Peer Assessment 2
Gary Martin
March 21, 2016
Introduction
Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events can result in fatalities, injuries, and property damage, and preventing such outcomes to the extent possible is a key concern.
This project involves exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. This database tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage.
Data
The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. You can download the file from the course web site:
StormData [47Mb] There is also some documentation of the database available. Here you will find how some of the variables are constructed/defined.
National Weather Service Storm Data Documentation
National Climatic Data Center Storm Events FAQ
The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete.
Assignment
The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. You must use the database to answer the questions below and show the code for your entire analysis. Your analysis can consist of tables, figures, or other summaries. You may use any R package you want to support your analysis.
Questions
Your data analysis must address the following questions:
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to popuulation health?
Across the United States, which types of events have the greatest economic consequences?
Consider writing your report as if it were to be read by a government or municipal manager who might be responsible for preparing for severe weather events and will need to prioritize resources for different types of events. However, there is no need to make any specific recommendations in your report.
Requirements
For this assignment you will need some specific tools
RStudio: You will need RStudio to publish your completed analysis document to RPubs. You can also use RStudio to edit/write your analysis.
knitr: You will need the knitr package in order to compile your R Markdown document and convert it to HTML
Document Layout
Language: Your document should be written in English.
Title: Your document should have a title that briefly summarizes your data analysis
Synopsis: Immediately after the title, there should be a synopsis which describes and summarizes your analysis in at most 10 complete sentences.
There should be a section titled Data Processing which describes (in words and code) how the data were loaded into R and processed for analysis. In particular, your analysis must start from the raw CSV file containing the data. You cannot do any preprocessing outside the document. If preprocessing is time- consuming you may consider using the cache = TRUE option for certain code chunks.
There should be a section titled Results in which your results are presented.
You may have other sections in your analysis, but Data Processing and Results are required.
The analysis document must have at least one figure containing a plot.
Your analyis must have no more than three figures. Figures may have multiple plots in them (i.e. panel plots), but there cannot be more than three figures total.
You must show all your code for the work in your analysis document. This may make the document a bit verbose, but that is okay. In general, you should ensure that echo = TRUE for every code chunk (this is the default setting in knitr).
Publishing Your Analysis
For this assignment you will need to publish your analysis on RPubs.com. If you do not already have an account, then you will have to create a new account. After you have completed writing your analysis in RStudio, you can publish it to RPubs by doing the following:
In RStudio, make sure your R Markdown document (.Rmd) document is loaded in the editor
Click the Knit HTML button in the doc toolbar to preview your document.
In the preview window, click the Publish button.
Once your document is published to RPubs, you should get a unique URL to that document. Make a note of this URL as you will need it to submit your assignment.
Submitting Your Assignment
In order to submit this assignment, you must copy the RPubs URL for your completed data analysis document in to the peer assessment question.
Comparison of states on outcomes from storms and other severe weather events
Synopsis
In order to compare outcomes from multiple weather types, The U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database
Data from 1193 to 2011 was used to determine the number of fatalities, injuries, and the amount in dollars of property damage was caused by various types of weather categories including convective, extreme temps, flooding, and various other types.
Illinois suffered the greatest number of fatalities due to extreme temperature events (998), Texas suffered the greatest number of injuries due to flooding (6,951), and the highest amount of property damage was reported by California ($117.4 Billion).
The following demonstrates these findings:
Data Processing
Load packages.
packages <- c("data.table", "ggplot2", "xtable", "downloader", "chron")
sapply(packages, require, character.only=TRUE, quietly=TRUE)## data.table ggplot2 xtable downloader chron
## TRUE TRUE TRUE TRUE TRUEFix URL reading for knitr. See Stackoverflow.
setInternet2(TRUE)Download and unzip files
Download the storm data documentation files.
- National Weather Service Storm Data Documentation
- National Climatic Data Center Storm Events FAQ
url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf"
f <- file.path(getwd(), "StormDataDocumentation.pdf")
download.file(url, f, mode="wb")
url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2FNCDC%20Storm%20Events-FAQ%20Page.pdf"
f <- file.path(getwd(), "StormEventsFAQ.pdf")
download.file(url, f, mode="wb")Download the zipped storm data file.
url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
filep <- file.path(getwd(), "StormData.csv.bz2")
download.file(url, filep, mode="wb")Unzip and read the data file.
Read the CSV file as a data frame
library(R.utils)## Loading required package: R.oo
## Loading required package: R.methodsS3
## R.methodsS3 v1.7.0 (2015-02-19) successfully loaded. See ?R.methodsS3 for help.
## R.oo v1.19.0 (2015-02-27) successfully loaded. See ?R.oo for help.
##
## Attaching package: 'R.oo'
##
## The following objects are masked from 'package:methods':
##
## getClasses, getMethods
##
## The following objects are masked from 'package:base':
##
## attach, detach, gc, load, save
##
## R.utils v2.1.0 (2015-05-27) successfully loaded. See ?R.utils for help.
##
## Attaching package: 'R.utils'
##
## The following object is masked from 'package:utils':
##
## timestamp
##
## The following objects are masked from 'package:base':
##
## cat, commandArgs, getOption, inherits, isOpen, parse, warningsbunzip2("StormData.csv.bz2", "StormData.csv", remove = FALSE, skip = TRUE)## [1] "StormData.csv"
## attr(,"temporary")
## [1] FALSEdataset <- read.csv("StormData.csv")
Storm <- data.table(dataset)
str(Storm)## Classes 'data.table' and '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 : Factor w/ 985 levels " HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
## $ 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/ 436774 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 ...
## - attr(*, ".internal.selfref")=<externalptr>Clean data
Rename the variables to lowercase for ease of coding.
old <- names(Storm)
new <- tolower(old)
setnames(Storm, old, new)Convert the bgn_date character class variable to a date class variable.
bgn_date <- as.character(Storm$BGN_DATE)
bgn_date <- unlist(Storm$bgn_date)
bgn_date <- as.numeric(bgn_date)
bgn_date <- matrix(bgn_date, nrow=nrow(Storm), byrow=TRUE)reformat the property damage variable into a new variable, propertyDamage.
- If
propdmgexp=B, then multiplypropdmgby 1,000,000,000 - Else if
propdmgexp=M, then multiplypropdmgby 1,000,000 - Else if
propdmgexp=K, then multiplypropdmgby 1,000 - Else if
propdmgexp=H, then multiplypropdmgby 100 - Else leave
propdmgas-is
Storm <- Storm[, propdmgexp := toupper(propdmgexp)]
Storm[, .N, propdmgexp]## propdmgexp N
## 1: K 424665
## 2: M 11337
## 3: 465934
## 4: B 40
## 5: + 5
## 6: 0 216
## 7: 5 28
## 8: 6 4
## 9: ? 8
## 10: 4 4
## 11: 2 13
## 12: 3 4
## 13: H 7
## 14: 7 5
## 15: - 1
## 16: 1 25
## 17: 8 1Storm <- Storm[, propertyDamage := ifelse(propdmgexp == "B", propdmg * 1E9, ifelse(propdmgexp == "M", propdmg * 1E6, ifelse(propdmgexp == "K", propdmg * 1E3, ifelse(propdmgexp == "H", propdmg * 1E2, propdmg))))]
summary(Storm$propertyDamage)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000e+00 0.000e+00 0.000e+00 4.736e+05 5.000e+02 1.150e+11Reformat the crop damage variable into a new variable, cropDamage.
