Report of most severe impact to health and economic due to natural disasters in the U.S.A..
M.J.M. Beuken
Saturday, october 7, 2017
Synopsis
This report identifies natural disasters in the U.S. which have had the greatest impact on human health and economic conditions.
For the purpose of this report the Storm Data was used. Which is published by the the National Oceanic and Atmospheric Administration (NOAA). In this dataset the occurrence of natural disasters leading to: death, injuries, severe property damage and severe damage to crops is documented.
Economic impact is analysed in terms of the damage caused to property and crops.The results of the analysis show that floods, storm surge, hurricanes and tornados have been most harmful to the economical situation of the U.S.A..
Effect on health is measured by the number of fatalities and injuries caused by natural disasters. Analysis of these variables show that tornados are dangerous to public health.
Processing the data
Loading data
### Working directory
setwd('D:/users/beukenmjm/Onedrive - ZuydHogeschool/Documents/Data science cursus coursera/course5week4')
### Loading data and remove quotationmarks
data <- read.csv('./repdata%2Fdata%2FStormData.csv', quote = "\"")
head(data, 5)## 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
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END
## 1 TORNADO 0 0
## 2 TORNADO 0 0
## 3 TORNADO 0 0
## 4 TORNADO 0 0
## 5 TORNADO 0 0
## COUNTYENDN END_RANGE END_AZI END_LOCATI LENGTH WIDTH F MAG FATALITIES
## 1 NA 0 14.0 100 3 0 0
## 2 NA 0 2.0 150 2 0 0
## 3 NA 0 0.1 123 2 0 0
## 4 NA 0 0.0 100 2 0 0
## 5 NA 0 0.0 150 2 0 0
## INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP WFO STATEOFFIC ZONENAMES
## 1 15 25.0 K 0
## 2 0 2.5 K 0
## 3 2 25.0 K 0
## 4 2 2.5 K 0
## 5 2 2.5 K 0
## LATITUDE LONGITUDE LATITUDE_E LONGITUDE_ REMARKS REFNUM
## 1 3040 8812 3051 8806 1
## 2 3042 8755 0 0 2
## 3 3340 8742 0 0 3
## 4 3458 8626 0 0 4
## 5 3412 8642 0 0 5### installing some packages
library(knitr)## Warning: package 'knitr' was built under R version 3.4.2library(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, unionlibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 3.4.1library(plotly)## Warning: package 'plotly' was built under R version 3.4.2##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
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## layoutData processing
Bringing back dataset to only relevant variables
Because of the fact that the dataset consists out of 37 variables and only 7 are necessary to do a relevant analysis we bring the dataset back from 37 variables to seven variables
Variables necessary:
1. EVTYPE
2. FATALITIES
3. INJURIES
4. PROPDMG
5. PROPDMGEXP
6. CROPDMG
7. CROPDMGEXP
Preparing the data considering public health.
Due to the fact that we try to generate an overview of the most harmful natural disasters to public health, we have to calculate the sum of fatalities and injuries per disaster. Based on that sum we can create a descending order of most harmful disasters. To give a good readable diagram we generated a stacked bar chart with bars for the number of injuries and stacked on those bars the bars for the number of fatalities. When looking at this chart one can see immediateley which disaster is most harmful.
subdata <- c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")
subsetdata <- data[subdata]
names(subsetdata)## [1] "EVTYPE" "FATALITIES" "INJURIES" "PROPDMG" "PROPDMGEXP"
## [6] "CROPDMG" "CROPDMGEXP"head(subsetdata)## EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 TORNADO 0 15 25.0 K 0
## 2 TORNADO 0 0 2.5 K 0
## 3 TORNADO 0 2 25.0 K 0
## 4 TORNADO 0 2 2.5 K 0
## 5 TORNADO 0 2 2.5 K 0
## 6 TORNADO 0 6 2.5 K 0### Looking at population health, on one hand to fatalities and on the other hand looking at injuries.
fatal <- aggregate(FATALITIES ~ EVTYPE, data = data, FUN = sum)
injury <- aggregate(INJURIES ~ EVTYPE, data = data, FUN = sum)
