Synopsis

In this report we analyze the impact of weather related events such as tornodas and storms on human life in terms of it’s economic impact and the damage caused by them. To investigate this we use data available from the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. The data set has information on impact of weather related events such as storms and tornados in terms of fatalities, injuries and property damage. The data set also lists the date and location of the event occurence.

Data Processing

Read Weather Data in R

Weather data is avaialble from the following url https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2. To load this table in R we download the table and save it on the user’s local machine. If the file is already present on the local machine it is not downloaded again to save time and bandwidth.

url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
destinationDownload <- "~/Desktop/StormData/"
if(!file.exists(destinationDownload)){
  dir.create(destinationDownload)
}

filename <- paste(destinationDownload, "stormdata.csv.bz2", sep="/", collapse="/")

if(!file.exists(filename)){
  download.file(url, filename)
}else{
  print("Remote file already exists locally.")
}
## [1] "Remote file already exists locally."
weatherdata <- read.csv(bzfile(filename))

Change working directory to the folder contain weather data.

Summary of the weather data.

print(str(weatherdata))
## '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 ...
## NULL

Load dplyr library for processing.

## 
## 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

Preprocessing of Weather Data

In this report we are interested in the finding the most harmful natural events which impact population health and the economy. To optimize the processing time we reduce the data set to contain only the fields EVTYPE, FATALITIES and PROPDMG which have this information.

fatalitiesdata <- select(weatherdata, EVTYPE, FATALITIES)
damagedata <- select(weatherdata, EVTYPE, PROPDMG)

Results

Most harmful events with respect to population health across the United States

Below we plot the list of events which led to highest loss of lives. This information was obtained by grouping and sorting the FATALITIES field on the basis of various events types.

by_fatalities <- group_by(fatalitiesdata, EVTYPE)
populationhealth <- summarize(by_fatalities,
                              TOTALFATALITIES=sum(FATALITIES))
populationhealth <- arrange(populationhealth, desc(TOTALFATALITIES))
populationhealth <- head(populationhealth, 10)
par(mfrow=c(1,1), las=1)
barplot(populationhealth$TOTALFATALITIES, main="Fatalities due to natural disasters", horiz=TRUE, names.arg=populationhealth$EVTYPE, cex.names=0.5, xlab="Number of Fatalities")

Most harmful natural disaster for human population was TORNADO which led to 5633 loss of lives in total.

Most harmful events with respect to economic consequences across the United States

Below we plot the list of events which led to greatest economic consequences such as property damage. This information was obtained by grouping and sorting the PROPDMG field on the basis of various events types.

by_damage <- group_by(damagedata, EVTYPE)
economicdamage <- summarize(by_damage,
                              TOTALPROPDMG=sum(PROPDMG))
economicdamage <- arrange(economicdamage, desc(TOTALPROPDMG))
economicdamage <- head(economicdamage, 10)
par(mfrow=c(1,1), las=1)
barplot(economicdamage$TOTALPROPDMG, main="Economic loss from Property Damage due to natural disasters", horiz=TRUE, names.arg=economicdamage$EVTYPE, cex.names=0.5, xlab="Loss in U.S. Dollars")

Most harmful natural disaster for the economy was TORNADO which led to $3212258.16 worth property damage.