2.104 – Compte des ménages simplifié et ratios d’épargne (En milliards d’euros et %) - t_2104_2018

Données - INSEE

Auteur

Code

t_2104_2018 <- read_parquet("t_2104_2018_old.parquet")
load_data("insee/CNA-2014-PIB.RData")
nber_recessions <- read_parquet(here::here("data", "us", "nber_recessions.parquet"))

Organisation

Revenu et pouvoir d’achat des ménages en 2018. html

variable

Code

t_2104_2018 |>
  group_by(line, Line) |>
  summarise(Nobs = n()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

gdp

Code

gdp <- `CNA-2014-PIB` |>
  yearend_to_date() |>
  filter(OPERATION == "PIB",
         UNIT_MEASURE %in% c("EUROS_COURANTS")) |>
  mutate(value = (OBS_VALUE |> as.numeric())/1000,
         variable = paste0("PIB_", UNIT_MEASURE),
         variable_desc = "Produit Intérieur Brut") |>
  select(date, value)

Code

gdp |>
  mutate(value = round(value) |> paste0(" Mds€")) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Ex 1: Table in 2018

Code

t_2104_2018 |>
  filter(date == as.Date("2018-12-31")) |>
  select(-date) |>
  mutate(value = round(value) |> paste0(" Mds€")) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Ex: Rémunération des salariés

Code

t_2104_2018 |>
  group_by(date) |>
  mutate(value = value/value[line == 18]) |>
  filter(line %in% c(5, 4)) |>
  ggplot() + theme_minimal() + 
  geom_line(aes(x = date, y = value, color = Line, linetype = Line)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.9)) +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1949-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
               limits = c(1949, 2020) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  ylab("% du RDBA") + xlab("") +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1))

Ex: Loyers, Business

Code

t_2104_2018 |>
  group_by(date) |>
  mutate(value = value/value[line == 18]) |>
  filter(line %in% c(2, 3)) |>
  ggplot() + theme_minimal() + 
  geom_line(aes(x = date, y = value, color = Line, linetype = Line)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.9)) +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1949-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
               limits = c(1949, 2020) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  ylab("% du RDBA") + xlab("") +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1))

Ex: Revenus de la propriété

Code

t_2104_2018 |>
  group_by(date) |>
  mutate(value = value/value[line == 18]) |>
  filter(line %in% c(9)) |>
  ggplot() + theme_minimal() + 
  geom_line(aes(x = date, y = value, color = Line, linetype = Line)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.9)) +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1949-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
               limits = c(1949, 2020) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  ylab("% du RDBA") + xlab("") +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = scales::percent_format(accuracy = 1))