A brief NOAA-storm database analysis
Mauricio Paletta
November 25, 2017
Synopsis
The U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database is briefly analyzed aiming to know which types of events are both most harmful with respect to population health and have the greatest economic consequences. The programming language R is used to perform the neccesary calculations and plots for this analysis. The data used belongs to the period between the years 1950 and 2011. This report can be useful for all those who have the responsibility for preparing for severe weather events and, as a consequence, will need to prioritize resources for different types of events. At the end of this report there is a section with the results and conclusions of the analysis and some corresponding recommendations.
This report has been prepared using the following R environment:
sessionInfo()## R version 3.4.2 (2017-09-28)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 16299)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Mexico.1252 LC_CTYPE=Spanish_Mexico.1252
## [3] LC_MONETARY=Spanish_Mexico.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Mexico.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] compiler_3.4.2 backports_1.1.1 magrittr_1.5 rprojroot_1.2
## [5] tools_3.4.2 htmltools_0.3.6 yaml_2.1.14 Rcpp_0.12.13
## [9] stringi_1.1.5 rmarkdown_1.6 knitr_1.17 stringr_1.2.0
## [13] digest_0.6.12 evaluate_0.10.1Data
The database used in this report tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage. Data could be downloaded from the following link:
https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2
Once the data is downloaded and the working directory has been properly configured in RStudio, it can be read with the following R code:
if (!exists("NOAA.storm")) NOAA.storm <- read.csv("NOAADataStorm.csv")There are 902297 observations / rows. Because the variable “ZONENAMES” does not contribute in the analysis, it is removed from dataset.
NOAA.storm <- subset(NOAA.storm, select = -ZONENAMES)Here a summary of the data:
summary(NOAA.storm)## STATE__ BGN_DATE BGN_TIME
## Min. : 1.0 5/25/2011 0:00:00: 1202 12:00:00 AM: 10163
## 1st Qu.:19.0 4/27/2011 0:00:00: 1193 06:00:00 PM: 7350
## Median :30.0 6/9/2011 0:00:00 : 1030 04:00:00 PM: 7261
## Mean :31.2 5/30/2004 0:00:00: 1016 05:00:00 PM: 6891
## 3rd Qu.:45.0 4/4/2011 0:00:00 : 1009 12:00:00 PM: 6703
## Max. :95.0 4/2/2006 0:00:00 : 981 03:00:00 PM: 6700
## (Other) :895866 (Other) :857229
## TIME_ZONE COUNTY COUNTYNAME STATE
## CST :547493 Min. : 0.0 JEFFERSON : 7840 TX : 83728
## EST :245558 1st Qu.: 31.0 WASHINGTON: 7603 KS : 53440
## MST : 68390 Median : 75.0 JACKSON : 6660 OK : 46802
## PST : 28302 Mean :100.6 FRANKLIN : 6256 MO : 35648
## AST : 6360 3rd Qu.:131.0 LINCOLN : 5937 IA : 31069
## HST : 2563 Max. :873.0 MADISON : 5632 NE : 30271
## (Other): 3631 (Other) :862369 (Other):621339
## EVTYPE BGN_RANGE BGN_AZI
## HAIL :288661 Min. : 0.000 :547332
## TSTM WIND :219940 1st Qu.: 0.000 N : 86752
## THUNDERSTORM WIND: 82563 Median : 0.000 W : 38446
## TORNADO : 60652 Mean : 1.484 S : 37558
## FLASH FLOOD : 54277 3rd Qu.: 1.000 E : 33178
## FLOOD : 25326 Max. :3749.000 NW : 24041
## (Other) :170878 (Other):134990
## BGN_LOCATI END_DATE END_TIME
## :287743 :243411 :238978
## COUNTYWIDE : 19680 4/27/2011 0:00:00: 1214 06:00:00 PM: 9802
## Countywide : 993 5/25/2011 0:00:00: 1196 05:00:00 PM: 8314
## SPRINGFIELD : 843 6/9/2011 0:00:00 : 1021 04:00:00 PM: 8104
## SOUTH PORTION: 810 4/4/2011 0:00:00 : 1007 12:00:00 PM: 7483
## NORTH PORTION: 784 5/30/2004 0:00:00: 998 11:59:00 PM: 7184
## (Other) :591444 (Other) :653450 (Other) :622432
## COUNTY_END COUNTYENDN END_RANGE END_AZI
## Min. :0 Mode:logical Min. : 0.0000 :724837
## 1st Qu.:0 NA's:902297 1st Qu.: 0.0000 N : 28082
## Median :0 Median : 0.0000 S : 22510
## Mean :0 Mean : 0.9862 W : 20119
## 3rd Qu.:0 3rd Qu.: 0.0000 E : 20047
## Max. :0 Max. :925.0000 NE : 14606
## (Other): 72096
## END_LOCATI LENGTH WIDTH
## :499225 Min. : 0.0000 Min. : 0.000
## COUNTYWIDE : 19731 1st Qu.: 0.0000 1st Qu.: 0.000
## SOUTH PORTION : 833 Median : 0.0000 Median : 0.000
## NORTH PORTION : 780 Mean : 0.2301 Mean : 7.503
## CENTRAL PORTION: 617 3rd Qu.: 0.0000 3rd Qu.: 0.000
## SPRINGFIELD : 575 Max. :2315.0000 Max. :4400.000
## (Other) :380536
## 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 :465934 Min. : 0.000 :618413
## 1st Qu.: 0.00 K :424665 1st Qu.: 0.000 K :281832
## Median : 0.00 M : 11330 Median : 0.000 M : 1994
## Mean : 12.06 0 : 216 Mean : 1.527 k : 21
## 3rd Qu.: 0.50 B : 40 3rd Qu.: 0.000 0 : 19
## Max. :5000.00 5 : 28 Max. :990.000 B : 9
## (Other): 84 (Other): 9
## WFO STATEOFFIC
## :142069 :248769
## OUN : 17393 TEXAS, North : 12193
## JAN : 13889 ARKANSAS, Central and North Central: 11738
## LWX : 13174 IOWA, Central : 11345
## PHI : 12551 KANSAS, Southwest : 11212
## TSA : 12483 GEORGIA, North and Central : 11120
## (Other):690738 (Other) :595920
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_
## Min. : 0 Min. :-14451 Min. : 0 Min. :-14455
## 1st Qu.:2802 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0
## Median :3540 Median : 8707 Median : 0 Median : 0
## Mean :2875 Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.:4019 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. :9706 Max. : 17124 Max. :9706 Max. :106220
## NA's :47 NA's :40
## REMARKS REFNUM
## :287433 Min. : 1
## : 24013 1st Qu.:225575
## Trees down.\n : 1110 Median :451149
## Several trees were blown down.\n : 569 Mean :451149
## Trees were downed.\n : 446 3rd Qu.:676723
## Large trees and power lines were blown down.\n: 432 Max. :902297
## (Other) :588294Note that there are a lot of NA values in column “F”. Here the column names (there are 36 columns) and its corresponding classes:
lapply(NOAA.storm, class)## $STATE__
## [1] "numeric"
##
## $BGN_DATE
## [1] "factor"
##
## $BGN_TIME
## [1] "factor"
##
## $TIME_ZONE
## [1] "factor"
##
## $COUNTY
## [1] "numeric"
##
## $COUNTYNAME
## [1] "factor"
##
## $STATE
## [1] "factor"
##
## $EVTYPE
## [1] "factor"
##
## $BGN_RANGE
## [1] "numeric"
##
## $BGN_AZI
## [1] "factor"
##
## $BGN_LOCATI
## [1] "factor"
##
## $END_DATE
## [1] "factor"
##
## $END_TIME
## [1] "factor"
##
## $COUNTY_END
## [1] "numeric"
##
## $COUNTYENDN
## [1] "logical"
##
## $END_RANGE
## [1] "numeric"
##
## $END_AZI
## [1] "factor"
##
## $END_LOCATI
## [1] "factor"
##
## $LENGTH
## [1] "numeric"
##
## $WIDTH
## [1] "numeric"
##
## $F
## [1] "integer"
##
## $MAG
## [1] "numeric"
##
## $FATALITIES
## [1] "numeric"
##
## $INJURIES
## [1] "numeric"
##
## $PROPDMG
## [1] "numeric"
##
## $PROPDMGEXP
## [1] "factor"
##
## $CROPDMG
## [1] "numeric"
##
## $CROPDMGEXP
## [1] "factor"
##
## $WFO
## [1] "factor"
##
## $STATEOFFIC
## [1] "factor"
##
## $LATITUDE
## [1] "numeric"
##
## $LONGITUDE
## [1] "numeric"
##
## $LATITUDE_E
## [1] "numeric"
##
## $LONGITUDE_
## [1] "numeric"
##
## $REMARKS
## [1] "factor"
##
## $REFNUM
## [1] "numeric"Here the first 10 observations:
head(NOAA.storm, 10)## 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
## 7 1 11/16/1951 0:00:00 0100 CST 9 BLOUNT AL
## 8 1 1/22/1952 0:00:00 0900 CST 123 TALLAPOOSA AL
## 9 1 2/13/1952 0:00:00 2000 CST 125 TUSCALOOSA AL
## 10 1 2/13/1952 0:00:00 2000 CST 57 FAYETTE 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
## 7 TORNADO 0 0
## 8 TORNADO 0 0
## 9 TORNADO 0 0
## 10 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
## 7 NA 0 1.5 33 2 0 0
## 8 NA 0 0.0 33 1 0 0
## 9 NA 0 3.3 100 3 0 1
## 10 NA 0 2.3 100 3 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC LATITUDE
## 1 15 25.0 K 0 3040
## 2 0 2.5 K 0 3042
## 3 2 25.0 K 0 3340
## 4 2 2.5 K 0 3458
## 5 2 2.5 K 0 3412
## 6 6 2.5 K 0 3450
## 7 1 2.5 K 0 3405
## 8 0 2.5 K 0 3255
## 9 14 25.0 K 0 3334
## 10 0 25.0 K 0 3336
## LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 8812 3051 8806 1
## 2 8755 0 0 2
## 3 8742 0 0 3
## 4 8626 0 0 4
## 5 8642 0 0 5
## 6 8748 0 0 6
## 7 8631 0 0 7
## 8 8558 0 0 8
## 9 8740 3336 8738 9
## 10 8738 3337 8737 10According to the available database:
https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf
There are the following 48 types of events:
- Astronomical Low Tide (Z)
- Avalanche (Z)
- Blizzard (Z)
- Coastal Flood (Z)
- Cold/Wind Chill (Z)
- Debris Flow (C)
- Dense Fog (Z)
- Dense Smoke (Z)
- Drought (Z)
- Dust Devil (C)
- Dust Storm (Z)
- Excessive Heat (Z)
- Extreme Cold/Wind Chill (Z)
- Flash Flood (C)
- Flood (C)
- Frost/Freeze (Z)
- Funnel Cloud (C)
- Freezing Fog (Z)
- Hail (C)
- Heat (Z)
- Heavy Rain (C)
- Heavy Snow (Z)
- High Surf (Z)
- High Wind (Z)
- Hurricane (Typhoon) (Z)
- Ice Storm (Z)
- Lake-Effect Snow (Z)
- Lakeshore Flood (Z)
- Lightning (C)
- Marine Hail (M)
- Marine High Wind (M)
- Marine Strong Wind (M)
- Marine Thunderstorm Wind (M)
- Rip Current (Z)
- Seiche (Z)
- Sleet (Z)
- Storm Surge/Tide (Z)
- Strong Wind (Z)
- Thunderstorm Wind (C)
- Tornado (C)
- Tropical Depression (Z)
- Tropical Storm (Z)
- Tsunami (Z)
- Volcanic Ash (Z)
- Waterspout (M)
- Wildfire (Z)
- Winter Storm (Z)
- Winter Weather (Z)
Where C, Z and M are the following designators:
- C: County/Parish
- Z: Zone
- M: Marine.
