Exploring the NOAA’s storm dataset
Shikhar
27/07/2020
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.
Sypnosis.
The Goal of this Exploration of NOAA’s storm Dataset is to analyze and find the answers of two basic questions :
1.Across the United States, which types of events are most harmful with respect to population health?
2.Across the United States, which types of events have the greatest economic consequences?
1.Addressing the problem finding which type of events are most harmful for Public Health
Checking and Processing of the data
sessionInfo()## R version 4.0.0 (2020-04-24)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18363)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_India.1252 LC_CTYPE=English_India.1252
## [3] LC_MONETARY=English_India.1252 LC_NUMERIC=C
## [5] LC_TIME=English_India.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] compiler_4.0.0 magrittr_1.5 tools_4.0.0 htmltools_0.4.0
## [5] yaml_2.2.1 Rcpp_1.0.4.6 stringi_1.4.6 rmarkdown_2.1
## [9] knitr_1.28 stringr_1.4.0 xfun_0.13 digest_0.6.25
## [13] rlang_0.4.6 evaluate_0.14rm(list=ls())
setwd("D:/R/Class/5Reproducible Research/Assessment2")
read <- read.csv("./repdata_data_StormData.csv.bz2")## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
## EOF within quoted stringnames(read)## [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"head(read)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE EVTYPE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL TORNADO
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL TORNADO
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL TORNADO
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL TORNADO
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL TORNADO
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL TORNADO
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1 0 0 NA
## 2 0 0 NA
## 3 0 0 NA
## 4 0 0 NA
## 5 0 0 NA
## 6 0 0 NA
## END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES INJURIES PROPDMG
## 1 0 14.0 100 3 0 0 15 25.0
## 2 0 2.0 150 2 0 0 0 2.5
## 3 0 0.1 123 2 0 0 2 25.0
## 4 0 0.0 100 2 0 0 2 2.5
## 5 0 0.0 150 2 0 0 2 2.5
## 6 0 1.5 177 2 0 0 6 2.5
## PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES LATITUDE LONGITUDE
## 1 K 0 3040 8812
## 2 K 0 3042 8755
## 3 K 0 3340 8742
## 4 K 0 3458 8626
## 5 K 0 3412 8642
## 6 K 0 3450 8748
## LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3051 8806 1
## 2 0 0 2
## 3 0 0 3
## 4 0 0 4
## 5 0 0 5
## 6 0 0 6summary(read)## STATE__ BGN_DATE BGN_TIME TIME_ZONE
## Min. : 1.00 Length:281945 Length:281945 Length:281945
## 1st Qu.:20.00 Class :character Class :character Class :character
## Median :31.00 Mode :character Mode :character Mode :character
## Mean :30.72
## 3rd Qu.:45.00
## Max. :78.00
##
## COUNTY COUNTYNAME STATE EVTYPE
## Min. : 0.0 Length:281945 Length:281945 Length:281945
## 1st Qu.: 33.0 Class :character Class :character Class :character
## Median : 81.0 Mode :character Mode :character Mode :character
## Mean :101.2
## 3rd Qu.:133.0
## Max. :840.0
##
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE
## Min. : 0.00 Length:281945 Length:281945 Length:281945
## 1st Qu.: 0.00 Class :character Class :character Class :character
## Median : 0.00 Mode :character Mode :character Mode :character
## Mean : 0.51
## 3rd Qu.: 0.00
## Max. :3749.00
##
## END_TIME COUNTY_END COUNTYENDN END_RANGE
## Length:281945 Min. :0 Mode:logical Min. : 0.0000
## Class :character 1st Qu.:0 NA's:281945 1st Qu.: 0.0000
## Mode :character Median :0 Median : 0.0000
## Mean :0 Mean : 0.2343
## 3rd Qu.:0 3rd Qu.: 0.0000
## Max. :0 Max. :925.0000
##
## END_AZI END_LOCATI LENGTH WIDTH
## Length:281945 Length:281945 Min. : 0.0000 Min. : 0.00
## Class :character Class :character 1st Qu.: 0.0000 1st Qu.: 0.00
## Mode :character Mode :character Median : 0.0000 Median : 0.00
## Mean : 0.5117 Mean : 13.72
## 3rd Qu.: 0.0000 3rd Qu.: 0.00
## Max. :2315.0000 Max. :3330.00
##
## F MAG FATALITIES INJURIES
## Min. :0.00 Min. : 0.00 Min. : 0.0000 Min. : 0.0000
## 1st Qu.:0.00 1st Qu.: 0.00 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median :1.00 Median : 0.00 Median : 0.0000 Median : 0.0000
## Mean :1.11 Mean : 50.95 Mean : 0.0247 Mean : 0.3027
## 3rd Qu.:2.00 3rd Qu.: 75.00 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :5.00 Max. :999.00 Max. :583.0000 Max. :1700.0000
## NA's :245086
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 Length:281945 Min. : 0.0000 Length:281945
## 1st Qu.: 0.00 Class :character 1st Qu.: 0.0000 Class :character
## Median : 0.00 Mode :character Median : 0.0000 Mode :character
## Mean : 13.05 Mean : 0.8725
## 3rd Qu.: 0.00 3rd Qu.: 0.0000
## Max. :900.00 Max. :950.0000
##
## WFO STATEOFFIC ZONENAMES LATITUDE
## Length:281945 Length:281945 Length:281945 Min. : 0
## Class :character Class :character Class :character 1st Qu.:2555
## Mode :character Mode :character Mode :character Median :3458
## Mean :2807
## 3rd Qu.:4000
## Max. :5273
##
## LONGITUDE LATITUDE_E LONGITUDE_ REMARKS
## Min. : 0 Min. : 0 Min. : 0 Length:281945
## 1st Qu.: 6959 1st Qu.: 0 1st Qu.: 0 Class :character
## Median : 8819 Median : 0 Median : 0 Mode :character
## Mean : 6893 Mean : 452 Mean : 1114
## 3rd Qu.: 9643 3rd Qu.: 0 3rd Qu.: 0
## Max. :15941 Max. :4902 Max. :15930
##
## REFNUM
## Min. : 1
## 1st Qu.: 70487
## Median :140973
## Mean :140973
## 3rd Qu.:211458
## Max. :283448
## NA's :1read$EVTYPE <- as.factor(read$EVTYPE)Loading Packages
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionCalculations and assigning values for Injuries and Fatalities Explicitly!
