Time Use Analysis

The categorizations are based on https://unstats.un.org/unsd/gender/timeuse/23012019%20ICATUS.pdf

1 Average sleep over the ages

timeuse_df %>%
  filter(activity == "Sleep", age < 90) %>%
  group_by(person_id, age, mult) %>%
  summarise(total_sleep_mins = sum(duration_mins, na.rm = TRUE),
            .groups = 'drop') %>% group_by(age) %>%
  summarise(avg_sleep_weighted_mins = weighted.mean(total_sleep_mins, w = mult, na.rm = TRUE)) %>%
  mutate(avg_sleep_hours = avg_sleep_weighted_mins / 60) %>%
  ggplot(aes(x = age, y = avg_sleep_hours)) +
  geom_smooth(
    method = "loess",
    se = TRUE,
    color = "black",
    fill = "gray80"
  ) +
  #geom_line()+
  scale_y_continuous(limits = c(8, 13), breaks = seq(8, 13, by = 1)) +
  scale_x_continuous(breaks = seq(0, 90, by = 10), limits = c(6, 95)) +
    labs(
    title = "Average Daily Sleep Declines Through Adolescence",
    x = "Age",
    y = "Average Sleep (hours per day)",
    caption = "Source: MoSPI Time Use Survey, 2024"
  ) +
    theme_fivethirtyeight() +
    theme(
    text = element_text(family = "ath"),
    plot.title = element_text(
      family = "ath",
      face = "bold",
      size = 18,
      margin = margin(b = 20)
    ),
    plot.caption = element_text(
      family = "ath",
      size = 10,
      color = "gray50"
    ),
    axis.title = element_text(family = "ath", size = 12),
    axis.title.y = element_text(margin = margin(r = 15)),
    axis.title.x = element_text(margin = margin(t = 15)),
    axis.text.y = element_text(
      family = "ath",
      size = 12,
      margin = margin(r = 8)
    ),
    axis.text.x = element_text(
      family = "ath",
      size = 12,
      margin = margin(t = 8)
    ),
    
    panel.grid.major = element_line(linewidth = 0.5, color = "gray90"),
    panel.grid.minor = element_blank()
  )

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

1.1 Difference in activities by gender

all_persons <- timeuse_df %>%
  distinct(person_id, gender, mult)

person_activity_summary <- timeuse_df %>%
  filter(!is.na(activity) & activity != "Unclassified") %>%
  group_by(person_id, activity) %>%
  summarise(total_duration_mins = sum(duration_mins, na.rm = TRUE), .groups = 'drop')

gender_summary <- all_persons %>%
  tidyr::crossing(activity = unique(person_activity_summary$activity)) %>%
  left_join(person_activity_summary, by = c("person_id", "activity")) %>%
  mutate(total_duration_mins = ifelse(is.na(total_duration_mins), 0, total_duration_mins)) %>%
  group_by(gender, activity) %>%
  summarise(
    avg_hours_per_person = weighted.mean(total_duration_mins, w = mult, na.rm = TRUE) / 60,
    .groups = 'drop'
  )

gender_summary_wide <- gender_summary %>%
  pivot_wider(names_from = gender, values_from = avg_hours_per_person) %>%
  mutate(gap = female - male) %>%
  slice_max(order_by = abs(gap), n = 15) %>%
  mutate(activity = fct_reorder(activity, gap))

ggplot(gender_summary_wide, aes(y = activity)) +
  geom_segment(aes(x = male, xend = female), color = "gray", linewidth = 1.5, alpha = 0.5) +
  geom_point(aes(x = female, color = "Female"), size = 4) +
  geom_point(aes(x = male, color = "Male"), size = 4) +
  
  theme_fivethirtyeight() +
  scale_color_manual(name = "", values = c("Female" = "#0072B2", "Male" = "#D55E00")) +
  labs(
    title = "Unpaid Work Accounts for the Largest Gender Gap in Time Use",
    subtitle = "Comparing the average daily hours for men and women",
    x = "Average Hours Per Day (per person)",
    y = "",
    caption = "Source: MoSPI Time Use Survey, 2024"
  ) +
    theme(
    text = element_text(family = "ath"),
    plot.title = element_text(family = "ath", size = 16, margin = margin(b = 10, l = -130)),
    plot.subtitle = element_text(family = "ath", size = 14, margin = margin(b = 20, l = -130), color = "gray40"),
    plot.caption = element_text(family = "ath", size = 10, color = "gray50", margin = margin(t = 20)),
    axis.title = element_text(family = "ath", size = 12),
    axis.text = element_text(family = "ath", size = 11),
    plot.margin = margin(15, 50, 10, 0),
    
