Crime and Place: Mapping Misdeeds Across America Plots

all_crime <- read_excel("all_crime_data.xlsx", sheet=6)


data2 <- all_crime %>%
  select(state, year, population, gambling, drugs, auto_theft, petty_theft, larceny,
                  fraud, sexual_assault, human_trafficking, assaults, homicide, prostitution1,
                weapon_law_violations, pornography, sports_tampering, animal_cruelty,
                  robbery, hacking, blackmail, embezzlement, vandalism, forgery, burgulary, bribery,
                  arson, abduction)

data2 <- data2 %>%
  group_by(state) %>%
  mutate(across(gambling:abduction, ~replace(., is.na(.), 0))) %>%  # Replace NAs with 0
  mutate(total_crime = rowSums(across(gambling:abduction), na.rm = TRUE)) %>%
  mutate(total_crime_per_capita = total_crime / population) %>%
  ungroup()

Total Crime in the Continental United States

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State of Crime: An Interactive Crime Map

https://prm4dq.shinyapps.io/State_of_crime/

Crime Trends Per Capita Mapper

# Summarize crime data for total crime per capita
crime_summary_per_capita <- data2 %>%
  filter(year %in% c(2020, 2021, 2022, 2023), !is.na(total_crime_per_capita)) %>% 
  group_by(state, year) %>%                             
  summarise(total_crime_per_capita = sum(total_crime_per_capita, na.rm = TRUE))

## `summarise()` has grouped output by 'state'. You can override using the
## `.groups` argument.

# Compare total crime per capita between 2020 and 2023, filling missing years
crime_comparison_per_capita <- crime_summary_per_capita %>%
  mutate(year = as.character(year)) %>%  # Ensure `year` is a character
  complete(year = c("2020", "2021", "2022", "2023"), fill = list(total_crime_per_capita = NA)) %>%  # Ensure all years are present
  spread(key = year, value = total_crime_per_capita) %>%  # Spread into wide format
  rename(
    crime_2020 = `2020`,
    crime_2021 = `2021`,
    crime_2022 = `2022`,
    crime_2023 = `2023`
  ) %>%
  mutate(
    adjusted_crime_2020 = case_when(
      !is.na(crime_2020) ~ crime_2020,
      is.na(crime_2020) & !is.na(crime_2021) ~ crime_2021,
      is.na(crime_2020) & is.na(crime_2021) & !is.na(crime_2022) ~ crime_2022,
      TRUE ~ crime_2023  # Default to 2023 if all others are missing
    ),
    crime_pctdiff = ((crime_2023 - adjusted_crime_2020) / adjusted_crime_2020) * 100
  )

# If percent difference is NA (no data for a state), replace with 0
crime_comparison_per_capita <- crime_comparison_per_capita %>%
  mutate(crime_pctdiff = ifelse(is.na(crime_pctdiff), 0, crime_pctdiff))

crime_comparison_per_capita <- crime_comparison_per_capita %>% 
  mutate(
    hover_label = paste("State: ", state, 
                        "<br>\nPercent Change in Crimes per Capita: ", 
                        round(crime_pctdiff, 3), "%")
  )
# Plotting the map with percent change in crime per capita
p_per_capita <- plot_usmap(data = crime_comparison_per_capita, 
                           values = "crime_pctdiff",
                           regions = "states", 
                           labels = FALSE) +  # Add state abbreviations
  scale_fill_gradientn(
    colors = c("lightblue", "white", "orange", "red"),
    values = c(0, 0.25, 0.5, 1),
    limits = c(-100, 700),
    name = "Crime % Change (per capita)"
  ) +
  theme(legend.position = "right") +
  labs(
    title = "Percent Change in Crime Rates Per Capita (2020-2023)",
  ) +
  theme(
    legend.position = "right",
    plot.title = element_text(size=14, face="bold", hjust=0.5),
    plot.caption = element_text(size = 9, hjust = 0, margin = margin(t = 10))
  )

# Add a custom caption below the plot
interactive_map_per_capita <- ggplotly(p_per_capita, tooltip = c("hover_label", "crime_pctdiff")) %>%
  layout(
    annotations = list(
      x = 0.5,  # Position on the x-axis (center)
      y = -0.1,  # Position on the y-axis (below the plot)
      showarrow = FALSE,
      font = list(size = 10, color = "black"),
      xref = "paper", yref = "paper",
      align = "center"
    )
  )

interactive_map_per_capita

State of Crime: A Stacked Perspective

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violent_crimes <- data2[, c("state", "year", "assaults", "homicide", "robbery", 
                            "sexual_assault", "human_trafficking", "animal_cruelty", 
                            "arson", "abduction")]

non_violent_crimes <- data2[, c("state", "year", "larceny", "fraud", "embezzlement", 
                                "petty_theft", "gambling", "auto_theft", "prostitution1", 
                                "sports_tampering", "blackmail", "hacking", 
                                "vandalism", "forgery")]

violent_sums <- rowSums(violent_crimes[, 3:ncol(violent_crimes)], na.rm = TRUE)
non_violent_sums <- rowSums(non_violent_crimes[, 3:ncol(non_violent_crimes)], na.rm = TRUE)