- If
cropdmgexp=B, then multiplycropdmgby 1,000,000,000 - Else if
cropdmgexp=M, then multiplycropdmgby 1,000,000 - Else if
cropdmgexp=K, then multiplycropdmgby 1,000 - Else leave
cropdmgas-is
Storm <- Storm[, cropdmgexp := toupper(cropdmgexp)]
Storm[, .N, cropdmgexp]## cropdmgexp N
## 1: 618413
## 2: M 1995
## 3: K 281853
## 4: B 9
## 5: ? 7
## 6: 0 19
## 7: 2 1Storm <- Storm[, cropDamage := ifelse(cropdmgexp == "B", cropdmg * 1E9, ifelse(cropdmgexp == "M", cropdmg * 1E6, ifelse(cropdmgexp == "K", cropdmg * 1E3, cropdmg)))]
summary(Storm$cropDamage)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000e+00 0.000e+00 0.000e+00 5.442e+04 0.000e+00 5.000e+09Group event types
List the number of unique values of evtype.
message(sprintf("Number of unique values of evtype: %.0d", length(unique(Storm$evtype))))## Number of unique values of evtype: 985Group event types as found in the 2009 Annual Summaries
evtype needs a lot of data cleaning.
Create indicator variables for event types that are most frequent and define a helper function freqtab to help with grouping evtype values.
indicator <- function (regex) {
indicator <- grepl(regex, Storm$evtype, ignore.case=TRUE)
uniqueEventTypes <- unique(Storm[indicator, evtype])
show(uniqueEventTypes[order(uniqueEventTypes)])
indicator
}Create an indicators for variations of weather: Lightning, Tornado, Thunderstorm Wind, Hail, Cold, Heat, Flood, Rain,Snow, Ice, Freeze, and Winter Weather. List the event types that fall into groups of Convection, Extreme Temperatures, Flood, and Winter.
regexLightning <- "\\bL\\S+?G\\b"
regexTornado <- "(NADO)|(\\bTOR\\S+?O\\b|(\\bFUN))"
regexThunderstorm <- "THUNDERSTORM|TSTM"
regexWind <- "(WIND)|(WND)"
regexHail <- "HAIL"
regex <- paste(regexLightning, regexTornado, regexThunderstorm, regexWind, regexHail, sep="|")
