This report examines the severity of various natural events. Both the population health and economic damages are considered. The most commonly reported events are thunderstorm related, including high winds, hail, floods, tornadoes, lightning, and heavy rain. Considering population health, tsunamis are clearly the most dangerous both by fatalities and by injuries, though tsunamis are very rare - heat and hurricanes are more common and also significantly fatal and injurious. Considering economic consequences, the greatest property damage comes from coastal events, including hurricanes, storm surges/tides, tropical storms and tsunamis. Hurricanes are also the greatest damage to crops, thought droughts and temperature-related events such as ice storms and frosts/freezes are also significant. Overall attention should be devoted to coastal events such as hurricanes, tsunamis, tropical storms, storm surges and related events such as high winds and heavy rains.
The data are from the National Weather Service. Documentations of the database are available at National Weather Service Storm Data Documentation and at National Climatic Data Center Storm Events FAQ. The data spans from 1950 to 2011. Earlier years may be less complete reflecting a lack of good records. Later years should have a more complete presentation of storm events.
The data is read directly from the bz2 zip file into a dataframe, raw.data. The data column names and “evtype” column are then converted to lowercase values for ease of coding.
raw.data <- read.csv("repdata-data-StormData.csv.bz2" ##read data from zip file
,
header = TRUE, nrows = 902297, comment.char = "", fileEncoding = "ISO-8859-15")
colnames(raw.data) <- tolower(colnames(raw.data)) ##lowercase column names for easier coding
raw.data$evtype <- tolower(raw.data$evtype) ##lowercase evtype for easier coding
After the load, the data is summarized and the first few rows are checked. The dataframe has 902,297 observations of 37 variables.
str(raw.data)
## 'data.frame': 902297 obs. of 37 variables:
## $ state__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ bgn_date : Factor w/ 16335 levels "1/1/1966 0:00:00",..: 6523 6523 4242 11116 2224 2224 2260 383 3980 3980 ...
## $ bgn_time : Factor w/ 3608 levels "00:00:00 AM",..: 272 287 2705 1683 2584 3186 242 1683 3186 3186 ...
## $ time_zone : Factor w/ 22 levels "ADT","AKS","AST",..: 7 7 7 7 7 7 7 7 7 7 ...
## $ county : num 97 3 57 89 43 77 9 123 125 57 ...
## $ countyname: Factor w/ 29601 levels "","5NM E OF MACKINAC BRIDGE TO PRESQUE ISLE LT MI",..: 13513 1873 4598 10592 4372 10094 1973 23873 24418 4598 ...
## $ state : Factor w/ 72 levels "AK","AL","AM",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ evtype : chr "tornado" "tornado" "tornado" "tornado" ...
## $ bgn_range : num 0 0 0 0 0 0 0 0 0 0 ...
## $ bgn_azi : Factor w/ 35 levels ""," N"," NW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ bgn_locati: Factor w/ 54429 levels "","- 1 N Albion",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ end_date : Factor w/ 6663 levels "","1/1/1993 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ end_time : Factor w/ 3647 levels ""," 0900CST",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 : Factor w/ 24 levels "","E","ENE","ESE",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ end_locati: Factor w/ 34506 levels "","- .5 NNW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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: Factor w/ 19 levels "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
## $ cropdmg : num 0 0 0 0 0 0 0 0 0 0 ...
## $ cropdmgexp: Factor w/ 9 levels "","?","0","2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ wfo : Factor w/ 542 levels ""," CI","$AC",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ stateoffic: Factor w/ 250 levels "","ALABAMA, Central",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ zonenames : Factor w/ 25112 levels ""," "| __truncated__,..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 : Factor w/ 436781 levels "","-2 at Deer Park\n",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ refnum : num 1 2 3 4 5 6 7 8 9 10 ...
head(raw.data)
## state__ bgn_date bgn_time time_zone county countyname state
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL
## evtype bgn_range bgn_azi bgn_locati end_date end_time county_end
## 1 tornado 0 0
## 2 tornado 0 0
## 3 tornado 0 0
## 4 tornado 0 0
## 5 tornado 0 0
## 6 tornado 0 0
## countyendn end_range end_azi end_locati length width f mag fatalities
## 1 NA 0 14.0 100 3 0 0
## 2 NA 0 2.0 150 2 0 0
## 3 NA 0 0.1 123 2 0 0
## 4 NA 0 0.0 100 2 0 0
## 5 NA 0 0.0 150 2 0 0
## 6 NA 0 1.5 177 2 0 0
## injuries propdmg propdmgexp cropdmg cropdmgexp wfo stateoffic zonenames
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## 3 2 25.0 K 0
## 4 2 2.5 K 0
## 5 2 2.5 K 0
## 6 6 2.5 K 0
## latitude longitude latitude_e longitude_ remarks refnum
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3
## 4 3458 8626 0 0 4
## 5 3412 8642 0 0 5
## 6 3450 8748 0 0 6
propdmg, propdmgexp, cropdmg, and cropdmgexp
propdmg and cropdmg represent the numeric value of property damage and crop damage, respectively. propdmgexp and cropdmgexp represent the units (i.e. “M” represents units in millions).
