Student: Al Shain

Email: al_shain@me.com

School: John Hopkins, Bloomberg School of Public Health

Class, Instructor: Reproducible Research, Roger Peng, PhD

Synopsis:

The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events.

We will answer the following questions using our data analysis:

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

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

Our analysis of the data will demonstrate that the most harmful event to public health is “tornado”, while the most harmful event to the Economy is “flood”.

Data Processing:

There are 6 basic steps required for loading and preprocessing the data: 1 set workding directory to project

echo = TRUE  
setwd("~/Desktop/Coursera/ReproducibleResearch/PeerAssessment2")

2 make sure the required libraries are loaded

library(knitr)
library(dplyr)

## 
## Attaching package: 'dplyr'
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)
library("gridExtra")

## Loading required package: grid

3 set the download, and unzip file name

downloadFile <- "data/repdata-data-StormData.csv.bz2"
downloadURL <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"

4 test for data foloder and zip file, if NOT found create

if(!file.exists("./data")) { dir.create("./data")}
if (!file.exists(downloadFile)) {
  download.file(downloadURL, downloadFile, method = "curl");
  unzip(downloadFile, overwrite = T, exdir = ".")
}

5 read in the csv data, take a quick view of the file structures and data

data <- read.csv("./data/repdata-data-StormData.csv", header=TRUE)
str(data)

## 'data.frame':    902297 obs. of  37 variables:
##  $ STATE__   : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ BGN_DATE  : Factor w/ 16335 levels "1/1/1966 0:00:00",..: 6523 6523 4242 11116 2224 2224 2260 383 3980 3980 ...
##  $ BGN_TIME  : Factor w/ 3608 levels "00:00:00 AM",..: 272 287 2705 1683 2584 3186 242 1683 3186 3186 ...
##  $ TIME_ZONE : Factor w/ 22 levels "ADT","AKS","AST",..: 7 7 7 7 7 7 7 7 7 7 ...
##  $ COUNTY    : num  97 3 57 89 43 77 9 123 125 57 ...
##  $ COUNTYNAME: Factor w/ 29601 levels "","5NM E OF MACKINAC BRIDGE TO PRESQUE ISLE LT MI",..: 13513 1873 4598 10592 4372 10094 1973 23873 24418 4598 ...
##  $ STATE     : Factor w/ 72 levels "AK","AL","AM",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ EVTYPE    : Factor w/ 985 levels "   HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
##  $ BGN_RANGE : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BGN_AZI   : Factor w/ 35 levels "","  N"," NW",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ BGN_LOCATI: Factor w/ 54429 levels ""," Christiansburg",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ END_DATE  : Factor w/ 6663 levels "","1/1/1993 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ END_TIME  : Factor w/ 3647 levels ""," 0900CST",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ 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   : Factor w/ 24 levels "","E","ENE","ESE",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ END_LOCATI: Factor w/ 34506 levels ""," CANTON"," TULIA",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ 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: Factor w/ 19 levels "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
##  $ CROPDMG   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ CROPDMGEXP: Factor w/ 9 levels "","?","0","2",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ WFO       : Factor w/ 542 levels ""," CI","%SD",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ STATEOFFIC: Factor w/ 250 levels "","ALABAMA, Central",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ ZONENAMES : Factor w/ 25112 levels "","                                                                                                                               "| __truncated__,..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ 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   : Factor w/ 436781 levels "","\t","\t\t",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ REFNUM    : num  1 2 3 4 5 6 7 8 9 10 ...

head(data, n=2)

##   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
##    EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO         0                                               0
## 2 TORNADO         0                                               0
##   COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1         NA         0                        14   100 3   0          0
## 2         NA         0                         2   150 2   0          0
##   INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1       15    25.0          K       0                                    
## 2        0     2.5          K       0                                    
##   LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1     3040      8812       3051       8806              1
## 2     3042      8755          0          0              2

Note: There are 902297 observations and 37 variables. The events in the database start in the year 1950 and end in November 2011.

There are 7 variables we are interested regarding the two questions. They are:

EVTYPE as a measure of event type (e.g. tornado, flood, etc.)

FATALITIES as a measure of harm to human health

INJURIES as a measure of harm to human health

PROPDMG as a measure of property damage and hence economic damage in USD

PROPDMGEXP as a measure of magnitude of property damage (e.g. thousands, millions USD, etc.)

CROPDMG as a measure of crop damage and hence economic damage in USD

CROPDMGEXP as a measure of magnitude of crop damage (e.g. thousands, millions USD, etc.)

To make our analysis more efficient, we can select only the columns we need for computation and analysis.