Storm <- Storm[, eventConvection := indicator(regex)]## [1] LIGHTNING TSTM WIND
## [3] TSTM WIND (G45) WIND
## [5] BITTER WIND CHILL BITTER WIND CHILL TEMPERATURES
## [7] BLIZZARD AND EXTREME WIND CHIL BLIZZARD/HIGH WIND
## [9] BLOWING SNOW- EXTREME WIND CHI BLOWING SNOW & EXTREME WIND CH
## [11] BLOWING SNOW/EXTREME WIND CHIL COLD AIR FUNNEL
## [13] COLD AIR FUNNELS COLD AIR TORNADO
## [15] COLD WIND CHILL TEMPERATURES COLD/WIND CHILL
## [17] COLD/WINDS DEEP HAIL
## [19] DOWNBURST WINDS DRY MICROBURST WINDS
## [21] DRY MIRCOBURST WINDS DUST STORM/HIGH WINDS
## [23] EXTREME COLD/WIND CHILL EXTREME WIND CHILL
## [25] EXTREME WIND CHILL/BLOWING SNO EXTREME WIND CHILLS
## [27] EXTREME WINDCHILL EXTREME WINDCHILL TEMPERATURES
## [29] FLASH FLOOD WINDS FLASH FLOODING/THUNDERSTORM WI
## [31] FLOOD/RAIN/WIND FLOOD/RAIN/WINDS
## [33] Flood/Strong Wind FUNNEL
## [35] Funnel Cloud FUNNEL CLOUD
## [37] FUNNEL CLOUD. FUNNEL CLOUD/HAIL
## [39] FUNNEL CLOUDS FUNNELS
## [41] gradient wind Gradient wind
## [43] GRADIENT WIND GRADIENT WINDS
## [45] GUSTNADO GUSTNADO AND
## [47] GUSTY LAKE WIND GUSTY THUNDERSTORM WIND
## [49] GUSTY THUNDERSTORM WINDS Gusty Wind
## [51] GUSTY WIND GUSTY WIND/HAIL
## [53] GUSTY WIND/HVY RAIN Gusty wind/rain
## [55] Gusty winds Gusty Winds
## [57] GUSTY WINDS HAIL
## [59] HAIL 0.75 HAIL 0.88
## [61] HAIL 075 HAIL 088
## [63] HAIL 1.00 HAIL 1.75
## [65] HAIL 1.75) HAIL 100
## [67] HAIL 125 HAIL 150
## [69] HAIL 175 HAIL 200
## [71] HAIL 225 HAIL 275
## [73] HAIL 450 HAIL 75
## [75] HAIL 80 HAIL 88
## [77] HAIL ALOFT HAIL DAMAGE
## [79] HAIL FLOODING HAIL STORM
## [81] Hail(0.75) HAIL/ICY ROADS
## [83] HAIL/WIND HAIL/WINDS
## [85] HAILSTORM HAILSTORMS
## [87] Heavy Rain and Wind HEAVY RAIN/LIGHTNING
## [89] HEAVY RAIN/WIND HEAVY RAIN; URBAN FLOOD WINDS;
## [91] HEAVY SNOW AND HIGH WINDS HEAVY SNOW AND STRONG WINDS
## [93] HEAVY SNOW/HIGH WIND HEAVY SNOW/HIGH WINDS
## [95] HEAVY SNOW/HIGH WINDS & FLOOD HEAVY SNOW/HIGH WINDS/FREEZING
## [97] HEAVY SNOW/WIND Heavy surf and wind
## [99] HIGH WINDS High Wind
## [101] HIGH WIND HIGH WIND (G40)
## [103] HIGH WIND 48 HIGH WIND 63
## [105] HIGH WIND 70 HIGH WIND AND HEAVY SNOW
## [107] HIGH WIND AND HIGH TIDES HIGH WIND AND SEAS
## [109] HIGH WIND DAMAGE HIGH WIND/ BLIZZARD
## [111] HIGH WIND/BLIZZARD HIGH WIND/BLIZZARD/FREEZING RA
## [113] HIGH WIND/HEAVY SNOW HIGH WIND/LOW WIND CHILL
## [115] HIGH WIND/SEAS HIGH WIND/WIND CHILL
## [117] HIGH WIND/WIND CHILL/BLIZZARD HIGH WINDS
## [119] HIGH WINDS 55 HIGH WINDS 57
## [121] HIGH WINDS 58 HIGH WINDS 63
## [123] HIGH WINDS 66 HIGH WINDS 67
## [125] HIGH WINDS 73 HIGH WINDS 76
## [127] HIGH WINDS 80 HIGH WINDS 82
## [129] HIGH WINDS AND WIND CHILL HIGH WINDS DUST STORM
## [131] HIGH WINDS HEAVY RAINS HIGH WINDS/
## [133] HIGH WINDS/COASTAL FLOOD HIGH WINDS/COLD
## [135] HIGH WINDS/FLOODING HIGH WINDS/HEAVY RAIN
## [137] HIGH WINDS/SNOW HURRICANE OPAL/HIGH WINDS
## [139] ICE/STRONG WINDS LATE SEASON HAIL
## [141] LIGHTING LIGHTNING
## [143] LIGHTNING WAUSEON LIGHTNING AND HEAVY RAIN
## [145] LIGHTNING AND THUNDERSTORM WIN LIGHTNING AND WINDS
## [147] LIGHTNING DAMAGE LIGHTNING FIRE
## [149] LIGHTNING INJURY LIGHTNING THUNDERSTORM WINDS
## [151] LIGHTNING THUNDERSTORM WINDSS LIGHTNING.
## [153] LIGHTNING/HEAVY RAIN LIGNTNING
## [155] LOW WIND CHILL MARINE HAIL
## [157] MARINE HIGH WIND MARINE STRONG WIND
## [159] MARINE THUNDERSTORM WIND MARINE TSTM WIND
## [161] MICROBURST WINDS NON-SEVERE WIND DAMAGE
## [163] NON-TSTM WIND NON SEVERE HAIL
## [165] NON TSTM WIND RAIN AND WIND
## [167] RAIN/WIND RECORD COLD AND HIGH WIND
## [169] SEVERE THUNDERSTORM SEVERE THUNDERSTORM WINDS
## [171] SEVERE THUNDERSTORMS small hail
## [173] Small Hail SMALL HAIL
## [175] SNOW- HIGH WIND- WIND CHILL SNOW AND WIND
## [177] SNOW/HIGH WINDS STORM FORCE WINDS
## [179] Strong Wind STRONG WIND
## [181] STRONG WIND GUST Strong winds
## [183] Strong Winds STRONG WINDS
## [185] THUDERSTORM WINDS THUNDEERSTORM WINDS
## [187] THUNDERESTORM WINDS THUNDERSTORM
## [189] THUNDERSTORM WINDS THUNDERSTORM DAMAGE
## [191] THUNDERSTORM DAMAGE TO THUNDERSTORM HAIL
## [193] THUNDERSTORM W INDS Thunderstorm Wind
## [195] THUNDERSTORM WIND THUNDERSTORM WIND (G40)
## [197] THUNDERSTORM WIND 50 THUNDERSTORM WIND 52
## [199] THUNDERSTORM WIND 56 THUNDERSTORM WIND 59
## [201] THUNDERSTORM WIND 59 MPH THUNDERSTORM WIND 59 MPH.
