Reproducible Research: Storms and other weather events that cause both public health and economic problems in USA
- Author - Sanjeev Kumar Maheve
- Dated - August 23, 2015
Synopsis
In this report we aim to explore 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. From these data, we found that Tornado causes major harmful injuries whereas Tornado, Heat, Lightening and Flood are major fatal weather events causes death in USA.
library(knitr)
# Setting the global options.
opts_chunk$set(echo = TRUE)Reading in the dataset from year 1950 till end in November 2011
## Download the said file from the location given below
##
inputfile <- paste("storm_data", ".bz2", sep="")
if(!file.exists(inputfile)) {
fileUrl <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(fileUrl, destfile = inputfile, method = "curl")
}
# Load the data as per default column type information.
df <- read.table(inputfile,
header=TRUE,
sep=",",
na.strings="",
strip.white = TRUE,
stringsAsFactors = FALSE)
str(df)## '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 NA NA NA NA ...
## $ BGN_LOCATI: chr NA NA NA NA ...
## $ END_DATE : chr NA NA NA NA ...
## $ END_TIME : chr NA NA NA NA ...
## $ 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 NA NA NA NA ...
## $ END_LOCATI: chr NA NA NA NA ...
## $ 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 NA NA NA NA ...
## $ WFO : chr NA NA NA NA ...
## $ STATEOFFIC: chr NA NA NA NA ...
## $ ZONENAMES : chr NA NA NA NA ...
## $ 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 NA NA NA NA ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...After reading in U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database between 1950 - 2011, there are 902297 observations in the dataset.
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
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(datasets)
library(ggplot2)
# Group the data based on the event type and aggregate for
# injuries and fatalities.
injuryStats <- aggregate (INJURIES~EVTYPE, data=df, FUN=sum, na.rm=TRUE) %>%
filter(INJURIES > 0) %>%
arrange(desc(INJURIES))
fatalStats <- aggregate (FATALITIES~EVTYPE, data=df, FUN=sum, na.rm=TRUE) %>%
filter(FATALITIES > 0) %>%
arrange(desc(FATALITIES))Results (entire USA analysis) - showing health impacts due to weather events.
# Lets show the first few observations in 2 columns plot.
par(mfrow = c(1, 2))
par(mar=c(5.1,4.1,4.1,2.1))
barplot(injuryStats$INJURIES[1:10],
names.arg = injuryStats$EVTYPE[1:10],
main = "Top 10 harmful injuries",
ylab = "Injuries",
col="darkblue",
cex.axis = 0.8,
cex.names = 0.7,
las = 2)
barplot(fatalStats$FATALITIES[1:10],
names.arg = fatalStats$EVTYPE[1:10],
main = "Top 10 harmful fatalities",
ylab = "Fatalities",
col="red",
cex.axis = 0.8,
cex.names = 0.7,
las = 2)
title("Health impact between 1950-2011 in USA",
outer = TRUE)
Summary Report The weather event that was the most harmful to human health between 1950 - 2011 is Tornado followed by TSTM Wind and Flood. Refer the results below:
head(injuryStats)## EVTYPE INJURIES
## 1 TORNADO 91346
## 2 TSTM WIND 6957
## 3 FLOOD 6789
## 4 EXCESSIVE HEAT 6525
## 5 LIGHTNING 5230
## 6 HEAT 2100head(fatalStats)## EVTYPE FATALITIES
## 1 TORNADO 5633
## 2 EXCESSIVE HEAT 1903
## 3 FLASH FLOOD 978
## 4 HEAT 937
## 5 LIGHTNING 816
## 6 TSTM WIND 504Across the United States, which types of events have the greatest economic consequences?
The variables PROPDMGEXP and CROPDMGEXP have the factor of multiplicity of the variables PROPDMG and CROPDMG with the values:
- H -> 100
- K -> 1000
- M -> 1000,000
- B -> 1000,000,000
# Map all the unit mentioned above to common base unit.
# PROP
df$PROPDMGEXP <- gsub("NA", 1e0, df$PROPDMGEXP, ignore.case = TRUE)
df$PROPDMGEXP <- gsub("H", 1e2, df$PROPDMGEXP, ignore.case = TRUE)
df$PROPDMGEXP <- gsub("K", 1e3, df$PROPDMGEXP, ignore.case = TRUE)
df$PROPDMGEXP <- gsub("M", 1e6, df$PROPDMGEXP, ignore.case = TRUE)
df$PROPDMGEXP <- gsub("B", 1e9, df$PROPDMGEXP, ignore.case = TRUE)
df$PROPDMGEXP <- as.numeric(df$PROPDMGEXP)## Warning: NAs introduced by coercion# CROP
df$CROPDMGEXP <- gsub("NA", 1e0, df$CROPDMGEXP, ignore.case = TRUE)
df$CROPDMGEXP <- gsub("H", 1e2, df$CROPDMGEXP, ignore.case = TRUE)
df$CROPDMGEXP <- gsub("K", 1e3, df$CROPDMGEXP, ignore.case = TRUE)
df$CROPDMGEXP <- gsub("M", 1e6, df$CROPDMGEXP, ignore.case = TRUE)
df$CROPDMGEXP <- gsub("B", 1e9, df$CROPDMGEXP, ignore.case = TRUE)
df$CROPDMGEXP <- as.numeric(df$CROPDMGEXP)## Warning: NAs introduced by coercion# Create new column(s) using combination of existing columns for
# PROP and CROP
df <- df %>%
mutate(PROPTOTALDMG = PROPDMG * PROPDMGEXP) %>%
mutate(CROPTOTALDMG = CROPDMG * CROPDMGEXP)
df <- df %>% mutate(ECONOMYTOTALDMG = PROPTOTALDMG + CROPTOTALDMG)
str(df)## 'data.frame': 902297 obs. of 40 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 NA NA NA NA ...
