Impact of Severe Weather Events on Public Health and Economy (NOAA Storm Database Analysis)
Zahra Esmaeilinezhad
2026-08-04
Synopsis
This report analyzes the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database to identify weather events most harmful to public health and those with the greatest economic consequences. The database covers storm events from 1950 to November 2011 across the United States. After loading and cleaning the data, we aggregated fatalities, injuries, and property/crop damages by event type. Our analysis reveals that tornadoes are by far the most harmful weather events with respect to population health, accounting for the highest number of both fatalities and injuries. For economic consequences, floods cause the greatest total property and crop damage in dollar terms. These findings suggest that emergency management resources should prioritize preparedness for tornadoes in terms of protecting human life and floods in terms of protecting economic assets. The analysis uses only the raw data provided and all transformations are documented herein.
Data Processing
Loading Required Libraries
# Load required libraries
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, unionlibrary(tidyr)
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(scales)
# Set seed for reproducibility
set.seed(42)Downloading and Loading the Data
# Download the file if it doesn't exist
storm_data <- read.csv("repdata_data_StormData.csv",
header = TRUE,
sep = ",",
stringsAsFactors = FALSE,
na.strings = c("", "NA"))
# Display basic structure
dim(storm_data)## [1] 902297 37Initial Data Exploration
# View the structure of the dataset
str(storm_data)## '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 NA NA NA NA ...
## $ BGN_LOCATI: chr NA NA NA NA ...
## $ END_DATE : chr NA NA NA NA ...
## $ END_TIME : chr NA NA NA NA ...
## $ 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 NA NA NA NA ...
## $ END_LOCATI: chr NA NA NA NA ...
## $ 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 NA NA NA NA ...
## $ WFO : chr NA NA NA NA ...
## $ STATEOFFIC: chr NA NA NA NA ...
## $ ZONENAMES : chr NA NA NA NA ...
## $ 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 NA NA NA NA ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...# Check the first few rows
head(storm_data[, c("BGN_DATE", "EVTYPE", "FATALITIES", "INJURIES",
"PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")], 10)## BGN_DATE EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG
## 1 4/18/1950 0:00:00 TORNADO 0 15 25.0 K 0
## 2 4/18/1950 0:00:00 TORNADO 0 0 2.5 K 0
## 3 2/20/1951 0:00:00 TORNADO 0 2 25.0 K 0
## 4 6/8/1951 0:00:00 TORNADO 0 2 2.5 K 0
## 5 11/15/1951 0:00:00 TORNADO 0 2 2.5 K 0
## 6 11/15/1951 0:00:00 TORNADO 0 6 2.5 K 0
## 7 11/16/1951 0:00:00 TORNADO 0 1 2.5 K 0
## 8 1/22/1952 0:00:00 TORNADO 0 0 2.5 K 0
## 9 2/13/1952 0:00:00 TORNADO 1 14 25.0 K 0
## 10 2/13/1952 0:00:00 TORNADO 0 0 25.0 K 0
## CROPDMGEXP
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## 7 <NA>
## 8 <NA>
## 9 <NA>
## 10 <NA># Summary of key variables
summary(storm_data[, c("FATALITIES", "INJURIES", "PROPDMG", "CROPDMG")])## FATALITIES INJURIES PROPDMG CROPDMG
