Reproducible Research Assignment 2
2024-05-05
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.
Data
The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. You can download the file from the course web site:
Storm Data [47Mb]
There is also some documentation of the database available. Here you will find how some of the variables are constructed/defined.
National Weather Service Storm Data Documentation
National Climatic Data Center Storm Events FAQ
The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete.
Assignment
The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. You must use the database to answer the questions below and show the code for your entire analysis. Your analysis can consist of tables, figures, or other summaries. You may use any R package you want to support your analysis.
Questions
Your data analysis must address the following questions:
1 Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
2 Across the United States, which types of events have the greatest economic consequences?
Consider writing your report as if it were to be read by a government or municipal manager who might be responsible for preparing for severe weather events and will need to prioritize resources for different types of events. However, there is no need to make any specific recommendations in your report.Setting up the working environment
setwd(“../Reproducible-Research-Course-Project-2”)
Reading Data
noaaDF <- read.csv('repdata_data_StormData.csv',header=TRUE, stringsAsFactors = FALSE)Loading Packages
require(dplyr)## Loading required package: 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(tidyr)## Loading required package: tidyrrequire(lubridate)## Loading required package: lubridate##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionrequire(ggplot2)## Loading required package: ggplot2Exploratory Data Analysis
Check the six summary of the data in term of the min, 1st quantile, median, mean, 3rd quantile and max.
summary(noaaDF)## 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
## Checking the structure of the data to see details like the number of observations, number of variables, each variable with the data type and example of the input in each.
str(noaaDF)## '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 ...Question One
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Calculating the fatalities
totFatalities <- aggregate(noaaDF$FATALITIES, by = list(noaaDF$EVTYPE), "sum")
names(totFatalities) <- c("Event", "Fatalities")
totFatalitiesSorted <- totFatalities[order(-totFatalities$Fatalities), ][1:20, ]
totFatalitiesSorted## Event Fatalities
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504
## 170 FLOOD 470
## 585 RIP CURRENT 368
## 359 HIGH WIND 248
## 19 AVALANCHE 224
## 972 WINTER STORM 206
## 586 RIP CURRENTS 204
## 278 HEAT WAVE 172
## 140 EXTREME COLD 160
## 760 THUNDERSTORM WIND 133
## 310 HEAVY SNOW 127
## 141 EXTREME COLD/WIND CHILL 125
## 676 STRONG WIND 103
## 30 BLIZZARD 101
## 350 HIGH SURF 101Calculating the injuries
totInjuries <- aggregate(noaaDF$INJURIES, by = list(noaaDF$EVTYPE), "sum")
names(totInjuries) <- c("Event", "Injuries")
totInjuriesSorted <- totInjuries[order(-totInjuries$Injuries), ][1:20, ]
totInjuriesSorted## Event 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 1361
## 972 WINTER STORM 1321
## 411 HURRICANE/TYPHOON 1275
## 359 HIGH WIND 1137
## 310 HEAVY SNOW 1021
## 957 WILDFIRE 911
## 786 THUNDERSTORM WINDS 908
## 30 BLIZZARD 805
## 188 FOG 734
## 955 WILD/FOREST FIRE 545
## 117 DUST STORM 440Plotting the fatalities and injuries together
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 = 'red',
main = 'Top 20 Weather Events for Fatalities', ylab = 'Number of Fatalities')
barplot(totInjuriesSorted$Injuries, names.arg = totInjuriesSorted$Event, col = 'orange',
main = 'Top 20 Weather Events for Injuries', ylab = 'Number of Injuries')
Question One Result Interpretation
The analysis of the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database reveals that tornadoes are the primary cause of both fatalities and injuries. However, excessive heat ranks second in terms of fatalities, while the causes from second to fourth places in injuries exhibit similar values.
Question Two
Across the United States, which types of events have the greatest economic consequences?
