Analysis on Weather Impact
Cynthia
Thursday, March 19, 2015
Synonpsis
This report is aimed to analyze impact of different weather events on public health and economy, based on storm database from the U.S. National Oceanic and Atmospheric Administration’s (NOAA) from 1950 - 2011. Estimates of fatalities, injuries, property and crop damage will be used to decide which types of event are most harmful. From analysis, we found that excessive heat and tornado are most harmful to population health, while flood, drought, and hurricane/typhoon have the greatest economic consequences.
Basic settings
echo = TRUE # Always make code visible
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.
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## Attaching package: 'R.oo'
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## The following objects are masked from 'package:methods':
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## getClasses, getMethods
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## The following objects are masked from 'package:base':
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## attach, detach, gc, load, save
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## R.utils v2.0.0 (2015-02-28) successfully loaded. See ?R.utils for help.
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## Attaching package: 'R.utils'
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## The following object is masked from 'package:utils':
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## timestamp
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## The following objects are masked from 'package:base':
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## cat, commandArgs, getOption, inherits, isOpen, parse, warningslibrary(ggplot2)
library(plyr)
require(gridExtra)## Loading required package: gridExtra
## Loading required package: gridData Processing
Download data and unzip
if (!"stormData.csv.bz2" %in% dir("./data/")) {
print("hhhh")
download.file("http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", destfile = "stormData.csv.bz2")
bunzip2("stormData.csv.bz2", overwrite=T, remove=F)
}## [1] "hhhh"Read in and look at data
if (!"stormData" %in% ls()) {
stormData <- read.csv("stormData.csv", sep = ",")
}
dim(stormData)## [1] 902297 37head(stormData, n = 2)## 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
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14 100 3 0 0
## 2 NA 0 2 150 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2Subset data with better data collections
if (dim(stormData)[2] == 37) {
stormData$year <- as.numeric(format(as.Date(stormData$BGN_DATE, format = "%m/%d/%Y %H:%M:%S"), "%Y"))
}
hist(stormData$year, breaks = 30)
storm <- stormData[stormData$year >= 1995, ]
dim(storm)## [1] 681500 38Impact on Public Health
Analyze number of fatalities and injuries that are caused by the severe weather events
sortHelper <- function(fieldName, top = 15, dataset = stormData) {
index <- which(colnames(dataset) == fieldName)
field <- aggregate(dataset[, index], by = list(dataset$EVTYPE), FUN = "sum")
names(field) <- c("EVTYPE", fieldName)
field <- arrange(field, field[, 2], decreasing = T)
field <- head(field, n = top)
field <- within(field, EVTYPE <- factor(x = EVTYPE, levels = field$EVTYPE))
return(field)
}
fatalities <- sortHelper("FATALITIES", dataset = storm)
injuries <- sortHelper("INJURIES", dataset = storm)Impact on Economy
Convert property damage and crop damage data into comparable numerical forms
convertHelper <- function(dataset = storm, fieldName, newFieldName) {
totalLen <- dim(dataset)[2]
index <- which(colnames(dataset) == fieldName)
dataset[, index] <- as.character(dataset[, index])
logic <- !is.na(toupper(dataset[, index]))
dataset[logic & toupper(dataset[, index]) == "B", index] <- "9"
dataset[logic & toupper(dataset[, index]) == "M", index] <- "6"
dataset[logic & toupper(dataset[, index]) == "K", index] <- "3"
dataset[logic & toupper(dataset[, index]) == "H", index] <- "2"
dataset[logic & toupper(dataset[, index]) == "", index] <- "0"
dataset[, index] <- as.numeric(dataset[, index])
dataset[is.na(dataset[, index]), index] <- 0
dataset <- cbind(dataset, dataset[, index - 1] * 10^dataset[, index])
names(dataset)[totalLen + 1] <- newFieldName
return(dataset)
}
