Coursera Reproducible Research: Course Project 2
Kenneth Wong
30th September, 2017
1 - 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.
2 - 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?
3 - Loading the data into R
3.1 - R libraries
getwd()## [1] "/Users/kennethwong/Desktop/Week 4"library(knitr)
library(markdown)
library(rmarkdown)
library(plyr)
library(stats)3.2 - Loading NOAA data into R
storm <- read.csv(file = "repdata-data-StormData.csv", header = TRUE, sep = ",")
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 "000","0000","0001",..: 152 167 2645 1563 2524 3126 122 1563 3126 3126 ...
## $ TIME_ZONE : Factor w/ 22 levels "ADT","AKS","AST",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ 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",..: 826 826 826 826 826 826 826 826 826 826 ...
## $ 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 ""," Christiansburg",..: 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 ""," CANTON"," TULIA",..: 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 "","+","-","0",..: 16 16 16 16 16 16 16 16 16 16 ...
## $ 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/ 436781 levels "","\t","\t\t",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...3.3 - Wrangling the Data
Defining variables that will be used:
EVTYPE: Event Type (Tornados, Flood, ….)
FATALITIES: Number of Fatalities
INJURIES: Number of Injuries
PROGDMG: Property Damage
PROPDMGEXP: Units for Property Damage (magnitudes - K,B,M)
CROPDMG: Crop Damage
CROPDMGEXP: Units for Crop Damage (magnitudes - K,BM,B)
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",..: 826 826 826 826 826 826 826 826 826 826 ...
## $ 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 "","+","-","0",..: 16 16 16 16 16 16 16 16 16 16 ...
## $ 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 ...3.3.1 - Total for Property Damage
- Refactor of variable PROPDNGEXP
unique(storm$PROPDMGEXP)## [1] K M B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels: + - 0 1 2 3 4 5 6 7 8 ? B H K M h mstorm$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$CROPDMGEXP)## [1] M K m B ? 0 k 2
## Levels: 0 2 ? B K M k mstorm$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)/10000000004 - Processing the data for analysis
https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2
4.1 - Events are most harmful to population Health?
To determine which type of events are most harmful to the population health we must look at the variables fatalities and Injuries.
4.1.1 - Fatalities
sumFatalities <- aggregate(FATALITIES ~ EVTYPE, data = storm, FUN="sum")
dim(sumFatalities) ## 985 observations## [1] 985 2- Ordering Number of Fatalities and defining the top 10 Weather events
fatalities10events <- sumFatalities[order(-sumFatalities$FATALITIES), ][1:10, ]
dim(fatalities10events)## [1] 10 2fatalities10events## EVTYPE FATALITIES
## 826 TORNADO 5633
## 124 EXCESSIVE HEAT 1903
## 151 FLASH FLOOD 978
## 271 HEAT 937
## 453 LIGHTNING 816
## 846 TSTM WIND 504
## 167 FLOOD 470
## 572 RIP CURRENT 368
## 343 HIGH WIND 248
## 19 AVALANCHE 224- BarPlot of the 10 Fatalities Events most harmful to population Health.
par(mfrow = c(1,1), mar = c(12, 4, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(fatalities10events$FATALITIES, names.arg = fatalities10events$EVTYPE, las = 3, main = "10 Fatalities Highest Events", ylab = "Number of Fatalities")
dev.copy(png, "fatalities-events.png", width = 480, height = 480)## quartz_off_screen
## 3dev.off()## quartz_off_screen
## 24.1.2 - Injuries
Number of Injuries per type of Event (EVTYPE)
sumInjuries <- aggregate(INJURIES ~ EVTYPE, data = storm, FUN="sum")
dim(sumInjuries) ## 985 observations## [1] 985 2- Ordering Number of INJURIES and defining the top 10 Weather events in this category
injuries10events <- sumInjuries[order(-sumInjuries$INJURIES), ][1:10, ]
dim(injuries10events)## [1] 10 2injuries10events## EVTYPE INJURIES
## 826 TORNADO 91346
## 846 TSTM WIND 6957
## 167 FLOOD 6789
## 124 EXCESSIVE HEAT 6525
## 453 LIGHTNING 5230
## 271 HEAT 2100
## 422 ICE STORM 1975
## 151 FLASH FLOOD 1777
## 753 THUNDERSTORM WIND 1488
## 241 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")
dev.copy(png, "injuries-events.png", width = 480, height = 480)## quartz_off_screen
## 3dev.off()## quartz_off_screen
## 24.2 - 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.
4.2.1 - Property Damage
*Calculation of property Damage
sumPropertyDamage <- aggregate(PROPDMGTOTAL ~ EVTYPE, data = storm, FUN="sum")
dim(sumPropertyDamage) ## 985 observations## [1] 985 2- Top 10 highest Property damage Events
propdmg10Total <- sumPropertyDamage[order(-sumPropertyDamage$PROPDMGTOTAL), ][1:10, ]
propdmg10Total## EVTYPE PROPDMGTOTAL
## 167 FLOOD 144.657710
## 393 HURRICANE/TYPHOON 69.305840
## 826 TORNADO 56.947381
## 656 STORM SURGE 43.323536
## 151 FLASH FLOOD 16.822674
## 241 HAIL 15.735268
## 385 HURRICANE 11.868319
## 839 TROPICAL STORM 7.703891
## 962 WINTER STORM 6.688497
## 343 HIGH WIND 5.270046- BarPlot of the top 10 events most harmful to population economic.
par(mfrow = c(1,1), mar = c(12, 6, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(propdmg10Total$PROPDMGTOTAL, names.arg = propdmg10Total$EVTYPE, las = 3, main = "10 Property Damages Highest Events", ylab = "Damage Property Values (in Billions)")
dev.copy(png, "propdmg-total.png", width = 480, height = 480)## quartz_off_screen
## 3dev.off()## quartz_off_screen
## 24.2.2 - Crop Damage
- Calculation of crop damage
sumCropDamage <- aggregate(CROPDMGTOTAL ~ EVTYPE, data = storm, FUN="sum")
dim(sumCropDamage) ## 985 observations## [1] 985 2- Top 10 highest crop damage events
cropdmg10Total <- sumCropDamage[order(-sumCropDamage$CROPDMGTOTAL), ][1:10, ]
cropdmg10Total## EVTYPE CROPDMGTOTAL
## 91 DROUGHT 13.972566
## 167 FLOOD 5.661968
## 577 RIVER FLOOD 5.029459
## 422 ICE STORM 5.022113
## 241 HAIL 3.025954
## 385 HURRICANE 2.741910
## 393 HURRICANE/TYPHOON 2.607873
## 151 FLASH FLOOD 1.421317
## 132 EXTREME COLD 1.292973
## 198 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) ")
dev.copy(png, "cropdmg-total.png", width = 480, height = 480)## quartz_off_screen
## 3dev.off()## quartz_off_screen
## 25 - Results
5.1 - Question 1
The results tells us that Tornados causes the highest number of Fatalities and Injuries.
5.2 - Question 2
The results tells us that Floods causes highest Property Damage.
The results tells us that Droughts causes highest Crop damages.