unique(raw.data$propdmgexp)
## [1] K M B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels: - ? + 0 1 2 3 4 5 6 7 8 B h H K m M
unique(raw.data$cropdmgexp)
## [1] M K m B ? 0 k 2
## Levels: ? 0 2 B k K m M
The values of PROPDMG and CROPDMG are modified to match to the units of PROPDMGEXP and CROPDMGEXP, respectively (i.e. if PROPDMGEXP is 'M', then multiply PROPDMG by one million).
options(scipen = 999) ##turn off scientific notation
## the following line of code multiplies the propdmg/cropdmg column based
## upon the -exp field.
raw.data$propdmg <- ifelse(raw.data$propdmgexp == "H", raw.data$propdmg * 100,
raw.data$propdmg)
raw.data$propdmg <- ifelse(raw.data$propdmgexp == "h", raw.data$propdmg * 100,
raw.data$propdmg)
raw.data$propdmg <- ifelse(raw.data$propdmgexp == "K", raw.data$propdmg * 1000,
raw.data$propdmg)
raw.data$propdmg <- ifelse(raw.data$propdmgexp == "M", raw.data$propdmg * 1e+06,
raw.data$propdmg)
raw.data$propdmg <- ifelse(raw.data$propdmgexp == "m", raw.data$propdmg * 1e+06,
raw.data$propdmg)
raw.data$propdmg <- ifelse(raw.data$propdmgexp == "B", raw.data$propdmg * 1e+09,
raw.data$propdmg)
raw.data$cropdmg <- ifelse(raw.data$cropdmgexp == "K", raw.data$cropdmg * 1000,
raw.data$cropdmg)
raw.data$cropdmg <- ifelse(raw.data$cropdmgexp == "k", raw.data$cropdmg * 1000,
raw.data$cropdmg)
raw.data$cropdmg <- ifelse(raw.data$cropdmgexp == "M", raw.data$cropdmg * 1e+06,
raw.data$cropdmg)
raw.data$cropdmg <- ifelse(raw.data$cropdmgexp == "m", raw.data$cropdmg * 1e+06,
raw.data$cropdmg)
raw.data$cropdmg <- ifelse(raw.data$cropdmgexp == "B", raw.data$cropdmg * 1e+09,
raw.data$cropdmg)
Relevant Columns
The evtype, fatalities, injuries, propdmg and cropdmg columns are of interest. This code creates a dataframe for each area of interest - event fatalities, injuries, property damage and crop damage. The following code creates a new dataframe, “event”, which eliminates the unnecessary columns.
event <- raw.data[, c("evtype" ##drop irrelevant data columns
,
"fatalities", "injuries", "propdmg", "cropdmg")]
evtype
The following is a list of the various evtype variables recorded. Notice these do not match the event types listed in the description of the date (linked above).
unique(event$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" "record dry month"
