Public health and economic problems as a result of the storms and other severe weather events in the United States
12/11/2022
library(knitr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(data.table)##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
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## between, first, lastlibrary(ggplot2)
library(tidyr)
directory=getwd()
workspace=getwd()
workspace=sprintf("%s/.RData",workspace)1.- 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.
The purpose of this analysis is to prevent such negative outcomes to the extent possible.
The two questions to be answered are:
Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
Across the United States, which types of events have the greatest economic consequences?
2.- Data Processing
2.1 Data Loading
2.1.- Code for reading in the dataset and/or processing the data.
dataset <- read.csv(bzfile("repdata_data_StormData.csv.bz2"))
head(dataset)## 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 62.2.- 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.
dataset$BGN_DATE <- as.Date(dataset$BGN_DATE, "%m/%d/%Y %H:%M:%S")
summary(dataset$BGN_DATE)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## "1950-01-03" "1995-04-20" "2002-03-18" "1998-12-27" "2007-07-28" "2011-11-30"2.3.- Prepare data for question 1:
¿Most harmful event types to population health?
Group data by variable “EVTYPE” and calculate harmful by FATALITIES and by INJURIES.
healthdata <- dataset %>%
group_by(EVTYPE) %>%
summarise(FATALITIES = sum(FATALITIES), INJURIES = sum(INJURIES) ) %>% arrange(desc(FATALITIES+INJURIES)) %>% as.data.table()
mostHarm <- healthdata[1:10,]
print(mostHarm)## EVTYPE FATALITIES INJURIES
## 1: TORNADO 5633 91346
## 2: EXCESSIVE HEAT 1903 6525
## 3: TSTM WIND 504 6957
## 4: FLOOD 470 6789
## 5: LIGHTNING 816 5230
## 6: HEAT 937 2100
## 7: FLASH FLOOD 978 1777
## 8: ICE STORM 89 1975
## 9: THUNDERSTORM WIND 133 1488
## 10: WINTER STORM 206 13212.4.- Prepare data for question 2:
¿Event types with the greatest economic consequences?
Property damage is indicated by two variables PROPDMG and PROPDMGEXP, same for crop damage. This step firstly converts magnitude characters in PROPDMGEXP and CROPDMGEXP to numeric values and multiplies the values with PROPDMG and CROPDMG respectively.
dataset1 <- dataset
dataset1$PROPDMGEXP <- recode(dataset1$PROPDMGEXP,'K'=1000,'M'=1000000,'B'=1000000000,.default=1)
dataset1$CROPDMGEXP <- recode(dataset1$CROPDMGEXP,'K'=1000,'M'=1000000,'B'=1000000000,.default=1)
dataset1$PROPDMGVALUE <- dataset1$PROPDMG*dataset1$PROPDMGEXP
dataset1$CROPDMGVALUE <- dataset1$CROPDMG*dataset1$CROPDMGEXPThe greatest economic consequences are evaluated by property and crop damage collectively. The top 10 event types with greatest economic consequences are shown below.
econdata <- dataset1 %>%
group_by(EVTYPE) %>%
summarise(PROPDMGVALUE = sum(PROPDMGVALUE), CROPDMGVALUE = sum(CROPDMGVALUE) ) %>% arrange(desc(PROPDMGVALUE + CROPDMGVALUE)) %>% as.data.table()
mostEcon <- econdata[1:10,]
print(mostEcon)## EVTYPE PROPDMGVALUE CROPDMGVALUE
## 1: FLOOD 144657709807 5661968450
## 2: HURRICANE/TYPHOON 69305840000 2607872800
## 3: TORNADO 56925660790 414953270
## 4: STORM SURGE 43323536000 5000
## 5: HAIL 15727367053 3025537890
## 6: FLASH FLOOD 16140812067 1421317100
## 7: DROUGHT 1046106000 13972566000
## 8: HURRICANE 11868319010 2741910000
## 9: RIVER FLOOD 5118945500 5029459000
## 10: ICE STORM 3944927860 50221135003.- Results.
3.1.- The harm of events to population health is evaluated by fatalities and injuries caused collectively. The top 10 most harmful event types are illustrated below.
plotdata <- gather(mostHarm, TYPE, VALUE, FATALITIES:INJURIES)
ggplot(plotdata, aes(x=reorder(EVTYPE,-VALUE), y=VALUE, fill=TYPE)) +
geom_bar(stat = "identity") +
labs(title = "Harmful Events to Population Health", x =" Event Type", y = "Count") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1.0, size = 5.5))
The plot reveals that tornados are the event the most harmful for population health followed by excessive heat
3.2.- The greatest economic consequences are evaluated by property and crop damage collectively. The top 10 event types with greatest economic consequences are shown below.
plotdata2 <- gather(mostEcon, TYPE, VALUE, PROPDMGVALUE:CROPDMGVALUE)
ggplot(plotdata2, aes(x=reorder(EVTYPE,-VALUE), y=VALUE,
fill=factor(TYPE, labels=c("crop damage", "property damage")))) +
geom_bar(stat = "identity") +
labs(title ="Economically Harmful Events", x = "Event Type", y = "Count") +
guides(fill = guide_legend(title = "Type of damage")) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1.0, size = 5.5))
It can be seen that floods cause the greatest economic consequences