## Min. : 0.00000 Min. : 0.0000 Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.00000 1st Qu.: 0.0000 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 0.00000 Median : 0.0000 Median : 0.00 Median : 0.000
## Mean : 0.01678 Mean : 0.1557 Mean : 12.06 Mean : 1.527
## 3rd Qu.: 0.00000 3rd Qu.: 0.0000 3rd Qu.: 0.50 3rd Qu.: 0.000
## Max. :583.00000 Max. :1700.0000 Max. :5000.00 Max. :990.000# Check number of unique event types
cat("Number of unique EVTYPE values:", length(unique(storm_data$EVTYPE)), "\n")## Number of unique EVTYPE values: 985Data Cleaning and Transformation
Step 1: Select Relevant Variables
# Select only the columns needed for our analysis
storm_clean <- storm_data %>%
select(
BGN_DATE,
EVTYPE,
FATALITIES,
INJURIES,
PROPDMG,
PROPDMGEXP,
CROPDMG,
CROPDMGEXP
)
cat("Dimensions after selecting relevant variables:", dim(storm_clean), "\n")## Dimensions after selecting relevant variables: 902297 8Step 2: Clean Event Types
# Convert EVTYPE to uppercase for consistency
storm_clean$EVTYPE <- toupper(trimws(storm_clean$EVTYPE))
# Check top event types by frequency
top_events <- storm_clean %>%
count(EVTYPE, sort = TRUE) %>%
head(20)
print(top_events)## EVTYPE n
## 1 HAIL 288661
## 2 TSTM WIND 219946
## 3 THUNDERSTORM WIND 82564
## 4 TORNADO 60652
## 5 FLASH FLOOD 54278
## 6 FLOOD 25327
## 7 THUNDERSTORM WINDS 20843
## 8 HIGH WIND 20214
## 9 LIGHTNING 15755
## 10 HEAVY SNOW 15708
## 11 HEAVY RAIN 11742
## 12 WINTER STORM 11433
## 13 WINTER WEATHER 7045
## 14 FUNNEL CLOUD 6844
## 15 MARINE TSTM WIND 6175
## 16 MARINE THUNDERSTORM WIND 5812
## 17 WATERSPOUT 3797
## 18 STRONG WIND 3569
## 19 URBAN/SML STREAM FLD 3392
## 20 WILDFIRE 2761Step 3: Convert Property Damage Exponents
The PROPDMGEXP and CROPDMGEXP columns contain multiplier codes that need to be converted to actual numeric values. According to the NOAA documentation:
K or k = thousands (10^3) M or m = millions (10^6) B or b = billions (10^9) H or h = hundreds (10^2) Numeric values 0-8 represent the exponent (10^n) Empty/unknown values are treated as 1 (10^0)
# Function to convert damage exponent codes to numeric multipliers
convert_exp <- function(exp_code) {
exp_code <- toupper(trimws(as.character(exp_code)))
multiplier <- case_when(
exp_code == "K" ~ 1e3,
exp_code == "M" ~ 1e6,
exp_code == "B" ~ 1e9,
exp_code == "H" ~ 1e2,
exp_code == "0" ~ 1e0,
exp_code == "1" ~ 1e1,
exp_code == "2" ~ 1e2,
exp_code == "3" ~ 1e3,
exp_code == "4" ~ 1e4,
exp_code == "5" ~ 1e5,
exp_code == "6" ~ 1e6,
exp_code == "7" ~ 1e7,
exp_code == "8" ~ 1e8,
exp_code == "NA" ~ 1,
TRUE ~ 1 # Default: treat as 1 for unknown codes
)
return(multiplier)
}
# Check unique exponent values before conversion
cat("Unique PROPDMGEXP values:\n")## Unique PROPDMGEXP values:print(table(storm_clean$PROPDMGEXP, useNA = "always"))##
## - ? + 0 1 2 3 4 5 6 7
## 1 8 5 216 25 13 4 4 28 4 5
## 8 B h H K m M <NA>
## 1 40 1 6 424665 7 11330 465934cat("\nUnique CROPDMGEXP values:\n")##
## Unique CROPDMGEXP values:print(table(storm_clean$CROPDMGEXP, useNA = "always"))##
## ? 0 2 B k K m M <NA>
## 7 19 1 9 21 281832 1 1994 618413# Apply the conversion function to calculate actual damage values
storm_clean <- storm_clean %>%
mutate(
PROPDMGEXP_NUM = convert_exp(PROPDMGEXP),
CROPDMGEXP_NUM = convert_exp(CROPDMGEXP),
PROP_DAMAGE = PROPDMG * PROPDMGEXP_NUM,
CROP_DAMAGE = CROPDMG * CROPDMGEXP_NUM,
TOTAL_DAMAGE = PROP_DAMAGE + CROP_DAMAGE
)
# Verify the transformation
cat("\nSummary of calculated damage values (in USD):\n")##
