Reproducible_research_week4
Anas
2024-07-04
Title
Health and Economic Impacts of US severe weather
Synopsis
This project enter into the framework of the work assignment of the data science course. Through, this document, we are trying to answer two major question. First, across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?, secondly, across the United States, which types of events have the greatest economic consequences?. To answer those questions we are using 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.
library(ggplot2)
library(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, union#downloading the file, reading the data, chekcing data, and view the parameters
url<- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
destfile<- "stormdata.csv"
download.file(url,destfile, method = "curl")
sotrmdata<- read.csv("stormdata.csv")
head(sotrmdata)## 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(sotrmdata)## '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 ...From the results of “str” function we can see that the data contain 37 variables and 902297 observations, the variables are either a numeric, charcter or integer. Based on this function, we can conclude that almost 7 variables arenof interst. In the following work, those 7 varibales will be slected for further analysis.
#Transforming ans subsetting data According to NOAA, the data recording strat from jan. 1950. However, at that time, only one event type was recroded. by jan. 1996 all event types have been recorded. Since we are interested in comparing the effects of different wheather events, the data base should contain only observation that started not earlier than jan. 1996. ## Transformin and subsetting
sotrmdata$BGN_DATE<- strptime(sotrmdata$BGN_DATE, "%m/%d/%Y %H:%M:%S")
data<- subset(sotrmdata, BGN_DATE> "1995-12-31")Selecting variables
The variables of interest are: * EVTYPE: Type of event * FATALITIES: Number of fatalities * INJURIES: Number of injuries * PROPDMG: Size of property damage * PROPDMGEXP: The exponent values for PROPDMG * CROPDMG: Size of crop damage * CROPDMGEXP: The exponent values for CROPDMG
varint<- c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")
maindata<- subset(data, select= varint)homoginizing and subseting non-zeor data
To homoginize the event in EVTYPE, we will transform data to capital letters. Further, we subset only non-zeo data regarding our traget numbers
maindata$EVTYPE <- toupper(maindata$EVTYPE)
maindata <- maindata[maindata$FATALITIES !=0 |
maindata$INJURIES !=0 |
maindata$PROPDMG !=0 |
maindata$CROPDMG !=0, ]Now our data are readi to be analyzed #RESULTS Q1: Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
fatalities <- aggregate(FATALITIES ~ EVTYPE, data=maindata, sum)
injuries <- aggregate(INJURIES ~ EVTYPE, data=maindata, sum)
fatalities <- arrange(fatalities,desc(FATALITIES),EVTYPE)[1:10,]
injuries <- arrange(injuries,desc(INJURIES),EVTYPE)[1:10,]
ggplot(fatalities, aes(x = EVTYPE, y= FATALITIES)) +
geom_bar(stat = "identity", fill = "red", width = NULL) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Event Type") + ylab("Fatalities")
ggplot(injuries, aes(x = EVTYPE, y= INJURIES)) +
geom_bar(stat = "identity", fill = "orange", width = NULL) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Event Type") + ylab("INJURIES")
Q2: across the United States, which types of events have the greatest
economic consequences?
maindata$PROPDMGEXP <- gsub("[Hh]", "2", maindata$PROPDMGEXP)
maindata$PROPDMGEXP <- gsub("[Kk]", "3", maindata$PROPDMGEXP)
maindata$PROPDMGEXP <- gsub("[Mm]", "6", maindata$PROPDMGEXP)
maindata$PROPDMGEXP <- gsub("[Bb]", "9", maindata$PROPDMGEXP)
maindata$PROPDMGEXP <- gsub("\\+", "1", maindata$PROPDMGEXP)
maindata$PROPDMGEXP <- gsub("\\?|\\-|\\ ", "0", maindata$PROPDMGEXP)
maindata$PROPDMGEXP <- as.numeric(maindata$PROPDMGEXP)
maindata$CROPDMGEXP <- gsub("[Hh]", "2", maindata$CROPDMGEXP)
maindata$CROPDMGEXP <- gsub("[Kk]", "3", maindata$CROPDMGEXP)
maindata$CROPDMGEXP <- gsub("[Mm]", "6", maindata$CROPDMGEXP)
maindata$CROPDMGEXP <- gsub("[Bb]", "9", maindata$CROPDMGEXP)
maindata$CROPDMGEXP <- gsub("\\+", "1", maindata$CROPDMGEXP)
maindata$CROPDMGEXP <- gsub("\\-|\\?|\\ ", "0", maindata$CROPDMGEXP)
maindata$CROPDMGEXP <- as.numeric(maindata$CROPDMGEXP)
maindata$PROPDMGEXP[is.na(maindata$PROPDMGEXP)] <- 0
maindata$CROPDMGEXP[is.na(maindata$CROPDMGEXP)] <- 0
maindata <- mutate(maindata,
PROPDMGTOTAL = PROPDMG * (10 ^ PROPDMGEXP),
CROPDMGTOTAL = CROPDMG * (10 ^ CROPDMGEXP))Summing economic consequencess
Economic_data <- aggregate(cbind(PROPDMGTOTAL, CROPDMGTOTAL) ~ EVTYPE, data = maindata, FUN=sum)
Economic_data$ECONOMIC_LOSS <- Economic_data$PROPDMGTOTAL + Economic_data$CROPDMGTOTAL
Economic_data <- Economic_data[order(Economic_data$ECONOMIC_LOSS, decreasing = TRUE), ]
worsteconomicevents <- Economic_data[1:10,c(1,4)]
worsteconomicevents## EVTYPE ECONOMIC_LOSS
## 48 FLOOD 147456390150
## 88 HURRICANE/TYPHOON 71913712800
## 141 STORM SURGE 43193541000
## 149 TORNADO 24887430720
## 66 HAIL 17056759620
## 46 FLASH FLOOD 16541801360
## 86 HURRICANE 14554229010
## 32 DROUGHT 14413057000
## 152 TROPICAL STORM 8320186550
## 83 HIGH WIND 5881288560Loss per event type
ggplot(worsteconomicevents, aes(x = EVTYPE, y = ECONOMIC_LOSS)) +
geom_bar(stat = "identity", fill = "green") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab("Event Type") + ylab("Total Prop & Crop Damages (USD)") +
ggtitle("Total economic loss in the US in the period 1996 - 2011 by weather event")