Health and Economic Impact Analysis of US Storms
Ryan Obuchowski
12/10/2019
1. Synopsis
The goal of this assignment and analysis is to explore the NOAA storm database and to ascertain the effect severe weather has on population and the economy. This NOAA storm database covers the time period between 1950 and November 2011.
This study analysis which types of severe weather event are the most harmful to: 1. Human Health (injuries and fatalities) 2. Economic Consequences (Property and crop damage)
2. Data Processing
2a: Download the raw data file from the internet and then extract the data into a dateframe. After, convert the data frame into a data table using data.table.
library("data.table")
library("ggplot2")
fileUrl <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
destfile0 <- "StormData.csv.bz2"
download.file(fileUrl, destfile = destfile0)
stormDF <- read.csv(destfile0)
stormDT <- as.data.table(stormDF)2b: Examining Data and Column Names
str(stormDT)## 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/ 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 ...
## - attr(*, ".internal.selfref")=<externalptr>colnames(stormDT)## [1] "STATE__" "BGN_DATE" "BGN_TIME" "TIME_ZONE" "COUNTY"
## [6] "COUNTYNAME" "STATE" "EVTYPE" "BGN_RANGE" "BGN_AZI"
## [11] "BGN_LOCATI" "END_DATE" "END_TIME" "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE" "END_AZI" "END_LOCATI" "LENGTH" "WIDTH"
## [21] "F" "MAG" "FATALITIES" "INJURIES" "PROPDMG"
## [26] "PROPDMGEXP" "CROPDMG" "CROPDMGEXP" "WFO" "STATEOFFIC"
## [31] "ZONENAMES" "LATITUDE" "LONGITUDE" "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS" "REFNUM"2c. Data subsetting
This dataset contains a lot of extra data that is not relevant forour analysis. THerefore, we subset the dataset to get rid of the columns that we do not need.
cols2Remove <- colnames(stormDT[, !c("EVTYPE"
, "FATALITIES"
, "INJURIES"
, "PROPDMG"
, "PROPDMGEXP"
, "CROPDMG"
, "CROPDMGEXP")])
stormDT[, c(cols2Remove) := NULL]
stormDT <- stormDT[(EVTYPE != "?" &
(INJURIES > 0 | FATALITIES > 0 | PROPDMG > 0 | CROPDMG > 0)), c("EVTYPE"
, "FATALITIES"
, "INJURIES"
, "PROPDMG"
, "PROPDMGEXP"
, "CROPDMG"
, "CROPDMGEXP") ]2d: Converting Exponent Columns into Actual Exponents instead of (-,+, H, K, etc)
Making the PROPDMGEXP and CROPDMGEXP columns cleaner so that we can calculate the propert and cost damage of the storms.
cols <- c("PROPDMGEXP", "CROPDMGEXP")
stormDT[, (cols) := c(lapply(.SD, toupper)), .SDcols = cols]
propDMGkey <- c("\"\"" = 10^0, "-" = 10^0, "+" = 10^0, "0" = 10^0, "1" = 10^1, "2" = 10^2, "3" = 10^3, "4" = 10^4, "5" = 10^5, "6" =10^6, "7" = 10^7, "8" = 10^8, "9" =10^9, "H" = 10^2, "K" = 10^3, "M" = 10^6, "B" = 10^9)
cropDMGkey <- c("\"\"" = 10^0, "?" = 10^0, "0" = 10^0, "K" = 10^3, "M" = 10^6, "B" = 10^9)
stormDT[, PROPDMGEXP := propDMGkey[as.character(stormDT[,PROPDMGEXP])]]
stormDT[is.na(PROPDMGEXP), PROPDMGEXP := 10^0]
stormDT[, CROPDMGEXP := cropDMGkey[as.character(stormDT[, CROPDMGEXP])]]
stormDT[is.na(CROPDMGEXP), CROPDMGEXP := 10^0]2e: Making Economic Cost Columns
stormDT <- stormDT[, .(EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, propCost = PROPDMG * PROPDMGEXP, CROPDMG, CROPDMGEXP, cropCost = CROPDMG * CROPDMGEXP)]2f: Calculating Total Property and Crop Cost
totalcostDT <- stormDT[, .(propCost = sum(propCost), cropCost = sum(cropCost), Total_Cost = sum(propCost) + sum(cropCost)), by = .(EVTYPE)]