Calculating the cost of property
totProperty <- aggregate(noaaDF$PROPDMG, by = list(noaaDF$EVTYPE), "sum")
names(totProperty) <- c("Event", "Property")
totPropertySorted <- totProperty[order(-totProperty$Property), ][1:20, ]
totPropertySorted## Event Property
## 834 TORNADO 3212258.16
## 153 FLASH FLOOD 1420124.59
## 856 TSTM WIND 1335965.61
## 170 FLOOD 899938.48
## 760 THUNDERSTORM WIND 876844.17
## 244 HAIL 688693.38
## 464 LIGHTNING 603351.78
## 786 THUNDERSTORM WINDS 446293.18
## 359 HIGH WIND 324731.56
## 972 WINTER STORM 132720.59
## 310 HEAVY SNOW 122251.99
## 957 WILDFIRE 84459.34
## 427 ICE STORM 66000.67
## 676 STRONG WIND 62993.81
## 376 HIGH WINDS 55625.00
## 290 HEAVY RAIN 50842.14
## 848 TROPICAL STORM 48423.68
## 955 WILD/FOREST FIRE 39344.95
## 164 FLASH FLOODING 28497.15
## 919 URBAN/SML STREAM FLD 26051.94Calculating the cost crop damages
totCrop <- aggregate(noaaDF$CROPDMG, by = list(noaaDF$EVTYPE), "sum")
names(totCrop) <- c("Event", "Crop")
totCropSorted <- totCrop[order(-totCrop$Crop), ][1:20, ]
totCropSorted## Event Crop
## 244 HAIL 579596.28
## 153 FLASH FLOOD 179200.46
## 170 FLOOD 168037.88
## 856 TSTM WIND 109202.60
## 834 TORNADO 100018.52
## 760 THUNDERSTORM WIND 66791.45
## 95 DROUGHT 33898.62
## 786 THUNDERSTORM WINDS 18684.93
## 359 HIGH WIND 17283.21
## 290 HEAVY RAIN 11122.80
## 212 FROST/FREEZE 7034.14
## 140 EXTREME COLD 6121.14
## 848 TROPICAL STORM 5899.12
## 402 HURRICANE 5339.31
## 164 FLASH FLOODING 5126.05
## 411 HURRICANE/TYPHOON 4798.48
## 957 WILDFIRE 4364.20
## 873 TSTM WIND/HAIL 4356.65
## 955 WILD/FOREST FIRE 4189.54
## 464 LIGHTNING 3580.61Plotting the cost of property and crop together
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))
Getting the total damage by adding both property and crop damages together
totTotalCost <- aggregate(noaaDF$CROPDMG+noaaDF$PROPDMG, by = list(noaaDF$EVTYPE), "sum")
names(totTotalCost) <- c("Event", "TotalCost")
totTotalCostSorted <- totTotalCost[order(-totTotalCost$TotalCost), ][1:20, ]
totTotalCostSorted## Event TotalCost
## 834 TORNADO 3312276.68
## 153 FLASH FLOOD 1599325.05
## 856 TSTM WIND 1445168.21
## 244 HAIL 1268289.66
## 170 FLOOD 1067976.36
## 760 THUNDERSTORM WIND 943635.62
## 464 LIGHTNING 606932.39
## 786 THUNDERSTORM WINDS 464978.11
## 359 HIGH WIND 342014.77
## 972 WINTER STORM 134699.58
## 310 HEAVY SNOW 124417.71
## 957 WILDFIRE 88823.54
## 427 ICE STORM 67689.62
## 676 STRONG WIND 64610.71
## 290 HEAVY RAIN 61964.94
## 376 HIGH WINDS 57384.60
## 848 TROPICAL STORM 54322.80
## 955 WILD/FOREST FIRE 43534.49
## 95 DROUGHT 37997.67
## 164 FLASH FLOODING 33623.20Plotting single plot
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))
Question Two Result Interpretation
From the result, it shows that total damage is mostly caused by tornadoes.