The storms most harmful events towards population health
Dan
2023-07-24
Assignment
The basic goal of this assignment is to explore the NOAA Storm Database and answer some basic questions about severe weather events. You must use the database to answer the questions below and show the code for your entire analysis. Your analysis can consist of tables, figures, or other summaries. You may use any R package you want to support your analysis.
Questions
Your data analysis must address the following questions:
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?
Consider writing your report as if it were to be read by a government or municipal manager who might be responsible for preparing for severe weather events and will need to prioritize resources for different types of events. However, there is no need to make any specific recommendations in your report.
Data Processing
Software Environment information
sessionInfo()## R version 4.3.0 (2023-04-21)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.4.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/London
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] digest_0.6.31 R6_2.5.1 fastmap_1.1.1 xfun_0.39
## [5] cachem_1.0.8 knitr_1.43 htmltools_0.5.5 rmarkdown_2.22
## [9] cli_3.6.1 sass_0.4.6 jquerylib_0.1.4 compiler_4.3.0
## [13] rstudioapi_0.14 tools_4.3.0 evaluate_0.21 bslib_0.5.0
## [17] yaml_2.3.7 rlang_1.1.1 jsonlite_1.8.5Loading required packages
library(dplyr)
library(ggplot2)
library(data.table)
library(R.utils)
library(tidyr)Read the data set
if (!file.exists("stormdata.csv.bz2")) {
url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
download.file(url, "stormdata.csv.bz2")
bunzip2("stormdata.csv.bz2", "stormdata.csv", remove=FALSE)
stormdf <- data.table::fread("stormdata.csv", fill=TRUE, header=TRUE)
} else{
stormdf <- data.table::fread("stormdata.csv", fill=TRUE, header=TRUE)
}
head(stormdf)## STATE__ BGN_DATE BGN_TIME TIME_ZONE COUNTY COUNTYNAME STATE
## 1: 1.00 4/18/1950 0:00:00 0130 CST 97 MOBILE AL
## 2: 1.00 4/18/1950 0:00:00 0145 CST 3 BALDWIN AL
## 3: 1.00 2/20/1951 0:00:00 1600 CST 57 FAYETTE AL
## 4: 1.00 6/8/1951 0:00:00 0900 CST 89 MADISON AL
## 5: 1.00 11/15/1951 0:00:00 1500 CST 43 CULLMAN AL
## 6: 1.00 11/15/1951 0:00:00 2000 CST 77 LAUDERDALE AL
## EVTYPE BGN_RANGE BGN_AZI BGN_LOCATI END_DATE END_TIME COUNTY_END COUNTYENDN
## 1: TORNADO 0 0 NA
## 2: TORNADO 0 0 NA
## 3: TORNADO 0 0 NA
## 4: TORNADO 0 0 NA
## 5: TORNADO 0 0 NA
## 6: TORNADO 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 6First look to dataset
dim(stormdf)## [1] 902297 37names(stormdf)## [1] "STATE__" "BGN_DATE" "BGN_TIME" "TIME_ZONE" "COUNTY"
## [6] "COUNTYNAME" "STATE" "EVTYPE" "BGN_RANGE" "BGN_AZI"
## [11] "BGN_LOCATI" "END_DATE" "END_TIME" "COUNTY_END" "COUNTYENDN"
## [16] "END_RANGE" "END_AZI" "END_LOCATI" "LENGTH" "WIDTH"
## [21] "F" "MAG" "FATALITIES" "INJURIES" "PROPDMG"
## [26] "PROPDMGEXP" "CROPDMG" "CROPDMGEXP" "WFO" "STATEOFFIC"
## [31] "ZONENAMES" "LATITUDE" "LONGITUDE" "LATITUDE_E" "LONGITUDE_"
## [36] "REMARKS" "REFNUM"str(stormdf)## Classes 'data.table' and 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : chr "1.00" "1.00" "1.00" "1.00" ...
## $ 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 ...
## - attr(*, ".internal.selfref")=<externalptr>summary(stormdf)## STATE__ BGN_DATE BGN_TIME TIME_ZONE
## Length:902297 Length:902297 Length:902297 Length:902297
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## COUNTY COUNTYNAME STATE EVTYPE
## Min. : 0.0 Length:902297 Length:902297 Length:902297
## 1st Qu.: 31.0 Class :character Class :character Class :character
## Median : 75.0 Mode :character Mode :character Mode :character
## Mean :100.6
## 3rd Qu.:131.0
## Max. :873.0
##
## BGN_RANGE BGN_AZI BGN_LOCATI END_DATE
## Min. : 0.000 Length:902297 Length:902297 Length:902297
## 1st Qu.: 0.000 Class :character Class :character Class :character
## Median : 0.000 Mode :character Mode :character Mode :character
## Mean : 1.484
## 3rd Qu.: 1.000
## Max. :3749.000
##
## END_TIME COUNTY_END COUNTYENDN END_RANGE
## Length:902297 Min. :0 Mode:logical Min. : 0.0000
## Class :character 1st Qu.:0 NA's:902297 1st Qu.: 0.0000
## Mode :character Median :0 Median : 0.0000
## Mean :0 Mean : 0.9862
## 3rd Qu.:0 3rd Qu.: 0.0000
## Max. :0 Max. :925.0000
##
## END_AZI END_LOCATI LENGTH WIDTH
## Length:902297 Length:902297 Min. : 0.0000 Min. : 0.000
## Class :character Class :character 1st Qu.: 0.0000 1st Qu.: 0.000
## Mode :character Mode :character Median : 0.0000 Median : 0.000
## Mean : 0.2301 Mean : 7.503
## 3rd Qu.: 0.0000 3rd Qu.: 0.000
## Max. :2315.0000 