# CHART 1
# Australians lost a record $32.2 billion to gambling
state <- read_excel(
"C:/Users/evita/OneDrive/Desktop/Data Vis A3/DATA/australian-gambling-statistics-40th-edn-1998-99-2023-24-state-tables (1).xlsx",
sheet = "AUS 20",
skip = 6
)
## New names:
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
# Clean data
state_clean <- state %>%
rename(
year = `Value ($ million)`,
gaming = ...2,
wagering = ...4,
total = ...6
) %>%
select(year, gaming, wagering, total) %>%
# Keep only rows that start with a year
filter(!is.na(year)) %>%
filter(str_detect(year, "^[0-9]")) %>%
# Convert columns to numeric
mutate(
gaming = as.numeric(gaming),
wagering = as.numeric(wagering),
total = as.numeric(total)
)
chart1 <- state_clean %>%
select(
year,
gaming,
wagering
) %>%
pivot_longer(
cols = c(gaming, wagering),
names_to = "type",
values_to = "expenditure"
)
chart1$type <- recode(
chart1$type,
gaming = "Gaming",
wagering = "Wagering"
)
#Plotting Chart 1
p1 <-ggplot(chart1,
aes(x = year,
y = expenditure,
colour = type,
group = type)) +
geom_line(linewidth = 1.6) +
scale_color_manual(
values = c(
"Gaming" = "darkorange3",
"Wagering" = "dodgerblue3"
)
) +
scale_y_continuous(
labels = function(x) paste0("$", x / 1000, "B"),
limits = c(0, 30000),
breaks = seq(0, 30000, 5000)
) +
annotate(
"segment",
x = "2019–20",
xend = "2019–20",
y = 18800,
yend = 17200,
colour = "grey40",
arrow = arrow(length = unit(0.2, "cm"))
) +
annotate(
"text",
x = "2016–17",
y = 14500,
label = "Losses briefly dipped during\nCOVID-19 venue closures",
size = 3.8,
colour = "grey20",
hjust = 0.5
) +
annotate(
"text",
x = "2021–22",
y = 25500,
label = "Gaming\n$23.8B",
colour = "darkorange3",
fontface = "bold",
size = 4
) +
annotate(
"text",
x = "2021–22",
y = 2500,
label = "Wagering\n$8.4B",
colour = "dodgerblue3",
fontface = "bold",
size = 4
) +
labs(
title = "Australians lost a record $32.2 billion to gambling",
x = "Financial Year",
y = "Gambling expenditure ($ billion)",
caption = "Source: Queensland Government Statistician's Office, Australian Gambling Statistics, 40th Edition (2024)"
) +
coord_cartesian(clip = "off") +
theme_minimal(base_size = 13) +
theme(
plot.title = element_text(
face = "bold",
size = 16,
hjust = 0.5
),
plot.subtitle = element_text(
size = 12,
colour = "grey30",
hjust = 0.5
),
axis.title.y = element_text(
face = "bold",
size = 11
),
axis.text.x = element_text(
angle = 45,
hjust = 1,
size = 9
),
axis.text.y = element_text(
size = 10
),
plot.caption = element_text(
size = 10,
colour = "grey30",
hjust = 0,
face = "italic",
margin = margin(t = 10)
),
legend.position = "none",
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
plot.margin = margin(
t = 10,
r = 40,
b = 10,
l = 10
)
)
ggplotly(p1) %>%
layout(
title = list(
text = paste(
"Australians lost a record $32.2 billion to gambling",
"<br><sup>Gaming remains dominant, while wagering expenditure has grown rapidly since 1998–99</sup>"
),
x = 0.5
),
margin = list(
l = 80,
r = 80,
t = 100,
b = 80
)
)
# CHART 2
# Gaming machines account for half of Australia's gambling losses
product_file <- "C:/Users/evita/OneDrive/Desktop/Data Vis A3/DATA/australian-gambling-statistics-40th-edn-1998-99-2023-24-product-tables.xlsx"
gaming_machines <- read_excel(
