Reprodicible Research Course Project 2

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.

Synopsis

This report consists in analyzing the NOAA storm database containing data on extreme climate events. This data was collected during the period from 1950 through 2011. The purpose of this analysis is to answer the following two questions:

Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Across the United States, which types of events have the greatest economic consequences?

QUESTION 1: Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?

library(dplyr)

## Warning: package 'dplyr' was built under R version 3.6.1

## 
## 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(ggplot2)

## Warning: package 'ggplot2' was built under R version 3.6.1

if(!file.exists("/StormData.csv.bz2")){
  download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2",destfile="./StormData.csv.bz2")
}

stormData <- read.csv("repdata_data_StormData.csv")

Data Processing

Download dataset

read in data subset data to only relevant variables (7 in total) convert PROPDMG and CROPDMG from units to numbers () omit variables PROPDMGEXP and CROPDMGEXP restructure data to have tidy dataset with only 3 variables ###Select the variables needed for this analysis

storm_Datasort <- stormData[, c(8, 23:28)]
head(storm_Datasort)

##    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

summary(storm_Datasort$FATALITIES)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##   0.0000   0.0000   0.0000   0.0168   0.0000 583.0000

summary(storm_Datasort$INJURIES)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##    0.0000    0.0000    0.0000    0.1557    0.0000 1700.0000

Create dataframe for INJURIES and subset the top 20

total_injuries <- storm_Datasort%>%
  group_by(EVTYPE)%>%
  summarise(INJURIES = sum(INJURIES))%>%
  arrange(desc(INJURIES))%>%
  top_n(10)

## Selecting by INJURIES

Create dataframe for FATALITIES and subset the top 20

total_FATALITIES <- storm_Datasort%>%
  group_by(EVTYPE)%>%
  summarise(FATALITIES = sum(FATALITIES))%>%
  arrange(desc(FATALITIES))%>%
  top_n(10)

## Selecting by FATALITIES

Create a historgram to show the results

par(mfrow=c(1,2), mar = c(10,3,3,2))
barplot(total_FATALITIES$FATALITIES, names.arg = total_FATALITIES$EVTYPE, las = 2, col = "blue", ylab="Fatalities", main = "Top 10 fatalities")
barplot(total_injuries$INJURIES, names.arg = total_injuries$EVTYPE, las = 2, col = "red", ylab="Injuries", main = "Top 10 Injuries")

Question 2: Across the United States, which types of events have the greatest economic consequences?

Data processing

H = Hundreds

K = Thousands

M = millions

B = billions

stormData$PROPDAMAGE = 0
stormData[stormData$PROPDMGEXP == "H", ]$PROPDAMAGE = stormData[stormData$PROPDMGEXP == "H", ]$PROPDMG * 10^2
stormData[stormData$PROPDMGEXP == "K", ]$PROPDAMAGE = stormData[stormData$PROPDMGEXP == "K", ]$PROPDMG * 10^3
stormData[stormData$PROPDMGEXP == "M", ]$PROPDAMAGE = stormData[stormData$PROPDMGEXP == "M", ]$PROPDMG * 10^6
stormData[stormData$PROPDMGEXP == "B", ]$PROPDAMAGE = stormData[stormData$PROPDMGEXP == "B", ]$PROPDMG * 10^9

stormData$CROPDAMAGE = 0
stormData[stormData$CROPDMGEXP == "H", ]$CROPDAMAGE = stormData[stormData$CROPDMGEXP == "H", ]$CROPDMG * 10^2
stormData[stormData$CROPDMGEXP == "K", ]$CROPDAMAGE = stormData[stormData$CROPDMGEXP == "K", ]$CROPDMG * 10^3
stormData[stormData$CROPDMGEXP == "M", ]$CROPDAMAGE = stormData[stormData$CROPDMGEXP == "M", ]$CROPDMG * 10^6
stormData[stormData$CROPDMGEXP == "B", ]$CROPDAMAGE = stormData[stormData$CROPDMGEXP == "B", ]$CROPDMG * 10^9
economicDamage <- aggregate(PROPDAMAGE + CROPDAMAGE ~ EVTYPE, stormData, sum)
names(economicDamage) <- c("EVENT_TYPE", "TOTAL_DAMAGE")

economicDamage <- arrange(economicDamage, desc(TOTAL_DAMAGE)) %>%
  top_n(20)

## Selecting by TOTAL_DAMAGE

economicDamage$TOTAL_DAMAGE <- economicDamage$TOTAL_DAMAGE/10^9
economicDamage$EVENT_TYPE <- factor(economicDamage$EVENT_TYPE, levels = economicDamage$EVENT_TYPE)
head(economicDamage)

##          EVENT_TYPE TOTAL_DAMAGE
## 1             FLOOD    150.31968
## 2 HURRICANE/TYPHOON     71.91371
## 3           TORNADO     57.34061
## 4       STORM SURGE     43.32354
## 5              HAIL     18.75290
## 6       FLASH FLOOD     17.56213

Create plot

par(mfrow=c(1,1), mar = c(10,3,3,2))
barplot(economicDamage$TOTAL_DAMAGE, names.arg = economicDamage$EVENT_TYPE, 
        las = 2, col = "yellow", ylab="Total Damage", main = "Total Property and Crop damage by 20 Event types")

Results

Main conclusions of the study are as follows: 1. Tornado is the most hazordous climate event with more than 5600 deaths and 91400 injuries. 2. Floods have caused the most significant economic damage - more than 157 billion USD.

Assignment week 5

Janneke Schipper

15 september 2019