Synopsis

United States gets effected with various calamities. Below analysis is used for projecting the impact caused by various calamities. The data is an official collected by National Oceanic and Atmospheric Administration (NOAA). This data has been collected from 1950 to 2011. The analysis explorers various weather events that effected various parts of United States. The analysis can be a feed for government or municipal manager who might be responsible for preparing for severe weather events.

Data Processing

Important Columns

BGN_DATE: Begin date of the calamity
BGN_TIME: Begin time of the calamity
END_DATE: End date of the calamity
END_TIME: End time of the calamity
TIME_ZONE: Timezone of the location
EVTYPE: Event Source
FATALITIES: Weather-related fatalities
INJURIES: Weather-related injuries
PROPDMG: Weather-related property damages
CROPDMG: Weather-related crop damages

Here we will be reading the data file and massaging the data to get fruitful results.

if (!exists("stormData")) {
 
  # ignore blank lines, if any
  stormData <- read.csv(bzfile("repdata-data-StormData.csv.bz2"), header = TRUE, blank.lines.skip = TRUE) 
}
head(stormData)

##   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

We will have to set the dates to Date format for validating year wise damages. We will even create a subset of storm data what will have rows which have proper values for columns: FATALITIES, INJURIES, PROPDMG and CROPDMG

# filter out data that doesn't have values.
cleanStormData <- stormData[stormData$FATALITIES>0 & stormData$INJURIES>0 & stormData$PROPDMG>0 & stormData$CROPDMG>0,]

Results

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

To address this question, we will have to concentrate on FATALITIES, INJURIES, as these two will be feed for analyzing population health

Aggregate FATALITIES based on the EVTYPE and order by FATALITIES (descending) to list top calamities that are harmful w.r.t population health

# aggreate FATALITIES
aggreateFacilties <- aggregate(FATALITIES ~ EVTYPE, cleanStormData, FUN = sum)
# sort FATALITIES in decending order
aggreateFacilties <- aggreateFacilties[order(-aggreateFacilties$FATALITIES),]
head(aggreateFacilties)

##            EVTYPE FATALITIES
## 15        TORNADO        190
## 4           FLOOD         58
## 2  EXCESSIVE HEAT         46
## 19        TSUNAMI         32
## 20       WILDFIRE         31
## 3     FLASH FLOOD         23

Plot FATALITIES that are harmful

library(ggplot2)
# use ggplot to generate a chart based on the aggreated object
ggplot(aggreateFacilties[1:5,], aes(EVTYPE, FATALITIES) ) + geom_bar(stat="identity")+ labs(title="Top facilties that are harmful with respect to population health",x="Event Source",y="Facitiles count")

Aggregate INJURIES based on the EVTYPE and order by INJURIES (descending) to list top calamities that are harmful w.r.t population health

# aggreate INJURIES
aggreateInjuries <- aggregate(INJURIES ~ EVTYPE, cleanStormData, FUN = sum)
# sort INJURIES in decending order
aggreateInjuries <- aggreateInjuries[order(-aggreateInjuries$INJURIES),]
head(aggreateInjuries)

##               EVTYPE INJURIES
## 4              FLOOD     2495
## 15           TORNADO     1630
## 12         ICE STORM     1568
## 11 HURRICANE/TYPHOON      884
## 1           BLIZZARD      402
## 5               HEAT      320

Plot INJURIES that are harmful

# use ggplot to generate a chart based on the aggreated object
ggplot(aggreateInjuries[1:5,], aes(EVTYPE, INJURIES) ) + geom_bar(stat="identity")+ labs(title="Top injuries harmful with respect to population health",x="Event Source",y="Injuries count")

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

To address this question, we will have to concentrate on PROPDMG, CROPDMG, as these two will be feed for analyzing greatest economic consequences

Aggregate PROPDMG based on the EVTYPE and order by PROPDMG (descending) to list top calamities that are harmful w.r.t economic consequences

# aggreate PROPDMG
aggreateProperty <- aggregate(PROPDMG ~ EVTYPE, cleanStormData, FUN = sum)
# sort PROPDMG in ascending order
aggreateProperty <- aggreateProperty[order(-aggreateProperty$PROPDMG),]
head(aggreateProperty)

##                EVTYPE PROPDMG
## 15            TORNADO 6888.06
## 8           HIGH WIND 1747.89
## 13  THUNDERSTORM WIND  800.00
## 14 THUNDERSTORM WINDS  755.00
## 4               FLOOD  720.50
## 16     TROPICAL STORM  678.47

Plot PROPDMG that are harmful

# use ggplot to generate a chart based on the aggreated object
ggplot(aggreateProperty[1:5,], aes(EVTYPE, PROPDMG) ) + geom_bar(stat="identity")+ labs(title="Top property damaged causing greatest economic consequences",x="Event Source",y="Property damages count")

Aggregate CROPDMG based on the EVTYPE and order by CROPDMG (descending) to list top calamities that are harmful w.r.t economic consequences

# aggreate CROPDMG
aggreateCrop <- aggregate(CROPDMG ~ EVTYPE, cleanStormData, FUN = sum)

# sort CROPDMG in ascending order
aggreateCrop <- aggreateCrop[order(-aggreateCrop$CROPDMG),]
head(aggreateCrop)

##                   EVTYPE CROPDMG
## 15               TORNADO  5313.3
## 4                  FLOOD  1746.5
## 3            FLASH FLOOD  1686.5
## 13     THUNDERSTORM WIND   600.0
## 9             HIGH WINDS   500.0
## 17 TROPICAL STORM GORDON   500.0