crime_comparison <- data.frame(
  State = data2$state,
  Violent = violent_sums,
  NonViolent = non_violent_sums,
  Assaults = data2$assaults,
  Homicide = data2$homicide,
  Robbery = data2$robbery,
  Sexual_Assault = data2$sexual_assault,
  Human_Trafficking = data2$human_trafficking,
  Animal_Cruelty = data2$animal_cruelty,
  Arson = data2$arson,
  Abduction = data2$abduction,
  Larceny = data2$larceny,
  Fraud = data2$fraud,
  Embezzlement = data2$embezzlement,
  Petty_Theft = data2$petty_theft,
  Gambling = data2$gambling,
  Auto_Theft = data2$auto_theft,
  Prostitution = data2$prostitution1,
  Sports_Tampering = data2$sports_tampering,
  Blackmail = data2$blackmail,
  Hacking = data2$hacking,
  Vandalism = data2$vandalism,
  Forgery = data2$forgery
)

crime_comparison_long <- crime_comparison %>%
  pivot_longer(
    cols = -c(State, Violent, NonViolent),
    names_to = "Crime_Type",
    values_to = "Count"
  )

crime_comparison_long <- crime_comparison_long %>%
  mutate(Crime_Category = ifelse(Crime_Type %in% c("Assaults", "Homicide", "Robbery", "Sexual_Assault", 
                                                 "Human_Trafficking", "Animal_Cruelty", "Arson", "Abduction"), 
                                 "Violent Crimes", "Non-Violent Crimes"))

High Stakes: Tracking Drug Offenses Across the States

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df_long <- all_crime %>%
  select(state, year, population, drug_offenses) %>%
  pivot_longer(cols = starts_with("drug_offenses"), names_to = "variable", values_to = "value") %>%
  mutate(per_capita = value / population)


# Get map data for US states
us_map <- map_data("state")

us_map <- us_map %>%
  rename(state = region)

# Merge the map data with your dataset (make sure state names match)
df_long$state <- tolower(df_long$state)

# Merge map data with your dataset
df_map_data <- merge(us_map, df_long, by = "state", all.x = TRUE)

https://prm4dq.shinyapps.io/interactive_map/

Highs and Lows: Regional Drug Density

df_avg_dot <- df_long %>%
  filter(year >= 2020 & year <= 2023) %>%
  group_by(state) %>%
  summarise(avg_per_capita = mean(per_capita, na.rm = TRUE),
            avg_population = mean(population, na.rm = TRUE)) %>%
  ungroup()

regions <- list(
  new_england = c("connecticut", "maine", "massachusetts", "new hampshire", "rhode island", "vermont"),
  great_plains = c("kansas", "nebraska", "north dakota", "south dakota", "oklahoma"),
  rocky_mountains = c("colorado", "idaho", "montana", "utah", "wyoming"),
  mid_atlantic = c("delaware", "maryland", "new jersey", "new york", "pennsylvania", "virginia", "west virginia"),
  south = c("alabama", "arkansas", "florida", "georgia", "kentucky", "louisiana", "mississippi", "north carolina", "south carolina", "tennessee", "texas"),
  west_coast = c("california", "oregon", "washington"),
  midwest = c("illinois", "indiana", "iowa", "michigan", "minnesota", "missouri", "ohio", "wisconsin"),
  southwest = c("arizona", "new mexico", "nevada"),
  alaska = c("alaska"),
  hawaii = c("hawaii")
)

df_avg_density <- df_avg_dot %>%
  mutate(region = case_when(
    state %in% regions$new_england ~ "new england",
    state %in% regions$great_plains ~ "great plains",
    state %in% regions$rocky_mountain ~ "rocky mountains",
    state %in% regions$mid_atlantic ~ "mid atlantic",
    state %in% regions$south ~ "south",
    state %in% regions$west_coast ~ "west coast",
    state %in% regions$midwest ~ "midwest",
    state %in% regions$southwest ~ "southwest",
    state %in% regions$alaska ~ "alaska",
    state %in% regions$hawaii ~ "hawaii",
    TRUE ~ "other"
  ))

gg_density_region <- ggplot(df_avg_density, aes(x = avg_per_capita * 100000, fill = region)) +
  geom_density(alpha = 0.7, color = "black", size = 0.3) + 
  scale_fill_viridis(discrete = T, option = "inferno", name = NULL) + 
  labs(
    title = "Density of Average Drug Offenses by Region (2020-2023)",
    subtitle = "Based on average drug offenses per 100,000 people",
    x = "Average Drug Offenses per 100,000 People",
    y = "Density",
    fill = "Region"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 16, hjust = 0.5), 
    plot.subtitle = element_text(face = "italic", size = 12, hjust = 0.5), 
    legend.position = "bottom", 
    legend.title = element_blank(), 
    legend.key.size = unit(0.8, "cm"),
    axis.text = element_text(size = 12), 
    axis.title = element_text(face = "bold"), 
    axis.title.y = element_text(margin = margin(r = 15)), 
    axis.title.x = element_text(margin = margin(t = 10))
  ) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0))

ggplotly(gg_density_region) %>%
  layout(width = 1000,
         height = 500,
    legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.2)  # Move legend explicitly in Plotly
  )

