Analysis of Health and Economic Consequences of Storm Events
Synopsis: This is an analysis that uses the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database to attempt to answer the following two questions
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Across the United States, which types of events have the greatest economic consequences?
Data Processing
Load required libraries
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(stringr)
library(ggplot2)
library(cowplot)##
## Attaching package: 'cowplot'## The following object is masked from 'package:lubridate':
##
## stamplibrary(forcats)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ purrr 1.0.4 ✔ tibble 3.2.1
## ✔ readr 2.1.5 ✔ tidyr 1.3.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ cowplot::stamp() masks lubridate::stamp()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsRead the data into R and run str() to get a sense of the
data.
storm <- read.csv("repdata_data_StormData.csv")
str(storm)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : chr "4/18/1950 0:00:00" "4/18/1950 0:00:00" "2/20/1951 0:00:00" "6/8/1951 0:00:00" ...
## $ BGN_TIME : chr "0130" "0145" "1600" "0900" ...
## $ TIME_ZONE : chr "CST" "CST" "CST" "CST" ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: chr "MOBILE" "BALDWIN" "FAYETTE" "MADISON" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : chr "" "" "" "" ...
## $ BGN_LOCATI: chr "" "" "" "" ...
## $ END_DATE : chr "" "" "" "" ...
## $ END_TIME : chr "" "" "" "" ...
## $ COUNTY_END: num 0 0 0 0 0 0 0 0 0 0 ...
## $ COUNTYENDN: logi NA NA NA NA NA NA ...
## $ END_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ END_AZI : chr "" "" "" "" ...
## $ END_LOCATI: chr "" "" "" "" ...
## $ LENGTH : num 14 2 0.1 0 0 1.5 1.5 0 3.3 2.3 ...
## $ WIDTH : num 100 150 123 100 150 177 33 33 100 100 ...
## $ F : int 3 2 2 2 2 2 2 1 3 3 ...
## $ MAG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ FATALITIES: num 0 0 0 0 0 0 0 0 1 0 ...
## $ INJURIES : num 15 0 2 2 2 6 1 0 14 0 ...
## $ PROPDMG : num 25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
## $ PROPDMGEXP: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...
## $ WFO : chr "" "" "" "" ...
## $ STATEOFFIC: chr "" "" "" "" ...
## $ ZONENAMES : chr "" "" "" "" ...
## $ LATITUDE : num 3040 3042 3340 3458 3412 ...
## $ LONGITUDE : num 8812 8755 8742 8626 8642 ...
## $ LATITUDE_E: num 3051 0 0 0 0 ...
## $ LONGITUDE_: num 8806 0 0 0 0 ...
## $ REMARKS : chr "" "" "" "" ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...We can see that there is already a BGN_TIME column, so
we can remove the “0:00:00” part from the BGN_DATE column.
We can do the same thing for END_DATE as well. Also create
a new column containing just the year so we can make a time-series plot
later.
storm = storm %>%
### Extract only the first part of the string for BGN_DATE and END_DATE columns
mutate(
BGN_DATE = str_extract(BGN_DATE, pattern = "^[^ ]+"),
END_DATE = str_extract(END_DATE, pattern = "^[^ ]+")
) %>%
### Convert date columns to date class and create new column containing the year
mutate(
BGN_DATE = mdy(BGN_DATE),
END_DATE = mdy(END_DATE),
BGN_YEAR = year(BGN_DATE)
)For the character variables, we will convert any empty strings to
NA. Also, we will convert EVTYPE to a factor
variable since that will be used later in our plots.
storm = storm %>%
mutate(
across(where(is.character), ~na_if(., "")), ### convert empty strings to NA
EVTYPE = as.factor(EVTYPE) ### convert EVTYPE to factor
)Now lets take a closer look at the PROPDMGEXP and
CROPDMGEXP variables
levels(as.factor(storm$PROPDMGEXP))## [1] "-" "?" "+" "0" "1" "2" "3" "4" "5" "6" "7" "8" "B" "h" "H" "K" "m" "M"levels(as.factor(storm$CROPDMGEXP))## [1] "?" "0" "2" "B" "k" "K" "m" "M"It seems that these are units including units for hundreds,
thousands, millions, and billions. I will recode the
PROPDMG and CROPDMG to be in dollars
instead
storm = storm %>%
mutate(
PROPDMGEXP = tolower(PROPDMGEXP), ### convert to lower case
PROPDMGEXP = case_when(
PROPDMGEXP == "h" ~ "2", ### hundreds
PROPDMGEXP == "k" ~ "3", ### thousands
PROPDMGEXP == "m" ~ "6", ### millions
PROPDMGEXP == "b" ~ "9", ### billions
TRUE ~ PROPDMGEXP
)
) %>%
mutate(
CROPDMGEXP = tolower(CROPDMGEXP), ### convert to lower case
CROPDMGEXP = case_when(
CROPDMGEXP == "h" ~ "2", ### hundreds
CROPDMGEXP == "k" ~ "3", ### thousands
CROPDMGEXP == "m" ~ "6", ### millions
CROPDMGEXP == "b" ~ "9", ### billions
TRUE ~ CROPDMGEXP
)
) %>%
mutate(
PROPDMGEXP = as.numeric(PROPDMGEXP), ### convert to numeric
CROPDMGEXP = as.numeric(CROPDMGEXP) ### convert to numeric
) %>%
filter(!is.na(PROPDMGEXP)) %>% ### filter out any rows with missing exponents
filter(!is.na(CROPDMGEXP)) %>% ### filter out any rows with missing exponents
mutate(
PROPDMG = PROPDMG * 10^PROPDMGEXP, ### recode PROPDMG and CROPDMG in dollars
CROPDMG = CROPDMG * 10^CROPDMGEXP
)## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `PROPDMGEXP = as.numeric(PROPDMGEXP)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.Lets take a closer look at the EVTYPE variable
levels(as.factor(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"We can see that there are some repeated EVTYPEs due to
case sensitivity, and some inconsistent naming conventions. We will
convert everything to lower case, then use stringr to
identify EVTYPEs that are the same type of weather event
and combine them into a consistent naming convention.
