Synopsis
* Tornadoes are the most harmful to public health because they produce the most fatalities.
* Tornadoes also produces the most expensive economic consequenses.

Data Processing
Load the file & review its unprocessed structure.

dat <- read.csv(file="F:/_rdev/data-science-data/ds5/repdata-data-StormData.csv")
str(dat)
## '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 "","- 1 N Albion",..: 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 "","- .5 NNW",..: 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","$AC",..: 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/ 436774 levels "","-2 at Deer Park\n",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ REFNUM    : num  1 2 3 4 5 6 7 8 9 10 ...

Tasks to Determine “Most Harmful to Public Health”
* Reduce the dataset to the fatalities
* Plot to review overall distribution
* Summarize deaths per event
* Display event type with greatest count

# Reduce dataset to fatalities greater than 0
dat.fatal <- dat[dat$FATALITIES > 0,]

# plot to review the distribution for normality or outliers
attach(dat.fatal)
plot(dat.fatal$FATALITIES,dat.fatal$EVTYPE)

# summarize count of fatalities per event type
library(plyr)
tbl.fatal.frq <- data.frame(ddply(dat.fatal,.(EVTYPE),summarise,sum=sum(FATALITIES)))
summary(tbl.fatal.frq)
##           EVTYPE         sum         
##  AVALANCE    :  1   Min.   :   1.00  
##  AVALANCHE   :  1   1st Qu.:   1.00  
##  BLACK ICE   :  1   Median :   4.00  
##  BLIZZARD    :  1   Mean   :  90.15  
##  blowing snow:  1   3rd Qu.:  19.75  
##  BLOWING SNOW:  1   Max.   :5633.00  
##  (Other)     :162
# display event type with greatest count
max(tbl.fatal.frq$sum)
## [1] 5633
tbl.fatal.frq$EVTYPE[tbl.fatal.frq$sum==max(tbl.fatal.frq$sum)]
## [1] TORNADO
## 985 Levels:    HIGH SURF ADVISORY  COASTAL FLOOD ... WND

Tasks to Determine “Greatest Economic Consequences”
* Sum property damages per event type
* Identify hazard event with largest propert damage dollars

sum.dmg.by.event <- data.frame(ddply(dat,.(EVTYPE),summarise,sum=sum(PROPDMG)))
summary(sum.dmg.by.event)
##                    EVTYPE         sum         
##     HIGH SURF ADVISORY:  1   Min.   :      0  
##   COASTAL FLOOD       :  1   1st Qu.:      0  
##   FLASH FLOOD         :  1   Median :      0  
##   LIGHTNING           :  1   Mean   :  11050  
##   TSTM WIND           :  1   3rd Qu.:     35  
##   TSTM WIND (G45)     :  1   Max.   :3212258  
##  (Other)              :979
sum.dmg.by.event$EVTYPE[sum.dmg.by.event$sum==max(sum.dmg.by.event$sum)]
## [1] TORNADO
## 985 Levels:    HIGH SURF ADVISORY  COASTAL FLOOD ... WND

Results
“Most Harmful to Public Health” = Tornadoes:
* Based on frequency (number of events) and severity (cost impact of the event), and assuming mortality is the greatest cost, tornadoes outpace all events as the most harmful to public health.
* Specifically: 5,633 fatalities over a course of 1,602 discrete events

“Greatest Economic Consequences” = Tornadoes:
* Property damage caused by tornadoes is $3.2 billion; more than double its closest competing event.