Data Processing
Checking the “EVTYPE” column
Reviewing the dataset “EVTYPE” column it can notice that there are a total of 985 types of events. This does not coincide with the 48 types of events that should be according to the database documentation (see above). The data contains the following list of types of events:
sort(unique(NOAA.storm$EVTYPE))## [1] HIGH SURF ADVISORY COASTAL FLOOD
## [3] FLASH FLOOD LIGHTNING
## [5] TSTM WIND TSTM WIND (G45)
## [7] WATERSPOUT WIND
## [9] ? ABNORMAL WARMTH
## [11] ABNORMALLY DRY ABNORMALLY WET
## [13] ACCUMULATED SNOWFALL AGRICULTURAL FREEZE
## [15] APACHE COUNTY ASTRONOMICAL HIGH TIDE
## [17] ASTRONOMICAL LOW TIDE AVALANCE
## [19] AVALANCHE BEACH EROSIN
## [21] Beach Erosion BEACH EROSION
## [23] BEACH EROSION/COASTAL FLOOD BEACH FLOOD
## [25] BELOW NORMAL PRECIPITATION BITTER WIND CHILL
## [27] BITTER WIND CHILL TEMPERATURES Black Ice
## [29] BLACK ICE BLIZZARD
## [31] BLIZZARD AND EXTREME WIND CHIL BLIZZARD AND HEAVY SNOW
## [33] Blizzard Summary BLIZZARD WEATHER
## [35] BLIZZARD/FREEZING RAIN BLIZZARD/HEAVY SNOW
## [37] BLIZZARD/HIGH WIND BLIZZARD/WINTER STORM
## [39] BLOW-OUT TIDE BLOW-OUT TIDES
## [41] BLOWING DUST blowing snow
## [43] Blowing Snow BLOWING SNOW
## [45] BLOWING SNOW- EXTREME WIND CHI BLOWING SNOW & EXTREME WIND CH
## [47] BLOWING SNOW/EXTREME WIND CHIL BREAKUP FLOODING
## [49] BRUSH FIRE BRUSH FIRES
## [51] COASTAL FLOODING/EROSION COASTAL EROSION
## [53] Coastal Flood COASTAL FLOOD
## [55] coastal flooding Coastal Flooding
## [57] COASTAL FLOODING COASTAL FLOODING/EROSION
## [59] Coastal Storm COASTAL STORM
## [61] COASTAL SURGE COASTAL/TIDAL FLOOD
## [63] COASTALFLOOD COASTALSTORM
## [65] Cold COLD
## [67] COLD AIR FUNNEL COLD AIR FUNNELS
## [69] COLD AIR TORNADO Cold and Frost
## [71] COLD AND FROST COLD AND SNOW
## [73] COLD AND WET CONDITIONS Cold Temperature
## [75] COLD TEMPERATURES COLD WAVE
## [77] COLD WEATHER COLD WIND CHILL TEMPERATURES
## [79] COLD/WIND CHILL COLD/WINDS
## [81] COOL AND WET COOL SPELL
## [83] CSTL FLOODING/EROSION DAM BREAK
## [85] DAM FAILURE Damaging Freeze
## [87] DAMAGING FREEZE DEEP HAIL
## [89] DENSE FOG DENSE SMOKE
## [91] DOWNBURST DOWNBURST WINDS
## [93] DRIEST MONTH Drifting Snow
## [95] DROUGHT DROUGHT/EXCESSIVE HEAT
## [97] DROWNING DRY
## [99] DRY CONDITIONS DRY HOT WEATHER
## [101] DRY MICROBURST DRY MICROBURST 50
## [103] DRY MICROBURST 53 DRY MICROBURST 58
## [105] DRY MICROBURST 61 DRY MICROBURST 84
## [107] DRY MICROBURST WINDS DRY MIRCOBURST WINDS
## [109] DRY PATTERN DRY SPELL
## [111] DRY WEATHER DRYNESS
## [113] DUST DEVEL Dust Devil
## [115] DUST DEVIL DUST DEVIL WATERSPOUT
## [117] DUST STORM DUST STORM/HIGH WINDS
## [119] DUSTSTORM EARLY FREEZE
## [121] Early Frost EARLY FROST
## [123] EARLY RAIN EARLY SNOW
## [125] Early snowfall EARLY SNOWFALL
## [127] Erosion/Cstl Flood EXCESSIVE
## [129] Excessive Cold EXCESSIVE HEAT
## [131] EXCESSIVE HEAT/DROUGHT EXCESSIVE PRECIPITATION
## [133] EXCESSIVE RAIN EXCESSIVE RAINFALL
## [135] EXCESSIVE SNOW EXCESSIVE WETNESS
## [137] EXCESSIVELY DRY Extended Cold
## [139] Extreme Cold EXTREME COLD
## [141] EXTREME COLD/WIND CHILL EXTREME HEAT
## [143] EXTREME WIND CHILL EXTREME WIND CHILL/BLOWING SNO
## [145] EXTREME WIND CHILLS EXTREME WINDCHILL
## [147] EXTREME WINDCHILL TEMPERATURES EXTREME/RECORD COLD
## [149] EXTREMELY WET FALLING SNOW/ICE
## [151] FIRST FROST FIRST SNOW
## [153] FLASH FLOOD FLASH FLOOD - HEAVY RAIN
## [155] FLASH FLOOD FROM ICE JAMS FLASH FLOOD LANDSLIDES
## [157] FLASH FLOOD WINDS FLASH FLOOD/
## [159] FLASH FLOOD/ FLOOD FLASH FLOOD/ STREET
## [161] FLASH FLOOD/FLOOD FLASH FLOOD/HEAVY RAIN
## [163] FLASH FLOOD/LANDSLIDE FLASH FLOODING
## [165] FLASH FLOODING/FLOOD FLASH FLOODING/THUNDERSTORM WI
## [167] FLASH FLOODS FLASH FLOOODING
## [169] Flood FLOOD
## [171] FLOOD & HEAVY RAIN FLOOD FLASH
## [173] FLOOD FLOOD/FLASH FLOOD WATCH/
## [175] FLOOD/FLASH Flood/Flash Flood
## [177] FLOOD/FLASH FLOOD FLOOD/FLASH FLOODING
## [179] FLOOD/FLASH/FLOOD FLOOD/FLASHFLOOD
## [181] FLOOD/RAIN/WIND FLOOD/RAIN/WINDS
## [183] FLOOD/RIVER FLOOD Flood/Strong Wind
## [185] FLOODING FLOODING/HEAVY RAIN
## [187] FLOODS FOG
## [189] FOG AND COLD TEMPERATURES FOREST FIRES
## [191] Freeze FREEZE
## [193] Freezing drizzle Freezing Drizzle
## [195] FREEZING DRIZZLE FREEZING DRIZZLE AND FREEZING
## [197] Freezing Fog FREEZING FOG
## [199] Freezing rain Freezing Rain
## [201] FREEZING RAIN FREEZING RAIN AND SLEET
## [203] FREEZING RAIN AND SNOW FREEZING RAIN SLEET AND
## [205] FREEZING RAIN SLEET AND LIGHT FREEZING RAIN/SLEET
## [207] FREEZING RAIN/SNOW Freezing Spray
## [209] Frost FROST
## [211] Frost/Freeze FROST/FREEZE
## [213] FROST\\FREEZE FUNNEL
## [215] Funnel Cloud FUNNEL CLOUD
## [217] FUNNEL CLOUD. FUNNEL CLOUD/HAIL
## [219] FUNNEL CLOUDS FUNNELS
## [221] Glaze GLAZE
## [223] GLAZE ICE GLAZE/ICE STORM
## [225] gradient wind Gradient wind
## [227] GRADIENT WIND GRADIENT WINDS
## [229] GRASS FIRES GROUND BLIZZARD
## [231] GUSTNADO GUSTNADO AND
## [233] GUSTY LAKE WIND GUSTY THUNDERSTORM WIND
## [235] GUSTY THUNDERSTORM WINDS Gusty Wind
## [237] GUSTY WIND GUSTY WIND/HAIL
## [239] GUSTY WIND/HVY RAIN Gusty wind/rain
## [241] Gusty winds Gusty Winds
## [243] GUSTY WINDS HAIL
## [245] HAIL 0.75 HAIL 0.88
## [247] HAIL 075 HAIL 088
## [249] HAIL 1.00 HAIL 1.75
## [251] HAIL 1.75) HAIL 100
## [253] HAIL 125 HAIL 150
## [255] HAIL 175 HAIL 200
## [257] HAIL 225 HAIL 275
## [259] HAIL 450 HAIL 75
## [261] HAIL 80 HAIL 88
## [263] HAIL ALOFT HAIL DAMAGE
## [265] HAIL FLOODING HAIL STORM
## [267] Hail(0.75) HAIL/ICY ROADS
## [269] HAIL/WIND HAIL/WINDS
## [271] HAILSTORM HAILSTORMS
## [273] HARD FREEZE HAZARDOUS SURF
## [275] HEAT HEAT DROUGHT
## [277] Heat Wave HEAT WAVE
## [279] HEAT WAVE DROUGHT HEAT WAVES
## [281] HEAT/DROUGHT Heatburst
## [283] HEAVY LAKE SNOW HEAVY MIX
## [285] HEAVY PRECIPATATION Heavy Precipitation
## [287] HEAVY PRECIPITATION Heavy rain
## [289] Heavy Rain