courseraproject2
2023-05-04
R Markdown
library(sass)## Warning: package 'sass' was built under R version 4.2.3This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00# load libraries ...
library(R.utils)## Warning: package 'R.utils' was built under R version 4.2.3## Loading required package: R.oo## Loading required package: R.methodsS3## R.methodsS3 v1.8.2 (2022-06-13 22:00:14 UTC) successfully loaded. See ?R.methodsS3 for help.## R.oo v1.25.0 (2022-06-12 02:20:02 UTC) successfully loaded. See ?R.oo for help.##
## Attaching package: 'R.oo'## The following object is masked from 'package:R.methodsS3':
##
## throw## The following objects are masked from 'package:methods':
##
## getClasses, getMethods## The following objects are masked from 'package:base':
##
## attach, detach, load, save## R.utils v2.12.2 (2022-11-11 22:00:03 UTC) successfully loaded. See ?R.utils for help.##
## Attaching package: 'R.utils'## The following object is masked from 'package:utils':
##
## timestamp## The following objects are masked from 'package:base':
##
## cat, commandArgs, getOption, isOpen, nullfile, parse, warnings## Loading required package: R.oo
## Loading required package: R.methodsS3
## R.methodsS3 v1.8.1 (2020-08-26 16:20:06 UTC) successfully loaded. See ?R.methodsS3 for help.
## R.oo v1.24.0 (2020-08-26 16:11:58 UTC) successfully loaded. See ?R.oo for help.
##
## Attaching package: 'R.oo'
## The following object is masked from 'package:R.methodsS3':
##
## throw
## The following objects are masked from 'package:methods':
##
## getClasses, getMethods
## The following objects are masked from 'package:base':
##
## attach, detach, load, save
## R.utils v2.11.0 (2021-09-26 08:30:02 UTC) successfully loaded. See ?R.utils for help.
##
## Attaching package: 'R.utils'
## The following object is masked from 'package:utils':
##
## timestamp
## The following objects are masked from 'package:base':
##
## cat, commandArgs, getOption, inherits, isOpen, nullfile, parse,
## warnings
library(rmarkdown)## Warning: package 'rmarkdown' was built under R version 4.2.3library(knitr)## Warning: package 'knitr' was built under R version 4.2.3temp <- tempfile()
##Performing the download
if(!file.exists("/stormData.csv.bz2")){
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2", destfile="./stormData.csv.bz2")
}
##Uncompressing the file
if(!file.exists("stormdata.csv"))
{
bunzip2("stormData.csv.bz2","stormdata.csv",remove=F)
}
# DP1.3 loading the data & reading the filestorm <- read.csv("stormdata.csv",header=TRUE,sep=",")
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 END_DATE
## Min. : 0.000 Length:902297 Length:902297 Length:902297
## 1st Qu.: 0.000 Class :character Class :character Class :character
## Median : 0.000 Mode :character Mode :character Mode :character
## Mean : 1.484
## 3rd Qu.: 1.000
## Max. :3749.000
##
## END_TIME COUNTY_END COUNTYENDN END_RANGE
## Length:902297 Min. :0 Mode:logical Min. : 0.0000
## Class :character 1st Qu.:0 NA's:902297 1st Qu.: 0.0000
## Mode :character Median :0 Median : 0.0000
## Mean :0 Mean : 0.9862
## 3rd Qu.:0 3rd Qu.: 0.0000
## Max. :0 Max. :925.0000
##
## END_AZI END_LOCATI LENGTH WIDTH
## Length:902297 Length:902297 Min. : 0.0000 Min. : 0.000
## Class :character Class :character 1st Qu.: 0.0000 1st Qu.: 0.000
## Mode :character Mode :character Median : 0.0000 Median : 0.000
## Mean : 0.2301 Mean : 7.503
## 3rd Qu.: 0.0000 3rd Qu.: 0.000
## Max. :2315.0000 Max. :4400.000
##
## F MAG FATALITIES INJURIES
## Min. :0.0 Min. : 0.0 Min. : 0.0000 Min. : 0.0000
## 1st Qu.:0.0 1st Qu.: 0.0 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median :1.0 Median : 50.0 Median : 0.0000 Median : 0.0000
## Mean :0.9 Mean : 46.9 Mean : 0.0168 Mean : 0.1557
## 3rd Qu.:1.0 3rd Qu.: 75.0 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :5.0 Max. :22000.0 Max. :583.0000 Max. :1700.0000
## NA's :843563
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 Length:902297 Min. : 0.000 Length:902297
