Geschlechtsspezifische Aufteilung von Sorgearbeit (https://x.com/matschnetzer/status/1747624651902099745?s=20)

timeuse.R

library(tidyverse)
library(readODS)

# Daten unter https://www.statistik.at/fileadmin/pages/298/Durchschnittliche_Zeitverwendung_2021-22.ods

longdata <- 
  read_ods("Durchschnittliche_Zeitverwendung_2021-22.ods", 
           sheet = "Tabelle_17", range = "A5:J129", col_names = F, 
           col_types = "ctcttcttct") |> 
  janitor::clean_names() |> 
  mutate(across(c(x3, x6, x9), ~as.numeric(str_replace_all(., ",", ".")))) |> 
  select(activity = x1, female_part = x3, female_time = x4, male_part = x6, male_time = x7) 

caredata <- longdata |> 
  filter(activity %in% c("Nahrungsmittelzubereitung", "Wäsche waschen", "Versorgung und Beaufsichtungung des Kindes", "Wege für Kinderbetreuung", "Einkaufen (inkl. Online-Shopping)", "Aufräumen und Reinigung von Wohnung oder Haus")) |> 
  mutate(activity = case_when( 
    activity == "Einkaufen (inkl. Online-Shopping)" ~ "Einkaufen",
    activity == "Aufräumen und Reinigung von Wohnung oder Haus" ~ "Aufräumen und Putzen",
    TRUE ~ activity
  )) |> 
  pivot_longer(-activity, names_to = c("gender", ".value"), 
               names_pattern = "(.+)_(.+)") |> 
  mutate(time = as.period(time))

caredata |> 
  ggplot(aes(x = part, y = time, color = gender, group = activity)) +
  geom_line(color = "gray95", linewidth = 5) +
  geom_point(size = 5) +
  geomtextpath::geom_textline(aes(label = activity, 
      vjust = ifelse(str_detect(activity, "Einkauf|Weg"), 1.8, 0.5)), linewidth = 0,
      color = "black", size = 3, family = "Roboto Condensed") +
  scale_y_time(labels = scales::label_timespan(unit = "hours"),
               breaks = scales::date_breaks("0.5 hours"),
               limits = c(as.period("0.5 hours"), as.period("2 hours")),
               expand = c(0, 0.1)) +
  scale_x_continuous(labels = scales::percent_format(scale = 1),
                     limits = c(-5, NA)) +
  scale_color_manual(values = c(female = "#802417", male = "#508ea2"), 
                     labels = c(female = "Frauen", male = "Männer")) +
  scale_size_continuous(range = c(3, 13)) +
  guides(size = guide_none(),
         color = guide_legend(
           label.theme = element_text(size = 12, family = "Roboto Condensed"),
           override.aes = list(size = 5))) + 
  labs(x = "Mehr Ausübende ⟶ ", y = "Mehr Zeitaufwand ⟶ ", color = NULL,
       title = "Unbezahlte Arbeit ist meist weiblich",
       subtitle = "Anteil der Ausübenden und täglicher Zeitaufwand für ausgewählte Tätigkeiten von Sorgearbeit",
       caption = "Quelle: Zeitverwendungserhebung 2021/22, Statistik Austria. Grafik: @matschnetzer") +
  coord_flip(clip = "off") +
  theme_minimal(base_family = "Roboto Condensed", base_size = 14) +
  theme(plot.title.position = "plot", 
        plot.title = element_text(size = 20, hjust = 0.5),
        plot.subtitle = element_text(size = 12, hjust = 0.5, 
                                     margin = margin(b = 2, unit = "lines")),
        plot.caption = element_text(size = 8, margin = margin(t = 2, unit = "lines")),
        axis.title = element_text(hjust = 0, size = 11, color = "gray20"),
        legend.position = c(0.8, 0.7), 
        panel.grid.minor = element_blank(),
        panel.grid.major = element_line(linewidth = 0.2, color = "gray90"))
        
 ggsave("timeuse_care.png", width = 8, height = 4.5, dpi = 320, bg = "white")