## [203] THUNDERSTORM WIND 60 MPH THUNDERSTORM WIND 65 MPH
## [205] THUNDERSTORM WIND 65MPH THUNDERSTORM WIND 69
## [207] THUNDERSTORM WIND 98 MPH THUNDERSTORM WIND G50
## [209] THUNDERSTORM WIND G51 THUNDERSTORM WIND G52
## [211] THUNDERSTORM WIND G55 THUNDERSTORM WIND G60
## [213] THUNDERSTORM WIND G61 THUNDERSTORM WIND TREES
## [215] THUNDERSTORM WIND. THUNDERSTORM WIND/ TREE
## [217] THUNDERSTORM WIND/ TREES THUNDERSTORM WIND/AWNING
## [219] THUNDERSTORM WIND/HAIL THUNDERSTORM WIND/LIGHTNING
## [221] THUNDERSTORM WINDS THUNDERSTORM WINDS LE CEN
## [223] THUNDERSTORM WINDS 13 THUNDERSTORM WINDS 2
## [225] THUNDERSTORM WINDS 50 THUNDERSTORM WINDS 52
## [227] THUNDERSTORM WINDS 53 THUNDERSTORM WINDS 60
## [229] THUNDERSTORM WINDS 61 THUNDERSTORM WINDS 62
## [231] THUNDERSTORM WINDS 63 MPH THUNDERSTORM WINDS AND
## [233] THUNDERSTORM WINDS FUNNEL CLOU THUNDERSTORM WINDS G
## [235] THUNDERSTORM WINDS G60 THUNDERSTORM WINDS HAIL
## [237] THUNDERSTORM WINDS HEAVY RAIN THUNDERSTORM WINDS LIGHTNING
## [239] THUNDERSTORM WINDS SMALL STREA THUNDERSTORM WINDS URBAN FLOOD
## [241] THUNDERSTORM WINDS. THUNDERSTORM WINDS/ FLOOD
## [243] THUNDERSTORM WINDS/ HAIL THUNDERSTORM WINDS/FLASH FLOOD
## [245] THUNDERSTORM WINDS/FLOODING THUNDERSTORM WINDS/FUNNEL CLOU
## [247] THUNDERSTORM WINDS/HAIL THUNDERSTORM WINDS/HEAVY RAIN
## [249] THUNDERSTORM WINDS53 THUNDERSTORM WINDSHAIL
## [251] THUNDERSTORM WINDSS THUNDERSTORM WINS
## [253] THUNDERSTORMS THUNDERSTORMS WIND
## [255] THUNDERSTORMS WINDS THUNDERSTORMW
## [257] THUNDERSTORMW 50 THUNDERSTORMW WINDS
## [259] THUNDERSTORMWINDS THUNDERSTROM WIND
## [261] THUNDERSTROM WINDS THUNDERTORM WINDS
## [263] THUNDERTSORM WIND THUNDESTORM WINDS
## [265] THUNERSTORM WINDS TORNADO
## [267] TORNADO DEBRIS TORNADO F0
## [269] TORNADO F1 TORNADO F2
## [271] TORNADO F3 TORNADO/WATERSPOUT
## [273] TORNADOES TORNADOES, TSTM WIND, HAIL
## [275] TORNADOS TORNDAO
## [277] TSTM TSTM HEAVY RAIN
## [279] Tstm Wind TSTM WIND
## [281] TSTM WIND (G45) TSTM WIND (41)
## [283] TSTM WIND (G35) TSTM WIND (G40)
## [285] TSTM WIND (G45) TSTM WIND 40
## [287] TSTM WIND 45 TSTM WIND 50
## [289] TSTM WIND 51 TSTM WIND 52
## [291] TSTM WIND 55 TSTM WIND 65)
## [293] TSTM WIND AND LIGHTNING TSTM WIND DAMAGE
## [295] TSTM WIND G45 TSTM WIND G58
## [297] TSTM WIND/HAIL TSTM WINDS
## [299] TSTM WND TSTMW
## [301] TUNDERSTORM WIND WAKE LOW WIND
## [303] WALL CLOUD/FUNNEL CLOUD WATERSPOUT-TORNADO
## [305] WATERSPOUT FUNNEL CLOUD WATERSPOUT TORNADO
## [307] WATERSPOUT/ TORNADO WATERSPOUT/TORNADO
## [309] Whirlwind WHIRLWIND
## [311] Wind WIND
## [313] WIND ADVISORY WIND AND WAVE
## [315] WIND CHILL WIND CHILL/HIGH WIND
## [317] Wind Damage WIND DAMAGE
## [319] WIND GUSTS WIND STORM
## [321] WIND/HAIL WINDS
## [323] WINTER STORM HIGH WINDS WINTER STORM/HIGH WIND
## [325] WINTER STORM/HIGH WINDS WND
## 985 Levels: HIGH SURF ADVISORY COASTAL FLOOD ... WNDregex <- "COLD|HEAT"
Storm <- Storm[, eventExtremeTemp := indicator(regex)]## [1] Cold COLD
## [3] COLD AIR FUNNEL COLD AIR FUNNELS
## [5] COLD AIR TORNADO Cold and Frost
## [7] COLD AND FROST COLD AND SNOW
## [9] COLD AND WET CONDITIONS Cold Temperature
## [11] COLD TEMPERATURES COLD WAVE
## [13] COLD WEATHER COLD WIND CHILL TEMPERATURES
## [15] COLD/WIND CHILL COLD/WINDS
## [17] DROUGHT/EXCESSIVE HEAT Excessive Cold
## [19] EXCESSIVE HEAT EXCESSIVE HEAT/DROUGHT
## [21] Extended Cold Extreme Cold
## [23] EXTREME COLD EXTREME COLD/WIND CHILL
## [25] EXTREME HEAT EXTREME/RECORD COLD
## [27] FOG AND COLD TEMPERATURES HEAT
## [29] HEAT DROUGHT Heat Wave
## [31] HEAT WAVE HEAT WAVE DROUGHT
## [33] HEAT WAVES HEAT/DROUGHT
## [35] Heatburst HIGH WINDS/COLD
## [37] Prolong Cold PROLONG COLD
## [39] PROLONG COLD/SNOW RECORD COLD
## [41] Record Cold RECORD COLD
## [43] RECORD COLD AND HIGH WIND RECORD COLD/FROST
## [45] Record Heat RECORD HEAT
## [47] RECORD HEAT WAVE RECORD SNOW/COLD
## [49] RECORD/EXCESSIVE HEAT SEVERE COLD
## [51] SNOW AND COLD SNOW/ BITTER COLD
## [53] SNOW/COLD SNOW\\COLD
## [55] Unseasonable Cold UNSEASONABLY COLD
## [57] UNUSUALLY COLD
## 985 Levels: HIGH SURF ADVISORY COASTAL FLOOD ... WNDregexFlood <- "(\\bFL\\S+?Storm)"
regexRain <- "RAIN|PRECIP|SHOWER"
regex <- paste(regexFlood, regexRain, sep="|")
Storm <- Storm[, eventFlood := indicator(regex)]## [1] BELOW NORMAL PRECIPITATION BLIZZARD/FREEZING RAIN
## [3] EARLY RAIN EXCESSIVE PRECIPITATION
## [5] EXCESSIVE RAIN EXCESSIVE RAINFALL
## [7] FLASH FLOOD - HEAVY RAIN FLASH FLOOD/HEAVY RAIN
## [9] FLASH FLOODING/THUNDERSTORM WI FLOOD & HEAVY RAIN
## [11] FLOOD/RAIN/WIND FLOOD/RAIN/WINDS
## [13] FLOODING/HEAVY RAIN Freezing rain
## [15] Freezing Rain FREEZING RAIN
## [17] FREEZING RAIN AND SLEET FREEZING RAIN AND SNOW
## [19] FREEZING RAIN SLEET AND FREEZING RAIN SLEET AND LIGHT
## [21] FREEZING RAIN/SLEET FREEZING RAIN/SNOW
## [23] GUSTY WIND/HVY RAIN Gusty wind/rain
## [25] HEAVY PRECIPATATION Heavy Precipitation
## [27] HEAVY PRECIPITATION Heavy rain
## [29] Heavy Rain HEAVY RAIN
## [31] HEAVY RAIN AND FLOOD Heavy Rain and Wind
## [33] HEAVY RAIN EFFECTS HEAVY RAIN/FLOODING
## [35] Heavy Rain/High Surf HEAVY RAIN/LIGHTNING
## [37] HEAVY RAIN/MUDSLIDES/FLOOD HEAVY RAIN/SEVERE WEATHER
## [39] HEAVY RAIN/SMALL STREAM URBAN HEAVY RAIN/SNOW
## [41] HEAVY RAIN/URBAN FLOOD HEAVY RAIN/WIND
## [43] HEAVY RAIN; URBAN FLOOD WINDS; HEAVY RAINFALL
## [45] HEAVY RAINS HEAVY RAINS/FLOODING
## [47] HEAVY SHOWER HEAVY SHOWERS
## [49] HEAVY SNOW FREEZING RAIN Heavy snow shower
## [51] HEAVY SNOW/FREEZING RAIN HIGH WINDS HEAVY RAINS
## [53] HIGH WINDS/HEAVY RAIN HVY RAIN
## [55] LIGHT FREEZING RAIN LIGHT SNOW/FREEZING PRECIP
## [57] LIGHTNING AND HEAVY RAIN LIGHTNING/HEAVY RAIN
## [59] LOCALLY HEAVY RAIN MIXED PRECIP
## [61] Mixed Precipitation MIXED PRECIPITATION
## [63] MONTHLY PRECIPITATION Monthly Rainfall
## [65] MONTHLY RAINFALL NORMAL PRECIPITATION
## [67] PROLONGED RAIN RAIN
## [69] RAIN (HEAVY) RAIN AND WIND
## [71] Rain Damage RAIN/SNOW
## [73] RAIN/WIND RAINSTORM
## [75] RECORD LOW RAINFALL RECORD PRECIPITATION
## [77] RECORD RAINFALL RECORD/EXCESSIVE RAINFALL
## [79] SLEET & FREEZING RAIN SLEET/FREEZING RAIN
## [81] SLEET/RAIN/SNOW SNOW FREEZING RAIN
## [83] SNOW SHOWERS SNOW/FREEZING RAIN
## [85] SNOW/RAIN SNOW/RAIN/SLEET
## [87] SNOW/SLEET/FREEZING RAIN SNOW/SLEET/RAIN
## [89] Thundersnow shower THUNDERSTORM WINDS HEAVY RAIN
## [91] THUNDERSTORM WINDS/HEAVY RAIN TORRENTIAL RAIN
## [93] Torrential Rainfall TSTM HEAVY RAIN
## [95] UNSEASONAL RAIN
## 985 Levels: HIGH SURF ADVISORY COASTAL FLOOD ... WNDregex <- "(SNOW)|(ICE)|(ICY)|(FREEZ)|(WINT)"
Storm <- Storm[, eventWinter := indicator(regex)]## [1] ACCUMULATED SNOWFALL AGRICULTURAL FREEZE
