualties[order(-casualties$TotalCasualties),]
# Just see top6 using head()
Top6 <- head(orderedCasualties)Step 4 Analyzing data
# Let's draw a bar plot
barplot(Top6$TotalCasualties, main = "Which event caused the most harmful with respect to population health", xlab = "Total No. of casualties", names.arg=Top6$EVTYPE)
so the answer for
The most harmful event is: “tornado”
————————————————————————
Question 2: Across the United States, which types of events have the greatest economic consequences?
Data Processing
Step 1 Downloading
Data to download is the same as the one downloaded in question 1.
Step 2 Loading
Loading is the same as that of question 1.
Step 3 Data Processing
# The loaded data will be explored for further analysis of question 2
str(stormData)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : chr "4/18/1950 0:00:00" "4/18/1950 0:00:00" "2/20/1951 0:00:00" "6/8/1951 0:00:00" ...
## $ BGN_TIME : chr "0130" "0145" "1600" "0900" ...
## $ TIME_ZONE : chr "CST" "CST" "CST" "CST" ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: chr "MOBILE" "BALDWIN" "FAYETTE" "MADISON" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ EVTYPE : chr "tornado" "tornado" "tornado" "tornado" ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : chr "" "" "" "" ...
## $ BGN_LOCATI: chr "" "" "" "" ...
## $ END_DATE : chr "" "" "" "" ...
## $ END_TIME : chr "" "" "" "" ...
## $ COUNTY_END: num 0 0 0 0 0 0 0 0 0 0 ...
## $ COUNTYENDN: logi NA NA NA NA NA NA ...
## $ END_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ END_AZI : chr "" "" "" "" ...
## $ END_LOCATI: chr "" "" "" "" ...
## $ LENGTH : num 14 2 0.1 0 0 1.5 1.5 0 3.3 2.3 ...
## $ WIDTH : num 100 150 123 100 150 177 33 33 100 100 ...
## $ F : int 3 2 2 2 2 2 2 1 3 3 ...
## $ MAG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ FATALITIES: num 0 0 0 0 0 0 0 0 1 0 ...
## $ INJURIES : num 15 0 2 2 2 6 1 0 14 0 ...
## $ PROPDMG : num 25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
## $ PROPDMGEXP: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...
## $ WFO : chr "" "" "" "" ...
## $ STATEOFFIC: chr "" "" "" "" ...
## $ ZONENAMES : chr "" "" "" "" ...
## $ LATITUDE : num 3040 3042 3340 3458 3412 ...
## $ LONGITUDE : num 8812 8755 8742 8626 8642 ...
## $ LATITUDE_E: num 3051 0 0 0 0 ...
## $ LONGITUDE_: num 8806 0 0 0 0 ...
## $ REMARKS : chr "" "" "" "" ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...str(stormData$CROPDMGEXP)## chr [1:902297] "" "" "" "" "" "" "" "" "" "" "" ...unique(stormData$CROPDMGEXP)## [1] "" "M" "K" "m" "B" "?" "0" "k" "2"unique(stormData$PROPDMGEXP)## [1] "K" "M" "" "B" "m" "+" "0" "5" "6" "?" "4" "2" "3" "h" "7" "H" "-"
## [18] "1" "8"str(stormData$PROPDMGEXP)## chr [1:902297] "K" "K" "K" "K" "K" "K" "K" "K" "K" ...table(stormData$CROPDMGEXP)##
## ? 0 2 B k K m M
## 618413 7 19 1 9 21 281832 1 1994table(stormData$PROPDMGEXP)##
## - ? + 0 1 2 3 4 5
## 465934 1 8 5 216 25 13 4 4 28
## 6 7 8 B h H K m M
## 4 5 1 40 1 6 424665 7 11330# 'CROPDMGEXP' and 'PROPDMGEXP' will be subsetted values with "K", "M", "B"
# First, 'CROPDMGEXP' subsetting
dataSet <- subset(stormData, (stormData$CROPDMGEXP == "K" | stormData$CROPDMGEXP == "M" | stormData$CROPDMGEXP == "B") | (stormData$PROPDMGEXP =="K" | stormData$PROPDMGEXP =="M" | stormData$PROPDMGEXP =="B") )
str(dataSet)## 'data.frame': 440355 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : chr "4/18/1950 0:00:00" "4/18/1950 0:00:00" "2/20/1951 0:00:00" "6/8/1951 0:00:00" ...
## $ BGN_TIME : chr "0130" "0145" "1600" "0900" ...
## $ TIME_ZONE : chr "CST" "CST" "CST" "CST" ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: chr "MOBILE" "BALDWIN" "FAYETTE" "MADISON" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ EVTYPE : chr "tornado" "tornado" "tornado" "tornado" ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : chr "" "" "" "" ...
