Sypnosis:Analysis of Weather Events.

The source of the data is NOAA Storm Database. The Analysis tries to deduce the effect of multiple weather events on Public Health and Economy. Weather events cause massive health hazards and economic problems every year and it’s important to understand their nature and characterstics. This analysis involves data visualization for studying the effects of major weather events on both puclic health and economy. The health effects include fatalities and inuries and economic factors include property damage and crop damage.

Data Processing:

Loading required Packages

Reading the data

storm<-fread("repdata_data_StormData.csv",sep=",",header=TRUE)

Exploratory Analysis

storm<-tbl_df(storm)
summary(storm)
##     STATE__       BGN_DATE           BGN_TIME          TIME_ZONE        
##  Min.   : 1.0   Length:902297      Length:902297      Length:902297     
##  1st Qu.:19.0   Class :character   Class :character   Class :character  
##  Median :30.0   Mode  :character   Mode  :character   Mode  :character  
##  Mean   :31.2                                                           
##  3rd Qu.:45.0                                                           
##  Max.   :95.0                                                           
##                                                                         
##      COUNTY       COUNTYNAME           STATE              EVTYPE         
##  Min.   :  0.0   Length:902297      Length:902297      Length:902297     
##  1st Qu.: 31.0   Class :character   Class :character   Class :character  
##  Median : 75.0   Mode  :character   Mode  :character   Mode  :character  
##  Mean   :100.6                                                           
##  3rd Qu.:131.0                                                           
##  Max.   :873.0                                                           
##                                                                          
##    BGN_RANGE          BGN_AZI           BGN_LOCATI       
##  Min.   :   0.000   Length:902297      Length:902297     
##  1st Qu.:   0.000   Class :character   Class :character  
##  Median :   0.000   Mode  :character   Mode  :character  
##  Mean   :   1.484                                        
##  3rd Qu.:   1.000                                        
##  Max.   :3749.000                                        
##                                                          
##    END_DATE           END_TIME           COUNTY_END COUNTYENDN    
##  Length:902297      Length:902297      Min.   :0    Mode:logical  
##  Class :character   Class :character   1st Qu.:0    NA's:902297   
##  Mode  :character   Mode  :character   Median :0                  
##                                        Mean   :0                  
##                                        3rd Qu.:0                  
##                                        Max.   :0                  
##                                                                   
##    END_RANGE          END_AZI           END_LOCATI       
##  Min.   :  0.0000   Length:902297      Length:902297     
##  1st Qu.:  0.0000   Class :character   Class :character  
##  Median :  0.0000   Mode  :character   Mode  :character  
##  Mean   :  0.9862                                        
##  3rd Qu.:  0.0000                                        
##  Max.   :925.0000                                        
##                                                          
##      LENGTH              WIDTH                F               MAG         
##  Min.   :   0.0000   Min.   :   0.000   Min.   :0.0      Min.   :    0.0  
##  1st Qu.:   0.0000   1st Qu.:   0.000   1st Qu.:0.0      1st Qu.:    0.0  
##  Median :   0.0000   Median :   0.000   Median :1.0      Median :   50.0  
##  Mean   :   0.2301   Mean   :   7.503   Mean   :0.9      Mean   :   46.9  
##  3rd Qu.:   0.0000   3rd Qu.:   0.000   3rd Qu.:1.0      3rd Qu.:   75.0  
##  Max.   :2315.0000   Max.   :4400.000   Max.   :5.0      Max.   :22000.0  
##                                         NA's   :843563                    
##    FATALITIES          INJURIES            PROPDMG       
##  Min.   :  0.0000   Min.   :   0.0000   Min.   :   0.00  
##  1st Qu.:  0.0000   1st Qu.:   0.0000   1st Qu.:   0.00  
##  Median :  0.0000   Median :   0.0000   Median :   0.00  
##  Mean   :  0.0168   Mean   :   0.1557   Mean   :  12.06  
##  3rd Qu.:  0.0000   3rd Qu.:   0.0000   3rd Qu.:   0.50  
##  Max.   :583.0000   Max.   :1700.0000   Max.   :5000.00  
##                                                          
##   PROPDMGEXP           CROPDMG         CROPDMGEXP       
##  Length:902297      Min.   :  0.000   Length:902297     
##  Class :character   1st Qu.:  0.000   Class :character  
##  Mode  :character   Median :  0.000   Mode  :character  
##                     Mean   :  1.527                     
##                     3rd Qu.:  0.000                     
##                     Max.   :990.000                     
##                                                         
##      WFO             STATEOFFIC         ZONENAMES            LATITUDE   
##  Length:902297      Length:902297      Length:902297      Min.   :   0  
##  Class :character   Class :character   Class :character   1st Qu.:2802  
##  Mode  :character   Mode  :character   Mode  :character   Median :3540  
##                                                           Mean   :2875  
##                                                           3rd Qu.:4019  
##                                                           Max.   :9706  
##                                                           NA's   :47    
##    LONGITUDE        LATITUDE_E     LONGITUDE_       REMARKS         
##  Min.   :-14451   Min.   :   0   Min.   :-14455   Length:902297     
##  1st Qu.:  7247   1st Qu.:   0   1st Qu.:     0   Class :character  
##  Median :  8707   Median :   0   Median :     0   Mode  :character  
##  Mean   :  6940   Mean   :1452   Mean   :  3509                     
##  3rd Qu.:  9605   3rd Qu.:3549   3rd Qu.:  8735                     
##  Max.   : 17124   Max.   :9706   Max.   :106220                     
##                   NA's   :40                                        
##      REFNUM      
##  Min.   :     1  
##  1st Qu.:225575  
##  Median :451149  
##  Mean   :451149  
##  3rd Qu.:676723  
##  Max.   :902297  
## 
str(storm)
## Classes 'tbl_df', 'tbl' and 'data.frame':    902297 obs. of  37 variables:
##  $ STATE__   : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ BGN_DATE  : chr  "4/18/1950 0:00:00" "4/18/1950 0:00:00" "2/20/1951 0:00:00" "6/8/1951 0:00:00" ...
##  $ BGN_TIME  : chr  "0130" "0145" "1600" "0900" ...
##  $ TIME_ZONE : chr  "CST" "CST" "CST" "CST" ...
##  $ COUNTY    : num  97 3 57 89 43 77 9 123 125 57 ...
##  $ COUNTYNAME: chr  "MOBILE" "BALDWIN" "FAYETTE" "MADISON" ...
##  $ STATE     : chr  "AL" "AL" "AL" "AL" ...
##  $ EVTYPE    : chr  "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
##  $ BGN_RANGE : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BGN_AZI   : chr  "" "" "" "" ...
##  $ BGN_LOCATI: chr  "" "" "" "" ...
##  $ END_DATE  : chr  "" "" "" "" ...
##  $ END_TIME  : chr  "" "" "" "" ...
##  $ 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   : chr  "" "" "" "" ...
##  $ END_LOCATI: chr  "" "" "" "" ...
##  $ 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: chr  "K" "K" "K" "K" ...
##  $ CROPDMG   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ CROPDMGEXP: chr  "" "" "" "" ...
##  $ WFO       : chr  "" "" "" "" ...
##  $ STATEOFFIC: chr  "" "" "" "" ...
##  $ ZONENAMES : chr  "" "" "" "" ...
##  $ 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   : chr  "" "" "" "" ...
##  $ REFNUM    : num  1 2 3 4 5 6 7 8 9 10 ...
##  - attr(*, ".internal.selfref")=<externalptr>