HEAVY RAIN
## [291] HEAVY RAIN AND FLOOD Heavy Rain and Wind
## [293] HEAVY RAIN EFFECTS HEAVY RAIN/FLOODING
## [295] Heavy Rain/High Surf HEAVY RAIN/LIGHTNING
## [297] HEAVY RAIN/MUDSLIDES/FLOOD HEAVY RAIN/SEVERE WEATHER
## [299] HEAVY RAIN/SMALL STREAM URBAN HEAVY RAIN/SNOW
## [301] HEAVY RAIN/URBAN FLOOD HEAVY RAIN/WIND
## [303] HEAVY RAIN; URBAN FLOOD WINDS; HEAVY RAINFALL
## [305] HEAVY RAINS HEAVY RAINS/FLOODING
## [307] HEAVY SEAS HEAVY SHOWER
## [309] HEAVY SHOWERS HEAVY SNOW
## [311] HEAVY SNOW-SQUALLS HEAVY SNOW FREEZING RAIN
## [313] HEAVY SNOW & ICE HEAVY SNOW AND
## [315] HEAVY SNOW AND HIGH WINDS HEAVY SNOW AND ICE
## [317] HEAVY SNOW AND ICE STORM HEAVY SNOW AND STRONG WINDS
## [319] HEAVY SNOW ANDBLOWING SNOW Heavy snow shower
## [321] HEAVY SNOW SQUALLS HEAVY SNOW/BLIZZARD
## [323] HEAVY SNOW/BLIZZARD/AVALANCHE HEAVY SNOW/BLOWING SNOW
## [325] HEAVY SNOW/FREEZING RAIN HEAVY SNOW/HIGH
## [327] HEAVY SNOW/HIGH WIND HEAVY SNOW/HIGH WINDS
## [329] HEAVY SNOW/HIGH WINDS & FLOOD HEAVY SNOW/HIGH WINDS/FREEZING
## [331] HEAVY SNOW/ICE HEAVY SNOW/ICE STORM
## [333] HEAVY SNOW/SLEET HEAVY SNOW/SQUALLS
## [335] HEAVY SNOW/WIND HEAVY SNOW/WINTER STORM
## [337] HEAVY SNOWPACK Heavy Surf
## [339] HEAVY SURF Heavy surf and wind
## [341] HEAVY SURF COASTAL FLOODING HEAVY SURF/HIGH SURF
## [343] HEAVY SWELLS HEAVY WET SNOW
## [345] HIGH HIGH SWELLS
## [347] HIGH WINDS HIGH SEAS
## [349] High Surf HIGH SURF
## [351] HIGH SURF ADVISORIES HIGH SURF ADVISORY
## [353] HIGH SWELLS HIGH TEMPERATURE RECORD
## [355] HIGH TIDES HIGH WATER
## [357] HIGH WAVES High Wind
## [359] HIGH WIND HIGH WIND (G40)
## [361] HIGH WIND 48 HIGH WIND 63
## [363] HIGH WIND 70 HIGH WIND AND HEAVY SNOW
## [365] HIGH WIND AND HIGH TIDES HIGH WIND AND SEAS
## [367] HIGH WIND DAMAGE HIGH WIND/ BLIZZARD
## [369] HIGH WIND/BLIZZARD HIGH WIND/BLIZZARD/FREEZING RA
## [371] HIGH WIND/HEAVY SNOW HIGH WIND/LOW WIND CHILL
## [373] HIGH WIND/SEAS HIGH WIND/WIND CHILL
## [375] HIGH WIND/WIND CHILL/BLIZZARD HIGH WINDS
## [377] HIGH WINDS 55 HIGH WINDS 57
## [379] HIGH WINDS 58 HIGH WINDS 63
## [381] HIGH WINDS 66 HIGH WINDS 67
## [383] HIGH WINDS 73 HIGH WINDS 76
## [385] HIGH WINDS 80 HIGH WINDS 82
## [387] HIGH WINDS AND WIND CHILL HIGH WINDS DUST STORM
## [389] HIGH WINDS HEAVY RAINS HIGH WINDS/
## [391] HIGH WINDS/COASTAL FLOOD HIGH WINDS/COLD
## [393] HIGH WINDS/FLOODING HIGH WINDS/HEAVY RAIN
## [395] HIGH WINDS/SNOW HIGHWAY FLOODING
## [397] Hot and Dry HOT PATTERN
## [399] HOT SPELL HOT WEATHER
## [401] HOT/DRY PATTERN HURRICANE
## [403] HURRICANE-GENERATED SWELLS Hurricane Edouard
## [405] HURRICANE EMILY HURRICANE ERIN
## [407] HURRICANE FELIX HURRICANE GORDON
## [409] HURRICANE OPAL HURRICANE OPAL/HIGH WINDS
## [411] HURRICANE/TYPHOON HVY RAIN
## [413] HYPERTHERMIA/EXPOSURE HYPOTHERMIA
## [415] Hypothermia/Exposure HYPOTHERMIA/EXPOSURE
## [417] ICE ICE AND SNOW
## [419] ICE FLOES Ice Fog
## [421] ICE JAM Ice jam flood (minor
## [423] ICE JAM FLOODING ICE ON ROAD
## [425] ICE PELLETS ICE ROADS
## [427] ICE STORM ICE STORM AND SNOW
## [429] ICE STORM/FLASH FLOOD Ice/Snow
## [431] ICE/SNOW ICE/STRONG WINDS
## [433] Icestorm/Blizzard Icy Roads
## [435] ICY ROADS LACK OF SNOW
## [437] LAKE-EFFECT SNOW Lake Effect Snow
## [439] LAKE EFFECT SNOW LAKE FLOOD
## [441] LAKESHORE FLOOD LANDSLIDE
## [443] LANDSLIDE/URBAN FLOOD LANDSLIDES
## [445] Landslump LANDSLUMP
## [447] LANDSPOUT LARGE WALL CLOUD
## [449] Late-season Snowfall LATE FREEZE
## [451] LATE SEASON HAIL LATE SEASON SNOW
## [453] Late Season Snowfall LATE SNOW
## [455] LIGHT FREEZING RAIN Light snow
## [457] Light Snow LIGHT SNOW
## [459] LIGHT SNOW AND SLEET Light Snow/Flurries
## [461] LIGHT SNOW/FREEZING PRECIP Light Snowfall
## [463] LIGHTING LIGHTNING
## [465] LIGHTNING WAUSEON LIGHTNING AND HEAVY RAIN
## [467] LIGHTNING AND THUNDERSTORM WIN LIGHTNING AND WINDS
## [469] LIGHTNING DAMAGE LIGHTNING FIRE
## [471] LIGHTNING INJURY LIGHTNING THUNDERSTORM WINDS
## [473] LIGHTNING THUNDERSTORM WINDSS LIGHTNING.
## [475] LIGHTNING/HEAVY RAIN LIGNTNING
## [477] LOCAL FLASH FLOOD LOCAL FLOOD
## [479] LOCALLY HEAVY RAIN LOW TEMPERATURE
## [481] LOW TEMPERATURE RECORD LOW WIND CHILL
## [483] MAJOR FLOOD Marine Accident
## [485] MARINE HAIL MARINE HIGH WIND
## [487] MARINE MISHAP MARINE STRONG WIND
## [489] MARINE THUNDERSTORM WIND MARINE TSTM WIND
## [491] Metro Storm, May 26 Microburst
## [493] MICROBURST MICROBURST WINDS
## [495] Mild and Dry Pattern MILD PATTERN
## [497] MILD/DRY PATTERN MINOR FLOOD
## [499] Minor Flooding MINOR FLOODING
## [501] MIXED PRECIP Mixed Precipitation
## [503] MIXED PRECIPITATION MODERATE SNOW
## [505] MODERATE SNOWFALL MONTHLY PRECIPITATION
## [507] Monthly Rainfall MONTHLY RAINFALL
## [509] Monthly Snowfall MONTHLY SNOWFALL
## [511] MONTHLY TEMPERATURE Mountain Snows
## [513] MUD SLIDE MUD SLIDES
## [515] MUD SLIDES URBAN FLOODING MUD/ROCK SLIDE
## [517] Mudslide MUDSLIDE
## [519] MUDSLIDE/LANDSLIDE Mudslides
## [521] MUDSLIDES NEAR RECORD SNOW
## [523] No Severe Weather NON-SEVERE WIND DAMAGE
## [525] NON-TSTM WIND NON SEVERE HAIL
## [527] NON TSTM WIND NONE
## [529] NORMAL PRECIPITATION NORTHERN LIGHTS
## [531] Other OTHER
## [533] PATCHY DENSE FOG PATCHY ICE
## [535] Prolong Cold PROLONG COLD
## [537] PROLONG COLD/SNOW PROLONG WARMTH
## [539] PROLONGED RAIN RAIN
## [541] RAIN (HEAVY) RAIN AND WIND
## [543] Rain Damage RAIN/SNOW
## [545] RAIN/WIND RAINSTORM
## [547] RAPIDLY RISING WATER RECORD COLD
## [549] Record Cold RECORD COLD
## [551] RECORD COLD AND HIGH WIND RECORD COLD/FROST
## [553] RECORD COOL Record dry month
## [555] RECORD DRYNESS Record Heat
## [557] RECORD HEAT RECORD HEAT WAVE
## [559] Record High RECORD HIGH
## [561] RECORD HIGH TEMPERATURE RECORD HIGH TEMPERATURES
## [563] RECORD LOW RECORD LOW RAINFALL
## [565] Record May Snow RECORD PRECIPITATION
## [567] RECORD RAINFALL RECORD SNOW
## [569] RECORD SNOW/COLD RECORD SNOWFALL
## [571] Record temperature RECORD TEMPERATURE
## [573] Record Temperatures RECORD TEMPERATURES
## [575] RECORD WARM RECORD WARM TEMPS.
