title: “Storm_data” author: “Akshat Jain” date: “7/4/2020” output: html_document
An impact analysis of Severe Weather Events on Public Health and Economy in the United States
Synonpsis
Storms and other severe weather events have huge impact on public health and economic problems for municipalities and their inhabitants. Some of severe events can cause injuries property damage and even lead to death. In this report, we aim to analyze the impact of different weather events on public health and economy Based on the storm database collected from the U.S. National Oceanic and Atmospheric Administration’s (NOAA) from 1950 - 2011. We will use the estimates of fatalities, injuries, property and crop damage to decide which types of event are most harmful to the population health and economy. 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.
Basic settings
Please install dplyr,ggplot2, and xtable
echo = TRUE # Always make code visible
options(scipen = 1) # Turn off scientific notations for numbers
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 4.0.2library(xtable)## Warning: package 'xtable' was built under R version 4.0.2Data Processing
First, we download the data file and unzip it.
link <- "http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url = link, destfile = "StormData")
StormData <- read.csv(bzfile("StormData"),sep = ",",header=TRUE)There are 902297 rows and 37 columns in total.
###Examine the structure of the data
str(StormData)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : chr "4/18/1950 0:00:00" "4/18/1950 0:00:00" "2/20/1951 0:00:00" "6/8/1951 0:00:00" ...
## $ BGN_TIME : chr "0130" "0145" "1600" "0900" ...
## $ TIME_ZONE : chr "CST" "CST" "CST" "CST" ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: chr "MOBILE" "BALDWIN" "FAYETTE" "MADISON" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : chr "" "" "" "" ...
## $ BGN_LOCATI: chr "" "" "" "" ...
## $ END_DATE : chr "" "" "" "" ...
## $ END_TIME : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ END_LOCATI: chr "" "" "" "" ...
## $ 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: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...
## $ WFO : chr "" "" "" "" ...
## $ STATEOFFIC: chr "" "" "" "" ...
## $ ZONENAMES : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...###Extracting variables of interest for analysis of weather impact on health and economy When processing a large dataset, compute performance can be improved by taking a subset of the variables required for the analysis.Only observations with value > 0 will be included. The selected variables are “EVTYPE”, “FATALITIES”, “INJURIES”, “PROPDMG”, “PROPDMGEXP”, “CROPDMG”, “CROPDMGEXP”, “END_DATE”, “BGN_DATE”, and “STATE”
stormDataTidy <- subset(StormData, EVTYPE != "?"
&
(FATALITIES > 0 | INJURIES > 0 | PROPDMG > 0 | CROPDMG > 0),
select = c("EVTYPE",
"FATALITIES",
"INJURIES",
"PROPDMG",
"PROPDMGEXP",
"CROPDMG",
"CROPDMGEXP",
"BGN_DATE",
"END_DATE",
"STATE"))
dim(stormDataTidy)## [1] 254632 10sum(is.na(stormDataTidy))## [1] 0Clean Event Type Data
length(unique(stormDataTidy$EVTYPE))## [1] 487The dataset was normalized by converting all Event Type values to uppercase and combining similar Event Type values into unique categories.
stormDataTidy$EVTYPE <- toupper(stormDataTidy$EVTYPE)
# AVALANCHE
stormDataTidy$EVTYPE <- gsub('.*AVALANCE.*', 'AVALANCHE', stormDataTidy$EVTYPE)
# BLIZZARD
stormDataTidy$EVTYPE <- gsub('.*BLIZZARD.*', 'BLIZZARD', stormDataTidy$EVTYPE)
# CLOUD
stormDataTidy$EVTYPE <- gsub('.*CLOUD.*', 'CLOUD', stormDataTidy$EVTYPE)
# COLD
stormDataTidy$EVTYPE <- gsub('.*COLD.*', 'COLD', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*FREEZ.*', 'COLD', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*FROST.*', 'COLD', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*ICE.*', 'COLD', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*LOW TEMPERATURE RECORD.*', 'COLD', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*LO.*TEMP.*', 'COLD', stormDataTidy$EVTYPE)
# DRY
stormDataTidy$EVTYPE <- gsub('.*DRY.*', 'DRY', stormDataTidy$EVTYPE)
# DUST
stormDataTidy$EVTYPE <- gsub('.*DUST.*', 'DUST', stormDataTidy$EVTYPE)
# FIRE
stormDataTidy$EVTYPE <- gsub('.*FIRE.*', 'FIRE', stormDataTidy$EVTYPE)
