Analysis of NOAA Storm-related data: Health and Economic Impact
Rich Huebner
January 25, 2016
Introduction & Synopsis
The basic goal of this assignment was to explore the NOAA Storm Database and answer some basic questions about severe weather events. Data is from the National Weather Service storm data, and is available here. The data contains the occurrence of storms and other significant weather phenomena having sufficient intensity to cause loss of life, injuries, property damage, etc. The analysis uses the Storm database to answer the questions listed below. All code and processing steps are shown below, along with the results. The data is processed to standarize/normalize some of the values.
The data had to be preprocessed prior to conducting the actual analysis, and prior to determining the answers to the questions. Preprocessing involved ensuring that NAs (no value) were converted to zeros. Additionally, the data had to be aggregated by the event type (EVTYPE) as well, so there are functions in the preprocessing that do that as well.
The data analysis addressed the following questions:
- Across the U.S., which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
- Across the U.S., which types of events have the greatest economic consequences?
Libraries Used in this Analysis
The following libraries are used in the analysis.
ggplot2 for graphing and plotting
dplyr for subsetting and data manipulation
Data Set Information
Detailed Documentation here: https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf
Data Set here: https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2
The data set file, when extracted, is about 400Mb. Loading the file upon first running the script takes time, so please be patient.
Data Processing
Load the required libraries for R.
library(ggplot2)
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, unionLoad the data set into R.
setwd("D:\\data\\RProjects\\RResearch_Project2")
data <- read.csv("repdata-data-StormData.csv", header=TRUE, sep=",")
head(data)## 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 6Clean the data by normalizing the data set.
Update event names – essentially normalizing them, “wind, WiNd” = “WIND”
#### First, I will only use the variables I need in this analysis. So, create a subset.
scols = c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")
subdata <- data[scols]
#### Fill in 0s for anything that's missing in the dataset
subdata[subdata$FATALITIES == ""] <- 0
subdata[subdata$INJURIES == ""] <- 0
subdata[subdata$PROPDMG == ""] <- 0
subdata[subdata$CROPDMG == ""] <- 0
head(subdata)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25.0 K 0
## 2 TORNADO 0 0 2.5 K 0
## 3 TORNADO 0 2 25.0 K 0
## 4 TORNADO 0 2 2.5 K 0
## 5 TORNADO 0 2 2.5 K 0
## 6 TORNADO 0 6 2.5 K 0#### Clean the data by normalizing the data set. "WInd, wind" = "WIND"
subdata$PROPDMGEXP[subdata$PROPDMGEXP == ""] <- 0
subdata$EVTYPE <- gsub("^HEAT$", "EXCESSIVE HEAT", subdata$EVTYPE )
subdata$EVTYPE <- gsub("^TSTM WIND$", "THUNDERSTORM WIND", subdata$EVTYPE)
subdata$EVTYPE <- gsub("^THUNDERSTORM WIND$", "THUNDERSTORM WIND", subdata$EVTYPE)Get aggregated data on fatalities.
f <- aggregate(subdata$FATALITIES, by=list(subdata$EVTYPE), sum, na.rm = TRUE)
names(f) <- c("Category", "Total")
fsort <- f[order(-f$Total), ]
topf <- fsort[1:10, ]
topf$Category <- factor(topf$Category, levels=topf$Category, ordered=TRUE)Get aggregated data on economic consequences
e <- aggregate(subdata$PROPDMG, by=list(subdata$EVTYPE), sum, na.rm=TRUE)
names(e) <- c("Category", "Total")
esort <- e[order(-e$Total), ]
tope <- esort[1:10, ]
tope$Category <- factor(tope$Category, levels=tope$Category, ordered=TRUE)Results
Q 1. Across the U.S., which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Here’s a plot of the data to answer question #1.
gplot <- ggplot(topf, aes(x=Category, y=Total, fill=Category)) +
geom_bar(stat="identity") +
labs(x="Category", y="Total Fatalities", title="Fatalities by Event Type") +
theme(axis.text.x = element_text(angle=45, hjust=1))
print(gplot)
Solution: Based on the graph, the MOST harmful with respect to population health is the TORNADO, followed by EXCESSIVE HEAT and FLASH FLOODING.
Q 2. Across the U.S., which types of events have the greatest economic consequences?
Here’s the second plot. This one addresses question #2.
gplot <- ggplot(tope, aes(x=Category, y=Total, fill=Category)) +
geom_bar(stat="identity") +
coord_flip() +
labs(x="Category", y="$$$", title="Total Economic Consequences per Event Type") +
theme(axis.text.x = element_text(angle=45, hjust=1)) +
scale_y_continuous(labels = scales::dollar)
print(gplot)
In terms of greatest economic impact, the TORNADO is the highest, followed by THUNDERSTORM WINDS and FLASH FLOODING.