Severe Weather Events Report
zrseah
2024-03-30
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.
Key Findings
- Generally, tornades stand out as the most catastrophic event to both the population health and the economy.
- The five events with the highest fatality rates include tornadoes, excessive heat, flash floods, heatwaves, and lightning strikes.
- Tornadoes, thunderstorm winds, floods, excessive heat, and lightning strikes rank among the top five events responsible for the highest number of injuries.
- When considering property damage, tornadoes, flash floods, thunderstorm winds, floods, and tsunami winds emerge as the top five culprits.
- Hailstorms, flash floods, floods, tsunami winds, and tornadoes are the primary contributors to crop damage, ranking in the top five in this regard.
Data Processing
Load data
stormdf <- read.csv("~/Online Courses/Johns Hopkins University Data Science Foundations using R Specialization/Assignment/Module 5/RepData_PeerAssessment2/repdata_data_StormData.csv")Load libraries
library('magrittr')
library('dplyr')
library('ggplot2')
library('patchwork')Extract useful columns for analysis
stormdf2 <- stormdf %>%
select(REFNUM, COUNTYNAME, STATE, EVTYPE, FATALITIES, INJURIES, PROPDMG, CROPDMG)See structure of data
str(stormdf2)## 'data.frame': 902297 obs. of 8 variables:
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...
## $ COUNTYNAME: chr "MOBILE" "BALDWIN" "FAYETTE" "MADISON" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ EVTYPE : chr "TORNADO" "TORNADO" "TORNADO" "TORNADO" ...
## $ 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 ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...Data Cleaning
## Ensure all character entries are capitalized
stormdf2$COUNTYNAME <- toupper(stormdf2$COUNTYNAME)
stormdf2$STATE <- toupper(stormdf2$STATE)
stormdf2$EVTYPE <- toupper(stormdf2$EVTYPE)
## Remove leading and trailing spaces
stormdf2$COUNTYNAME <- trimws(as.character(stormdf2$COUNTYNAME))
stormdf2$STATE <- trimws(as.character(stormdf2$STATE))
stormdf2$EVTYPE <- trimws(as.character(stormdf2$EVTYPE))Results
Impact of event types on both population health and economy
## Create a subset of data on total fatalities and injuries combined for each event type
pophealthdf <- aggregate(cbind(FATALITIES, INJURIES) ~ EVTYPE, data = stormdf2, sum) %>%
mutate(TOTAL_FATALITIES_AND_INJURIES = FATALITIES + INJURIES) %>%
select(EVTYPE, TOTAL_FATALITIES_AND_INJURIES)## Create a subset of data on total property and crop damages combined for each event type
econdf <- aggregate(cbind(CROPDMG, PROPDMG) ~ EVTYPE, data = stormdf2, sum) %>%
mutate(TOTAL_PROP_CROPDMG = CROPDMG + PROPDMG) %>%
select(EVTYPE, TOTAL_PROP_CROPDMG)Summary of total fatalities and injuries combined across the top 5 event types
top5_pophealthdf <- pophealthdf %>%
arrange(desc(TOTAL_FATALITIES_AND_INJURIES)) %>%
top_n(5, TOTAL_FATALITIES_AND_INJURIES)
summary(top5_pophealthdf)## EVTYPE TOTAL_FATALITIES_AND_INJURIES
## Length:5 Min. : 6046
## Class :character 1st Qu.: 7259
## Mode :character Median : 7461
## Mean :25235
## 3rd Qu.: 8428
## Max. :96979Summary of total property and crop damage combined across the top 5 event types
top5_econdf <- econdf %>%
arrange(desc(TOTAL_PROP_CROPDMG)) %>%
top_n(5, TOTAL_PROP_CROPDMG)
summary(top5_econdf)## EVTYPE TOTAL_PROP_CROPDMG
## Length:5 Min. :1067976
## Class :character 1st Qu.:1268290
## Mode :character Median :1445306
## Mean :1738645
## 3rd Qu.:1599375
## Max. :3312277plot1_colour <- c("HAIL" = "honeydew4",
"FLOOD" = "maroon4",
"FLASH FLOOD" = "lightsteelblue4",
"THUNDERSTORM WIND" ="plum4",
"LIGHTNING" = "paleturquoise4",
"TORNADO" = "bisque4",
"TSTM WIND" = "lavenderblush4",
"EXCESSIVE HEAT" = "indianred4")
pophealthplot <- ggplot(top5_pophealthdf, aes(x = EVTYPE, y = TOTAL_FATALITIES_AND_INJURIES, fill = EVTYPE)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = plot1_colour) +
labs(x = "Event_Type", y = "Total Fatalities and Injuries",
title = "Total Fatalities and Injuries by Event Type",
fill = "Event Type") +
theme(axis.title = element_text(size = 10),
plot.title = element_text(size = 14),
legend.title = element_text(size = 10),
legend.text = element_text(size = 8))
econplot <- ggplot(top5_econdf, aes(x = EVTYPE, y = TOTAL_PROP_CROPDMG, fill = EVTYPE)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = plot1_colour) +
labs(x = "Event_Type", y = "Total_Properties_and_Crops_Damage_($)",
