title: “How Australians Spend Their Time” author: “DHANUSHA” output: flexdashboard::flex_dashboard: orientation: columns storyboard: true —
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()
Australian Bureau of Statistics (ABS)
Time Use Survey, 2020–21
https://www.abs.gov.au/statistics/people/people-and-communities/how-australians-use-their-time/latest-release
ABS Data Portal
General access to downloadable datasets and metadata
https://www.abs.gov.au/statistics