Severe Weather Events: Impacts on Population Health and the Economy
Bianca Sieveritz
2026-11-14
Synopsis
This data analysis uses the National Oceanic and Atmospheric Administration (NOAA) Storm Data to explore which severe weather events have the largest impacts on population health and the economy. The NOAA Storm Data only permits entry of specific storm data events, but inspection of unique storm data events in the data set revealed that many entries deviated from permitted storm data events. In addition, capitalization and formatting differed between entries and other entries were misspelled. Thus, event descriptions were cleaned to remove formatting variations, capitalize all letters, and autmatically correct misspellings. Next, the analysis quantified how often each single word was contained in event descriptions, and derived a list of any weather-related words with more than 100 occurences. Severe weather events that contained one of these weather-related words were then re-coded as this weather-related event, going through the list of weather-related word from most to least common occurence. Afterward, the analysis totals the reported fatalities and injuries by these standardized weather-related events to determine which severe weather events have the largest impact on population health. Property and crop damage amounts are converted to dollar amounts using damage-magnitude indicators from the data set, and are also totaled by standardized weather-related events to determine which severe weather events have the largest impact on the economy. Given that standardization of weather-related events utilizes a text-matching heuristic, the resulting categories must be interpreted as approximate groupings rather than official NOAA classifications.
Data Processing
Loading the data
The analysis uses multiple libraries to summarize, manipulate, correct, and visualize the data. The following lines of code load these libraries.
library(ggplot2) # Used to create data visualizations.
library(patchwork) # Allows multiple ggplot figures to be combined.
library(hunspell) # Used to identify misspelled words and suggest corrections.
library(stringr) # Provides functions for string manipulation.
library(dplyr) # Used for data manipulation and summarization.##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionData Processing
Loading the data
The following lines of code load the data directly from the compressed csv-file, ‘repdata_data_StormData.csv.bz2’, by utilizing the read_csv function.
# Load the NOAA Storm Data
storm_data <- read.csv('./repdata_data_StormData.csv.bz2')Preprocessing the data
Remove formatting variations and capitalize all letters
The following lines of code print the first 10 rows of the loaded data file, revealing that the column ‘EVTYPE’ contains strings classifying each severe weather event. In addition, the data contains the columns ‘FATALITIES’, ‘INJURIES’, ‘PROPDMG’, ‘CROPDMG’, ‘PROPDMGEXP’, and ‘CROPDMGEXP’ that respectively list the fatalities and injuries, the property and crop damage, and a magnitude multiplier for the property and crop damage amiunts for each severe weather event. This analysis will inspect these quantities later to determine which severe weather events have the largest impact on population health and the economy.
The following lines of code then print each unique severe weather event entry, revealing that severe weather event entries in this data set do not adhere to the NOAA storm data permitted severe weather events, that there are many variations in formatting and capitalization, and that some entries are misspelled.
# Print the first rows of data
head(storm_data, 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 COUNTYENDN
## 1 TORNADO 0 0 NA
## 2 TORNADO 0 0 NA
## 3 TORNADO 0 0 NA
## 4 TORNADO 0 0 NA
## 5 TORNADO 0 0 NA
## 6 TORNADO 0 0 NA
## 7 TORNADO 0 0 NA
## 8 TORNADO 0 0 NA
## 9 TORNADO 0 0 NA
## 10 TORNADO 0 0 NA
## END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES INJURIES PROPDMG
## 1 0 14.0 100 3 0 0 15 25.0
## 2 0 2.0 150 2 0 0 0 2.5
## 3 0 0.1 123 2 0 0 2 25.0
## 4 0 0.0 100 2 0 0 2 2.5
## 5 0 0.0 150 2 0 0 2 2.5
## 6 0 1.5 177 2 0 0 6 2.5
## 7 0 1.5 33 2 0 0 1 2.5
## 8 0 0.0 33 1 0 0 0 2.5
## 9 0 3.3 100 3 0 1 14 25.0
## 10 0 2.3 100 3 0 0 0 25.0
## PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES LATITUDE LONGITUDE
## 1 K 0 3040 8812
## 2 K 0 3042 8755
## 3 K 0 3340 8742
## 4 K 0 3458 8626
## 5 K 0 3412 8642
## 6 K 0 3450 8748
## 7 K 0 3405 8631
## 8 K 0 3255 8558
## 9 K 0 3334 8740
## 10 K 0 3336 8738
## LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3051 8806 1
## 2 0 0 2
## 3 0 0 3
## 4 0 0 4
## 5 0 0 5
## 6 0 0 6
## 7 0 0 7
## 8 0 0 8
## 9 3336 8738 9
## 10 3337 8737 10# Print unique severe weather events
unique(storm_data$EVTYPE)## [1] "TORNADO" "TSTM WIND"