## Summary of calculated damage values (in USD):summary(storm_clean[, c("PROP_DAMAGE", "CROP_DAMAGE", "TOTAL_DAMAGE")])## PROP_DAMAGE CROP_DAMAGE TOTAL_DAMAGE
## Min. :0.000e+00 Min. :0.000e+00 Min. :0.00e+00
## 1st Qu.:0.000e+00 1st Qu.:0.000e+00 1st Qu.:0.00e+00
## Median :0.000e+00 Median :0.000e+00 Median :0.00e+00
## Mean :4.746e+05 Mean :5.442e+04 Mean :5.29e+05
## 3rd Qu.:5.000e+02 3rd Qu.:0.000e+00 3rd Qu.:1.00e+03
## Max. :1.150e+11 Max. :5.000e+09 Max. :1.15e+11Step 4: Filter Records with Non-Zero Values
# For health analysis: filter records with fatalities or injuries
health_data <- storm_clean %>%
filter(FATALITIES > 0 | INJURIES > 0)
cat("Records with health impact:", nrow(health_data), "\n")## Records with health impact: 21929# For economic analysis: filter records with property or crop damage
econ_data <- storm_clean %>%
filter(TOTAL_DAMAGE > 0)
cat("Records with economic impact:", nrow(econ_data), "\n")## Records with economic impact: 245031Step 5: Aggregate Data by Event Type
# Aggregate health impacts by event type
health_by_event <- storm_clean %>%
group_by(EVTYPE) %>%
summarise(
Total_Fatalities = sum(FATALITIES, na.rm = TRUE),
Total_Injuries = sum(INJURIES, na.rm = TRUE),
Total_Health = Total_Fatalities + Total_Injuries,
.groups = "drop"
) %>%
arrange(desc(Total_Health))
# Top 15 events by total health impact
top15_health <- head(health_by_event, 15)
cat("Top 15 Events by Total Health Impact:\n")## Top 15 Events by Total Health Impact:print(top15_health)## # A tibble: 15 × 4
## EVTYPE Total_Fatalities Total_Injuries Total_Health
## <chr> <dbl> <dbl> <dbl>
## 1 TORNADO 5633 91346 96979
## 2 EXCESSIVE HEAT 1903 6525 8428
## 3 TSTM WIND 504 6957 7461
## 4 FLOOD 470 6789 7259
## 5 LIGHTNING 816 5230 6046
## 6 HEAT 937 2100 3037
## 7 FLASH FLOOD 978 1777 2755
## 8 ICE STORM 89 1975 2064
## 9 THUNDERSTORM WIND 133 1488 1621
## 10 WINTER STORM 206 1321 1527
## 11 HIGH WIND 248 1137 1385
## 12 HAIL 15 1361 1376
## 13 HURRICANE/TYPHOON 64 1275 1339
## 14 HEAVY SNOW 127 1021 1148
## 15 WILDFIRE 75 911 986# Aggregate economic impacts by event type
econ_by_event <- storm_clean %>%
group_by(EVTYPE) %>%
summarise(
Total_PropDamage = sum(PROP_DAMAGE, na.rm = TRUE),
Total_CropDamage = sum(CROP_DAMAGE, na.rm = TRUE),
Total_Damage = sum(TOTAL_DAMAGE, na.rm = TRUE),
.groups = "drop"
) %>%
arrange(desc(Total_Damage))
# Top 15 events by total economic impact
top15_econ <- head(econ_by_event, 15)
cat("Top 15 Events by Total Economic Impact (USD):\n")## Top 15 Events by Total Economic Impact (USD):print(top15_econ)## # A tibble: 15 × 4
## EVTYPE Total_PropDamage Total_CropDamage Total_Damage
## <chr> <dbl> <dbl> <dbl>
## 1 FLOOD 144657709807 5661968450 150319678257
## 2 HURRICANE/TYPHOON 69305840000 2607872800 71913712800
## 3 TORNADO 56947380676. 414953270 57362333946.
## 4 STORM SURGE 43323536000 5000 43323541000
## 5 HAIL 15735267513. 3025954473 18761221986.
## 6 FLASH FLOOD 16822723978. 1421317100 18244041078.
## 7 DROUGHT 1046106000 13972566000 15018672000
## 8 HURRICANE 11868319010 2741910000 14610229010
## 9 RIVER FLOOD 5118945500 5029459000 10148404500
## 10 ICE STORM 3944927860 5022113500 8967041360
## 11 TROPICAL STORM 7703890550 678346000 8382236550
## 12 WINTER STORM 6688497251 26944000 6715441251
## 13 HIGH WIND 5270046295 638571300 5908617595
## 14 WILDFIRE 4765114000 295472800 5060586800
## 15 TSTM WIND 4493058495 554007350 5047065845Results
Question 1: Which Events Are Most Harmful to Population Health?