6 Remove unwanted columns not used in this assignment

desiredColumns <- c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")
storm <- data[desiredColumns]
str(storm)

## 'data.frame':    902297 obs. of  7 variables:
##  $ EVTYPE    : Factor w/ 985 levels "   HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
##  $ 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: Factor w/ 19 levels "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
##  $ CROPDMG   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ CROPDMGEXP: Factor w/ 9 levels "","?","0","2",..: 1 1 1 1 1 1 1 1 1 1 ...

Results

Graphs and Analysis of Weather Impacts on Public Health

We need to determine which weather events caused the highest number of fatalities, and the most injuries. There are 8 steps in determining weather impacts on Public Health:

What weather events caused the highest number fatalities?

1 Here are the top 10 weather events that caused the highest number of fatalities:

FATAL <- group_by(storm, EVTYPE)
FATAL10 <- summarise(FATAL,
                     total = sum(FATALITIES))%>% 
                     arrange(desc(total))%>% 
                     top_n(10)

## Selecting by total

FATAL10

## Source: local data frame [10 x 2]
## 
##            EVTYPE total
## 1         TORNADO  5633
## 2  EXCESSIVE HEAT  1903
## 3     FLASH FLOOD   978
## 4            HEAT   937
## 5       LIGHTNING   816
## 6       TSTM WIND   504
## 7           FLOOD   470
## 8     RIP CURRENT   368
## 9       HIGH WIND   248
## 10      AVALANCHE   224

Note: The most fatalities are related to Tornados from our data.

2 Here are the top 10 weather events that caused the highest number of injuries:

INJURY <- group_by(storm, EVTYPE)
INJURY10 <- summarise(INJURY,
    total = sum(INJURIES))%>% 
    arrange(desc(total))%>% 
    top_n(10)

## Selecting by total

INJURY10

## Source: local data frame [10 x 2]
## 
##               EVTYPE total
## 1            TORNADO 91346
## 2          TSTM WIND  6957
## 3              FLOOD  6789
## 4     EXCESSIVE HEAT  6525
## 5          LIGHTNING  5230
## 6               HEAT  2100
## 7          ICE STORM  1975
## 8        FLASH FLOOD  1777
## 9  THUNDERSTORM WIND  1488
## 10              HAIL  1361

Note: The most injuries are related to Tornados from our data

For our Economic Impact, we’ll look at the correlations between weather events measurable property and crop damage, using the following storm variables: PROPDMGEXP and CROPDMGEXP

But the storm data for these variables needs some work, as thier raw values require reformatting: 3 Raw Property Damages Values:

unique(storm$PROPDMGEXP)

##  [1] K M   B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels:  - ? + 0 1 2 3 4 5 6 7 8 B h H K m M

4 Raw Storm Damage Values:

unique(storm$CROPDMGEXP)

## [1]   M K m B ? 0 k 2
## Levels:  ? 0 2 B k K m M

5 Transform Storm Property Damage Expenses

storm$PROPDMGEXP <- as.character(storm$PROPDMGEXP)
storm$PROPDMGEXP = gsub("\\-|\\+|\\?","0", storm$PROPDMGEXP)
storm$PROPDMGEXP = gsub("B|b", "9", storm$PROPDMGEXP)
storm$PROPDMGEXP = gsub("M|m", "6", storm$PROPDMGEXP)
storm$PROPDMGEXP = gsub("K|k", "3", storm$PROPDMGEXP)
storm$PROPDMGEXP = gsub("H|h", "2", storm$PROPDMGEXP)
storm$PROPDMGEXP <- as.numeric(storm$PROPDMGEXP)
storm$PROPDMGEXP[is.na(storm$PROPDMGEXP)] = 0

6 Aggregate and Sort Storm Property Damage

storm$ActPropDMG<- storm$PROPDMG * 10^storm$PROPDMGEXP
propDMG <- aggregate(ActPropDMG~EVTYPE, data=storm, sum)
propDMG_reorder<- propDMG[order(-propDMG$ActPropDMG),]
PropDMG10<-propDMG_reorder[1:10,]

7 Transform Storm Crop Damage Expenses

storm$CROPDMGEXP <- as.character(storm$CROPDMGEXP)
storm$CROPDMGEXP = gsub("\\-|\\+|\\?","0",storm$CROPDMGEXP)
storm$CROPDMGEXP = gsub("B|b", "9", storm$CROPDMGEXP)
storm$CROPDMGEXP = gsub("M|m", "6", storm$CROPDMGEXP)
storm$CROPDMGEXP = gsub("K|k", "3", storm$CROPDMGEXP)
storm$CROPDMGEXP = gsub("H|h", "2", storm$CROPDMGEXP)
storm$CROPDMGEXP <- as.numeric(storm$CROPDMGEXP)
storm$CROPDMGEXP[is.na(storm$CROPDMGEXP)] = 0

