Storm Data Peer Assesment 2
Impact of Severe Weather Events on Public Health and Economy in the USA
Synopsis
We analyze the impact of different weather events on the public health and the economy of thte United States. We use data from the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. We look at estimates of fatalities, injuries, and property damage.
We address the following questions:
Across the United States, which types of events (as indicated in the
EVTYPEvariable) are most harmful with respect to population health?Across the United States, which types of events have the greatest economic consequences?
We conclude that the deadliest events were tornados and excessive heat. The events which resulted in the greatest number of injuries were tornados and thunderstorm wind. The events leading to the greatest economic damage were tornados and flash floods.
Data Processing
Load the necessary libraries.
library(R.utils)## Loading required package: R.oo
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## cat, commandArgs, getOption, inherits, isOpen, parse, warningslibrary(ggplot2)
library(knitr)Download, unzip, and load the data file.
download.file("http://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", destfile = "storm_data.csv.bz2")
bunzip2("storm_data.csv.bz2", overwrite=T, remove=F)
storm_data <- read.csv("storm_data.csv",header = TRUE, stringsAsFactors = FALSE)
# We look at the structure of this data set.
str(storm_data)## '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 ...We want to subset the data by the type of damamge - either economic or population health.
data_col <- c("BGN_DATE", "EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")
storm_data_sub <- storm_data[data_col]
storm_data_sub$YEAR <- as.integer(format(as.Date(storm_data_sub$BGN_DATE, "%m/%d/%Y 0:00:00"), "%Y"))
head(storm_data_sub)## BGN_DATE EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP
## 1 4/18/1950 0:00:00 TORNADO 0 15 25.0 K
## 2 4/18/1950 0:00:00 TORNADO 0 0 2.5 K
## 3 2/20/1951 0:00:00 TORNADO 0 2 25.0 K
## 4 6/8/1951 0:00:00 TORNADO 0 2 2.5 K
## 5 11/15/1951 0:00:00 TORNADO 0 2 2.5 K
## 6 11/15/1951 0:00:00 TORNADO 0 6 2.5 K
## CROPDMG CROPDMGEXP YEAR
## 1 0 1950
## 2 0 1950
## 3 0 1951
## 4 0 1951
## 5 0 1951
## 6 0 1951str(storm_data_sub)## 'data.frame': 902297 obs. of 9 variables:
## $ 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" ...
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ 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 "" "" "" "" ...
## $ YEAR : int 1950 1950 1951 1951 1951 1951 1951 1952 1952 1952 ...According to the Storm Data Documentation, PROPDMGEXP and CROPDMGEXP are exponents. We convert these to numerical exponents.
convert_exp <- function(x){
x[x==" " || x==""] <- "0"
x[x=="H" || x=="h"] <- "100"
x[x=="K" || x=="k"] <- "1000"
x[x=="M" || x=="m"] <- "1000000"
x[x=="B" || x=="b"] <- "1000000000"
return(as.numeric(x))
}
storm_data_sub$PROPDMGEXP <- convert_exp(storm_data_sub$PROPDMGEXP)
storm_data_sub$PROPDMGEXP[is.na(storm_data_sub$PROPDMGEXP)] = 0
storm_data_sub$PropertyDamage = storm_data_sub$PROPDMG * 10^storm_data_sub$PROPDMGEXP
summary(storm_data_sub$PropertyDamage)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## Inf Inf Inf Inf Inf Inf 663123storm_data_sub$CROPDMGEXP <- convert_exp(storm_data_sub$CROPDMGEXP)
storm_data_sub$CROPDMGEXP[is.na(storm_data_sub$CROPDMGEXP)] = 0
storm_data_sub$CropDamage = storm_data_sub$CROPDMG * 10^storm_data_sub$CROPDMGEXP
summary(storm_data_sub$CropDamage)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 1.527 0.000 990.000Compute the combined property and crop damage
TOTAL_DMG <- storm_data_sub$PROPDMG + storm_data_sub$CROPDMG
clean_storm_data <- cbind(storm_data_sub, TOTAL_DMG)
str(clean_storm_data)## 'data.frame': 902297 obs. of 12 variables:
## $ 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" ...
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ 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 : num 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP : num 0 0 0 0 0 0 0 0 0 0 ...
## $ YEAR : int 1950 1950 1951 1951 1951 1951 1951 1952 1952 1952 ...
## $ PropertyDamage: num Inf Inf Inf Inf Inf ...
## $ CropDamage : num 0 0 0 0 0 0 0 0 0 0 ...
## $ TOTAL_DMG : num 25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...Results
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
First, we consider the 10 most harmful events with respect to fatalities and injuries and plot a bar chart.
fatal_data <- aggregate(clean_storm_data$FATALITIES, by = list(clean_storm_data$EVTYPE), "sum")
fatal_data <- fatal_data[order(-fatal_data$x), ][1:10, ]
str(fatal_data)## 'data.frame': 10 obs. of 2 variables:
## $ Group.1: chr "TORNADO" "EXCESSIVE HEAT" "FLASH FLOOD" "HEAT" ...
## $ x : num 5633 1903 978 937 816 ...fatal_plot<- ggplot(fatal_data, aes(x = Group.1, y = x)) + geom_bar(stat = "identity", fill = "#FF0011",
las = 3) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + xlab("Event Type") +
ylab("Count") + ggtitle("Top Ten Fatal Events")
fatal_plot
Next, we consider the 10 events leading to the greatest number of injuries.
injury_data <- aggregate(clean_storm_data$INJURIES, by = list(clean_storm_data$EVTYPE), "sum")
injury_data <- injury_data[order(-injury_data$x), ][1:10, ]
str(injury_data)## 'data.frame': 10 obs. of 2 variables:
## $ Group.1: chr "TORNADO" "TSTM WIND" "FLOOD" "EXCESSIVE HEAT" ...
## $ x : num 91346 6957 6789 6525 5230 ...injury_plot<- ggplot(injury_data, aes(x = Group.1, y = x)) + geom_bar(stat = "identity", fill = "#0022FF",
las = 3) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + xlab("Event Type") +
ylab("Count") + ggtitle("Top Ten Causes of Injuries")
injury_plot
Across the United States, which types of events have the greatest economic consequences?
We consider the 10 costliest events with respect to economic damage and plot a bar chart.
damage_data <- aggregate(clean_storm_data$TOTAL_DMG, by = list(clean_storm_data$EVTYPE), "sum")
damage_data <- damage_data[order(-damage_data$x), ][1:10, ]
str(damage_data)## 'data.frame': 10 obs. of 2 variables:
## $ Group.1: chr "TORNADO" "FLASH FLOOD" "TSTM WIND" "HAIL" ...
## $ x : num 3312277 1599325 1445168 1268290 1067976 ...damage_plot<- ggplot(damage_data, aes(x = Group.1, y = x)) + geom_bar(stat = "identity", fill = "#05FF22",
las = 3) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + xlab("Event Type") +
ylab("Count") + ggtitle("Top Ten Events Resulting in Economic Damage")
damage_plot
Conclusions
Between the years 1950 and 2011, tornados were the leading cause of both injuries and fatalitites, and property and crop damamge.