## [3] "HAIL" "FREEZING RAIN"
## [5] "SNOW" "ICE STORM/FLASH FLOOD"
## [7] "SNOW/ICE" "WINTER STORM"
## [9] "HURRICANE OPAL/HIGH WINDS" "THUNDERSTORM WINDS"
## [11] "RECORD COLD" "HURRICANE ERIN"
## [13] "HURRICANE OPAL" "HEAVY RAIN"
## [15] "LIGHTNING" "THUNDERSTORM WIND"
## [17] "DENSE FOG" "RIP CURRENT"
## [19] "THUNDERSTORM WINS" "FLASH FLOOD"
## [21] "FLASH FLOODING" "HIGH WINDS"
## [23] "FUNNEL CLOUD" "TORNADO F0"
## [25] "THUNDERSTORM WINDS LIGHTNING" "THUNDERSTORM WINDS/HAIL"
## [27] "HEAT" "WIND"
## [29] "LIGHTING" "HEAVY RAINS"
## [31] "LIGHTNING AND HEAVY RAIN" "FUNNEL"
## [33] "WALL CLOUD" "FLOODING"
## [35] "THUNDERSTORM WINDS HAIL" "FLOOD"
## [37] "COLD" "HEAVY RAIN/LIGHTNING"
## [39] "FLASH FLOODING/THUNDERSTORM WI" "WALL CLOUD/FUNNEL CLOUD"
## [41] "THUNDERSTORM" "WATERSPOUT"
## [43] "EXTREME COLD" "HAIL 1.75)"
## [45] "LIGHTNING/HEAVY RAIN" "HIGH WIND"
## [47] "BLIZZARD" "BLIZZARD WEATHER"
## [49] "WIND CHILL" "BREAKUP FLOODING"
## [51] "HIGH WIND/BLIZZARD" "RIVER FLOOD"
## [53] "HEAVY SNOW" "FREEZE"
## [55] "COASTAL FLOOD" "HIGH WIND AND HIGH TIDES"
## [57] "HIGH WIND/BLIZZARD/FREEZING RA" "HIGH TIDES"
## [59] "HIGH WIND AND HEAVY SNOW" "RECORD COLD AND HIGH WIND"
## [61] "RECORD HIGH TEMPERATURE" "RECORD HIGH"
## [63] "HIGH WINDS HEAVY RAINS" "HIGH WIND/ BLIZZARD"
## [65] "ICE STORM" "BLIZZARD/HIGH WIND"
## [67] "HIGH WIND/LOW WIND CHILL" "HEAVY SNOW/HIGH"
## [69] "RECORD LOW" "HIGH WINDS AND WIND CHILL"
## [71] "HEAVY SNOW/HIGH WINDS/FREEZING" "LOW TEMPERATURE RECORD"
## [73] "AVALANCHE" "MARINE MISHAP"
## [75] "WIND CHILL/HIGH WIND" "HIGH WIND/WIND CHILL/BLIZZARD"
## [77] "HIGH WIND/WIND CHILL" "HIGH WIND/HEAVY SNOW"
## [79] "HIGH TEMPERATURE RECORD" "FLOOD WATCH/"
## [81] "RECORD HIGH TEMPERATURES" "HIGH WIND/SEAS"
## [83] "HIGH WINDS/HEAVY RAIN" "HIGH SEAS"
## [85] "SEVERE TURBULENCE" "RECORD RAINFALL"
## [87] "RECORD SNOWFALL" "RECORD WARMTH"
## [89] "HEAVY SNOW/WIND" "EXTREME HEAT"
## [91] "WIND DAMAGE" "DUST STORM"
## [93] "APACHE COUNTY" "SLEET"
## [95] "HAIL STORM" "FUNNEL CLOUDS"
## [97] "FLASH FLOODS" "DUST DEVIL"
## [99] "EXCESSIVE HEAT" "THUNDERSTORM WINDS/FUNNEL CLOU"
## [101] "WINTER STORM/HIGH WIND" "WINTER STORM/HIGH WINDS"
## [103] "GUSTY WINDS" "STRONG WINDS"
## [105] "FLOODING/HEAVY RAIN" "SNOW AND WIND"
## [107] "HEAVY SURF COASTAL FLOODING" "HEAVY SURF"
## [109] "HEAVY PRECIPATATION" "URBAN FLOODING"
## [111] "HIGH SURF" "BLOWING DUST"
## [113] "URBAN/SMALL" "WILD FIRES"
## [115] "HIGH" "URBAN/SMALL FLOODING"
## [117] "WATER SPOUT" "HIGH WINDS DUST STORM"
## [119] "WINTER STORM HIGH WINDS" "LOCAL FLOOD"
## [121] "WINTER STORMS" "MUDSLIDES"
## [123] "RAINSTORM" "SEVERE THUNDERSTORM"
## [125] "SEVERE THUNDERSTORMS" "SEVERE THUNDERSTORM WINDS"
## [127] "THUNDERSTORMS WINDS" "DRY MICROBURST"
## [129] "FLOOD/FLASH FLOOD" "FLOOD/RAIN/WINDS"
## [131] "WINDS" "DRY MICROBURST 61"
## [133] "THUNDERSTORMS" "FLASH FLOOD WINDS"
## [135] "URBAN/SMALL STREAM FLOODING" "MICROBURST"
## [137] "STRONG WIND" "HIGH WIND DAMAGE"
## [139] "STREAM FLOODING" "URBAN AND SMALL"
## [141] "HEAVY SNOWPACK" "ICE"
## [143] "FLASH FLOOD/" "DOWNBURST"
## [145] "GUSTNADO AND" "FLOOD/RAIN/WIND"
## [147] "WET MICROBURST" "DOWNBURST WINDS"
## [149] "DRY MICROBURST WINDS" "DRY MIRCOBURST WINDS"
## [151] "DRY MICROBURST 53" "SMALL STREAM URBAN FLOOD"
## [153] "MICROBURST WINDS" "HIGH WINDS 57"
## [155] "DRY MICROBURST 50" "HIGH WINDS 66"
## [157] "HIGH WINDS 76" "HIGH WINDS 63"
## [159] "HIGH WINDS 67" "BLIZZARD/HEAVY SNOW"
## [161] "HEAVY SNOW/HIGH WINDS" "BLOWING SNOW"
## [163] "HIGH WINDS 82" "HIGH WINDS 80"
## [165] "HIGH WINDS 58" "FREEZING DRIZZLE"
## [167] "LIGHTNING THUNDERSTORM WINDSS" "DRY MICROBURST 58"
## [169] "HAIL 75" "HIGH WINDS 73"
## [171] "HIGH WINDS 55" "LIGHT SNOW AND SLEET"
## [173] "URBAN FLOOD" "DRY MICROBURST 84"
## [175] "THUNDERSTORM WINDS 60" "HEAVY RAIN/FLOODING"
## [177] "THUNDERSTORM WINDSS" "TORNADOS"
## [179] "GLAZE" "RECORD HEAT"
## [181] "COASTAL FLOODING" "HEAT WAVE"
## [183] "FIRST SNOW" "FREEZING RAIN AND SLEET"
## [185] "UNSEASONABLY DRY" "UNSEASONABLY WET"
## [187] "WINTRY MIX" "WINTER WEATHER"
## [189] "UNSEASONABLY COLD" "EXTREME/RECORD COLD"
## [191] "RIP CURRENTS HEAVY SURF" "SLEET/RAIN/SNOW"
## [193] "UNSEASONABLY WARM" "DROUGHT"
## [195] "NORMAL PRECIPITATION" "HIGH WINDS/FLOODING"
## [197] "DRY" "RAIN/SNOW"
## [199] "SNOW/RAIN/SLEET" "WATERSPOUT/TORNADO"
## [201] "WATERSPOUTS" "WATERSPOUT TORNADO"
## [203] "URBAN/SMALL STREAM FLOOD" "STORM SURGE"
## [205] "WATERSPOUT-TORNADO" "WATERSPOUT-"
## [207] "TORNADOES, TSTM WIND, HAIL" "TROPICAL STORM ALBERTO"
## [209] "TROPICAL STORM" "TROPICAL STORM GORDON"
## [211] "TROPICAL STORM JERRY" "LIGHTNING THUNDERSTORM WINDS"
## [213] "WAYTERSPOUT" "MINOR FLOODING"
## [215] "LIGHTNING INJURY" "URBAN/SMALL STREAM FLOOD"
## [217] "LIGHTNING AND THUNDERSTORM WIN" "THUNDERSTORM WINDS53"
## [219] "URBAN AND SMALL STREAM FLOOD" "URBAN AND SMALL STREAM"
## [221] "WILDFIRE" "DAMAGING FREEZE"
## [223] "THUNDERSTORM WINDS 13" "SMALL HAIL"
## [225] "HEAVY SNOW/HIGH WIND" "HURRICANE"
## [227] "WILD/FOREST FIRE" "SMALL STREAM FLOODING"
## [229] "MUD SLIDE" "LIGNTNING"
## [231] "FROST" "FREEZING RAIN/SNOW"
## [233] "HIGH WINDS/" "THUNDERSNOW"
## [235] "FLOODS" "EXTREME WIND CHILLS"
## [237] "COOL AND WET" "HEAVY RAIN/SNOW"
## [239] "SMALL STREAM AND URBAN FLOODIN" "SMALL STREAM/URBAN FLOOD"
## [241] "SNOW/SLEET/FREEZING RAIN" "SEVERE COLD"
## [243] "GLAZE ICE" "COLD WAVE"
## [245] "EARLY SNOW" "SMALL STREAM AND URBAN FLOOD"
## [247] "HIGH WINDS" "RURAL FLOOD"
## [249] "SMALL STREAM AND" "MUD SLIDES"
## [251] "HAIL 80" "EXTREME WIND CHILL"
## [253] "COLD AND WET CONDITIONS" "EXCESSIVE WETNESS"
## [255] "GRADIENT WINDS" "HEAVY SNOW/BLOWING SNOW"
## [257] "SLEET/ICE STORM" "THUNDERSTORM WINDS URBAN FLOOD"
## [259] "THUNDERSTORM WINDS SMALL STREA" "ROTATING WALL CLOUD"
## [261] "LARGE WALL CLOUD" "COLD AIR FUNNEL"
## [263] "GUSTNADO" "COLD AIR FUNNELS"
## [265] "BLOWING SNOW- EXTREME WIND CHI" "SNOW AND HEAVY SNOW"
## [267] "GROUND BLIZZARD" "MAJOR FLOOD"
## [269] "SNOW/HEAVY SNOW" "FREEZING RAIN/SLEET"
## [271] "ICE JAM FLOODING" "SNOW- HIGH WIND- WIND CHILL"
## [273] "STREET FLOOD" "COLD AIR TORNADO"
## [275] "SMALL STREAM FLOOD" "FOG"
## [277] "THUNDERSTORM WINDS 2" "FUNNEL CLOUD/HAIL"
## [279] "ICE/SNOW" "TSTM WIND 51"
## [281] "TSTM WIND 50" "TSTM WIND 52"
## [283] "TSTM WIND 55" "HEAVY SNOW/BLIZZARD"
## [285] "THUNDERSTORM WINDS 61" "HAIL 0.75"
## [287] "THUNDERSTORM DAMAGE" "THUNDERTORM WINDS"
## [289] "HAIL 1.00" "HAIL/WINDS"
## [291] "SNOW AND ICE" "WIND STORM"
## [293] "SNOWSTORM" "GRASS FIRES"
## [295] "LAKE FLOOD" "PROLONG COLD"
## [297] "HAIL/WIND" "HAIL 1.75"
## [299] "THUNDERSTORMW 50" "WIND/HAIL"
## [301] "SNOW AND ICE STORM" "URBAN AND SMALL STREAM FLOODIN"
## [303] "THUNDERSTORMS WIND" "THUNDERSTORM WINDS"
## [305] "HEAVY SNOW/SLEET" "AGRICULTURAL FREEZE"
## [307] "DROUGHT/EXCESSIVE HEAT" "TUNDERSTORM WIND"
## [309] "TROPICAL STORM DEAN" "THUNDERTSORM WIND"
## [311] "THUNDERSTORM WINDS/ HAIL" "THUNDERSTORM WIND/LIGHTNING"
## [313] "HEAVY RAIN/SEVERE WEATHER" "THUNDESTORM WINDS"
## [315] "WATERSPOUT/ TORNADO" "LIGHTNING."