forfatalities <- aggregate(FATALITIES ~ EVTYPE,read,"sum")
forfatalities <- arrange(forfatalities,desc(forfatalities[,2]))
forfatalities$EVTYPE <- as.factor(forfatalities$EVTYPE)
ff <- head(forfatalities,8)
ff$EVTYPE <- as.factor(ff$EVTYPE)
forinjuries <- aggregate(INJURIES ~ EVTYPE,read,"sum")
forinjuries <- arrange(forinjuries,desc(forinjuries[,2]))
forinjuries$EVTYPE <- as.factor(forinjuries$EVTYPE)
fi <- head(forinjuries,8)
fi$EVTYPE <- as.factor(fi$EVTYPE)Bar Plot Representation for Fatalities and Injuries Repectively.
par(mfrow = c(1, 2), las = 3, cex = 0.7, cex.main = 1.4, cex.lab = 1.2)
barplot(names.arg = ff$EVTYPE , ff$FATALITIES,col= "maroon",main="FATALITIES")
barplot(names.arg = fi$EVTYPE , fi$INJURIES,col= "grey",main="INJURIES")
Finding the final Population/Human Damage the Events causes.
Results :
finalhumandamage <- aggregate(read$FATALITIES+read$INJURIES , by= list(read$EVTYPE),"sum")
finalhumandamage <- arrange(finalhumandamage,desc(finalhumandamage[,2]))
fhd <- head(finalhumandamage,8)
head(fhd)## Group.1 x
## 1 TORNADO 75580
## 2 TSTM WIND 3943
## 3 ICE STORM 1797
## 4 LIGHTNING 1637
## 5 HEAT 1578
## 6 THUNDERSTORM WINDS 972par(mfrow = c(1, 1), las = 3, cex = 0.7, cex.main = 1.4, cex.lab = 1.2)
barplot(names.arg = fhd$Group.1,fhd$x,col="darkgreen",main="Final Most Human Damage Causing EVTYPE")
## Thus, the final graph , where we have added the Injuries and Fatalities suggests that the most number of Damages are done by TORNADOs.
2.Addressing the problem finding greatest economic consequences.
Damage done to the Properties and Crops.
forproperties <- aggregate(read$PROPDMG,by=list(read$EVTYPE),"sum")
forproperties <- arrange(forproperties,desc(forproperties[,2]))
forproperties$Group.1 <- as.factor(forproperties$Group.1)
fp <- head(forproperties,8)
forcrops <- aggregate(read$CROPDMG,by=list(read$EVTYPE),"sum")
forcrops <- arrange(forcrops,desc(forcrops[,2]))
forcrops$Group.1 <- as.factor(forcrops$Group.1)
fc <- head(forcrops,8)Bar Plot Representation for Properties and Crops Repectively.
par(mfrow = c(1, 2), las = 3, cex = 0.7, cex.main = 1.4, cex.lab = 1.2)
barplot(names.arg= fp$Group.1,fp$x,col="darkblue",main="PROPERTY DAMAGE")
barplot(names.arg= fc$Group.1,fc$x,col="orange",main="CROPS DAMAGE")
Final damage done to the Crops and Properties and finding the Event causing most damage.
Results :
finaldamage <- aggregate(read$PROPDMG+read$CROPDMG,by=list(read$EVTYPE),"sum")
finaldamage <- arrange(finaldamage,desc(finaldamage[,2]))
fd <- head(finaldamage,8)
par(mfrow = c(1, 1), las = 3, cex = 0.7, cex.main = 1.4, cex.lab = 1.2)
barplot(names.arg=fd$Group.1,fd$x,col="brown",main="Final most Economic Damage causing EVTYPE")