# FLOOD
stormDataTidy$EVTYPE <- gsub('.*FLOOD.*', 'FLOOD', stormDataTidy$EVTYPE)
# FOG
stormDataTidy$EVTYPE <- gsub('.*FOG.*', 'FOG', stormDataTidy$EVTYPE)
# HAIL
stormDataTidy$EVTYPE <- gsub('.*HAIL.*', 'HAIL', stormDataTidy$EVTYPE)
# HEAT
stormDataTidy$EVTYPE <- gsub('.*HEAT.*', 'HEAT', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*WARM.*', 'HEAT', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*HIGH.*TEMP.*', 'HEAT', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*RECORD HIGH TEMPERATURES.*', 'HEAT', stormDataTidy$EVTYPE)
# HYPOTHERMIA/EXPOSURE
stormDataTidy$EVTYPE <- gsub('.*HYPOTHERMIA.*', 'HYPOTHERMIA/EXPOSURE', stormDataTidy$EVTYPE)
# LANDSLIDE
stormDataTidy$EVTYPE <- gsub('.*LANDSLIDE.*', 'LANDSLIDE', stormDataTidy$EVTYPE)
# LIGHTNING
stormDataTidy$EVTYPE <- gsub('^LIGHTNING.*', 'LIGHTNING', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('^LIGNTNING.*', 'LIGHTNING', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('^LIGHTING.*', 'LIGHTNING', stormDataTidy$EVTYPE)
# MICROBURST
stormDataTidy$EVTYPE <- gsub('.*MICROBURST.*', 'MICROBURST', stormDataTidy$EVTYPE)
# MUDSLIDE
stormDataTidy$EVTYPE <- gsub('.*MUDSLIDE.*', 'MUDSLIDE', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*MUD SLIDE.*', 'MUDSLIDE', stormDataTidy$EVTYPE)
# RAIN
stormDataTidy$EVTYPE <- gsub('.*RAIN.*', 'RAIN', stormDataTidy$EVTYPE)
# RIP CURRENT
stormDataTidy$EVTYPE <- gsub('.*RIP CURRENT.*', 'RIP CURRENT', stormDataTidy$EVTYPE)
# STORM
stormDataTidy$EVTYPE <- gsub('.*STORM.*', 'STORM', stormDataTidy$EVTYPE)
# SUMMARY
stormDataTidy$EVTYPE <- gsub('.*SUMMARY.*', 'SUMMARY', stormDataTidy$EVTYPE)
# TORNADO
stormDataTidy$EVTYPE <- gsub('.*TORNADO.*', 'TORNADO', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*TORNDAO.*', 'TORNADO', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*LANDSPOUT.*', 'TORNADO', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*WATERSPOUT.*', 'TORNADO', stormDataTidy$EVTYPE)
# SURF
stormDataTidy$EVTYPE <- gsub('.*SURF.*', 'SURF', stormDataTidy$EVTYPE)
# VOLCANIC
stormDataTidy$EVTYPE <- gsub('.*VOLCANIC.*', 'VOLCANIC', stormDataTidy$EVTYPE)
# WET
stormDataTidy$EVTYPE <- gsub('.*WET.*', 'WET', stormDataTidy$EVTYPE)
# WIND
stormDataTidy$EVTYPE <- gsub('.*WIND.*', 'WIND', stormDataTidy$EVTYPE)
# WINTER
stormDataTidy$EVTYPE <- gsub('.*WINTER.*', 'WINTER', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*WINTRY.*', 'WINTER', stormDataTidy$EVTYPE)
stormDataTidy$EVTYPE <- gsub('.*SNOW.*', 'WINTER', stormDataTidy$EVTYPE)
length(unique(stormDataTidy$EVTYPE))## [1] 81Clean Date Data
Format date variables for any type of optional reporting or further analysis
stormDataTidy$DATE_START <- as.Date(stormDataTidy$BGN_DATE, format = "%m/%d/%Y")
stormDataTidy$DATE_END <- as.Date(stormDataTidy$END_DATE, format = "%m/%d/%Y")
stormDataTidy$YEAR <- as.integer(format(stormDataTidy$DATE_START, "%Y"))
stormDataTidy$DURATION <- as.numeric(stormDataTidy$DATE_END - stormDataTidy$DATE_START)/3600Clean Economic Data
table(toupper(stormDataTidy$PROPDMGEXP))##
## - + 0 2 3 4 5 6 7 B
## 11585 1 5 210 1 1 4 18 3 3 40
## H K M
## 7 231427 11327table(toupper(stormDataTidy$CROPDMGEXP))##
## ? 0 B K M
## 152663 6 17 7 99953 1986# function to get multiplier factor
getMultiplier <- function(exp) {
exp <- toupper(exp);
if (exp == "") return (10^0);
if (exp == "-") return (10^0);
if (exp == "?") return (10^0);
if (exp == "+") return (10^0);
if (exp == "0") return (10^0);
if (exp == "1") return (10^1);
if (exp == "2") return (10^2);
if (exp == "3") return (10^3);
if (exp == "4") return (10^4);
if (exp == "5") return (10^5);
if (exp == "6") return (10^6);
if (exp == "7") return (10^7);
if (exp == "8") return (10^8);
if (exp == "9") return (10^9);
if (exp == "H") return (10^2);
if (exp == "K") return (10^3);
if (exp == "M") return (10^6);
if (exp == "B") return (10^9);
return (NA);
}
# Property damage and crop damage costs (in billions)
stormDataTidy$PROP_COST <- with(stormDataTidy, as.numeric(PROPDMG) * sapply(PROPDMGEXP, getMultiplier))/10^9
stormDataTidy$CROP_COST <- with(stormDataTidy, as.numeric(CROPDMG) * sapply(CROPDMGEXP, getMultiplier))/10^9Summarize Data
A summarized dataset sorted in descending order by health impact(property damage + crop damage).