8 Aggregate and Sort Storm Crop Damage Expenses

storm$ActCropDMG<- storm$CROPDMG * 10^storm$CROPDMGEXP
cropDMG <- aggregate(ActCropDMG~EVTYPE, data=storm, sum)
cropDMG_reorder<- cropDMG[order(-cropDMG$ActCropDMG),]
CropDMG10<-cropDMG_reorder[1:10,]
TotalDMG <- aggregate(ActPropDMG + ActCropDMG~EVTYPE, data=storm, sum)
names(TotalDMG)[2] <- "total"
TotalDMG10 <- arrange(TotalDMG, desc(total))%>%
    top_n(10)

## Selecting by total

Health Impact Assessment

As shown above Tornados lead by a major factor in injuries and deaths, here are two graphs that reflect the results.

par(mfrow=c(1,2), mar = c(12, 6, 3, 2),mgp = c(4, 1, 0), cex = 0.7)
barplot(FATAL10$total, 
        names = FATAL10$EVTYPE,
        col = "red",
        ylab = "Total Deaths",
        main = "Top 10 Weather Events \n Resulting in a Fatality", 
        las  = 3, xpd = TRUE)

barplot(INJURY10$total, 
        names = INJURY10$EVTYPE,
        col = "yellow",
        ylab = "Total Deaths",
        main = "Top 10 Weather Events \n Resulting in Injuries",
        las  = 3, xpd = TRUE)

Economic Impact Assessment

Economic Impact Tables:

1 Property Damage due to Weather Events:

PropDMG10

##                EVTYPE   ActPropDMG
## 170             FLOOD 144657709807
## 411 HURRICANE/TYPHOON  69305840000
## 834           TORNADO  56947380676
## 670       STORM SURGE  43323536000
## 153       FLASH FLOOD  16822673978
## 244              HAIL  15735267513
## 402         HURRICANE  11868319010
## 848    TROPICAL STORM   7703890550
## 972      WINTER STORM   6688497251
## 359         HIGH WIND   5270046295

2 Crop Damage due to Weather Events:

CropDMG10

##                EVTYPE  ActCropDMG
## 95            DROUGHT 13972566000
## 170             FLOOD  5661968450
## 590       RIVER FLOOD  5029459000
## 427         ICE STORM  5022113500
## 244              HAIL  3025954473
## 402         HURRICANE  2741910000
## 411 HURRICANE/TYPHOON  2607872800
## 153       FLASH FLOOD  1421317100
## 140      EXTREME COLD  1292973000
## 212      FROST/FREEZE  1094086000

3 Total Economic Impact due to Weather Events:

TotalDMG10

##               EVTYPE        total
## 1              FLOOD 150319678257
## 2  HURRICANE/TYPHOON  71913712800
## 3            TORNADO  57362333946
## 4        STORM SURGE  43323541000
## 5               HAIL  18761221986
## 6        FLASH FLOOD  18243991078
## 7            DROUGHT  15018672000
## 8          HURRICANE  14610229010
## 9        RIVER FLOOD  10148404500
## 10         ICE STORM   8967041360

As shown in the above tables, Floods were the leading cause of ecnomic impact (even when Drought leads the economic impact for Crop Damages), here are three graphs that reflect the results.

Note: The Left farmost graph shows TOTAL damages, with Flood Events as the leading cause of Economic Impact.

par(mfrow = c(1, 3), mar = c(12, 6, 3, 2), mgp = c(3, 1, 0), cex = 0.6)
barplot(PropDMG10$ActPropDMG/(10^6), 
        names = PropDMG10$EVTYPE,
        col = "green",
        ylab = "Total Property Damage (million $)",
        main = "Top 10 Events \n Resulting in Property Damage",
        las  = 3, xpd = TRUE)
barplot(CropDMG10$ActCropDMG/(10^6), 
        names = CropDMG10$EVTYPE,
        ylab = "Total Crop Damage (million $)",
        col = "purple",
        main = "Top 10 Events \n Resulting in Crop Damage",
        las  = 3, xpd = TRUE)
barplot(TotalDMG10$total/(10^6), 
        names = TotalDMG10$EVTYPE,
        col = "orange",
        ylab = "Total Crop Damage (million $)",
        main = "Top 10 Events \n Resulting in Total Damage",
        las  = 3, xpd = TRUE)

Reproducible Research: Peer Assessment 2