Insight into Storms and severe weather events
Ebenezer Bediam Nonbo
December 17, 2019
Introduction:
Public health and econimic problems are affected by a number of reasons i am now going to analysize how storms and other severe weather events play a part. Storms and severe weather conditions causes fatalities and injuries and substantial property damage. Hence to minimze damages we should analyse the given data.
This project requires us to analyse the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. This database tracks when and where major storms and weather events occur in the United States, estimated of any fatalities, injuries, and property damage figures are also provided.
Synopsis:
This report will provide a better insight into Storms and severe weather events in United States and the Fatalities, Injuries and property damages left behind. Two questions to be answered: 1 - which types of events are most harmful to population health? 2 - which types of events have the greatest economic consequences?
Loading the data into R:
library(knitr)
library(markdown)
library(rmarkdown)
library(plyr)
library(stats)
repdata_data_StormData <- read.csv("C:/Users/hp.000/Desktop/DATA SCIENCE/Reproducible Research/week4/repdata_data_StormData.csv/repdata_data_StormData.csv")
storm<-repdata_data_StormData
dim(storm)## [1] 902297 37names(storm)## [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(storm)## '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 ...Wrangling the Data:
varsNedeed <- c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")
storm <- storm[varsNedeed]
dim(storm)## [1] 902297 7names(storm)## [1] "EVTYPE" "FATALITIES" "INJURIES" "PROPDMG" "PROPDMGEXP"
## [6] "CROPDMG" "CROPDMGEXP"str(storm)## 'data.frame': 902297 obs. of 7 variables:
## $ EVTYPE : Factor w/ 985 levels " HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
## $ 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 ...Total for Property Damage
#Refactor of variable PROPDNGEXP
unique(storm$PROPDMGEXP)## [1] K M B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels: - ? + 0 1 2 3 4 5 6 7 8 B h H K m Mstorm$PROPDMGEXP <- mapvalues(storm$PROPDMGEXP, from = c("K", "M","", "B", "m", "+", "0", "5", "6", "?", "4", "2", "3", "h", "7", "H", "-", "1", "8"), to = c(10^3, 10^6, 1, 10^9, 10^6, 0,1,10^5, 10^6, 0, 10^4, 10^2, 10^3, 10^2, 10^7, 10^2, 0, 10, 10^8))
storm$PROPDMGEXP <- as.numeric(as.character(storm$PROPDMGEXP))
storm$PROPDMGTOTAL <- (storm$PROPDMG * storm$PROPDMGEXP)/1000000000
#Refactor of variable CROPDMGEXP variable
unique(storm$CROPDMGEXP)## [1] M K m B ? 0 k 2
## Levels: ? 0 2 B k K m Mstorm$CROPDMGEXP <- mapvalues(storm$CROPDMGEXP, from = c("","M", "K", "m", "B", "?", "0", "k","2"), to = c(1,10^6, 10^3, 10^6, 10^9, 0, 1, 10^3, 10^2))
storm$CROPDMGEXP <- as.numeric(as.character(storm$CROPDMGEXP))
storm$CROPDMGTOTAL <- (storm$CROPDMG * storm$CROPDMGEXP)/1000000000Processing the data for analysis Events are most harmful to population Health?
#Fatalities
sumFatalities <- aggregate(FATALITIES ~ EVTYPE, data = storm, FUN="sum")
dim(sumFatalities)## [1] 985 2#Ordering Number of Fatalities and defining the top 10 Weather events
fatalities10events <- sumFatalities[order(-sumFatalities$FATALITIES), ][1:10, ]
dim(fatalities10events)## [1] 10 2fatalities10events## EVTYPE FATALITIES
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504
## 170 FLOOD 470
## 585 RIP CURRENT 368
## 359 HIGH WIND 248
## 19 AVALANCHE 224#BarPlot of the 10 Fatalities Events most harmful to population Health
par(mfrow = c(1,1), mar = c(12, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(fatalities10events$FATALITIES, names.arg = fatalities10events$EVTYPE, las = 3, main = "10 Fatalities Highest Events", ylab = "Number of Fatalities")
Injuries:
#Number of Injuries per type of Event (EVTYPE)
sumInjuries <- aggregate(INJURIES ~ EVTYPE, data = storm, FUN="sum")
dim(sumInjuries)## [1] 985 2#Ordering Number of INJURIES and defining the top 10 Weather events in this category
injuries10events <- sumInjuries[order(-sumInjuries$INJURIES), ][1:10, ]
