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.

Synopsis

This project aims to explore the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database in order to discuss events that are most harmful to population health as well as those related to greatest economic consequences. 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 events in the database start in the year 1950 and end in November 2011. the analysis revealed that across US, tornadoes result in the highest fatalities and injuries counts as well as highest property damage. However hails followed by flash floods are the top causes of crop damage across US.

first: loading required libraries

library(R.utils)
## Warning: package 'R.utils' was built under R version 4.3.3
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library(dplyr)
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library(tidyr)
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library(ggplot2)
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library(gridExtra)
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library(xtable)

Second: Loading and preprocessing the data

2.1. Downloading and unzipping the necessary files

URL <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"

if(!file.exists("repdata_data_StormData.csv.bz2")){
    download.file(URL, destfile = "repdata_data_StormData.csv.bz2")
}

if(file.exists("repdata_data_StormData.csv.bz2")){
    bunzip2("repdata_data_StormData.csv.bz2", overwrite = TRUE)
}

raw.data <- read.csv("D:/r project/r project/repdata_data_StormData.csv")
names_1 <- names(raw.data)

The names of the raw data columns are STATE_, BGN_DATE, BGN_TIME, TIME_ZONE, COUNTY, COUNTYNAME, STATE, EVTYPE, BGN_RANGE, BGN_AZI, BGN_LOCATI, END_DATE, END_TIME, COUNTY_END, COUNTYENDN, END_RANGE, END_AZI, END_LOCATI, LENGTH, WIDTH, F, MAG, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP, WFO, STATEOFFIC, ZONENAMES, LATITUDE, LONGITUDE, LATITUDE_E, LONGITUDE, REMARKS, REFNUM

2.3. dealing with NA values

raw.data <- raw.data[complete.cases(raw.data),]

The data are now ready for downstream analysis

3.Processing code

3.1. grouping and summarising the data by the event type

cleaned_data <- raw.data %>%
    group_by(EVTYPE) %>%
    summarize(
        Total_Fatalities = sum(FATALITIES, na.rm = TRUE),
        Total_PropDmg = sum(PROPDMG, na.rm = TRUE),
        Total_CropDmg = sum(CROPDMG, na.rm = TRUE),
        Total_Injuries = sum(INJURIES, na.rm = TRUE)
    )

4. Results

top_fatalities <- cleaned_data %>% 
    arrange(desc(Total_Fatalities)) 
top_injures <- cleaned_data %>% 
    arrange(desc(Total_Injuries))  
top_cropdmg <- cleaned_data %>% 
    arrange(desc(Total_CropDmg)) 
top_propdmg <- cleaned_data %>% 
    arrange(desc(Total_PropDmg)) 
head(top_fatalities)
## # A tibble: 6 × 5
##   EVTYPE         Total_Fatalities Total_PropDmg Total_CropDmg Total_Injuries
##   <chr>                     <dbl>         <dbl>         <dbl>          <dbl>
## 1 TORNADO                    5633      3212258.       100019.          91346
## 2 EXCESSIVE HEAT             1903         1460           494.           6525
## 3 FLASH FLOOD                 978      1420125.       179200.           1777
## 4 HEAT                        937          298.          663.           2100
## 5 LIGHTNING                   816       603352.         3581.           5230
## 6 TSTM WIND                   504      1335966.       109203.           6957
head(top_propdmg)
## # A tibble: 6 × 5
##   EVTYPE            Total_Fatalities Total_PropDmg Total_CropDmg Total_Injuries
##   <chr>                        <dbl>         <dbl>         <dbl>          <dbl>
## 1 TORNADO                       5633      3212258.       100019.          91346
## 2 FLASH FLOOD                    978      1420125.       179200.           1777
## 3 TSTM WIND                      504      1335966.       109203.           6957
## 4 FLOOD                          470       899938.       168038.           6789
## 5 THUNDERSTORM WIND              133       876844.        66791.           1488
## 6 HAIL                            15       688693.       579596.           1361

from analyzing the data, the results showed that Tornadoes result in the highest fatalities with estimate count of 5633, additionally, it results in the highest injuries count and property damage with counts of 91346 and 3212258.2 respectively.

However, hails resulted in the highest crop damage among all events across US with estimate count of 579596.28.

Which type of events are most harmful to population health?

The following plots shows the top five events that are most harmful to population health and economy.

plot_1 <- ggplot(top_fatalities[1:5, ], aes(x = reorder(EVTYPE, -Total_Fatalities), y = Total_Fatalities)) +
    geom_point(stat = "identity", color = "red", size=3) +
    scale_y_continuous(limits = range(top_fatalities$Total_Fatalities+200)) +
    geom_text(aes(label = Total_Fatalities), vjust = -0.5, size = 3) +  # Add values on top of bars
    labs(x = "Event Type", y = "Total Fatalities", title = "Top five fatal events", size=10) +
    theme(axis.text.x = element_text(size = 6)) 

plot_2 <- ggplot(top_injures[1:5, ], aes(x = reorder(EVTYPE, -Total_Injuries), y = Total_Injuries)) +
    geom_point(stat = "identity", color = "skyblue", size= 3) +
    geom_text(aes(label = Total_Injuries), vjust = -0.5, size = 3) +  # Add values on top of bars
    scale_y_continuous(limits = range(top_injures$Total_Injuries)) +
    labs(x = "Event Type", y = "Total Injuries", title = "Top five injury-causing events") +
    theme(axis.text.x = element_text(size = 6)) # Rotate x-axis labels for better readability

grid.arrange(plot_1, plot_2, ncol = 2)

which type of events have the greatest economic consequences?

plot_3 <- ggplot(top_cropdmg[1:5, ], aes(x = reorder(EVTYPE, -Total_CropDmg), y = Total_CropDmg)) +
    geom_point(stat = "identity", color = "red") +
    scale_y_continuous(limits = range(top_cropdmg$Total_CropDmg+1000)) +
    geom_text(aes(label = Total_CropDmg), vjust = -0.5, size = 3) +  # Add values on top of bars
    labs(x = "Event Type", y = "Total crop damage", title = "Top five crop-damaging events", size=10) +
    theme(axis.text.x = element_text(size = 6)) 

plot_4 <- ggplot(top_propdmg[1:5, ], aes(x = reorder(EVTYPE, -Total_PropDmg), y = Total_PropDmg)) +
    geom_point(stat = "identity", color = "skyblue") +
    geom_text(aes(label = Total_PropDmg), vjust = -0.5, size = 3) +  # Add values on top of bars
    labs(x = "Event Type", y = "Total Property Damage", title = "Top five property damaging events") +
    theme(axis.text.x = element_text(size = 6)) # Rotate x-axis labels for better readability

grid.arrange(plot_3, plot_4, ncol = 2)