Exploring the US national oceanic and atmospheric administration’s (noaa) storm database : impacts of severe weather on economy and health.
ASSIGNMENT
The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. You must use the database to answer the questions below and show the code for your entire analysis. Your analysis can consist of tables, figures, or other summaries. You may use any R package you want to support your analysis.
SYNOPSIS
As we know that severe weather conditions can cause both health and property damage. So we are going to explore the dataset to determine that which weather condition has the largest impact on health as well as on economy.
Question 1
Across the United States, which types of events i.e Flood, Lightning, Tornado etc. are most harmful with respect to population health?
Data Processing
- Download the data from the URL.
fileURL <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(fileURL, "./repdata-data-StormData.csv.bz2")- Now create a data frame
dataframe <- read.table(bzfile("./repdata-data-StormData.csv.bz2"), sep = ",", header=TRUE)- Let’s give a look to our data
head(dataframe)## 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 6- We only want the health related data for our question. So we are extracting the data we need.
healthData <- dataframe[,c("EVTYPE","FATALITIES","INJURIES")]
head(healthData)## 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 6Now let’s find out the top 10 conditions that has effect on the health of the population i.e the number of fatalities and injuries.
Fatalities
1. Number of Fatalities by top 10 conditions.
aggData <- aggregate(FATALITIES ~ EVTYPE,data = healthData,sum)
head(aggData)## EVTYPE FATALITIES
## 1 HIGH SURF ADVISORY 0
## 2 COASTAL FLOOD 0
## 3 FLASH FLOOD 0
## 4 LIGHTNING 0
## 5 TSTM WIND 0
## 6 TSTM WIND (G45) 0But Remember we need top 10 not all.
library(tidyverse)## Warning: package 'tidyverse' was built under R version 3.4.4## -- Attaching packages ------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 2.2.1 v purrr 0.2.4
## v tibble 1.4.2 v dplyr 0.7.4
## v tidyr 0.7.2 v stringr 1.2.0
## v readr 1.1.1 v forcats 0.2.0## Warning: package 'ggplot2' was built under R version 3.4.4## -- Conflicts ---------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()topData <- head(arrange(aggData,desc(FATALITIES)), n = 10)
head(topData)## EVTYPE FATALITIES
## 1 TORNADO 5633
## 2 EXCESSIVE HEAT 1903
## 3 FLASH FLOOD 978
## 4 HEAT 937
## 5 LIGHTNING 816
## 6 TSTM WIND 504Now we have found the top 10 weather conditions causing maximum fatalities, let’s create our first histogram.
library(ggplot2)
ggplot(topData, aes(x = EVTYPE, y = FATALITIES)) +
geom_bar(stat = "identity", fill = "darkgreen", las = 3) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Event Type") + ylab("Fatalities") + ggtitle("Number of fatalities by top 10 Weather Events")## Warning: Ignoring unknown parameters: las
## Injuries
2. Number of Injuries by top 10 conditions.
aggData1 <- aggregate(INJURIES ~ EVTYPE,data = healthData,sum)
head(aggData1)## EVTYPE INJURIES
## 1 HIGH SURF ADVISORY 0
## 2 COASTAL FLOOD 0
## 3 FLASH FLOOD 0
## 4 LIGHTNING 0
## 5 TSTM WIND 0
## 6 TSTM WIND (G45) 0Now Let’s find the top 10 weather conditions causing maximum injuries.
topData1 <- head(arrange(aggData1,desc(INJURIES)), n = 10)
head(topData1)## EVTYPE INJURIES
## 1 TORNADO 91346
## 2 TSTM WIND 6957
## 3 FLOOD 6789
## 4 EXCESSIVE HEAT 6525
## 5 LIGHTNING 5230
## 6 HEAT 2100Now we have found the top 10 weather conditions causing maximum fatalities, let’s create histogram.
ggplot(topData1,aes(x=EVTYPE,y=INJURIES)) +
geom_bar(stat = "identity",fill="darkgreen") +
theme(axis.text.x = element_text(angle = 90,hjust = 1))+
xlab("Event Type") + ylab("INJURIES") + ggtitle("Number of Injuries by top 10 Weather Conditions")
Result
Weather Condition i.e TORNADO is the most disastrous one causing highest number of injuries and fatalities.