## [577] Record Warmth RECORD WARMTH
## [579] Record Winter Snow RECORD/EXCESSIVE HEAT
## [581] RECORD/EXCESSIVE RAINFALL RED FLAG CRITERIA
## [583] RED FLAG FIRE WX REMNANTS OF FLOYD
## [585] RIP CURRENT RIP CURRENTS
## [587] RIP CURRENTS HEAVY SURF RIP CURRENTS/HEAVY SURF
## [589] RIVER AND STREAM FLOOD RIVER FLOOD
## [591] River Flooding RIVER FLOODING
## [593] ROCK SLIDE ROGUE WAVE
## [595] ROTATING WALL CLOUD ROUGH SEAS
## [597] ROUGH SURF RURAL FLOOD
## [599] Saharan Dust SAHARAN DUST
## [601] Seasonal Snowfall SEICHE
## [603] SEVERE COLD SEVERE THUNDERSTORM
## [605] SEVERE THUNDERSTORM WINDS SEVERE THUNDERSTORMS
## [607] SEVERE TURBULENCE SLEET
## [609] SLEET & FREEZING RAIN SLEET STORM
## [611] SLEET/FREEZING RAIN SLEET/ICE STORM
## [613] SLEET/RAIN/SNOW SLEET/SNOW
## [615] small hail Small Hail
## [617] SMALL HAIL SMALL STREAM
## [619] SMALL STREAM AND SMALL STREAM AND URBAN FLOOD
## [621] SMALL STREAM AND URBAN FLOODIN SMALL STREAM FLOOD
## [623] SMALL STREAM FLOODING SMALL STREAM URBAN FLOOD
## [625] SMALL STREAM/URBAN FLOOD Sml Stream Fld
## [627] SMOKE Snow
## [629] SNOW SNOW- HIGH WIND- WIND CHILL
## [631] Snow Accumulation SNOW ACCUMULATION
## [633] SNOW ADVISORY SNOW AND COLD
## [635] SNOW AND HEAVY SNOW Snow and Ice
## [637] SNOW AND ICE SNOW AND ICE STORM
## [639] Snow and sleet SNOW AND SLEET
## [641] SNOW AND WIND SNOW DROUGHT
## [643] SNOW FREEZING RAIN SNOW SHOWERS
## [645] SNOW SLEET SNOW SQUALL
## [647] Snow squalls Snow Squalls
## [649] SNOW SQUALLS SNOW/ BITTER COLD
## [651] SNOW/ ICE SNOW/BLOWING SNOW
## [653] SNOW/COLD SNOW/FREEZING RAIN
## [655] SNOW/HEAVY SNOW SNOW/HIGH WINDS
## [657] SNOW/ICE SNOW/ICE STORM
## [659] SNOW/RAIN SNOW/RAIN/SLEET
## [661] SNOW/SLEET SNOW/SLEET/FREEZING RAIN
## [663] SNOW/SLEET/RAIN SNOW\\COLD
## [665] SNOWFALL RECORD SNOWMELT FLOODING
## [667] SNOWSTORM SOUTHEAST
## [669] STORM FORCE WINDS STORM SURGE
## [671] STORM SURGE/TIDE STREAM FLOODING
## [673] STREET FLOOD STREET FLOODING
## [675] Strong Wind STRONG WIND
## [677] STRONG WIND GUST Strong winds
## [679] Strong Winds STRONG WINDS
## [681] Summary August 10 Summary August 11
## [683] Summary August 17 Summary August 2-3
## [685] Summary August 21 Summary August 28
## [687] Summary August 4 Summary August 7
## [689] Summary August 9 Summary Jan 17
## [691] Summary July 23-24 Summary June 18-19
## [693] Summary June 5-6 Summary June 6
## [695] Summary of April 12 Summary of April 13
## [697] Summary of April 21 Summary of April 27
## [699] Summary of April 3rd Summary of August 1
## [701] Summary of July 11 Summary of July 2
## [703] Summary of July 22 Summary of July 26
## [705] Summary of July 29 Summary of July 3
## [707] Summary of June 10 Summary of June 11
## [709] Summary of June 12 Summary of June 13
## [711] Summary of June 15 Summary of June 16
## [713] Summary of June 18 Summary of June 23
## [715] Summary of June 24 Summary of June 3
## [717] Summary of June 30 Summary of June 4
## [719] Summary of June 6 Summary of March 14
## [721] Summary of March 23 Summary of March 24
## [723] SUMMARY OF MARCH 24-25 SUMMARY OF MARCH 27
## [725] SUMMARY OF MARCH 29 Summary of May 10
## [727] Summary of May 13 Summary of May 14
## [729] Summary of May 22 Summary of May 22 am
## [731] Summary of May 22 pm Summary of May 26 am
## [733] Summary of May 26 pm Summary of May 31 am
## [735] Summary of May 31 pm Summary of May 9-10
## [737] Summary Sept. 25-26 Summary September 20
## [739] Summary September 23 Summary September 3
## [741] Summary September 4 Summary: Nov. 16
## [743] Summary: Nov. 6-7 Summary: Oct. 20-21
## [745] Summary: October 31 Summary: Sept. 18
## [747] Temperature record THUDERSTORM WINDS
## [749] THUNDEERSTORM WINDS THUNDERESTORM WINDS
## [751] THUNDERSNOW Thundersnow shower
## [753] THUNDERSTORM THUNDERSTORM WINDS
## [755] THUNDERSTORM DAMAGE THUNDERSTORM DAMAGE TO
## [757] THUNDERSTORM HAIL THUNDERSTORM W INDS
## [759] Thunderstorm Wind THUNDERSTORM WIND
## [761] THUNDERSTORM WIND (G40) THUNDERSTORM WIND 50
## [763] THUNDERSTORM WIND 52 THUNDERSTORM WIND 56
## [765] THUNDERSTORM WIND 59 THUNDERSTORM WIND 59 MPH
## [767] THUNDERSTORM WIND 59 MPH. THUNDERSTORM WIND 60 MPH
## [769] THUNDERSTORM WIND 65 MPH THUNDERSTORM WIND 65MPH
## [771] THUNDERSTORM WIND 69 THUNDERSTORM WIND 98 MPH
## [773] THUNDERSTORM WIND G50 THUNDERSTORM WIND G51
## [775] THUNDERSTORM WIND G52 THUNDERSTORM WIND G55
## [777] THUNDERSTORM WIND G60 THUNDERSTORM WIND G61
## [779] THUNDERSTORM WIND TREES THUNDERSTORM WIND.
## [781] THUNDERSTORM WIND/ TREE THUNDERSTORM WIND/ TREES
## [783] THUNDERSTORM WIND/AWNING THUNDERSTORM WIND/HAIL
## [785] THUNDERSTORM WIND/LIGHTNING THUNDERSTORM WINDS
## [787] THUNDERSTORM WINDS LE CEN THUNDERSTORM WINDS 13
## [789] THUNDERSTORM WINDS 2 THUNDERSTORM WINDS 50
## [791] THUNDERSTORM WINDS 52 THUNDERSTORM WINDS 53
## [793] THUNDERSTORM WINDS 60 THUNDERSTORM WINDS 61
## [795] THUNDERSTORM WINDS 62 THUNDERSTORM WINDS 63 MPH
## [797] THUNDERSTORM WINDS AND THUNDERSTORM WINDS FUNNEL CLOU
## [799] THUNDERSTORM WINDS G THUNDERSTORM WINDS G60
## [801] THUNDERSTORM WINDS HAIL THUNDERSTORM WINDS HEAVY RAIN
## [803] THUNDERSTORM WINDS LIGHTNING THUNDERSTORM WINDS SMALL STREA
## [805] THUNDERSTORM WINDS URBAN FLOOD THUNDERSTORM WINDS.