## [317] "WARM DRY CONDITIONS" "HURRICANE-GENERATED SWELLS"
## [319] "HEAVY SNOW/ICE STORM" "RIVER AND STREAM FLOOD"
## [321] "HIGH WIND 63" "COASTAL SURGE"
## [323] "HEAVY SNOW AND ICE STORM" "MINOR FLOOD"
## [325] "HIGH WINDS/COASTAL FLOOD" "RAIN"
## [327] "RIVER FLOODING" "SNOW/RAIN"
## [329] "ICE FLOES" "HIGH WAVES"
## [331] "SNOW SQUALLS" "SNOW SQUALL"
## [333] "THUNDERSTORM WIND G50" "LIGHTNING FIRE"
## [335] "BLIZZARD/FREEZING RAIN" "HEAVY LAKE SNOW"
## [337] "HEAVY SNOW/FREEZING RAIN" "LAKE EFFECT SNOW"
## [339] "HEAVY WET SNOW" "DUST DEVIL WATERSPOUT"
## [341] "THUNDERSTORM WINDS/HEAVY RAIN" "THUNDERSTROM WINDS"
## [343] "THUNDERSTORM WINDS LE CEN" "HAIL 225"
## [345] "BLIZZARD AND HEAVY SNOW" "HEAVY SNOW AND ICE"
## [347] "ICE STORM AND SNOW" "HEAVY SNOW ANDBLOWING SNOW"
## [349] "HEAVY SNOW/ICE" "BLIZZARD AND EXTREME WIND CHIL"
## [351] "LOW WIND CHILL" "BLOWING SNOW & EXTREME WIND CH"
## [353] "WATERSPOUT/" "URBAN/SMALL STREAM"
## [355] "TORNADO F3" "FUNNEL CLOUD."
## [357] "TORNDAO" "HAIL 0.88"
## [359] "FLOOD/RIVER FLOOD" "MUD SLIDES URBAN FLOODING"
## [361] "TORNADO F1" "THUNDERSTORM WINDS G"
## [363] "DEEP HAIL" "GLAZE/ICE STORM"
## [365] "HEAVY SNOW/WINTER STORM" "AVALANCE"
## [367] "BLIZZARD/WINTER STORM" "DUST STORM/HIGH WINDS"
## [369] "ICE JAM" "FOREST FIRES"
## [371] "THUNDERSTORM WIND G60" "FROST\\FREEZE"
## [373] "THUNDERSTORM WINDS." "HAIL 88"
## [375] "HAIL 175" "HVY RAIN"
## [377] "HAIL 100" "HAIL 150"
## [379] "HAIL 075" "THUNDERSTORM WIND G55"
## [381] "HAIL 125" "THUNDERSTORM WINDS G60"
## [383] "HARD FREEZE" "HAIL 200"
## [385] "THUNDERSTORM WINDS FUNNEL CLOU" "THUNDERSTORM WINDS 62"
## [387] "WILDFIRES" "RECORD HEAT WAVE"
## [389] "HEAVY SNOW AND HIGH WINDS" "HEAVY SNOW/HIGH WINDS & FLOOD"
## [391] "HAIL FLOODING" "THUNDERSTORM WINDS/FLASH FLOOD"
## [393] "HIGH WIND 70" "WET SNOW"
## [395] "HEAVY RAIN AND FLOOD" "LOCAL FLASH FLOOD"
## [397] "THUNDERSTORM WINDS 53" "FLOOD/FLASH FLOODING"
## [399] "TORNADO/WATERSPOUT" "RAIN AND WIND"
## [401] "THUNDERSTORM WIND 59" "THUNDERSTORM WIND 52"
## [403] "COASTAL/TIDAL FLOOD" "SNOW/ICE STORM"
## [405] "BELOW NORMAL PRECIPITATION" "RIP CURRENTS/HEAVY SURF"
## [407] "FLASH FLOOD/FLOOD" "EXCESSIVE RAIN"
## [409] "RECORD/EXCESSIVE HEAT" "HEAT WAVES"
## [411] "LIGHT SNOW" "THUNDERSTORM WIND 69"
## [413] "HAIL DAMAGE" "LIGHTNING DAMAGE"
## [415] "RECORD TEMPERATURES" "LIGHTNING AND WINDS"
## [417] "FOG AND COLD TEMPERATURES" "OTHER"
## [419] "RECORD SNOW" "SNOW/COLD"
## [421] "FLASH FLOOD FROM ICE JAMS" "TSTM WIND G58"
## [423] "MUDSLIDE" "HEAVY SNOW SQUALLS"
## [425] "HEAVY SNOW/SQUALLS" "HEAVY SNOW-SQUALLS"
## [427] "ICY ROADS" "HEAVY MIX"
## [429] "SNOW FREEZING RAIN" "LACK OF SNOW"
## [431] "SNOW/SLEET" "SNOW/FREEZING RAIN"
## [433] "SNOW DROUGHT" "THUNDERSTORMW WINDS"
## [435] "THUNDERSTORM WIND 60 MPH" "THUNDERSTORM WIND 65MPH"
## [437] "THUNDERSTORM WIND/ TREES" "THUNDERSTORM WIND/AWNING"
## [439] "THUNDERSTORM WIND 98 MPH" "THUNDERSTORM WIND TREES"
## [441] "TORRENTIAL RAIN" "TORNADO F2"
## [443] "RIP CURRENTS" "HURRICANE EMILY"
## [445] "HURRICANE GORDON" "HURRICANE FELIX"
## [447] "THUNDERSTORM WIND 59 MPH" "THUNDERSTORM WINDS 63 MPH"
## [449] "THUNDERSTORM WIND/ TREE" "THUNDERSTORM DAMAGE TO"
## [451] "THUNDERSTORM WIND 65 MPH" "FLASH FLOOD - HEAVY RAIN"
## [453] "THUNDERSTORM WIND." "FLASH FLOOD/ STREET"
## [455] "THUNDERSTORM WIND 59 MPH." "HEAVY SNOW FREEZING RAIN"
## [457] "DAM FAILURE" "THUNDERSTORM HAIL"
## [459] "HAIL 088" "THUNDERSTORM WINDSHAIL"
## [461] "LIGHTNING WAUSEON" "THUDERSTORM WINDS"
## [463] "ICE AND SNOW" "RECORD COLD/FROST"
## [465] "STORM FORCE WINDS" "FREEZING RAIN AND SNOW"
## [467] "FREEZING RAIN SLEET AND" "SOUTHEAST"
## [469] "HEAVY SNOW & ICE" "FREEZING DRIZZLE AND FREEZING"
## [471] "THUNDERSTORM WINDS AND" "HAIL/ICY ROADS"
## [473] "FLASH FLOOD/HEAVY RAIN" "HEAVY RAIN; URBAN FLOOD WINDS;"
## [475] "HEAVY PRECIPITATION" "TSTM WIND DAMAGE"
## [477] "HIGH WATER" "FLOOD FLASH"
## [479] "RAIN/WIND" "THUNDERSTORM WINDS 50"
## [481] "THUNDERSTORM WIND G52" "FLOOD FLOOD/FLASH"
## [483] "THUNDERSTORM WINDS 52" "SNOW SHOWERS"
## [485] "THUNDERSTORM WIND G51" "HEAT WAVE DROUGHT"
## [487] "HEAVY SNOW/BLIZZARD/AVALANCHE" "RECORD SNOW/COLD"
## [489] "WET WEATHER" "UNSEASONABLY WARM AND DRY"
## [491] "FREEZING RAIN SLEET AND LIGHT" "RECORD/EXCESSIVE RAINFALL"
## [493] "TIDAL FLOOD" "BEACH EROSIN"
## [495] "THUNDERSTORM WIND G61" "FLOOD/FLASH"
## [497] "LOW TEMPERATURE" "SLEET & FREEZING RAIN"
## [499] "HEAVY RAINS/FLOODING" "THUNDERESTORM WINDS"
## [501] "THUNDERSTORM WINDS/FLOODING" "THUNDEERSTORM WINDS"
## [503] "HIGHWAY FLOODING" "THUNDERSTORM W INDS"
## [505] "HYPOTHERMIA" "FLASH FLOOD/ FLOOD"
## [507] "THUNDERSTORM WIND 50" "THUNERSTORM WINDS"
## [509] "HEAVY RAIN/MUDSLIDES/FLOOD" "MUD/ROCK SLIDE"
## [511] "HIGH WINDS/COLD" "BEACH EROSION/COASTAL FLOOD"
## [513] "COLD/WINDS" "SNOW/ BITTER COLD"
## [515] "THUNDERSTORM WIND 56" "SNOW SLEET"
## [517] "DRY HOT WEATHER" "COLD WEATHER"
## [519] "RAPIDLY RISING WATER" "HAIL ALOFT"
## [521] "EARLY FREEZE" "ICE/STRONG WINDS"
## [523] "EXTREME WIND CHILL/BLOWING SNO" "SNOW/HIGH WINDS"
## [525] "HIGH WINDS/SNOW" "EARLY FROST"
## [527] "SNOWMELT FLOODING" "HEAVY SNOW AND STRONG WINDS"
## [529] "SNOW ACCUMULATION" "BLOWING SNOW/EXTREME WIND CHIL"
## [531] "SNOW/ ICE" "SNOW/BLOWING SNOW"
## [533] "TORNADOES" "THUNDERSTORM WIND/HAIL"
## [535] "FLASH FLOODING/FLOOD" "HAIL 275"
## [537] "HAIL 450" "FLASH FLOOODING"
## [539] "EXCESSIVE RAINFALL" "THUNDERSTORMW"
## [541] "HAILSTORM" "TSTM WINDS"
## [543] "BEACH FLOOD" "HAILSTORMS"
## [545] "TSTMW" "FUNNELS"
## [547] "TSTM WIND 65)" "THUNDERSTORM WINDS/ FLOOD"
## [549] "HEAVY RAINFALL" "HEAT/DROUGHT"
## [551] "HEAT DROUGHT" "NEAR RECORD SNOW"
## [553] "LANDSLIDE" "HIGH WIND AND SEAS"
## [555] "THUNDERSTORMWINDS" "THUNDERSTORM WINDS HEAVY RAIN"
## [557] "SLEET/SNOW" "EXCESSIVE"
## [559] "SNOW/SLEET/RAIN" "WILD/FOREST FIRES"
## [561] "HEAVY SEAS" "DUSTSTORM"
## [563] "FLOOD & HEAVY RAIN" "?"
