Title - Reproducible Research - Assignment 2
Synopsis
The purpose of this analyses is to identify the weather events that have the most destructive impact on human life and economy in the United States. The data for the study are obtained from NOAA Storm Database, US and comprised of 37 Variables, 902297 cases. The data was analysed using the R Studio package, and results are discussed through use of bar graph. The analyses suggests that the worst events include tornado, excessive heat, flash flood, heat, lightening, TSTM wind, flood, thudestorm wind, and hail. These worst event factos varied by their impact on human life and economic impact.
Data Processing
The specific R-code used for data processing is presented below. In summary, the download.file, read.table and read.csv functions were applied to get the data. Also, the dplyr package was used to select the variables of interest, and to filter the records. For two variables namely CROPDMGEEXP and PROPDMGEEXP, the data transformation techniques were used where character data was transformed to numeric data, and the string values were replaced with their numeric codes given in codebook. For these two variables, the missing values were replaced with the mean value of the non missing data. However, these two variables are not analysed given the assignment limitation of three graphs.
Results
QUESTION 1: Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Fatalities: The worst five events are TORNADO, EXCESSIVE HEAT, FLASH FLOOD, HEAT and LIGHTENING.
Injuries: The worst five events are TORNADO, TSTM WIND, FLOOD, EXCESSIVE HEAT, LIGHTENING
QUESTION 2: Across the United States, which types of events have the greatest economic consequences?
Property Damage: The worst five events are TORNADO, FLASH FLOOD, TSTM WIND, FLOOD, THUNDERSTORM WIND
Crop Damage: The worst five events are HAIL, FLASH FLOOD, FLOOD, TSTM WIND, TORNADO
Data Processing Code
fileUrl <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(fileUrl, destfile = "C:/Users/rvai007/Documents/repdata%2Fdata%2FStormData.csv.bz2")
raw_dataset <- read.csv("C:/Users/rvai007/Documents/repdata%2Fdata%2FStormData.csv/repdata%2Fdata%2FStormData.csv", sep = ",", header = TRUE, stringsAsFactors=FALSE)Understanding Data Structure
# Understanding the Structure of the Raw Dataset (37 Variables, 902297 cases)
class(raw_dataset)## [1] "data.frame"dim(raw_dataset)## [1] 902297 37str(raw_dataset)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : chr "4/18/1950 0:00:00" "4/18/1950 0:00:00" "2/20/1951 0:00:00" "6/8/1951 0:00:00" ...
## $ BGN_TIME : chr "0130" "0145" "1600" "0900" ...
## $ TIME_ZONE : chr "CST" "CST" "CST" "CST" ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: chr "MOBILE" "BALDWIN" "FAYETTE" "MADISON" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : chr "" "" "" "" ...
## $ BGN_LOCATI: chr "" "" "" "" ...
## $ END_DATE : chr "" "" "" "" ...
## $ END_TIME : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ END_LOCATI: chr "" "" "" "" ...
## $ 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: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...
## $ WFO : chr "" "" "" "" ...
## $ STATEOFFIC: chr "" "" "" "" ...
## $ ZONENAMES : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...Selecting required variables (Measures on: Event Type, Loss/ damage to Human Life, Economic Repurcussions)
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, unionrequired_data <- raw_dataset %>% select(EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP)
str(required_data)## 'data.frame': 902297 obs. of 7 variables:
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ 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: chr "K" "K" "K" "K" ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: chr "" "" "" "" ...Exploratory Analysis (Initial Level - to explore the variables of interest)
Mean cannot be generated for the variables $PROPDMGEXP, CROPDMGEXP as these are chr. Will explore these variables further.
mean(required_data$PROPDMG)## [1] 12.0631mean(required_data$CROPDMG)## [1] 1.527022mean(required_data$FATALITIES)## [1] 0.01678494mean(required_data$INJURIES)## [1] 0.1557447mean(required_data$INJURIES)## [1] 0.1557447mean(required_data$PROPDMGEXP)## Warning in mean.default(required_data$PROPDMGEXP): argument is not numeric
## or logical: returning NA## [1] NAmean(required_data$CROPDMGEXP)## Warning in mean.default(required_data$CROPDMGEXP): argument is not numeric
## or logical: returning NA## [1] NAMean cannot be generated for the variables $PROPDMGEXP, CROPDMGEXP as these are chr. Will explore these variables further.
copy_required_data <- required_data
unique(copy_required_data$CROPDMGEXP)## [1] "" "M" "K" "m" "B" "?" "0" "k" "2"Finding the unique values of PROPDMGEXP i.e. [1] “K” “M” “” “B” “m” “+” “0” “5” “6” “?” “4” “2” “3” “h” “7” “H” “-” “1” “8” The codebook specifies the values for K, M, B (as thousands, millions and Billions, but does not specifies the values for 1, 2, 4, etc.). These two variables are not suitable for analysis.
Data Transformations - for CROPDMGEXP
The instruction manual mentions that Alphabetical characters used to signify magnitude include “K” for thousands, “M” for millions, and “B” for billions. For CROPDMGEXP, I will replace these values with 1000, 100000, 1000000000 respectively. Since other numeric values are not specified, they remain as it is. Finding the unique values of CROPDMGEXP i.e. [1] “” “M” “K” “m” “B” “?” “0” “k” “2”
copy_required_data[copy_required_data$CROPDMGEXP == "K",]$CROPDMGEXP = 1000
copy_required_data[copy_required_data$CROPDMGEXP == "m",]$CROPDMGEXP = 100000
copy_required_data[copy_required_data$CROPDMGEXP == "M",]$CROPDMGEXP = 100000
copy_required_data[copy_required_data$CROPDMGEXP == "B",]$CROPDMGEXP = 1000000000
copy_required_data[copy_required_data$CROPDMGEXP == "k",]$CROPDMGEXP = 1000
unique(copy_required_data$CROPDMGEXP)## [1] "" "1e+05" "1000" "1e+09" "?" "0" "2"[1] “” “1e+05” “1000” “1e+09” “?” “0” “2”
also there are blank values “”. These will need to be replaced. I will replace them with the non missing average. Replacing the blanks
copy_required_data$CROPDMGEXP[copy_required_data$CROPDMGEXP==""] <- NA
unique(copy_required_data$CROPDMGEXP)## [1] NA "1e+05" "1000" "1e+09" "?" "0" "2"copy_required_data$CROPDMGEXP <- as.numeric(copy_required_data$CROPDMGEXP)## Warning: NAs introduced by coercionunique(copy_required_data$CROPDMGEXP)## [1] NA 1e+05 1e+03 1e+09 0e+00 2e+00mean(copy_required_data$CROPDMGEXP, na.rm=TRUE)## [1] 33399.51[1] 33399.51 (This is the non missing average)
copy_required_data$CROPDMGEXP[is.na(copy_required_data$CROPDMGEXP)] <- 33399.51Data Transformations - for PROPDMGEXP
The instruction manual mentions that Alphabetical characters used to signify magnitude include “K” for thousands, “M” for millions, and “B” for billions. “K” “M” “” “B” “m” “+” “0” “5” “6” “?” “4” “2” “3” “h” “7” “H” “-” “1” “8. Note that +, - will coerced as NA when this is converted to numeric.