## [807] THUNDERSTORM WINDS/ FLOOD THUNDERSTORM WINDS/ HAIL
## [809] THUNDERSTORM WINDS/FLASH FLOOD THUNDERSTORM WINDS/FLOODING
## [811] THUNDERSTORM WINDS/FUNNEL CLOU THUNDERSTORM WINDS/HAIL
## [813] THUNDERSTORM WINDS/HEAVY RAIN THUNDERSTORM WINDS53
## [815] THUNDERSTORM WINDSHAIL THUNDERSTORM WINDSS
## [817] THUNDERSTORM WINS THUNDERSTORMS
## [819] THUNDERSTORMS WIND THUNDERSTORMS WINDS
## [821] THUNDERSTORMW THUNDERSTORMW 50
## [823] THUNDERSTORMW WINDS THUNDERSTORMWINDS
## [825] THUNDERSTROM WIND THUNDERSTROM WINDS
## [827] THUNDERTORM WINDS THUNDERTSORM WIND
## [829] THUNDESTORM WINDS THUNERSTORM WINDS
## [831] TIDAL FLOOD Tidal Flooding
## [833] TIDAL FLOODING TORNADO
## [835] TORNADO DEBRIS TORNADO F0
## [837] TORNADO F1 TORNADO F2
## [839] TORNADO F3 TORNADO/WATERSPOUT
## [841] TORNADOES TORNADOES, TSTM WIND, HAIL
## [843] TORNADOS TORNDAO
## [845] TORRENTIAL RAIN Torrential Rainfall
## [847] TROPICAL DEPRESSION TROPICAL STORM
## [849] TROPICAL STORM ALBERTO TROPICAL STORM DEAN
## [851] TROPICAL STORM GORDON TROPICAL STORM JERRY
## [853] TSTM TSTM HEAVY RAIN
## [855] Tstm Wind TSTM WIND
## [857] TSTM WIND (G45) TSTM WIND (41)
## [859] TSTM WIND (G35) TSTM WIND (G40)
## [861] TSTM WIND (G45) TSTM WIND 40
## [863] TSTM WIND 45 TSTM WIND 50
## [865] TSTM WIND 51 TSTM WIND 52
## [867] TSTM WIND 55 TSTM WIND 65)
## [869] TSTM WIND AND LIGHTNING TSTM WIND DAMAGE
## [871] TSTM WIND G45 TSTM WIND G58
## [873] TSTM WIND/HAIL TSTM WINDS
## [875] TSTM WND TSTMW
## [877] TSUNAMI TUNDERSTORM WIND
## [879] TYPHOON Unseasonable Cold
## [881] UNSEASONABLY COLD UNSEASONABLY COOL
## [883] UNSEASONABLY COOL & WET UNSEASONABLY DRY
## [885] UNSEASONABLY HOT UNSEASONABLY WARM
## [887] UNSEASONABLY WARM & WET UNSEASONABLY WARM AND DRY
## [889] UNSEASONABLY WARM YEAR UNSEASONABLY WARM/WET
## [891] UNSEASONABLY WET UNSEASONAL LOW TEMP
## [893] UNSEASONAL RAIN UNUSUAL WARMTH
## [895] UNUSUAL/RECORD WARMTH UNUSUALLY COLD
## [897] UNUSUALLY LATE SNOW UNUSUALLY WARM
## [899] URBAN AND SMALL URBAN AND SMALL STREAM
## [901] URBAN AND SMALL STREAM FLOOD URBAN AND SMALL STREAM FLOODIN
## [903] Urban flood Urban Flood
## [905] URBAN FLOOD URBAN FLOOD LANDSLIDE
## [907] Urban Flooding URBAN FLOODING
## [909] URBAN FLOODS URBAN SMALL
## [911] URBAN SMALL STREAM FLOOD URBAN/SMALL
## [913] URBAN/SMALL FLOODING URBAN/SMALL STREAM
## [915] URBAN/SMALL STREAM FLOOD URBAN/SMALL STREAM FLOOD
## [917] URBAN/SMALL STREAM FLOODING URBAN/SMALL STRM FLDG
## [919] URBAN/SML STREAM FLD URBAN/SML STREAM FLDG
## [921] URBAN/STREET FLOODING VERY DRY
## [923] VERY WARM VOG
## [925] Volcanic Ash VOLCANIC ASH
## [927] Volcanic Ash Plume VOLCANIC ASHFALL
## [929] VOLCANIC ERUPTION WAKE LOW WIND
## [931] WALL CLOUD WALL CLOUD/FUNNEL CLOUD
## [933] WARM DRY CONDITIONS WARM WEATHER
## [935] WATER SPOUT WATERSPOUT
## [937] WATERSPOUT- WATERSPOUT-TORNADO
## [939] WATERSPOUT FUNNEL CLOUD WATERSPOUT TORNADO
## [941] WATERSPOUT/ WATERSPOUT/ TORNADO
## [943] WATERSPOUT/TORNADO WATERSPOUTS
## [945] WAYTERSPOUT wet micoburst
## [947] WET MICROBURST Wet Month
## [949] WET SNOW WET WEATHER
## [951] Wet Year Whirlwind
## [953] WHIRLWIND WILD FIRES
## [955] WILD/FOREST FIRE WILD/FOREST FIRES
## [957] WILDFIRE WILDFIRES
## [959] Wind WIND
## [961] WIND ADVISORY WIND AND WAVE
## [963] WIND CHILL WIND CHILL/HIGH WIND
## [965] Wind Damage WIND DAMAGE
## [967] WIND GUSTS WIND STORM
## [969] WIND/HAIL WINDS
## [971] WINTER MIX WINTER STORM
## [973] WINTER STORM HIGH WINDS WINTER STORM/HIGH WIND
## [975] WINTER STORM/HIGH WINDS WINTER STORMS
## [977] Winter Weather WINTER WEATHER
## [979] WINTER WEATHER MIX WINTER WEATHER/MIX
## [981] WINTERY MIX Wintry mix
## [983] Wintry Mix WINTRY MIX
## [985] WND
## 985 Levels: HIGH SURF ADVISORY COASTAL FLOOD ... WNDIt can be noted that there are several cases involving transcription error, uppercase and lowercase, among others. These observations have to be adequately adjusted and/or grouped. For example:
- AVALANCE (should be AVALANCHE)
- Black Ice vs BLACK ICE
- WINTER MIX instead of WINTRY MIX
On the other hand, there are even values that do not represent a valid type of event. In my opinion these observations have to be removed from the analysis and therefore of the dataset. For example:
- ?
- APACHE COUNTY
- SUMMARY …
Here the code to normalize / correct the types of events (from 985 to 48):
if (!require("stringr")) install.packages("stringr")## Loading required package: stringrrequire("stringr")
# Chage event type descriptions to uppercase and remove white spaces
# at the beginning and the end
#
NOAA.storm$EVTYPE <- toupper(NOAA.storm$EVTYPE)
NOAA.storm$EVTYPE <- str_trim(NOAA.storm$EVTYPE, side = "both")
# Remove irrelevant observations for the analysis
#
prevRow <- nrow(NOAA.storm)
NOAA.storm <- NOAA.storm[-grep("APACHE COUNTY", NOAA.storm$EVTYPE), ]
NOAA.storm <- NOAA.storm[-grep("SUMMARY", NOAA.storm$EVTYPE), ]
# Adjust the event type descriptions
#
NOAA.storm$EVTYPE[grep("AVALANCE", NOAA.storm$EVTYPE)] <- "AVALANCHE"
NOAA.storm$EVTYPE[grep("BLIZZARD", NOAA.storm$EVTYPE)] <- "BLIZZARD"
NOAA.storm$EVTYPE[grep("BLOWING SNOW", NOAA.storm$EVTYPE)] <- "BLOWING SNOW"
NOAA.storm$EVTYPE[grep("COASTAL", NOAA.storm$EVTYPE)] <- "COASTAL FLOOD"
NOAA.storm$EVTYPE[grep("COLD", NOAA.storm$EVTYPE)] <- "COLD/WIND CHILL"
NOAA.storm$EVTYPE[grep("DRY MICROBURST", NOAA.storm$EVTYPE)] <- "DRY MICROBURST"
NOAA.storm$EVTYPE[grep("DRY[A-Z]*.[^MICROBURST]", NOAA.storm$EVTYPE)] <- "DRY CONDITIONS"
NOAA.storm$EVTYPE[grep("[^MICROBURST].DRY", NOAA.storm$EVTYPE)] <- "DRY CONDITIONS"
NOAA.storm$EVTYPE[grep("FLASH FLOOD", NOAA.storm$EVTYPE)] <- "FLASH FLOOD"
NOAA.storm$EVTYPE[grep("FLOOD/FLASH", NOAA.storm$EVTYPE)] <- "FLASH FLOOD"
NOAA.storm$EVTYPE[grep("FLOOD FLASH", NOAA.storm$EVTYPE)] <- "FLASH FLOOD"
NOAA.storm$EVTYPE[grep("FLASH FLOOODING", NOAA.storm$EVTYPE)] <- "FLASH FLOOD"
NOAA.storm$EVTYPE[grep("FLOOD.[^FLASH]", NOAA.storm$EVTYPE)] <- "FLOOD"
NOAA.storm$EVTYPE[grep("[^FLASH].FLOOD", NOAA.storm$EVTYPE)] <- "FLOOD"
NOAA.storm$EVTYPE[grep("FLOODS", NOAA.storm$EVTYPE)] <- "FLOOD"
NOAA.storm$EVTYPE[grep("FREEZING", NOAA.storm$EVTYPE)] <- "FROST/FREEZE"
NOAA.storm$EVTYPE[grep("FREEZE", NOAA.storm$EVTYPE)] <- "FROST/FREEZE"
NOAA.storm$EVTYPE[grep("HAIL", NOAA.storm$EVTYPE)] <- "HAIL"
NOAA.storm$EVTYPE[grep("HEAT", NOAA.storm$EVTYPE)] <- "HEAT"
NOAA.storm$EVTYPE[grep("HEAVY RAIN", NOAA.storm$EVTYPE)] <- "HEAVY RAIN"
NOAA.storm$EVTYPE[grep("HVY RAIN", NOAA.storm$EVTYPE)] <- "HEAVY RAIN"
NOAA.storm$EVTYPE[grep("RAIN \\(HEAVY\\)", NOAA.storm$EVTYPE)] <- "HEAVY RAIN"