## [565] "THUNDERSTROM WIND" "FLOOD/FLASHFLOOD"
## [567] "SNOW AND COLD" "HOT PATTERN"
## [569] "PROLONG COLD/SNOW" "BRUSH FIRES"
## [571] "SNOW\\COLD" "WINTER MIX"
## [573] "EXCESSIVE PRECIPITATION" "SNOWFALL RECORD"
## [575] "HOT/DRY PATTERN" "DRY PATTERN"
## [577] "MILD/DRY PATTERN" "MILD PATTERN"
## [579] "LANDSLIDES" "HEAVY SHOWERS"
## [581] "HEAVY SNOW AND" "HIGH WIND 48"
## [583] "LAKE-EFFECT SNOW" "BRUSH FIRE"
## [585] "WATERSPOUT FUNNEL CLOUD" "URBAN SMALL STREAM FLOOD"
## [587] "SAHARAN DUST" "HEAVY SHOWER"
## [589] "URBAN FLOOD LANDSLIDE" "HEAVY SWELLS"
## [591] "URBAN SMALL" "URBAN FLOODS"
## [593] "SMALL STREAM" "HEAVY RAIN/URBAN FLOOD"
## [595] "FLASH FLOOD/LANDSLIDE" "LANDSLIDE/URBAN FLOOD"
## [597] "HEAVY RAIN/SMALL STREAM URBAN" "FLASH FLOOD LANDSLIDES"
## [599] "EXTREME WINDCHILL" "URBAN/SML STREAM FLD"
## [601] "TSTM WIND/HAIL" "Other"
## [603] "Record dry month" "Temperature record"
## [605] "Minor Flooding" "Ice jam flood (minor"
## [607] "High Wind" "Tstm Wind"
## [609] "ROUGH SURF" "Wind"
## [611] "Heavy Surf" "Dust Devil"
## [613] "Wind Damage" "Marine Accident"
## [615] "Snow" "Freeze"
## [617] "Snow Squalls" "Coastal Flooding"
## [619] "Heavy Rain" "Strong Wind"
## [621] "COASTAL STORM" "COASTALFLOOD"
## [623] "Erosion/Cstl Flood" "Heavy Rain and Wind"
## [625] "Light Snow/Flurries" "Wet Month"
## [627] "Wet Year" "Tidal Flooding"
## [629] "River Flooding" "Damaging Freeze"
## [631] "Beach Erosion" "Hot and Dry"
## [633] "Flood/Flash Flood" "Icy Roads"
## [635] "High Surf" "Heavy Rain/High Surf"
## [637] "Thunderstorm Wind" "Rain Damage"
## [639] "Unseasonable Cold" "Early Frost"
## [641] "Wintry Mix" "blowing snow"
## [643] "STREET FLOODING" "Record Cold"
## [645] "Extreme Cold" "Ice Fog"
## [647] "Excessive Cold" "Torrential Rainfall"
## [649] "Freezing Rain" "Landslump"
## [651] "Late-season Snowfall" "Hurricane Edouard"
## [653] "Coastal Storm" "Flood"
## [655] "HEAVY RAIN/WIND" "TIDAL FLOODING"
## [657] "Winter Weather" "Snow squalls"
## [659] "Strong Winds" "Strong winds"
## [661] "RECORD WARM TEMPS." "Ice/Snow"
## [663] "Mudslide" "Glaze"
## [665] "Extended Cold" "Snow Accumulation"
## [667] "Freezing Fog" "Drifting Snow"
## [669] "Whirlwind" "Heavy snow shower"
## [671] "Heavy rain" "LATE SNOW"
## [673] "Record May Snow" "Record Winter Snow"
## [675] "Heavy Precipitation" " COASTAL FLOOD"
## [677] "Record temperature" "Light snow"
## [679] "Late Season Snowfall" "Gusty Wind"
## [681] "small hail" "Light Snow"
## [683] "MIXED PRECIP" "Black Ice"
## [685] "Mudslides" "Gradient wind"
## [687] "Snow and Ice" "Freezing Spray"
## [689] "Summary Jan 17" "Summary of March 14"
## [691] "Summary of March 23" "Summary of March 24"
## [693] "Summary of April 3rd" "Summary of April 12"
## [695] "Summary of April 13" "Summary of April 21"
## [697] "Summary August 11" "Summary of April 27"
## [699] "Summary of May 9-10" "Summary of May 10"
## [701] "Summary of May 13" "Summary of May 14"
## [703] "Summary of May 22 am" "Summary of May 22 pm"
## [705] "Heatburst" "Summary of May 26 am"
## [707] "Summary of May 26 pm" "Metro Storm, May 26"
## [709] "Summary of May 31 am" "Summary of May 31 pm"
## [711] "Summary of June 3" "Summary of June 4"
## [713] "Summary June 5-6" "Summary June 6"
## [715] "Summary of June 11" "Summary of June 12"
## [717] "Summary of June 13" "Summary of June 15"
## [719] "Summary of June 16" "Summary June 18-19"
## [721] "Summary of June 23" "Summary of June 24"
## [723] "Summary of June 30" "Summary of July 2"
## [725] "Summary of July 3" "Summary of July 11"
## [727] "Summary of July 22" "Summary July 23-24"
## [729] "Summary of July 26" "Summary of July 29"
## [731] "Summary of August 1" "Summary August 2-3"
## [733] "Summary August 7" "Summary August 9"
## [735] "Summary August 10" "Summary August 17"
## [737] "Summary August 21" "Summary August 28"
## [739] "Summary September 4" "Summary September 20"
## [741] "Summary September 23" "Summary Sept. 25-26"
## [743] "Summary: Oct. 20-21" "Summary: October 31"
## [745] "Summary: Nov. 6-7" "Summary: Nov. 16"
## [747] "Microburst" "wet micoburst"
## [749] "Hail(0.75)" "Funnel Cloud"
## [751] "Urban Flooding" "No Severe Weather"
## [753] "Urban flood" "Urban Flood"
## [755] "Cold" "Summary of May 22"
## [757] "Summary of June 6" "Summary August 4"
## [759] "Summary of June 10" "Summary of June 18"
## [761] "Summary September 3" "Summary: Sept. 18"
## [763] "Coastal Flood" "coastal flooding"
## [765] "Small Hail" "Record Temperatures"
## [767] "Light Snowfall" "Freezing Drizzle"
## [769] "Gusty wind/rain" "GUSTY WIND/HVY RAIN"
## [771] "Blowing Snow" "Early snowfall"
## [773] "Monthly Snowfall" "Record Heat"
## [775] "Seasonal Snowfall" "Monthly Rainfall"
## [777] "Cold Temperature" "Sml Stream Fld"
## [779] "Heat Wave" "MUDSLIDE/LANDSLIDE"
## [781] "Saharan Dust" "Volcanic Ash"
## [783] "Volcanic Ash Plume" "Thundersnow shower"
## [785] "NONE" "COLD AND SNOW"
## [787] "DAM BREAK" "TSTM WIND (G45)"
## [789] "SLEET/FREEZING RAIN" "BLACK ICE"
## [791] "BLOW-OUT TIDES" "UNSEASONABLY COOL"
## [793] "TSTM HEAVY RAIN" "Gusty Winds"
## [795] "GUSTY WIND" "TSTM WIND 40"
## [797] "TSTM WIND 45" "TSTM WIND (41)"
## [799] "TSTM WIND (G40)" "TSTM WND"
## [801] "Wintry mix" " TSTM WIND"
## [803] "Frost" "Frost/Freeze"
## [805] "RAIN (HEAVY)" "Record Warmth"
## [807] "Prolong Cold" "Cold and Frost"
## [809] "URBAN/SML STREAM FLDG" "STRONG WIND GUST"
## [811] "LATE FREEZE" "BLOW-OUT TIDE"
## [813] "Hypothermia/Exposure" "HYPOTHERMIA/EXPOSURE"
## [815] "Lake Effect Snow" "Mixed Precipitation"
## [817] "Record High" "COASTALSTORM"
## [819] "Snow and sleet" "Freezing rain"
## [821] "Gusty winds" "Blizzard Summary"
## [823] "SUMMARY OF MARCH 24-25" "SUMMARY OF MARCH 27"
## [825] "SUMMARY OF MARCH 29" "GRADIENT WIND"
## [827] "Icestorm/Blizzard" "Flood/Strong Wind"
## [829] "TSTM WIND AND LIGHTNING" "gradient wind"
## [831] "Freezing drizzle" "Mountain Snows"
## [833] "URBAN/SMALL STRM FLDG" "Heavy surf and wind"
## [835] "Mild and Dry Pattern" "COLD AND FROST"
## [837] "TYPHOON" "HIGH SWELLS"
## [839] "HIGH SWELLS" "VOLCANIC ASH"
## [841] "DRY SPELL" " LIGHTNING"
## [843] "BEACH EROSION" "UNSEASONAL RAIN"
## [845] "EARLY RAIN" "PROLONGED RAIN"
## [847] "WINTERY MIX" "COASTAL FLOODING/EROSION"
## [849] "HOT SPELL" "UNSEASONABLY HOT"
## [851] " TSTM WIND (G45)" "TSTM WIND (G45)"
## [853] "HIGH WIND (G40)" "TSTM WIND (G35)"
## [855] "DRY WEATHER" "ABNORMAL WARMTH"
## [857] "UNUSUAL WARMTH" "WAKE LOW WIND"
## [859] "MONTHLY RAINFALL" "COLD TEMPERATURES"
## [861] "COLD WIND CHILL TEMPERATURES" "MODERATE SNOW"
## [863] "MODERATE SNOWFALL" "URBAN/STREET FLOODING"
## [865] "COASTAL EROSION" "UNUSUAL/RECORD WARMTH"
## [867] "BITTER WIND CHILL" "BITTER WIND CHILL TEMPERATURES"
## [869] "SEICHE" "TSTM"
## [871] "COASTAL FLOODING/EROSION" "UNSEASONABLY WARM YEAR"
## [873] "HYPERTHERMIA/EXPOSURE" "ROCK SLIDE"
## [875] "ICE PELLETS" "PATCHY DENSE FOG"
## [877] "RECORD COOL" "RECORD WARM"
## [879] "HOT WEATHER" "RECORD TEMPERATURE"
## [881] "TROPICAL DEPRESSION" "VOLCANIC ERUPTION"
## [883] "COOL SPELL" "WIND ADVISORY"
## [885] "GUSTY WIND/HAIL" "RED FLAG FIRE WX"
## [887] "FIRST FROST" "EXCESSIVELY DRY"
## [889] "SNOW AND SLEET" "LIGHT SNOW/FREEZING PRECIP"
## [891] "VOG" "MONTHLY PRECIPITATION"
## [893] "MONTHLY TEMPERATURE" "RECORD DRYNESS"
## [895] "EXTREME WINDCHILL TEMPERATURES" "MIXED PRECIPITATION"
## [897] "DRY CONDITIONS" "REMNANTS OF FLOYD"
## [899] "EARLY SNOWFALL" "FREEZING FOG"
## [901] "LANDSPOUT" "DRIEST MONTH"
## [903] "RECORD COLD" "LATE SEASON HAIL"
## [905] "EXCESSIVE SNOW" "DRYNESS"
## [907] "FLOOD/FLASH/FLOOD" "WIND AND WAVE"
## [909] "LIGHT FREEZING RAIN" " WIND"
## [911] "MONTHLY SNOWFALL" "RECORD PRECIPITATION"
## [913] "ICE ROADS" "ROUGH SEAS"
## [915] "UNSEASONABLY WARM/WET" "UNSEASONABLY COOL & WET"
## [917] "UNUSUALLY WARM" "TSTM WIND G45"
## [919] "NON SEVERE HAIL" "NON-SEVERE WIND DAMAGE"
## [921] "UNUSUALLY COLD" "WARM WEATHER"
## [923] "LANDSLUMP" "THUNDERSTORM WIND (G40)"
## [925] "UNSEASONABLY WARM & WET" " FLASH FLOOD"
## [927] "LOCALLY HEAVY RAIN" "WIND GUSTS"
## [929] "UNSEASONAL LOW TEMP" "HIGH SURF ADVISORY"
## [931] "LATE SEASON SNOW" "GUSTY LAKE WIND"
## [933] "ABNORMALLY DRY" "WINTER WEATHER MIX"
## [935] "RED FLAG CRITERIA" "WND"
## [937] "CSTL FLOODING/EROSION" "SMOKE"
## [939] " WATERSPOUT" "SNOW ADVISORY"
## [941] "EXTREMELY WET" "UNUSUALLY LATE SNOW"
## [943] "VERY DRY" "RECORD LOW RAINFALL"
## [945] "ROGUE WAVE" "PROLONG WARMTH"
## [947] "ACCUMULATED SNOWFALL" "FALLING SNOW/ICE"
## [949] "DUST DEVEL" "NON-TSTM WIND"
## [951] "NON TSTM WIND" "GUSTY THUNDERSTORM WINDS"
## [953] "PATCHY ICE" "HEAVY RAIN EFFECTS"
## [955] "EXCESSIVE HEAT/DROUGHT" "NORTHERN LIGHTS"
## [957] "MARINE TSTM WIND" " HIGH SURF ADVISORY"
## [959] "HAZARDOUS SURF" "FROST/FREEZE"
## [961] "WINTER WEATHER/MIX" "ASTRONOMICAL HIGH TIDE"
## [963] "WHIRLWIND" "VERY WARM"
## [965] "ABNORMALLY WET" "TORNADO DEBRIS"
## [967] "EXTREME COLD/WIND CHILL" "ICE ON ROAD"
## [969] "DROWNING" "GUSTY THUNDERSTORM WIND"
## [971] "MARINE HAIL" "HIGH SURF ADVISORIES"
## [973] "HURRICANE/TYPHOON" "HEAVY SURF/HIGH SURF"
## [975] "SLEET STORM" "STORM SURGE/TIDE"
## [977] "COLD/WIND CHILL" "MARINE HIGH WIND"
## [979] "TSUNAMI" "DENSE SMOKE"
## [981] "LAKESHORE FLOOD" "MARINE THUNDERSTORM WIND"
## [983] "MARINE STRONG WIND" "ASTRONOMICAL LOW TIDE"
## [985] "VOLCANIC ASHFALL"Differences in formatting and capitalization, and misspellings of severe weather events can cause entries that describe the same or similar events to be counted as separate severe weather events.
The following lines of code duplicate the column ‘EVTYPE’ as ‘EVTYPE_CLEANED’ to retain the original severe weather events in ‘EVTYPE’, whereas the duplicated severe weather events in ‘EVTYPE_CLEANED’ will be cleaned to standardize formatting and capitalization, and correct misspellings.
The following transformations will standardize the capitalization, replace selected punctuations with whitespaces, and trim leading, trailing, and repeated whitespaces.
# Duplicate the EVTYPE column
storm_data$EVTYPE_CLEANED <- storm_data$EVTYPE
# Capitalize all letters for each event type
storm_data$EVTYPE_CLEANED <- toupper(storm_data$EVTYPE_CLEANED)
# Replace commas, periods, forward slashes,
# backslashes, and hyphens with spaces
storm_data <- storm_data %>%
mutate(EVTYPE_CLEANED = str_replace_all(EVTYPE_CLEANED, ',', ' '))
storm_data <- storm_data %>%
mutate(EVTYPE_CLEANED = str_replace_all(EVTYPE_CLEANED, '\\.', ' '))
storm_data <- storm_data %>%
mutate(EVTYPE_CLEANED = str_replace_all(EVTYPE_CLEANED, '/', ' '))
storm_data <- storm_data %>%
mutate(EVTYPE_CLEANED = str_replace_all(EVTYPE_CLEANED, '\\\\', ' '))
storm_data <- storm_data %>%
mutate(EVTYPE_CLEANED = str_replace_all(EVTYPE_CLEANED, '-', ' '))
# Then remove extra leading and trailing whitespaces,
# and multiple consecutive whitespaces between words
storm_data <- storm_data %>%
mutate(EVTYPE_CLEANED = str_squish(EVTYPE_CLEANED))Correct misspellings
The following lines of code utilize the library hunspell to identify any words across the severe weather event entries that are not in a standard English dictionary and to replace it with the first suggested correct spelling, if there are any suggestions for correct spellings.
# Extract unique severe weather event entries
unique_evtypes <- c(unique(storm_data$EVTYPE_CLEANED))
# Identify any words that are not in a standard English dictionary
misspelled_evtypes <- hunspell_find(unique_evtypes)
unique_misspelled_evtypes <- unique(unlist(misspelled_evtypes))
# Generate spelling suggestions for each of these misspelled words
suggestions <- hunspell_suggest(unique_misspelled_evtypes)
# Select the first spelling suggestion for each word, or
# assign NA, if there are no spelling suggestions
top_suggestion <- sapply(suggestions, function(x) if (length(x) > 0) x[1] else NA_character_)
# Identify any words for with assigned spelling suggestions
valid_idx <- !is.na(top_suggestion)
# Create a lookup table containing each misspelled word and its
# suggested replacement, if there was a suggestion available
lookup_table <- setNames(top_suggestion[valid_idx], unique_misspelled_evtypes[valid_idx])
# Define a function that replaces each misspelled word with its
# suggested replacement, if there was a suggestion avaialable
replace_misspellings <- function(text, lookup) {
for (misspelled in names(lookup)) {
pattern <- paste0("\\b", misspelled, "\\b")
text <- gsub(pattern, lookup[misspelled], text, perl = TRUE)
}
return(text)
}
# Apply the function to the cleaned severe weather event entries
storm_data$EVTYPE_CLEANED <- replace_misspellings(storm_data$EVTYPE_CLEANED, lookup_table)Consolidate similar severe weather event entries
The following lines of code print how often each unique word occurs across the cleaned severe weather event entries in descending order.
