Reproducible Research - Peer Assessment 2
Divya Shree H P
December 28, 2019
Impact of Severe Weather Events on Public Health and Economy in the United States
Synopsis
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. File can be downloaded from the course web site:
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 [https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf]
National Climatic Data Center Storm Events FAQ [https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2FNCDC%20Storm%20Events-FAQ%20Page.pdf]
Basic settings
knitr::opts_chunk$set(fig.path='Plots/')echo = TRUE
options(scipen = 1) # Turn off scientific notations for numbers
library(R.utils)
library(ggplot2)
library(plyr)
library(gridExtra)
options(warn=-1)Data Processing
First, we download the data file form the website and unzip it.
download.file("http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", destfile = "stormData.csv.bz2")
bunzip2("stormData.csv.bz2", overwrite=T, remove=F)Then, we read the generated csv file.
storm <- read.csv("stormData.csv", sep = ",")
dim(storm)## [1] 902297 37head(storm,2)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14 100 3 0 0
## 2 NA 0 2 150 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2There are 902297 rows and 37 columns in total. The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete.
Impact on Public Health
In this section, we check the number of fatalities and injuries that are caused by the severe weather events. We would like to get the first 15 most severe types of weather events.
func1 <- function(fieldName, top = 15, dataset = stormData) {
index <- which(colnames(dataset) == fieldName)
field <- aggregate(dataset[, index], by = list(dataset$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 <- func1("FATALITIES", dataset = storm)
injuries <- func1("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).
func_convert <- 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 <- func_convert(storm, "PROPDMGEXP", "propertyDamage")
storm <- func_convert(storm, "CROPDMGEXP", "cropDamage")
names(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" "propertyDamage" "cropDamage"options(scipen=999)
property <- func1("propertyDamage", dataset = storm)
crop <- func1("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 TORNADO 5633
## 2 EXCESSIVE HEAT 1903
## 3 FLASH FLOOD 978
## 4 HEAT 937
## 5 LIGHTNING 816
## 6 TSTM WIND 504
## 7 FLOOD 470
## 8 RIP CURRENT 368
## 9 HIGH WIND 248
## 10 AVALANCHE 224
## 11 WINTER STORM 206
## 12 RIP CURRENTS 204
## 13 HEAT WAVE 172
## 14 EXTREME COLD 160
## 15 THUNDERSTORM WIND 133injuries## EVTYPE INJURIES
## 1 TORNADO 91346
## 2 TSTM WIND 6957
## 3 FLOOD 6789
## 4 EXCESSIVE HEAT 6525
## 5 LIGHTNING 5230
## 6 HEAT 2100
## 7 ICE STORM 1975
## 8 FLASH FLOOD 1777
## 9 THUNDERSTORM WIND 1488
## 10 HAIL 1361
## 11 WINTER STORM 1321
## 12 HURRICANE/TYPHOON 1275
## 13 HIGH WIND 1137
## 14 HEAVY SNOW 1021
## 15 WILDFIRE 911We graph the top 10 causes of fatalities.
ggplot(data=fatalities[1:10,], aes(x=EVTYPE, y=FATALITIES)) +
geom_bar(stat="identity", fill = "#CC79A7", colour = "Black") + xlab("Event type") + ylab("Total fatalities") + ggtitle("Fatalities By Event Type") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
We do the same for injuries.
ggplot(data=injuries[1:10,], aes(x=EVTYPE, y=INJURIES)) +
geom_bar(stat="identity", fill = "#E69F00", colour = "Black") + xlab("Event type") + ylab("Total injuries") + ggtitle("Injuries By Event Type") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
Based on the above plots, 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 144657709807
## 2 HURRICANE/TYPHOON 69305840000
## 3 TORNADO 56947380676
## 4 STORM SURGE 43323536000
## 5 FLASH FLOOD 16822673978
## 6 HAIL 15735267513
## 7 HURRICANE 11868319010
## 8 TROPICAL STORM 7703890550
## 9 WINTER STORM 6688497251
## 10 HIGH WIND 5270046295
## 11 RIVER FLOOD 5118945500
## 12 WILDFIRE 4765114000
## 13 STORM SURGE/TIDE 4641188000
## 14 TSTM WIND 4484928495
## 15 ICE STORM 3944927860crop## EVTYPE cropDamage
## 1 DROUGHT 13972566000
## 2 FLOOD 5661968450
## 3 RIVER FLOOD 5029459000
## 4 ICE STORM 5022113500
## 5 HAIL 3025954473
## 6 HURRICANE 2741910000
## 7 HURRICANE/TYPHOON 2607872800
## 8 FLASH FLOOD 1421317100
## 9 EXTREME COLD 1292973000
## 10 FROST/FREEZE 1094086000
## 11 HEAVY RAIN 733399800
## 12 TROPICAL STORM 678346000
## 13 HIGH WIND 638571300
## 14 TSTM WIND 554007350
## 15 EXCESSIVE HEAT 492402000And the following is a pair of graphs of total property damage and total crop damage affected by these severe weather events.
ggplot(data=property[1:10,], aes(x=EVTYPE, y=propertyDamage)) +
geom_bar(stat="identity", fill = "#009E73", colour = "Black") + xlab("Severe Weather Type") +
ylab("Property Damage in US dollars") + ggtitle("Total Property Damage by Severe Weather Events") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Based on the above plot, we find that flood and hurricane/typhoon cause most property damage in the United States from 1995 to 2011.