### Convert everything to lower case
storm = storm %>%
mutate(
EVTYPE = tolower(EVTYPE)
)
### Identify EVTYPEs that are the same and recode them to have a consistent naming convention
storm <- storm %>%
mutate(EVTYPE = case_when(
str_detect(EVTYPE, "hurricane") ~ "hurricane",
str_detect(EVTYPE, "tropical storm|tropical depression") ~ "tropical storm",
str_detect(EVTYPE, "flood|fld") ~ "flood",
str_detect(EVTYPE, "thunderstorm") ~ "thunderstorm",
str_detect(EVTYPE, "fire") ~ "fire",
str_detect(EVTYPE, "tornado") ~ "tornado",
str_detect(EVTYPE, "rain") ~ "rain",
str_detect(EVTYPE, "heat") ~ "heat",
str_detect(EVTYPE, "wind|funnel cloud|gustnado") ~ "wind",
str_detect(EVTYPE, "wintry mix") ~ "wintry mix", # Explicitly categorize it
str_detect(EVTYPE, "ice|freez|winter|snow|blizzard|cold|icy") ~ "cold",
str_detect(EVTYPE, "hail|sleet") ~ "hail",
str_detect(EVTYPE, "surf|tsunami|storm surge|waterspout|rip current|seiche") ~ "surf",
str_detect(EVTYPE, "fog") ~ "fog",
str_detect(EVTYPE, "dust") ~ "dust",
TRUE ~ EVTYPE # Retain original if no match
))Results
To answer question 1:
- Across the United States, which types of events (as indicated in the
EVTYPEvariable) are most harmful with respect to population health?
We can compare EVTYPE against injuries and
fatalities.
### Storm events with the top 5 highest average injuries
injuries <- storm %>%
group_by(EVTYPE) %>%
summarise(
sum_injuries = sum(INJURIES, na.rm = TRUE)
) %>%
arrange(desc(sum_injuries)) %>%
mutate(EVTYPE = fct_reorder(EVTYPE, sum_injuries)) %>%
head(5)
### Storm events with the top 5 highest average fatalities
fatalities <- storm %>%
group_by(EVTYPE) %>%
summarise(
sum_fatalities = sum(FATALITIES, na.rm = TRUE)
) %>%
arrange(desc(sum_fatalities)) %>%
mutate(EVTYPE = fct_reorder(EVTYPE, sum_fatalities)) %>%
head(5)
### Take the set union of the top 5 storm events from the two categories above (average injuries and average fatalities)
combined <- unique(union(injuries$EVTYPE, fatalities$EVTYPE))
### Filter `s` dataframe for the combined storm events taken above, then calculate the avg injuries, fatalities, and the sum of those (which I call harm_index) and arrange descending by harm_index
combined_df <- storm %>%
filter(EVTYPE %in% combined) %>%
group_by(EVTYPE) %>%
summarise(
sum_injuries = sum(INJURIES, na.rm = TRUE),
sum_fatalities = sum(FATALITIES, na.rm = TRUE),
sum_total_combined = sum_injuries + sum_fatalities
) %>%
arrange(desc(sum_total_combined)) %>%
mutate(EVTYPE = fct_reorder(EVTYPE, sum_total_combined))
### Pivot to a longer format
combined_df_longer = combined_df %>%
pivot_longer(
cols = sum_injuries:sum_total_combined,
names_to = "harm_type",
values_to = "count"
)
### Plot bar charts and facet by harm_type to see bar charts for avg injuries, fatalities, and harm_index, and how they compare
ggplot(combined_df_longer, aes(x = EVTYPE, y = count)) +
geom_col() +
theme_bw() +
labs(
title = "Population Health Impacts of Storm Events",
x = "Storm Type",
y = "Number of Injuries/Fatalities"
) +
coord_flip() +
facet_wrap(~harm_type)
Tornado is at the top spot for both number of injuries and fatalities, although it has a larger lead compared to the others for injuries. Flood, cold, and heat are the runners up when it comes to numbers of injuries, whereas Flood and heat are runners up when it comes to number of fatalities.