# Split the cleaned severe weather events into individual words, reduce words
# to their root forms by utilizing the hunspell_stem function, capitalize each
# letter of the resulting individual words, and store them in a data frame
single_words_evtypes <- data.frame(words = toupper(unlist(hunspell_stem(unlist(strsplit(storm_data$EVTYPE_CLEANED, split = " "))))))
# Count the frequency of each word across severe weather event entries
word_counts <- single_words_evtypes %>%
count(words, sort = TRUE)
# Display the word frequency table
print(word_counts)## words n
## 1 WIND 364679
## 2 HAIL 290396
## 3 UTMOST 227235
## 4 THUNDERSTORM 109588
## 5 FLOOD 83395
## 6 TORNADO 60698
## 7 FLASH 55677
## 8 SNOW 35179
## 9 HEAVY 27983
## 10 MARINE 25230
## 11 HIGH 23069
## 12 WINTER 19604
## 13 LIGHTNING 15777
## 14 STORM 15037
## 15 RAIN 12201
## 16 WEATHER 8171
## 17 URBAN 7660
## 18 STRONG 7652
## 19 FUNNEL 6991
## 20 CLOUD 6947
## 21 FL 6788
## 22 ICE 4390
## 23 WATERSPOUT 3862
## 24 STREAM 3480
## 25 ML 3395
## 26 FOREST 2918
## 27 FROST 2826
## 28 WILDFIRE 2769
## 29 BLIZZARD 2745
## 30 HEAT 2648
## 31 DROUGHT 2512
## 32 COLD 2463
## 33 EXTREME 1923
## 34 FOG 1883
## 35 FREEZE 1854
## 36 EXCESSIVE 1736
## 37 EXCESS 1735
## 38 CHILL 1584
## 39 RIP 1554
## 40 FIRE 1471
## 41 WILD 1462
## 42 DENSE 1306
## 43 SURF 1294
## 44 MIX 1264
## 45 COASTAL 866
## 46 TIDE 857
## 47 TROPICAL 757
## 48 LAKE 709
## 49 EFFECT 660
## 50 LANDSLIDE 613
## 51 DUST 589
## 52 CURRENT 470
## 53 CORD 449
## 54 SURGE 411
## 55 AVALANCHE 387
## 56 WARM 327
## 57 CURRENTS 307
## 58 DRY 305
## 59 HURRICANE 288
## 60 SMALL 284
## 61 ASTRONOMICAL 277
## 62 SEASONABLY 266
## 63 WINDCHILL 223
## 64 BURST 222
## 65 MICRO 217
## 66 LIGHT 211
## 67 RIV 205
## 68 RIVER 205
## 69 LOW 194
## 70 WARMTH 174
## 71 WAVE 169
## 72 DEVIL 150
## 73 AND 139
## 74 SNOWFALL 126
## 75 SLEET 122
## 76 TEMPERATURE 118
## 77 MODERATE 102
## 78 GUSTY 101
## 79 TYPHOON 99
## 80 WINTRY 94
## 81 SQUALL 90
## 82 PRECIPITATE 84
## 83 PRECIPITATION 84
## 84 SUMMARY 70
## 85 MONTH 61
## 86 DEPRESSION 60
## 87 MONTHLY 55
## 88 SEVERE 54
## 89 OTHER 52
## 90 DAMAGE 50
## 91 OF 50
## 92 MIXED 47
## 93 WET 47
## 94 GLAZE 46
## 95 RAINFALL 38
## 96 BLOW 37
## 97 ROAD 35
## 98 ICY 33
## 99 LONG 31
## 100 VOLCANIC 29
## 101 MUDSLIDE 28
## 102 TIDAL 28
## 103 ASH 27
## 104 MAY 26
## 105 DRIZZLE 25
## 106 USUAL 25
## 107 AUGUST 24
## 108 WALL 24
## 109 SHORE 23
## 110 CHEESE 21
## 111 COOL 21
## 112 SMOKE 21
## 113 OPAL 20
## 114 TSUNAMI 20
## 115 0 19
## 116 ADVISORY 19
## 117 HOT 18
## 118 JUNE 18
## 119 BLACK 17
## 120 GRADIENT 17
## 121 BEACH 16
## 122 EROSION 16
## 123 JAM 15
## 124 SEA 15
## 125 EAR 14
## 126 EARLY 14
## 127 MARCH 14
## 128 1 12
## 129 SLIDE 12
## 130 NON 11
## 131 PRECIS 11
## 132 SHOW 11
## 133 FIRST 10
## 134 MUD 10
## 135 STREET 10
## 136 SWELL 10
## 137 AM 9
## 138 LATE 9
## 139 MPH 9
## 140 PM 9
## 141 CHI 8
## 142 CONDITION 8
## 143 CST 8
## 144 HYPOTHERMIA 8
## 145 LAND 8
## 146 WATER 8
## 147 ABNORMAL 7
## 148 AIR 7
## 149 APRIL 7
## 150 ERIN 7
## 151 EXPOSURE 7
## 152 GUSTAVO 7
## 153 HARD 7
## 154 JULY 7
## 155 ROUGH 7
## 156 SPELL 7
## 157 ACCUMULATE 6
## 158 AGRICULTURAL 6
## 159 FLAG 6
## 160 MAJOR 6
## 161 PATTERN 6
## 162 ROCK 6
## 163 TREE 6
## 164 YEAR 6
## 165 3 5
## 166 BITTER 5
## 167 DAM 5
## 168 FALL 5
## 169 FROM 5
## 170 GUST 5
## 171 MELT 5
## 172 MINOR 5
## 173 NORMAL 5
## 174 ROTATE 5
## 175 SEASON 5
## 176 SEASONABLE 5
## 177 SEPTEMBER 5
## 178 6 4
## 179 BREAK 4
## 180 BRUSH 4
## 181 CH 4
## 182 DEAN 4
## 183 DOWN 4
## 184 G 4
## 185 HAILSTORM 4
## 186 PATCHY 4
## 187 RE 4
## 188 RED 4
## 189 SAHARAN 4
## 190 SHOWER 4
## 191 WAKE 4
## 192 2 3
## 193 4 3
## 194 CLOY 3
## 195 DRYNESS 3
## 196 FLURRY 3
## 197 GENERATE 3
## 198 HEY 3
## 199 JERRY 3
## 200 LOCAL 3
## 201 MILD 3
## 202 NOV 3
## 203 SPOUT 3
## 204 TEMP 3
## 205 TORNADOES 3
## 206 VERY 3
## 207 WHIRLWIND 3
## 208 7 2
## 209 9 2
## 210 ACCUMULATION 2
## 211 AN 2
## 212 BELOW 2
## 213 CLOUDBURST 2
## 214 CRITERIA 2
## 215 EDUARDO 2
## 216 END 2
## 217 ERUPTION 2
## 218 EX 2
## 219 FELIX 2
## 220 FLOE 2
## 221 FLOYD 2
## 222 GORDON 2
## 223 GRASS 2
## 224 GROUND 2
## 225 IND 2
## 226 MISHAP 2
## 227 NO 2
## 228 NONE 2
## 229 ON 2
## 230 OUT 2
## 231 REMNANT 2
## 232 RURAL 2
## 233 SEPT 2
## 234 SLUMP 2
## 235 SON 2
## 236 SOUTHEAST 2
## 237 THUNDER 2
## 238 TORRENTIAL 2
## 239 W 2
## 240 WIN 2
## 241 3RD 1
## 242 5 1
## 243 ACCIDENT 1
## 244 ALBERTO 1
## 245 ALOFT 1
## 246 APACHE 1
## 247 AWN 1
## 248 AWNING 1
## 249 BREAKUP 1
## 250 COUNTY 1
## 251 DEBRIS 1
## 252 DEEP 1
## 253 DRIFT 1
## 254 DROWN 1
## 255 DROWNING 1
## 256 EMILY 1
## 257 EN 1
## 258 EXTEND 1
## 259 FAILURE 1
## 260 FORCE 1
## 261 GOV 1
## 262 HAZARDOUS 1
## 263 HIGHWAY 1
## 264 JAN 1
## 265 JURY 1
## 266 LACK 1
## 267 LARGE 1
## 268 LE 1
## 269 METRO 1
## 270 MOUNTAIN 1
## 271 NAUSEOUS 1
## 272 NEAR 1
## 273 NORTHERN 1
## 274 OCT 1
## 275 OCTOBER 1
## 276 PACK 1
## 277 PELLET 1
## 278 PLUME 1
## 279 RA 1
## 280 RAINSTORM 1
## 281 RAPID 1
## 282 RISE 1
## 283 RISING 1
## 284 ROGUE 1
## 285 ROSIN 1
## 286 SEASONAL 1
## 287 SNOWSTORM 1
## 288 SPRAY 1
## 289 STEM 1
## 290 TO 1
## 291 TSWANA 1
## 292 TURBULENCE 1
## 293 VALANCE 1
## 294 WATCH 1
## 295 WETNESS 1
## 296 WI 1I then manually defined a list of words that contains any weather-related words with more than 100 occurences as well as ‘TYPHOON’. The weather event ‘TYPHOON’ only had 99 occurences, but typhoons are the same as hurricanes and the weather event ‘HURRICANE’ had more than 100 occurences. Any weather event coded as ‘TYPHOON’ will be re-coded to ‘HURRICANE’ later on.
I then proceed through the list in descending order of occurences of each weather-related words. For each cleaned severe weather event entry that contains a match for the currently assessed weather-related word, I replace the severe weather event entry with the currently assessed weather-related word. This consolidates similar severe weather event entries into the same category of severe weather events.
The last lines of the following code re-codes any weather event coded as ‘TYPHOON’ to ‘HURRICANE’.
# Manually define a list of words that contains any weather-related
# words with more than 100 occurences as well as 'TYPHOON'
most_common_events <- c(
"WIND", "HAIL", "THUNDERSTORM", "FLOOD", "TORNADO",
"SNOW", "WINTER", "LIGHTNING", "STORM", "RAIN",
"FUNNEL", "ICE", "WATERSPOUT", "STREAM", "FROST",
"WILDFIRE", "BLIZZARD", "HEAT", "DROUGHT", "COLD",
"FOG", "FREEZE", "FIRE", "SURF", "TIDE",
"LANDSLIDE", "DUST", "CURRENT", "SURGE", "AVALANCHE",
"WARM", "DRY", "HURRICANE", "WINDCHILL", "WAVE",
"SNOWFALL", "SLEET", "TEMPERATURE", "TYPHOON"
)
# Loop through each commonly occurring weather-related word.
for (e in seq_along(most_common_events)) {
# Construct a regular expression that searches for the specified word
# anywhere within an event classification.
# \\w* allows for zero or more word characters before and after the term.
# This allows variations such as "HIGH WINDS" and "STRONG WIND"
# to be identified as containing the common term WIND.
pattern <- paste0("\\w*", most_common_events[e], "\\w*")
# Check whether each event classification contains the current
# commonly occurring weather-related word.
#
# If a match is found, replace the entire event classification
# with the corresponding standardized term.
# If no match is found, retain the original event classification.
#
# Note that classifications containing multiple terms may be
# reassigned more than once as the loop progresses.
storm_data <- storm_data %>%
mutate(EVTYPE_CLEANED = if_else(str_detect(EVTYPE_CLEANED, pattern),most_common_events[e],EVTYPE_CLEANED))
}
# Standardize all TYPHOON classifications as HURRICANE.
# This combines the two equivalent tropical cyclone classifications
# into one category for the subsequent analysis.
storm_data <- storm_data %>%
mutate(EVTYPE_CLEANED = str_replace_all(EVTYPE_CLEANED, 'TYPHOON', 'HURRICANE'))To determine whether consolidation of the severe weather types was successful, the following lines of code print how often each unique severe weather event tyoe occurs across the cleaned and consolidated severe weather event entries in descending order.
