Importations par produit - Valeurs aux prix courants - t_imports_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, 01:48

Comptes trimestriels : importations par produit, à prix courants.

Structure

Principales branches

Code 
`t_imports_val` |>
  filter(variable %in% big) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Importations (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_imports_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_imports_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_imports_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 5.2 16.0 173.4 15.7 9.6 40.3 35.5 72.3 0.7 2.3 15.2 9.8 8.4 27.4 0.2 1.1
2026-01-01 5.3 13.0 165.4 15.9 8.2 38.2 32.5 70.7 0.6 2.2 14.8 9.6 8.4 26.8 0.2 1.0
2025-10-01 5.1 11.3 165.5 16.1 7.6 37.5 33.7 70.6 0.6 2.2 14.4 9.5 8.5 27.0 0.2 1.0
2025-07-01 5.3 12.3 167.1 16.5 8.0 37.2 33.6 71.9 0.6 2.2 14.2 9.4 8.5 27.2 0.2 1.0
2025-04-01 5.3 12.8 165.1 16.2 8.1 36.7 32.7 71.4 0.6 2.2 14.4 9.3 8.5 27.0 0.2 1.0
2025-01-01 5.2 15.5 164.5 16.1 8.5 37.0 30.8 72.3 0.7 2.2 14.5 9.2 8.4 27.5 0.2 1.0
2024-10-01 5.0 13.7 163.7 15.4 8.9 36.8 31.9 70.8 0.6 2.2 14.3 9.0 8.2 27.2 0.2 1.1