1: Synopsis

The goal of the assignment is to explore the NOAA Storm Database and explore the effects of severe weather events on both population and economy.The database covers the time period between 1950 and November 2011.

The following analysis investigates which types of severe weather events are most harmful on:

  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?

Information on the Data: Documentation

2: Data Processing

2.1: Data Loading

Download the raw data file and extract the data into a dataframe.Then convert to a data.table

library(data.table)
library(ggplot2)

stormDF <- read.csv("repdata_data_StormData.csv")
stormDT <- as.data.table(stormDF)

2.2: Loking at the data

dim(stormDF)
## [1] 902297     37
colnames(stormDF)
##  [1] "STATE__"    "BGN_DATE"   "BGN_TIME"   "TIME_ZONE"  "COUNTY"    
##  [6] "COUNTYNAME" "STATE"      "EVTYPE"     "BGN_RANGE"  "BGN_AZI"   
## [11] "BGN_LOCATI" "END_DATE"   "END_TIME"   "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE"  "END_AZI"    "END_LOCATI" "LENGTH"     "WIDTH"     
## [21] "F"          "MAG"        "FATALITIES" "INJURIES"   "PROPDMG"   
## [26] "PROPDMGEXP" "CROPDMG"    "CROPDMGEXP" "WFO"        "STATEOFFIC"
## [31] "ZONENAMES"  "LATITUDE"   "LONGITUDE"  "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS"    "REFNUM"
str(stormDF)
## '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 ...

Removing Unwanted coloumn

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

Viewing the Dataset

Subsetting with health and economic consequences

subset(x = stormDT,INJURIES > 0 | FATALITIES > 0 | PROPDMG > 0 | CROPDMG > 0)
##               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           
##     ---                                                                       
## 254629: WINTER STORM          0        0     5.0          K       0          K
## 254630:  STRONG WIND          0        0     0.6          K       0          K
## 254631:  STRONG WIND          0        0     1.0          K       0          K
## 254632:      DROUGHT          0        0     2.0          K       0          K
## 254633:    HIGH WIND          0        0     7.5          K       0          K
head(stormDT,10)
##      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           
##  7: TORNADO          0        1     2.5          K       0           
##  8: TORNADO          0        0     2.5          K       0           
##  9: TORNADO          1       14    25.0          K       0           
## 10: TORNADO          0        0    25.0          K       0

Converting Exponent Columns into Actual Exponents instead of (-,+, H, K, etc)

Making the PROPDMGEXP and CROPDMGEXP columns cleaner so they can be used to calculate property and crop cost.

stormDT$PROPDMGEXP <- toupper(stormDT$PROPDMGEXP)
stormDT$CROPDMGEXP <- toupper(stormDT$CROPDMGEXP)
Key <-  c("\"\"" = 10^0,
                 "-" = 10^0, 
                 "+" = 10^0,
                 "0" = 10^0,
                 "?" = 10^0,
                 "1" = 10^1,
                 "2" = 10^2,
                 "3" = 10^3,
                 "4" = 10^4,
                 "5" = 10^5,
                 "6" = 10^6,
                 "7" = 10^7,
                 "8" = 10^8,
                 "9" = 10^9,
                 "H" = 10^2,
                 "K" = 10^3,
                 "M" = 10^6,
                 "B" = 10^9)
stormDT <- stormDT %>% mutate(PROPDMGEXP = Key[PROPDMGEXP])
stormDT <- stormDT %>% mutate(CROPDMGEXP = Key[CROPDMGEXP])
stormDT[is.na(PROPDMGEXP),PROPDMGEXP :=10^1]
stormDT[is.na(CROPDMGEXP),CROPDMGEXP := 10^1]
head(stormDT)
##     EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1: TORNADO          0       15    25.0       1000       0         10
## 2: TORNADO          0        0     2.5       1000       0         10
## 3: TORNADO          0        2    25.0       1000       0         10
## 4: TORNADO          0        2     2.5       1000       0         10
## 5: TORNADO          0        2     2.5       1000       0         10
## 6: TORNADO          0        6     2.5       1000       0         10

Making Economic Cost Columns

stormDT <- stormDT %>% mutate(propCost = PROPDMG * PROPDMGEXP,cropCost = CROPDMG * CROPDMGEXP,tolal_cost = propCost + cropCost)