title = "Total Properties and Crop Damage by Event Type",
fill = "Event Type") +
theme(axis.title = element_text(size = 10),
plot.title = element_text(size = 14),
legend.title = element_text(size = 10),
legend.text = element_text(size = 8))
combined_pophealth_econ_plot <- pophealthplot + econplot + plot_layout(nrow = 2)
combined_pophealth_econ_plot
Impact of event types on population health
## Create subset of data on total fatalities and injuries for each event type
fatdf <- aggregate(FATALITIES ~ EVTYPE, data = stormdf2, sum)
injdf <- aggregate(INJURIES ~ EVTYPE, data = stormdf2, sum)Summary of total number of fatalities across the top 5 event types
top5_evtypes_fat <- fatdf %>%
arrange(desc(FATALITIES)) %>%
top_n(5, FATALITIES)
summary(top5_evtypes_fat)## EVTYPE FATALITIES
## Length:5 Min. : 816
## Class :character 1st Qu.: 937
## Mode :character Median : 978
## Mean :2053
## 3rd Qu.:1903
## Max. :5633Summary of total number of injuries across the top 5 event types
top5_evtypes_inj <- injdf %>%
arrange(desc(INJURIES)) %>%
top_n(5, INJURIES)
summary(top5_evtypes_inj)## EVTYPE INJURIES
## Length:5 Min. : 5230
## Class :character 1st Qu.: 6525
## Mode :character Median : 6789
## Mean :23369
## 3rd Qu.: 6957
## Max. :91346fat_inj_colour <- c("EXCESSIVE HEAT" = "indianred4",
"FLOOD" = "maroon4",
"FLASH FLOOD" = "lightsteelblue4",
"HEAT" ="lightpink4",
"LIGHTNING" = "paleturquoise4",
"TORNADO" = "bisque4",
"TSTM WIND" = "lavenderblush4")
fat <- ggplot(top5_evtypes_fat, aes(x = EVTYPE, y = FATALITIES, fill = EVTYPE)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = fat_inj_colour) +
labs(x = "Event_Type" , y = "Fatalities_Count",
title = "Total Fatalities by Event Type",
fill = "Event Type") +
theme(axis.title = element_text(size = 10),
plot.title = element_text(size = 14),
legend.title = element_text(size = 10),
legend.text = element_text(size = 8))
inj <- ggplot(top5_evtypes_inj, aes(x = EVTYPE, y = INJURIES, fill = EVTYPE)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = fat_inj_colour) +
labs(x = "Event_Type" , y = "Injuries_Count",
title = "Total Injuries by Event Type",
fill = "Event Type") +
theme(axis.title = element_text(size = 10),
plot.title = element_text(size = 14),
legend.title = element_text(size = 10),
legend.text = element_text(size = 8))
combined_fat_inj <- fat + inj + plot_layout(nrow = 2)
combined_fat_inj
Impact of event types on the economy
## Create a subset of data on total property and crop damage for each event type
property <- aggregate(PROPDMG ~ EVTYPE, data = stormdf2, sum)
crop <- aggregate(CROPDMG ~ EVTYPE, data = stormdf2, sum)Summary of total property damage across the top 5 event types
top5_propdmg <- property %>%
arrange(desc(PROPDMG)) %>%
top_n(5, PROPDMG)
summary(top5_propdmg)## EVTYPE PROPDMG
## Length:5 Min. : 876844
## Class :character 1st Qu.: 899938
## Mode :character Median :1336104
## Mean :1549064
## 3rd Qu.:1420175
## Max. :3212258Summary of total crop damage across the top 5 event types
top5_cropdmg <- crop %>%
arrange(desc(CROPDMG)) %>%
top_n(5, CROPDMG)
summary(top5_cropdmg)## EVTYPE CROPDMG
## Length:5 Min. :100019
## Class :character 1st Qu.:109203
## Mode :character Median :168038
## Mean :227211
## 3rd Qu.:179201
## Max. :579596prop_cropdmg_colour <- c( "HAIL" = "honeydew4",
"FLOOD" = "maroon4",
"FLASH FLOOD" = "lightsteelblue4",
"THUNDERSTORM WIND" ="plum4",
"LIGHTNING" = "paleturquoise4",
"TORNADO" = "bisque4",
"TSTM WIND" = "lavenderblush4")
propdmg <- ggplot(top5_propdmg, aes(x = EVTYPE, y = PROPDMG, fill = EVTYPE)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = prop_cropdmg_colour) +
labs(x = "Event_Type", y = "Property_Damage_($)",
title = "Total Property Damage by Event Type",
fill = "Event Type") +
theme(axis.title = element_text(size = 10),
plot.title = element_text(size = 14),
legend.title = element_text(size = 10),
legend.text = element_text(size = 8))
cropdmg <- ggplot(top5_cropdmg, aes(x = EVTYPE, y = CROPDMG, fill = EVTYPE)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = prop_cropdmg_colour) +
labs(x = "Event_Type", y = "Crop_Damage_($)",
title = "Total Crop Damage by Event Type",
fill = "Event Type") +
theme(axis.title = element_text(size = 10),
plot.title = element_text(size = 14),
legend.title = element_text(size = 10),
legend.text = element_text(size = 8))
combined_prop_cropdmg <- propdmg + cropdmg + plot_layout(nrow = 2)
combined_prop_cropdmg