Reproducible Research Peer Assessment 2
Thomas J James
2/12/2021
Impact of Severe Weather Events on Public Health and Economy in the United States
Synonpsis
In this report our goal is 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. Estimates of fatalities, injuries, property and crop damage will be utilized to decide which types of event are most harmful to the population health and economy. Data observations revealed that excessive heat and tornado are most harmful with respect to population health; whereas flood, drought, and hurricane/typhoon have the greatest economic consequences.
Data Processing
Loads the needed libraries
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)
library(reshape2)Downloads and Reads the dataset into R.
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", "data.csv.bz2")
rawData <- read.csv("data.csv.bz2")
rawData <- rawData %>% group_by(EVTYPE) Summarises the relevant variables (Fatalities, INJURIES, and PROPDMG) to make it easier to plot them. Also filters out the data points to remove some of the “noise”" (insignificantly small values) to make the plot more readable.
summarisedData <- rawData %>% group_by(EVTYPE) %>% summarise(totalFatalities = sum(FATALITIES), totalInjuries = sum(INJURIES))## `summarise()` ungrouping output (override with `.groups` argument)humanDamageData <- summarisedData %>% filter(totalFatalities>10, totalInjuries>10)Attempts to estimate the total damage to human health as a function of the totalFatilities and totalInjuries. I chose to weight them such that a fatality is worth twice as much as an injury.
humanDamageData <- humanDamageData %>% mutate(totalHumanDamage = 2*totalFatalities + totalInjuries) %>%
arrange(desc(totalHumanDamage))Calculates the total property and crop damage using the PROPDMGEXP column before filtering datapoints with low values to make the plot more readable
propDamageData <- rawData %>% mutate(completePropDamage = ifelse(tolower(PROPDMGEXP)=="k",PROPDMG*1000,ifelse(tolower(PROPDMGEXP)=="m", PROPDMG*1000000, ifelse(tolower(PROPDMGEXP)=="b",PROPDMG*1000000000,PROPDMG)))) %>%
mutate(completeCropDamage = ifelse(tolower(CROPDMGEXP)=="k",CROPDMG*1000,ifelse(tolower(CROPDMGEXP)=="m", CROPDMG*1000000, ifelse(tolower(CROPDMGEXP)=="b",CROPDMG*1000000000,CROPDMG)))) %>%
select(EVTYPE, completePropDamage, completeCropDamage) %>%
mutate(totalDamage=completePropDamage+completeCropDamage) %>%
filter(completePropDamage>1000000, completeCropDamage>1000000)
propDamageData <- propDamageData %>% group_by(EVTYPE) %>%
summarise(totalPropDamage=sum(completePropDamage), totalCropDamage=sum(completeCropDamage), totalDamage=sum(totalDamage))## `summarise()` ungrouping output (override with `.groups` argument)Results
####Damage to Human Life
Plots only the fatality and injury data to help visualise any overall trends.
## Melts the data to make it a long dataset (which can be plotted in ggplot2)
fatalityAndInjuryData <- melt(humanDamageData %>% select(EVTYPE, totalFatalities, totalInjuries), id="EVTYPE")
g <- ggplot(fatalityAndInjuryData, aes(EVTYPE, value)) +
facet_grid(variable~., scales="free") +
geom_point() +
theme(axis.text.x = element_text(angle=90, hjust=1)) +
ggtitle("Plot showing the total number of fatalities and injuries for different events")
print(g)
Plots the eariler calculated metric for total human health damage. This helps to visualise the data from the 2 earlier plots and any trends between them.
g <- ggplot(humanDamageData, aes(EVTYPE, totalHumanDamage)) +
geom_point() +
theme(axis.text.x = element_text(angle=90, hjust=1)) +
ggtitle("Plot showing the total human damage for different \nevents (based on the earlier calculated metric)")
print(g)
Displays the 10 events which are most damaging to human health to help inform any decision making (by providing precise information)
head(arrange(humanDamageData, desc(totalHumanDamage)), 10)## # A tibble: 10 x 4
## EVTYPE totalFatalities totalInjuries totalHumanDamage
## <chr> <dbl> <dbl> <dbl>
## 1 TORNADO 5633 91346 102612
## 2 EXCESSIVE HEAT 1903 6525 10331
## 3 TSTM WIND 504 6957 7965
## 4 FLOOD 470 6789 7729
## 5 LIGHTNING 816 5230 6862
## 6 HEAT 937 2100 3974
## 7 FLASH FLOOD 978 1777 3733
## 8 ICE STORM 89 1975 2153
## 9 THUNDERSTORM WIND 133 1488 1754
## 10 WINTER STORM 206 1321 1733####Property Damage
Plots the property and crop damage data side by side to help visualise any overall trends
##Melts the data to make it a long dataset (which can be plotted in ggplot2)
meltedDamageData <- melt(propDamageData %>% select(-totalDamage), id="EVTYPE")
g <- ggplot(meltedDamageData, aes(EVTYPE, value)) +
facet_grid(variable~., scales="free") +
geom_point() +
theme(axis.text.x = element_text(angle=90, hjust=1)) +
ggtitle("Plot showing the total property and crop damage for different events")
print(g)
Displays the 10 events which cause the most overall financial impact to help inform any decision making (by providing precise information)
head(arrange(propDamageData, desc(totalDamage), desc(totalPropDamage), desc(totalCropDamage)),10)## # A tibble: 10 x 4
## EVTYPE totalPropDamage totalCropDamage totalDamage
## <chr> <dbl> <dbl> <dbl>
## 1 FLOOD 117668720000 2368860000 120037580000
## 2 HURRICANE/TYPHOON 25073720000 2593840000 27667560000
## 3 HURRICANE 7586870000 2658510000 10245380000
## 4 RIVER FLOOD 5022800000 5019000000 10041800000
## 5 HURRICANE OPAL 2168000000 19000000 2187000000
## 6 FLASH FLOOD 1359230000 816510000 2175740000
## 7 HAIL 1247370000 400800000 1648170000
## 8 HIGH WIND 1140390000 478500000 1618890000
## 9 TORNADOES, TSTM WIND, HAIL 1600000000 2500000 1602500000
## 10 WILDFIRE 1296220000 88900000 1385120000