## [3] Black Ice BLACK ICE
## [5] BLIZZARD AND HEAVY SNOW BLIZZARD/FREEZING RAIN
## [7] BLIZZARD/HEAVY SNOW BLIZZARD/WINTER STORM
## [9] blowing snow Blowing Snow
## [11] BLOWING SNOW BLOWING SNOW- EXTREME WIND CHI
## [13] BLOWING SNOW & EXTREME WIND CH BLOWING SNOW/EXTREME WIND CHIL
## [15] COLD AND SNOW Damaging Freeze
## [17] DAMAGING FREEZE Drifting Snow
## [19] EARLY FREEZE EARLY SNOW
## [21] Early snowfall EARLY SNOWFALL
## [23] EXCESSIVE SNOW FALLING SNOW/ICE
## [25] FIRST SNOW FLASH FLOOD FROM ICE JAMS
## [27] Freeze FREEZE
## [29] Freezing drizzle Freezing Drizzle
## [31] FREEZING DRIZZLE FREEZING DRIZZLE AND FREEZING
## [33] Freezing Fog FREEZING FOG
## [35] Freezing rain Freezing Rain
## [37] FREEZING RAIN FREEZING RAIN AND SLEET
## [39] FREEZING RAIN AND SNOW FREEZING RAIN SLEET AND
## [41] FREEZING RAIN SLEET AND LIGHT FREEZING RAIN/SLEET
## [43] FREEZING RAIN/SNOW Freezing Spray
## [45] Frost/Freeze FROST/FREEZE
## [47] FROST\\FREEZE GLAZE ICE
## [49] GLAZE/ICE STORM HAIL/ICY ROADS
## [51] HARD FREEZE HEAVY LAKE SNOW
## [53] HEAVY RAIN/SNOW HEAVY SNOW
## [55] HEAVY SNOW-SQUALLS HEAVY SNOW FREEZING RAIN
## [57] HEAVY SNOW & ICE HEAVY SNOW AND
## [59] HEAVY SNOW AND HIGH WINDS HEAVY SNOW AND ICE
## [61] HEAVY SNOW AND ICE STORM HEAVY SNOW AND STRONG WINDS
## [63] HEAVY SNOW ANDBLOWING SNOW Heavy snow shower
## [65] HEAVY SNOW SQUALLS HEAVY SNOW/BLIZZARD
## [67] HEAVY SNOW/BLIZZARD/AVALANCHE HEAVY SNOW/BLOWING SNOW
## [69] HEAVY SNOW/FREEZING RAIN HEAVY SNOW/HIGH
## [71] HEAVY SNOW/HIGH WIND HEAVY SNOW/HIGH WINDS
## [73] HEAVY SNOW/HIGH WINDS & FLOOD HEAVY SNOW/HIGH WINDS/FREEZING
## [75] HEAVY SNOW/ICE HEAVY SNOW/ICE STORM
## [77] HEAVY SNOW/SLEET HEAVY SNOW/SQUALLS
## [79] HEAVY SNOW/WIND HEAVY SNOW/WINTER STORM
## [81] HEAVY SNOWPACK HEAVY WET SNOW
## [83] HIGH WIND AND HEAVY SNOW HIGH WIND/BLIZZARD/FREEZING RA
## [85] HIGH WIND/HEAVY SNOW HIGH WINDS/SNOW
## [87] ICE ICE AND SNOW
## [89] ICE FLOES Ice Fog
## [91] ICE JAM Ice jam flood (minor
## [93] ICE JAM FLOODING ICE ON ROAD
## [95] ICE PELLETS ICE ROADS
## [97] ICE STORM ICE STORM AND SNOW
## [99] ICE STORM/FLASH FLOOD Ice/Snow
## [101] ICE/SNOW ICE/STRONG WINDS
## [103] Icestorm/Blizzard Icy Roads
## [105] ICY ROADS LACK OF SNOW
## [107] LAKE-EFFECT SNOW Lake Effect Snow
## [109] LAKE EFFECT SNOW Late-season Snowfall
## [111] LATE FREEZE LATE SEASON SNOW
## [113] Late Season Snowfall LATE SNOW
## [115] LIGHT FREEZING RAIN Light snow
## [117] Light Snow LIGHT SNOW
## [119] LIGHT SNOW AND SLEET Light Snow/Flurries
## [121] LIGHT SNOW/FREEZING PRECIP Light Snowfall
## [123] MODERATE SNOW MODERATE SNOWFALL
## [125] Monthly Snowfall MONTHLY SNOWFALL
## [127] Mountain Snows NEAR RECORD SNOW
## [129] PATCHY ICE PROLONG COLD/SNOW
## [131] RAIN/SNOW Record May Snow
## [133] RECORD SNOW RECORD SNOW/COLD
## [135] RECORD SNOWFALL Record Winter Snow
## [137] Seasonal Snowfall SLEET & FREEZING RAIN
## [139] SLEET/FREEZING RAIN SLEET/ICE STORM
## [141] SLEET/RAIN/SNOW SLEET/SNOW
## [143] Snow SNOW
## [145] SNOW- HIGH WIND- WIND CHILL Snow Accumulation
## [147] SNOW ACCUMULATION SNOW ADVISORY
## [149] SNOW AND COLD SNOW AND HEAVY SNOW
## [151] Snow and Ice SNOW AND ICE
## [153] SNOW AND ICE STORM Snow and sleet
## [155] SNOW AND SLEET SNOW AND WIND
## [157] SNOW DROUGHT SNOW FREEZING RAIN
## [159] SNOW SHOWERS SNOW SLEET
## [161] SNOW SQUALL Snow squalls
## [163] Snow Squalls SNOW SQUALLS
## [165] SNOW/ BITTER COLD SNOW/ ICE
## [167] SNOW/BLOWING SNOW SNOW/COLD
## [169] SNOW/FREEZING RAIN SNOW/HEAVY SNOW
## [171] SNOW/HIGH WINDS SNOW/ICE
## [173] SNOW/ICE STORM SNOW/RAIN
## [175] SNOW/RAIN/SLEET SNOW/SLEET
## [177] SNOW/SLEET/FREEZING RAIN SNOW/SLEET/RAIN
## [179] SNOW\\COLD SNOWFALL RECORD
## [181] SNOWMELT FLOODING SNOWSTORM
## [183] THUNDERSNOW Thundersnow shower
## [185] UNUSUALLY LATE SNOW WET SNOW
## [187] WINTER MIX WINTER STORM
## [189] WINTER STORM HIGH WINDS WINTER STORM/HIGH WIND
## [191] WINTER STORM/HIGH WINDS WINTER STORMS
## [193] Winter Weather WINTER WEATHER
## [195] WINTER WEATHER MIX WINTER WEATHER/MIX
## [197] WINTERY MIX Wintry mix
## [199] Wintry Mix WINTRY MIX
## 985 Levels: HIGH SURF ADVISORY COASTAL FLOOD ... WNDCalculate the proportion of records that don’t satisfy any one of the defined indicators. Calculate the number of unique event types among these records. List the ungrouped unique event types.
