1. Introduction

Road accidents are an important road-safety concern. Exploring how recorded crashes vary by time, weekday, road type, weather, and severity can help identify patterns for further investigation. This report uses R and ggplot2 to summarize a supplied cleaned dataset and applies a chi-square test to examine the association between road type and accident severity.

2. Objectives

3. Dataset Description

The supplied dataset is described as containing 20,000 rows and 24 columns, including date, time, city, state, road type, weather, visibility, accident severity, vehicles involved, and casualties. The report reads cleaned_road_accidents.csv from the working directory or its data/ subfolder. Header variants are normalized for the fields used here. If your source uses different names, update the alias list in the setup chunk.

The dataset was obtained from a community repository. Its provenance and accuracy as a representation of actual Indian road accidents have not been independently verified. Results should be understood as patterns in this supplied file, not official road-safety statistics.

4. Data Preparation and Validation

The setup code checks that the CSV is present and non-empty, normalizes common column name variants, parses dates and hours, and treats blank category values as missing. Invalid or unparseable values are retained as missing rather than silently converted to valid values. Each chart excludes records missing the variables required for that chart.

cat("Rows read:", nrow(clean_accidents), "\n")
## Rows read: 20000
cat("Columns read:", ncol(clean_accidents), "\n")
## Columns read: 24
cat("Exact duplicate rows:", sum(duplicated(clean_accidents)), "\n")
## Exact duplicate rows: 0
cat("Rows with any missing value:", sum(!complete.cases(clean_accidents)), "\n")
## Rows with any missing value: 0
if ("date" %in% names(clean_accidents)) {
  cat("Missing or invalid dates:", sum(is.na(clean_accidents$date)), "\n")
}
## Missing or invalid dates: 0
cat("Missing or invalid hours:", sum(is.na(clean_accidents$hour) |
                                           clean_accidents$hour < 0 |
                                           clean_accidents$hour > 23, na.rm = TRUE), "\n")
## Missing or invalid hours: 0

Exact duplicates are reported, not removed, so the analysis preserves the supplied records. Check the source and data dictionary before deciding whether repeated rows represent duplicate entries or separate events.

5. Exploratory Data Analysis

The plots show counts of records in the file. They are not population rates: there are no exposure denominators such as traffic volume, distance traveled, or time spent on the road.

5.1 Accident Records by Hour

hour_data <- clean_accidents[!is.na(clean_accidents$hour) &
                               clean_accidents$hour >= 0 & clean_accidents$hour <= 23, ]
ggplot(hour_data, aes(x = hour)) +
  geom_bar(fill = "#2878B5", width = 0.8) +
  scale_x_continuous(breaks = 0:23) +
  labs(title = "Recorded Accidents by Hour", x = "Hour of day", y = "Records") +
  theme_minimal()

5.2 Accident Records by Weekday

weekday_levels <- c("monday", "tuesday", "wednesday", "thursday", "friday",
                    "saturday", "sunday")
weekday_data <- clean_accidents[!is.na(clean_accidents$weekday), ]
weekday_values <- tolower(weekday_data$weekday)
weekday_data$weekday_plot <- factor(weekday_values,
  levels = c(weekday_levels, setdiff(unique(weekday_values), weekday_levels)))
ggplot(weekday_data, aes(x = weekday_plot)) +
  geom_bar(fill = "#55A868") +
  labs(title = "Recorded Accidents by Weekday", x = "Weekday", y = "Records") +
  theme_minimal() + theme(axis.text.x = element_text(angle = 35, hjust = 1))

5.3 Accident Records by Road Type

road_data <- clean_accidents[!is.na(clean_accidents$road_type), ]
ggplot(road_data, aes(x = reorder(road_type, road_type, FUN = length))) +
  geom_bar(fill = "#C44E52") +
  coord_flip() +
  labs(title = "Recorded Accidents by Road Type", x = "Road type", y = "Records") +
  theme_minimal()

5.4 Severity by Road Type

severity_road_data <- clean_accidents[!is.na(clean_accidents$severity) &
                                       !is.na(clean_accidents$road_type), ]
ggplot(severity_road_data, aes(x = road_type, fill = severity)) +
  geom_bar(position = "fill") +
  coord_flip() +
  scale_y_continuous(labels = function(x) paste0(round(100 * x), "%")) +
  labs(title = "Severity Composition by Road Type", x = "Road type",
       y = "Share of records", fill = "Severity") +
  theme_minimal()

5.5 Severity by Weather Condition

severity_weather_data <- clean_accidents[!is.na(clean_accidents$severity) &
                                          !is.na(clean_accidents$weather), ]
ggplot(severity_weather_data, aes(x = weather, fill = severity)) +
  geom_bar(position = "fill") +
  coord_flip() +
  scale_y_continuous(labels = function(x) paste0(round(100 * x), "%")) +
  labs(title = "Severity Composition by Weather Condition", x = "Weather",
       y = "Share of records", fill = "Severity") +
  theme_minimal()

6. Chi-Square Test: Road Type and Severity

The chi-square test of independence evaluates whether the distribution of recorded severity differs across road-type categories. It assumes independent records and adequate expected cell counts. A small p-value is evidence of an association in this dataset; it does not show that road type causes severity.

test_data <- clean_accidents[!is.na(clean_accidents$road_type) &
                               !is.na(clean_accidents$severity), ]
test_data$road_type <- droplevels(factor(test_data$road_type))
test_data$severity <- droplevels(factor(test_data$severity))
test_table <- table(test_data$road_type, test_data$severity)
if (nrow(test_table) < 2 || ncol(test_table) < 2) {
  cat("The test was not run: at least two road types and two severity categories are required.\n")
} else {
  chi_result <- suppressWarnings(chisq.test(test_table, correct = FALSE))
  print(test_table)
  print(chi_result)
  cat("\nInterpretation: ")
  if (any(chi_result$expected < 5)) {
    cat("Some expected cell counts are below 5, so the chi-square approximation may be unreliable. Consider combining substantively similar categories or using a simulated p-value.\n")
  } else if (chi_result$p.value < 0.05) {
    cat("At the 5% significance level, the data provide evidence of an association between road type and severity (p = ",
        format.pval(chi_result$p.value, digits = 3), "). This is an association, not a causal effect.\n", sep = "")
  } else {
    cat("At the 5% significance level, the data do not provide sufficient evidence of an association between road type and severity (p = ",
        format.pval(chi_result$p.value, digits = 3), "). This does not prove that no association exists.\n", sep = "")
  }
}
##          
##           fatal major minor
##   highway   983  1967  3666
##   rural     965  2014  3660
##   urban    1039  2007  3699
## 
##  Pearson's Chi-squared test
## 
## data:  test_table
## X-squared = 2.448, df = 4, p-value = 0.654
## 
## 
## Interpretation: At the 5% significance level, the data do not provide sufficient evidence of an association between road type and severity (p = 0.654). This does not prove that no association exists.

7. Limitations

8. Conclusion

This report provides a reproducible descriptive analysis of the supplied road-accident records, with five visualizations and a test of association between road type and severity. The plots and test output describe the data that were provided. They should guide questions for further investigation rather than be treated as estimates of real-world accident risk or as evidence of causal effects. Stronger conclusions would require verified data provenance, consistent definitions, and exposure information.