dblogr/

Crime In Canada

Graphs of crime statistics using STATCAN data

Prepare Data

# Prep data
myCaption <- "derekmichaelwright.github.io/dblogr/ | Data: STATCAN"
myColors <- c("darkorange", "darkgreen", "darkred")
myAreas <- c("Canada", 
    "British Columbia", "Alberta", "Saskatchewan", "Manitoba",
    "Ontario", "Quebec", "Newfoundland and Labrador", 
    "Prince Edward Island", "Nova Scotia", "New Brunswick",
    "Yukon", "Northwest Territories", "Nunavut",
             
    "Kelowna, British Columbia", "Abbotsford-Mission, British Columbia",
    "Vancouver, British Columbia", "Victoria, British Columbia",
    "Lethbridge, Alberta", "Calgary, Alberta", "Edmonton, Alberta",
    "Regina, Saskatchewan", "Saskatoon, Saskatchewan", "Winnipeg, Manitoba",
    "Saguenay, Quebec", "Québec, Quebec", "Sherbrooke, Quebec",
    "Trois-Rivières, Quebec", "Montréal, Quebec", "Ottawa-Gatineau, Quebec part",
    "Ottawa-Gatineau, Ontario/Quebec", "Ottawa-Gatineau, Ontario part",
    "Kingston, Ontario", "Belleville, Ontario", "Peterborough, Ontario",
    "Toronto, Ontario", "Hamilton, Ontario", "St.Catharines-Niagara, Ontario",
    "Kitchener-Cambridge-Waterloo, Ontario", "Brantford, Ontario",
    "Guelph, Ontario", "London, Ontario", "Windsor, Ontario", "Barrie, Ontario",
    "Greater Sudbury, Ontario", "Thunder Bay, Ontario",
    "St. John's, Newfoundland and Labrador", "Halifax, Nova Scotia",
    "Moncton, New Brunswick", "Saint John, New Brunswick" )
d1 <- read.csv("3510002601_databaseLoadingData.csv") %>%
  select(Year=1, Area=GEO, Measurement=Statistics, Unit=UOM, Value=VALUE) %>%
  mutate(Area = ifelse(Area == "Canada", Area, 
                       substr(.$Area, 1, regexpr("\\[", .$Area)-2)),
         Area = factor(Area, levels = myAreas))
d1.1 <- d1 %>% filter(!grepl("Youth",Measurement))
d1.2 <- d1 %>% filter(grepl("Youth",Measurement))
d1.3 <- d1 %>% filter(Measurement %in% c("Violent crime severity index",
                                       "Youth violent crime severity index"))
#
myCities <- c("Vancouver, British Columbia", "Victoria, British Columbia",
              "Kelowna, British Columbia", "Calgary, Alberta", 
              "Edmonton, Alberta", "Lethbridge, Alberta", 
              "Regina, Saskatchewan", "Saskatoon, Saskatchewan",
              "Winnipeg, Manitoba")
d2 <- read.csv("3510019101_databaseLoadingData.csv") %>%
  select(Year=1, Area=GEO, Measurement=Statistics, Unit=UOM, Value=VALUE) %>% 
  arrange(desc(Value)) %>%
  mutate(Area = factor(Area, levels = unique(.$Area)),
         Group = ifelse(Area %in% myCities, "West", "East"),
         Group = factor(Group, levels = c("West", "East")))
#
d3 <- read.csv("3510006601_databaseLoadingData.csv") %>%
  select(Year=1, Area=GEO, Motive=Type.of.motivation, Unit=UOM, Value=VALUE) %>%
  arrange(desc(Value)) %>%
  mutate(Motive = factor(Motive, levels = unique(.$Motive)))
# 
d4 <- read_xlsx("data_canada_crime.xlsx", "Sexual Assault")
#
myTraits <- c("Total", "Property crimes", "Violent crimes", "Other crimes")
d5 <- read_xlsx("data_canada_crime.xlsx", "Crime Rates") %>%
  gather(Trait, Value, 3:ncol(.)) %>%
  mutate(Trait = factor(Trait, levels = myTraits))
#
myTraits <- c("Total fraud", "General fraud", "Identity fraud", "Identity theft")
d6 <- read_xlsx("data_canada_crime.xlsx", "Fraud") %>%
  gather(Trait, Value, 3:ncol(.)) %>%
  mutate(Trait = factor(Trait, levels = myTraits))

Crime Severity Index

Canada

# Prep data
xx <- d1.1 %>% filter(Area == "Canada")
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, color = Measurement)) + 
  geom_line(size = 1.5, alpha = 0.7) +
  facet_wrap(Area ~ .) +
  scale_color_manual(name = NULL, values = myColors) +
  scale_x_continuous(minor_breaks = 1995:2025) +
  theme_agData(legend.position = "bottom") +
  labs(y = "Crime Severity Index", x = NULL,
       caption = myCaption)
ggsave("canada_crime_1_01.png", mp, width = 7, height = 4)