## [603] "temperature record" "ice jam flood (minor"
## [605] "rough surf" "marine accident"
## [607] "coastal storm" "coastalflood"
## [609] "erosion/cstl flood" "heavy rain and wind"
## [611] "light snow/flurries" "wet month"
## [613] "wet year" "tidal flooding"
## [615] "beach erosion" "hot and dry"
## [617] "heavy rain/high surf" "rain damage"
## [619] "unseasonable cold" "street flooding"
## [621] "ice fog" "excessive cold"
## [623] "torrential rainfall" "landslump"
## [625] "late-season snowfall" "hurricane edouard"
## [627] "heavy rain/wind" "record warm temps."
## [629] "extended cold" "freezing fog"
## [631] "drifting snow" "whirlwind"
## [633] "heavy snow shower" "late snow"
## [635] "record may snow" "record winter snow"
## [637] " coastal flood" "record temperature"
## [639] "late season snowfall" "gusty wind"
## [641] "mixed precip" "black ice"
## [643] "gradient wind" "freezing spray"
## [645] "summary jan 17" "summary of march 14"
## [647] "summary of march 23" "summary of march 24"
## [649] "summary of april 3rd" "summary of april 12"
## [651] "summary of april 13" "summary of april 21"
## [653] "summary august 11" "summary of april 27"
## [655] "summary of may 9-10" "summary of may 10"
## [657] "summary of may 13" "summary of may 14"
## [659] "summary of may 22 am" "summary of may 22 pm"
## [661] "heatburst" "summary of may 26 am"
## [663] "summary of may 26 pm" "metro storm, may 26"
## [665] "summary of may 31 am" "summary of may 31 pm"
## [667] "summary of june 3" "summary of june 4"
## [669] "summary june 5-6" "summary june 6"
## [671] "summary of june 11" "summary of june 12"
## [673] "summary of june 13" "summary of june 15"
## [675] "summary of june 16" "summary june 18-19"
## [677] "summary of june 23" "summary of june 24"
## [679] "summary of june 30" "summary of july 2"
## [681] "summary of july 3" "summary of july 11"
## [683] "summary of july 22" "summary july 23-24"
## [685] "summary of july 26" "summary of july 29"
## [687] "summary of august 1" "summary august 2-3"
## [689] "summary august 7" "summary august 9"
## [691] "summary august 10" "summary august 17"
## [693] "summary august 21" "summary august 28"
## [695] "summary september 4" "summary september 20"
## [697] "summary september 23" "summary sept. 25-26"
## [699] "summary: oct. 20-21" "summary: october 31"
## [701] "summary: nov. 6-7" "summary: nov. 16"
## [703] "wet micoburst" "hail(0.75)"
## [705] "no severe weather" "summary of may 22"
## [707] "summary of june 6" "summary august 4"
## [709] "summary of june 10" "summary of june 18"
## [711] "summary september 3" "summary: sept. 18"
## [713] "light snowfall" "gusty wind/rain"
## [715] "gusty wind/hvy rain" "early snowfall"
## [717] "monthly snowfall" "seasonal snowfall"
## [719] "monthly rainfall" "cold temperature"
## [721] "sml stream fld" "mudslide/landslide"
## [723] "volcanic ash" "volcanic ash plume"
## [725] "thundersnow shower" "none"
## [727] "cold and snow" "dam break"
## [729] "tstm wind (g45)" "sleet/freezing rain"
## [731] "blow-out tides" "unseasonably cool"
## [733] "tstm heavy rain" "tstm wind 40"
## [735] "tstm wind 45" "tstm wind (41)"
## [737] "tstm wind (g40)" "tstm wnd"
## [739] " tstm wind" "frost/freeze"
## [741] "rain (heavy)" "cold and frost"
## [743] "urban/sml stream fldg" "strong wind gust"
## [745] "late freeze" "blow-out tide"
## [747] "hypothermia/exposure" "mixed precipitation"
## [749] "coastalstorm" "snow and sleet"
## [751] "blizzard summary" "summary of march 24-25"
## [753] "summary of march 27" "summary of march 29"
## [755] "icestorm/blizzard" "flood/strong wind"
## [757] "tstm wind and lightning" "mountain snows"
## [759] "urban/small strm fldg" "heavy surf and wind"
## [761] "mild and dry pattern" "typhoon"
## [763] "high swells" "high swells"
## [765] "dry spell" " lightning"
## [767] "unseasonal rain" "early rain"
## [769] "prolonged rain" "wintery mix"
## [771] "coastal flooding/erosion" "hot spell"
## [773] "unseasonably hot" " tstm wind (g45)"
## [775] "tstm wind (g45)" "high wind (g40)"
## [777] "tstm wind (g35)" "dry weather"
## [779] "abnormal warmth" "unusual warmth"
## [781] "wake low wind" "cold temperatures"
## [783] "cold wind chill temperatures" "moderate snow"
## [785] "moderate snowfall" "urban/street flooding"
## [787] "coastal erosion" "unusual/record warmth"
## [789] "bitter wind chill" "bitter wind chill temperatures"
## [791] "seiche" "tstm"
## [793] "coastal flooding/erosion" "unseasonably warm year"
## [795] "hyperthermia/exposure" "rock slide"
## [797] "ice pellets" "patchy dense fog"
## [799] "record cool" "record