Production par branche - Volume (CVS-CJO) - t_prod_vol

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, en volume.

Structure

Principales branches

Code 
`t_prod_vol` |>
  filter(variable %in% big) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Production (volume)") +
  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_vol` |>
  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_vol` |>
  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_vol` |>
  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 21.1 38.4 205.8 40.8 8.3 22.7 49.5 84.7 81.8 114.2 64.4 34.1 75.5 71.4 99.9 179.2 180.5 36.2
2026-01-01 21.4 38.4 205.6 40.9 8.5 22.6 49.2 84.5 82.1 113.9 64.4 34.1 75.4 71.2 99.7 178.6 180.1 35.9
2025-10-01 21.9 39.1 204.6 40.6 8.4 22.5 48.6 84.4 83.5 114.4 64.2 34.3 74.7 71.4 99.5 177.9 179.4 35.7
2025-07-01 22.1 38.7 205.0 40.4 8.4 22.5 49.0 84.7 83.3 114.3 63.4 34.2 74.5 71.4 99.1 176.5 179.0 35.8
2025-04-01 22.1 37.2 203.0 40.9 7.5 22.5 47.9 84.2 83.1 113.4 64.0 34.3 73.5 71.1 98.8 175.1 178.4 35.7
2025-01-01 21.9 38.3 202.2 41.6 8.2 22.4 45.3 84.4 83.2 112.7 64.0 33.7 73.4 70.7 98.3 174.5 178.4 36.0
2024-10-01 21.6 38.4 200.6 40.8 8.2 22.3 44.5 84.6 83.8 112.8 64.4 33.6 73.6 69.9 97.9 173.5 178.5 36.2