Production par branche - Valeurs aux prix courants - t_prod_val

Données - INSEE

Auteur

François Geerolf

Last observation: Q2 2026 (N = 22)

First observation: Q1 1949 (N = 22)

Last data update: 03 sept. 2026, 05:31

Last compile: 24 sept. 2026, 01:49

Comptes trimestriels : production par branche, à prix courants.

Structure

Principales branches

Code 
`t_prod_val` |>
  filter(variable %in% big) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Production (Mds €)") +
  geom_line(aes(x = date, y = value, color = a17_label(variable))) +
  scale_x_date(date_breaks = "10 years", date_labels = "%Y") +
  theme(legend.title = element_blank(), legend.position = "bottom") +
  guides(color = guide_legend(nrow = 2))

Industrie manufacturière — glissement annuel

Code 
`t_prod_val` |>
  filter(variable == "C") |>
  arrange(date) |> mutate(g = value / lag(value, 4) - 1) |>
  filter(date >= as.Date("1980-01-01")) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Glissement annuel") +
  geom_hline(yintercept = 0, color = "grey60") +
  geom_line(aes(x = date, y = g)) +
  scale_x_date(date_breaks = "10 years", date_labels = "%Y") +
  scale_y_continuous(labels = percent_format(accuracy = 1))

Répartition par branche

Code 
`t_prod_val` |>
  filter(grepl("^[A-Z][A-Z0-9]?$", variable), variable != "C") |>
  group_by(date) |> mutate(part = value / sum(value)) |> ungroup() |>
  arrange(desc(variable)) |> mutate(variable = factor(a17_label(variable), levels = unique(a17_label(variable)))) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Part du total") +
  geom_area(aes(x = date, y = part, fill = variable)) +
  scale_x_date(date_breaks = "10 years", date_labels = "%Y") +
  scale_y_continuous(labels = percent_format(accuracy = 1)) +
  scale_fill_viridis_d() +
  theme(legend.title = element_blank(), legend.position = "bottom") +
  guides(fill = guide_legend(nrow = 3))

Derniers trimestres

Code 
`t_prod_val` |>
  filter(grepl("^[A-Z][A-Z0-9]?$", variable), date >= last_d %m-% months(18)) |>
  mutate(value = round(value, 1)) |>
  tidyr::pivot_wider(names_from = variable, values_from = value) |>
  arrange(desc(date)) |>
  print_table_conditional()
date AZ DE C C1 C2 C3 C4 C5 FZ GZ HZ IZ JZ KZ LZ MN OQ RU
2026-04-01 26.3 56.2 255.0 51.4 12.4 26.4 55.3 109.5 101.1 135.6 75.2 41.2 79.6 82.7 109.6 203.9 205.0 37.8
2026-01-01 26.8 54.0 249.2 51.7 10.0 26.1 54.5 106.9 100.5 133.6 72.9 40.8 78.8 81.8 109.0 201.9 203.5 37.3
2025-10-01 27.0 54.8 246.4 51.8 8.7 25.9 54.0 105.8 101.5 134.0 72.4 40.6 78.1 79.9 108.6 200.3 201.9 37.1
2025-07-01 27.1 56.3 246.4 51.5 8.9 25.9 54.4 105.8 100.8 134.1 71.8 40.4 77.7 79.8 107.9 198.1 201.1 37.0
2025-04-01 26.7 53.8 245.2 52.1 8.2 26.0 53.6 105.3 100.4 133.3 72.4 40.2 76.4 78.7 107.2 195.6 200.0 37.0
2025-01-01 26.0 59.6 245.5 52.4 9.5 25.9 51.7 105.9 100.3 133.0 73.5 39.5 76.4 77.3 106.3 194.8 199.4 36.9
2024-10-01 25.6 61.8 242.1 51.4 9.4 25.7 50.5 105.2 100.4 132.5 74.1 39.2 76.5 74.5 105.3 193.2 197.8 36.8