Explore the NOAA Storm Database

Synopsis

Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events can result in fatalities, injuries, and property damage, and preventing such outcomes to the extent possible is a key concern.

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.

In this report,effect of weather events on personal as well as property damages was studied. Barplots were plotted seperately for the top 8 weather events that causes highest fatalities and highest injuries. Results indicate that most Fatalities and injuries were caused by Tornados.Also, barplots were plotted for the top 8 weather events that causes the highest property damage and crop damage.

Data Processing

Loading packages and dataset

# load packages

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, union
library(reshape2)
sd<-download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2","./storm_dataset")
sd <- read.csv(bzfile("storm_dataset"))
sd

Answering the following questions

  1. Which types of events (as indicated in the EVTYPE #variable) are most harmful with respect to population health?

Compare the events of health problems (injuries and fatalities)

# aggregate the EVTYPE of  injuries
injuries <- arrange(aggregate(INJURIES ~ EVTYPE, sd, sum), desc(INJURIES))

injuries = injuries[1:20,] # select top 20 events of injuries


# aggregate the EVTYPE of  fatalities
fatalities <- arrange(aggregate(FATALITIES ~ EVTYPE, sd, sum), desc(FATALITIES))

fatalities = fatalities[1:20,] # select top 20 events of fatalities

Make plot

par(mfrow = c(1, 2), mar = c(12, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)

barplot(injuries$INJURIES, las = 3, names.arg = injuries$EVTYPE, main = "Events with Highest Injuries", 
        ylab = "Number of injuries", col = "red")


barplot(fatalities$FATALITIES, las = 3, names.arg = fatalities$EVTYPE, main = "Events with Highest Fatalities", 
        ylab = "Number of fatalities", col = "blue")

Restuls

The maximum of fatalities and injuries were caused by Tornados. The second major cause of fatalities and injuries was Excessive Heat and Thunderstorm wind, respectively.

Second question

  1. Which types of events have the greatest economic consequences across the US?

Here we have property damage and crop damage in economic

# property damage
propertyDamage <- aggregate(PROPDMG ~ EVTYPE, data = sd, FUN = sum)

propertyDamage <- propertyDamage[order(propertyDamage$PROPDMG, decreasing = TRUE), ]

# Select top 10 harmful events

maxPropertyDamage <- propertyDamage[1:10, ]
print(maxPropertyDamage)
##                 EVTYPE   PROPDMG
## 834            TORNADO 3212258.2
## 153        FLASH FLOOD 1420124.6
## 856          TSTM WIND 1335965.6
## 170              FLOOD  899938.5
## 760  THUNDERSTORM WIND  876844.2
## 244               HAIL  688693.4
## 464          LIGHTNING  603351.8
## 786 THUNDERSTORM WINDS  446293.2
## 359          HIGH WIND  324731.6
## 972       WINTER STORM  132720.6
# crop damage data
cropDamage <- aggregate(CROPDMG ~ EVTYPE, data = sd, FUN = sum)
cropDamage <- cropDamage[order(cropDamage$CROPDMG, decreasing = TRUE), ]

# 10 most harmful events
maxCropDamage <- cropDamage[1:10, ]
print(maxCropDamage)
##                 EVTYPE   CROPDMG
## 244               HAIL 579596.28
## 153        FLASH FLOOD 179200.46
## 170              FLOOD 168037.88
## 856          TSTM WIND 109202.60
## 834            TORNADO 100018.52
## 760  THUNDERSTORM WIND  66791.45
## 95             DROUGHT  33898.62
## 786 THUNDERSTORM WINDS  18684.93
## 359          HIGH WIND  17283.21
## 290         HEAVY RAIN  11122.80

Make plot

par(mfrow = c(1, 2), mar = c(15, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)

barplot(maxPropertyDamage$PROPDMG, las = 3, names.arg = maxPropertyDamage$EVTYPE, 
        main = "Top 10 Events with\n Greatest Property Damages", 
        ylab = "Number of Injuries", col = maxPropertyDamage$PROPDMG)
        
        
barplot(maxCropDamage$CROPDMG, las = 3, names.arg = maxCropDamage$EVTYPE, 
        main = "Top 10 Events with\n Greatest Crop Damages", 
        ylab = "Number of Injuries", col = maxCropDamage$CROPDMG)

Results

On the other hand, the most cause for property damage was Floods and the second important event was Hurricanes/Typhoos. Moreover, crop damages first were caused by Drought and the next major cause was Floods.