Exportations par produit - Valeurs aux prix courants - t_exports_val

Données - INSEE

Auteur

François Geerolf

Last observation: Q2 2026 (N = 21)

First observation: Q1 1949 (N = 21)

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

Last compile: 24 sept. 2026, 01:47

Comptes trimestriels : exportations par produit, à prix courants.

Structure

Principales branches

Code 
`t_exports_val` |>
  filter(variable %in% big) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Exportations (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_exports_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_exports_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_exports_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 JZ KZ MN OQ RU
2026-04-01 5.1 7.1 162.3 16.2 4.7 30.0 42.0 69.5 0.2 2.2 14.9 7.4 11.2 33.3 0.5 1.3
2026-01-01 5.0 6.2 151.7 16.3 4.0 28.5 34.0 68.7 0.2 2.1 14.0 7.4 11.0 32.1 0.7 1.3
2025-10-01 5.2 6.1 158.4 16.2 4.0 28.0 42.0 68.2 0.2 2.2 13.4 7.5 11.0 31.8 0.5 1.3
2025-07-01 5.4 5.6 156.4 16.2 3.9 27.3 40.5 68.5 0.2 2.2 13.3 7.5 11.0 31.6 0.5 1.4
2025-04-01 5.2 5.1 150.2 16.6 3.5 26.9 36.0 67.1 0.2 2.2 13.8 7.4 11.2 31.6 0.5 1.4
2025-01-01 4.8 7.2 152.3 16.7 4.1 26.6 37.8 67.1 0.2 2.1 14.7 7.3 11.4 31.7 0.5 1.4
2024-10-01 4.7 7.2 154.4 16.6 4.1 26.5 38.8 68.6 0.2 2.1 15.3 7.3 11.4 31.2 0.4 1.4