warm"
## [801] "hot weather" "tropical depression"
## [803] "volcanic eruption" "cool spell"
## [805] "wind advisory" "gusty wind/hail"
## [807] "red flag fire wx" "first frost"
## [809] "excessively dry" "light snow/freezing precip"
## [811] "vog" "monthly precipitation"
## [813] "monthly temperature" "record dryness"
## [815] "extreme windchill temperatures" "dry conditions"
## [817] "remnants of floyd" "landspout"
## [819] "driest month" "record cold"
## [821] "late season hail" "excessive snow"
## [823] "dryness" "flood/flash/flood"
## [825] "wind and wave" "light freezing rain"
## [827] " wind" "record precipitation"
## [829] "ice roads" "rough seas"
## [831] "unseasonably warm/wet" "unseasonably cool & wet"
## [833] "unusually warm" "tstm wind g45"
## [835] "non severe hail" "non-severe wind damage"
## [837] "unusually cold" "warm weather"
## [839] "thunderstorm wind (g40)" "unseasonably warm & wet"
## [841] " flash flood" "locally heavy rain"
## [843] "wind gusts" "unseasonal low temp"
## [845] "high surf advisory" "late season snow"
## [847] "gusty lake wind" "abnormally dry"
## [849] "winter weather mix" "red flag criteria"
## [851] "wnd" "cstl flooding/erosion"
## [853] "smoke" " waterspout"
## [855] "snow advisory" "extremely wet"
## [857] "unusually late snow" "very dry"
## [859] "record low rainfall" "rogue wave"
## [861] "prolong warmth" "accumulated snowfall"
## [863] "falling snow/ice" "dust devel"
## [865] "non-tstm wind" "non tstm wind"
## [867] "gusty thunderstorm winds" "patchy ice"
## [869] "heavy rain effects" "excessive heat/drought"
## [871] "northern lights" "marine tstm wind"
## [873] " high surf advisory" "hazardous surf"
## [875] "winter weather/mix" "astronomical high tide"
## [877] "very warm" "abnormally wet"
## [879] "tornado debris" "extreme cold/wind chill"
## [881] "ice on road" "drowning"
## [883] "gusty thunderstorm wind" "marine hail"
## [885] "high surf advisories" "hurricane/typhoon"
## [887] "heavy surf/high surf" "sleet storm"
## [889] "storm surge/tide" "cold/wind chill"
## [891] "marine high wind" "tsunami"
## [893] "dense smoke" "lakeshore flood"
## [895] "marine thunderstorm wind" "marine strong wind"
## [897] "astronomical low tide" "volcanic ashfall"
The evtype column is a factor with 985 variables, most of which need to be cleaned. The description of the data (linked above) describes 48 different event types. The following code attempts to clean the data and coerce values into the 48 different types as best as possible. While the following code may innappropriately group events, it is impossible to accurately allocate many values to the appropriate events with further extensive and costly research, so logical assumptions were used to categorize events.
event$evtype <- gsub(".*blizzard.*", "blizzard", event$evtype)
event$evtype <- gsub(".*chill.*", "cold/wind chill", event$evtype)
event$evtype <- gsub(".*dry.*", "drought", event$evtype)
event$evtype <- gsub(".*fog.*", "dense fog", event$evtype)
event$evtype <- gsub(".*frost.*", "frost/freeze", event$evtype)
event$evtype <- gsub(".*freeze.*", "frost/freeze", event$evtype)
event$evtype <- gsub(".*funnel.*", "funnel cloud", event$evtype)
event$evtype <- gsub(".*hurricane.*", "hurricane (typhoon)", event$evtype)
event$evtype <- gsub(".*ice.*", "ice storm", event$evtype)
event$evtype <- gsub(".*lightning.*", "lightning", event$evtype)
event$evtype <- gsub(".*current.*", "rip current", event$evtype)
event$evtype <- gsub(".*surge.*", "storm surge/tide", event$evtype)
event$evtype <- gsub(".*fire.*", "wildfire", event$evtype)
event$evtype <- gsub(".*cold.*", "cold/wind chill", event$evtype)
event$evtype <- gsub(".*dust.*", "dust storm", event$evtype)
event$evtype <- gsub(".*heat.*", "heat", event$evtype)
event$evtype <- gsub(".*flood.*", "flood", event$evtype)
event$evtype <- gsub(".*torn.*", "tornado", event$evtype)
event$evtype <- gsub(".*freezing.*", "ice storm", event$evtype)
event$evtype <- gsub(".*rain.*", "heavy rain", event$evtype)
event$evtype <- gsub(".*snow.*", "heavy snow", event$evtype)
event$evtype <- gsub(".*surf.*", "high surf", event$evtype)
event$evtype <- gsub(".*cold.*", "cold", event$evtype) ##removes "wind" from cold/wind chill evtype to protect from "wind" gsub
event$evtype <- gsub(".*wind.*", "high wind", event$evtype)
event$evtype <- gsub(".*cold.*", "cold/wind chill", event$evtype) ##converts back to appropriate evtype
event$evtype <- gsub(".*thunderstorm.*", "thunderstorm wind", event$evtype)
event$evtype <- gsub(".*tide.*", "storm surge/tide", event$evtype)
event$evtype <- gsub(".*hail.*", "hail", event$evtype)