1) PUBLIC HEALTH:Fatalities and Injuries

stormpop<-subset(storm,select = c("EVTYPE","FATALITIES","INJURIES"))
fatalities<-aggregate(data=stormpop,FATALITIES~EVTYPE,FUN = sum)
fatalities<-fatalities[order(fatalities$FATALITIES,decreasing = TRUE),]
injuries<-aggregate(data=stormpop,INJURIES~EVTYPE,FUN=sum)
injuries<-injuries[order(injuries$INJURIES,decreasing = TRUE),]
topfatalities<-fatalities[1:20,]
topinjuries<-injuries[1:20,]

Plotting Most effective Weather Events vs Fatalities and Most effective Weather Events and Injuries.

par(mfrow = c(1, 2), las = 2,cex=0.7,font.lab=2,mar=c(8,4,1,1))
barplot(topfatalities$FATALITIES,names.arg=topfatalities$EVTYPE,col="red",legend.text = "FATALITIES")
barplot(topinjuries$INJURIES,names.arg = topinjuries$EVTYPE,col="pink",legend.text = "INJURIES")

  • We can Observe that most of the Public health effects (fatalities and injuries) are caused by Tornadoes.

2) Economic Consequences:

stormprop<-subset(storm,select=c("EVTYPE","PROPDMG","CROPDMG"))
propdmg<-aggregate(data=stormprop,PROPDMG~EVTYPE,FUN=sum)
cropdmg<-aggregate(data=stormprop,CROPDMG~EVTYPE,FUN=sum)
propdmg<-propdmg[order(propdmg$PROPDMG,decreasing = TRUE),]
cropdmg<-cropdmg[order(cropdmg$CROPDMG,decreasing=TRUE),]
topprop<-propdmg[1:20,]
topcrop<-cropdmg[1:20,]

Visualizing Significant Weather Events vs Property Damage and Significant Weather Events vs Crop Damage:

par(mfrow = c(1, 2), las = 3,cex=0.6,font.lab=2,mar=c(10,4,1,1))
barplot(topprop$PROPDMG,names.arg = topprop$EVTYPE,col="blue",legend.text = "Property DMG")
barplot(topcrop$CROPDMG,names.arg = topcrop$EVTYPE,col="green",legend.text = "Crop DMG")

## Aggregating Property and Crop Damages:

economicdmg<-merge(propdmg,cropdmg,by="EVTYPE")
economicdmg$ecodmg<- economicdmg$PROPDMG+economicdmg$CROPDMG
economicdmg<-economicdmg[order(economicdmg$ecodmg,decreasing = TRUE),]
topecodmg<-economicdmg[1:20,]

Significant Weather Events vs Economic Loss

par(mfrow=c(1,1),mar=c(12,8,3,3),cex=0.6)
barplot(topecodmg$ecodmg,names.arg=topecodmg$EVTYPE,col="brown",legend.text = "Total Economic Damage")

* We can observe that most of the Economic loss was due to, yet again, Tornadoes!

Results:

From the Analysis done above on the NOAA Storm Database it is quite clear that there are huge health effects and economic consequences due to multiple Weather events in USA alone. Most of these Harmful Health Hazards and Economic loss can be associated largely with Tornadoes. There are many other weather events which also harm population health and cause economic losses. Second most deaths are caused by Excessive Heat and Second most injuries by TSTM WINDS. Second most Property Damage and Crop Damage is done by Flash Floods.