copy_required_data[copy_required_data$PROPDMGEXP == "K",]$PROPDMGEXP = 1000
copy_required_data[copy_required_data$PROPDMGEXP == "M",]$PROPDMGEXP = 100000
copy_required_data[copy_required_data$PROPDMGEXP == "m",]$PROPDMGEXP = 100000
copy_required_data[copy_required_data$PROPDMGEXP == "B",]$PROPDMGEXP = 1000000000
copy_required_data[copy_required_data$PROPDMGEXP == "H",]$PROPDMGEXP = 100
copy_required_data[copy_required_data$PROPDMGEXP == "h",]$PROPDMGEXP = 100
copy_required_data$PROPDMGEXP[copy_required_data$PROPDMGEXP=="+"] <- NA
copy_required_data$PROPDMGEXP[copy_required_data$PROPDMGEXP=="-"] <- NA
copy_required_data$PROPDMGEXP[copy_required_data$PROPDMGEXP=="?"] <- NA
copy_required_data$PROPDMGEXP <- as.numeric(copy_required_data$PROPDMGEXP)
mean(copy_required_data$PROPDMGEXP, na.rm=TRUE)## [1] 95241.12[1] 95241.12 (This is the non missing average)
copy_required_data$PROPDMGEXP[is.na(copy_required_data$PROPDMGEXP)] <- 95241.12
str(copy_required_data)## 'data.frame': 902297 obs. of 7 variables:
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ 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: num 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: num 33400 33400 33400 33400 33400 ...All variables are numeric
Data Analysis: Question 1
QUESTION 1: Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Fatalities
fatalities_splittedby_event <- tapply(copy_required_data$FATALITIES, copy_required_data$EVTYPE, sum)
fatalities_splittedby_event <- as.data.frame.table(fatalities_splittedby_event)
class(fatalities_splittedby_event)## [1] "data.frame"dim(fatalities_splittedby_event)## [1] 985 2head(fatalities_splittedby_event)## Var1 Freq
## 1 HIGH SURF ADVISORY 0
## 2 COASTAL FLOOD 0
## 3 FLASH FLOOD 0
## 4 LIGHTNING 0
## 5 TSTM WIND 0
## 6 TSTM WIND (G45) 0fatalities_splittedby_event <- setNames(fatalities_splittedby_event, c("Event_Type","Frequency"))
library(dplyr)
graph_fatalities_splittedby_event <- fatalities_splittedby_event %>% filter(Frequency != 0)
arrange(graph_fatalities_splittedby_event, desc(graph_fatalities_splittedby_event$Frequency))## Event_Type Frequency
## 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 224
## 11 WINTER STORM 206
## 12 RIP CURRENTS 204
## 13 HEAT WAVE 172
## 14 EXTREME COLD 160
## 15 THUNDERSTORM WIND 133
## 16 HEAVY SNOW 127
## 17 EXTREME COLD/WIND CHILL 125
## 18 STRONG WIND 103
## 19 BLIZZARD 101
## 20 HIGH SURF 101
## 21 HEAVY RAIN 98
## 22 EXTREME HEAT 96
## 23 COLD/WIND CHILL 95
## 24 ICE STORM 89
## 25 WILDFIRE 75
## 26 HURRICANE/TYPHOON 64
## 27 THUNDERSTORM WINDS 64
## 28 FOG 62
## 29 HURRICANE 61
## 30 TROPICAL STORM 58
## 31 HEAVY SURF/HIGH SURF 42
## 32 LANDSLIDE 38
## 33 COLD 35
## 34 HIGH WINDS 35
## 35 TSUNAMI 33
## 36 WINTER WEATHER 33
## 37 UNSEASONABLY WARM AND DRY 29
## 38 URBAN/SML STREAM FLD 28
## 39 WINTER WEATHER/MIX 28
## 40 TORNADOES, TSTM WIND, HAIL 25
## 41 WIND 23
## 42 DUST STORM 22
## 43 FLASH FLOODING 19
## 44 DENSE FOG 18
## 45 EXTREME WINDCHILL 17
## 46 FLOOD/FLASH FLOOD 17
## 47 RECORD/EXCESSIVE HEAT 17
## 48 HAIL 15
## 49 COLD AND SNOW 14
## 50 FLASH FLOOD/FLOOD 14
## 51 MARINE STRONG WIND 14
## 52 STORM SURGE 13
## 53 WILD/FOREST FIRE 12
## 54 STORM SURGE/TIDE 11
## 55 UNSEASONABLY WARM 11
## 56 MARINE THUNDERSTORM WIND 10
## 57 WINTER STORMS 10
## 58 MARINE TSTM WIND 9
## 59 ROUGH SEAS 8
## 60 TROPICAL STORM GORDON 8
## 61 FREEZING RAIN 7
## 62 GLAZE 7
## 63 HEAVY SURF 7
## 64 LOW TEMPERATURE 7
## 65 MARINE MISHAP 7
## 66 STRONG WINDS 7
## 67 FLOODING 6
## 68 HURRICANE ERIN 6
## 69 ICE 6
## 70 COLD WEATHER 5
## 71 FLASH FLOODING/FLOOD 5
## 72 HEAT WAVES 5
## 73 HIGH SEAS 5
## 74 ICY ROADS 5
## 75 RIP CURRENTS/HEAVY SURF 5
## 76 SNOW 5
## 77 TSTM WIND/HAIL 5
## 78 GUSTY WINDS 4
## 79 HEAT WAVE DROUGHT 4
## 80 HIGH WIND/SEAS 4
## 81 Hypothermia/Exposure 4
## 82 Mudslide 4
## 83 RAIN/SNOW 4
## 84 ROUGH SURF 4
## 85 SNOW AND ICE 4
## 86 COASTAL FLOOD 3
## 87 COASTAL STORM 3
## 88 Cold 3
## 89 COLD WAVE 3
## 90 DRY MICROBURST 3
## 91 HEAVY SEAS 3
## 92 Heavy surf and wind 3
## 93 High Surf 3
## 94 HIGH WATER 3
## 95 HIGH WIND AND SEAS 3
## 96 HIGH WINDS/SNOW 3
## 97 HYPOTHERMIA/EXPOSURE 3
## 98 WATERSPOUT 3
## 99 WATERSPOUT/TORNADO 3
## 100 WILD FIRES 3
## 101 Coastal