event$evtype <- gsub(".*sea.*", "high surf", event$evtype)
event$evtype <- gsub(".*winter storm.*", "winter storm", event$evtype)
event$evtype <- gsub(".*winter weather.*", "winter weather", event$evtype)
event$evtype <- gsub(".*waterspout.*", "waterspout", event$evtype)
event$evtype <- gsub(".*tropical storm.*", "tropical storm", event$evtype)
event$evtype <- gsub(".*heavy shower.*", "heavy rain", event$evtype)
event$evtype <- gsub(".*mix.*", "winter weather", event$evtype)
event$evtype <- gsub(".*water.*", "flood", event$evtype)
event$evtype <- gsub(".*fld.*", "flood", event$evtype)
event$evtype <- gsub(".*wave.*", "high surf", event$evtype)
event$evtype <- gsub(".*light.*", "lightning", event$evtype)
event$evtype <- gsub(".*lignt.*", "lightning", event$evtype)
event$evtype <- gsub(".*slide.*", "debris flow", event$evtype)
event$evtype <- gsub(".*vol.*", "volcanic ash", event$evtype)
event$evtype <- gsub(".*sleet.*", "sleet", event$evtype)
event$evtype <- gsub(".*warm.*", "heat", event$evtype)
event$evtype <- gsub(".*smoke.*", "dense smoke", event$evtype)
event$evtype <- gsub(".*driest.*", "drought", event$evtype)
event$evtype <- gsub(".*vog.*", "dense fog", event$evtype)
event$evtype <- gsub(".*cool.*", "cold/wind chill", event$evtype)
event$evtype <- gsub(".*hot.*", "heat", event$evtype)
event$evtype <- gsub(".*swell.*", "high surf", event$evtype)
event$evtype <- gsub(".*astro.*", "astro", event$evtype) ##removes "low" to protect from "low" gsub
event$evtype <- gsub(".*low.*", "cold/wind chill", event$evtype)
event$evtype <- gsub(".*astro.*", "astronomical low tide", event$evtype) ##recodes back to appropriate evtype
event$evtype <- gsub(".*icy.*", "winter weather", event$evtype)
event$evtype <- gsub(".*below.*", "drought", event$evtype)
event$evtype <- gsub(".*wall.*", "funnel cloud", event$evtype)
event$evtype <- gsub(".*wayter.*", "waterspout", event$evtype)
event$evtype <- gsub(".*burst.*", "thunderstorm wind", event$evtype)
event$evtype <- gsub(".*record high.*", "excessive heat", event$evtype)
event$evtype <- gsub(".*high temperature.*", "heat", event$evtype)
event$evtype <- gsub(".*flooo.*", "flood", event$evtype)
event$evtype <- gsub(".*wet.*", "heavy rain", event$evtype)
event <- event[event$evtype %in% c("astronomical low tide", ##drops remaining unclear observations
"avalanche",
"blizzard",
"coastal flood",
"cold/wind chill",
"debris flow",
"dense fog",
"dense smoke",
"drought",
"dust devil",
"dust storm",
"excessive heat",
"extreme cold/wind chill",
"flash flood",
"flood",
"frost/freeze",
"funnel cloud",
"freezing fog",
"hail",
"heat",
"heavy rain",
"heavy snow",
"high surf",
"high wind",
"hurricane (typhoon)",
"ice storm",
"lake-effect snow",
"lakeshore flood",
"lightning",
"marine hail",
"marine high wind",
"marine strong wind",
"marine thunderstorm wind",
"rip current",
"seiche",
"sleet",
"storm surge/tide",
"strong wind",
"thunderstorm wind",
"tornado",
"tropical depression",
"tropical storm",
"tsunami",
"volcanic ash",
"waterspout",
"wildfire",
"winter storm",
"winter weather"
),]
Aggregating Values
The raw data is reshaped into dataframes for each pertinent column calculating the mean by evtype.
library(plyr)
## Warning: package 'plyr' was built under R version 3.0.3
## reshapes new dataframes for each column of concern, aggregating by mean
mean.fatalities <- ddply(event, c("evtype"), function(event) data.frame(mean_fatalities = mean(event$fatalities,
na.rm = TRUE)))
mean.injuries <- ddply(event, c("evtype"), function(event) data.frame(mean_injuries = mean(event$injuries,
na.rm = TRUE)))
mean.propdmg <- ddply(event, c("evtype"), function(event) data.frame(mean_propdmg = mean(event$propdmg,
na.rm = TRUE)))
mean.cropdmg <- ddply(event, c("evtype"), function(event) data.frame(mean_cropdmg = mean(event$cropdmg,
na.rm = TRUE)))
The raw data is reshaped into dataframes for each pertinent column calculating the sum by evtype.
## reshapes new dataframes for each column of concern, aggregating by sum
sum.fatalities <- ddply(event, c("evtype"), function(event) data.frame(sum_fatalities = sum(event$fatalities,
na.rm = TRUE)))
sum.injuries <- ddply(event, c("evtype"), function(event) data.frame(sum_injuries = sum(event$injuries,
na.rm = TRUE)))
sum.propdmg <- ddply(event, c("evtype"), function(event) data.frame(sum_propdmg = sum(event$propdmg,
na.rm = TRUE)))
sum.cropdmg <- ddply(event, c("evtype"), function(event) data.frame(sum_cropdmg = sum(event$cropdmg,
na.rm = TRUE)))
The standard deviation from the mean is calculated for each evtype mean and these values are appended to the dataframes.
