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Reproducible Research

Storm Data Analysis - Effecs on Population and Economy

TLJ

Date: 10/22/2018

I. SNYPOSIS

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.

The focus of the analysis will look at severe weather events and identify the ones that are the most catastrophic in nature and analyze the impact on the overall population and the economy.

II. Data Processing

The data for this project come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. You can download the file from the course web site:

“https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2”

There is also some documentation of the database available. Here you will find how some of the variables are constructed/defined.

National Weather Service Storm Data Documentation: (“https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf”)

National Climatic Data Center Storm Events FAQ: (“https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2FNCDC%20Storm%20Events-FAQ%20Page.pdf”)

Note: The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete.

II.A. Download Data

The file was retrieved and downloaded to local computer from the following website:

“https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2”

Using a Zip file utility package, the .bz2 file was unzipped and placed in the working directory (“C:/Users/tljon/datasciencecoursera”) with shortened file name – “StormData.csv”

##set working directory
setwd("C:/Users/tljon/datasciencecoursera")

##load libraries that may be needed to develop/support analysis and file prep
library(ggplot2)
library(plyr)

##load data into R
storm <- read.csv("StormData.csv", header=TRUE)
head(storm)

##   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

Review and determine which variables will need to be extracted to support data analysis

II.B. Extract data for health and economic impact analysis

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

##Due to the size of this list, it will not be displayed but will be used to produce the top 5 causes for fatalities and injuries respectively

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

##Order by category in decreasing value the total number of fatalities (top 5)
fatal <- ddply(et, .(EVTYPE), summarize, FATALITIES=sum(FATALITIES))
fatal <- fatal[order(fatal$FATALITIES, decreasing=TRUE), ]
head(fatal,5)

##             EVTYPE FATALITIES
## 834        TORNADO       5633
## 130 EXCESSIVE HEAT       1903
## 153    FLASH FLOOD        978
## 275           HEAT        937
## 464      LIGHTNING        816

#Order by category in decreasing value the total number injured (top 5)
injured <- ddply(et, .(EVTYPE), summarize, INJURED=sum(INJURIES))
injured <- injured[order(injured$INJURED, decreasing=TRUE), ]
head(injured,5)

##             EVTYPE INJURED
## 834        TORNADO   91346
## 856      TSTM WIND    6957
## 170          FLOOD    6789
## 130 EXCESSIVE HEAT    6525
## 464      LIGHTNING    5230

We will now take a look at a panel graph for the top 5 causes of fatalities and injuries

##Plot the Fatalities graph
plot1 <- ggplot(data=head(fatal,5), aes(x=reorder(EVTYPE, FATALITIES), y=FATALITIES)) +
        geom_bar(fill="red",stat="identity")  + coord_flip() + 
        ylab("Total Weather Fatalities") + xlab("Event Type") +
        ggtitle("US Weather Fatalities - Top 5") +
        theme(legend.position="none")
plot1

##Plot the Injuries graph
plot2 <- ggplot(data=head(injured,5), aes(x=reorder(EVTYPE, INJURED), y=INJURED)) +
        geom_bar(fill="blue",stat="identity")  + coord_flip() + 
        ylab("Total Weather Injuries") + xlab("Event Type") +
        ggtitle("US Weather Injuries - Top 5") +
        theme(legend.position="none")
plot2

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

We will now take a look at property and crop damage expenses.

##Analyze the categorization values for Property Damage Expense
unique(et$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

##Analyze the categorization values for Crop Damage Expense
unique(et$CROPDMGEXP)

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

The following steps will combine weather related property and crop damage records in a new dataset and ranked for the top 10 in economic loss.

The property and crop damage expenses will be combined and lower case characters will be converted to uppercase then we will look at other values.

##Raise lower case letters to upper case.
et$PROPDMGEXP <- toupper(et$PROPDMGEXP)
et$CROPDMGEXP <- toupper(et$CROPDMGEXP)
unique(c(et$PROPDMGEXP, et$CROPDMGEXP))

##  [1] "K" "M" ""  "B" "+" "0" "5" "6" "?" "4" "2" "3" "H" "7" "-" "1" "8"

It looks like there are some other irregularities in the categorization of the data. We will now convert missing values, +, -, and ?, to “0”.