Flooding 2
## 102 Cold Temperature 2
## 103 DROUGHT/EXCESSIVE HEAT 2
## 104 DUST DEVIL 2
## 105 EXCESSIVE RAINFALL 2
## 106 Extreme Cold 2
## 107 FLASH FLOODS 2
## 108 FREEZING DRIZZLE 2
## 109 HEAVY SNOW AND HIGH WINDS 2
## 110 HURRICANE OPAL/HIGH WINDS 2
## 111 MIXED PRECIP 2
## 112 RECORD HEAT 2
## 113 RIVER FLOOD 2
## 114 RIVER FLOODING 2
## 115 SLEET 2
## 116 SNOW SQUALL 2
## 117 UNSEASONABLY COLD 2
## 118 AVALANCE 1
## 119 BLACK ICE 1
## 120 blowing snow 1
## 121 BLOWING SNOW 1
## 122 COASTAL FLOODING 1
## 123 COASTALSTORM 1
## 124 COLD/WINDS 1
## 125 DROWNING 1
## 126 Extended Cold 1
## 127 FALLING SNOW/ICE 1
## 128 FLOOD & HEAVY RAIN 1
## 129 FLOOD/RIVER FLOOD 1
## 130 FOG AND COLD TEMPERATURES 1
## 131 FREEZE 1
## 132 FREEZING RAIN/SNOW 1
## 133 Freezing Spray 1
## 134 FROST 1
## 135 GUSTY WIND 1
## 136 Heavy Surf 1
## 137 HIGH SWELLS 1
## 138 HIGH WAVES 1
## 139 HURRICANE FELIX 1
## 140 HURRICANE OPAL 1
## 141 HYPERTHERMIA/EXPOSURE 1
## 142 HYPOTHERMIA 1
## 143 ICE ON ROAD 1
## 144 LANDSLIDES 1
## 145 LIGHT SNOW 1
## 146 LIGHTNING. 1
## 147 Marine Accident 1
## 148 MARINE HIGH WIND 1
## 149 MINOR FLOODING 1
## 150 Mudslides 1
## 151 RAIN/WIND 1
## 152 RAPIDLY RISING WATER 1
## 153 RECORD COLD 1
## 154 Snow Squalls 1
## 155 SNOW/ BITTER COLD 1
## 156 Strong Winds 1
## 157 THUNDERSNOW 1
## 158 THUNDERSTORM 1
## 159 THUNDERSTORM WIND (G40) 1
## 160 THUNDERSTORM WIND G52 1
## 161 THUNDERTORM WINDS 1
## 162 TSTM WIND (G35) 1
## 163 URBAN AND SMALL STREAM FLOODIN 1
## 164 Whirlwind 1
## 165 WIND STORM 1
## 166 WINDS 1
## 167 WINTER STORM HIGH WINDS 1
## 168 WINTRY MIX 1graph_fatalities_splittedby_event_TOP20 <- graph_fatalities_splittedby_event %>% filter(Frequency >= 100)Clearly the Top 5 (i.e. worst 5) events are TORNADO, EXCESSIVE HEAT, FLASH FLOOD, HEAT and LIGHTENING. This is shown graphically below:
barplot (height = graph_fatalities_splittedby_event_TOP20$Frequency, names.arg = graph_fatalities_splittedby_event_TOP20$Event_Type, las = 2, cex.names = 0.7, col = rainbow (30, start=0, end=1))
Injuries (i.e. are patterns consistent across both injuries and fatalities)
str(copy_required_data)## 'data.frame': 902297 obs. of 7 variables:
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ 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: num 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: num 33400 33400 33400 33400 33400 ...injuries_splittedby_event <- tapply(copy_required_data$INJURIES, copy_required_data$EVTYPE, sum)
injuries_splittedby_event <- as.data.frame.table(injuries_splittedby_event)
injuries_splittedby_event <- setNames(injuries_splittedby_event, c("Event_Type","Frequency"))
library(dplyr)
graph_injuries_splittedby_event <- injuries_splittedby_event %>% filter(Frequency != 0)
arrange(graph_injuries_splittedby_event, desc(graph_injuries_splittedby_event$Frequency))## Event_Type Frequency
## 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 1361
## 11 WINTER STORM 1321
## 12 HURRICANE/TYPHOON 1275
## 13 HIGH WIND 1137
## 14 HEAVY SNOW 1021
## 15 WILDFIRE 911
## 16 THUNDERSTORM WINDS 908
## 17 BLIZZARD 805
## 18 FOG 734
## 19 WILD/FOREST FIRE 545
## 20 DUST STORM 440
## 21 WINTER WEATHER 398
## 22 DENSE FOG 342
## 23 TROPICAL STORM 340
## 24 HEAT WAVE 309
## 25 HIGH WINDS 302
## 26 RIP CURRENTS 297
## 27 STRONG WIND 280
## 28 HEAVY RAIN 251
## 29 RIP CURRENT 232
## 30 EXTREME COLD 231
## 31 GLAZE 216
## 32 AVALANCHE 170
## 33 EXTREME HEAT 155
## 34 HIGH SURF 152
## 35 WILD FIRES 150
## 36 ICE 137
## 37 TSUNAMI 129
## 38 TSTM WIND/HAIL 95
## 39 WIND 86
## 40 URBAN/SML STREAM FLD 79
## 41 WINTRY MIX 77
## 42 WINTER WEATHER/MIX 72
## 43 Heat Wave 70
## 44 WINTER WEATHER MIX 68
## 45 LANDSLIDE 52
## 46 RECORD HEAT 50
## 47 COLD 48
## 48 HEAVY SURF/HIGH SURF 48
## 49 HURRICANE 46
## 50 TROPICAL STORM GORDON 43
## 51 DUST DEVIL 42
## 52 WATERSPOUT/TORNADO 42
## 53 HEAVY SURF 40
## 54 STORM SURGE 38
## 55 SNOW/HIGH WINDS 36
## 56 SNOW SQUALL 35
## 57 ICY ROADS 31
## 58 SNOW 29
## 59 WATERSPOUT 29
## 60 DRY MICROBURST 28
## 61 THUNDERSTORMW 27
## 62 MARINE THUNDERSTORM WIND 26
## 63 MIXED PRECIP 26
## 64 BLACK ICE 24
## 65 EXTREME COLD/WIND CHILL 24
## 66 FREEZING RAIN 23
## 67 MARINE STRONG WIND 22
## 68 EXCESSIVE RAINFALL 