# Split the cleaned severe weather events into individual words, reduce words
# to their root forms by utilizing the hunspell_stem function, capitalize each
# letter of the resulting individual words, and store them in a data frame
cleaned_evtypes <- data.frame(words = storm_data$EVTYPE_CLEANED)
# Count the frequency of each word across severe weather event entries
word_counts <- cleaned_evtypes %>%
count(words, sort = TRUE)
# Display the word frequency table
print(word_counts)## words n
## 1 WIND 364901
## 2 HAIL 289277
## 3 FLOOD 82712
## 4 TORNADO 60700
## 5 WINTER 19597
## 6 SNOW 17677
## 7 LIGHTNING 15764
## 8 RAIN 12152
## 9 FUNNEL 6987
## 10 FIRE 4239
## 11 WATERSPOUT 3847
## 12 STORM 3662
## 13 STREAM 3408
## 14 BLIZZARD 2723
## 15 HEAT 2648
## 16 DROUGHT 2488
## 17 FOG 1880
## 18 FROST 1413
## 19 SURF 1063
## 20 COLD 884
## 21 CURRENT 774
## 22 LANDSLIDE 609
## 23 AVALANCHE 386
## 24 WARM 327
## 25 HURRICANE 298
## 26 DRY 287
## 27 TIDE 281
## 28 DUST 158
## 29 FREEZE 99
## 30 WINTRY MIX 94
## 31 ICE 92
## 32 TEMPERATURE 83
## 33 TROPICAL DEPRESSION 60
## 34 SLEET 59
## 35 OTHER 52
## 36 GLAZE 43
## 37 MIXED PRECIPITATION 37
## 38 MONTHLY PRECIPITATION 36
## 39 ICY ROADS 32
## 40 FREEZING DRIZZLE 24
## 41 VOLCANIC ASH 23
## 42 CHEESE 21
## 43 TSUNAMI 20
## 44 UNSEASONABLY WET 19
## 45 MUDSLIDE 17
## 46 UNSEASONABLY COOL 12
## 47 SMOKE 11
## 48 DENSE SMOKE 10
## 49 MIXED PRECIS 10
## 50 UNSEASONABLY HOT 10
## 51 MICRO BURST 9
## 52 MUDSLIDES 9
## 53 HEAVY MIX 8
## 54 HIGH SEAS 8
## 55 HYPOTHERMIA EXPOSURE 7
## 56 MUD SLIDE 7
## 57 RECORD HIGH 7
## 58 GUSTAVO 6
## 59 HIGH SWELLS 6
## 60 HIGH WATER 6
## 61 WET MICRO BURST 6
## 62 RECORD COOL 5
## 63 ROTATING WALL CLOUD 5
## 64 WALL CLOUD 5
## 65 BEACH EROSION 4
## 66 DAM BREAK 4
## 67 HEAVY PRECIPITATION 4
## 68 RECORD LOW 4
## 69 WAVE 4
## 70 WET MONTH 4
## 71 WET YEAR 4
## 72 LIGHTING 3
## 73 NORMAL PRECIPITATION 3
## 74 ROUGH SEAS 3
## 75 URBAN SMALL 3
## 76 VOLCANIC ASH FALL 3
## 77 BELOW NORMAL PRECIPITATION 2
## 78 DOWN BURST 2
## 79 HEAVY SEAS 2
## 80 HEAVY SHOWER 2
## 81 HOT SPELL 2
## 82 LAND SLUMP 2
## 83 LAND SPOUT 2
## 84 MARINE MISHAP 2
## 85 NONE 2
## 86 RED FLAG CRITERIA 2
## 87 REMNANTS OF FLOYD 2
## 88 ROCK SLIDE 2
## 89 SUMMARY AUGUST 10 2
## 90 SUMMARY AUGUST 11 2
## 91 SUMMARY OF APRIL 12 2
## 92 SUMMARY OF APRIL 21 2
## 93 SUMMARY OF JUNE 13 2
## 94 SUMMARY OF JUNE 3 2
## 95 SUMMARY OF MARCH 23 2
## 96 SUMMARY SEPTEMBER 23 2
## 97 SUMMARY: NOV 16 2
## 98 SURGE 2
## 99 UNSEASONABLE LOW TEMP 2
## 100 UNSEASONABLY COOL & WET 2
## 101 URBAN AND SMALL 2
## 102 VOLCANIC ERUPTION 2
## 103 ? 1
## 104 ABNORMALLY WET 1
## 105 APACHE COUNTY 1
## 106 BEACH ROSIN 1
## 107 COASTAL EROSION 1
## 108 COOL AND WET 1
## 109 COOL SPELL 1
## 110 DAM FAILURE 1
## 111 DRIEST MONTH 1
## 112 DROWNING 1
## 113 END 1
## 114 EXCESSIVE 1
## 115 EXCESSIVE PRECIPITATION 1
## 116 EXCESSIVE WETNESS 1
## 117 EXTREMELY WET 1
## 118 FREEZING DRIZZLE AND FREEZING 1
## 119 FREEZING SPRAY 1
## 120 GOV 1
## 121 GUSTAVO AND 1
## 122 HEAVY SHOWERS 1
## 123 HEAVY SWELLS 1
## 124 HIGH 1
## 125 HOT PATTERN 1
## 126 HOT WEATHER 1
## 127 HYPOTHERMIA 1
## 128 LARGE WALL CLOUD 1
## 129 MARINE ACCIDENT 1
## 130 MILD PATTERN 1
## 131 MUD ROCK SLIDE 1
## 132 MUD SLIDES 1
## 133 NO SEVERE WEATHER 1
## 134 NORTHERN LIGHTS 1
## 135 RAPIDLY RISING WATER 1
## 136 RECORD PRECIPITATION 1
## 137 SEVERE TURBULENCE 1
## 138 SOUTHEAST 1
## 139 SUMMARY AUGUST 17 1
## 140 SUMMARY AUGUST 2 3 1
## 141 SUMMARY AUGUST 21 1
## 142 SUMMARY AUGUST 28 1
## 143 SUMMARY AUGUST 4 1
## 144 SUMMARY AUGUST 7 1
## 145 SUMMARY AUGUST 9 1
## 146 SUMMARY JAN 17 1
## 147 SUMMARY JULY 23 24 1
## 148 SUMMARY JUNE 18 19 1
## 149 SUMMARY JUNE 5 6 1
## 150 SUMMARY JUNE 6 1
## 151 SUMMARY OF APRIL 13 1
## 152 SUMMARY OF APRIL 27 1
## 153 SUMMARY OF APRIL 3RD 1
## 154 SUMMARY OF AUGUST 1 1
## 155 SUMMARY OF JULY 11 1
## 156 SUMMARY OF JULY 2 1
## 157 SUMMARY OF JULY 22 1
## 158 SUMMARY OF JULY 26 1
## 159 SUMMARY OF JULY 29 1
## 160 SUMMARY OF JULY 3 1
## 161 SUMMARY OF JUNE 10 1
## 162 SUMMARY OF JUNE 11 1
## 163 SUMMARY OF JUNE 12 1
## 164 SUMMARY OF JUNE 15 1
## 165 SUMMARY OF JUNE 16 1
## 166 SUMMARY OF JUNE 18 1
## 167 SUMMARY OF JUNE 23 1
## 168 SUMMARY OF JUNE 24 1
## 169 SUMMARY OF JUNE 30 1
## 170 SUMMARY OF JUNE 4 1
## 171 SUMMARY OF JUNE 6 1
## 172 SUMMARY OF MARCH 14 1
## 173 SUMMARY OF MARCH 24 1
## 174 SUMMARY OF MARCH 24 25 1
## 175 SUMMARY OF MARCH 27 1
## 176 SUMMARY OF MARCH 29 1
## 177 SUMMARY OF MAY 10 1
## 178 SUMMARY OF MAY 13 1
## 179 SUMMARY OF MAY 14 1
## 180 SUMMARY OF MAY 22 1
## 181 SUMMARY OF MAY 22 AM 1
## 182 SUMMARY OF MAY 22 PM 1
## 183 SUMMARY OF MAY 26 AM 1
## 184 SUMMARY OF MAY 26 PM 1
## 185 SUMMARY OF MAY 31 AM 1
## 186 SUMMARY OF MAY 31 PM 1
## 187 SUMMARY OF MAY 9 10 1
## 188 SUMMARY SEPT 25 26 1
## 189 SUMMARY SEPTEMBER 20 1
## 190 SUMMARY SEPTEMBER 3 1
## 191 SUMMARY SEPTEMBER 4 1
## 192 SUMMARY: NOV 6 7 1
## 193 SUMMARY: OCT 20 21 1
## 194 SUMMARY: OCTOBER 31 1
## 195 SUMMARY: SEPT 18 1
## 196 TSWANA 1
## 197 URBAN SMALL STEM FLAG 1
## 198 UTMOST 1
## 199 UTMOST END 1
## 200 VALANCE 1
## 201 VOLCANIC ASH PLUME 1
## 202 WATER SPOUT 1
## 203 WET CLOUDBURST 1
## 204 WET WEATHER 1Quantifying missing population health and economic data
The following lines of code quantify the number of missing entries for fatalities and injuries. No severe weather events are missing entries for fatalities or injuries. Therefore, no additional data cleaning is necessary before determining which severe weather events have the largest impact on population health.
