Reproducible Research: Peer Assessment 2
Jose Antonio R Neto (joseantonio@me.com)
Health/Economic Consequences in U.S. caused by Storms ans Weather Events
GitHub: GitHub NOAA Project
1 - Introduction
Storms and other severe weather events can cause both public health and economic problems for communities and municipalities. Many severe events can result in fatalities, injuries, and property damage, and preventing such outcomes to the extent possible is a key concern.
This Data Science Project involves exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. This database tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage.
Look to link: Storm Data Documentation. At pag.51 of this document, there is an example, evolving Hurricane Andrew where the powerful winds resulted in 4 fatalities, 50 injuries, $13B in property damage and $ 750M in crop damage (Notation Example pag.51: FLZ018-021 >023 24 0325EST 4 50 13B 750M Hurricane/Typhoon 0900EST)
2 - Synopsis
This report address questions related to Weather Events and Storms in U.S. that are most damaging in terms of Fatalities, Injuries and damages to properties and crop.
The two mainly questions to be answered are:
1 - Which types of events are most harmful with respect to population health?
2 - Which types of events have the greatest economic consequences?
3 - Loading the Data
3.1 - R libraries
getwd()## [1] "/Volumes/Dados/GITHUB/RepData_PeerAssessment3"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 - Calculating 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:
Lets answer the question about Which Types of Events are most Harmful for population HEALTH? The variables involved are FATALITIES and INJURIES.
4.1 - Events are most harmful to population Health?
The item 2.6 (page 9) of National Weather Service Storm Data documentation describes about Fatalities and Injuries. So, it is necessary to assess these Variables to define which of EVENTS (EVTYPE) are most harmful. Look to link: Storm Data Documentation
4.1.1 - Total Number of Fatalities per Event
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$EVTYPE, las = 3, main = "10 Fatalities Highest Events", ylab = "Number of Fatalities")
dev.copy(png, "fatalities-events.png", width = 480, height = 480)## quartz_off_screen
## 3dev.off()## quartz_off_screen
## 24.1.2 - Total Number of Injuries per Event
Using the same reasoning of Fatalities, let’s evaluate the Number of Injuries per type of Event (EVTYPE)
sumInjuries <- aggregate(INJURIES ~ EVTYPE, data = storm, FUN="sum")
dim(sumInjuries) ## 985 observations## [1] 985 2- Ordering Number of INJURIES and defining the top 10 Weather events in this category
injuries10events <- sumInjuries[order(-sumInjuries$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?
The item 2.7 (page 12 and the APPENDIX B) of National Weather Service Storm Data documentation describes about Damage. Two variables, PROPDMG (for Property Damage) and CROPDMG (for Crop Damage) are used to represent these losts. If you want, read more about theses damages, please connect with National Weather Service using the link Storm Data Documentation
4.2.1 - Property Damage
- Calculating Property Damage for type of Event
sumPropertyDamage <- aggregate(PROPDMGTOTAL ~ EVTYPE, data = storm, FUN="sum")
dim(sumPropertyDamage) ## 985 observations## [1] 985 2- We have 985 observations, which is a great number of Events to present in a Plot.
- Lets stay with the 10 most Property Damage Events
propdmg10Total <- sumPropertyDamage[order(-sumPropertyDamage$PROPDMGTOTAL), ][1:10, ]
propdmg10Total## EVTYPE PROPDMGTOTAL
## 167 FLOOD 144.657710
## 393 HURRICANE/TYPHOON 69.305840
## 826 TORNADO 56.947381
## 656 STORM SURGE 43.323536
## 151 FLASH FLOOD 16.822674
## 241 HAIL 15.735268
## 385 HURRICANE 11.868319
## 839 TROPICAL STORM 7.703891
## 962 WINTER STORM 6.688497
## 343 HIGH WIND 5.270046- BarPlot of the 10 Proprietary Damage Events most harmful to population economic.
par(mfrow = c(1,1), mar = c(12, 6, 3, 2), mgp = c(3, 1, 0), cex = 0.8)
barplot(propdmg10Total$PROPDMGTOTAL, names.arg = propdmg10Total$EVTYPE, las = 3, main = "10 Property Damages Highest Events", ylab = "Damage Property Values (in Billions)")
dev.copy(png, "propdmg-total.png", width = 480, height = 480)## quartz_off_screen
## 3dev.off()## quartz_off_screen
## 24.2.2 - Crop Damage
- Calculating Crop Damage for type of Event
sumCropDamage <- aggregate(CROPDMGTOTAL ~ EVTYPE, data = storm, FUN="sum")
dim(sumCropDamage) ## 985 observations## [1] 985 2- We have 985 observations, which is a great number of Events to present in a Plot.
- Lets stay with the 10 most Crop Damage Events
cropdmg10Total <- sumCropDamage[order(-sumCropDamage$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 and Conclusions
5.1 - Answering Question 1
As demonstrated by the Graphs, Tornados causes the greatest number of Fatalities and Injuries.
Specifically in FATALITIES, after Tornados, EXCESSIVE HEAT, FLASH FLOOD and HEAT are the next ones.
Specifically in INJURIES, after tornados we have TSTM WIND, FLOOD and EXCESSIVE HEAT.
5.2 - Answering Question 2
Floods are the Weather Event that cause most Property Damage, followed by Hurrucanes.
Drought are the Weather Event that causes most Crop damages, follwed by Flood.
5.3 - Conclusions
Based on evidences demonstrated previously, tornados, floods and droughts have priorities to minize the impact in human and economic costs of Weather Events. Government and society have to be alert and prepared for each type of events. For safety, it’s important to population to know what to do during these events.