Reproducible Research Assignment 2
Saiful Ramadzan
Sunday, September 27, 2015
Reproducible Research: Peer Assessment 2
Impact of Severe Weather Events on Public Health and Economy in the United States
Synopsis
This report aims to analyze the impact of multiple weather events on public health and economy based on the storm data from the U.S National Oceanic and Atmospheric Administration’s (NOAAA) from the year 1950-2011. This data is used to estimates fatalities, injuries, property and crop damages to decide which weather events give negative impact towards the U.s population health and economy. As a result, excessive heat and tornado have been determined as the most harmful events towards the population health and floods, droughts and typhoon have been the greatest economic threat.
Basic settings
library(ggplot2)
library(plyr)
require(gridExtra)## Loading required package: gridExtraData Processing
Reading the csv file.
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 2Rows = 90229 Columns = 37
Few records have been obtained in the earlier years of the dataset. Most recent years data will be used.
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$year, breaks = 30)
Above histogram is showing the number of weather events data from 1950-2011. However, the histograms shows that the increase of number of events started to increased starting from 1995. Therefore, we will use 1995 as my starting point to get the best possible record.
storm <- stormData[stormData$year >= 1995, ]
dim(storm)## [1] 681500 38Rows = 681500 Colums = 38
Impact on Public Health
In this section, we check the number of fatalities and injuries that are caused by the severe weather events. We would like to get the first 15 most severe types of 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
We will convert the property damage and crop damage data into comparable numerical forms according to the meaning of units described in the code book (Storm Events). Both PROPDMGEXP and CROPDMGEXP columns record a multiplier for each observation where we have Hundred (H), Thousand (K), Million (M) and Billion (B).
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)Results
As for the impact on public health, we have got two sorted lists of severe weather events below by the number of people badly affected.
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 718And the following is a pair of graphs of total fatalities and total injuries affected by these severe weather events.
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)
Based on the above histograms, we find that excessive heat and tornado cause most fatalities; tornato causes most injuries in the United States from 1995 to 2011.
As for the impact on economy, we have got two sorted lists below by the amount of money cost by damages.
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 401411500And the following is a pair of graphs of total property damage and total crop damage affected by these severe weather events.
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)
Based on the above histograms, we find that flood and hurricane/typhoon cause most property damage; drought and flood causes most crop damage in the United States from 1995 to 2011.
Conclusion
From these data, we found that excessive heat and tornado are most harmful with respect to population health, while flood, drought, and hurricane/typhoon have the greatest economic consequences.