## creates formula to create a numeric object containing the deviations from
## average of means for each evtype
deviation <- function(meancolumn) {
dev <- as.numeric()
mean.value <- mean(meancolumn)
st.deviation <- sd(meancolumn)
for (x in meancolumn) {
deviation <- (x - mean.value)/st.deviation ##calculates number of deviations from mean
dev <- c(dev, deviation)
}
return(dev)
}
## appends deviation numeric object to dataframe for each variable
mean.fatalities$mean_fatalities_deviation <- deviation(mean.fatalities$mean_fatalities)
mean.injuries$mean_injuries_deviation <- deviation(mean.injuries$mean_injuries)
mean.propdmg$mean_propdmg_deviation <- deviation(mean.propdmg$mean_propdmg)
mean.cropdmg$mean_cropdmg_deviation <- deviation(mean.cropdmg$mean_cropdmg)
The mean and sum dataframes are combined.
mean.fatalities$sum_fatalities <- sum.fatalities$sum_fatalities
mean.injuries$sum_injuries <- sum.injuries$sum_injuries
mean.propdmg$sum_propdmg <- sum.propdmg$sum_propdmg
mean.cropdmg$sum_cropdmg <- sum.cropdmg$sum_cropdmg
Frequency of Events
The following code displays the number of observations of each type of event.
count.evtype <- data.frame(table(event$evtype)) ##creates frequency table
colnames(count.evtype) <- c("evtype", "frequency")
count.evtype
## evtype frequency
## 1 avalanche 386
## 2 blizzard 2745
## 3 cold/wind chill 3373
## 4 dense fog 1884
## 5 dense smoke 21
## 6 drought 2796
## 7 dust storm 589
## 8 excessive heat 11
## 9 flood 89969
## 10 frost/freeze 1512
## 11 funnel cloud 7002
## 12 hail 289274
## 13 heat 2837
## 14 heavy rain 11890
## 15 heavy snow 17558
## 16 high surf 1261
## 17 high wind 362994
## 18 hurricane (typhoon) 288
## 19 ice storm 2554
## 20 lightning 15781
## 21 rip current 777
## 22 seiche 21
## 23 sleet 71
## 24 storm surge/tide 692
## 25 thunderstorm wind 111
## 26 tornado 60700
## 27 tropical depression 60
## 28 tropical storm 697
## 29 tsunami 20
## 30 volcanic ash 29
## 31 waterspout 1
## 32 wildfire 4239
## 33 winter storm 11436
## 34 winter weather 8341
High winds, hail, floods, tornados, lightning, and heavy rains are among the highest occurences, indicating that storm-related events are the most commonly recorded.
Fatalities and Injuries
The following code creates a table displaying the mean, deviation from the mean, and sum for both fatalities and injuries by evtype.
population.health <- merge(mean.fatalities, mean.injuries) ##merges two dataframes
population.health
## evtype mean_fatalities mean_fatalities_deviation
## 1 avalanche 0.58031088 1.17025
## 2 blizzard 0.03679417 -0.34168
## 3 cold/wind chill 0.15357249 -0.01683
## 4 dense fog 0.04299363 -0.32444
## 5 dense smoke 0.00000000 -0.44403
## 6 drought 0.01144492 -0.41220
## 7 dust storm 0.04074703 -0.33068
## 8 excessive heat 0.00000000 -0.44403
## 9 flood 0.01733931 -0.39580
## 10 frost/freeze 0.00132275 -0.44035
## 11 funnel cloud 0.00000000 -0.44403
## 12 hail 0.00005185 -0.44389
## 13 heat 1.10609799 2.63286
## 14 heavy rain 0.00883095 -0.41947
## 15 heavy snow 0.00820139 -0.42122
## 16 high surf 0.15067407 -0.02489
## 17 high wind 0.00324248 -0.43501
## 18 hurricane (typhoon) 0.46875000 0.85991
## 19 ice storm 0.04424432 -0.32096
## 20 lightning 0.05177112 -0.30002
## 21 rip current 0.74259974 1.62170
## 22 seiche 0.00000000 -0.44403
## 23 sleet 0.02816901 -0.36567
## 24 storm surge/tide 0.03468208 -0.34756
## 25 thunderstorm wind 0.00900901 -0.41897
## 26 tornado 0.09326194 -0.18460
## 27 tropical depression 0.00000000 -0.44403
## 28 tropical storm 0.09469154 -0.18062
## 29 tsunami 1.65000000 4.14586
## 30 volcanic ash 0.00000000 -0.44403
## 31 waterspout 0.00000000 -0.44403
## 32 wildfire 0.02123142 -0.38497
## 33 winter storm 0.01888772 -0.39149
## 34 winter weather 0.00827239 -0.42102
## sum_fatalities mean_injuries mean_injuries_deviation sum_injuries
## 1 224 0.4404145 -0.14595 170
## 2 101 0.2936248 -0.25067 806
## 3 518 0.1123629 -0.37999 379
## 4 81 0.5716561 -0.05231 1077
## 5 0 0.0000000 -0.46016 0
## 6 32 0.0118026 -0.45174 33
## 7 24 