21
## 69 STRONG WINDS 21
## 70 HIGH WIND AND SEAS 20
## 71 UNSEASONABLY WARM 17
## 72 WINTER STORMS 17
## 73 TORNADO F2 16
## 74 FLOOD/FLASH FLOOD 15
## 75 FREEZING DRIZZLE 15
## 76 GLAZE/ICE STORM 15
## 77 HEAT WAVE DROUGHT 15
## 78 WINTER STORM HIGH WINDS 15
## 79 BLOWING SNOW 13
## 80 COLD/WIND CHILL 12
## 81 THUNDERSTORM 12
## 82 HEAVY SNOW/ICE 10
## 83 SMALL HAIL 10
## 84 THUNDERSTORM WINDS 10
## 85 FLASH FLOODING 8
## 86 GUSTY WINDS 8
## 87 HIGH SEAS 8
## 88 MARINE TSTM WIND 8
## 89 NON-SEVERE WIND DAMAGE 7
## 90 HIGH WINDS/SNOW 6
## 91 COASTAL FLOODING/EROSION 5
## 92 EXTREME WINDCHILL 5
## 93 MARINE MISHAP 5
## 94 ROUGH SEAS 5
## 95 STORM SURGE/TIDE 5
## 96 TYPHOON 5
## 97 DROUGHT 4
## 98 HEAVY RAINS 4
## 99 High Surf 4
## 100 HIGH WINDS/COLD 4
## 101 OTHER 4
## 102 THUNDERSTORM WINDSS 4
## 103 Torrential Rainfall 4
## 104 FROST 3
## 105 FUNNEL CLOUD 3
## 106 TSTM WIND (G45) 3
## 107 BRUSH FIRE 2
## 108 COASTAL FLOOD 2
## 109 EXCESSIVE SNOW 2
## 110 FLOODING 2
## 111 Gusty winds 2
## 112 Heavy snow shower 2
## 113 HURRICANE-GENERATED SWELLS 2
## 114 Hurricane Edouard 2
## 115 ICE STORM/FLASH FLOOD 2
## 116 LIGHT SNOW 2
## 117 Marine Accident 2
## 118 Mudslide 2
## 119 RAIN/SNOW 2
## 120 RIVER FLOOD 2
## 121 ROGUE WAVE 2
## 122 Snow 2
## 123 TORNADO F3 2
## 124 WARM WEATHER 2
## 125 blowing snow 1
## 126 Coastal Storm 1
## 127 COASTAL STORM 1
## 128 DRY MIRCOBURST WINDS 1
## 129 Dust Devil 1
## 130 FALLING SNOW/ICE 1
## 131 FOG AND COLD TEMPERATURES 1
## 132 GUSTY WIND 1
## 133 Gusty Winds 1
## 134 HAZARDOUS SURF 1
## 135 HEAVY SNOW/BLIZZARD/AVALANCHE 1
## 136 HIGH 1
## 137 HIGH WIND 48 1
## 138 HIGH WIND/HEAVY SNOW 1
## 139 HURRICANE EMILY 1
## 140 HURRICANE ERIN 1
## 141 HURRICANE OPAL 1
## 142 ICE ROADS 1
## 143 LANDSLIDES 1
## 144 LIGHTNING AND THUNDERSTORM WIN 1
## 145 LIGHTNING INJURY 1
## 146 MARINE HIGH WIND 1
## 147 NON TSTM WIND 1
## 148 River Flooding 1
## 149 ROUGH SURF 1
## 150 SNOW AND ICE 1
## 151 THUNDERSNOW 1
## 152 THUNDERSTORM WINDS 13 1
## 153 THUNDERSTORM WINDS/HAIL 1
## 154 THUNDERSTORMS WINDS 1
## 155 TIDAL FLOODING 1
## 156 TSTM WIND (G40) 1
## 157 WATERSPOUT TORNADO 1
## 158 WINDS 1Clearly, the top 5 (i.e. worst 5) events are TORNADO, TSTM WIND, FLOOD, EXCESSIVE HEAT, LIGHTENING
library(dplyr)
graph_injuries_splittedby_event_TOP20 <- graph_injuries_splittedby_event %>% filter(Frequency >= 440)
barplot (height = graph_injuries_splittedby_event_TOP20$Frequency, names.arg = graph_injuries_splittedby_event_TOP20$Event_Type, las = 2, cex.names = 0.7, col = rainbow (30, start=0, end=1))
Lets find out the events that are present in both FATALITIES and INJURIES
common <- c("TORNADO", "EXCESSIVE HEAT", "FLASH FLOOD", "HEAT", "LIGHTENING", "TORNADO", "TSTM WIND", "FLOOD", "EXCESSIVE HEAT", "LIGHTENING")
unique(common)## [1] "TORNADO" "EXCESSIVE HEAT" "FLASH FLOOD" "HEAT"
## [5] "LIGHTENING" "TSTM WIND" "FLOOD"ANSWER: Q1 [1] “TORNADO” “EXCESSIVE HEAT” “FLASH FLOOD” “HEAT” “LIGHTENING” “TSTM WIND” “FLOOD”
Data Analysis: Question 2
Across the United States, which types of events have the greatest economic consequences? Property Damage
propdmge_splittedby_event <- tapply(copy_required_data$PROPDMG, copy_required_data$EVTYPE, sum)
propdmge_splittedby_event <- as.data.frame.table(propdmge_splittedby_event)
propdmge_splittedby_event <- setNames(propdmge_splittedby_event, c("Event_Type","Frequency"))
library(dplyr)
graph_propdmge_splittedby_event <- propdmge_splittedby_event %>% filter(Frequency != 0)
arrange(graph_propdmge_splittedby_event, desc(graph_propdmge_splittedby_event$Frequency))## Event_Type Frequency
