Title

Among a Total of 984 Events:

The Most Harmful Event is TORNADO, and the Greatest Economic Consequence Event is FLOOD

Synopsis

This report analyzes storm event data from the NOAA Storm Database. Web interface is available here.

We focus on:
- Health impact: Fatalities and Injuries.
- Economic impact: Property and Crop damages.

Key steps:
- Download and load the dataset. - Subset the dataset to relevant columns for efficiency.
- Aggregate health and economic impacts by event type.
- Visualize top contributors with bar charts and percentages.

The following sections present detailed results with plots and commentary.

Load required libraries (Messages loading the packages are suppressed on purpose)

suppressMessages(library(dplyr))
suppressMessages(library(ggplot2))

Function ‘report’ is created for graphs and figures reporting wrt the scope

report <- function(agg_tbl, scope){
    top_items <- agg_tbl %>%
        slice_max(order_by = figure, n = 9)
    others <- agg_tbl %>%
        slice(-(1:9)) %>%
        summarise(EVTYPE = "OTHERS", figure = sum(figure)) 
    tbl <- bind_rows(top_items, others) %>%
        mutate(percentage = 100 * figure / sum(figure))
    
    print(paste0('Total number of events: ', length(agg_tbl$EVTYPE)))
    print(paste0('Events with Zero impact: ', sum(agg_tbl$figure == 0)))
    print(paste0('Events with impact: ', sum(agg_tbl$figure != 0)))
    
    print(paste0('The Event Impacting ',scope, ' the Most is ', top_items$EVTYPE[which.max(top_items$figure)]))
    
    ggplot(tbl, aes(x = reorder(EVTYPE, percentage), y = percentage)) +
        geom_bar(stat = "identity", fill = "cyan") +
        geom_text(aes(label = paste0(round(percentage, 1), "%")), hjust = 0.5, size = 3) +
        coord_flip() +
        labs(title = paste0("Top 10 Events Impacting ", scope), x = "Event Type", y = "Percentage") +
        theme_minimal(base_size = 10)
}

Data Processing

Download and Load the Source File

url <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"
zip_file <- "stormdata.bz2"
if (!file.exists(zip_file)){
    download.file(url, zip_file, mode="wb")
    }
data <- read.csv(bzfile(zip_file))

data <- data %>% 
    select(EVTYPE, FATALITIES, INJURIES, PROPDMG, PROPDMGEXP, CROPDMG, CROPDMGEXP)

Handling Exponent Values of PROPDMGEXP and CROPDMGEXP

suppressWarnings(
data <- data %>%
    filter(!PROPDMGEXP %in% c('?','-')) %>%
    mutate(PROPDMG = case_when(
        PROPDMGEXP %in% c('0','1','2','3','4','5','6','7','8') ~ PROPDMG * 10 + as.numeric(PROPDMGEXP),
        PROPDMGEXP %in% c('H','h') ~ PROPDMG * 100,
        PROPDMGEXP %in% c('K','k') ~ PROPDMG * 1e+03,
        PROPDMGEXP %in% c('M','m') ~ PROPDMG * 1e+06,
        PROPDMGEXP %in% c('B','b') ~ PROPDMG * 1e+09,
        TRUE ~ PROPDMG))
)

suppressWarnings(
data <- data %>%
    filter(!CROPDMGEXP %in% c('?','-')) %>%
    mutate(CROPDMG = case_when(
        CROPDMGEXP %in% c('0','1','2','3','4','5','6','7','8') ~ CROPDMG * 10 + as.numeric(CROPDMGEXP),
        CROPDMGEXP %in% c('H','h') ~ CROPDMG * 100,
        CROPDMGEXP %in% c('K','k') ~ CROPDMG * 1e+03,
        CROPDMGEXP %in% c('M','m') ~ CROPDMG * 1e+06,
        CROPDMGEXP %in% c('B','b') ~ CROPDMG * 1e+09,
        TRUE ~ CROPDMG))
)

Aggregate health and economic impacts by event type

evt_health <- data %>% group_by(EVTYPE) %>% 
    summarise(figure = sum(FATALITIES + INJURIES, na.rm = TRUE), .groups = "drop")
evt_econ <- data %>% group_by(EVTYPE) %>% 
    summarise(figure = sum(PROPDMG + CROPDMG, na.rm = TRUE), .groups = "drop")

Results

Bar Charts for Health and Economy Impact

report(evt_health, 'Health')

## [1] "Total number of events: 984"
## [1] "Events with Zero impact: 764"
## [1] "Events with impact: 220"
## [1] "The Event Impacting Health the Most is TORNADO"

report(evt_econ, 'Economy')

## [1] "Total number of events: 984"
## [1] "Events with Zero impact: 553"
## [1] "Events with impact: 431"
## [1] "The Event Impacting Economy the Most is FLOOD"