Provinces

# Prep data
xx <- d1.1 %>% filter(Area %in% myAreas[1:14])
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, color = Measurement)) + 
  geom_line(size = 1.5, alpha = 0.7) +
  facet_wrap(Area ~ ., scales = "free_y", ncol = 7) +
  scale_color_manual(name = NULL, values = myColors) +
  scale_x_continuous(minor_breaks = 1998:2020) +
  theme_agData(legend.position = "bottom") +
  labs(y = "Crime Severity Index", x = NULL, caption = myCaption)
ggsave("canada_crime_1_02.png", mp, width = 16, height = 6)

Cities

# Prep data
xx <- d1.1 %>% filter(Area %in% myAreas[15:length(myAreas)])
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, color = Measurement)) + 
  geom_line(size = 1.5, alpha = 0.7) +
  facet_wrap(Area ~ ., scales = "free_y", ncol = 6) +
  scale_color_manual(name = NULL, values = myColors) +
  scale_x_continuous(minor_breaks = 1998:2020) +
  theme_agData(legend.position = "bottom") +
  labs(y = "Crime Severity Index", x = NULL, caption = myCaption)
ggsave("canada_crime_1_03.png", mp, width = 16, height = 10)

Youth Crime Severity Index

Canada

# Prep data
xx <- d1.2 %>% filter(Area == "Canada")
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, color = Measurement)) + 
  geom_line(size = 1.5, alpha = 0.7) +
  facet_wrap(Area ~ .) +
  scale_color_manual(name = NULL, values = myColors) +
  scale_x_continuous(minor_breaks = 1995:2025) +
  theme_agData(legend.position = "bottom") +
  labs(y = "Crime Severity Index", x = NULL, caption = myCaption)
ggsave("canada_crime_1_04.png", mp, width = 7, height = 4)

Provinces

# Prep data
xx <- d1.2 %>% filter(Area %in% myAreas[1:14])
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, color = Measurement)) + 
  geom_line(size = 1.5, alpha = 0.7) +
  facet_wrap(Area ~ ., scales = "free_y", ncol = 7) +
  scale_color_manual(name = NULL, values = myColors) +
  scale_x_continuous(minor_breaks = 1998:2020) +
  theme_agData(legend.position = "bottom") +
  labs(y = "Crime Severity Index", x = NULL, caption = myCaption)
ggsave("canada_crime_1_05.png", mp, width = 16, height = 6)

Violent Crime

Canada

# Prep data
xx <- d1.3 %>% filter(Area == "Canada")
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, color = Measurement)) + 
  geom_line(size = 1.5, alpha = 0.7) +
  facet_wrap(Area ~ .) +
  scale_color_manual(name = NULL, values = c("darkred", "steelblue")) +
  scale_x_continuous(minor_breaks = 1995:2025) +
  theme_agData(legend.position = "bottom") +
  labs(y = "Crime Severity Index", x = NULL, caption = myCaption)
ggsave("canada_crime_1_06.png", mp, width = 7, height = 4)

Provinces

# Prep data
xx <- d1.3 %>% filter(Area %in% myAreas[1:14])
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, color = Measurement)) + 
  geom_line(size = 1.5, alpha = 0.7) +
  facet_wrap(Area ~ ., scales = "free_y", ncol = 7) +
  scale_color_manual(name = NULL, values = c("darkred", "steelblue")) +
  scale_x_continuous(minor_breaks = 1998:2020) +
  theme_agData(legend.position = "bottom") +
  labs(y = "Crime Severity Index", x = NULL, caption = myCaption)
ggsave("canada_crime_07.png", mp, width = 16, height = 6)

Saskatchewan

Crime Severity Index

# Prep data
xx <- d1.1 %>% filter(Area == "Saskatchewan")
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, color = Measurement)) + 
  geom_line(size = 1.5, alpha = 0.7) +
  facet_wrap(Area ~ .) +
  scale_color_manual(name = NULL, values = myColors) +
  scale_x_continuous(minor_breaks = 1995:2025) +
  theme_agData(legend.position = "bottom") +
  labs(y = "Crime Severity Index", x = NULL, caption = myCaption)
ggsave("canada_crime_1_08.png", mp, width = 8, height = 4)

Youth Crime Severity Index

# Prep data
xx <- d1.2 %>% filter(Area == "Saskatchewan")
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, color = Measurement)) + 
  geom_line(size = 1.5, alpha = 0.7) +
  facet_wrap(Area ~ .) +
  scale_color_manual(name = NULL, values = myColors) +
  scale_x_continuous(minor_breaks = 1995:2025) +
  theme_agData(legend.position = "bottom") +
  labs(y = "Crime Severity Index", x = NULL, caption = myCaption)
ggsave("canada_crime_1_09.png", mp, width = 8, height = 4)