storm <- convertHelper(storm, "PROPDMGEXP", "propertyDamage")## Warning in convertHelper(storm, "PROPDMGEXP", "propertyDamage"): NAs
## introduced by coercionstorm <- convertHelper(storm, "CROPDMGEXP", "cropDamage")## Warning in convertHelper(storm, "CROPDMGEXP", "cropDamage"): NAs
## introduced by coercionnames(storm)## [1] "STATE__" "BGN_DATE" "BGN_TIME" "TIME_ZONE"
## [5] "COUNTY" "COUNTYNAME" "STATE" "EVTYPE"
## [9] "BGN_RANGE" "BGN_AZI" "BGN_LOCATI" "END_DATE"
## [13] "END_TIME" "COUNTY_END" "COUNTYENDN" "END_RANGE"
## [17] "END_AZI" "END_LOCATI" "LENGTH" "WIDTH"
## [21] "F" "MAG" "FATALITIES" "INJURIES"
## [25] "PROPDMG" "PROPDMGEXP" "CROPDMG" "CROPDMGEXP"
## [29] "WFO" "STATEOFFIC" "ZONENAMES" "LATITUDE"
## [33] "LONGITUDE" "LATITUDE_E" "LONGITUDE_" "REMARKS"
## [37] "REFNUM" "year" "propertyDamage" "cropDamage"options(scipen=999)
property <- sortHelper("propertyDamage", dataset = storm)
crop <- sortHelper("cropDamage", dataset = storm)Result - Impact on Public Health
Sorted list
fatalities## EVTYPE FATALITIES
## 1 EXCESSIVE HEAT 1903
## 2 TORNADO 1545
## 3 FLASH FLOOD 934
## 4 HEAT 924
## 5 LIGHTNING 729
## 6 FLOOD 423
## 7 RIP CURRENT 360
## 8 HIGH WIND 241
## 9 TSTM WIND 241
## 10 AVALANCHE 223
## 11 RIP CURRENTS 204
## 12 WINTER STORM 195
## 13 HEAT WAVE 161
## 14 THUNDERSTORM WIND 131
## 15 EXTREME COLD 126injuries## EVTYPE INJURIES
## 1 TORNADO 21765
## 2 FLOOD 6769
## 3 EXCESSIVE HEAT 6525
## 4 LIGHTNING 4631
## 5 TSTM WIND 3630
## 6 HEAT 2030
## 7 FLASH FLOOD 1734
## 8 THUNDERSTORM WIND 1426
## 9 WINTER STORM 1298
## 10 HURRICANE/TYPHOON 1275
## 11 HIGH WIND 1093
## 12 HAIL 916
## 13 WILDFIRE 911
## 14 HEAVY SNOW 751
## 15 FOG 718Graph presentation
fatalitiesPlot <- qplot(EVTYPE, data = fatalities, weight = FATALITIES, geom = "bar", binwidth = 1) +
scale_y_continuous("Number of Fatalities") +
theme(axis.text.x = element_text(angle = 45,
hjust = 1)) + xlab("Severe Weather Type") +
ggtitle("Total Fatalities by Severe Weather\n Events in the U.S.\n from 1995 - 2011")
injuriesPlot <- qplot(EVTYPE, data = injuries, weight = INJURIES, geom = "bar", binwidth = 1) +
scale_y_continuous("Number of Injuries") +
theme(axis.text.x = element_text(angle = 45,
hjust = 1)) + xlab("Severe Weather Type") +
ggtitle("Total Injuries by Severe Weather\n Events in the U.S.\n from 1995 - 2011")
grid.arrange(fatalitiesPlot, injuriesPlot, ncol = 2)
Result - Impact on Economy
Sorted list
property## EVTYPE propertyDamage
## 1 FLOOD 144022037057
## 2 HURRICANE/TYPHOON 69305840000
## 3 STORM SURGE 43193536000
## 4 TORNADO 24935939545
## 5 FLASH FLOOD 16047794571
## 6 HAIL 15048722103
## 7 HURRICANE 11812819010
## 8 TROPICAL STORM 7653335550
## 9 HIGH WIND 5259785375
## 10 WILDFIRE 4759064000
## 11 STORM SURGE/TIDE 4641188000
## 12 TSTM WIND 4482361440
## 13 ICE STORM 3643555810
## 14 THUNDERSTORM WIND 3399282992
## 15 HURRICANE OPAL 3172846000crop## EVTYPE cropDamage
## 1 DROUGHT 13922066000
## 2 FLOOD 5422810400
## 3 HURRICANE 2741410000
## 4 HAIL 2614127070
## 5 HURRICANE/TYPHOON 2607872800
## 6 FLASH FLOOD 1343915000
## 7 EXTREME COLD 1292473000
## 8 FROST/FREEZE 1094086000
## 9 HEAVY RAIN 728399800
## 10 TROPICAL STORM 677836000
## 11 HIGH WIND 633561300
## 12 TSTM WIND 553947350
## 13 EXCESSIVE HEAT 492402000
## 14 THUNDERSTORM WIND 414354000
## 15 HEAT 401411500Graph presentation
propertyPlot <- qplot(EVTYPE, data = property, weight = propertyDamage, geom = "bar", binwidth = 1) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_y_continuous("Property Damage in US dollars")+
xlab("Severe Weather Type") + ggtitle("Total Property Damage by\n Severe Weather Events in\n the U.S. from 1995 - 2011")
cropPlot<- qplot(EVTYPE, data = crop, weight = cropDamage, geom = "bar", binwidth = 1) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_y_continuous("Crop Damage in US dollars") +
xlab("Severe Weather Type") + ggtitle("Total Crop Damage by \nSevere Weather Events in\n the U.S. from 1995 - 2011")
grid.arrange(propertyPlot, cropPlot, ncol = 2)
From the above histograms, we find that flood and hurricane/typhoon cause most property damage; drought and flood causes most crop damage in the US from 1995 to 2011.
Conclusion
From these data, we found that excessive heat and tornado are most harmful to population health; whereas flood, drought, and hurricane/typhoon have the greatest economic consequences.