where <- expression(eventConvection == FALSE & eventExtremeTemp == FALSE & eventFlood == FALSE & eventWinter == FALSE)
ungrouped <- Storm[eval(where), list(n = .N, prop = .N / nrow(Storm))]
prop <- Storm[eval(where), .N / nrow(Storm)]
message(sprintf("Number of records that does not satisfy any single indicator: %.0d (%.2f%%)", ungrouped$n, ungrouped$prop * 100))## Number of records that does not satisfy any single indicator: 107716 (11.94%)uniqueEvtype <- unique(Storm[eval(where), evtype])
message(sprintf("Number of unique event types: %.0d", length(uniqueEvtype)))## Number of unique event types: 391uniqueEvtype[order(uniqueEvtype)]## [1] HIGH SURF ADVISORY COASTAL FLOOD
## [3] FLASH FLOOD WATERSPOUT
## [5] ? ABNORMAL WARMTH
## [7] ABNORMALLY DRY ABNORMALLY WET
## [9] APACHE COUNTY ASTRONOMICAL HIGH TIDE
## [11] ASTRONOMICAL LOW TIDE AVALANCE
## [13] AVALANCHE BEACH EROSIN
## [15] Beach Erosion BEACH EROSION
## [17] BEACH EROSION/COASTAL FLOOD BEACH FLOOD
## [19] BLIZZARD Blizzard Summary
## [21] BLIZZARD WEATHER BLOW-OUT TIDE
## [23] BLOW-OUT TIDES BLOWING DUST
## [25] BREAKUP FLOODING BRUSH FIRE
## [27] BRUSH FIRES COASTAL FLOODING/EROSION
## [29] COASTAL EROSION Coastal Flood
## [31] COASTAL FLOOD coastal flooding
## [33] Coastal Flooding COASTAL FLOODING
## [35] COASTAL FLOODING/EROSION Coastal Storm
## [37] COASTAL STORM COASTAL SURGE
## [39] COASTAL/TIDAL FLOOD COASTALFLOOD
## [41] COASTALSTORM COOL AND WET
## [43] COOL SPELL CSTL FLOODING/EROSION
## [45] DAM BREAK DAM FAILURE
## [47] DENSE FOG DENSE SMOKE
## [49] DOWNBURST DRIEST MONTH
## [51] DROUGHT DROWNING
## [53] DRY DRY CONDITIONS
## [55] DRY HOT WEATHER DRY MICROBURST
## [57] DRY MICROBURST 50 DRY MICROBURST 53
## [59] DRY MICROBURST 58 DRY MICROBURST 61
## [61] DRY MICROBURST 84 DRY PATTERN
## [63] DRY SPELL DRY WEATHER
## [65] DRYNESS DUST DEVEL
## [67] Dust Devil DUST DEVIL
## [69] DUST DEVIL WATERSPOUT DUST STORM
## [71] DUSTSTORM Early Frost
## [73] EARLY FROST Erosion/Cstl Flood
## [75] EXCESSIVE EXCESSIVE WETNESS
## [77] EXCESSIVELY DRY EXTREMELY WET
## [79] FIRST FROST FLASH FLOOD
## [81] FLASH FLOOD LANDSLIDES FLASH FLOOD/
## [83] FLASH FLOOD/ FLOOD FLASH FLOOD/ STREET
## [85] FLASH FLOOD/FLOOD FLASH FLOOD/LANDSLIDE
## [87] FLASH FLOODING FLASH FLOODING/FLOOD
## [89] FLASH FLOODS FLASH FLOOODING
## [91] Flood FLOOD
## [93] FLOOD FLASH FLOOD FLOOD/FLASH
## [95] FLOOD WATCH/ FLOOD/FLASH
## [97] Flood/Flash Flood FLOOD/FLASH FLOOD
## [99] FLOOD/FLASH FLOODING FLOOD/FLASH/FLOOD
## [101] FLOOD/FLASHFLOOD FLOOD/RIVER FLOOD
## [103] FLOODING FLOODS
## [105] FOG FOREST FIRES
## [107] Frost FROST
## [109] Glaze GLAZE
## [111] GRASS FIRES GROUND BLIZZARD
## [113] HAZARDOUS SURF HEAVY MIX
## [115] HEAVY SEAS Heavy Surf
## [117] HEAVY SURF HEAVY SURF COASTAL FLOODING
## [119] HEAVY SURF/HIGH SURF HEAVY SWELLS
## [121] HIGH HIGH SWELLS
## [123] HIGH SEAS High Surf
## [125] HIGH SURF HIGH SURF ADVISORIES
## [127] HIGH SURF ADVISORY HIGH SWELLS
## [129] HIGH TEMPERATURE RECORD HIGH TIDES
## [131] HIGH WATER HIGH WAVES
## [133] HIGHWAY FLOODING Hot and Dry
## [135] HOT PATTERN HOT SPELL
## [137] HOT WEATHER HOT/DRY PATTERN
## [139] HURRICANE HURRICANE-GENERATED SWELLS
## [141] Hurricane Edouard HURRICANE EMILY
## [143] HURRICANE ERIN HURRICANE FELIX
## [145] HURRICANE GORDON HURRICANE OPAL
## [147] HURRICANE/TYPHOON HYPERTHERMIA/EXPOSURE
## [149] HYPOTHERMIA Hypothermia/Exposure
## [151] HYPOTHERMIA/EXPOSURE LAKE FLOOD
## [153] LAKESHORE FLOOD LANDSLIDE
## [155] LANDSLIDE/URBAN FLOOD LANDSLIDES
## [157] Landslump LANDSLUMP
## [159] LANDSPOUT LARGE WALL CLOUD
## [161] LOCAL FLASH FLOOD LOCAL FLOOD
## [163] LOW TEMPERATURE LOW TEMPERATURE RECORD
## [165] MAJOR FLOOD Marine Accident
## [167] MARINE MISHAP Metro Storm, May 26
## [169] Microburst MICROBURST
## [171] Mild and Dry Pattern MILD PATTERN
## [173] MILD/DRY PATTERN MINOR FLOOD
## [175] Minor Flooding MINOR FLOODING
## [177] MONTHLY TEMPERATURE MUD SLIDE
## [179] MUD SLIDES MUD SLIDES URBAN FLOODING
## [181] MUD/ROCK SLIDE Mudslide
## [183] MUDSLIDE MUDSLIDE/LANDSLIDE
## [185] Mudslides MUDSLIDES
## [187] No Severe Weather NONE
## [189] NORTHERN LIGHTS Other
## [191] OTHER PATCHY DENSE FOG
## [193] PROLONG WARMTH RAPIDLY RISING WATER
## [195] RECORD COOL Record dry month
## [197] RECORD DRYNESS Record High
## [199] RECORD HIGH RECORD HIGH TEMPERATURE
## [201] RECORD HIGH TEMPERATURES RECORD LOW
## [203] Record temperature RECORD TEMPERATURE
## [205] Record Temperatures RECORD TEMPERATURES
## [207] RECORD WARM RECORD WARM TEMPS.