# Prepare data for plotting - top 10 by fatalities
top10_fatal <- health_by_event %>%
arrange(desc(Total_Fatalities)) %>%
head(10) %>%
mutate(EVTYPE = factor(EVTYPE, levels = rev(EVTYPE)))
# Top 10 by injuries
top10_injury <- health_by_event %>%
arrange(desc(Total_Injuries)) %>%
head(10) %>%
mutate(EVTYPE = factor(EVTYPE, levels = rev(EVTYPE)))
# Plot 1: Fatalities
p1 <- ggplot(top10_fatal,
aes(x = EVTYPE, y = Total_Fatalities, fill = Total_Fatalities)) +
geom_bar(stat = "identity", color = "black", size = 0.3) +
coord_flip() +
scale_fill_gradient(low = "#FFC0C0", high = "#CC0000",
name = "Fatalities",
labels = comma) +
scale_y_continuous(labels = comma) +
labs(
title = "Top 10 Events by Total Fatalities",
subtitle = "United States, 1950-2011",
x = "Event Type",
y = "Total Fatalities"
) +
theme_minimal(base_size = 11) +
theme(
plot.title = element_text(face = "bold", size = 12, color = "#CC0000"),
plot.subtitle = element_text(size = 10, color = "gray50"),
axis.text.y = element_text(size = 9, face = "bold"),
axis.text.x = element_text(size = 9),
legend.position = "none",
panel.grid.minor = element_blank()
)## Warning in geom_bar(stat = "identity", color = "black", size = 0.3): Ignoring
## unknown parameters: `size`# Plot 2: Injuries
p2 <- ggplot(top10_injury,
aes(x = EVTYPE, y = Total_Injuries, fill = Total_Injuries)) +
geom_bar(stat = "identity", color = "black", size = 0.3) +
coord_flip() +
scale_fill_gradient(low = "#FFD580", high = "#FF8C00",
name = "Injuries",
labels = comma) +
scale_y_continuous(labels = comma) +
labs(
title = "Top 10 Events by Total Injuries",
subtitle = "United States, 1950-2011",
x = "Event Type",
y = "Total Injuries"
) +
theme_minimal(base_size = 11) +
theme(
plot.title = element_text(face = "bold", size = 12, color = "#FF8C00"),
plot.subtitle = element_text(size = 10, color = "gray50"),
axis.text.y = element_text(size = 9, face = "bold"),
axis.text.x = element_text(size = 9),
legend.position = "none",
panel.grid.minor = element_blank()
)## Warning in geom_bar(stat = "identity", color = "black", size = 0.3): Ignoring
## unknown parameters: `size`# Combine plots side by side
grid.arrange(p1, p2,
ncol = 2,
top = grid::textGrob(
"Weather Events Most Harmful to Population Health (1950-2011)",
gp = grid::gpar(fontsize = 14, fontface = "bold")
))
Key Findings - Population Health:
The analysis clearly shows that TORNADO is by far the most dangerous weather event for human health:
# Summary table for top 10 health impacts
health_summary <- health_by_event %>%
head(10) %>%
mutate(
Total_Fatalities = format(Total_Fatalities, big.mark = ","),
Total_Injuries = format(Total_Injuries, big.mark = ","),
Total_Health = format(Total_Health, big.mark = ",")
)
knitr::kable(
health_summary,
col.names = c("Event Type", "Total Fatalities",
"Total Injuries", "Total Health Impact"),
caption = "Table 1: Top 10 Weather Events by Total Health Impact (1950-2011)",
align = c("l", "r", "r", "r")
)| Event Type | Total Fatalities | Total Injuries | Total Health Impact |
|---|---|---|---|
| TORNADO | 5,633 | 91,346 | 96,979 |
| EXCESSIVE HEAT | 1,903 | 6,525 | 8,428 |
| TSTM WIND | 504 | 6,957 | 7,461 |
| FLOOD | 470 | 6,789 | 7,259 |
| LIGHTNING | 816 | 5,230 | 6,046 |
| HEAT | 937 | 2,100 | 3,037 |
| FLASH FLOOD | 978 | 1,777 | 2,755 |
| ICE STORM | 89 | 1,975 | 2,064 |
| THUNDERSTORM WIND | 133 | 1,488 | 1,621 |
| WINTER STORM | 206 | 1,321 | 1,527 |
Question 2: Which Events Have the Greatest Economic Consequences?
