Analysis of the Adverse Health and Economic Impacts of US Storms
knitr::opts_chunk$set(warning = FALSE, message = FALSE,echo = TRUE)1: Synopsis
The goal of the assignment is to explore the NOAA Storm Database and explore the effects of severe weather events on both population and economy.The database covers the time period between 1950 and November 2011.
The following analysis investigates which types of severe weather events are most harmful on:
- Health (injuries and fatalities)
- Property and crops (economic consequences)
Information on the Data: Documentation
2: Data Processing
2.1: Data Loading
Loading Required packages and downloading the raw data file from the url and reading data directly to form data.table. It requires R.utils package to extract bz2 files and to read files from it.
library(data.table)
library(ggplot2)
library(gridExtra)
library(grid)
library(R.utils)
path <- getwd()
url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
stormdata <- data.table::fread(url)2.2: Examining Column Names
#Column names and some values of the table.
names(stormdata)## [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"head(stormdata)## 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 62.3: Data Selection
Extracting only Neccessary Columns and Data.
stormdata <- stormdata[,c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]2.4: Converting Exponent Columns into Actual Exponents instead of (-,+, H, K, etc)
Making the PROPDMGEXP and CROPDMGEXP columns cleaner so they can be used to calculate Property and Crop cost and the values can be user readble.
#Checking for distint values.
unique(stormdata$PROPDMGEXP)## [1] "K" "M" "" "B" "m" "+" "0" "5" "6" "?" "4" "2" "3" "h" "7" "H" "-"
## [18] "1" "8"unique(stormdata$CROPDMGEXP)## [1] "" "M" "K" "m" "B" "?" "0" "k" "2"#Changing the values to user readable values.
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "K"] <- 1000
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "M"] <- 1e+06
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == ""] <- 1
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "B"] <- 1e+09
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "m"] <- 1e+06
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "0"] <- 1
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "5"] <- 1e+05
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "6"] <- 1e+06
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "4"] <- 10000
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "2"] <- 100
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "3"] <- 1000
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "h"] <- 100
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "7"] <- 1e+07
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "H"] <- 100
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "1"] <- 10
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "8"] <- 1e+08
# Assigning '0' to invalid exponent stormdata
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "+"] <- 0
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "-"] <- 0
stormdata$PROPDMGEXP[stormdata$PROPDMGEXP == "?"] <- 0
#Changing class of PROPDMGEXP to numeric.
stormdata$PROPDMGEXP <- as.numeric(stormdata$PROPDMGEXP)
# Assigning values for the crop exponent stormdata
stormdata$CROPDMGEXP[stormdata$CROPDMGEXP == "M"] <- 1e+06
stormdata$CROPDMGEXP[stormdata$CROPDMGEXP == "K"] <- 1000
stormdata$CROPDMGEXP[stormdata$CROPDMGEXP == "m"] <- 1e+06
stormdata$CROPDMGEXP[stormdata$CROPDMGEXP == "B"] <- 1e+09
stormdata$CROPDMGEXP[stormdata$CROPDMGEXP == "0"] <- 1
stormdata$CROPDMGEXP[stormdata$CROPDMGEXP == "k"] <- 1000
stormdata$CROPDMGEXP[stormdata$CROPDMGEXP == "2"] <- 100
stormdata$CROPDMGEXP[stormdata$CROPDMGEXP == ""] <- 1
# Assigning '0' to invalid exponent stormdata
stormdata$CROPDMGEXP[stormdata$CROPDMGEXP == "?"] <- 0
#Changing class of CROPDMGEXP to numeric.
stormdata$CROPDMGEXP <- as.numeric(stormdata$CROPDMGEXP)2.5: Calcuating Total Property and Crop Damage Cost.
# Calculating the property damage value
stormdata$PROPDMGVAL <- stormdata$PROPDMG * stormdata$PROPDMGEXP
# calculating the crop damage value
stormdata$CROPDMGVAL <- stormdata$CROPDMG * stormdata$CROPDMGEXP2.6:Calculating total numbers by events and selecting data with values greater than 0.