## 1 TORNADO 3212258.16
## 2 FLASH FLOOD 1420124.59
## 3 TSTM WIND 1335965.61
## 4 FLOOD 899938.48
## 5 THUNDERSTORM WIND 876844.17
## 6 HAIL 688693.38
## 7 LIGHTNING 603351.78
## 8 THUNDERSTORM WINDS 446293.18
## 9 HIGH WIND 324731.56
## 10 WINTER STORM 132720.59
## 11 HEAVY SNOW 122251.99
## 12 WILDFIRE 84459.34
## 13 ICE STORM 66000.67
## 14 STRONG WIND 62993.81
## 15 HIGH WINDS 55625.00
## 16 HEAVY RAIN 50842.14
## 17 TROPICAL STORM 48423.68
## 18 WILD/FOREST FIRE 39344.95
## 19 FLASH FLOODING 28497.15
## 20 URBAN/SML STREAM FLD 26051.94
## 21 BLIZZARD 25318.48
## 22 STORM SURGE 19393.49
## 23 FLOOD/FLASH FLOOD 19153.25
## 24 LANDSLIDE 18961.94
## 25 HURRICANE 15513.68
## 26 LAKE-EFFECT SNOW 14141.00
## 27 RIVER FLOOD 13855.70
## 28 URBAN FLOOD 13293.00
## 29 COASTAL FLOOD 12610.84
## 30 WINTER WEATHER 11974.90
## 31 WATERSPOUT 9353.70
## 32 FOG 8849.81
## 33 TSTM WIND/HAIL 8371.50
## 34 DENSE FOG 8225.45
## 35 ICE 7660.00
## 36 EXTREME COLD 7657.54
## 37 STORM SURGE/TIDE 6777.05
## 38 HURRICANE/TYPHOON 5839.37
## 39 URBAN FLOODING 5605.60
## 40 FLOODING 5463.90
## 41 DUST STORM 5049.50
## 42 WINTER WEATHER/MIX 4873.50
## 43 DROUGHT 4099.05
## 44 COASTAL FLOODING 3613.65
## 45 HIGH SURF 3041.62
## 46 SNOW 3004.32
## 47 FREEZING RAIN 2916.70
## 48 EXTREME COLD/WIND CHILL 2654.00
## 49 WIND 2650.54
## 50 MARINE TSTM WIND 2424.00
## 51 HEAVY RAINS 2295.05
## 52 COLD/WIND CHILL 1990.00
## 53 EXCESSIVE SNOW 1935.00
## 54 LIGHT SNOW 1918.00
## 55 THUNDERSTORM WINDSS 1885.60
## 56 FLASH FLOOD/FLOOD 1826.90
## 57 FLASH FLOODING/FLOOD 1750.00
## 58 DRY MICROBURST 1737.60
## 59 FLASH FLOODS 1635.60
## 60 AVALANCHE 1623.90
## 61 EXCESSIVE HEAT 1460.00
## 62 TYPHOON 1429.40
## 63 HEAVY SURF 1390.00
## 64 HEAVY SURF/HIGH SURF 1378.50
## 65 HEAT WAVE 1269.25
## 66 RIVER AND STREAM FLOOD 1200.00
## 67 WATERSPOUT/TORNADO 1160.00
## 68 STRONG WINDS 1115.99
## 69 FLOODS 1005.00
## 70 RECORD SNOW 1000.00
## 71 SNOW/COLD 1000.00
## 72 SEICHE 980.00
## 73 FROST/FREEZE 968.52
## 74 Gusty Winds 938.00
## 75 ASTRONOMICAL HIGH TIDE 933.50
## 76 Coastal Flood 926.00
## 77 HURRICANE OPAL 921.10
## 78 THUNDERSTORM 920.55
## 79 TSUNAMI 905.30
## 80 TORNADO F1 871.50
## 81 HEAVY RAINS/FLOODING 864.00
## 82 Coastal Flooding 860.47
## 83 COASTAL EROSION 766.00
## 84 EXTREME WINDCHILL 755.00
## 85 TROPICAL DEPRESSION 738.00
## 86 TORNADO F3 725.00
## 87 WILD FIRES 724.00
## 88 HEAVY MIX 705.60
## 89 THUNDERSTORM WINDS HAIL 705.00
## 90 DUST DEVIL 700.33
## 91 HEAVY SNOW SQUALLS 695.00
## 92 SNOW SQUALL 680.00
## 93 HEAVY SNOW/ICE 655.00
## 94 HIGH WINDS/COLD 610.00
## 95 THUNDERTORM WINDS 606.00
## 96 TROPICAL STORM JERRY 604.00
## 97 TORNADO F2 601.00
## 98 MUD SLIDE 600.10
## 99 HEAVY RAIN AND FLOOD 600.00
## 100 WILDFIRES 600.00
## 101 Light Snow 590.00
## 102 Landslump 570.00
## 103 RECORD COLD 555.50
## 104 MIXED PRECIPITATION 555.00
## 105 ICE JAM FLOODING 521.00
## 106 THUNDERSTORMS WINDS 513.05
## 107 HIGH WATER 505.00
## 108 SEVERE THUNDERSTORMS 501.50
## 109 BLIZZARD/WINTER STORM 500.00
## 110 COASTAL SURGE 500.00
## 111 COLD 500.00
## 112 FLASH FLOOD/ 500.00
## 113 FLASH FLOODING/THUNDERSTORM WI 500.00
## 114 FLOOD/RIVER FLOOD 500.00
## 115 FROST\\FREEZE 500.00
## 116 HAIL/WINDS 500.00
## 117 HEAVY LAKE SNOW 500.00
## 118 HEAVY PRECIPITATION 500.00
## 119 HEAVY RAIN/SNOW 500.00
## 120 HEAVY SNOW/WINTER STORM 500.00
## 121 HIGH WIND/SEAS 500.00
## 122 HURRICANE FELIX 500.00
## 123 HURRICANE GORDON 500.00
## 124 SLEET/ICE STORM 500.00
## 125 SNOW AND ICE STORM 500.00
## 126 SNOW/HEAVY SNOW 500.00
## 127 TROPICAL STORM GORDON 500.00
## 128 VOLCANIC ASH 500.00
## 129 WINTER STORMS 500.00
## 130 LIGHT FREEZING RAIN 451.00
## 131 MICROBURST WINDS 450.00
## 132 MARINE THUNDERSTORM WIND 436.40
## 133 MARINE STRONG WIND 418.33
## 134 TROPICAL STORM DEAN 400.00
## 135 High Surf 380.00
## 136 SEVERE THUNDERSTORM 366.20
## 137 THUNDERSTORM WINDS/HAIL 365.00
## 138 GUSTY WIND 360.00
## 139 HURRICANE ERIN 358.00
## 140 GUSTY WINDS 343.00
## 141 ICY ROADS 341.20
## 142 ASTRONOMICAL LOW TIDE 320.00
## 143 GLAZE 310.60
## 144 SNOW FREEZING RAIN 306.00
## 145 RAIN 305.05
## 146 WIND STORM 300.00
## 147 HEAT 298.50
## 148 MARINE HIGH WIND 298.01
## 149 MUDSLIDE 276.00
## 150 River Flooding 270.99
## 151 FLASH FLOOD FROM ICE JAMS 250.00
## 152 HAILSTORM 241.00
## 153 Mixed Precipitation 235.00
## 154 TSTM WIND (G45) 221.50
## 155 WATERSPOUT- 210.50
## 156 FREEZE 205.00
## 157 HIGH SURF ADVISORY 200.00
## 158 HEAT WAVE DROUGHT 200.00
## 159 STORM FORCE WINDS 200.00
## 160 FUNNEL CLOUD 194.60
## 161 THUNDERSTORM WINDS LIGHTNING 190.50
## 162 FREEZING FOG 184.00
## 163 SNOW SQUALLS 180.50
## 164 LATE SEASON SNOW 180.00
## 165 SEVERE THUNDERSTORM WINDS 175.00
## 166 RIP CURRENTS 162.00
## 167 HAIL DAMAGE 150.00
## 168 HIGH TIDES 150.00
## 169 HIGH WINDS/SNOW 150.00
## 170 ROCK SLIDE 150.00
## 171 