### creating data frames of the relevant data.
D1 <- data.frame(fatal$FATALITIES, row.names = fatal$EVTYPE)
D2 <- data.frame(injury$INJURIES, row.names = injury$EVTYPE)
### merge two data frames by row name
merged.data <- merge(D1,D2,by=0,all=TRUE)
merged.data[is.na(merged.data)] <- 0
head(merged.data)## Row.names fatal.FATALITIES injury.INJURIES
## 1 HIGH SURF ADVISORY 0 0
## 2 COASTAL FLOOD 0 0
## 3 FLASH FLOOD 0 0
## 4 LIGHTNING 0 0
## 5 TSTM WIND 0 0
## 6 TSTM WIND (G45) 0 0### 1 calculate sum rows merged data
merged.data <- transform(merged.data, sum=rowSums(merged.data[,2:3]))
### Order data frame in descending order
sorted.data <- merged.data[order(merged.data$sum, decreasing=TRUE), ][1:10, ]
print(sorted.data)## Row.names fatal.FATALITIES injury.INJURIES sum
## 834 TORNADO 5633 91346 96979
## 130 EXCESSIVE HEAT 1903 6525 8428
## 856 TSTM WIND 504 6957 7461
## 170 FLOOD 470 6789 7259
## 464 LIGHTNING 816 5230 6046
## 275 HEAT 937 2100 3037
## 153 FLASH FLOOD 978 1777 2755
## 427 ICE STORM 89 1975 2064
## 760 THUNDERSTORM WIND 133 1488 1621
## 972 WINTER STORM 206 1321 1527### Drop sum column
sorted.data$sum <- NULL
print(sorted.data)## Row.names fatal.FATALITIES injury.INJURIES
## 834 TORNADO 5633 91346
## 130 EXCESSIVE HEAT 1903 6525
## 856 TSTM WIND 504 6957
## 170 FLOOD 470 6789
## 464 LIGHTNING 816 5230
## 275 HEAT 937 2100
## 153 FLASH FLOOD 978 1777
## 427 ICE STORM 89 1975
## 760 THUNDERSTORM WIND 133 1488
## 972 WINTER STORM 206 1321### Create som new columnnames
colnames(sorted.data) <- c("Event", "Fatal", "Injury")Preparing the data considering economic damage.
Due to the fact that we try to generate an overview of the most harmful natural disasters to economics, we have to calculate the sum of damage to property and crops per disaster. Based on that sum we can create a descending order of most harmful disasters. To give a good readable diagram we generate a stacked bar chart with bars for the value of damage to property and stacked on those bars the bars for the value of damage to crops. When looking at this chart one can see immediateley which disaster is most harmful. Looking at the dataset we can notice that one variable records the estimate rounded to three significant digits, whereas the other variable has an alphabetical character which signifies that magnitute for example, include “K” for thousands, “M” for millions, and “B” for billions. Thus, as part of data processing these alphabetical characters were converted to their numeric value. A new variable was generated which was the product of these two variables.
### first prepare property damage
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="K"] <- 1000
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="M"] <- 10^6
subsetdata$PROPEXP[subsetdata$PROPDMGEXP ==""] <- 1
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="B"] <- 10^9
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="m"] <- 10^6
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="+"] <- 0
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="0"] <- 1
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="5"] <- 10^5
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="6"] <- 10^6
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="?"] <- 0
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="4"] <- 10000
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="2"] <- 100
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="3"] <- 1000
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="h"] <- 100
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="7"] <- 10^7
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="H"] <- 100
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="-"] <- 0
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="1"] <- 10
subsetdata$PROPEXP[subsetdata$PROPDMGEXP =="8"] <- 10^8
subsetdata$PROPDMGVAL <- subsetdata$PROPDMG * subsetdata$PROPEXP
### second prop damage
subsetdata$CROPEXP[subsetdata$CROPDMGEXP == ""] <- 1
subsetdata$CROPEXP[subsetdata$CROPDMGEXP =="M"] <- 10^6
subsetdata$CROPEXP[subsetdata$CROPDMGEXP =="K"] <- 1000
subsetdata$CROPEXP[subsetdata$CROPDMGEXP =="m"] <- 10^9
subsetdata$CROPEXP[subsetdata$CROPDMGEXP =="B"] <- 10^6
subsetdata$CROPEXP[subsetdata$CROPDMGEXP =="?"] <- 0
subsetdata$CROPEXP[subsetdata$CROPDMGEXP =="0"] <- 1
subsetdata$CROPEXP[subsetdata$CROPDMGEXP =="k"] <- 1000
subsetdata$CROPEXP[subsetdata$CROPDMGEXP =="2"] <- 100
subsetdata$CROPDMGVAL <- subsetdata$CROPDMG * subsetdata$CROPEXP
### Looking on one hand to damage to crop and on the other hand looking to damage at properties.
prop.damage <- aggregate(subsetdata$PROPDMGVAL ~ EVTYPE, data = data, FUN = sum)
crop.damage <- aggregate(subsetdata$CROPDMGVAL ~ EVTYPE, data = data, FUN = sum)
colnames(prop.damage)[2] <- c("Value prop damage")
colnames(crop.damage)[2] <- c("Value crop damage")
head(prop.damage)## EVTYPE Value prop damage
## 1 HIGH SURF ADVISORY 200000
## 2 COASTAL FLOOD 0
## 3 FLASH FLOOD 50000
## 4 LIGHTNING 0
## 5 TSTM WIND 8100000
## 6 TSTM WIND (G45) 8000### creating data frames of the relevant data.
Data1 <- data.frame(prop.damage$`Value prop damage`, row.names = prop.damage$EVTYPE)
Data2 <- data.frame(crop.damage$`Value crop damage`, row.names = crop.damage$EVTYPE)
head(Data1)## prop.damage..Value.prop.damage.