## [209] Record Warmth RECORD WARMTH
## [211] RED FLAG CRITERIA RED FLAG FIRE WX
## [213] REMNANTS OF FLOYD RIP CURRENT
## [215] RIP CURRENTS RIP CURRENTS HEAVY SURF
## [217] RIP CURRENTS/HEAVY SURF RIVER AND STREAM FLOOD
## [219] RIVER FLOOD River Flooding
## [221] RIVER FLOODING ROCK SLIDE
## [223] ROGUE WAVE ROTATING WALL CLOUD
## [225] ROUGH SEAS ROUGH SURF
## [227] RURAL FLOOD Saharan Dust
## [229] SAHARAN DUST SEICHE
## [231] SEVERE TURBULENCE SLEET
## [233] SLEET STORM SMALL STREAM
## [235] SMALL STREAM AND SMALL STREAM AND URBAN FLOOD
## [237] SMALL STREAM AND URBAN FLOODIN SMALL STREAM FLOOD
## [239] SMALL STREAM FLOODING SMALL STREAM URBAN FLOOD
## [241] SMALL STREAM/URBAN FLOOD Sml Stream Fld
## [243] SMOKE SOUTHEAST
## [245] STORM SURGE STORM SURGE/TIDE
## [247] STREAM FLOODING STREET FLOOD
## [249] STREET FLOODING Summary August 10
## [251] Summary August 11 Summary August 17
## [253] Summary August 2-3 Summary August 21
## [255] Summary August 28 Summary August 4
## [257] Summary August 7 Summary August 9
## [259] Summary Jan 17 Summary July 23-24
## [261] Summary June 18-19 Summary June 5-6
## [263] Summary June 6 Summary of April 12
## [265] Summary of April 13 Summary of April 21
## [267] Summary of April 27 Summary of April 3rd
## [269] Summary of August 1 Summary of July 11
## [271] Summary of July 2 Summary of July 22
## [273] Summary of July 26 Summary of July 29
## [275] Summary of July 3 Summary of June 10
## [277] Summary of June 11 Summary of June 12
## [279] Summary of June 13 Summary of June 15
## [281] Summary of June 16 Summary of June 18
## [283] Summary of June 23 Summary of June 24
## [285] Summary of June 3 Summary of June 30
## [287] Summary of June 4 Summary of June 6
## [289] Summary of March 14 Summary of March 23
## [291] Summary of March 24 SUMMARY OF MARCH 24-25
## [293] SUMMARY OF MARCH 27 SUMMARY OF MARCH 29
## [295] Summary of May 10 Summary of May 13
## [297] Summary of May 14 Summary of May 22
## [299] Summary of May 22 am Summary of May 22 pm
## [301] Summary of May 26 am Summary of May 26 pm
## [303] Summary of May 31 am Summary of May 31 pm
## [305] Summary of May 9-10 Summary Sept. 25-26
## [307] Summary September 20 Summary September 23
## [309] Summary September 3 Summary September 4
## [311] Summary: Nov. 16 Summary: Nov. 6-7
## [313] Summary: Oct. 20-21 Summary: October 31
## [315] Summary: Sept. 18 Temperature record
## [317] TIDAL FLOOD Tidal Flooding
## [319] TIDAL FLOODING TROPICAL DEPRESSION
## [321] TROPICAL STORM TROPICAL STORM ALBERTO
## [323] TROPICAL STORM DEAN TROPICAL STORM GORDON
## [325] TROPICAL STORM JERRY TSUNAMI
## [327] TYPHOON UNSEASONABLY COOL
## [329] UNSEASONABLY COOL & WET UNSEASONABLY DRY
## [331] UNSEASONABLY HOT UNSEASONABLY WARM
## [333] UNSEASONABLY WARM & WET UNSEASONABLY WARM AND DRY
## [335] UNSEASONABLY WARM YEAR UNSEASONABLY WARM/WET
## [337] UNSEASONABLY WET UNSEASONAL LOW TEMP
## [339] UNUSUAL WARMTH UNUSUAL/RECORD WARMTH
## [341] UNUSUALLY WARM URBAN AND SMALL
## [343] URBAN AND SMALL STREAM URBAN AND SMALL STREAM FLOOD
## [345] URBAN AND SMALL STREAM FLOODIN Urban flood
## [347] Urban Flood URBAN FLOOD
## [349] URBAN FLOOD LANDSLIDE Urban Flooding
## [351] URBAN FLOODING URBAN FLOODS
## [353] URBAN SMALL URBAN SMALL STREAM FLOOD
## [355] URBAN/SMALL URBAN/SMALL FLOODING
## [357] URBAN/SMALL STREAM URBAN/SMALL STREAM FLOOD
## [359] URBAN/SMALL STREAM FLOOD URBAN/SMALL STREAM FLOODING
## [361] URBAN/SMALL STRM FLDG URBAN/SML STREAM FLD
## [363] URBAN/SML STREAM FLDG URBAN/STREET FLOODING
## [365] VERY DRY VERY WARM
## [367] VOG Volcanic Ash
## [369] VOLCANIC ASH Volcanic Ash Plume
## [371] VOLCANIC ASHFALL VOLCANIC ERUPTION
## [373] WALL CLOUD WARM DRY CONDITIONS
## [375] WARM WEATHER WATER SPOUT
## [377] WATERSPOUT WATERSPOUT-
## [379] WATERSPOUT/ WATERSPOUTS
## [381] WAYTERSPOUT wet micoburst
## [383] WET MICROBURST Wet Month
## [385] WET WEATHER Wet Year
## [387] WILD FIRES WILD/FOREST FIRE
## [389] WILD/FOREST FIRES WILDFIRE
## [391] WILDFIRES
## 985 Levels: HIGH SURF ADVISORY COASTAL FLOOD ... WNDCreate an Other variable for ungrouped event types.
Storm <- Storm[, eventOther := eventConvection == FALSE & eventExtremeTemp == FALSE & eventFlood == FALSE & eventWinter == FALSE]A crosstabulation for the event type categories is below.
