Importations par produit - Volume (CVS-CJO) - t_imports_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, 01:48

Comptes trimestriels : importations par produit, en volume.

Structure

Principales branches

Code 
`t_imports_vol` |>
  filter(variable %in% big) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Importations (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_imports_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_imports_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_imports_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 JZ KZ MN OQ RU
2026-04-01 4.3 6.4 141.4 11.9 4.3 36.2 31.5 59.0 0.6 2.2 13.5 9.3 6.5 24.1 0.2 1.0
2026-01-01 4.2 6.6 139.1 12.0 4.7 35.0 28.9 59.7 0.5 2.1 13.8 9.1 6.6 23.7 0.2 0.9
2025-10-01 4.0 6.5 140.7 12.1 4.8 34.6 30.1 60.3 0.5 2.1 13.5 9.0 6.7 24.0 0.2 0.9
2025-07-01 4.0 6.5 142.2 12.2 4.8 34.3 30.0 62.0 0.5 2.1 13.3 8.9 6.7 24.3 0.2 0.9
2025-04-01 3.9 6.5 140.1 12.0 4.9 33.7 28.9 61.5 0.5 2.1 13.4 8.8 6.7 24.2 0.2 0.9
2025-01-01 3.8 6.6 137.6 12.0 4.7 33.6 26.9 61.3 0.5 2.1 13.2 8.7 6.5 24.6 0.2 0.9
2024-10-01 3.8 6.4 137.3 11.8 5.0 33.6 28.1 59.6 0.5 2.1 13.0 8.5 6.3 24.5 0.2 1.0