## 1st Qu.: 0.00 Class :character 1st Qu.: 0.000 Class :character
## Median : 0.00 Mode :character Median : 0.000 Mode :character
## Mean : 12.06 Mean : 1.527
## 3rd Qu.: 0.50 3rd Qu.: 0.000
## Max. :5000.00 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
## ## 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 END_DATE
## Min. : 0.000 Length:902297 Length:902297 Length:902297
## 1st Qu.: 0.000 Class :character Class :character Class :character
## Median : 0.000 Mode :character Mode :character Mode :character
## Mean : 1.484
## 3rd Qu.: 1.000
## Max. :3749.000
##
## END_TIME COUNTY_END COUNTYENDN END_RANGE
## Length:902297 Min. :0 Mode:logical Min. : 0.0000
## Class :character 1st Qu.:0 NA's:902297 1st Qu.: 0.0000
## Mode :character Median :0 Median : 0.0000
## Mean :0 Mean : 0.9862
## 3rd Qu.:0 3rd Qu.: 0.0000
## Max. :0 Max. :925.0000
##
## END_AZI END_LOCATI LENGTH WIDTH
## Length:902297 Length:902297 Min. : 0.0000 Min. : 0.000
## Class :character Class :character 1st Qu.: 0.0000 1st Qu.: 0.000
## Mode :character Mode :character Median : 0.0000 Median : 0.000
## Mean : 0.2301 Mean : 7.503
## 3rd Qu.: 0.0000 3rd Qu.: 0.000
## Max. :2315.0000 Max. :4400.000
##
## F MAG FATALITIES INJURIES
## Min. :0.0 Min. : 0.0 Min. : 0.0000 Min. : 0.0000
## 1st Qu.:0.0 1st Qu.: 0.0 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median :1.0 Median : 50.0 Median : 0.0000 Median : 0.0000
## Mean :0.9 Mean : 46.9 Mean : 0.0168 Mean : 0.1557
## 3rd Qu.:1.0 3rd Qu.: 75.0 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :5.0 Max. :22000.0 Max. :583.0000 Max. :1700.0000
## NA's :843563
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 Length:902297 Min. : 0.000 Length:902297
## 1st Qu.: 0.00 Class :character 1st Qu.: 0.000 Class :character
## Median : 0.00 Mode :character Median : 0.000 Mode :character
## Mean : 12.06 Mean : 1.527
## 3rd Qu.: 0.50 3rd Qu.: 0.000
## Max. :5000.00 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
###QUESTION 1. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
##Q1.1 Variable selection (reducing the data set to only needed columns and variables)
variables<-c("EVTYPE","FATALITIES","INJURIES","PROPDMG", "PROPDMGEXP","CROPDMG","CROPDMGEXP")
strmdata<-storm[variables]
dim(strmdata)## [1] 902297 7## [1] 902297 7
names(strmdata)## [1] "EVTYPE" "FATALITIES" "INJURIES" "PROPDMG" "PROPDMGEXP"
## [6] "CROPDMG" "CROPDMGEXP"## [1] "EVTYPE" "FATALITIES" "INJURIES" "PROPDMG" "PROPDMGEXP"
## [6] "CROPDMG" "CROPDMGEXP"
# Q1.2 Reviewing events that cause the most fatalities ( The Top-10 Fatalities by Weather Event )
## Procedure = aggregate the top 10 fatalities by the event type and sort the output in descending orderFatalities <- aggregate(FATALITIES ~ EVTYPE, data = strmdata, FUN = sum)
Top10_Fatalities <- Fatalities[order(-Fatalities$FATALITIES), ][1:10, ]
Top10_Fatalities ## EVTYPE FATALITIES
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504
## 170 FLOOD 470
## 585 RIP CURRENT 368
## 359 HIGH WIND 248
## 19 AVALANCHE 224## EVTYPE FATALITIES
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504
## 170 FLOOD 470
## 585 RIP CURRENT 368
## 359 HIGH WIND 248Injuries <- aggregate(INJURIES ~ EVTYPE, data = strmdata, FUN = sum)
Top10_Injuries <- Injuries[order(-Injuries$INJURIES), ][1:10, ]
Top10_Injuries ## 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 1361par(mfrow=c(1,2),mar=c(10,3,3,2))
barplot(Top10_Fatalities$FATALITIES,names.arg=Top10_Fatalities$EVTYPE,las=2,col="purple",ylab="fatalities",main="Top 10 fatalities")
