Exploring U.S. National Oceanic and Atmospheric Administration’s (NOAA) Storm Database
Dhruvin Shah
5/22/2020
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.
Goal
The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events.
Across the United States, which types of events are most harmful with respect to population health?
Across the United States, which types of events have the greatest economic consequences?
Synopsis
This report invloves the analysis of NOAA Storm Database which includes details about occurance of Fatal events like Natutal Calamaties. It depicts the top 10 Event causing most Fatalities and Injuries as well as CropDMG and PropDMG.
Data Description
Downloading Data and Data Information File
if(!file.exists("StormData.csv.bz2")) {
download.file(url = "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2",destfile = "StormData.csv.bz2")
}
if(!file.exists("StormDataDocumentation.pdf")) {
download.file(url = "https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf", destfile = "StormDataDocumentation.pdf")
}Information About Session:
sessionInfo()## R version 3.6.1 (2019-07-05)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] compiler_3.6.1 magrittr_1.5 tools_3.6.1 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_0.14Reading the Data File:
library(utils)
require(utils)
data_raw <- read.csv("StormData.csv.bz2")library(base)
require(base)
dim(data_raw)## [1] 902297 37Structure of Dataset:
str(data_raw)## '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 ""," 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 "","-","?","+",..: 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","%SD",..: 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 ...Glimpse of Dataset:
head(data_raw, n = 10)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL
## 7 1 11/16/1951 0:00:00 0100 CST 9 BLOUNT AL
## 8 1 1/22/1952 0:00:00 0900 CST 123 TALLAPOOSA AL
## 9 1 2/13/1952 0:00:00 2000 CST 125 TUSCALOOSA AL
## 10 1 2/13/1952 0:00:00 2000 CST 57 FAYETTE AL
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1 TORNADO 0 0 NA
## 2 TORNADO 0 0 NA
## 3 TORNADO 0 0 NA
## 4 TORNADO 0 0 NA
## 5 TORNADO 0 0 NA
## 6 TORNADO 0 0 NA
## 7 TORNADO 0 0 NA
## 8 TORNADO 0 0 NA
## 9 TORNADO 0 0 NA
## 10 TORNADO 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
## 7 0 1.5 33 2 0 0 1 2.5
## 8 0 0.0 33 1 0 0 0 2.5
## 9 0 3.3 100 3 0 1 14 25.0
## 10 0 2.3 100 3 0 0 0 25.0
## 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
## 7 K 0 3405 8631
## 8 K 0 3255 8558
## 9 K 0 3334 8740
## 10 K 0 3336 8738
## 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 6
## 7 0 0 7
## 8 0 0 8
## 9 3336 8738 9
## 10 3337 8737 10Column Names of Dataset:
names(data_raw)## [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"Summary of Dataset:
summary(data_raw)## STATE__ BGN_DATE BGN_TIME
## Min. : 1.0 5/25/2011 0:00:00: 1202 12:00:00 AM: 10163
## 1st Qu.:19.0 4/27/2011 0:00:00: 1193 06:00:00 PM: 7350
## Median :30.0 6/9/2011 0:00:00 : 1030 04:00:00 PM: 7261
## Mean :31.2 5/30/2004 0:00:00: 1016 05:00:00 PM: 6891
## 3rd Qu.:45.0 4/4/2011 0:00:00 : 1009 12:00:00 PM: 6703
## Max. :95.0 4/2/2006 0:00:00 : 981 03:00:00 PM: 6700
## (Other) :895866 (Other) :857229
## TIME_ZONE COUNTY COUNTYNAME STATE
## CST :547493 Min. : 0.0 JEFFERSON : 7840 TX : 83728
## EST :245558 1st Qu.: 31.0 WASHINGTON: 7603 KS : 53440
## MST : 68390 Median : 75.0 JACKSON : 6660 OK : 46802
## PST : 28302 Mean :100.6 FRANKLIN : 6256 MO : 35648
## AST : 6360 3rd Qu.:131.0 LINCOLN : 5937 IA : 31069
## HST : 2563 Max. :873.0 MADISON : 5632 NE : 30271
## (Other): 3631 (Other) :862369 (Other):621339
## EVTYPE BGN_RANGE BGN_AZI
## HAIL :288661 Min. : 0.000 :547332
## TSTM WIND :219940 1st Qu.: 0.000 N : 86752
## THUNDERSTORM WIND: 82563 Median : 0.000 W : 38446
## TORNADO : 60652 Mean : 1.484 S : 37558
## FLASH FLOOD : 54277 3rd Qu.: 1.000 E : 33178
## FLOOD : 25326 Max. :3749.000 NW : 24041
## (Other) :170878 (Other):134990
## BGN_LOCATI