Reproducible Research: Peer Assessment 2 - Storm Data Analysis
Introduction
#This project involves exploring 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.
Synopsis
#In this project we try to explore the NOAA data storm database (1950 - 2011). Our prime objective is to find out what events are most significant in terms of fatalities, injuries and economic damage (property and crop). We’ll focus our analysis to answer below two questions: #Q1. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health? #Q2. Across the United States, which types of events have the greatest economic consequences?
Reading Dataset
library(ggplot2)
library(knitr)
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, union#getwd()
#dir()
storm<- read.csv2("repdata_data_StormData.csv", header = TRUE, sep = ",")
#head(storm)
summary(storm)## STATE__ BGN_DATE BGN_TIME TIME_ZONE
## Length:902297 Length:902297 Length:902297 Length:902297
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## COUNTY COUNTYNAME STATE EVTYPE
## Length:902297 Length:902297 Length:902297 Length:902297
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE
## Length:902297 Length:902297 Length:902297 Length:902297
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## END_TIME COUNTY_END COUNTYENDN END_RANGE
## Length:902297 Length:902297 Mode:logical Length:902297
## Class :character Class :character NA's:902297 Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## END_AZI END_LOCATI LENGTH WIDTH
## Length:902297 Length:902297 Length:902297 Length:902297
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## F MAG FATALITIES INJURIES
## Min. :0.0 Length:902297 Length:902297 Length:902297
## 1st Qu.:0.0 Class :character Class :character Class :character
## Median :1.0 Mode :character Mode :character Mode :character
## Mean :0.9
## 3rd Qu.:1.0
## Max. :5.0
## NA's :843563
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Length:902297 Length:902297 Length:902297 Length:902297
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## WFO STATEOFFIC ZONENAMES LATITUDE
## Length:902297 Length:902297 Length:902297 Length:902297
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
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## LONGITUDE LATITUDE_E LONGITUDE_ REMARKS
## Length:902297 Length:902297 Length:902297 Length:902297
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
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##
##
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## REFNUM
## Length:902297
## Class :character
## Mode :character
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##
## Data Processing
#Trim the dataset to keep only relevant columns
storm_sub <- storm[, c("BGN_DATE", "EVTYPE", "FATALITIES", "INJURIES",
"PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]
head(storm_sub)## BGN_DATE EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG
## 1 4/18/1950 0:00:00 TORNADO 0.00 15.00 25.00 K 0.00
## 2 4/18/1950 0:00:00 TORNADO 0.00 0.00 2.50 K 0.00
## 3 2/20/1951 0:00:00 TORNADO 0.00 2.00 25.00 K 0.00
## 4 6/8/1951 0:00:00 TORNADO 0.00 2.00 2.50 K 0.00
## 5 11/15/1951 0:00:00 TORNADO 0.00 2.00 2.50 K 0.00
## 6 11/15/1951 0:00:00 TORNADO 0.00 6.00 2.50 K 0.00
## CROPDMGEXP
## 1
## 2
## 3
## 4
## 5
## 6# Create subset for Question 1 & 2
# Select data for Fatalities and injuries from subset (Q1)
harm <- subset(storm_sub, !storm_sub$FATALITIES == 0 & !storm_sub$INJURIES ==
0, select = c(EVTYPE, FATALITIES, INJURIES))
harm$FATALITIES <- as.numeric(as.character(harm$FATALITIES))
harm$INJURIES <- as.numeric(as.character(harm$INJURIES))
head(harm)## EVTYPE FATALITIES INJURIES
## 1 TORNADO 0 15
## 2 TORNADO 0 0
## 3 TORNADO 0 2
## 4 TORNADO 0 2
## 5 TORNADO 0 2
## 6 TORNADO 0 6# Select data for various damages from subset (Q2)
Dam <- subset(storm_sub, !storm_sub$PROPDMG == 0 & !storm_sub$CROPDMG ==
0, select = c(EVTYPE, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP))
head(Dam)## EVTYPE PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 25.00 K 0.00
## 2 TORNADO 2.50 K 0.00
## 3 TORNADO 25.00 K 0.00
## 4 TORNADO 2.50 K 0.00
## 5 TORNADO 2.50 K 0.00
## 6 TORNADO 2.50 K 0.00Q1 - Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
# Create separate data set for Fatalities
fatal <- harm %>%
group_by(EVTYPE) %>%
summarise_each(funs(sum))%>%
arrange(desc(FATALITIES))## Warning: `summarise_each_()` is deprecated as of dplyr 0.7.0.