RIVER FLOODING 149.00
## 172 WINDS 146.50
## 173 HAIL 275 125.10
## 174 THUNDERSTORMW 125.00
## 175 TSTM WIND 108.00
## 176 LANDSLIDES 105.00
## 177 MAJOR FLOOD 105.00
## 178 GUSTNADO 102.05
## 179 Beach Erosion 100.00
## 180 DENSE SMOKE 100.00
## 181 Extended Cold 100.00
## 182 GROUND BLIZZARD 100.00
## 183 HEAVY SNOW/SQUALLS 100.00
## 184 ICE FLOES 100.00
## 185 SNOW AND HEAVY SNOW 100.00
## 186 SNOW/HIGH WINDS 100.00
## 187 SNOW/ICE STORM 100.00
## 188 SNOW/SLEET/FREEZING RAIN 100.00
## 189 Strong Winds 100.00
## 190 Glaze 90.00
## 191 HEAVY SNOW-SQUALLS 88.00
## 192 Light Snowfall 85.00
## 193 TSTM WIND AND LIGHTNING 80.00
## 194 WIND DAMAGE 77.00
## 195 TORNADO F0 76.20
## 196 FREEZING DRIZZLE 75.00
## 197 HURRICANE-GENERATED SWELLS 75.00
## 198 TUNDERSTORM WIND 75.00
## 199 SMALL HAIL 70.00
## 200 THUNDERSTORM WIND 60 MPH 70.00
## 201 Snow 65.00
## 202 MICROBURST 60.00
## 203 WINTER STORM HIGH WINDS 60.00
## 204 WINTER WEATHER MIX 60.00
## 205 TSTM WINDS 59.00
## 206 BRUSH FIRE 55.00
## 207 Freezing Drizzle 55.00
## 208 Snow Squalls 55.00
## 209 SNOWMELT FLOODING 55.00
## 210 Cold 54.00
## 211 SNOW/SLEET 51.30
## 212 URBAN/SMALL STREAM FLOOD 51.05
## 213 FLASH FLOOD 50.00
## 214 Coastal Storm 50.00
## 215 DUST STORM/HIGH WINDS 50.00
## 216 EXTREME WIND CHILL 50.00
## 217 FLASH FLOOD LANDSLIDES 50.00
## 218 FREEZING RAIN/SLEET 50.00
## 219 HEAVY SNOW/BLIZZARD 50.00
## 220 HEAVY SNOW/WIND 50.00
## 221 Heavy Surf 50.00
## 222 HEAVY SURF COASTAL FLOODING 50.00
## 223 HIGH WIND AND SEAS 50.00
## 224 HIGH WIND/BLIZZARD 50.00
## 225 HIGH WIND/HEAVY SNOW 50.00
## 226 HIGH WINDS/ 50.00
## 227 HURRICANE EMILY 50.00
## 228 Lake Effect Snow 50.00
## 229 Marine Accident 50.00
## 230 MUD SLIDES 50.00
## 231 MUDSLIDES 50.00
## 232 Other 50.00
## 233 RAINSTORM 50.00
## 234 SEVERE TURBULENCE 50.00
## 235 SNOW/ BITTER COLD 50.00
## 236 SNOW/BLOWING SNOW 50.00
## 237 THUDERSTORM WINDS 50.00
## 238 THUNDERSNOW 50.00
## 239 THUNDERSTORM WINDS 13 50.00
## 240 THUNDERSTORM WINDS53 50.00
## 241 TSTMW 50.00
## 242 LAKESHORE FLOOD 47.50
## 243 TSTM WIND (G40) 45.00
## 244 NON-TSTM WIND 40.00
## 245 THUNDERSTORM WINDSHAIL 40.00
## 246 WATERSPOUT TORNADO 40.00
## 247 Freezing Rain 35.00
## 248 WET MICROBURST 35.00
## 249 LAKE FLOOD 30.00
## 250 LIGHTNING WAUSEON 30.00
## 251 SNOW/ICE 30.00
## 252 Tstm Wind 30.00
## 253 TSTM WIND (G35) 30.00
## 254 TSTM WIND 55 30.00
## 255 LIGHTNING AND HEAVY RAIN 28.00
## 256 THUNDERSTORM WINDS 25.00
## 257 THUNDERSTORM WIND 98 MPH 25.00
## 258 THUNDERSTORM WINDS 63 MPH 25.00
## 259 HAIL 175 20.10
## 260 COASTAL FLOODING/EROSION 20.03
## 261 BLOWING DUST 20.00
## 262 BREAKUP FLOODING 20.00
## 263 GUSTY WIND/HAIL 20.00
## 264 Microburst 20.00
## 265 THUNDERSTORM WIND/ TREES 20.00
## 266 THUNDERSTORM WINDS/ FLOOD 20.00
## 267 TSTM WIND 65) 20.00
## 268 WILD/FOREST FIRES 20.00
## 269 Dust Devil 18.30
## 270 HIGH WIND (G40) 18.00
## 271 Strong Wind 18.00
## 272 gradient wind 17.00
## 273 LAKE EFFECT SNOW 17.00
## 274 Erosion/Cstl Flood 16.20
## 275 HIGH SEAS 15.50
## 276 THUNDERSTORMS WIND 15.50
## 277 blowing snow 15.00
## 278 COASTAL FLOODING/EROSION 15.00
## 279 Freezing drizzle 15.00
## 280 FROST 15.00
## 281 HEAVY SWELLS 15.00
## 282 WATERSPOUT-TORNADO 15.00
## 283 Gradient wind 14.00
## 284 Heavy Rain/High Surf 13.50
## 285 FLASH FLOOD - HEAVY RAIN 12.00
## 286 ICE ROADS 12.00
## 287 FLOOD & HEAVY RAIN 10.00
## 288 FLOOD/FLASH/FLOOD 10.00
## 289 GRASS FIRES 10.00
## 290 Heavy snow shower 10.00
## 291 HIGH WIND 48 10.00
## 292 LIGHTNING THUNDERSTORM WINDS 10.00
## 293 ROUGH SURF 10.00
## 294 Tidal Flooding 10.00
## 295 TSTM WIND 45 10.00
## 296 Wind 10.00
## 297 Wind Damage 10.00
## 298 WINTRY MIX 10.00
## 299 TSTM WIND (G45) 8.00
## 300 DAMAGING FREEZE 8.00
## 301 TSTM WIND (41) 8.00
## 302 HIGH WINDS HEAVY RAINS 7.50
## 303 LANDSPOUT 7.00
## 304 WHIRLWIND 7.00
## 305 GRADIENT WIND 6.00
## 306 OTHER 5.50
## 307 EXTREME HEAT 5.11
## 308 FREEZING RAIN/SNOW 5.07
## 309 ? 