Reproducible Research / Week 4 / Course Project 2
Pingping
6/28/2020
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 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.
SYNOPSIS
- This is an exploration of the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database.
- The FATALITIES and INJURIES variables are selected to evaluate which event is the most harmful with respect to public health.
- The PROPDMG, PRODMGEXP, CROPDMG and CROPDMGEXP variables are selected to evaluate which event is the most harmful with respect to economic consequences.
- The PROPDMG and PRODMGEXP variables are processed into one variable showing the final property damage.
- The CORPDMG and CROPDMGEXP variables are processed into one variable showing the final crop damage.
- Across the United States, Tornados are the most harmful events with respect to population health, creating the largest number of fatalities and injuries.
- Across the United States, Floods have the greatest economic consequences with repect to property damage; Droughts have the greatest economic consequences with repect to crop damage.
DATA PROCESSING
Install Packages
library(rmarkdown)
library(R.utils)
library(knitr)
library(tidyr)
library(dplyr)
library(ggplot2)Download and read the data
if(!file.exists("C:/Users/pmeng2/Documents/R.Studio/Reproducible_research/RepData_PeerAssessment2/stormData.csv.bz2"))
{
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", destfile="C:/Users/pmeng2/Documents/R.Studio/Reproducible_research/RepData_PeerAssessment2/stormData.csv.bz2")
}if(!file.exists("C:/Users/pmeng2/Documents/R.Studio/Reproducible_research/RepData_PeerAssessment2/stormdata.csv"))
{
bunzip2("stormData.csv.bz2","stormdata.csv",remove=F)
}
stormdata <- read.csv("stormdata.csv", header = T)
str(stormdata)## '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 ...Queation 1. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
1.1 First, select the variables that are needed to answer this question.
healthdata <- select(stormdata, EVTYPE, FATALITIES, INJURIES)1.2 Get the sum fatalities and injuries for each events
sumfatal <- as.vector(tapply(healthdata$FATALITIES, healthdata$EVTYPE, sum))
suminjur <- as.vector(tapply(healthdata$INJURIES, healthdata$EVTYPE, sum))
events <- levels(healthdata$EVTYPE)
sumhealth <- data.frame(events, sumfatal, suminjur)
colnames(sumhealth) <- c("event", "fatalities","injuries")1.3 arrange the dataframe in the desc order, get the top 10 events
fatality10 <- arrange(sumhealth, desc(fatalities))[1:10, 1:2]
injury10 <- arrange(sumhealth, desc(injuries))[1:10, c(1,3)]
topfatality <- fatality10 [1,1]
topinjury <- injury10 [1,1]Question 2. Across the United States, which types of events have the greatest economic consequences?
2.1 First, select the variables that are needed to answer this question.
dmgdata <- select(stormdata, EVTYPE, PROPDMG:CROPDMGEXP)2.2 combine the PRODMG and PRODMGEXP, CROPDMG and CROPDMGEXP columns
levels(dmgdata$PROPDMGEXP)## [1] "" "-" "?" "+" "0" "1" "2" "3" "4" "5" "6" "7" "8" "B" "h" "H" "K" "m" "M"levels(dmgdata$CROPDMGEXP)## [1] "" "?" "0" "2" "B" "k" "K" "m" "M"dmgdata <- mutate(dmgdata,PROPDMGEXP = as.character(PROPDMGEXP), CROPDMGEXP = as.character(CROPDMGEXP))
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == ""] <- 1
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "0"] <- 1
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "1"] <- 10
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "2"] <- 100
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "3"] <- 1000
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "4"] <- 10000
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "5"] <- 100000
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "6"] <- 1000000
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "7"] <- 10000000
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "8"] <- 100000000
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "B"] <- 1000000000
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "h"] <- 100
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "H"] <- 100
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "K"] <- 1000
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "m"] <- 1000000
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "M"] <- 1000000