## $ BGN_LOCATI : chr NA NA NA NA ...
## $ END_DATE : chr NA NA NA NA ...
## $ END_TIME : chr NA NA NA NA ...
## $ 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 NA NA NA NA ...
## $ END_LOCATI : chr NA NA NA NA ...
## $ 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 : 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 NA NA NA NA NA NA NA NA NA NA ...
## $ WFO : chr NA NA NA NA ...
## $ STATEOFFIC : chr NA NA NA NA ...
## $ ZONENAMES : chr NA NA NA NA ...
## $ 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 NA NA NA NA ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...
## $ PROPTOTALDMG : num 25000 2500 25000 2500 2500 2500 2500 2500 25000 25000 ...
## $ CROPTOTALDMG : num NA NA NA NA NA NA NA NA NA NA ...
## $ ECONOMYTOTALDMG: num NA NA NA NA NA NA NA NA NA NA ...# Group the data based on the event type and aggregate for
# property, crop and combined for economy.
propStats <- aggregate (PROPTOTALDMG~EVTYPE, data=df, FUN=sum, na.rm=TRUE) %>%
filter(PROPTOTALDMG > 0) %>%
arrange(desc(PROPTOTALDMG))
cropStats <- aggregate (CROPTOTALDMG~EVTYPE, data=df, FUN=sum, na.rm=TRUE) %>%
filter(CROPTOTALDMG > 0) %>%
arrange(desc(CROPTOTALDMG))
economyStats <- aggregate (ECONOMYTOTALDMG~EVTYPE, data=df, FUN=sum, na.rm=TRUE) %>%
filter(ECONOMYTOTALDMG > 0) %>%
arrange(desc(ECONOMYTOTALDMG))Results (entire USA analysis) - showing economy consequencies due to weather events.
# Lets show the first few observations in 2 columns plot.
barplot(economyStats$ECONOMYTOTALDMG[1:10],
names.arg = economyStats$EVTYPE[1:10],
main = "Economy Damage (USD)",
ylab = "Economy (USD)",
col="red",
cex.axis = 0.8,
cex.names = 0.7,
las = 2)
par(mfrow = c(1, 2))
par(mar=c(5.1,4.1,4.1,2.1))
barplot(propStats$PROPTOTALDMG[1:10],
names.arg = propStats$EVTYPE[1:10],
main = "Property Damage (USD)",
ylab = "Property (USD)",
col="magenta",
cex.axis = 0.8,
cex.names = 0.7,
las = 2)
barplot(cropStats$CROPTOTALDMG[1:10],
names.arg = cropStats$EVTYPE[1:10],
main = "Crop Damage (USD)",
ylab = "Crop (USD)",
col="skyblue",
cex.axis = 0.8,
cex.names = 0.7,
las = 2)
title("Economy consequence between 1950-2011 in USA",
outer = TRUE)
Summary Report
The weather event that resulted in bad economy consequences between 1950 - 2011 is Flood followed by Hurricane/Typhoon and Tornado for property and Drought followed by Flood for Crops.
head(economyStats)## EVTYPE ECONOMYTOTALDMG
## 1 FLOOD 138007444500
## 2 HURRICANE/TYPHOON 29348167800
## 3 TORNADO 16570326150
## 4 HURRICANE 12405268000
## 5 RIVER FLOOD 10108369000
## 6 HAIL 10045596740head(propStats)## EVTYPE PROPTOTALDMG
## 1 FLOOD 144657709800
## 2 HURRICANE/TYPHOON 69305840000
## 3 TORNADO 56937160991
## 4 STORM SURGE 43323536000
## 5 FLASH FLOOD 16140812087
## 6 HAIL 15732267370head(cropStats)## EVTYPE CROPTOTALDMG
## 1 DROUGHT 13972566000
## 2 FLOOD 5661968450
## 3 RIVER FLOOD 5029459000
## 4 ICE STORM 5022113500
## 5 HAIL 3025954450
## 6 HURRICANE 2741910000