END_DATE END_TIME
## :287743 :243411 :238978
## COUNTYWIDE : 19680 4/27/2011 0:00:00: 1214 06:00:00 PM: 9802
## Countywide : 993 5/25/2011 0:00:00: 1196 05:00:00 PM: 8314
## SPRINGFIELD : 843 6/9/2011 0:00:00 : 1021 04:00:00 PM: 8104
## SOUTH PORTION: 810 4/4/2011 0:00:00 : 1007 12:00:00 PM: 7483
## NORTH PORTION: 784 5/30/2004 0:00:00: 998 11:59:00 PM: 7184
## (Other) :591444 (Other) :653450 (Other) :622432
## COUNTY_END COUNTYENDN END_RANGE END_AZI
## Min. :0 Mode:logical Min. : 0.0000 :724837
## 1st Qu.:0 NA's:902297 1st Qu.: 0.0000 N : 28082
## Median :0 Median : 0.0000 S : 22510
## Mean :0 Mean : 0.9862 W : 20119
## 3rd Qu.:0 3rd Qu.: 0.0000 E : 20047
## Max. :0 Max. :925.0000 NE : 14606
## (Other): 72096
## END_LOCATI LENGTH WIDTH
## :499225 Min. : 0.0000 Min. : 0.000
## COUNTYWIDE : 19731 1st Qu.: 0.0000 1st Qu.: 0.000
## SOUTH PORTION : 833 Median : 0.0000 Median : 0.000
## NORTH PORTION : 780 Mean : 0.2301 Mean : 7.503
## CENTRAL PORTION: 617 3rd Qu.: 0.0000 3rd Qu.: 0.000
## SPRINGFIELD : 575 Max. :2315.0000 Max. :4400.000
## (Other) :380536
## 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 :465934 Min. : 0.000 :618413
## 1st Qu.: 0.00 K :424665 1st Qu.: 0.000 K :281832
## Median : 0.00 M : 11330 Median : 0.000 M : 1994
## Mean : 12.06 0 : 216 Mean : 1.527 k : 21
## 3rd Qu.: 0.50 B : 40 3rd Qu.: 0.000 0 : 19
## Max. :5000.00 5 : 28 Max. :990.000 B : 9
## (Other): 84 (Other): 9
## WFO STATEOFFIC
## :142069 :248769
## OUN : 17393 TEXAS, North : 12193
## JAN : 13889 ARKANSAS, Central and North Central: 11738
## LWX : 13174 IOWA, Central : 11345
## PHI : 12551 KANSAS, Southwest : 11212
## TSA : 12483 GEORGIA, North and Central : 11120
## (Other):690738 (Other) :595920
## ZONENAMES
## :594029
## :205988
## GREATER RENO / CARSON CITY / M - GREATER RENO / CARSON CITY / M : 639
## GREATER LAKE TAHOE AREA - GREATER LAKE TAHOE AREA : 592
## JEFFERSON - JEFFERSON : 303
## MADISON - MADISON : 302
## (Other) :100444
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_
## Min. : 0 Min. :-14451 Min. : 0 Min. :-14455
## 1st Qu.:2802 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0
## Median :3540 Median : 8707 Median : 0 Median : 0
## Mean :2875 Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.:4019 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. :9706 Max. : 17124 Max. :9706 Max. :106220
## NA's :47 NA's :40
## REMARKS REFNUM
## :287433 Min. : 1
## : 24013 1st Qu.:225575
## Trees down.\n : 1110 Median :451149
## Several trees were blown down.\n : 568 Mean :451149
## Trees were downed.\n : 446 3rd Qu.:676723
## Large trees and power lines were blown down.\n: 432 Max. :902297
## (Other) :588295Data Processing
Checking for missing values:
data <- data_raw
any(is.na(data))## [1] TRUENumber of Missing Values:
sum(is.na(data))## [1] 1745947Missing values in each column:
sapply(data, FUN = function(x) (sum(is.na(x))))## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 0 0 0 0 0 0 0
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 0 0 0 0 0 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F
## 902297 0 0 0 0 0 843563
## MAG FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 0 0 0 0 0 0 0
## WFO STATEOFFIC ZONENAMES LATITUDE LONGITUDE LATITUDE_E LONGITUDE_
## 0 0 0 47 0 40 0
## REMARKS REFNUM
## 0 0Percentage of Missing Values in each column:
sapply(data, FUN = function(x) round(mean(is.na(x))*100, 2))## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F
## 100.00 0.00 0.00 0.00 0.00 0.00 93.49
## MAG FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## WFO STATEOFFIC ZONENAMES LATITUDE LONGITUDE LATITUDE_E LONGITUDE_
## 0.00 0.00 0.00 0.01 0.00 0.00 0.00
## REMARKS REFNUM
## 0.00 0.00Dropping columns with more than 80% Null Values:
data <- data[, which(colMeans(!is.na(data)) > 0.8)]
dim(data)## [1] 902297 351) Data to determine most Harmful Events with respect to Population
Creating Data Frames for Fatalities and Injuries