groupby <- expression(list(eventConvection, eventExtremeTemp, eventFlood, eventWinter, eventOther))
Storm[, .N, eval(groupby)][order(eventConvection, eventExtremeTemp, eventFlood, eventWinter, eventOther, decreasing=TRUE)]## eventConvection eventExtremeTemp eventFlood eventWinter eventOther
## 1: TRUE TRUE FALSE FALSE FALSE
## 2: TRUE FALSE TRUE FALSE FALSE
## 3: TRUE FALSE FALSE TRUE FALSE
## 4: TRUE FALSE FALSE FALSE FALSE
## 5: FALSE TRUE FALSE TRUE FALSE
## 6: FALSE TRUE FALSE FALSE FALSE
## 7: FALSE FALSE TRUE TRUE FALSE
## 8: FALSE FALSE TRUE FALSE FALSE
## 9: FALSE FALSE FALSE TRUE FALSE
## 10: FALSE FALSE FALSE FALSE TRUE
## N
## 1: 1561
## 2: 31
## 3: 29
## 4: 736119
## 5: 9
## 6: 3541
## 7: 354
## 8: 11963
## 9: 40974
## 10: 107716Create Event Type Categories
Records can have multiple events listed in evtype and will need a categorized heirarchy of event types, The hierarchy is as follows:
- Convection (including lightning, tornado, thunderstorm, wind, and hail)
- Extreme temperature (including hot and cold)
- Flood (including flood, flash flood, rain)
- Winter (including snow, ice, freeze, or winter weather)
- Other
THUNDERSTORM WINDS/FLASH FLOOD would be assigned to the Convection category.
Storm <- Storm[, eventCategory := ifelse(eventConvection, 1, ifelse(eventExtremeTemp, 2, ifelse(eventFlood, 3, ifelse(eventWinter, 4, ifelse(eventOther, 5, NA)))))]
labels <- c("Convection", "Extreme temperature", "Flood", "Winter", "Other")
Storm <- Storm[, eventCategory := factor(eventCategory, labels=labels)]
Storm[, .N, eventCategory]## eventCategory N
## 1: Convection 737740
## 2: Flood 12317
## 3: Winter 40974
## 4: Extreme temperature 3550
## 5: Other 107716Restrict to 50 states
Restrict the data to records from the 50 states. Use R’s built-in state.abb dataset.
Storm <- Storm[state %in% state.abb]Reshape data
Reshape data table with essential variables for tabulation and plotting.
labels <- c("Convection", "Extreme temperature", "Flood", "Winter", "Other")
D2 <- rbind(Storm[, list(state, eventCategory = factor(eventCategory, labels=labels), outcome = "Fatalities (thousands)", value = fatalities / 1E3)],
Storm[, list(state, eventCategory = factor(eventCategory, labels=labels), outcome = "Injuries (thousands)", value = injuries / 1E3)],
Storm[, list(state, eventCategory = factor(eventCategory, labels=labels), outcome = "Property damage ($, billions)", value = propertyDamage / 1E9)])Tabulate by state and category, and by state. For the state tabulation, rank each state according to outcome.
tabulationStateCategory <- D2[, list(value = sum(value)), list(state, eventCategory, outcome)]
tabulationState <- D2[, list(value = sum(value)), list(state, outcome)]
tabulationState <- tabulationState[, rank := abs(rank(value, ties.method="random") - 51), list(outcome)]Results
Plot each state as a coxcomb. Each rose shows the magnitude of each outcome Fatalities, Injuries and Property Damage. The color of each bar segment corresponds to event category.
tabulationStateCategory <- tabulationStateCategory[, valueScaled := scale(value, center=FALSE), list(outcome)]
ggplot(tabulationStateCategory, aes(x=outcome, y=valueScaled, fill=eventCategory)) +
geom_bar(alpha=1, stat="identity") +
coord_polar(theta="x") +
scale_fill_brewer(name="Category", palette="Set1") +
scale_x_discrete(name="", labels=c("F", "I", "P")) +
scale_y_continuous(name="", labels=NULL) +
facet_wrap(~ state, nrow=5, ncol=10) +
labs(title="Comparison of states on outcomes from storms and other severe weather events") +
theme(legend.position="top") +
theme(axis.ticks.y=element_blank(),
panel.grid=element_blank())
Tabulate the highest ranking state for each outcome.
Fatalities
top <- tabulationState[grepl("Fatal", outcome) & rank <= 1, state]
where <- expression(state %in% top & grepl("Fatal", outcome))
select <- expression(list(state, value = format(value * 1E3, big.mark=","), eventCategory))
tabulation <- tabulationStateCategory[eval(where), eval(select)]
tabulation <- tabulation[order(value, decreasing=TRUE)]
print(xtable(tabulation, digits=0), type="html", include.rownames=FALSE)| state | value | eventCategory |
|---|---|---|
| IL | 998 | Extreme temperature |
| IL | 375 | Convection |
| IL | 28 | Other |
| IL | 20 | Winter |
| IL | 0 | Flood |
Injuries
top <- tabulationState[grepl("Inj", outcome) & rank <= 1, state]
where <- expression(state %in% top & grepl("Inj", outcome))
select <- expression(list(state, value = format(value * 1E3, big.mark=","), eventCategory))
tabulation <- tabulationStateCategory[eval(where), eval(select)]
tabulation <- tabulation[order(value, decreasing=TRUE)]
print(xtable(tabulation, digits=0), type="html", include.rownames=FALSE)| state | value | eventCategory |
|---|---|---|
| TX | 9,532 | Convection |
| TX | 7,111 | Other |
| TX | 787 | Extreme temperature |
| TX | 211 | Winter |
| TX |
|
Property damage
top <- tabulationState[grepl("Prop", outcome) & rank <= 1, state]
where <- expression(state %in% top & grepl("Prop", outcome))
select <- expression(list(state, value = sprintf("$%s billion", format(round(value, digits=1), big.mark=",")), eventCategory))
tabulation <- tabulationStateCategory[eval(where), eval(select)]
tabulation <- tabulation[order(value, decreasing=TRUE)]
print(xtable(tabulation, digits=0), type="html", include.rownames=FALSE)| state | value | eventCategory |
|---|---|---|
| CA | $122.5 billion | Other |
| CA | $ 0.7 billion | Convection |
| CA | $ 0.3 billion | Flood |
| CA | $ 0.1 billion | Winter |
| CA | $ 0.0 billion | Extreme temperature |