Project 2 Reproducible Research
Illya Pavao Bjazevic
10/5/2020
Analysis
When we see the histograms of injuries and fatalities, it is clear that Tornados produces the bigger number of injuries and fatalities. For that reason, there is no doubt that Tornados are the natural disaster most harmful with respect to population health.
Additionally, considering than in the United States, individuals and corporations have insurance against property damage and crop damages, we consider that injuries and fatalities are the only ones that individuals usually do not have insurance against those events.
Also the insurances companies, as they are experts in statistics, have already calculated and charged by the risk of natural disasters. For them, this kind of situations are part of their business.
Even, when the risk is too high, they pay to big companies for sharing the risk. Additionally, the price of a life is infinite.
When we analyze the graphs, there is no doubt that Tornados are by far the most damaging natural disaster in the economic aspecto, as well.
Code for reading in the dataset and/or processing the data
Before to start, We need to download all the data that we will user for this homework.
# Download the data from the url of the homework.
library(ggplot2)
library('R.utils')## Loading required package: R.oo## Loading required package: R.methodsS3## R.methodsS3 v1.8.1 (2020-08-26 16:20:06 UTC) successfully loaded. See ?R.methodsS3 for help.## R.oo v1.24.0 (2020-08-26 16:11:58 UTC) successfully loaded. See ?R.oo for help.##
## Attaching package: 'R.oo'## The following object is masked from 'package:R.methodsS3':
##
## throw## The following objects are masked from 'package:methods':
##
## getClasses, getMethods## The following objects are masked from 'package:base':
##
## attach, detach, load, save## R.utils v2.10.1 (2020-08-26 22:50:31 UTC) successfully loaded. See ?R.utils for help.##
## Attaching package: 'R.utils'## The following object is masked from 'package:tidyr':
##
## extract## The following object is masked from 'package:utils':
##
## timestamp## The following objects are masked from 'package:base':
##
## cat, commandArgs, getOption, inherits, isOpen, nullfile, parse,
## warningsif (!file.exists("NOAA.csv") ) {download.file('https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2','NOAA.csv.bz2',method='curl')
bunzip2('NOAA.csv.bz2')}
myDATAFRAME <- read.csv("NOAA.csv", header = TRUE)
head(myDATAFRAME)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE EVTYPE
## 1 1 4/18/1950 0:00:00 0130 CST 97 MOBILE AL TORNADO
## 2 1 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL TORNADO
## 3 1 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL TORNADO
## 4 1 6/8/1951 0:00:00 0900 CST 89 MADISON AL TORNADO
## 5 1 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL TORNADO
## 6 1 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL TORNADO
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1 0 0 NA
## 2 0 0 NA
## 3 0 0 NA
## 4 0 0 NA
## 5 0 0 NA
## 6 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
## 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
## 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 6str(myDATAFRAME)## '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 ...Fatalities Histogram
#The first step is to sum all the fatalities per natural disaster.
numberOFdeathsDATAFRAME <- aggregate(myDATAFRAME$FATALITIES, by = list(EVTYPE = myDATAFRAME$EVTYPE), sum)
numberOFdeathsDATAFRAME <- numberOFdeathsDATAFRAME[order(numberOFdeathsDATAFRAME$x, decreasing = TRUE), ]
ggplot(numberOFdeathsDATAFRAME[1:6,], aes(EVTYPE, y = x)) +
geom_bar(stat = "identity") +
xlab("Natural Disaster") +
ylab("Number of Deaths") +
ggtitle("Fatalities Histogram")
Injuries Histogram
#The first step is to sum all the fatalities per natural disaster.
numberOFinjuriesDATAFRAME <- aggregate(myDATAFRAME$INJURIES, by = list(EVTYPE = myDATAFRAME$EVTYPE), sum)
numberOFinjuriesDATAFRAME <- numberOFinjuriesDATAFRAME[order(numberOFinjuriesDATAFRAME$x, decreasing = TRUE), ]
#Histogram
ggplot(numberOFinjuriesDATAFRAME[1:6,], aes(EVTYPE, y = x)) +
geom_bar(stat = "identity") +
xlab("Natural Disaster") +
ylab("Number of Injuries") +
ggtitle("Injuries Histogram")