title: Project 2, Exploration of NOAA Database for Harmful Events! author: “Phillip”
Synopsis:
This document will depict the results from data analysis of the National Weather Service Database on the most harmful types of events to the U.S. Population. There are many types of weather events that occurs across America, which have devastating and lasting effects upon the lives of people. This document will dive into this question by using the variable “EVTYPE” from the Storms Database to discover a pattern. Additionally, this document will depict the analysis of the various types of events, which have the greatest economic consequences.
My analysis will focus on answering two primary questions: (1.) Across the United States, which types of events are most harmful with respect to population health? (2.) Across the United States, which types of events have the greatest economic consequences? The results will be provided to my Senior Manager who is responsible for preparing for severe weather events and has to prioritize resources for the different types of events.
Data Processing:
• There should be a section titled Data Processing which describes (in words and code) how the data were loaded into R and processed for analysis. In particular, your analysis must start from the raw CSV file containing the data. You cannot do any preprocessing outside the document. If preprocessing is time-consuming you may consider using the cache = TRUE option for certain code chunks.
## Import data
NoSt_1 <- read.csv("repdata_data_StormData.csv")
head(NoSt_1)## 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 6# libraries
library(ggplot2)
library(dplyr)## Warning: package 'dplyr' was built under R version 3.4.4##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionstr(NoSt_1)## '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 ...The code below will depict the types of harmful events in the US population.
library(ggplot2)
No_1 <- (c(NoSt_1$EVTYPE, NoSt_1$FATILITIES, data = NoSt_1, na.rm=TRUE))
totalNoSt_1 <- aggregate(NoSt_1$FATALITIES ~ NoSt_1$PROPDMG, NoSt_1, FUN = "mean", na.rm=TRUE)
totalNoSt_2 <- aggregate(NoSt_1$FATALITIES ~ NoSt_1$INJURIES, totalNoSt_1, FUN= "mean", na.rm=FALSE)
head(totalNoSt_2)## NoSt_1$INJURIES NoSt_1$FATALITIES
## 1 0 0.008548728
## 2 1 0.101856627
## 3 2 0.144862795
## 4 3 0.164948454
## 5 4 0.191192266
## 6 5 0.299012694totalNoSt_3 <- aggregate(NoSt_1$PROPDMG ~ NoSt_1$STATE, totalNoSt_2, FUN = "mean", na.rm=FALSE)
head(totalNoSt_3)## NoSt_1$STATE NoSt_1$PROPDMG
## 1 AK 7.74208836
## 2 AL 15.99044197
## 3 AM 3.00894093
## 4 AN 0.09046154
## 5 AR 13.32453620
## 6 AS 11.49610895plot(totalNoSt_3, NoSt_1$FATALITIES, type = "o", col = "steelblue3",
main = expression("TYPE OF HARMFUL EVENTS IN US BY STATES"),
ylab = expression("EVENT TYPES"), xlab = "STATES") 
png("plot8271.png")
plot(totalNoSt_3, NoSt_1$FATALITIES, type = "o", col = "steelblue3",
main = expression("TYPE OF HARMFUL EVENTS IN US BY STATES"),
ylab = expression("EVENT TYPES"), xlab = "STATES")
dev.off()## png
## 2Results:
The result depicts the total number of events in the United States and the economic consequences. The code for the data analysis is shown in “Plot877”.
totalNoSt_1 <- aggregate(NoSt_1$FATALITIES ~ NoSt_1$PROPDMG, NoSt_1, FUN = "mean", na.rm=TRUE)
head(totalNoSt_1)## NoSt_1$PROPDMG NoSt_1$FATALITIES
## 1 0.00 0.011284483
## 2 0.01 0.003222342
## 3 0.02 0.000000000
## 4 0.03 0.004347826
## 5 0.04 0.000000000
## 6 0.05 0.005102041PD1 <- mean(na.omit(NoSt_1$PROPDMG))
PD2 <- sum(na.omit(NoSt_1$PROPDMG))
mean(NoSt_1$PROPDMG)## [1] 12.0631PD <- mean(NoSt_1$PROPDMG)
mean(na.omit(NoSt_1$CROPDMG))## [1] 1.527022mean(NoSt_1$CROPDMG)## [1] 1.527022CD <- mean(NoSt_1$CROPDMG)
CD2 <- sum(NoSt_1$CROPDMG)
Con_Data2 <- aggregate(CD2 ~ PD2, NoSt_1, FUN = "mean", na.rm=TRUE)
head(Con_Data2)## PD2 CD2
## 1 10884500 1377827plot(NoSt_1$STATE, xlab="EVTYPE", ylab="Economic Consequences (K$)",main="Total Events in US")
png("plot877.png")
plot(NoSt_1$STATE, xlab="EVTYPE", ylab="Economic Consequences (K$)",main="Total Events in US")
dev.off()## png
## 2