5.00
## 310 APACHE COUNTY 5.00
## 311 FLOOD FLASH 5.00
## 312 FLOOD/FLASHFLOOD 5.00
## 313 FOREST FIRES 5.00
## 314 HAIL 100 5.00
## 315 HEAVY RAIN/LIGHTNING 5.00
## 316 HEAVY RAIN/SMALL STREAM URBAN 5.00
## 317 HEAVY SNOW/BLIZZARD/AVALANCHE 5.00
## 318 HEAVY SNOW/FREEZING RAIN 5.00
## 319 HEAVY SNOWPACK 5.00
## 320 HIGH SWELLS 5.00
## 321 HIGH WINDS/COASTAL FLOOD 5.00
## 322 HIGH WINDS/HEAVY RAIN 5.00
## 323 ICE AND SNOW 5.00
## 324 ICE JAM 5.00
## 325 Light snow 5.00
## 326 LIGHTING 5.00
## 327 LIGHTNING FIRE 5.00
## 328 LIGHTNING/HEAVY RAIN 5.00
## 329 LIGNTNING 5.00
## 330 MINOR FLOODING 5.00
## 331 MUD SLIDES URBAN FLOODING 5.00
## 332 NON-SEVERE WIND DAMAGE 5.00
## 333 SNOW ACCUMULATION 5.00
## 334 SNOW/ ICE 5.00
## 335 THUNDEERSTORM WINDS 5.00
## 336 THUNDERSTORM HAIL 5.00
## 337 THUNDERSTORM WIND G55 5.00
## 338 THUNDERSTORM WIND/LIGHTNING 5.00
## 339 THUNDERSTORM WINDS AND 5.00
## 340 THUNDERSTORM WINDS. 5.00
## 341 THUNDERSTORM WINDS/FLOODING 5.00
## 342 THUNDERSTORM WINDS/FUNNEL CLOU 5.00
## 343 THUNDERSTORMS 5.00
## 344 THUNDERSTROM WIND 5.00
## 345 TROPICAL STORM ALBERTO 5.00
## 346 TSTM WIND (G45) 5.00
## 347 TSTM WIND DAMAGE 5.00
## 348 URBAN AND SMALL 5.00
## 349 URBAN/SMALL STREAM 5.00
## 350 Whirlwind 5.00
## 351 GLAZE ICE 4.80
## 352 MARINE HAIL 4.00
## 353 THUNDERSTORM WIND 65 MPH 4.00
## 354 THUNDERSTORM WIND 65MPH 4.00
## 355 THUNDERSTORM WIND TREES 4.00
## 356 ICE/STRONG WINDS 3.50
## 357 DAM BREAK 3.00
## 358 HIGH WINDS 3.00
## 359 TIDAL FLOODING 3.00
## 360 HEAVY RAIN/SEVERE WEATHER 2.50
## 361 Wintry Mix 2.50
## 362 DOWNBURST 2.00
## 363 FLASH FLOOD/ STREET 2.00
## 364 FLOODING/HEAVY RAIN 2.00
## 365 GUSTY WIND/HVY RAIN 2.00
## 366 Gusty wind/rain 2.00
## 367 THUNDERSTORM DAMAGE TO 2.00
## 368 THUNDERSTORM WIND G50 2.00
## 369 THUNDERSTORM WIND/AWNING 2.00
## 370 HEAVY SNOW AND STRONG WINDS 1.70
## 371 TORNADOES, TSTM WIND, HAIL 1.60
## 372 HEAVY SNOW/HIGH WINDS & FLOOD 1.50
## 373 URBAN FLOODS 1.50
## 374 RURAL FLOOD 1.20
## 375 SNOW/FREEZING RAIN 1.20
## 376 HIGH WIND DAMAGE 1.10
## 377 FLASH FLOOD/LANDSLIDE 1.00
## 378 FLOOD/FLASH 1.00
## 379 Frost/Freeze 1.00
## 380 HAIL 0.75 1.00
## 381 HAIL 75 1.00
## 382 Ice jam flood (minor 1.00
## 383 RIP CURRENT 1.00
## 384 THUNDERESTORM WINDS 1.00
## 385 THUNDERSTORM WIND/ TREE 1.00
## 386 THUNDERSTORM WINS 1.00
## 387 THUNERSTORM WINDS 1.00
## 388 TSTM WIND 40 1.00
## 389 TSTM WIND G45 1.00
## 390 WATERSPOUT/ TORNADO 1.00
## 391 WIND AND WAVE 1.00
## 392 DUST DEVIL WATERSPOUT 0.50
## 393 HAIL/WIND 0.50
## 394 HEAVY SHOWER 0.50
## 395 RECORD RAINFALL 0.50
## 396 THUNDERSTORM WIND/HAIL 0.50
## 397 THUNDERSTORMWINDS 0.50
## 398 TORNDAO 0.50
## 399 WIND/HAIL 0.50
## 400 FLASH FLOOD WINDS 0.41
## 401 HAIL 450 0.20
## 402 HURRICANE OPAL/HIGH WINDS 0.10
## 403 TSTM WIND G58 0.10
## 404 COLD AIR TORNADO 0.05
## 405 SNOW AND ICE 0.05
## 406 URBAN SMALL 0.05Clearly, the top 5 (i.e. worst 5) events are TORNADO, FLASH FLOOD, TSTM WIND, FLOOD, THUNDERSTORM WIND
library(dplyr)
graph_propdmge_splittedby_event_TOP20 <- graph_propdmge_splittedby_event %>% filter(Frequency >= 26000)
barplot (height = graph_propdmge_splittedby_event_TOP20$Frequency, names.arg = graph_propdmge_splittedby_event_TOP20$Event_Type, las = 2, cex.names = 0.7, col = rainbow (30, start=0, end=1))
Crop Damage
cropdmge_splittedby_event <- tapply(copy_required_data$CROPDMG, copy_required_data$EVTYPE, sum)
cropdmge_splittedby_event <- as.data.frame.table(cropdmge_splittedby_event)
cropdmge_splittedby_event <- setNames(cropdmge_splittedby_event, c("Event_Type","Frequency"))
library(dplyr)
graph_cropdmge_splittedby_event <- cropdmge_splittedby_event %>% filter(Frequency != 0)
arrange(graph_cropdmge_splittedby_event, desc(graph_cropdmge_splittedby_event$Frequency))## Event_Type Frequency
## 1 HAIL 579596.28
## 2 FLASH FLOOD 179200.46
## 3 FLOOD 168037.88
## 4 TSTM WIND 109202.60
## 5 TORNADO 100018.52
## 6 THUNDERSTORM WIND 66791.45
## 7 DROUGHT 33898.62
## 8 THUNDERSTORM WINDS 18684.93
## 9 HIGH WIND 17283.21
## 10 HEAVY RAIN 11122.80
## 11 FROST/FREEZE 7034.14
## 12 EXTREME COLD 6121.14
## 13 TROPICAL STORM 5899.12
## 14 HURRICANE 5339.31
## 15 FLASH FLOODING 5126.05
## 16 HURRICANE/TYPHOON 4798.48
## 17 WILDFIRE 4364.20
## 18 TSTM WIND/HAIL 4356.65
## 19 WILD/FOREST FIRE 4189.54
## 20 LIGHTNING 3580.61
## 21 RIVER FLOOD 3490.00
## 22 FLOODING 3361.00
## 23 URBAN/SML STREAM FLD 2793.80
## 24 HEAVY SNOW 2165.72
## 25 HIGH WINDS/COLD 2005.00
## 26 WINTER STORM 1978.99
## 27 HIGH WINDS 