## HIGH SURF ADVISORY 200000
## COASTAL FLOOD 0
## FLASH FLOOD 50000
## LIGHTNING 0
## TSTM WIND 8100000
## TSTM WIND (G45) 8000### merge two data frames by row name
merged.data.economic <- merge(Data1,Data2,by=0,all=TRUE)
head(merged.data.economic)## Row.names prop.damage..Value.prop.damage.
## 1 HIGH SURF ADVISORY 200000
## 2 COASTAL FLOOD 0
## 3 FLASH FLOOD 50000
## 4 LIGHTNING 0
## 5 TSTM WIND 8100000
## 6 TSTM WIND (G45) 8000
## crop.damage..Value.crop.damage.
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0colnames(merged.data.economic) <- c("Events", "Value prop damage", "Value crop damage")
head(merged.data.economic)## Events Value prop damage Value crop damage
## 1 HIGH SURF ADVISORY 200000 0
## 2 COASTAL FLOOD 0 0
## 3 FLASH FLOOD 50000 0
## 4 LIGHTNING 0 0
## 5 TSTM WIND 8100000 0
## 6 TSTM WIND (G45) 8000 0### 1 calculate sum rows merged data
merged.data.economic <- transform(merged.data.economic, sum=rowSums(merged.data.economic[,2:3]))
### Order data frame in descending order
sorted.data.economic <- merged.data.economic[order(merged.data.economic$sum, decreasing=TRUE), ][1:10, ]
print(sorted.data.economic)## Events Value.prop.damage Value.crop.damage sum
## 170 FLOOD 144657709807 5661968450 150319678257
## 411 HURRICANE/TYPHOON 69305840000 1099382800 70405222800
## 834 TORNADO 56947380617 414953270 57362333887
## 670 STORM SURGE 43323536000 5000 43323541000
## 244 HAIL 15735267513 3025954473 18761221986
## 153 FLASH FLOOD 16822673979 1421317100 18243991079
## 402 HURRICANE 11868319010 2741910000 14610229010
## 95 DROUGHT 1046106000 12474066000 13520172000
## 409 HURRICANE OPAL 3172846000 10009000000 13181846000
## 848 TROPICAL STORM 7703890550 678346000 8382236550### Drop sum column
sorted.data.economic$sum <- NULL
print(sorted.data.economic)## Events Value.prop.damage Value.crop.damage
## 170 FLOOD 144657709807 5661968450
## 411 HURRICANE/TYPHOON 69305840000 1099382800
## 834 TORNADO 56947380617 414953270
## 670 STORM SURGE 43323536000 5000
## 244 HAIL 15735267513 3025954473
## 153 FLASH FLOOD 16822673979 1421317100
## 402 HURRICANE 11868319010 2741910000
## 95 DROUGHT 1046106000 12474066000
## 409 HURRICANE OPAL 3172846000 10009000000
## 848 TROPICAL STORM 7703890550 678346000Rusults
Results of the analysis of damage to the public health
### create a stacked bar chart
p <- plot_ly(sorted.data, x= ~Event, y= ~Fatal, type = 'bar', name = 'Fatal', width = 800, height = 500) %>%
add_trace(y= ~Injury, name='Injury') %>%
layout(title = "Top 10 events most hurtful to public health", yaxis = list(title = 'Number'), xaxis = list(title = 'Eventtype', tickangle = -45), barmode = 'stack', margin = list(b = 150))
print(p)
###The plot above shows that tornado has caused the highest number of fatalities and injuries.
webshot::install_phantomjs()## phantomjs has been installed to C:\Users\beukenmjm\AppData\Roaming\PhantomJSexport(p, file = "public health.png")
Results economic consequences.
### create a stacked bar chart
q <- plot_ly(sorted.data.economic, x= ~Events, y= ~Value.prop.damage, type = 'bar', name = 'Value property damage', width = 800, height = 500) %>%
add_trace(y= ~Value.crop.damage, name='Value crop damage') %>%
layout(title = "Top 10 events highest economic damage", yaxis = list(title = 'Value of damage'), xaxis = list(title = 'Eventtype', tickangle = -45), barmode = 'stack', margin = list(b = 150))
print(q)
### The plot above shows that flood has caused the most severe economic consequences.
webshot::install_phantomjs()## phantomjs has been installed to C:\Users\beukenmjm\AppData\Roaming\PhantomJSexport(q, file = "economicdamage.png")