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, unionrequire(dplyr)
FatalitiesData <- data %>%
group_by(EVTYPE) %>%
summarise(COUNT = sum(FATALITIES)) %>%
arrange(desc(COUNT))
names(FatalitiesData) <- c("Event", "Count")
InjuriesData <- data %>%
group_by(EVTYPE) %>%
summarise(COUNT = sum(INJURIES)) %>%
arrange(desc(COUNT))
names(InjuriesData) <- c("Event", "Count")Selecting Top 10 Event Type with maximum Injuries and Fatalities:
FatalitiesData <- as.data.frame(top_n(FatalitiesData, 10))## Selecting by CountFatalitiesData$Type <- "Fatalities"
InjuriesData <- as.data.frame(top_n(InjuriesData, 10))## Selecting by CountInjuriesData$Type <- "Injuries"Merging Fatalities and Injuries DataFrame
data_1 <- rbind(FatalitiesData, InjuriesData)
arrange(data_1, desc(Count))## Event Count Type
## 1 TORNADO 91346 Injuries
## 2 TSTM WIND 6957 Injuries
## 3 FLOOD 6789 Injuries
## 4 EXCESSIVE HEAT 6525 Injuries
## 5 TORNADO 5633 Fatalities
## 6 LIGHTNING 5230 Injuries
## 7 HEAT 2100 Injuries
## 8 ICE STORM 1975 Injuries
## 9 EXCESSIVE HEAT 1903 Fatalities
## 10 FLASH FLOOD 1777 Injuries
## 11 THUNDERSTORM WIND 1488 Injuries
## 12 HAIL 1361 Injuries
## 13 FLASH FLOOD 978 Fatalities
## 14 HEAT 937 Fatalities
## 15 LIGHTNING 816 Fatalities
## 16 TSTM WIND 504 Fatalities
## 17 FLOOD 470 Fatalities
## 18 RIP CURRENT 368 Fatalities
## 19 HIGH WIND 248 Fatalities
## 20 AVALANCHE 224 Fatalities2) Data to determine most Harmful Event with respect to Economy
Creating Data Frames for CropDMP and PropDMP
library(dplyr)
require(dplyr)
CropData <- data %>%
group_by(EVTYPE) %>%
summarise(COUNT = sum(CROPDMG)/1000000) %>%
arrange(desc(COUNT))
names(CropData) <- c("Event", "Damage")
PropData <- data %>%
group_by(EVTYPE) %>%
summarise(COUNT = sum(PROPDMG)/1000000) %>%
arrange(desc(COUNT))
names(PropData) <- c("Event", "Damage")Selecting Top 10 Event Type with maximum Damage CropDMP and PropDMP:
CropData <- as.data.frame(top_n(CropData, 10))## Selecting by DamageCropData$Type <- "CropDMG"
PropData <- as.data.frame(top_n(PropData, 10))## Selecting by DamagePropData$Type <- "PropDMG"Merging Fatalities and Injuries DataFrame
data_2 <- rbind(CropData, PropData)
data_2$Damage <- round(data_2$Damage,2)
arrange(data_2, desc(Damage))## Event Damage Type
## 1 TORNADO 3.21 PropDMG
## 2 FLASH FLOOD 1.42 PropDMG
## 3 TSTM WIND 1.34 PropDMG
## 4 FLOOD 0.90 PropDMG
## 5 THUNDERSTORM WIND 0.88 PropDMG
## 6 HAIL 0.69 PropDMG
## 7 LIGHTNING 0.60 PropDMG
## 8 HAIL 0.58 CropDMG
## 9 THUNDERSTORM WINDS 0.45 PropDMG
## 10 HIGH WIND 0.32 PropDMG
## 11 FLASH FLOOD 0.18 CropDMG
## 12 FLOOD 0.17 CropDMG
## 13 WINTER STORM 0.13 PropDMG
## 14 TSTM WIND 0.11 CropDMG
## 15 TORNADO 0.10 CropDMG
## 16 THUNDERSTORM WIND 0.07 CropDMG
## 17 DROUGHT 0.03 CropDMG
## 18 THUNDERSTORM WINDS 0.02 CropDMG
## 19 HIGH WIND 0.02 CropDMG
## 20 HEAVY RAIN 0.01 CropDMGResults
- The Types of Events that are Most Harmful with respect to Population Health:
library(ggplot2)
ggplot(data = data_1, aes(x = Event, y = Count, fill = Type)) +
geom_bar(stat = "identity", position = "dodge" ) +
labs(x = "Event", y = "Count") +
labs(title = "Top 10 harmful events by type of harmful") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0)) +
scale_fill_manual(values=c("#6b5b95", "#feb236"))
- The Types of Events that are Most Harmful with respect to Economic Consequences:
library(ggplot2)
ggplot(data = data_2, aes(x = Event, y = Damage, fill = Type)) +
geom_bar(stat = "identity", position = "dodge" ) +
labs(x = "Event", y = "Damage (Million USD)") +
labs(title = "Top 10 Economic Consequnces by Event") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0)) +
scale_fill_manual(values=c("#379683", "#4056A1"))
Conclusion
- From Analysis we can conclude that most destruction was occurred during Tornado followed by Flash Floods and TSTM Wind.
- Tornado cause was resposible for damage of approx 3.21M USD and claming 91000+ injuries and 5500+ fatalities which is more than doubled the damage caused by Flash Floods i.e. 1.42M USD.