NOAA.storm$EVTYPE[grep("HEAVY SNOW", NOAA.storm$EVTYPE)] <- "HEAVY SNOW"
NOAA.storm$EVTYPE[grep("HEAVY SURF", NOAA.storm$EVTYPE)] <- "HEAVY SURF"
NOAA.storm$EVTYPE[grep("HEAVY WAVE", NOAA.storm$EVTYPE)] <- "HEAVY WAVES"
NOAA.storm$EVTYPE[grep("HIGH WIND", NOAA.storm$EVTYPE)] <- "HIGH WIND"
NOAA.storm$EVTYPE[grep("HURRICANE", NOAA.storm$EVTYPE)] <- "HURRICANE"
NOAA.storm$EVTYPE[grep("ICE ", NOAA.storm$EVTYPE)] <- "ICE"
NOAA.storm$EVTYPE[grep("LIGHTING", NOAA.storm$EVTYPE)] <- "LIGHTING"
NOAA.storm$EVTYPE[grep("LIGHTNING", NOAA.storm$EVTYPE)] <- "LIGHTING"
NOAA.storm$EVTYPE[grep("LIGNTNING", NOAA.storm$EVTYPE)] <- "LIGHTING"
NOAA.storm$EVTYPE[grep("THUND[A-Z]*", NOAA.storm$EVTYPE)] <- "THUNDERSTORM"
NOAA.storm$EVTYPE[grep("THUDERSTORM", NOAA.storm$EVTYPE)] <- "THUNDERSTORM"
NOAA.storm$EVTYPE[grep("THUNERSTORM", NOAA.storm$EVTYPE)] <- "THUNDERSTORM"
NOAA.storm$EVTYPE[grep("TUNDERSTORM WIND", NOAA.storm$EVTYPE)] <- "THUNDERSTORM"
NOAA.storm$EVTYPE[grep("TSTM", NOAA.storm$EVTYPE)] <- "THUNDERSTORM"
NOAA.storm$EVTYPE[grep("TORNADO", NOAA.storm$EVTYPE)] <- "TORNADO"
NOAA.storm$EVTYPE[grep("TORNDAO", NOAA.storm$EVTYPE)] <- "TORNADO"
NOAA.storm$EVTYPE[grep("TROPICAL STORM", NOAA.storm$EVTYPE)] <- "TROPICAL STORM"
NOAA.storm$EVTYPE[grep("VOLCANIC ASH", NOAA.storm$EVTYPE)] <- "VOLCANIC ASH"
NOAA.storm$EVTYPE[grep("WATERSPOUT", NOAA.storm$EVTYPE)] <- "WATERSPOUT"
NOAA.storm$EVTYPE[grep("WINTER STORM", NOAA.storm$EVTYPE)] <- "WINTER STORM"
NOAA.storm$EVTYPE[grep("WINTER WEATHER", NOAA.storm$EVTYPE)] <- "WINTER WEATHER"
NOAA.storm$EVTYPE[grep("WIN[A-Z]*.MIX", NOAA.storm$EVTYPE)] <- "WINTER WEATHER"
NOAA.storm$EVTYPE[grep("SNOW", NOAA.storm$EVTYPE)] <- "SNOW STORM"
NOAA.storm$EVTYPE[grep("RAINSTORM", NOAA.storm$EVTYPE)] <- "RAIN STORM"
NOAA.storm$EVTYPE[grep("BEACH EROSIN", NOAA.storm$EVTYPE)] <- "BEACH EROSION"
NOAA.storm$EVTYPE[grep("BLOW-OUT TIDES", NOAA.storm$EVTYPE)] <- "BLOW-OUT TIDE"
NOAA.storm$EVTYPE[grep("BRUSH FIRES", NOAA.storm$EVTYPE)] <- "BRUSH FIRE"
NOAA.storm$EVTYPE[grep("DAM BREAK", NOAA.storm$EVTYPE)] <- "DAM FAILURE"
NOAA.storm$EVTYPE[grep("DUSTSTORM", NOAA.storm$EVTYPE)] <- "DUST STORM"
NOAA.storm$EVTYPE[grep("EXTREME WIND CHILLS", NOAA.storm$EVTYPE)] <-
"EXTREME WIND CHILL"
NOAA.storm$EVTYPE[grep("EXTREME WINDCHILL", NOAA.storm$EVTYPE)] <-
"EXTREME WIND CHILL"
NOAA.storm$EVTYPE[grep("HIGH SURF ADVISOR[A-Z]*", NOAA.storm$EVTYPE)] <-
"HIGH SURF"
NOAA.storm$EVTYPE[grep("HYPOTHERMIA", NOAA.storm$EVTYPE)] <- "HYPOTHERMIA/EXPOSURE"
NOAA.storm$EVTYPE[grep("MUD SLIDES", NOAA.storm$EVTYPE)] <- "MUD SLIDE"
NOAA.storm$EVTYPE[grep("MUDSLIDE[A-Z]*", NOAA.storm$EVTYPE)] <- "MUD SLIDE"
NOAA.storm$EVTYPE[grep("STRONG WINDS", NOAA.storm$EVTYPE)] <- "STRONG WIND"
NOAA.storm$EVTYPE[grep("TORRENTIAL RAINFALL", NOAA.storm$EVTYPE)] <-
"TORRENTIAL RAIN"
NOAA.storm$EVTYPE[grep("UNSEASONABLY WARM & WET", NOAA.storm$EVTYPE)] <- "UNSEASONABLY WARM/WET"
NOAA.storm$EVTYPE[grep("WET MICOBURST", NOAA.storm$EVTYPE)] <-
"WET MICROBURST"
NOAA.storm$EVTYPE[grep("WINDS", NOAA.storm$EVTYPE)] <- "WIND"
NOAA.storm$EVTYPE[grep("WND", NOAA.storm$EVTYPE)] <- "WIND"
NOAA.storm$EVTYPE[grep("WILDFIRE[A-Z]*", NOAA.storm$EVTYPE)] <- "WILD FIRES"
NOAA.storm$EVTYPE[grep("WILD/FOREST FIRE", NOAA.storm$EVTYPE)] <-
"WILD/FOREST FIRES"
NOAA.storm$EVTYPE[grep("WAYTERSPOUT", NOAA.storm$EVTYPE)] <- "WATER SPOUT"
NOAA.storm$EVTYPE[grep("UNUSUALLY WARM", NOAA.storm$EVTYPE)] <- "UNUSUAL WARMTH"
NOAA.storm$EVTYPE[grep("UNUSUAL/RECORD WARMTH", NOAA.storm$EVTYPE)] <-
"UNUSUAL WARMTH"
NOAA.storm$EVTYPE[grep("RIP CURRENTS", NOAA.storm$EVTYPE)] <- "RIP CURRENT"
NOAA.storm$EVTYPE[grep("RECORD HIGH TEMPERATURES", NOAA.storm$EVTYPE)] <- "RECORD HIGH TEMPERATURE"
NOAA.storm$EVTYPE[grep("LANDSLIDES", NOAA.storm$EVTYPE)] <- "LANDSLIDE"
NOAA.storm$EVTYPE[grep("HIGH SWELLS", NOAA.storm$EVTYPE)] <- "HIGH SWELLS"
NOAA.storm$EVTYPE[grep("HEAVY PRECIPATATION", NOAA.storm$EVTYPE)] <-
"HEAVY PRECIPITATION"
NOAA.storm$EVTYPE[grep("GUSTNADO AND", NOAA.storm$EVTYPE)] <- "GUSTNADO"
NOAA.storm$EVTYPE[grep("FUNNEL CLOUD[A-Z]*", NOAA.storm$EVTYPE)] <- "FUNNEL CLOUD"
NOAA.storm$EVTYPE[grep("EXCESSIVE RAINFALL", NOAA.storm$EVTYPE)] <-
"EXCESSIVE RAIN"
NOAA.storm$EVTYPE[grep("DUST DEVEL", NOAA.storm$EVTYPE)] <- "DUST DEVIL"
NOAA.storm$EVTYPE[grep("NONE", NOAA.storm$EVTYPE)] <- "OTHER"
NOAA.storm$EVTYPE[grep("\\?", NOAA.storm$EVTYPE)] <- "OTHER"77 rows where removed from dataset. There are now 216 types of events:
sort(unique(NOAA.storm$EVTYPE))## [1] "ABNORMAL WARMTH" "ABNORMALLY WET"
## [3] "ASTRONOMICAL HIGH TIDE" "ASTRONOMICAL LOW TIDE"
## [5] "AVALANCHE" "BEACH EROSION"
## [7] "BEACH FLOOD" "BELOW NORMAL PRECIPITATION"
## [9] "BITTER WIND CHILL" "BITTER WIND CHILL TEMPERATURES"
## [11] "BLACK ICE" "BLIZZARD"
## [13] "BLOW-OUT TIDE" "BLOWING DUST"
## [15] "BRUSH FIRE" "COASTAL FLOOD"
## [17] "COLD/WIND CHILL" "COOL AND WET"
## [19] "COOL SPELL" "DAM FAILURE"
## [21] "DENSE FOG" "DENSE SMOKE"
## [23] "DOWNBURST" "DRIEST MONTH"
## [25] "DROUGHT" "DROWNING"
## [27] "DRY" "DRY CONDITIONS"
## [29] "DRY MICROBURST" "DRY SPELL"
## [31] "DUST DEVIL" "DUST STORM"
## [33] "EARLY FROST" "EARLY RAIN"
## [35] "EROSION/CSTL FLOOD" "EXCESSIVE"
## [37] "EXCESSIVE PRECIPITATION" "EXCESSIVE RAIN"
## [39] "EXCESSIVE WETNESS" "EXTREME WIND CHILL"
## [41] "EXTREME WIND CHILL/BLOWING SNO" "EXTREMELY WET"
## [43] "FIRST FROST" "FLASH FLOOD"
## [45] "FLOOD" "FLOOD/STRONG WIND"
## [47] "FOG" "FOREST FIRES"
## [49] "FROST" "FROST/FREEZE"
## [51] "FUNNEL" "FUNNEL CLOUD"
## [53] "FUNNELS" "GLAZE"
## [55] "GLAZE ICE" "GRADIENT WIND"
## [57] "GRASS FIRES" "GUSTNADO"
## [59] "GUSTY LAKE WIND" "GUSTY WIND"
## [61] "GUSTY WIND/RAIN" "HAIL"
## [63] "HAZARDOUS SURF" "HEAT"
## [65] "HEAVY MIX" "HEAVY PRECIPITATION"
## [67] "HEAVY RAIN" "HEAVY SEAS"
## [69] "HEAVY SHOWER" "HEAVY SHOWERS"
## [71] "HEAVY SURF" "HEAVY SWELLS"
## [73] "HIGH" "HIGH SEAS"
## [75] "HIGH SURF" "HIGH SWELLS"
## [77] "HIGH TEMPERATURE RECORD" "HIGH