barplot(Top10_Injuries$INJURIES,names.arg=Top10_Injuries$EVTYPE,las=2,col="purple",ylab="injuries",main="Top 10 Injuries")
unique(strmdata$PROPDMGEXP)## [1] "K" "M" "" "B" "m" "+" "0" "5" "6" "?" "4" "2" "3" "h" "7" "H" "-" "1" "8"## [1] "K" "M" "" "B" "m" "+" "0" "5" "6" "?" "4" "2" "3" "h" "7" "H" "-" "1" "8"
# Assigning values for the property exponent strmdata
strmdata$PROPEXP[strmdata$PROPDMGEXP == "K"] <- 1000
strmdata$PROPEXP[strmdata$PROPDMGEXP == "M"] <- 1e+06
strmdata$PROPEXP[strmdata$PROPDMGEXP == ""] <- 1
strmdata$PROPEXP[strmdata$PROPDMGEXP == "B"] <- 1e+09
strmdata$PROPEXP[strmdata$PROPDMGEXP == "m"] <- 1e+06
strmdata$PROPEXP[strmdata$PROPDMGEXP == "0"] <- 1
strmdata$PROPEXP[strmdata$PROPDMGEXP == "5"] <- 1e+05
strmdata$PROPEXP[strmdata$PROPDMGEXP == "6"] <- 1e+06
strmdata$PROPEXP[strmdata$PROPDMGEXP == "4"] <- 10000
strmdata$PROPEXP[strmdata$PROPDMGEXP == "2"] <- 100
strmdata$PROPEXP[strmdata$PROPDMGEXP == "3"] <- 1000
strmdata$PROPEXP[strmdata$PROPDMGEXP == "h"] <- 100
strmdata$PROPEXP[strmdata$PROPDMGEXP == "7"] <- 1e+07
strmdata$PROPEXP[strmdata$PROPDMGEXP == "H"] <- 100
strmdata$PROPEXP[strmdata$PROPDMGEXP == "1"] <- 10
strmdata$PROPEXP[strmdata$PROPDMGEXP == "8"] <- 1e+08
# Assigning '0' to invalid exponent strmdata
strmdata$PROPEXP[strmdata$PROPDMGEXP == "+"] <- 0
strmdata$PROPEXP[strmdata$PROPDMGEXP == "-"] <- 0
strmdata$PROPEXP[strmdata$PROPDMGEXP == "?"] <- 0
# Calculating the property damage value
strmdata$PROPDMGVAL <- strmdata$PROPDMG * strmdata$PROPEXP
# Q2.3 Defining & Calcuating [ Crop Damage ]
## Crop damage exponents for each level listed out & assigned those values for the crop exponent data.
## Invalid data was excluded by assigning the value as '0'.
## Then, the crop damage value was calculated by multiplying the crop damage and crop exponent value.
unique(strmdata$CROPDMGEXP)## [1] "" "M" "K" "m" "B" "?" "0" "k" "2"## [1] "" "M" "K" "m" "B" "?" "0" "k" "2"
# Assigning values for the crop exponent strmdata
strmdata$CROPEXP[strmdata$CROPDMGEXP == "M"] <- 1e+06
strmdata$CROPEXP[strmdata$CROPDMGEXP == "K"] <- 1000
strmdata$CROPEXP[strmdata$CROPDMGEXP == "m"] <- 1e+06
strmdata$CROPEXP[strmdata$CROPDMGEXP == "B"] <- 1e+09
strmdata$CROPEXP[strmdata$CROPDMGEXP == "0"] <- 1
strmdata$CROPEXP[strmdata$CROPDMGEXP == "k"] <- 1000
strmdata$CROPEXP[strmdata$CROPDMGEXP == "2"] <- 100
strmdata$CROPEXP[strmdata$CROPDMGEXP == ""] <- 1
# Assigning '0' to invalid exponent strmdata
strmdata$CROPEXP[strmdata$CROPDMGEXP == "?"] <- 0
# calculating the crop damage
strmdata$CROPDMGVAL <- strmdata$CROPDMG * strmdata$CROPEXPprop <- aggregate(PROPDMGVAL~EVTYPE,data=strmdata,FUN=sum,na.rm=TRUE)
prop <- prop[with(prop,order(-PROPDMGVAL)),]
prop <- head(prop,10)
print(prop)## EVTYPE PROPDMGVAL
## 170 FLOOD 144657709807
## 411 HURRICANE/TYPHOON 69305840000
## 834 TORNADO 56947380617
## 670 STORM SURGE 43323536000
## 153 FLASH FLOOD 16822673979
## 244 HAIL 15735267513
## 402 HURRICANE 11868319010
## 848 TROPICAL STORM 7703890550
## 972 WINTER STORM 6688497251
## 359 HIGH WIND 5270046260crop <- aggregate(CROPDMGVAL~EVTYPE,data=strmdata,FUN=sum,na.rm=TRUE)
crop <- crop[with(crop,order(-CROPDMGVAL)),]
crop <- head(crop,10)
print(crop)## EVTYPE CROPDMGVAL
## 95 DROUGHT 13972566000
## 170 FLOOD 5661968450
## 590 RIVER FLOOD 5029459000
## 427 ICE STORM 5022113500
## 244 HAIL 3025954473
## 402 HURRICANE 2741910000
## 411 HURRICANE/TYPHOON 2607872800
## 153 FLASH FLOOD 1421317100
## 140 EXTREME COLD 1292973000
## 212 FROST/FREEZE 1094086000par(mfrow=c(1,2),mar=c(11,3,3,2))
barplot(prop$PROPDMGVAL/(10^9),names.arg=prop$EVTYPE,las=2,col="gold",ylab="Prop.damage(billions)",main="Top10 Prop.Damages")
barplot(crop$CROPDMGVAL/(10^9),names.arg=crop$EVTYPE,las=2,col="gold",ylab="Crop damage(billions)",main="Top10 Crop.Damages")