    legend.position = "top",
    legend.direction = "horizontal", 
    legend.background = element_rect(fill = "transparent"),
    legend.key = element_rect(fill = "transparent", color = NA), 
    legend.title = element_blank(), 
    legend.text = element_text(size = 10, family = "ath")
  )

all_persons_sector <- timeuse_df %>%
  distinct(person_id, sector, mult)

person_activity_summary_sector <- timeuse_df %>%
  filter(!is.na(activity) & activity != "Unclassified") %>%
  group_by(person_id, activity) %>%
  summarise(total_duration_mins = sum(duration_mins, na.rm = TRUE), .groups = 'drop')

sector_summary <- all_persons_sector %>%
  tidyr::crossing(activity = unique(person_activity_summary_sector$activity)) %>%
  left_join(person_activity_summary_sector, by = c("person_id", "activity")) %>%
  mutate(total_duration_mins = ifelse(is.na(total_duration_mins), 0, total_duration_mins)) %>%
  group_by(sector, activity) %>%
  summarise(
    avg_hours_per_person = weighted.mean(total_duration_mins, w = mult, na.rm = TRUE) / 60,
    .groups = 'drop'
  )

sector_summary_wide <- sector_summary %>%
  pivot_wider(names_from = sector, values_from = avg_hours_per_person) %>%
  mutate(gap = Urban - Rural) %>%
  slice_max(order_by = abs(gap), n = 15) %>%
  mutate(activity = fct_reorder(activity, gap))

ggplot(sector_summary_wide, aes(y = activity)) +
  geom_segment(aes(x = Rural, xend = Urban), color = "gray", linewidth = 1.5, alpha = 0.5) +
  geom_point(aes(x = Urban, color = "Urban"), size = 4) +
  geom_point(aes(x = Rural, color = "Rural"), size = 4) +
  
  theme_fivethirtyeight() +
  scale_color_manual(name = "", values = c("Urban" = "#0072B2", "Rural" = "#D55E00")) +
  
  labs(
    title = "Rural-Urban Divide in Daily Time Use",
    subtitle = "Comparing daily hours for the 15 activities with the largest urban-rural disparities",
    x = "Average Hours Per Day (per person)",
    y = "",
    caption = "Source: MoSPI Time Use Survey, 2024"
  ) +
  
  theme(
    text = element_text(family = "ath"),
    plot.title = element_text(family = "ath", size = 20, margin = margin(b = 10)),
    plot.subtitle = element_text(family = "ath", size = 14, margin = margin(b = 20), color = "gray40"),
    plot.caption = element_text(family = "ath", size = 10, color = "gray50", margin = margin(t = 20)),
    axis.title = element_text(family = "ath", size = 12),
    axis.text = element_text(family = "ath", size = 11),
    plot.margin = margin(15, 15, 10, 15),
    
    legend.position = "top",
    legend.direction = "horizontal", 
    legend.background = element_rect(fill = "transparent"),
    legend.key = element_rect(fill = "transparent", color = NA), 
    legend.title = element_blank(), 
    legend.text = element_text(size = 14, family = "ath")
  )

2 Activity strips by age groups

high_level_summary <- timeuse_df %>%
  filter(!is.na(activity) & activity != "Unclassified") %>%
  mutate(
    activity_group = case_when(
      activity == "Sleep" ~ "Sleep",
      activity %in% c("Eating & Drinking", "Personal Hygiene & Health", "Receiving Care") ~ "Personal Care",
      activity %in% c(
        "Formal Employment",
        "Household Enterprise (Goods)",
        "Household Enterprise (Services)",
        "Work-Related Training",
        "Seeking Employment",
        "Setting up a Business"
      ) ~ "Paid Work",
      activity %in% c(
        "Food & Meal Management",
        "Cleaning & Maintenance",
        "Childcare & Instruction",
        "Shopping",
        "Agriculture & Fishing (Own-Use)",
        "Community Volunteering",
        "Direct Volunteering"
      ) ~ "Unpaid Work & Care",
      activity %in% c("Formal Education", "Homework", "Additional Study") ~ "Learning",
      # Group all leisure, social, and travel into one bucket for this high-level view
      TRUE ~ "Leisure, Social & Travel"
    )
  )

age_group_summary <- high_level_summary %>%
  mutate(age_group = cut(
    age,
    breaks = c(5, 14, 24, 59, Inf),
    labels = c(
      "Children (6-14)",
      "Youth (15-24)",
      "Adults (25-59)",
      "Seniors (60+)"
    ),
    right = TRUE,
    include.lowest = TRUE
  )) %>%
  filter(!is.na(age_group)) %>%
  group_by(person_id, age_group, activity_group, mult) %>%
  summarise(total_duration = sum(duration_mins, na.rm = TRUE),
            .groups = "drop") %>%
  group_by(age_group, activity_group) %>%
  summarise(avg_hours = weighted.mean(total_duration, w = mult, na.rm = TRUE) / 60,
            .groups = "drop")