dim(injuries10events)## [1] 10 2injuries10events## EVTYPE INJURIES
## 834 TORNADO 91346
## 856 TSTM WIND 6957
## 170 FLOOD 6789
## 130 EXCESSIVE HEAT 6525
## 464 LIGHTNING 5230
## 275 HEAT 2100
## 427 ICE STORM 1975
## 153 FLASH FLOOD 1777
## 760 THUNDERSTORM WIND 1488
## 244 HAIL 1361#BarPlot of the 10 INJURIES Events most harmful to population Health.
par(mfrow = c(1,1), mar = c(12, 6, 3, 2), mgp = c(4, 1, 0), cex = 0.8)
barplot(injuries10events$INJURIES, names.arg = injuries10events$EVTYPE, las = 3, main = "10 Injuries Highest Events", ylab = "Number of Injuries")
Which type of Events have the greatest Economic consequences?
To determine which type of events have the greatest econimic consequences the variables, PROPDMG (Property Damage) and CROPDMG (Crop Damage) have to be taken into consideration
#Calculation of property Damage
sumPropertyDamage <- aggregate(PROPDMGTOTAL ~ EVTYPE, data = storm, FUN="sum")
dim(sumPropertyDamage)## [1] 985 2#Top 10 highest Property damage Events
propdmg10Total <- sumPropertyDamage[order(-sumPropertyDamage$PROPDMGTOTAL), ][1:10, ]
propdmg10Total## EVTYPE PROPDMGTOTAL
## 170 FLOOD 144.657710
## 411 HURRICANE/TYPHOON 69.305840
## 834 TORNADO 56.947381
## 670 STORM SURGE 43.323536
## 153 FLASH FLOOD 16.822674
## 244 HAIL 15.735268
## 402 HURRICANE 11.868319
## 848 TROPICAL STORM 7.703891
## 972 WINTER STORM 6.688497
## 359 HIGH WIND 5.270046#BarPlot of the top 10 events most harmful to population economic
par(mfrow = c(1,1), mar = c(12, 6, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(propdmg10Total$PROPDMGTOTAL, names.arg = propdmg10Total$EVTYPE, las = 3, main = "10 Property Damages Highest Events", ylab = "Damage Property Values (in Billions)")
Crop Damage
#Calculation of crop damage
sumCropDamage <- aggregate(CROPDMGTOTAL ~ EVTYPE, data = storm, FUN="sum")
dim(sumCropDamage)## [1] 985 2#Top 10 highest crop damage events
cropdmg10Total <- sumCropDamage[order(-sumCropDamage$CROPDMGTOTAL), ][1:10, ]
cropdmg10Total## EVTYPE CROPDMGTOTAL
## 95 DROUGHT 13.972566
## 170 FLOOD 5.661968
## 590 RIVER FLOOD 5.029459
## 427 ICE STORM 5.022113
## 244 HAIL 3.025954
## 402 HURRICANE 2.741910
## 411 HURRICANE/TYPHOON 2.607873
## 153 FLASH FLOOD 1.421317
## 140 EXTREME COLD 1.292973
## 212 FROST/FREEZE 1.094086#BarPlot of the 10 Crop Damage Events most harmful to population economic.
par(mfrow = c(1,1), mar = c(10, 6, 3, 2), mgp = c(3, 1, 0), cex = 0.6)
barplot(cropdmg10Total$CROPDMGTOTAL, names.arg = cropdmg10Total$EVTYPE, las = 2, main = "10 Crop Damages Highest Events", ylab = "Damage Crop Values (in Billions) ")
Results:
Question 1 The results tells us that Tornados causes the highest number of Fatalities and Injuries.
Question 2 The results tells us that Floods causes highest Property Damage.
The results tells us that Droughts causes highest Crop damages.