Breaking Down Crime: The Hidden Connections Between Drug Offenses and Crime Trends

long_data <- data2 %>%
  pivot_longer(cols = c(larceny, auto_theft, petty_theft, assaults, homicide, robbery, burgulary, sexual_assault, animal_cruelty, abduction, arson, vandalism),  
               names_to = "crime_type",
               values_to = "crime_count")

correlation_summary <- long_data %>%
  group_by(crime_type) %>%
  summarise(
    correlation = cor(drugs, crime_count, use = "complete.obs"),
    .groups = 'drop'
  ) %>%
  arrange(desc(correlation))

facet_plot <- ggplot(long_data, aes(x = drugs, y = crime_count)) + 
  geom_point(aes(text = paste("State: ", state, "<br>Drug Offenses: ", drugs, "<br>Crime: ", crime_count))) + 
  geom_smooth(method = "lm", color = "blue", se = TRUE) +  
  facet_wrap(~ crime_type, scales = "free_y", ncol = 3) +  
  labs(title = "Drug Offenses vs Various Crimes", x = "Drug Offenses", y = "Crime Count") +
  scale_y_continuous(labels = label_number(scale = 1)) +  
  scale_x_continuous(labels = label_number(scale = 1)) +  
  theme_minimal() +
  theme(
    strip.text = element_text(size = 10),  
    axis.text.x = element_text(angle = 45, hjust = 1),  
    axis.title.x = element_text(margin = margin(t = 20)),  
    axis.title.y = element_text(margin = margin(r = 20))  
  )

interactive_facet_plot <- ggplotly(facet_plot, tooltip = "text")  

## `geom_smooth()` using formula = 'y ~ x'

interactive_facet_plot <- interactive_facet_plot %>% layout(
  width = 1200, 
  height = 800,  
  margin = list(l = 100, r = 100, t = 100, b = 100) 
)

interactive_facet_plot

summary_table <- ggplot(correlation_summary, aes(x = crime_type, y = correlation)) +
  geom_bar(stat = "identity", fill = "lightblue") +
  geom_text(aes(label = round(correlation, 2)), vjust = -0.5) +
  labs(title = "Correlation Between Drug Offenses and Crimes",
       x = "Crime Type",
       y = "Correlation Coefficient") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))


summary_table

Highs and Lows: Visualizing Marijuana Laws and Drug Trends

weed <- read_excel("weed_laws.xlsx")
weed_merged <- merge(data2, weed, by = "state", all.x = TRUE)

weed_merged <- weed_merged %>%
  mutate(legalized_marijuana = case_when(
    legal_status == "legal" ~ "Legal",
    legal_status == "mixed" ~ "Mixed",
    TRUE ~ "Illegal"
  ))

weed_merged <- weed_merged %>%
  mutate(hover_text = paste(state, 
                            "\nLegal Status: ", legal_status, 
                            "\nDecriminalized: ", decriminalized))

violin_plot <- ggplot(weed_merged, aes(x = legalized_marijuana, y = drugs, fill = legalized_marijuana)) +
  geom_violin(trim = FALSE, color = "black") +
  labs(title = "Distribution of Drug Offenses by Marijuana Legalization Status",
       subtitle = "Comparison across 'Legal', 'Mixed', and 'Illegal' States",
       x = "Marijuana Legalization Status", y = "Drug Offenses") +
  theme_minimal(base_size = 15) +
  scale_fill_manual(values = c("Legal" = "forestgreen", "Mixed" = "goldenrod", "Illegal" = "firebrick")) +
  scale_y_continuous(labels = label_comma()) + 
  theme(axis.title = element_text(size = 10), axis.text = element_text(size = 10),
        plot.title = element_text(size = 12, face = "bold"), plot.subtitle = element_text(size = 14, face = "italic")) +
  theme(legend.position = "top") +
  guides(fill = guide_legend(title = "Legalization Status"))

plotly_violin <- ggplotly(violin_plot)
plotly_violin

boxplot_plot <- ggplot(weed_merged, aes(x = decriminalized, y = drugs, fill = decriminalized)) +
  geom_boxplot(outlier.colour = "red", outlier.size = 2, color = "black") +
  labs(title = "Comparison of Drug Offenses in Decriminalized vs Non-Decriminalized States",
       subtitle = "States with Decriminalized Marijuana vs Others",
       x = "Decriminalization Status", y = "Drug Offenses") +
  theme_minimal(base_size = 15) +
  scale_fill_manual(values = c("yes" = "cornflowerblue", "no" = "gray")) +
  scale_y_continuous(labels = label_comma()) + 
  theme(axis.title = element_text(size = 10), axis.text = element_text(size = 10),
        plot.title = element_text(size = 12, face = "bold"), plot.subtitle = element_text(size = 14, face = "italic")) +
  theme(legend.position = "top") +
  guides(fill = guide_legend(title = "Decriminalized?"))

# Convert to plotly
plotly_boxplot <- ggplotly(boxplot_plot)
plotly_boxplot