##Convert missing values, +, -, and ? to "0"
etsub <- et[, c("EVTYPE", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]
etsub[etsub$PROPDMGEXP %in% c("", "+", "-", "?"), "PROPDMGEXP"] <- "0"
etsub[etsub$CROPDMGEXP %in% c("", "+", "-", "?"), "CROPDMGEXP"] <- "0"
unique(c(etsub$PROPDMGEXP, etsub$CROPDMGEXP))

##  [1] "K" "M" "0" "B" "5" "6" "4" "2" "3" "H" "7" "1" "8"

Since we are working with the power of 10, we need to convert our character values to numeric values to support our continued data analysis of the total damage amount. Billions “B” will be converted to 9, Millions “M” will be converted to 6, Thousands “K” will be converted to 3, and Hundreds “H” will be converted to 2.

etsub[etsub$PROPDMGEXP == "B", "PROPDMGEXP"] <- 9
etsub[etsub$CROPDMGEXP == "B", "CROPDMGEXP"] <- 9
etsub[etsub$PROPDMGEXP == "M", "PROPDMGEXP"] <- 6
etsub[etsub$CROPDMGEXP == "M", "CROPDMGEXP"] <- 6
etsub[etsub$PROPDMGEXP == "K", "PROPDMGEXP"] <- 3
etsub[etsub$CROPDMGEXP == "K", "CROPDMGEXP"] <- 3
etsub[etsub$PROPDMGEXP == "H", "PROPDMGEXP"] <- 2
etsub[etsub$CROPDMGEXP == "H", "CROPDMGEXP"] <- 2
unique(c(etsub$PROPDMGEXP, etsub$CROPDMGEXP))

##  [1] "3" "6" "0" "9" "5" "4" "2" "7" "1" "8"

Now we will convert the PROPDMGEXP and CROPDMGEXP to numeric values. This will give us the damage amount in dollars by calculating the total damage by the multiplier.

##Generate a value 
etsub$PROPDMGEXP <- 10^(as.numeric(etsub$PROPDMGEXP))
etsub$CROPDMGEXP <- 10^(as.numeric(etsub$CROPDMGEXP))
etsub[is.na(etsub$PROPDMG), "PROPDMG"] <- 0
etsub[is.na(etsub$CROPDMG), "CROPDMG"] <- 0

## Calculate the total weather related damages
etsub <- within(etsub, TOTALDMG <- PROPDMG * PROPDMGEXP + CROPDMG * CROPDMGEXP)

TypeDmg <- aggregate(etsub$TOTALDMG, by = list(EVTYPE = etsub$EVTYPE), 
                          FUN = sum)
TypeDmg <- TypeDmg[order(TypeDmg$x, decreasing = TRUE), ]

## view the top 10 types of weather related damage
head(TypeDmg, 10)

##                EVTYPE            x
## 170             FLOOD 150319678257
## 411 HURRICANE/TYPHOON  71913712800
## 834           TORNADO  57362333947
## 670       STORM SURGE  43323541000
## 244              HAIL  18761221986
## 153       FLASH FLOOD  18243991079
## 95            DROUGHT  15018672000
## 402         HURRICANE  14610229010
## 590       RIVER FLOOD  10148404500
## 427         ICE STORM   8967041360

The Top 10 in Severe Weather Damage by Type are presented in this graph

##Plot the severe weather damage graph for the top 10
plot3 <- ggplot(data=head(TypeDmg,10), aes(EVTYPE, y=x)) +
                geom_bar(fill="green",stat="identity")  + coord_flip() + 
                ylab("Dollar Amount") + xlab("Event Type") +
                ggtitle("Severe Weather Damage by Type - Top 10") +
                theme(legend.position="none")
plot3

III. Results

It appears that in terms of fatalities and injuries, Tornadoes are the deadliest and also cause the most injuries – no other weather related conditions appear to come close. As for economic damage, flooding causes the most economic loss while Hurricanes/Typhoons come in a distant second, with tornadoes coming in a close third.