product_file,
sheet = "GAMING_MACHINES 5",
skip = 5
) %>%
select(
year = `Value ($ million)`,
expenditure = ...18
) %>%
filter(!is.na(year)) %>%
filter(str_detect(year, "^[0-9]")) %>%
mutate(product = "Gaming Machines")
## New names:
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13`
## • `` -> `...14`
## • `` -> `...15`
## • `` -> `...16`
## • `` -> `...17`
## • `` -> `...18`
## • `` -> `...19`
wagering <- read_excel(
product_file,
sheet = "WAGERING 5",
skip = 5
) %>%
select(
year = `Value ($ million)`,
expenditure = ...18
) %>%
filter(!is.na(year)) %>%
filter(str_detect(year, "^[0-9]")) %>%
mutate(product = "Wagering")
## New names:
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13`
## • `` -> `...14`
## • `` -> `...15`
## • `` -> `...16`
## • `` -> `...17`
## • `` -> `...18`
## • `` -> `...19`
casino <- read_excel(
product_file,
sheet = "CASINO 5",
skip = 5
) %>%
select(
year = `Value ($ million)`,
expenditure = ...18
) %>%
filter(!is.na(year)) %>%
filter(str_detect(year, "^[0-9]")) %>%
mutate(product = "Casino")
## New names:
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13`
## • `` -> `...14`
## • `` -> `...15`
## • `` -> `...16`
## • `` -> `...17`
## • `` -> `...18`
## • `` -> `...19`
lotteries <- read_excel(
product_file,
sheet = "LOTTERIES 5",
skip = 5
) %>%
select(
year = `Value ($ million)`,
expenditure = ...18
) %>%
filter(!is.na(year)) %>%
filter(str_detect(year, "^[0-9]")) %>%
mutate(product = "Lotteries")
## New names:
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13`
## • `` -> `...14`
## • `` -> `...15`
## • `` -> `...16`
## • `` -> `...17`
## • `` -> `...18`
## • `` -> `...19`
keno <- read_excel(
product_file,
sheet = "KENO 5",
skip = 5
) %>%
select(
year = `Value ($ million)`,
expenditure = ...18
) %>%
filter(!is.na(year)) %>%
filter(str_detect(year, "^[0-9]")) %>%
mutate(product = "Keno")
## New names:
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13`
## • `` -> `...14`
## • `` -> `...15`
## • `` -> `...16`
## • `` -> `...17`
## • `` -> `...18`
## • `` -> `...19`
chart2 <- bind_rows(
gaming_machines,
wagering,
casino,
lotteries,
keno
)
chart2_2024 <- chart2 %>%
filter(year == "2023–24") %>%
arrange(desc(expenditure)) %>%
mutate(
product = factor(
product,
levels = rev(product)
)
)
#Plotting Chart 2
product_order <- c(
"Gaming Machines",
"Wagering",
"Casino",
"Lotteries",
"Keno"
)
product_colours <- c(
"Gaming Machines" = "#D55E00",
"Wagering" = "#0072B2",
"Casino" = "#009E73",
"Lotteries" = "#E69F00",
"Keno" = "#CC79A7"
)
chart2_plot <- chart2 %>%
mutate(
product = factor(product, levels = product_order),
expenditure_b = expenditure / 1000
)
p2 <- ggplot(
chart2_plot,
aes(
x = year,
y = expenditure_b,
fill = product,
group = product
)
) +
geom_area(
alpha = 0.9,
colour = "white",
linewidth = 0.3
) +
scale_fill_manual(
values = product_colours
) +
scale_y_continuous(
labels = function(x) paste0("$", x, "B"),
breaks = seq(0, 35, 5),
expand = expansion(mult = c(0, 0.01))
) +
labs(
x = NULL,
y = "Gambling expenditure ($ billion)",
fill = NULL
) +
scale_x_discrete(
breaks = c(
"1998–99",
"2003–04",
"2008–09",
"2013–14",
"2018–19",
"2023–24"
)
) +
theme_minimal(base_size = 13) +
theme(
plot.title = element_text(
face = "bold",
size = 15,