stacking_order <- c(
  "Sleep",
  "Personal Care",
  "Paid Work",
  "Unpaid Work & Care",
  "Learning",
  "Leisure, Social & Travel"
)

age_group_summary$activity_group <- factor(age_group_summary$activity_group, levels = stacking_order)


ggplot(age_group_summary,
       aes(x = age_group, y = avg_hours, fill = activity_group)) +
  geom_col(position = "fill", colour = "transparent") +
  scale_y_continuous(labels = scales::percent) +
  
  scale_fill_brewer(palette = "Dark2", name = "") +
  labs(
    title = "Share of a 24-hour day spent on major activities, by age group",
    x = "",
    y = "Percentage of Day",
    caption = "Source: MoSPI Time Use Survey, 2024"
  ) +
  theme_fivethirtyeight() +
  
  theme(
    text = element_text(family = "ath"),
    plot.title = element_text(
      family = "ath",
      size = 20,
      margin = margin(b = 10)
    ),
    plot.subtitle = element_text(
      family = "ath",
      size = 14,
      margin = margin(b = 20),
      color = "gray40"
    ),
    plot.caption = element_text(
      family = "ath",
      size = 10,
      color = "gray50",
      margin = margin(t = 20)
    ),
    axis.title = element_text(family = "ath", size = 12),
    axis.text = element_text(family = "ath", size = 11),
    plot.margin = margin(15, 15, 10, 15),
    
    legend.position = "bottom",
    legend.direction = "horizontal",
    legend.background = element_rect(fill = "transparent"),
    legend.key = element_rect(fill = "transparent", color = NA),
    legend.title = element_blank(),
    legend.text = element_text(size = 14, family = "ath")
  )

3 Time use on key activities in children and youth

“children” are defined as persons under 15 years of age, and “youth” are defined as those aged 15–24.

youth_activities <- c(
  "Formal Education", "Homework", 
  "Food & Meal Management", "Cleaning & Maintenance", "Childcare & Instruction",
  "Mass Media", "Sports & Exercise", "Hobbies & Games",
  "Socializing & Communication"
  )

youth_summary <- timeuse_df %>%
  filter(age >= 6 & age <= 24, activity %in% youth_activities, gender %in% c('male', 'female')) %>%
  group_by(person_id, age, gender, activity, mult) %>%
  summarise(total_duration = sum(duration_mins, na.rm = TRUE), .groups = "drop") %>%
  group_by(age, gender, activity) %>%
  summarise(avg_hours = weighted.mean(total_duration, w = mult, na.rm = TRUE) / 60, .groups = "drop")

ggplot(youth_summary, aes(x = age, y = avg_hours, color = gender)) +
  geom_smooth(se = FALSE, size = 1.2) +
  facet_wrap(~ activity, scales = "free", ncol = 3) + 
  
  scale_color_manual(name = "Gender", values = c("female" = "#0072B2", "male" = "#D55E00")) +
  labs(
    title = "Average daily hours spent on key activities for ages 6-24, by gender",
    x = "Age (years)",
    y = "Average Hours Per Day",
    caption = "Source: MoSPI Time Use Survey, 2024"
  ) +
  theme_minimal(base_family = "ath") +
  theme(
    plot.title = element_text(face = "bold", size = 20, margin = margin(b = 10)),
    plot.subtitle = element_text(size = 14, color = "gray40", margin = margin(b = 20)),
    strip.text = element_text(face = "bold", size = 11, hjust = 0),
    legend.position = "top",
    panel.spacing = unit(1.5, "lines")
  ) +
theme_fivethirtyeight() +
  
  theme(
    text = element_text(family = "ath"),
    plot.title = element_text(
      family = "ath",
      size = 20,
      margin = margin(b = 10)
    ),
    plot.subtitle = element_text(
      family = "ath",
      size = 14,
      margin = margin(b = 20),
      color = "gray40"
    ),
    plot.caption = element_text(
      family = "ath",
      size = 10,
      color = "gray50",
      margin = margin(t = 20)
    ),
    axis.title = element_text(family = "ath", size = 12),
    axis.text = element_text(family = "ath", size = 11),
    plot.margin = margin(15, 15, 10, 15),
    
    legend.position = "bottom",
    legend.direction = "horizontal",
    legend.background = element_rect(fill = "transparent"),
    legend.key = element_rect(fill = "transparent", color = NA),
    legend.title = element_blank(),
    legend.text = element_text(size = 14, family = "ath")
  )

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

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