Calcuating Total Property and Crop Cost

total_cost <-  stormDT %>% group_by(EVTYPE) %>% summarise(cropCost = sum(cropCost),propCost = sum(propCost),tolal_cost=sum(tolal_cost)) %>% arrange(desc(tolal_cost))
## `summarise()` ungrouping output (override with `.groups` argument)
total_cost <- total_cost[1:10,]
head(total_cost)
## # A tibble: 6 x 4
##   EVTYPE              cropCost      propCost    tolal_cost
##   <fct>                  <dbl>         <dbl>         <dbl>
## 1 FLOOD             5661968450 144657709870  150319678320 
## 2 HURRICANE/TYPHOON 2607872800  69305840000   71913712800 
## 3 TORNADO            414953270  56947380704.  57362333974.
## 4 STORM SURGE             5000  43323536000   43323541000 
## 5 HAIL              3025954500  15735268026.  18761222526.
## 6 FLASH FLOOD       1421317100  16822675842.  18243992942.

###Calcuating Total Fatalities and Injuries

totalInjuriesDT <- stormDT %>% group_by(EVTYPE) %>% summarise(FATALITIES = sum(FATALITIES), INJURIES = sum(INJURIES),TOTAL =FATALITIES+INJURIES)%>%
        arrange(desc(TOTAL))
## `summarise()` ungrouping output (override with `.groups` argument)
totalInjuriesDT <- totalInjuriesDT[1:10,]
head(totalInjuriesDT, 5)
## # A tibble: 5 x 4
##   EVTYPE         FATALITIES INJURIES TOTAL
##   <fct>               <dbl>    <dbl> <dbl>
## 1 TORNADO              5633    91346 96979
## 2 EXCESSIVE HEAT       1903     6525  8428
## 3 TSTM WIND             504     6957  7461
## 4 FLOOD                 470     6789  7259
## 5 LIGHTNING             816     5230  6046

Results

Events that are Most Harmful to Population Health

Melting data.table so that it is easier to put in bar graph format

event <- tidyr::pivot_longer(totalInjuriesDT,cols = c("FATALITIES","INJURIES","TOTAL"),names_to = "EVENT")
head(event)
## # A tibble: 6 x 3
##   EVTYPE         EVENT      value
##   <fct>          <chr>      <dbl>
## 1 TORNADO        FATALITIES  5633
## 2 TORNADO        INJURIES   91346
## 3 TORNADO        TOTAL      96979
## 4 EXCESSIVE HEAT FATALITIES  1903
## 5 EXCESSIVE HEAT INJURIES    6525
## 6 EXCESSIVE HEAT TOTAL       8428
ggplot(data = event,mapping = aes(x = EVTYPE,y = value,fill = EVENT))+geom_bar(stat = "identity",position = "dodge")+
        theme(axis.text.x = element_text(angle=45, hjust=1))+
        ggtitle("Top 10 US Killers") + theme(plot.title = element_text(hjust = 0.5))+ylab("Frequency Count") +xlab("Event Type") 

#### Events that have the Greatest Economic Consequences Melting data.table so that it is easier to put in bar graph format

event2 <- tidyr::pivot_longer(total_cost,cols =-c("EVTYPE"),names_to = "TYPE")
head(event2)
## # A tibble: 6 x 3
##   EVTYPE            TYPE              value
##   <fct>             <chr>             <dbl>
## 1 FLOOD             cropCost     5661968450
## 2 FLOOD             propCost   144657709870
## 3 FLOOD             tolal_cost 150319678320
## 4 HURRICANE/TYPHOON cropCost     2607872800
## 5 HURRICANE/TYPHOON propCost    69305840000
## 6 HURRICANE/TYPHOON tolal_cost  71913712800
options(scipen = 0)
ggplot(data = event2,mapping = aes(x = EVTYPE,y = value,fill = TYPE))+geom_bar(stat = "identity",position = "dodge")+
        theme(axis.text.x = element_text(angle=45, hjust=1))+
        ggtitle("Top 10 US Killers") + theme(plot.title = element_text(hjust = 0.5))+ylab("Cost") +xlab("Event Type")