hjust = 0.5
),
plot.subtitle = element_text(
size = 12,
colour = "grey30",
hjust = 0.5,
margin = margin(b = 12)
),
axis.title.y = element_text(
face = "bold",
size = 11,
margin = margin(r = 15)
),
axis.text.x = element_text(
angle = 45,
hjust = 1,
size = 6
),
axis.text.y = element_text(
size = 10
),
legend.position = "bottom",
legend.text = element_text(
size = 10
),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
plot.caption = element_text(
size = 10,
colour = "grey30",
hjust = 0,
face = "italic",
margin = margin(t = 10)
),
plot.margin = margin(
t = 10,
r = 20,
b = 10,
l = 10
)
)
ggplotly(p2) %>%
layout(
title = list(
text = paste(
"Gaming machines account for half of gambling losses",
"<br><sup>Australian gambling expenditure by product type, 1998–99 to 2023–24</sup>"
),
x = 0.5
),
xaxis = list(
title = "",
tickangle = -45
),
yaxis = list(
title = list(
text = "Gambling expenditure ($ billion)",
font = list(size = 16)
)
),
legend = list(
title = list(text = "")
),
margin = list(
l = 100,
r = 80,
t = 120,
b = 120
)
)
# CHART 3
# Relationship between gaming machine density and gambling losses
density <- read_excel(
"C:/Users/evita/OneDrive/Desktop/Data Vis A3/DATA/yearly_density_statistical_release_nov_24-(2).xlsx",
sheet = "Detail Data 2023-24",
skip = 11
)
## New names:
## • `as at June 2024` -> `as at June 2024...17`
## • `as at June 2024` -> `as at June 2024...18`
## • `as at June 2024` -> `as at June 2024...19`
chart3_scatter <- density %>%
select(
lga = `LGA Name`,
region = Region,
expenditure_per_adult = `per Adult 2024`,
egm_density = `per 1,000 Adults 2024`
) %>%
filter(
!is.na(expenditure_per_adult),
!is.na(egm_density),
!is.na(region),
expenditure_per_adult > 0
)
ggplot(
chart3_scatter,
aes(
x = egm_density,
y = expenditure_per_adult,
colour = region
)
) +
geom_point(
size = 3.5,
alpha = 0.75
) +
geom_smooth(
method = "lm",
se = FALSE,
colour = "grey30",
linewidth = 1
) +
annotate(
"text",
x = 3,
y = 900,
label = "Highest losses occur\nin large Metro LGAs",
size = 3,
colour = "steelblue4",
hjust = 0,
fontface = "italic"
) +
scale_colour_manual(
values = c(
"Metro" = "steelblue",
"Country" = "darkorange"
)
) +
scale_x_continuous(
breaks = seq(0, 10, 2)
) +
scale_y_continuous(
labels = scales::dollar_format()
) +
labs(
title = "Communities with more poker machines lose more money",
subtitle = "Higher gaming machine density is associated with greater gambling losses per adult",
x = "EGMs per 1,000 adults",
y = "Gambling expenditure per adult ($)",
colour = "Region",
caption = "Source: Victorian Gambling and Casino Control Commission (2024)"
) +
theme_minimal(base_size = 13) +
theme(
plot.title = element_text(
face = "bold",
size = 16,
margin = margin(b = 6)
),
plot.subtitle = element_text(
size = 12,
colour = "grey30",
margin = margin(b = 10)
),
axis.title.x = element_text(
face = "bold",
size = 11
),
axis.title.y = element_text(
face = "bold",
size = 11
),
axis.text = element_text(
size = 10
),
legend.position = c(0.83, 0.22),
legend.title = element_text(
face = "bold"
),
panel.grid.minor = element_blank(),
plot.caption = element_text(
size = 10,
colour = "grey30",
face = "italic",
hjust = 0,
margin = margin(t = 10)
),
plot.margin = margin(
t = 10,
r = 20,
b = 10,
l = 10
)
)
## Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
## 3.5.0.
## ℹ Please use the `legend.position.inside` argument of `theme()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## `geom_smooth()` using formula = 'y ~ x'
# CHART 4
# Governments collect up to one-third of gambling losses
state_file <- "C:/Users/evita/OneDrive/Desktop/Data Vis A3/DATA/australian-gambling-statistics-40th-edn-1998-99-2023-24-state-tables (1).xlsx"
states <- c(
"NSW",
"VIC",
"QLD",
"SA",
"WA",
"TAS",
"ACT",
"NT"
)
chart4 <- lapply(states, function(state) {
exp_data <- read_excel(
state_file,
sheet = paste0(state, " 20"),
skip = 5
)
rev_data <- read_excel(
state_file,
sheet = paste0(state, " 26"),
skip = 5
)
tibble(
state = state,
expenditure = exp_data %>%
filter(...1 == "2023–24") %>%
pull(TOTAL),
revenue = rev_data %>%
filter(...1 == "2023–24") %>%
pull(TOTAL)
)
}) %>%
bind_rows() %>%
mutate(
revenue_pct = (revenue / expenditure) * 100,
revenue_billion = revenue / 1000
)
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## • `` -> `...1`
## • `` -> `...3`
## • `` -> `...5`
## • `` -> `...7`
#Plotting Chart 4
ggplot(
chart4,
aes(
x = revenue_pct,
y = reorder(state, revenue_pct),
fill = revenue_billion
)
) +
geom_col() +
geom_text(
aes(label = paste0(round(revenue_pct, 1), "%")),
hjust = -0.2,
size = 4
) +
scale_fill_gradient(
low = "#fee8c8",
high = "#d7301f",
name = "Government revenue\n($ billion)"
) +
scale_x_continuous(
limits = c(0, max(chart4$revenue_pct) + 8)
) +
labs(
title = "Governments collect up to one-third of gambling losses",
subtitle = "SA captures the largest share of gambling losses, while NSW collects the most revenue",
x = "Government revenue (% of gambling expenditure)",
y = NULL,
caption = "Source: Queensland Government Statistician's Office, Australian Gambling Statistics, 40th Edition (2024)"
)+
theme_minimal(base_size = 13) +
theme(
plot.title = element_text(
face = "bold",
size = 15,
hjust = 0
),
plot.subtitle = element_text(
size = 12,
colour = "grey30",
margin = margin(b = 10)
),
legend.position = "right",
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
plot.caption = element_text(
size = 10,
colour = "grey30",
face = "italic",
hjust = 0
)
)
# CHART 5
# Who pays the price of gambling?
chart5 <- density %>%
transmute(
region = Region,
disadvantage = `DIS Score`,
expenditure_per_adult = `per Adult 2024`,
egm_density = `per 1,000 Adults 2024`
) %>%
filter(
!is.na(region),
!is.na(disadvantage),
!is.na(expenditure_per_adult),
!is.na(egm_density)
) %>%
mutate(
disadvantage_group = ntile(disadvantage, 5),
disadvantage_group = factor(
disadvantage_group,
levels = 1:5,
labels = c(
"Most disadvantaged",
"Disadvantaged",
"Average",
"Advantaged",
"Most advantaged"
)
)
)
chart5_summary <- chart5 %>%
group_by(disadvantage_group, region) %>%
summarise(
avg_expenditure = mean(expenditure_per_adult, na.rm = TRUE),
avg_egm = mean(egm_density, na.rm = TRUE),
.groups = "drop"
)
#Plotting Chart 5
ggplot(
chart5_summary,
aes(
x = region,
y = disadvantage_group,
fill = avg_expenditure
)
) +
geom_tile(
colour = "white",
linewidth = 1.5
) +
geom_text(
aes(
label = paste0(
"$", round(avg_expenditure),
"\n",
round(avg_egm, 1),
" EGMs"
)
),
colour = "black",
fontface = "bold",
lineheight = 0.9,
size = 4
) +
scale_fill_gradientn(
colours = c(
"#f2f0f7",
"#cbc9e2",
"#9e9ac8",
"#6a51a3"
),
name = "Losses ($)",
labels = scales::dollar_format()
) +
labs(
title = "Who pays the price of gambling?",
subtitle = "Gambling losses and EGM density are highest in disadvantaged Metro communities",
x = NULL,
y = NULL,
caption = "Source: Victorian Gambling and Casino Control Commission (2024)\nSEIFA disadvantage score used to classify LGAs. Lower scores indicate greater disadvantage."
) +
theme_minimal(base_size = 13) +
theme(
plot.title = element_text(
hjust = 0.5,
face = "bold",
size = 14,
margin = margin(b = 6)
),
plot.subtitle = element_text(
hjust = 0.5,
size = 11,
colour = "grey30",
margin = margin(b = 12)
),
axis.text = element_text(
size = 10,
face = "bold"
),
axis.title = element_blank(),
panel.grid = element_blank(),
legend.position = "right",
plot.caption = element_text(
size = 9,
colour = "grey35",
face = "italic",
hjust = 0,
margin = margin(t = 12)
),
plot.margin = margin(
t = 10,
r = 20,
b = 35,
l = 10
)
)