## $ BGN_LOCATI: chr "" "" "" "" ...
## $ END_DATE : chr "" "" "" "" ...
## $ END_TIME : chr "" "" "" "" ...
## $ COUNTY_END: num 0 0 0 0 0 0 0 0 0 0 ...
## $ COUNTYENDN: logi NA NA NA NA NA NA ...
## $ END_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ END_AZI : chr "" "" "" "" ...
## $ END_LOCATI: chr "" "" "" "" ...
## $ LENGTH : num 14 2 0.1 0 0 1.5 1.5 0 3.3 2.3 ...
## $ WIDTH : num 100 150 123 100 150 177 33 33 100 100 ...
## $ F : int 3 2 2 2 2 2 2 1 3 3 ...
## $ MAG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ FATALITIES: num 0 0 0 0 0 0 0 0 1 0 ...
## $ INJURIES : num 15 0 2 2 2 6 1 0 14 0 ...
## $ PROPDMG : num 25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
## $ PROPDMGEXP: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...
## $ WFO : chr "" "" "" "" ...
## $ STATEOFFIC: chr "" "" "" "" ...
## $ ZONENAMES : chr "" "" "" "" ...
## $ LATITUDE : num 3040 3042 3340 3458 3412 ...
## $ LONGITUDE : num 8812 8755 8742 8626 8642 ...
## $ LATITUDE_E: num 3051 0 0 0 0 ...
## $ LONGITUDE_: num 8806 0 0 0 0 ...
## $ REMARKS : chr "" "" "" "" ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...# CROPDMG value will be multipied accordingly with,
# "K", "M", "B" which mean 1000, 1000000, 1000000000 respectively.
summary(dataSet$CROPDMG)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 3.129 0.000 990.000table(dataSet$CROPDMGEXP)##
## ? 0 B k K M
## 156478 5 16 9 21 281832 1994# Character with 'K', 'M', 'B' are assigned values accordingly by multiplying corresponding multiplier as follows. Other than these, the multiplier will be 1.
for(i in 1:length(dataSet$CROPDMGEXP)) {
ifelse(dataSet$CROPDMGEXP[i] == "K", dataSet$CROPDMG[i] <- dataSet$CROPDMG[i] * 1000,
ifelse(dataSet$CROPDMGEXP[i] == "M", dataSet$CROPDMG[i] <- dataSet$CROPDMG[i] * 1000000,
ifelse(dataSet$CROPDMGEXP[i] == "B", dataSet$CROPDMG[i] <- dataSet$CROPDMG[i] * 1000000000, dataSet$CROPDMG[i] <- dataSet$CROPDMG[i] * 1)))
}
summary(dataSet$CROPDMG)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000e+00 0.000e+00 0.000e+00 1.115e+05 0.000e+00 5.000e+09# PROPDMG value will be multipied accordingly as
# "K", "M", "B" which mean 1000, 1000000, 1000000000 respectively.
summary(dataSet$PROPDMG)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 0.0 1.0 24.7 10.0 5000.0table(dataSet$PROPDMGEXP)##
## 0 3 5 B K M
## 4312 5 1 2 40 424665 11330# Character with 'K', 'M', 'B' are assigned values accordingly by multiplying corresponding multiplier as follows. Other than these, the multiplier will be 1.
for(i in 1:length(dataSet$PROPDMGEXP)) {
ifelse(dataSet$PROPDMGEXP[i] == "K", dataSet$PROPDMG[i] <- dataSet$PROPDMG[i] * 1000,
ifelse(dataSet$PROPDMGEXP[i] == "M", dataSet$PROPDMG[i] <- dataSet$PROPDMG[i] * 1000000,
ifelse(dataSet$PROPDMGEXP[i] == "B", dataSet$PROPDMG[i] <- dataSet$PROPDMG[i] * 1000000000, dataSet$PROPDMG[i] <- dataSet$PROPDMG[i] * 1)))
}
summary(dataSet$PROPDMG)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000e+00 0.000e+00 1.000e+03 9.703e+05 1.000e+04 1.150e+11Analysis
Step 4 Analyzing the data
# Installing ggplot2 and lattice packages
library(ggplot2)
library(lattice)
# Let's calculate how much economic values are lost using aggregate function.
economicDamageValue <- with(dataSet, aggregate(CROPDMG + PROPDMG ~ EVTYPE, stormData, FUN = "sum"))
# Changing the name
names(economicDamageValue)[2] <- "TotalEconomicDamage"
# Order the economic damage values by decending method
orderedEconomicDamage <- economicDamageValue[order(-economicDamageValue$TotalEconomicDamage),]
# We will just check top6 using head()
Top6EconomicDamage <- head(orderedEconomicDamage)Step 5 Report
# Draw the barplot
barplot(Top6EconomicDamage$TotalEconomicDamage, main = "Which event caused the greatest economic consequence", xlab = "Total Economic Consequences", names.arg=Top6$EVTYPE)
so the answer for
The greatest economic consequence event is: “tornado”