Violent Crime

# Prep data
xx <- d1.3 %>% filter(Area == "Saskatchewan")
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, color = Measurement)) + 
  geom_line(size = 1.5, alpha = 0.7) +
  facet_wrap(Area ~ .) +
  scale_color_manual(name = NULL, values = c("darkred", "steelblue")) +
  scale_x_continuous(minor_breaks = 1995:2025) +
  theme_agData(legend.position = "bottom") +
  labs(y = "Crime Severity Index", x = NULL, caption = myCaption)
ggsave("canada_crime_1_10.png", mp, width = 8, height = 4)

Hate Crimes

All Cities

# Plot
mp <- ggplot(d2, aes(x = Year, y = Value, fill = Group)) + 
  geom_col(color = "black", alpha = 0.7) +
  facet_wrap(Area ~ ., ncol = 6) +
  scale_fill_manual(values = c("steelblue", "darkred")) +
  scale_x_continuous(breaks = 2014:2021) +
  theme_agData(legend.position = "none",
               axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Hate Crime Rate Per 100,000 People", 
       y = NULL, x = NULL, caption = myCaption)
ggsave("canada_crime_2_01.png", mp, width = 16, height = 10)

2021

# Prep data
xx <- d2 %>% filter(Year == 2021) %>%
  arrange(desc(Value)) %>%
  mutate(Area = factor(Area, levels = .$Area))
# Plot
mp <- ggplot(xx, aes(x = Area, y = Value, fill = Group)) + 
  geom_col(color = "black", alpha = 0.7) +
  scale_fill_manual(values = c("steelblue", "darkred")) +
  theme_agData(legend.position = "none",
               axis.text.x = element_text(angle = 55, hjust = 1)) +
  labs(title = "Hate Crime By City", x = NULL,
       y = "Rate Per 100,000 People", caption = myCaption)
ggsave("canada_crime_2_02.png", mp, width = 8, height = 5)

Ottawa vs Regina

# Prep data
myCities <- c("Regina, Saskatchewan", "Ottawa, Ontario")
xx <- d2 %>% filter(Area %in% myCities) %>%
  mutate(Area = factor(Area, levels = myCities))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, fill = Area)) + 
  geom_col(position = "dodge", color = "black", alpha = 0.7) +
  scale_fill_manual(name = NULL, values = c("steelblue", "darkred")) +
  scale_x_continuous(breaks = 2014:2021) +
  theme_agData(legend.position = "bottom") +
  labs(title = "Hate Crime Rate Per 100,000 People", 
       y = NULL, x = NULL, caption = myCaption)
ggsave("canada_crime_2_03.png", mp, width = 6, height = 4)

Hate Crime Types

# Plot
mp <- ggplot(d3, aes(x = Year, y = Value, fill = Motive)) + 
  geom_col(color = "black", alpha = 0.7) +
  facet_wrap(Motive ~ ., scales = "free_y", ncol = 5) +
  scale_fill_manual(name = NULL, values = agData_Colors) +
  scale_x_continuous(minor_breaks = 2012:2021) +
  theme_agData(legend.position = "none") +
  labs(title = "Canada", x = NULL,
       y = "Crime Severity Index", caption = myCaption)
ggsave("canada_crime_3_01.png", mp, width = 14, height = 4)

Other Crimes

Sexual Assault

# Plot
mp <- ggplot(d4, aes(x = Year, y = Value)) + 
  geom_line(color = "darkred", size = 1.5, alpha = 0.7) +
  scale_x_continuous(breaks = seq(1985, 2025, by = 5), 
                     minor_breaks = 1985:2025) +
  theme_agData() +
  labs(title = "Sexual Assualts in Canada", y = "Rate per 100,000", x = NULL,
       caption = myCaption)
ggsave("canada_crime_4_01.png", mp, width = 6, height = 4)

Property vs Violent Crime

# Plot
mp <- ggplot(d5, aes(x = Year, y = Value, color = Trait)) + 
  geom_line(alpha = 0.7, size = 1.5) +
  scale_color_manual(name = NULL, values =  agData_Colors) +
  scale_x_continuous(breaks = seq(1960, 2025, by = 10), 
                     minor_breaks = seq(1960, 2025, by = 5)) +
  theme_agData(legend.position = "bottom") +
  labs(title  = "Crime Rate in Canada", x = NULL,
       y = "Rate per 100,000", caption = myCaption)
ggsave("canada_crime_5_01.png", mp, width = 6, height = 4)

Fraud

# Plot
mp <- ggplot(d6, aes(x = Year, y = Value, color = Trait)) + 
  geom_line(alpha = 0.7) +
  facet_wrap(Trait ~ ., ncol = 4, scales = "free_y") +
  scale_color_manual(name = NULL, values = agData_Colors) +
  scale_x_continuous(breaks = seq(1985, 2025, by = 5), 
                     minor_breaks = 1985:2025) +
  theme_agData(legend.position = "none") +
  labs(title = "Fraud in Canada", x = NULL,
       y = "Rate per 100,000", caption = myCaption)
ggsave("canada_crime_6_01.png", mp, width = 8, height = 4)

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© Derek Michael Wright