How Australians Spend Their Time

title: “How Australians Spend Their Time” author: “DHANUSHA” output: flexdashboard::flex_dashboard: orientation: columns storyboard: true —


🕒 Introduction


📊 Data Source


📈 Time Use Breakdown by Activity

library(ggplot2)
library(dplyr)

# Simulated ABS-style data
time_data <- data.frame(
  Activity = c("Paid Work", "Housework", "Caring", "Leisure", "Sleep", "Eating", "Travel", "Education"),
  Hours = c(3.5, 2.7, 1.2, 4.5, 8.7, 1.1, 1.3, 0.6)
)

ggplot(time_data, aes(x = reorder(Activity, Hours), y = Hours, fill = Activity)) +
  geom_col(show.legend = FALSE) +
  coord_flip() +
  labs(title = "Average Daily Hours by Activity",
       x = "Activity",
       y = "Hours per Day") +
  theme_minimal()

gender_data <- data.frame(
  Activity = rep(c("Paid Work", "Housework", "Caring", "Leisure"), each = 2),
  Gender = rep(c("Men", "Women"), times = 4),
  Hours = c(4.2, 2.8, 1.0, 1.4, 4.8, 4.2, 5.0, 4.0)
)

ggplot(gender_data, aes(x = Activity, y = Hours, fill = Gender)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Gender Differences in Time Use",
       y = "Hours per Day") +
  theme_minimal()

age_data <- data.frame(
  AgeGroup = rep(c("15–24", "25–44", "45–64", "65+"), each = 3),
  Activity = rep(c("Paid Work", "Leisure", "Sleep"), times = 4),
  Hours = c(2.5, 5.0, 8.5, 4.5, 3.5, 7.5, 3.0, 4.0, 8.0, 1.0, 6.0, 9.0)
)

ggplot(age_data, aes(x = AgeGroup, y = Hours, fill = Activity)) +
  geom_bar(stat = "identity", position = "stack") +
  labs(title = "Time Use by Age Group",
       y = "Hours per Day") +
  theme_minimal()

leisure_data <- data.frame(
  Activity = c("TV/Streaming", "Socializing", "Reading", "Gaming", "Sports"),
  Hours = c(2.1, 1.0, 0.5, 0.6, 0.3)
)

ggplot(leisure_data, aes(x = "", y = Hours, fill = Activity)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y") +
  labs(title = "Leisure Time Breakdown") +
  theme_void()

🔗 References