## Please use `across()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.## Warning: `funs()` is deprecated as of dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.#head(fatal)
# Take top 10 events for Fatalities and corresponding Injuries
fatal <- fatal[1:10,]
# Create separate data set for Injury
Inj <- harm %>%
group_by(EVTYPE) %>%
summarise_each(funs(sum))%>%
arrange(desc(INJURIES))
#head(Inj)
# Take top 10 events for Injuries
Inj <- Inj[1:10,]
#head(Inj)
# Plot Fatalities and store at fatal_plot
library(ggplot2)
fatal_plot <- ggplot() + geom_bar(data = fatal, aes(x = EVTYPE,
y =FATALITIES, fill = interaction(FATALITIES, EVTYPE)), stat = "identity",
show.legend = F) + theme(axis.text.x = element_text(angle = 30, hjust = 1)) +
xlab("Harmful Events") + ylab("FATALITIES") + ggtitle("Top 10 weather events causing FATALITIES") +
theme(axis.text.x = element_text(angle = 30, hjust = 1))
# Plot Fatalities and store at Inj_plot
Inj_plot <- ggplot() + geom_bar(data = Inj, aes(x = EVTYPE,
y =INJURIES, fill = interaction(INJURIES, EVTYPE)), stat = "identity",
show.legend = F) + theme(axis.text.x = element_text(angle = 30, hjust = 1)) +
xlab("Harmful Events") + ylab("INJURIES") + ggtitle("Top 10 weather events causing INJURIES") +
theme(axis.text.x = element_text(angle = 30, hjust = 1))
#Show both plots for Fatalities and Injuries
require("gridExtra")## Loading required package: gridExtra##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinegrid.arrange(fatal_plot, Inj_plot, ncol = 2)
## Assessment for Q1: Tornados are the most harmful in terms of Fatality and Injury based on given data
Q2 - Across the United States, which types of events have the greatest economic consequences?
# select required entries for economy damage
Dam <- subset(Dam, Dam$PROPDMGEXP == "K" | Dam$PROPDMGEXP ==
"k" | Dam$PROPDMGEXP == "M" | Dam$PROPDMGEXP == "m" |
Dam$PROPDMGEXP == "B" | Dam$PROPDMGEXP == "b")
Dam <- subset(Dam, Dam$CROPDMGEXP == "K" | Dam$CROPDMGEXP ==
"k" | Dam$CROPDMGEXP == "M" | Dam$CROPDMGEXP == "m" |
Dam$CROPDMGEXP == "B" | Dam$CROPDMGEXP == "b")
# Convert economic values to number
Dam$PROPDMGEXP <- gsub("m", 1e+06, Dam$PROPDMGEXP, ignore.case = TRUE)
Dam$PROPDMGEXP <- gsub("k", 1000, Dam$PROPDMGEXP, ignore.case = TRUE)
Dam$PROPDMGEXP <- gsub("b", 1e+09, Dam$PROPDMGEXP, ignore.case = TRUE)
Dam$PROPDMGEXP <- as.numeric(Dam$PROPDMGEXP)
Dam$CROPDMGEXP <- gsub("m", 1e+06, Dam$CROPDMGEXP, ignore.case = TRUE)
Dam$CROPDMGEXP <- gsub("k", 1000, Dam$CROPDMGEXP, ignore.case = TRUE)
Dam$CROPDMGEXP <- gsub("b", 1e+09, Dam$CROPDMGEXP, ignore.case = TRUE)
Dam$CROPDMGEXP <- as.numeric(Dam$CROPDMGEXP)
Dam$PROPDMGEXP <- as.numeric(Dam$PROPDMGEXP)
Dam$CROPDMG <- as.numeric(Dam$CROPDMG)
Dam$PROPDMG <- as.numeric(Dam$PROPDMG)
# then sum the damages by each event type
Dam$TOTALDMG <- (Dam$CROPDMG * Dam$CROPDMGEXP) +
(Dam$PROPDMG * Dam$PROPDMGEXP)
Dam <- aggregate(Dam$TOTALDMG, by = list(Dam$EVTYPE),
FUN = sum)
colnames(Dam) <- c("EVTYPE", "TOTALDMG")
# Rank the event type by highest damage cost and take top 5 columns
Dam <- Dam[order(Dam$TOTALDMG, decreasing = TRUE),
]
Dam <- Dam[1:5, ]
# Now plot the graph
ggplot() + geom_bar(data = Dam, aes(x = EVTYPE, y = TOTALDMG, fill = interaction(TOTALDMG,
EVTYPE)), stat = "identity", show.legend = F) + theme(axis.text.x = element_text(angle = 30,
hjust = 1)) + xlab("Event Type") + ylab("Total Damage")
## Q2 Assessment: Flood brings most economic damage as per given data.
Result
# As per the analysis of given data, we can infer that:
#1. Tornados are most harmful event in terms of fatality and Injuries.
#2. Flood is most harmful even in terms of economic damage.