# Prepare data for economic plots
# Top 10 by property damage
top10_prop <- econ_by_event %>%
arrange(desc(Total_PropDamage)) %>%
head(10) %>%
mutate(
EVTYPE = factor(EVTYPE, levels = rev(EVTYPE)),
Damage_Billions = Total_PropDamage / 1e9
)
# Top 10 by crop damage
top10_crop <- econ_by_event %>%
arrange(desc(Total_CropDamage)) %>%
head(10) %>%
mutate(
EVTYPE = factor(EVTYPE, levels = rev(EVTYPE)),
Damage_Billions = Total_CropDamage / 1e9
)
# Plot 3: Property Damage
p3 <- ggplot(top10_prop,
aes(x = EVTYPE, y = Damage_Billions, fill = Damage_Billions)) +
geom_bar(stat = "identity", color = "black", size = 0.3) +
coord_flip() +
scale_fill_gradient(low = "#B0C4DE", high = "#00008B",
name = "USD (Billions)") +
scale_y_continuous(labels = dollar_format(suffix = "B", prefix = "$")) +
labs(
title = "Top 10 Events by Property Damage",
subtitle = "United States, 1950-2011",
x = "Event Type",
y = "Total Property Damage (Billions USD)"
) +
theme_minimal(base_size = 11) +
theme(
plot.title = element_text(face = "bold", size = 12, color = "#00008B"),
plot.subtitle = element_text(size = 10, color = "gray50"),
axis.text.y = element_text(size = 9, face = "bold"),
axis.text.x = element_text(size = 8, angle = 15, hjust = 1),
legend.position = "none",
panel.grid.minor = element_blank()
)## Warning in geom_bar(stat = "identity", color = "black", size = 0.3): Ignoring
## unknown parameters: `size`# Plot 4: Crop Damage
p4 <- ggplot(top10_crop,
aes(x = EVTYPE, y = Damage_Billions, fill = Damage_Billions)) +
geom_bar(stat = "identity", color = "black", size = 0.3) +
coord_flip() +
scale_fill_gradient(low = "#90EE90", high = "#006400",
name = "USD (Billions)") +
scale_y_continuous(labels = dollar_format(suffix = "B", prefix = "$")) +
labs(
title = "Top 10 Events by Crop Damage",
subtitle = "United States, 1950-2011",
x = "Event Type",
y = "Total Crop Damage (Billions USD)"
) +
theme_minimal(base_size = 11) +
theme(
plot.title = element_text(face = "bold", size = 12, color = "#006400"),
plot.subtitle = element_text(size = 10, color = "gray50"),
axis.text.y = element_text(size = 9, face = "bold"),
axis.text.x = element_text(size = 8, angle = 15, hjust = 1),
legend.position = "none",
panel.grid.minor = element_blank()
)## Warning in geom_bar(stat = "identity", color = "black", size = 0.3): Ignoring
## unknown parameters: `size`# Combine plots
grid.arrange(p3, p4,
ncol = 2,
top = grid::textGrob(
"Weather Events with Greatest Economic Consequences (1950-2011)",
gp = grid::gpar(fontsize = 14, fontface = "bold")
))
# Top 10 by total damage
top10_total <- econ_by_event %>%
arrange(desc(Total_Damage)) %>%
head(10) %>%
mutate(EVTYPE = factor(EVTYPE, levels = rev(EVTYPE)))
# Reshape to long format for stacked bar
top10_long <- top10_total %>%
select(EVTYPE, Total_PropDamage, Total_CropDamage) %>%
pivot_longer(
cols = c(Total_PropDamage, Total_CropDamage),
names_to = "Damage_Type",
values_to = "Amount"
) %>%
mutate(
Amount_Billions = Amount / 1e9,
Damage_Type = recode(Damage_Type,
"Total_PropDamage" = "Property Damage",
"Total_CropDamage" = "Crop Damage")
)
# Figure 3: Stacked bar - Total Combined Economic Damage
p5 <- ggplot(top10_long,
aes(x = EVTYPE,
y = Amount_Billions,
fill = Damage_Type)) +
geom_bar(stat = "identity",
color = "black",
size = 0.3) +
coord_flip() +
scale_fill_manual(
values = c("Property Damage" = "#4472C4",
"Crop Damage" = "#70AD47"),
name = "Damage Type"
) +
scale_y_continuous(
labels = dollar_format(suffix = "B", prefix = "$")
) +
labs(
title = "Top 10 Weather Events by Total Economic Damage",
subtitle = "Combined Property and Crop Damage, United States (1950-2011)",
x = "Event Type",
y = "Total Damage (Billions USD)",
caption = "Source: NOAA Storm Database (1950-2011). Property damage dominates total economic losses."