totalcostDT <- totalcostDT[order(-Total_Cost), ]
totalcostDT <- totalcostDT[1:10, ]
head(totalcostDT, 5)## EVTYPE propCost cropCost Total_Cost
## 1: FLOOD 144657709807 5661968450 150319678257
## 2: HURRICANE/TYPHOON 69305840000 2607872800 71913712800
## 3: TORNADO 56947380677 414953270 57362333947
## 4: STORM SURGE 43323536000 5000 43323541000
## 5: HAIL 15735267513 3025954473 187612219862g: Calculating Total Fatalities and Injuries
totalInjuriesDT <- stormDT[, .(FATALITIES = sum(FATALITIES), INJURIES = sum(INJURIES), totals = sum(FATALITIES) + sum(INJURIES)), by = .(EVTYPE)]
totalInjuriesDT <- totalInjuriesDT[order(-FATALITIES), ]
totalInjuriesDT <- totalInjuriesDT[1:10, ]
head(totalInjuriesDT, 5)## EVTYPE FATALITIES INJURIES totals
## 1: TORNADO 5633 91346 96979
## 2: EXCESSIVE HEAT 1903 6525 8428
## 3: FLASH FLOOD 978 1777 2755
## 4: HEAT 937 2100 3037
## 5: LIGHTNING 816 5230 60463: Results
3a: Events that are Most Harmful to Human Population Health
We melt the data.table in order to make it easier to put into a bargraph format
Neg_Events <- melt(totalInjuriesDT, id.vars="EVTYPE", variable.name = "Neg_event")
head(Neg_Events, 5)## EVTYPE Neg_event value
## 1: TORNADO FATALITIES 5633
## 2: EXCESSIVE HEAT FATALITIES 1903
## 3: FLASH FLOOD FATALITIES 978
## 4: HEAT FATALITIES 937
## 5: LIGHTNING FATALITIES 816Now we create the bargraph
healthChart <- ggplot(Neg_Events, aes(x=reorder(EVTYPE, -value), y=value))
healthChart = healthChart + geom_bar(stat="identity", aes(fill=Neg_event), position="dodge")
healthChart = healthChart + ylab("Frequency Count")
healthChart = healthChart + xlab("Event Type")
healthChart = healthChart + theme(axis.text.x = element_text(angle=45, hjust=1))
healthChart = healthChart + ggtitle("Top 10 US Killers") + theme(plot.title = element_text(hjust = 0.5))
print(healthChart)
3b: Events that have the Greatest Economic Conseqences
Again, melt the data.table so that it is easier to put into a bargraph format.
econconsequences <- melt(totalcostDT, id.vars="EVTYPE", variable.name = "Damage_Type")
head(econconsequences, 5)## EVTYPE Damage_Type value
## 1: FLOOD propCost 144657709807
## 2: HURRICANE/TYPHOON propCost 69305840000
## 3: TORNADO propCost 56947380677
## 4: STORM SURGE propCost 43323536000
## 5: HAIL propCost 15735267513econchart <- ggplot(econconsequences, aes(x=reorder(EVTYPE, -value), y=value))
econchart = econchart + geom_bar(stat = "identity", aes(fill= Damage_Type), position = "dodge")
econchart =econchart + ylab("Cost (dollars)")
econchart=econchart + xlab("EVent Type")
econchart= econchart +theme(axis.text.x = element_text(angle = 45, hjust = 1))
econchart=econchart + ggtitle("Top 10 US Storm Events causing Economic Consequences") + theme(plot.title = element_text(hjust= 0.5))
print(econchart)
By looking at the first graph titled “Top 10 US Killers”, one can deduce that a tornado has the highest fatalities and injuries to the human population and looking at the table one can see that Tornadoes had a fatalities value of 5633 while the second highest fatalities value was Excessive Heat with a value of 1903.
By looking at the second graph titled “Top 10 US Storm Events causing Economic Consequences”, one can see that the greatest economic damage occurs with a flood event, which has a property cost value of 144657709807.