2.103 – Dépense de consommation finale pré-engagée des ménages à prix courants (En milliards d’euros et %) - t_2103

Data - INSEE

Info

  • Comptes de la Nation 2019. html

gdp

Code
gdp <- `CNA-2014-PIB` %>%
  year_to_date %>%
  filter(OPERATION == "PIB",
         UNIT_MEASURE %in% c("EUR2014", "EUROS_COURANTS")) %>%
  select(date, UNIT_MEASURE, OBS_VALUE) %>%
  mutate(OBS_VALUE = (OBS_VALUE %>% as.numeric)/1000,
         UNIT_MEASURE = paste0("PIB_", UNIT_MEASURE)) %>%
  spread(UNIT_MEASURE, OBS_VALUE)

gdp %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Table in 2000, 2005, 2010, 2015, 2020

Code
t_2103 %>%
  filter(year %in% paste0(seq(2000, 2020, 5))) %>%
  spread(year, value) %>%
  arrange(line) %>%
  mutate_at(vars(-line, -Line), funs(round(.) %>% paste0(" Mds€"))) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Logement VS dépenses pré-engagées

Code
t_2103 %>%
  filter(line %in% c(6, 7)) %>%
  year_to_date2 %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("% du revenu disponible brut") +
  geom_line(aes(x = date, y = value / 100, color = Line)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.65, 0.15)) +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_y_continuous(breaks = 0.01*seq(0, 30, 2),
                     labels = scales::percent_format(accuracy = 1))