0.8200340 0.12489 483
## 8 0 0.0000000 -0.46016 0
## 9 1560 0.0968111 -0.39109 8710
## 10 2 0.0019841 -0.45874 3
## 11 0 0.0004284 -0.45985 3
## 12 15 0.0047395 -0.45678 1371
## 13 3138 3.2520268 1.86000 9226
## 14 105 0.0237174 -0.44324 282
## 15 144 0.0655542 -0.41339 1151
## 16 190 0.2204600 -0.30287 278
## 17 1177 0.0314303 -0.43773 11409
## 18 135 4.6111111 2.82963 1328
## 19 113 0.8629601 0.15552 2204
## 20 817 0.3315379 -0.22362 5232
## 21 577 0.6808237 0.02557 529
## 22 0 0.0000000 -0.46016 0
## 23 2 0.0000000 -0.46016 0
## 24 24 0.0621387 -0.41583 43
## 25 1 0.3513514 -0.20949 39
## 26 5661 1.5058814 0.61421 91407
## 27 0 0.0000000 -0.46016 0
## 28 66 0.5494978 -0.06812 383
## 29 33 6.4500000 4.14159 129
## 30 0 0.0000000 -0.46016 0
## 31 0 0.0000000 -0.46016 0
## 32 90 0.3793347 -0.18952 1608
## 33 216 0.1169990 -0.37669 1338
## 34 69 0.0805659 -0.40268 672
The following tables display the top 10 fatal and injurious events, respectively:
top10.fatalities <- head(population.health[order(-population.health$mean_fatalities),
c(1, 2)], n = 10)
top10.fatalities
## evtype mean_fatalities
## 29 tsunami 1.65000
## 13 heat 1.10610
## 21 rip current 0.74260
## 1 avalanche 0.58031
## 18 hurricane (typhoon) 0.46875
## 3 cold/wind chill 0.15357
## 16 high surf 0.15067
## 28 tropical storm 0.09469
## 26 tornado 0.09326
## 20 lightning 0.05177
top10.injuries <- head(population.health[order(-population.health$mean_injuries),
c(1, 5)], n = 10)
top10.injuries
## evtype mean_injuries
## 29 tsunami 6.4500
## 18 hurricane (typhoon) 4.6111
## 13 heat 3.2520
## 26 tornado 1.5059
## 19 ice storm 0.8630
## 7 dust storm 0.8200
## 21 rip current 0.6808
## 4 dense fog 0.5717
## 28 tropical storm 0.5495
## 1 avalanche 0.4404
For both fatalities and injuries, tsunamis are dramatically more dangerous than other events, though it is important to consider that tsunamis are rare, with only 20 records since 1950. More common and significantly dangerous events are hurricanes and heat.
The follow graphic displays a positive quandrant for each event type. Along the x-axis are the mean number of injuries, and along the y-axis are the mean number of fatalities. In each plot, the higher the dot the more fatal the event, and the further to the right the more injurious the event (i.e. the plot point for “tsunami” is all the way in the upper right-hand corner, signifying both fatal and injurious, while the plot point for “hurricane (typhoon)” is more to the right, but less than the mid-way point in height, signifying a less fatal event that is fairly high in injuries).
library(lattice)
## Warning: package 'lattice' was built under R version 3.0.3
xyplot(population.health$mean_fatalities ~ population.health$mean_injuries |
evtype, data = population.health, ylab = "Mean Fatalities", xlab = "Mean injuries")
Property and Crop Damages
The following code creates a table displaying the mean, deviation from the mean, and sum for both property damage and crop damage by evtype.
economic.consequences <- merge(mean.propdmg, mean.cropdmg) ##merges two dataframes
economic.consequences
## evtype mean_propdmg mean_propdmg_deviation sum_propdmg
## 1 avalanche 9641.97 -0.22969 3721800
## 2 blizzard 242227.30 -0.22516 664913950
## 3 cold/wind chill 170237.34 -0.22656 574210550
## 4 dense fog 13275.74 -0.22962 25011500
## 5 dense smoke 4761.90 -0.22979 100000
## 6 drought 376551.72 -0.22254 1052838600
## 7 dust storm 10760.83 -0.22967 6338130
## 8 excessive heat 0.00 -0.22988 0
## 9 flood 1862778.88 -0.19360 167592352782
## 10 frost/freeze 12698.41 -0.22963 19200000
## 11 funnel cloud 28.51 -0.22988 199600
## 12 hail 55221.15 -0.22880 15974043543
## 13 heat 7164.52 -0.22974 20325750
## 14 heavy rain 271942.66 -0.22458 3233398190
## 15 heavy snow 57280.42 -0.22876 1005729702
## 16 high surf 80500.00 -0.22831 101510500
## 17 high wind 43804.21 -0.22903 15900665729
## 18 hurricane (typhoon) 294292291.70 5.50229 84756180010
## 19 ice storm 1562464.33 -0.19945 3990533910
## 20 lightning 59503.07 -0.22872 