) +
theme_minimal(base_size = 12) +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(size = 11, color = "gray50"),
plot.caption = element_text(size = 9, color = "gray60"),
axis.text.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(size = 10),
legend.position = "bottom",
legend.title = element_text(face = "bold"),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank()
)## Warning in geom_bar(stat = "identity", color = "black", size = 0.3): Ignoring
## unknown parameters: `size`print(p5)
Economic Summary Table
# Format economic summary table
econ_summary <- econ_by_event %>%
head(10) %>%
mutate(
Total_PropDamage = paste0("$", format(
round(Total_PropDamage / 1e9, 2),
big.mark = ","), "B"),
Total_CropDamage = paste0("$", format(
round(Total_CropDamage / 1e9, 2),
big.mark = ","), "B"),
Total_Damage = paste0("$", format(
round(Total_Damage / 1e9, 2),
big.mark = ","), "B")
)
knitr::kable(
econ_summary,
col.names = c("Event Type",
"Property Damage (USD)",
"Crop Damage (USD)",
"Total Damage (USD)"),
caption = "Table 2: Top 10 Weather Events by Total Economic Damage (1950-2011)",
align = c("l", "r", "r", "r")
)| Event Type | Property Damage (USD) | Crop Damage (USD) | Total Damage (USD) |
|---|---|---|---|
| FLOOD | $144.66B | $ 5.66B | $150.32B |
| HURRICANE/TYPHOON | $ 69.31B | $ 2.61B | $ 71.91B |
| TORNADO | $ 56.95B | $ 0.41B | $ 57.36B |
| STORM SURGE | $ 43.32B | $ 0.00B | $ 43.32B |
| HAIL | $ 15.74B | $ 3.03B | $ 18.76B |
| FLASH FLOOD | $ 16.82B | $ 1.42B | $ 18.24B |
| DROUGHT | $ 1.05B | $13.97B | $ 15.02B |
| HURRICANE | $ 11.87B | $ 2.74B | $ 14.61B |
| RIVER FLOOD | $ 5.12B | $ 5.03B | $ 10.15B |
| ICE STORM | $ 3.94B | $ 5.02B | $ 8.97B |
Key Findings Summary
# Print key numeric findings
cat("============================================\n")## ============================================cat(" KEY FINDINGS SUMMARY\n")## KEY FINDINGS SUMMARYcat("============================================\n\n")## ============================================# Health findings
top_fatal <- health_by_event$EVTYPE[1]
top_injury <- health_by_event %>%
arrange(desc(Total_Injuries)) %>%
pull(EVTYPE) %>%
first()
cat("--- POPULATION HEALTH ---\n")## --- POPULATION HEALTH ---cat(sprintf(
"Most fatal event : %s (%s fatalities)\n",
top_fatal,
format(health_by_event$Total_Fatalities[1], big.mark = ",")
))## Most fatal event : TORNADO (5,633 fatalities)cat(sprintf(
"Most injurious event : %s (%s injuries)\n\n",
top_injury,
format(
health_by_event %>%
arrange(desc(Total_Injuries)) %>%
pull(Total_Injuries) %>%
first(),
big.mark = ","
)
))## Most injurious event : TORNADO (91,346 injuries)# Economic findings
top_econ <- econ_by_event$EVTYPE[1]
top_prop <- econ_by_event %>%
arrange(desc(Total_PropDamage)) %>%
pull(EVTYPE) %>%
first()
top_crop <- econ_by_event %>%
arrange(desc(Total_CropDamage)) %>%
pull(EVTYPE) %>%
first()
cat("--- ECONOMIC CONSEQUENCES ---\n")## --- ECONOMIC CONSEQUENCES ---cat(sprintf(
"Greatest total damage : %s ($%.2fB)\n",
top_econ,
econ_by_event$Total_Damage[1] / 1e9
))## Greatest total damage : FLOOD ($150.32B)cat(sprintf(
"Greatest property damage : %s ($%.2fB)\n",
top_prop,
econ_by_event %>%
arrange(desc(Total_PropDamage)) %>%
pull(Total_PropDamage) %>%
first() / 1e9
))## Greatest property damage : FLOOD ($144.66B)cat(sprintf(
"Greatest crop damage : %s ($%.2fB)\n",
top_crop,
econ_by_event %>%
arrange(desc(Total_CropDamage)) %>%
pull(Total_CropDamage) %>%
first() / 1e9
))## Greatest crop damage : DROUGHT ($13.97B)cat("============================================\n")## ============================================