TIDES"
## [79] "HIGH WATER" "HIGH WAVES"
## [81] "HIGH WIND" "HOT PATTERN"
## [83] "HOT SPELL" "HOT WEATHER"
## [85] "HURRICANE" "HYPERTHERMIA/EXPOSURE"
## [87] "HYPOTHERMIA/EXPOSURE" "ICE"
## [89] "ICY ROADS" "LANDSLIDE"
## [91] "LANDSLUMP" "LANDSPOUT"
## [93] "LARGE WALL CLOUD" "LIGHTING"
## [95] "LOCAL FLOOD" "LOW TEMPERATURE"
## [97] "LOW TEMPERATURE RECORD" "LOW WIND CHILL"
## [99] "MARINE ACCIDENT" "MARINE MISHAP"
## [101] "MARINE STRONG WIND" "METRO STORM, MAY 26"
## [103] "MICROBURST" "MILD PATTERN"
## [105] "MIXED PRECIP" "MIXED PRECIPITATION"
## [107] "MONTHLY PRECIPITATION" "MONTHLY RAINFALL"
## [109] "MONTHLY TEMPERATURE" "MUD SLIDE"
## [111] "MUD/ROCK SLIDE" "NO SEVERE WEATHER"
## [113] "NON-SEVERE WIND DAMAGE" "NORMAL PRECIPITATION"
## [115] "NORTHERN LIGHTS" "OTHER"
## [117] "PATCHY DENSE FOG" "PATCHY ICE"
## [119] "PROLONG WARMTH" "PROLONGED RAIN"
## [121] "RAIN" "RAIN AND WIND"
## [123] "RAIN DAMAGE" "RAIN STORM"
## [125] "RAIN/WIND" "RAPIDLY RISING WATER"
## [127] "RECORD COOL" "RECORD HIGH"
## [129] "RECORD HIGH TEMPERATURE" "RECORD LOW"
## [131] "RECORD LOW RAINFALL" "RECORD PRECIPITATION"
## [133] "RECORD RAINFALL" "RECORD TEMPERATURE"
## [135] "RECORD TEMPERATURES" "RECORD WARM"
## [137] "RECORD WARM TEMPS." "RECORD WARMTH"
## [139] "RED FLAG CRITERIA" "RED FLAG FIRE WX"
## [141] "REMNANTS OF FLOYD" "RIP CURRENT"
## [143] "ROCK SLIDE" "ROGUE WAVE"
## [145] "ROTATING WALL CLOUD" "ROUGH SEAS"
## [147] "ROUGH SURF" "RURAL FLOOD"
## [149] "SAHARAN DUST" "SEICHE"
## [151] "SEVERE TURBULENCE" "SLEET"
## [153] "SLEET STORM" "SMALL STREAM"
## [155] "SMALL STREAM AND" "SML STREAM FLD"
## [157] "SMOKE" "SNOW STORM"
## [159] "SOUTHEAST" "STORM SURGE"
## [161] "STORM SURGE/TIDE" "STRONG WIND"
## [163] "STRONG WIND GUST" "TEMPERATURE RECORD"
## [165] "THUNDERSTORM" "TIDAL FLOOD"
## [167] "TORNADO" "TORRENTIAL RAIN"
## [169] "TROPICAL DEPRESSION" "TROPICAL STORM"
## [171] "TSUNAMI" "TYPHOON"
## [173] "UNSEASONABLY COOL" "UNSEASONABLY COOL & WET"
## [175] "UNSEASONABLY HOT" "UNSEASONABLY WARM"
## [177] "UNSEASONABLY WARM YEAR" "UNSEASONABLY WARM/WET"
## [179] "UNSEASONABLY WET" "UNSEASONAL LOW TEMP"
## [181] "UNSEASONAL RAIN" "UNUSUAL WARMTH"
## [183] "URBAN AND SMALL" "URBAN AND SMALL STREAM"
## [185] "URBAN SMALL" "URBAN/SMALL"
## [187] "URBAN/SMALL STREAM" "URBAN/SMALL STRM FLDG"
## [189] "URBAN/SML STREAM FLD" "URBAN/SML STREAM FLDG"
## [191] "VERY WARM" "VOG"
## [193] "VOLCANIC ASH" "VOLCANIC ERUPTION"
## [195] "WAKE LOW WIND" "WALL CLOUD"
## [197] "WARM DRY CONDITIONS" "WARM WEATHER"
## [199] "WATER SPOUT" "WATERSPOUT"
## [201] "WET MICROBURST" "WET MONTH"
## [203] "WET WEATHER" "WET YEAR"
## [205] "WHIRLWIND" "WILD FIRES"
## [207] "WILD/FOREST FIRES" "WIND"
## [209] "WIND ADVISORY" "WIND AND WAVE"
## [211] "WIND CHILL" "WIND DAMAGE"
## [213] "WIND GUSTS" "WIND STORM"
## [215] "WINTER STORM" "WINTER WEATHER"Analysis 1: population damage
Lets take a look which types of events are most harmful with respect to population (across the United States). To do this, there are two columns in the database (“FATALITIES” and “INJURIES”) that gives us the number of people who have died or were injured as a result of a storm event. Here the code that separately calculate the total of “FATALITIES” and “INJURIES” for each type of event:
TotFatalities <- with(NOAA.storm,
tapply(FATALITIES, EVTYPE, sum))
TotInjuries <- with(NOAA.storm,
tapply(INJURIES, EVTYPE, sum))Here the code to put the information into a data-set with the total of “FATALITIES” and “INJURIES” (together) for each type of event:
PopDamByEvent <- data.frame(names(TotFatalities),
TotFatalities + TotInjuries,
row.names = NULL)
names(PopDamByEvent) <- c("EventType", "PopulationDamage")Next step is removed from data those event types that do not have any population damage (TotFatalities + TotInjuries == 0) and order the data by population damage:
PopDamByEvent <- subset(PopDamByEvent, PopulationDamage > 0)
PopDamByEvent <- PopDamByEvent[order(PopDamByEvent$PopulationDamage,
decreasing = TRUE), ]
row.names(PopDamByEvent) <- "1":nrow(PopDamByEvent)Lets take a look of the data obtained:
PopDamByEvent## EventType PopulationDamage
## 1 TORNADO 97043
## 2 HEAT 12362
## 3 THUNDERSTORM 10174
## 4 FLOOD 7279
## 5 LIGHTING 6049
## 6 FLASH FLOOD 2837
## 7 ICE 2224
## 8 HIGH WIND 1815
## 9 WINTER STORM 1554
## 10 HAIL 1512
## 11 HURRICANE 1461
## 12 SNOW STORM 1271
## 13 WILD FIRES 1139
## 14 RIP CURRENT 1101
## 15 BLIZZARD 907
## 16 FOG 796
## 17 COLD/WIND CHILL 771
## 18 WINTER WEATHER 677
## 19 WILD/FOREST FIRES 557
## 20 DUST STORM 462
## 21 TROPICAL STORM 449
## 22 STRONG WIND 412
## 23 AVALANCHE 395
## 24 DENSE FOG 360
## 25 HEAVY RAIN 353
## 26 HIGH SURF 260
## 27 GLAZE 223
## 28 TSUNAMI 162
## 29 HEAVY SURF 146
## 30 WIND 127
## 31 URBAN/SML STREAM FLD 107
## 32 LANDSLIDE 92
## 33 STORM SURGE 51
## 34 FROST/FREEZE 50
## 35 DUST DEVIL 45
## 36 ICY ROADS 36
## 37 MARINE STRONG WIND 36
## 38 WATERSPOUT 32
## 39 DRY MICROBURST 31
## 40 DRY CONDITIONS 29
## 41 MIXED PRECIP 28
## 42 UNSEASONABLY WARM 28
## 43 BLACK ICE 25
## 44 EXCESSIVE RAIN 23
## 45 EXTREME WIND CHILL 22
## 46 COASTAL FLOOD 19
## 47 STORM SURGE/TIDE 16
## 48 HIGH SEAS 13
## 49 ROUGH SEAS 13
## 50 MARINE MISHAP 12
## 51 HYPOTHERMIA/EXPOSURE 8
## 52 LOW TEMPERATURE 7
## 53 MUD SLIDE 7
## 54 NON-SEVERE WIND DAMAGE 7
## 55 ROUGH SURF 5
## 56 TYPHOON 5
## 57 DROUGHT 4
## 58 FROST 4
## 59 OTHER 4
## 60 TORRENTIAL RAIN 4
## 61 FUNNEL CLOUD 3
## 62 HEAVY SEAS 3
## 63 HIGH WATER 3
## 64 MARINE ACCIDENT 3
## 65 BRUSH FIRE 2
## 66 GUSTY WIND 2
## 67 ROGUE WAVE 2
## 68 SLEET 2
## 69 WARM WEATHER 2
## 70 DROWNING 1
## 71 HAZARDOUS SURF 1
## 72 HIGH 1
## 73 HIGH SWELLS 1
## 74 HIGH WAVES 1
## 75 HYPERTHERMIA/EXPOSURE 1
## 76 RAIN/WIND 1
## 77 RAPIDLY RISING WATER 1
## 78 WHIRLWIND 1
## 79 WIND STORM 1Fig. 1 shows a bar plot with the population damage (fatalities + injuries) by type of event with at least 1 fatalty or injure.