healthImpactData <- aggregate(x = list(HEALTH_IMPACT = stormDataTidy$FATALITIES + stormDataTidy$INJURIES),
by = list(EVENT_TYPE = stormDataTidy$EVTYPE),
FUN = sum,
na.rm = TRUE)
healthImpactData <- healthImpactData[order(healthImpactData$HEALTH_IMPACT, decreasing = TRUE),]A summarized dataset sorted in descending order by damage cost(property damage + crop damage).
damageCostImpactData <- aggregate(x = list(DAMAGE_IMPACT = stormDataTidy$PROP_COST + stormDataTidy$CROP_COST),
by = list(EVENT_TYPE = stormDataTidy$EVTYPE),
FUN = sum,
na.rm = TRUE)
damageCostImpactData <- damageCostImpactData[order(damageCostImpactData$DAMAGE_IMPACT, decreasing = TRUE),]Results
Most Harmful to Population Health
The most harmful impact on population health is because of injuries and fatalities. In terms of population health, 10 most harmful weather events are shown below
print(xtable(head(healthImpactData, 10),
caption = "Top 10 Weather Events Most Harmful to Population Health"),
caption.placement = 'top',
type = "html",
include.rownames = FALSE,
html.table.attributes='class="table-bordered", width="100%"')| EVENT_TYPE | HEALTH_IMPACT |
|---|---|
| TORNADO | 97075.00 |
| HEAT | 12392.00 |
| FLOOD | 10127.00 |
| WIND | 9893.00 |
| LIGHTNING | 6049.00 |
| STORM | 4780.00 |
| COLD | 3100.00 |
| WINTER | 1924.00 |
| FIRE | 1698.00 |
| HAIL | 1512.00 |
healthImpactChart <- ggplot(head(healthImpactData, 10),
aes(x = reorder(EVENT_TYPE, HEALTH_IMPACT), y = HEALTH_IMPACT, fill = EVENT_TYPE)) +
coord_flip() +
geom_bar(stat = "identity") +
xlab("Event Type") +
ylab("Total Fatalities and Injures") +
theme(plot.title = element_text(size = 14, hjust = 0.5)) +
ggtitle("Top 10 Weather Events Most Harmful to\nPopulation Health")
print(healthImpactChart)
Economic Consequences
The most harmful impact on the economy is because of property and crop. The 10 most harmful weather events in terms economic consequences in the U.S.are as follows
print(xtable(head(damageCostImpactData, 10),
caption = "Top 10 Weather Events with Greatest Economic Consequences"),
caption.placement = 'top',
type = "html",
include.rownames = FALSE,
html.table.attributes='class="table-bordered", width="100%"')| EVENT_TYPE | DAMAGE_IMPACT |
|---|---|
| FLOOD | 180.58 |
| HURRICANE/TYPHOON | 71.91 |
| STORM | 70.45 |
| TORNADO | 57.43 |
| HAIL | 20.74 |
| DROUGHT | 15.02 |
| HURRICANE | 14.61 |
| COLD | 12.70 |
| WIND | 12.01 |
| FIRE | 8.90 |
damageCostImpactChart <- ggplot(head(damageCostImpactData, 10),
aes(x = reorder(EVENT_TYPE, DAMAGE_IMPACT), y = DAMAGE_IMPACT, fill = EVENT_TYPE)) +
coord_flip() +
geom_bar(stat = "identity") +
xlab("Event Type") +
ylab("Total Property / Crop Damage Cost\n(in Billions)") +
theme(plot.title = element_text(size = 14, hjust = 0.5)) +
ggtitle("Top 10 Weather Events with\nGreatest Economic Consequences")
print(damageCostImpactChart)
Conclusion
Based on the evidence demonstrated in this analysis and supported by the included data and graphs, the following conclusions can be drawn:
- Tornadoes are responsible for the greatest number of fatalities and injuries.
- Floods are responsible for causing the most property damage and crop damage costs.