To answer question 2:
- Across the United States, which types of events have the greatest economic consequences?
We can compare EVTYPE against property and crop
damage.
### Top 5 property damage by EVTYPE
prop_dmg <- storm %>%
group_by(EVTYPE) %>%
summarise(sum_property_dmg = sum(PROPDMG, na.rm = TRUE)) %>%
arrange(desc(sum_property_dmg)) %>%
mutate(EVTYPE = fct_reorder(EVTYPE, sum_property_dmg)) %>%
head(5)
### Top 5 crop damage by EVTYPE
crop_dmg <- storm %>%
group_by(EVTYPE) %>%
summarise(
sum_crop_dmg = sum(CROPDMG, na.rm = TRUE)
) %>%
arrange(desc(sum_crop_dmg)) %>%
mutate(EVTYPE = fct_reorder(EVTYPE, sum_crop_dmg)) %>%
head(5)
### Get union of top 5 EVTYPEs by Property damage and Crop damage
combined <- unique(union(prop_dmg$EVTYPE, crop_dmg$EVTYPE))
### Filter `s` dataframe for the combined storm events taken above, then calculate the avg property damage, crop damage, and the sum of those (which I call sum_avg) and arrange descending by sum_avg
combined_df <- storm %>%
filter(EVTYPE %in% combined) %>%
group_by(EVTYPE) %>%
summarise(
sum_property_dmg = sum(PROPDMG, na.rm = TRUE),
sum_crop_dmg = sum(CROPDMG, na.rm = TRUE),
sum_total_combined = sum_property_dmg + sum_crop_dmg
) %>%
arrange(desc(sum_total_combined)) %>%
mutate(EVTYPE = fct_reorder(EVTYPE, sum_total_combined))
### Pivot longer
combined_df_longer = combined_df %>%
pivot_longer(
cols = sum_property_dmg:sum_total_combined,
names_to = "damage_type",
values_to = "dollars"
)
### Plot economic damage effects for property, crops, and sum(property, crops)
p1 <- ggplot(combined_df_longer, aes(x = EVTYPE, y = dollars)) +
geom_col() +
labs(
title = "Economic Damages by Storm Type",
x = "Storm Type",
y = "Dollars"
) +
coord_flip() +
theme_bw() +
facet_wrap(~damage_type)
### Display the plot
p1
We can see that flood leads both in terms of property damage and crop damage. The lead it has for property damage is large, where as less so for crop damage. After flood, hurricane and tornado have the next largest property damage, followed by hail and surf. The main contributors to crop damage after flood are cold and hurricane. In general, the economic losses from crop damage are less than the economic losses from property damage.
We might also be interested in seeing the overall trend over time of population health effects and economic damages from all storm events combined. I have plotted that below.
### Average property damage and crop damage by year
economic_dmg_time <- storm %>%
group_by(BGN_YEAR) %>%
summarise(
sum_prop_dmg = sum(PROPDMG, na.rm = TRUE),
sum_crop_dmg = sum(CROPDMG, na.rm = TRUE),
)
### pivot longer
economic_dmg_time <- economic_dmg_time %>%
pivot_longer(
cols = c(sum_prop_dmg, sum_crop_dmg),
names_to = "damage_type",
values_to = "dollars"
)
### Plot economic damage over time for all storm types combined
p1 <- ggplot(economic_dmg_time,
aes(x = BGN_YEAR, y = dollars, colour = damage_type)) +
geom_point() +
geom_smooth(method = "lm") +
labs(
title = "Economic Damages Over Time",
x = "Year",
y = "Dollars"
)
### Average injuries and fatalities by year
harm_time <- storm %>%
group_by(BGN_YEAR) %>%
summarise(
sum_injuries = sum(INJURIES, na.rm = TRUE),
sum_fatalities = sum(FATALITIES, na.rm = TRUE),
)
### Pivot longer
harm_time <- harm_time %>%
pivot_longer(
cols = c(sum_injuries, sum_fatalities),
names_to = "harm_type",
values_to = "count"
)
### Plot population health effects over time for all storm types combined
p2 <- ggplot(harm_time,
aes(x = BGN_YEAR, y = count, colour = harm_type)) +
geom_point() +
geom_smooth(method = "lm") +
labs(
title = "Population Health Impacts Over time",
x = "Year",
y = "Number of Injuries/Fatalities"
)
### Display the plots in a grid
plot_grid(p2, p1, nrow = 2)## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
In terms of population health effects, the number of injuries for all storm events has increased, whereas the number of fatalities has remained steady. There is an especially high instance of injury at about 1996 and 2013. As far as economic damages, there is an increase in property damage from storms over time. There is a marked increase for the year 2006. Crop damages have been steady but decreased slightly.