Question 2
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to economy?
economyData <- dataframe[,c(8,25,26,27,28)]
head(economyData)## EVTYPE PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 25.0 K 0
## 2 TORNADO 2.5 K 0
## 3 TORNADO 25.0 K 0
## 4 TORNADO 2.5 K 0
## 5 TORNADO 2.5 K 0
## 6 TORNADO 2.5 K 0tail(economyData)## EVTYPE PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 902292 WINTER WEATHER 0 K 0 K
## 902293 HIGH WIND 0 K 0 K
## 902294 HIGH WIND 0 K 0 K
## 902295 HIGH WIND 0 K 0 K
## 902296 BLIZZARD 0 K 0 K
## 902297 HEAVY SNOW 0 K 0 KConvert H, K, M, B units to calculate the Property Damage
economyData$PROPDMGFIG = 0
economyData[economyData$PROPDMGEXP == "H", ]$PROPDMGFIG = economyData[economyData$PROPDMGEXP == "H", ]$PROPDMG * 10^2
economyData[economyData$PROPDMGEXP == "K", ]$PROPDMGFIG = economyData[economyData$PROPDMGEXP == "K", ]$PROPDMG * 10^3
economyData[economyData$PROPDMGEXP == "M", ]$PROPDMGFIG = economyData[economyData$PROPDMGEXP == "M", ]$PROPDMG * 10^6
economyData[economyData$PROPDMGEXP == "B", ]$PROPDMGFIG = economyData[economyData$PROPDMGEXP == "B", ]$PROPDMG * 10^9Convert H, K, M, B units to calculate the Crop Damage
economyData$CROPDMGFIG = 0
economyData[economyData$CROPDMGEXP == "H",]$CROPDMGFIG =
economyData[economyData$CROPDMGEXP == "H",]$CROPDMG * 10^2
economyData[economyData$CROPDMGEXP == "K",]$CROPDMGFIG =
economyData[economyData$CROPDMGEXP == "K",]$CROPDMG * 10^3
economyData[economyData$CROPDMGEXP == "M",]$CROPDMGFIG =
economyData[economyData$CROPDMGEXP == "M",]$CROPDMG * 10^6
economyData[economyData$CROPDMGEXP == "B",]$CROPDMGFIG =
economyData[economyData$CROPDMGEXP == "B",]$CROPDMG * 10^9
head(economyData)## EVTYPE PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP PROPDMGFIG CROPDMGFIG
## 1 TORNADO 25.0 K 0 25000 0
## 2 TORNADO 2.5 K 0 2500 0
## 3 TORNADO 25.0 K 0 25000 0
## 4 TORNADO 2.5 K 0 2500 0
## 5 TORNADO 2.5 K 0 2500 0
## 6 TORNADO 2.5 K 0 2500 0Let’s find out top 10 weather conditions affecting crops and property.
damageData <- aggregate(PROPDMGFIG + CROPDMGFIG ~ EVTYPE,data = economyData,sum)
head(damageData)## EVTYPE PROPDMGFIG + CROPDMGFIG
## 1 HIGH SURF ADVISORY 200000
## 2 COASTAL FLOOD 0
## 3 FLASH FLOOD 50000
## 4 LIGHTNING 0
## 5 TSTM WIND 8100000
## 6 TSTM WIND (G45) 8000Rename the column names for readability.
colnames(damageData) <- c("EVTYPE","Damages")
topData2 <- head(arrange(damageData,desc(Damages)), n = 10)
head(topData2)## EVTYPE Damages
## 1 FLOOD 150319678250
## 2 HURRICANE/TYPHOON 71913712800
## 3 TORNADO 57340613590
## 4 STORM SURGE 43323541000
## 5 HAIL 18752904670
## 6 FLASH FLOOD 17562128610plot for property and crop damage by top 10 weather conditions.
ggplot(topData2,aes(x=EVTYPE,y=Damages))+
geom_bar(stat = "identity",fill="darkgreen")+
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle("Property & Crop Damages by top 10 Weather Conditions ")
Result
Hence Flood is the main cause for maximum crop and property damage.