FatalitiesData <- aggregate(FATALITIES ~ EVTYPE, stormdata, FUN = sum, na.rm = TRUE)
FatalitiesData <- FatalitiesData[FatalitiesData$FATALITIES > 0,]
InjuriesData <- aggregate(INJURIES ~ EVTYPE, stormdata, FUN = sum, na.rm = TRUE)
InjuriesData <- InjuriesData[InjuriesData$INJURIES > 0,]
PropDmgValueData <- aggregate(PROPDMGVAL ~ EVTYPE, stormdata, FUN = sum, na.rm = TRUE)
PropDmgValueData <- PropDmgValueData[PropDmgValueData$PROPDMGVAL > 0,]
CropDmgValueData <- aggregate(CROPDMGVAL ~ EVTYPE, stormdata, FUN = sum, na.rm = TRUE)
CropDmgValueData <- CropDmgValueData[CropDmgValueData$CROPDMGVAL > 0,]2.7: Ordering Data with Highest Values
#Ordering events with highest fatalities.
FatalitiesData <- FatalitiesData[order(-FatalitiesData$FATALITIES), ]
#Ordering events with highest injuries.
InjuriesData <- InjuriesData[order(-InjuriesData$INJURIES), ]
#Ordering events with highest Property Damage.
PropDmgValueData <- PropDmgValueData[order(-PropDmgValueData$PROPDMGVAL), ]
#Ordering events with highest injuries.
CropDmgValueData <- CropDmgValueData[order(-CropDmgValueData$CROPDMGVAL), ]3: Results
3.1: Events with Highest Fatalities and Injuries.
head(FatalitiesData)## EVTYPE FATALITIES
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504head(InjuriesData)## EVTYPE INJURIES
## 834 TORNADO 91346
## 856 TSTM WIND 6957
## 170 FLOOD 6789
## 130 EXCESSIVE HEAT 6525
## 464 LIGHTNING 5230
## 275 HEAT 21003.2: Events with Highest Property Damage and Crop Damage.
head(PropDmgValueData)## EVTYPE PROPDMGVAL
## 170 FLOOD 144657709870
## 411 HURRICANE/TYPHOON 69305840000
## 834 TORNADO 56947381845
## 670 STORM SURGE 43323536000
## 153 FLASH FLOOD 16822678195
## 244 HAIL 15735270147head(CropDmgValueData)## EVTYPE CROPDMGVAL
## 95 DROUGHT 13972566000
## 170 FLOOD 5661968450
## 590 RIVER FLOOD 5029459000
## 427 ICE STORM 5022113500
## 244 HAIL 3025954473
## 402 HURRICANE 27419100003.3: Graph of Events with Highest Fatalitites and Injuries.
plot1 <- ggplot(data = FatalitiesData[1:10,], aes(x = reorder(EVTYPE, FATALITIES), y = FATALITIES/1000, color = EVTYPE, fill = EVTYPE)) +
geom_bar(stat = "identity") + coord_flip() +
ylab("Total number of Fatalities in Thousands") + xlab("Event type") + ggtitle("Fatalities in US by Top 10 Events")
#Plotting events with highest Injuries
plot2 <- ggplot(data = InjuriesData[1:10,], aes(x = reorder(EVTYPE, INJURIES), y = INJURIES/1000, color = EVTYPE, fill = EVTYPE)) +
geom_bar(stat = "identity") + coord_flip() +
ylab("Total number of Injuries in Thousands") + xlab("Event type") +
ggtitle("Injuries in US by Top 10 Events")
#Combining them to make 1 Plot
grid.arrange(plot1, plot2, nrow =2)
3.4: Graph of Events with Highest Property and Crop Damage.
#Plotting events with highest Property Damage.
plot3 <- ggplot(data = PropDmgValueData[1:10,], aes(x = reorder(EVTYPE, PROPDMGVAL),y = PROPDMGVAL/1000000, color = EVTYPE, fill = EVTYPE)) + geom_bar(stat = "identity") + coord_flip() + ylab("Total Damage to Property in Million") + xlab("Event type") + ggtitle("Property Damage in US by Top 10 Events")
#Plotting events with highest Crop Damage.
plot4 <- ggplot(data = CropDmgValueData[1:10,], aes(x = reorder(EVTYPE, CROPDMGVAL), y = CROPDMGVAL/1000000, color = EVTYPE, fill = EVTYPE)) + geom_bar(stat = "identity") + coord_flip() + ylab("Total Damage to Crop in Million") + xlab("Event type") + ggtitle("Crop Damage in US by Top 10 Events")
#Combining them to make 1 plot.
grid.arrange(plot3, plot4, nrow =2)