# Quantify missing values in the FATALITIES column
sum(is.na(storm_data$FATALITIES))## [1] 0# Quantify missing values in the INJURIES column
sum(is.na(storm_data$INJURIES))## [1] 0The following lines of code quantify the number of missing entries for property and crop damages, and the number of non-standard or missing entries for the magnitude multipliers for property and crop damages in dollar amounts. Standard magnitude multipliers for property and crop damages are “H” for a multiplier of $100, “K” for a multiplier of $1,000, “M” for a multiplier of $1,000,000, and “B” for a multiplier of $1,000,000,000. To quantify the number of non-standard or missing magnitude multipliers for property and crop damages, I identified any entries that were not “H”, “K”, “M”, or “B” and calculated the sum of them. No severe weather event are missing entries for property or crop damage. However, 466256 or 51.67% of property damage magnitude multiplier entries are non-standard or missing, and 618462 or 68.54% of crop damage magnitude multiplier entries are non-standard or missing.
# Quantify missing values for property damage in the PROPDMG column
sum(is.na(storm_data$PROPDMG))## [1] 0# Quantify missing values for crop damage in the CROPDMG column
sum(is.na(storm_data$CROPDMG))## [1] 0# Quantify non-standard and missing values for property damage
# magnitude multipliers in the PROPDMGEXP column, and quantify
# the percentage of missing values
sum(!storm_data$PROPDMGEXP %in% c("H","K","M","B"))## [1] 466256sum(!storm_data$PROPDMGEXP %in% c("H","K","M","B")) / nrow(storm_data)## [1] 0.5167434# Quantify non-standard and missing values for property damage
# magnitude multipliers in the PROPDMGEXP column, and quantify
# the percentage of missing values
sum(!storm_data$CROPDMGEXP %in% c("H","K","M","B"))## [1] 618462sum(!storm_data$CROPDMGEXP %in% c("H","K","M","B")) / nrow(storm_data)## [1] 0.6854306Setting missing economic data to NA values
The following lines of code duplicate the property and crop damage columns and set any entries with a non-standard or missing magnitude multipliers to NA values, so that these entries can be excluded from later analyses. I decided not to infer values for these entries, since it is not possible for me to know whether entries were supposed to be “H”,“K”,“M”, or “B”. Once entries with a non-standard or missing magnitude multipliers were set to NA values, each duplicated property and crop damage is scaled to its total dollar amount based on its corresponding magnitude multiplier.
# Duplicate PROPDMG column, and set any entries with a
# non-standard or missing magnitude multipliers to NA values
storm_data$PROPDMG_MAG <- storm_data$PROPDMG
storm_data$PROPDMG_MAG <- ifelse(!storm_data$PROPDMG_MAG %in% c("H","K","M","B"), storm_data$PROPDMG_MAG, NA)
# Scale each duplicated property damage to its total dollar
# amount based on its corresponding magnitude multiplier
storm_data$PROPDMG_MAG[grepl("H", storm_data$PROPDMGEXP)] <-
storm_data$PROPDMG_MAG[grepl("H", storm_data$PROPDMGEXP)] * 100
storm_data$PROPDMG_MAG[grepl("K", storm_data$PROPDMGEXP)] <-
storm_data$PROPDMG_MAG[grepl("K", storm_data$PROPDMGEXP)] * 1000
storm_data$PROPDMG_MAG[grepl("M", storm_data$PROPDMGEXP)] <-
storm_data$PROPDMG_MAG[grepl("M", storm_data$PROPDMGEXP)] * 1000000
storm_data$PROPDMG_MAG[grepl("B", storm_data$PROPDMGEXP)] <-
storm_data$PROPDMG_MAG[grepl("B", storm_data$PROPDMGEXP)] * 1000000000
# Duplicate PROPDMG column, and set any entries with a
# non-standard or missing magnitude multipliers to NA values
storm_data$CROPDMG_MAG <- storm_data$PROPDMG
storm_data$CROPDMG_MAG <- ifelse(!storm_data$CROPDMG_MAG %in% c("H","K","M","B"), storm_data$CROPDMG_MAG, NA)
# Scale each duplicated property damage to its total dollar
# amount based on its corresponding magnitude multiplier
storm_data$CROPDMG_MAG[grepl("H", storm_data$CROPDMGEXP)] <-
storm_data$CROPDMG_MAG[grepl("H", storm_data$CROPDMGEXP)] * 100
storm_data$CROPDMG_MAG[grepl("K", storm_data$CROPDMGEXP)] <-
storm_data$CROPDMG_MAG[grepl("K", storm_data$CROPDMGEXP)] * 1000
storm_data$CROPDMG_MAG[grepl("M", storm_data$CROPDMGEXP)] <-
storm_data$CROPDMG_MAG[grepl("M", storm_data$CROPDMGEXP)] * 1000000
storm_data$CROPDMG_MAG[grepl("B", storm_data$CROPDMGEXP)] <-
storm_data$CROPDMG_MAG[grepl("B", storm_data$CROPDMGEXP)] * 1000000000Results
Which severe weather events are most harmful to population health?
The following figure compares the 20 cleaned and consolidated severe weather events that result in the most fatalities and injuries in descending order. The two figure panels utilize separate rankings. Therefore, the position of a severe weather event does not necessarily remain the same between the fatalities and injuries panels.
# Calculate the total number of fatalities associated with each cleaned
# and consolidated severe weather event, and exclude missing values
health_fatalities <- storm_data %>%
group_by(EVTYPE_CLEANED) %>%
summarise(fatalities = sum(FATALITIES, na.rm = TRUE))
# Calculate the total number of injuries associated with each cleaned
# and consolidated severe weather event, and exclude missing values
health_injuries <- storm_data %>%
group_by(EVTYPE_CLEANED) %>%
summarise(injuries = sum(INJURIES, na.rm = TRUE))
# Sort the totals in descending order
health_fatalities <- arrange(health_fatalities, desc(fatalities))
health_injuries <- arrange(health_injuries, desc(injuries))
# Visualize the first 20 entries for fatalities and injuries using a scatter plot
p1 <- ggplot(data = head(health_fatalities, 20), aes(x = seq_along(fatalities), y = fatalities)) +
geom_point() + scale_x_continuous(breaks = (1:20), labels = health_fatalities$EVTYPE_CLEANED[1:20]) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
labs(x = "", y = "Fatalities")
p2 <- ggplot(data = head(health_injuries, 20), aes(x = seq_along(injuries), y = injuries)) +
geom_point() + scale_x_continuous(breaks = (1:20), labels = health_injuries$EVTYPE_CLEANED[1:20]) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
labs(x = "", y = "Injuries")
print(p1 + p2)
The figure panels show the total number of people, who died or were injured, for each of the 20 severe weather events with the most fatalities and injuries on their respective y-axes. Both fatalities and injuries are harmful to the population health. Although it is difficult to determine which is more harmful, it can be argued that dying has a more severe impact on a person’s life than being injured. The five cleaned and consolidated severe weather events with the largest number of fatalities are tornados, heat, floods, wind, and lightning. Likewise, the five cleaned and consolidated severe weather events with the largest number of injuries are tornados, wind, heat, flood, and lightning. Tornados cause both the most fatalities and injuries, and are therefore most harmful to population health. Heat, floods, wind, and lightning are the severe weather events that produce both the most fatalities and injuries after tornados, indicating that heat, floods, wind and lightning are the most harmful to population health after tornados.
Which severe weather events have the largest economic consequences?
The following figure compares the 20 cleaned and consolidated severe weather events that result in the largest property and crop damages in descending order. The two figure panels utilize separate rankings. Therefore, the position of a severe weather event does not necessarily remain the same between the property and crop damage panels.
# Calculate the total dollar amounts for property damages associated with each
# cleaned and consolidated severe weather event, and exclude missing values
damage_property <- storm_data %>%
group_by(EVTYPE_CLEANED) %>%
summarise(property = sum(PROPDMG_MAG, na.rm = TRUE))
# Calculate the total dollar amounts for crop damages associated with each
# cleaned and consolidated severe weather event, and exclude missing values
damage_crops <- storm_data %>%
group_by(EVTYPE_CLEANED) %>%
summarise(crops = sum(CROPDMG_MAG, na.rm = TRUE))
# Sort the totals in descending order
damage_property <- arrange(damage_property, desc(property))
damage_crops <- arrange(damage_crops, desc(crops))
# Visualize the first 20 entries for property and crop damages using a scatter plot
q1 <- ggplot(data = head(damage_property, 20), aes(x = seq_along(property), y = property)) +
geom_point() + scale_x_continuous(breaks = (1:20), labels = damage_property$EVTYPE_CLEANED[1:20]) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
labs(x = "", y = "Property damage")
q2 <- ggplot(data = head(damage_crops, 20), aes(x = seq_along(crops), y = crops)) +
geom_point() + scale_x_continuous(breaks = (1:20), labels = damage_crops$EVTYPE_CLEANED[1:20]) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
labs(x = "", y = "Crop damage")
print(q1 + q2)
The figure panels show the total dollar amounts for property and crop damages for each of the 20 severe weather events with the largest property and crop damages on their respective y-axes. Both property and crop damages have large economic consequences, and it is difficult to determine which has larger economic consequences. However, the top seven severe weather events that caused the largest property and crop damages are consistent, albeit with different orderings. The seven cleaned and consolidated severe weather events that caused the largest total dollar amounts for property damages are floods, hurricanes, storms, tornados, winds, hail and fire. The seven cleaned and consolidated severe weather events that caused the largest total dollar amounts for crop damages are storms, floods, hail, winds, hurricanes, tornados, and fire. Thus, the severe weather events that have the largest econmic consequences are floods, hurricanes, tornados, storms, winds, hail, and fire.