Max. :4400.000
##
## F MAG FATALITIES INJURIES
## Min. :0.0 Min. : 0.0 Min. : 0.0000 Min. : 0.0000
## 1st Qu.:0.0 1st Qu.: 0.0 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median :1.0 Median : 50.0 Median : 0.0000 Median : 0.0000
## Mean :0.9 Mean : 46.9 Mean : 0.0168 Mean : 0.1557
## 3rd Qu.:1.0 3rd Qu.: 75.0 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## Max. :5.0 Max. :22000.0 Max. :583.0000 Max. :1700.0000
## NA's :843563
## PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## Min. : 0.00 Length:902297 Min. : 0.000 Length:902297
## 1st Qu.: 0.00 Class :character 1st Qu.: 0.000 Class :character
## Median : 0.00 Mode :character Median : 0.000 Mode :character
## Mean : 12.06 Mean : 1.527
## 3rd Qu.: 0.50 3rd Qu.: 0.000
## Max. :5000.00 Max. :990.000
##
## WFO STATEOFFIC ZONENAMES LATITUDE
## Length:902297 Length:902297 Length:902297 Min. : 0
## Class :character Class :character Class :character 1st Qu.:2802
## Mode :character Mode :character Mode :character Median :3540
## Mean :2875
## 3rd Qu.:4019
## Max. :9706
## NA's :47
## LONGITUDE LATITUDE_E LONGITUDE_ REMARKS
## Min. :-14451 Min. : 0 Min. :-14455 Length:902297
## 1st Qu.: 7247 1st Qu.: 0 1st Qu.: 0 Class :character
## Median : 8707 Median : 0 Median : 0 Mode :character
## Mean : 6940 Mean :1452 Mean : 3509
## 3rd Qu.: 9605 3rd Qu.:3549 3rd Qu.: 8735
## Max. : 17124 Max. :9706 Max. :106220
## NA's :40
## REFNUM
## Min. : 1
## 1st Qu.:225575
## Median :451149
## Mean :451149
## 3rd Qu.:676723
## Max. :902297
## Processing data for analysis
storm2 <- stormdf %>%
select(c("EVTYPE","FATALITIES","INJURIES","PROPDMG","PROPDMGEXP","CROPDMG","CROPDMGEXP")) %>%
rename_all(tolower)
# select columns needed for the bar plot, group it by event type and calculate sum of both fatalities and injuries. Then, arrange it in descending order and slice the first 10 rows, then gather it and turning it into categorical variables for creating a grouped bar plot.
pop_health <-
storm2 %>% select(evtype, fatalities, injuries) %>%
group_by(evtype) %>%
summarize(fatalities = sum(fatalities), injuries = sum(injuries), .groups='drop') %>%
arrange(desc(fatalities), desc(injuries)) %>%
slice(1:10) %>%
gather(key = type, value = value, fatalities, injuries)
# the variable PROPDMGEXP is regarding property damage expenses, so it can be utilized to denote the events with greatest economic consequences
unique(storm2$propdmgexp)## [1] "K" "M" "" "B" "m" "+" "0" "5" "6" "?" "4" "2" "3" "h" "7" "H" "-" "1" "8"unique(storm2$cropdmgexp)## [1] "" "M" "K" "m" "B" "?" "0" "k" "2"cost <- function(x) {
if (x == "H")
1E-4
else if (x == "K")
1E-3
else if (x == "M")
1
else if (x == "B")
1E3
else
1-6
}
cost <- function(x) {
if (x == "H")
1E-4
else if (x == "K")
1E-3
else if (x == "M")
1
else if (x == "B")
1E3
else
1-6
}
economic <-
storm2 %>% select("evtype", "propdmg", "propdmgexp", "cropdmg", "cropdmgexp") %>%
mutate(prop_dmg = propdmg*sapply(propdmgexp, FUN = cost), crop_dmg = cropdmg*sapply(cropdmgexp, FUN = cost), .keep="unused") %>%
group_by(evtype) %>%
summarize(property = sum(prop_dmg), crop = sum(crop_dmg), .groups='drop') %>%
arrange(desc(property), desc(crop)) %>%
slice(1:10) %>%
gather(key = type, value = value, property, crop)Results
1. Across the United States, which types of events (as indicated in the EVTYPE variable) are most harmful with respect to population health?
ggplot(data=pop_health, aes(reorder(evtype, -value), value, fill=type)) +
geom_bar(position = "dodge", stat="identity") +
labs(x="Event Type", y="Count") +
theme_bw() +
theme(axis.text.x = element_text(angle = 20, vjust=0.7)) +
ggtitle("Total Number of Fatalities and Injuries of top 10 storm event types") +
scale_fill_manual(values=c("red", "blue"))
## 2. Across the United States, which types of events have the greatest
economic consequences?
ggplot(data=economic, aes(reorder(evtype, -value), value, fill=type)) +
geom_bar(position = "dodge", stat="identity") +
labs(x="Event Type", y="Count (millions)") +
theme_bw() +
theme(axis.text.x = element_text(angle = 25, vjust=0.5)) +
ggtitle("Total Cost of Property and Crop Damage by top 10 storm event types") +
scale_fill_manual(values=c("purple", "pink"))
# Conclusion
Based on the analysis, resources should be directed towards dealing with tornadoes for the safety and health of population by building better infrastructure or early warning systems. As for dealing with hurricanes and typhoons, there should be more funding for innovation in developing better systems and infrastructure to safeguard these properties and crops to prevent damages as much as possible.