939017947
## 21 rip current 209.78 -0.22987 163000
## 22 seiche 46666.67 -0.22897 980000
## 23 sleet 0.00 -0.22988 0
## 24 storm surge/tide 69327990.10 1.12048 47974969150
## 25 thunderstorm wind 10887707.66 -0.01781 1208535550
## 26 tornado 965289.93 -0.21108 58593098479
## 27 tropical depression 28950.00 -0.22931 1737000
## 28 tropical storm 11067992.18 -0.01430 7714390550
## 29 tsunami 7203100.00 -0.08958 144062000
## 30 volcanic ash 17241.38 -0.22954 500000
## 31 waterspout 0.00 -0.22988 0
## 32 wildfire 2004394.55 -0.19084 8496628500
## 33 winter storm 584907.07 -0.21849 6688997251
## 34 winter weather 3566.32 -0.22981 29746700
## mean_cropdmg mean_cropdmg_deviation sum_cropdmg
## 1 0.00 -0.26036 0
## 2 40823.32 -0.24821 112060000
## 3 432488.16 -0.13166 1458782550
## 4 0.00 -0.26036 0
## 5 0.00 -0.26036 0
## 6 4997346.57 1.22677 13972581000
## 7 6112.05 -0.25854 3600000
## 8 0.00 -0.26036 0
## 9 137642.93 -0.21940 12383597200
## 10 1320807.54 0.13269 1997061000
## 11 0.00 -0.26036 0
## 12 10532.70 -0.25722 3046837473
## 13 318811.87 -0.16548 904469280
## 14 79744.56 -0.23663 948162800
## 15 7669.61 -0.25807 134663100
## 16 7.93 -0.26035 10000
## 17 5533.95 -0.25871 2008789538
## 18 19150322.22 5.43845 5515292800
## 19 1968329.80 0.32538 5027114300
## 20 766.56 -0.26013 12097090
## 21 0.00 -0.26036 0
## 22 0.00 -0.26036 0
## 23 0.00 -0.26036 0
## 24 1235.55 -0.25999 855000
## 25 164459.46 -0.21142 18255000
## 26 6877.45 -0.25831 417461470
## 27 0.00 -0.26036 0
## 28 996981.35 0.03633 694896000
## 29 1000.00 -0.26006 20000
## 30 0.00 -0.26036 0
## 31 0.00 -0.26036 0
## 32 95136.03 -0.23205 403281630
## 33 2399.79 -0.25964 27444000
## 34 1798.35 -0.25982 15000000
The following tables display the top 10 property-damaging and crop-damaging events, respectively:
top5.propdmg <- head(economic.consequences[order(-economic.consequences$mean_propdmg),
c(1, 2)], n = 10)
top5.propdmg
## evtype mean_propdmg
## 18 hurricane (typhoon) 294292292
## 24 storm surge/tide 69327990
## 28 tropical storm 11067992
## 25 thunderstorm wind 10887708
## 29 tsunami 7203100
## 32 wildfire 2004395
## 9 flood 1862779
## 19 ice storm 1562464
## 26 tornado 965290
## 33 winter storm 584907
top5.cropdmg <- head(economic.consequences[order(-economic.consequences$mean_cropdmg),
c(1, 5)], n = 10)
top5.cropdmg
## evtype mean_cropdmg
## 18 hurricane (typhoon) 19150322
## 6 drought 4997347
## 19 ice storm 1968330
## 10 frost/freeze 1320808
## 28 tropical storm 996981
## 3 cold/wind chill 432488
## 13 heat 318812
## 25 thunderstorm wind 164459
## 9 flood 137643
## 32 wildfire 95136
The follow graphic displays a positive quandrant for each event type, similar to the plot for fatalities and injuries above. Along the x-axis is crop damage and along the y-axis is property damage. Again, a point high on the y-axis represents high property damages, whereas a point further to the right represents high crop damages.
xyplot(economic.consequences$mean_propdmg ~ economic.consequences$mean_cropdmg |
evtype, data = economic.consequences, xlab = "Mean Crop Damages", ylab = "Mean Property Damage")
Again, hurricanes and coastal events rank score dangerous for property damage. Hurricanes are also the most significant damage to crops, but the following top damages are droughts and temperature-related events such as ice storms, frosts/freezes, cold and heat.
This report indicates that coastal events such as tsunamis, hurricanes, tropical thunderstorms, and storm surges are the most catastrophic events that warrant significant attention. Hurricanes are present in the top 10 lists for fatalities, injuries, property damage and crop damage. Policy makers are encouraged to focus disaster-prevention and remediation efforts toward coastal events.
sessionInfo()
## R version 3.0.2 (2013-09-25)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] lattice_0.20-29 plyr_1.8.1 knitr_1.5
##
## loaded via a namespace (and not attached):
## [1] evaluate_0.5.5 formatR_0.10 grid_3.0.2 Rcpp_0.11.1
## [5] stringr_0.6.2 tools_3.0.2