1759.60
## 28 SMALL HAIL 1732.08
## 29 ICE STORM 1688.95
## 30 STRONG WIND 1616.90
## 31 DUST STORM 1601.50
## 32 FLOOD/FLASH FLOOD 1427.70
## 33 River Flooding 1206.84
## 34 FROST 1065.00
## 35 OTHER 1034.40
## 36 URBAN FLOOD 923.50
## 37 FREEZE 920.75
## 38 STORM SURGE/TIDE 850.00
## 39 TYPHOON 825.00
## 40 RAIN 750.00
## 41 HEAT 662.70
## 42 COLD/WIND CHILL 600.00
## 43 HEAVY RAINS 560.00
## 44 FLASH FLOOD/FLOOD 555.00
## 45 DUST STORM/HIGH WINDS 500.00
## 46 FOREST FIRES 500.00
## 47 HURRICANE FELIX 500.00
## 48 TROPICAL STORM GORDON 500.00
## 49 WILDFIRES 500.00
## 50 WINTER STORMS 500.00
## 51 EXCESSIVE HEAT 494.40
## 52 URBAN FLOODING 366.50
## 53 DAMAGING FREEZE 362.00
## 54 HEAVY RAINS/FLOODING 353.00
## 55 WIND 300.00
## 56 HEAT WAVE 255.30
## 57 GUSTY WINDS 200.00
## 58 FLASH FLOODING/FLOOD 175.00
## 59 BLIZZARD 172.00
## 60 HURRICANE ERIN 146.00
## 61 EXCESSIVE WETNESS 142.00
## 62 FLOOD/RAIN/WINDS 112.80
## 63 Frost/Freeze 100.00
## 64 UNSEASONABLY COLD 67.50
## 65 COLD AND WET CONDITIONS 66.00
## 66 Damaging Freeze 64.10
## 67 THUNDERSTORM WINDSS 59.55
## 68 COASTAL FLOODING 56.00
## 69 WIND DAMAGE 55.00
## 70 HAIL/WINDS 50.05
## 71 EXTREME COLD/WIND CHILL 50.00
## 72 FLOODS 50.00
## 73 HAIL 150 50.00
## 74 HEAT WAVE DROUGHT 50.00
## 75 MARINE THUNDERSTORM WIND 50.00
## 76 THUNDERSTORM HAIL 50.00
## 77 TROPICAL STORM DEAN 50.00
## 78 THUNDERSTORM WINDS HAIL 49.00
## 79 Early Frost 42.00
## 80 LANDSLIDE 37.00
## 81 THUNDERSTORM WINDS/ FLOOD 30.00
## 82 THUNDERSTORM WINDS/HAIL 30.00
## 83 SEVERE THUNDERSTORM WINDS 29.00
## 84 AGRICULTURAL FREEZE 28.82
## 85 Extreme Cold 20.00
## 86 HEAVY SNOW/HIGH WINDS & FLOOD 20.00
## 87 TSUNAMI 20.00
## 88 HURRICANE OPAL 19.00
## 89 EXTREME WINDCHILL 17.00
## 90 SEVERE THUNDERSTORMS 17.00
## 91 TROPICAL STORM JERRY 16.00
## 92 DRY MICROBURST 15.00
## 93 WINTER WEATHER 15.00
## 94 HARD FREEZE 13.10
## 95 WILD/FOREST FIRES 12.00
## 96 Freeze 10.50
## 97 GUSTY WIND 10.00
## 98 HAIL 075 10.00
## 99 HAIL 100 10.00
## 100 HAIL 125 10.00
## 101 HAIL 200 10.00
## 102 HIGH WINDS HEAVY RAINS 10.00
## 103 HURRICANE OPAL/HIGH WINDS 10.00
## 104 SNOW 10.00
## 105 THUNDERSTORM WIND G60 10.00
## 106 THUNDERSTORM WINDS G60 10.00
## 107 UNSEASONABLY WARM 10.00
## 108 UNSEASONAL RAIN 10.00
## 109 TORNADO F0 7.20
## 110 DROUGHT/EXCESSIVE HEAT 5.78
## 111 WINDS 5.50
## 112 Unseasonable Cold 5.10
## 113 COOL AND WET 5.00
## 114 EXTREME HEAT 5.00
## 115 ICE JAM FLOODING 5.00
## 116 RIVER FLOODING 5.00
## 117 SMALL STREAM FLOOD 5.00
## 118 STORM SURGE 5.00
## 119 STRONG WINDS 5.00
## 120 THUDERSTORM WINDS 5.00
## 121 THUNDERSTORM WINDS LIGHTNING 5.00
## 122 THUNDERSTORMS 5.00
## 123 THUNDERSTORMS WIND 5.00
## 124 WINTER STORM HIGH WINDS 5.00
## 125 HVY RAIN 3.00
## 126 THUNDERSTORM WIND. 3.00
## 127 TORNADOES, TSTM WIND, HAIL 2.50
## 128 GUSTNADO 1.55
## 129 Heavy Rain/High Surf 1.50
## 130 THUNDERSTORM 1.00
## 131 THUNDERSTORMS WINDS 1.00
## 132 TORNADOES 1.00
## 133 GLAZE ICE 0.80
## 134 SEVERE THUNDERSTORM 0.20
## 135 HAIL/WIND 0.10
## 136 COLD AIR TORNADO 0.05Clearly, the top 5 (i.e. worst crop damagers) are HAIL, FLASH FLOOD, FLOOD, TSTM WIND, TORNADO
library(dplyr)
graph_cropdmge_splittedby_event_TOP20 <- graph_cropdmge_splittedby_event %>% filter(Frequency >= 3500)The assignment instructions limits the graphs to 3, and hence the following graph is not shown
barplot (height = graph_cropdmge_splittedby_event_TOP20\(Frequency, names.arg = graph_cropdmge_splittedby_event_TOP20\)Event_Type, las = 2, cex.names = 0.7, col = rainbow (30, start=0, end=1))
common_econmomic <- c("TORNADO", "FLASH FLOOD", "TSTM WIND", "FLOOD", "THUNDERSTORM WIND", "HAIL", "FLASH FLOOD", "FLOOD", "TSTM WIND", "TORNADO")
unique(common_econmomic)## [1] "TORNADO" "FLASH FLOOD" "TSTM WIND"
## [4] "FLOOD" "THUNDERSTORM WIND" "HAIL"ANSWER Q2: [1] “TORNADO” “FLASH FLOOD” “TSTM WIND” “FLOOD” “THUNDERSTORM WIND” “HAIL”.
all_in_all <- c(common, common_econmomic)
unique(all_in_all)## [1] "TORNADO" "EXCESSIVE HEAT" "FLASH FLOOD"
## [4] "HEAT" "LIGHTENING" "TSTM WIND"
## [7] "FLOOD" "THUNDERSTORM WIND" "HAIL"All in all, the worst events are: [1] “TORNADO” “EXCESSIVE HEAT” “FLASH FLOOD” “HEAT” “LIGHTENING” “TSTM WIND” “FLOOD” “THUNDERSTORM WIND” “HAIL”.