Course_Project Assignment 2
Chien-Hua Wang
January 21, 2019
Synopsis:
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.
# download file containing data if it hasn't already been downloaded
Url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
File <- "StormData.csv"
if (!file.exists(File)) {
download.file(Url, File, mode = "wb")
}
SD = read.csv('StormData.csv')
str(SD)## '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/ 436781 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 ...1. address the question of which types of events are most harmful to population health
Calculate the fatalities
totFatalities = aggregate(SD$FATALITIES, by=list(SD$EVTYPE),"sum")
names(totFatalities) = c('Event', 'Fatalities')
totFatalitiesSorted = totFatalities %>%
arrange(desc(Fatalities)) %>%
head(20)
totFatalitiesSorted## 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 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 101Calculate the injuries
totInjuries <- aggregate(SD$INJURIES, by = list(SD$EVTYPE), "sum")
names(totInjuries) <- c("Event", "Injuries")
totInjuriesSorted = totInjuries %>%
arrange(desc(Injuries)) %>%
head(20)
totInjuriesSorted## 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 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 440Visualization
par(mfrow=c(1,2), mar = c(10,4,2,2),las = 3,cex = 0.7,cex.main = 1.4, cex.lab = 1.2)
barplot(totFatalitiesSorted$Fatalities, names.arg = totFatalitiesSorted$Event, col = 'blue',
main = 'Top 20 Weather Events for Fatalities', ylab = 'Number of Fatalities')
barplot(totInjuriesSorted$Injuries, names.arg = totInjuriesSorted$Event, col = 'red',
main = 'Top 20 Weather Events for Injuries', ylab = 'Number of Injuries')
CONCLUSION:
Thus we see that Tornados cause most deaths and injuries in the U.S. National Oceanic
and Atmospheric Administration’s (NOAA) storm database. But Excessive heat causes second
most deaths, whereas as far as injuries are conserned second to fourth causes have very similar values.
2. address the question of which types of events have the greatest economic consequences
The property
totProperty <- aggregate(SD$PROPDMG, by = list(SD$EVTYPE), "sum")
names(totProperty) <- c("Event", "Property")
totPropertySorted = totProperty %>%
arrange(desc(Property)) %>% head(20)
totPropertySorted## Event Property
## 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 26051.94The Crop
totCrop <- aggregate(SD$CROPDMG, by = list(SD$EVTYPE), "sum")
names(totCrop) <- c("Event", "Crop")
totCropSorted = totCrop %>% arrange(desc(Crop)) %>% head(20)
totCropSorted## Event Crop
## 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 3580.61Visualization
par(mfrow = c(1, 2), mar = c(10, 4, 2, 2), las = 3, cex = 0.7, cex.main = 1.4, cex.lab = 1.2)
barplot(totPropertySorted$Property, names.arg = totPropertySorted$Event, col = 'Brown',
main = 'Top 20 Weather Events for Property Damage ', ylab = 'Amount of Property Damage', ylim = c(0, 3500000))
barplot(totCropSorted$Crop, names.arg = totCropSorted$Event, col = 'Green',
main = 'Top 20 Weather Events for Crop Damage', ylab = 'Amount of Crop Damage', ylim = c(0, 3500000))
The totl damage by adding both costs (property and crop damage)
totTotalCost <- aggregate(SD$CROPDMG+SD$PROPDMG, by = list(SD$EVTYPE), "sum")
names(totTotalCost) <- c("Event", "TotalCost")
totTotalCostSorted = totTotalCost %>% arrange(desc(TotalCost)) %>% head(20)
totTotalCostSorted## Event TotalCost
## 1 TORNADO 3312276.68
## 2 FLASH FLOOD 1599325.05
## 3 TSTM WIND 1445168.21
## 4 HAIL 1268289.66
## 5 FLOOD 1067976.36
## 6 THUNDERSTORM WIND 943635.62
## 7 LIGHTNING 606932.39
## 8 THUNDERSTORM WINDS 464978.11
## 9 HIGH WIND 342014.77
## 10 WINTER STORM 134699.58
## 11 HEAVY SNOW 124417.71
## 12 WILDFIRE 88823.54
## 13 ICE STORM 67689.62
## 14 STRONG WIND 64610.71
## 15 HEAVY RAIN 61964.94
## 16 HIGH WINDS 57384.60
## 17 TROPICAL STORM 54322.80
## 18 WILD/FOREST FIRE 43534.49
## 19 DROUGHT 37997.67
## 20 FLASH FLOODING 33623.20Visualization
par(mfrow = c(1,1), mar = c(10, 4, 2, 2), las = 3, cex = 0.7, cex.main = 1.4, cex.lab = 1.2)
barplot(totTotalCostSorted$TotalCost, names.arg = totTotalCostSorted$Event, col = 'Black',
main = 'Top 20 Weather Events for total Damage ', ylab = 'Amount of total Damage', ylim = c(0, 3500000))