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "-"] <- 0
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "+"] <- 0
dmgdata$PROPDMGEXP[dmgdata$PROPDMGEXP == "?"] <- 0
dmgdata$CROPDMGEXP[dmgdata$CROPDMGEXP == ""] <- 1
dmgdata$CROPDMGEXP[dmgdata$CROPDMGEXP == "0"] <- 1
dmgdata$CROPDMGEXP[dmgdata$CROPDMGEXP == "2"] <- 100
dmgdata$CROPDMGEXP[dmgdata$CROPDMGEXP == "k"] <- 1000
dmgdata$CROPDMGEXP[dmgdata$CROPDMGEXP == "K"] <- 1000
dmgdata$CROPDMGEXP[dmgdata$CROPDMGEXP == "m"] <- 1000000
dmgdata$CROPDMGEXP[dmgdata$CROPDMGEXP == "M"] <- 1000000
dmgdata$CROPDMGEXP[dmgdata$CROPDMGEXP == "B"] <- 1000000000
dmgdata$CROPDMGEXP[dmgdata$CROPDMGEXP == "?"] <- 0
dmgdata1 <- mutate(dmgdata,PROPDMGEXP = as.numeric(PROPDMGEXP), CROPDMGEXP = as.numeric(CROPDMGEXP))
dmgdata2 <- mutate(dmgdata1, PROPDMG = PROPDMG*PROPDMGEXP)
dmgdata3 <- mutate(dmgdata2, CROPDMG = CROPDMG*CROPDMGEXP)
DMGDATA <- select(dmgdata3, EVTYPE, PROPDMG, CROPDMG)2.3 Get the sum damage for each events
sumpropdmg <- as.vector(tapply(DMGDATA$PROPDMG, DMGDATA$EVTYPE, sum))
sumcropdmg <- as.vector(tapply(DMGDATA$CROPDMG, DMGDATA$EVTYPE, sum))
events <- levels(DMGDATA$EVTYPE)
DMGSUM <- data.frame(events, sumpropdmg, sumcropdmg)
colnames(DMGSUM) <- c("event", "propdmg","cropdmg")2.4 arrange the dataframe in the desc order, get the top 10 events
propdmg10 <- arrange(DMGSUM, desc(propdmg))[1:10, 1:2]
cropdmg10 <- arrange(DMGSUM, desc(cropdmg))[1:10, c(1,3)]
toppropdmg <- propdmg10 [1,1]
topcropdmg <- cropdmg10 [1,1]RESULTS
Harm to Public Health
- The top10 and top 1 harmful events with repect to fatalities is :
fatality10## event fatalities
## 1 TORNADO 5633
## 2 EXCESSIVE HEAT 1903
## 3 FLASH FLOOD 978
## 4 HEAT 937
## 5 LIGHTNING 816
## 6 TSTM WIND 504
## 7 FLOOD 470
## 8 RIP CURRENT 368
## 9 HIGH WIND 248
## 10 AVALANCHE 224topfatality## [1] TORNADO
## 985 Levels: HIGH SURF ADVISORY COASTAL FLOOD FLASH FLOOD ... WND- The top10 and top 1 harmful events with repect to injuries is :
injury10## event injuries
## 1 TORNADO 91346
## 2 TSTM WIND 6957
## 3 FLOOD 6789
## 4 EXCESSIVE HEAT 6525
## 5 LIGHTNING 5230
## 6 HEAT 2100
## 7 ICE STORM 1975
## 8 FLASH FLOOD 1777
## 9 THUNDERSTORM WIND 1488
## 10 HAIL 1361topinjury## [1] TORNADO
## 985 Levels: HIGH SURF ADVISORY COASTAL FLOOD FLASH FLOOD ... WND- Making the plots of the top 10 fatalities and top 10 injuries
par(mfrow=c(1,2),mar=c(10,3,3,2))
barplot(fatality10$fatalities, names.arg = fatality10$event, las = 2, ylab = "Fatalities", main = "Top 10 Fatalities Events")
barplot(injury10$injuries, names.arg = injury10$event, las = 2, ylab = "Injuries", main = "Top 10 Injuries Events")
Harm to Economics
- The top10 and top1 harmful events with repect to the property damage.
propdmg10## event propdmg
## 1 FLOOD 144657709870
## 2 HURRICANE/TYPHOON 69305840000
## 3 TORNADO 56947381845
## 4 STORM SURGE 43323536000
## 5 FLASH FLOOD 16822678195
## 6 HAIL 15735270147
## 7 HURRICANE 11868319010
## 8 TROPICAL STORM 7703890550
## 9 WINTER STORM 6688497260
## 10 HIGH WIND 5270046260toppropdmg## [1] FLOOD
## 985 Levels: HIGH SURF ADVISORY COASTAL FLOOD FLASH FLOOD ... WND- The top10 and top1 harmful events with repect to the crop damage.
cropdmg10## event cropdmg
## 1 DROUGHT 13972566000
## 2 FLOOD 5661968450
## 3 RIVER FLOOD 5029459000
## 4 ICE STORM 5022113500
## 5 HAIL 3025954473
## 6 HURRICANE 2741910000
## 7 HURRICANE/TYPHOON 2607872800
## 8 FLASH FLOOD 1421317100
## 9 EXTREME COLD 1292973000
## 10 FROST/FREEZE 1094086000topcropdmg## [1] DROUGHT
## 985 Levels: HIGH SURF ADVISORY COASTAL FLOOD FLASH FLOOD ... WND- Making the plots of the top 10 property damage and top 10 crop damage events
par(mfrow=c(1,2),mar=c(10,3,3,2))
barplot(propdmg10$propdmg, names.arg = propdmg10$event, las = 2, ylab = "Property Damage", main = "Top 10 Property Damege Events")
barplot(cropdmg10$cropdmg, names.arg = cropdmg10$event, las = 2, ylab = "Crop Damage", main = "Top 10 Crop Damage Evenets")
SUMMARY
- Across the United States, Tornados are the most harmful events with respect to population health, creating the largest number of fatalities and injuries.
- Across the United States, Floods have the greatest economic consequences with repect to property damage; Droughts have the greatest economic consequences with repect to crop damage.