if (!require("ggplot2")) install.packages("ggplot2")## Loading required package: ggplot2require("ggplot2")
MaxEveTypePos <- which(sort(PopDamByEvent$EventType) == PopDamByEvent$EventType[1])
ggplot(data = PopDamByEvent,
aes(x = EventType, y = PopulationDamage)) +
geom_col(fill = "steelblue") +
labs(title = "Fig. 1 Population damage by type of event",
x = "Event type", y = "Population damage (fatalities and injuries)") +
theme(axis.text.x = element_text(size = 4, angle = 90),
axis.text.y = element_text(size = 6)) +
geom_vline(aes(xintercept = MaxEveTypePos),
colour = "red", linetype = "dashed")
As it can be seen in Fig. 1, the type of event that is the most harmful with respect to population (across the United States) is TORNADO
Following a similar analysis but based only in the most harmful type of event previously obtained, and with respect to the U.S. state:
NOAA.storm.MostHarmful = subset(NOAA.storm,
NOAA.storm$EVTYPE == PopDamByEvent$EventType[1])
TotFatalities <- with(NOAA.storm.MostHarmful,
tapply(FATALITIES, STATE, sum))
TotInjuries <- with(NOAA.storm.MostHarmful,
tapply(INJURIES, STATE, sum))
PopDamByState <- data.frame(names(TotFatalities),
TotFatalities + TotInjuries,
row.names = NULL)
names(PopDamByState) <- c("State", "PopulationDamage")
PopDamByState <- subset(PopDamByState, PopulationDamage > 0)
PopDamByState <- PopDamByState[order(PopDamByState$PopulationDamage,
decreasing = TRUE), ]
row.names(PopDamByState) <- "1":nrow(PopDamByState)PopDamByState## State PopulationDamage
## 1 TX 8745
## 2 AL 8546
## 3 MS 6696
## 4 AR 5495
## 5 OK 5125
## 6 TN 5116
## 7 MO 4718
## 8 OH 4633
## 9 IN 4476
## 10 IL 4348
## 11 GA 4106
## 12 MI 3605
## 13 FL 3505
## 14 KS 2957
## 15 KY 2931
## 16 LA 2832
## 17 NC 2674
## 18 IA 2289
## 19 MN 2075
## 20 MA 1866
## 21 WI 1697
## 22 SC 1373
## 23 PA 1323
## 24 NE 1212
## 25 VA 950
## 26 CT 707
## 27 SD 470
## 28 ND 351
## 29 NY 337
## 30 MD 321
## 31 WA 309
## 32 CO 266
## 33 NM 160
## 34 AZ 150
## 35 WV 117
## 36 WY 105
## 37 UT 92
## 38 CA 88
## 39 DE 75
## 40 NJ 71
## 41 NH 31
## 42 MT 25
## 43 RI 23
## 44 ME 20
## 45 VT 10
## 46 ID 9
## 47 HI 6
## 48 OR 5
## 49 NV 2MaxStatePos <- which(sort(PopDamByState$State) == PopDamByState$State[1])
ggplot(data = PopDamByState,
aes(x = State, y = PopulationDamage)) +
geom_col(fill = "steelblue") +
labs(title = "Fig. 2 Population damage by U.S. State",
subtitle = paste("Most harmful type of event: ",
PopDamByEvent$EventType[1]),
x = "State", y = "Population damage (fatalities and injuries)") +
theme(axis.text.x = element_text(size = 4, angle = 90),
axis.text.y = element_text(size = 6)) +
geom_vline(aes(xintercept = MaxStatePos),
colour = "red", linetype = "dashed")
As it can be seen in Fig. 2, the U.S. State that has the most harmful with respect to population due to the type of event TORNADO is TX with a total of 8745 fatalities and/or injuries.
Analysis 2: economic consequences
Lets take a look which types of events have the greatest economic consequences (across the United States). The National Weather Service makes a best guess using all available data at the time of the publication. The damage amounts are received from a variety of sources, including those listed above in the Data Sources section. Property and Crop damage should be considered as a broad estimate. There are two columns in the database (“PROPDMG” and “CROPDMG”) that gives us an estimation of the property and crop damage because of a storm event. Here the code that separately calculate the total of “PROPDMG” and “CROPDMG” for each type of event:
TotProperties <- with(NOAA.storm,
tapply(PROPDMG, EVTYPE, sum))
TotCrops <- with(NOAA.storm,
tapply(CROPDMG, EVTYPE, sum))Here the code to put the information into a data-set with the total of “PROPDMG” and “CROPDMG” (together) for each type of event:
EcoDamByEvent <- data.frame(names(TotProperties),
TotProperties + TotCrops,
row.names = NULL)
names(EcoDamByEvent) <- c("EventType", "EconomicConsequence")Next step is removed from data those event types that do not have any ecenomic consequebce and order the data by this total:
EcoDamByEvent <- subset(EcoDamByEvent, EconomicConsequence > 0)
EcoDamByEvent <- EcoDamByEvent[order(EcoDamByEvent$EconomicConsequence,
decreasing = TRUE), ]
row.names(EcoDamByEvent) <- "1":nrow(EcoDamByEvent)Lets take a look of the data obtained:
EcoDamByEvent## EventType EconomicConsequence
## 1 TORNADO 3315775.08
## 2 THUNDERSTORM 2863115.59
## 3 FLASH FLOOD 1660858.11
## 4 HAIL 1285277.04
## 5 FLOOD 1121605.74
## 6 LIGHTING 607267.89
## 7 HIGH WIND 401201.58
## 8 SNOW STORM 151744.58
## 9 WINTER STORM 135699.58
## 10 WILD FIRES 90647.54
## 11 ICE 76571.62
## 12 STRONG WIND 65853.20
## 13 HEAVY RAIN 65407.99
## 14 TROPICAL STORM 56397.80
## 15 WILD/FOREST FIRES 43566.49
## 16 DROUGHT 37997.67
## 17 HURRICANE 34559.94
## 18 URBAN/SML STREAM FLD 28845.74
## 19 BLIZZARD 26195.48
## 20 COLD/WIND CHILL 24105.88
## 21 COASTAL FLOOD 19472.99
## 22 STORM SURGE 19398.49
## 23 LANDSLIDE 19103.94
## 24 WINTER WEATHER 16935.90
## 25 FROST/FREEZE 14414.90
## 26 WATERSPOUT 9564.70
## 27 FOG 8849.81
## 28 DENSE FOG 8225.45
## 29 STORM SURGE/TIDE 7627.05
## 30 DUST STORM 6651.00
## 31 WIND 5246.54
## 32 HEAT 4706.04
## 33 HIGH SURF 3621.62
## 34 HEAVY SURF 2818.50
## 35 TYPHOON 2254.40
## 36 DRY MICROBURST 1752.60
## 37 AVALANCHE 1623.90
## 38 OTHER 1094.90
## 39 FROST 1080.00
## 40 RAIN 1055.05
## 41 SEICHE 980.00
## 42 MUD SLIDE 976.10
## 43 ASTRONOMICAL HIGH TIDE 933.50
## 44 TSUNAMI 925.30
## 45 EXTREME WIND CHILL 822.00
## 46 MIXED PRECIPITATION 790.00
## 47 TROPICAL DEPRESSION 738.00
## 48 DUST DEVIL 718.63
## 49 HEAVY MIX 705.60
## 50 LANDSLUMP 570.00
## 51 FOREST FIRES 505.00
## 52 HIGH WATER 505.00
## 53 HEAVY PRECIPITATION 500.00
## 54 VOLCANIC ASH 500.00
## 55 MARINE STRONG WIND 418.33
## 56 GLAZE 400.60
## 57 GUSTY WIND 370.00
## 58 ICY ROADS 341.20
## 59 ASTRONOMICAL LOW TIDE 320.00
## 60 WIND STORM 300.00
## 61 FUNNEL CLOUD 194.60
## 62 RIP CURRENT 163.00
## 63 HIGH TIDES 150.00
## 64 ROCK SLIDE 150.00
## 65 EXCESSIVE WETNESS 142.00
## 66 WIND DAMAGE 142.00
## 67 GUSTNADO 103.60
## 68 BEACH EROSION 100.00
## 69 DENSE SMOKE 100.00
## 70 MICROBURST 80.00
## 71 BRUSH FIRE 55.00
## 72 MARINE ACCIDENT 50.00
## 73 RAIN STORM 50.00
## 74 SEVERE TURBULENCE 50.00
## 75 EARLY FROST 42.00
## 76 GRADIENT WIND 37.00
## 77 WET MICROBURST 35.00
## 78 BLOWING DUST 20.00
## 79 EROSION/CSTL FLOOD 16.20
## 80 HIGH SEAS 15.50
## 81 HEAVY SWELLS 15.00
## 82 WHIRLWIND 12.00
## 83 GRASS FIRES 10.00
## 84 ROUGH SURF 10.00
## 85 UNSEASONABLY WARM 10.00
## 86 UNSEASONAL RAIN 10.00
## 87 LANDSPOUT 7.00
## 88 GLAZE ICE 5.60
## 89 COOL AND WET 5.00
## 90 HIGH SWELLS 5.00
## 91 NON-SEVERE WIND DAMAGE 5.00
## 92 URBAN AND SMALL 5.00
## 93 URBAN/SMALL STREAM 5.00
## 94 DAM FAILURE 3.00
## 95 DOWNBURST 2.00
## 96 GUSTY WIND/RAIN 2.00
## 97 RURAL FLOOD 1.20
## 98 WIND AND WAVE 1.00
## 99 HEAVY SHOWER 0.50
## 100 RECORD RAINFALL 0.50
## 101 URBAN SMALL 0.05Fig. 3 shows a bar plot with the total of economic consequences by type of event with at least any consequence.
MaxEveTypePos <- which(sort(EcoDamByEvent$EventType) ==
EcoDamByEvent$EventType[1])
ggplot(data = EcoDamByEvent,
aes(x = EventType, y = EconomicConsequence)) +
geom_col(fill = "steelblue") +
labs(title = "Fig. 3 Economic consequence by type of event",
x = "Event type", y = "Economic consequence") +
theme(axis.text.x = element_text(size = 4, angle = 90),
axis.text.y = element_text(size = 6)) +
geom_vline(aes(xintercept = MaxEveTypePos),
colour = "red", linetype = "dashed")
As it can be seen in Fig. 3, the type of event that is the most harmful with respect to economic consequences (across the United States) is TORNADO
Results
From the analysis previously done there are the following results:
- TORNADO is the most harmful type of event with respect to population (across the United States).
- TX is the U.S. State that has the most harmful with respect to population due to the type of event TORNADO.
- TORNADO is the most harmful type of event with respect to economic consequences.
Population damage was estimated by totalizing the number of fatalities and injuries registered.
Economic consequences was estimated by totalizing the property and crop damages registered.
As happened with the type of event, in general, the data must be properly reviewed and the required adjustments must be made in order to have more reliable results after every analysis.