Commerce extérieur de la France - COM-EXT

Données - INSEE

Info

Last observation: 2026-05

First observation: 1999-01

Number of observations: 536 721

Last data update: 24 jul 2026, 03:59. Last compile: 24 jul 2026, 05:40

Structure

FREQ

SERIE_ARRETEE

CORRECTION

REF_AREA

BASIND

COM_EXT_NAF2

NATURE

ZONE_ECO

INDICATEUR

LAST_DOWNLOAD

LAST_DOWNLOAD
2026-07-23

Solde FAB-FAB

`COM-EXT` %>%
  filter(INDICATEUR == "SFF")

# # A tibble: 250 × 35
#    FREQ  SERIE_ARRETEE CORRECTION REF_AREA BASIND COM_EXT_NAF2 NATURE   ZONE_ECO
#    <chr> <chr>         <chr>      <chr>    <chr>  <chr>        <chr>    <chr>   
#  1 M     FALSE         CVS-CJO    FE       SO     R1001        VALEUR_… SO      
#  2 M     FALSE         CVS-CJO    FE       SO     R1001        VALEUR_… SO      
#  3 M     FALSE         CVS-CJO    FE       SO     R1001        VALEUR_… SO      
#  4 M     FALSE         CVS-CJO    FE       SO     R1001        VALEUR_… SO      
#  5 M     FALSE         CVS-CJO    FE       SO     R1001        VALEUR_… SO      
#  6 M     FALSE         CVS-CJO    FE       SO     R1001        VALEUR_… SO      
#  7 M     FALSE         CVS-CJO    FE       SO     R1001        VALEUR_… SO      
#  8 M     FALSE         CVS-CJO    FE       SO     R1001        VALEUR_… SO      
#  9 M     FALSE         CVS-CJO    FE       SO     R1001        VALEUR_… SO      
# 10 M     FALSE         CVS-CJO    FE       SO     R1001        VALEUR_… SO      
# # ℹ 240 more rows
# # ℹ 27 more variables: UNIT_MULT <chr>, UNIT_MEASURE <chr>, INDICATEUR <chr>,
# #   IDBANK <chr>, TITLE_FR <chr>, TITLE_EN <chr>, LAST_UPDATE <chr>,
# #   DECIMALS <chr>, TIME_PERIOD <chr>, OBS_VALUE <dbl>, OBS_STATUS <chr>,
# #   OBS_QUAL <chr>, OBS_TYPE <chr>, OBS_REV <chr>, Freq <chr>,
# #   Serie_arretee <chr>, Correction <chr>, Ref_area <chr>, Basind <chr>,
# #   Com_ext_naf2 <chr>, Nature <chr>, Zone_eco <chr>, Indicateur <chr>, …

Automobiles

Graph

`COM-EXT` %>%
  
  filter(COM_EXT_NAF2 == "29-1") %>%
  month_to_date() %>%
  arrange(date) %>%
  mutate(OBS_VALUE = OBS_VALUE %>% as.numeric) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Zone_eco)) + 
  theme_minimal() + xlab("") + ylab("Indice de Volume des Exportations Automobiles") +
  scale_x_date(breaks = seq(1960, 2020, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  scale_y_log10(breaks = seq(0, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank())

Table

`COM-EXT` %>%
  
  filter(COM_EXT_NAF2 == "29-1") %>%
  month_to_date() %>%
  arrange(date) %>%
  mutate(OBS_VALUE = OBS_VALUE %>% as.numeric) %>%
  arrange(desc(date)) %>%
  head(30) %>%
  select(date, ZONE_ECO, Zone_eco, OBS_VALUE) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Exportations Monde Entier

Table

`COM-EXT` %>%
  filter(INDICATEUR == "E",
         ZONE_ECO == "190",
         TIME_PERIOD == "2020-07") %>%
  
  select(COM_EXT_NAF2, Com_ext_naf2, TITLE_FR, OBS_VALUE) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Cuir, Meubles

`COM-EXT` %>%
  filter(COM_EXT_NAF2 %in% c("15", "16"),
         ZONE_ECO == "190",
         NATURE == "INDICE_VOL") %>%
  
  
  month_to_date() %>%
  select(COM_EXT_NAF2, Com_ext_naf2, INDICATEUR, Indicateur, date, OBS_VALUE) %>%
  arrange(date) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Com_ext_naf2, linetype = Indicateur)) + 
  theme_minimal() + xlab("") + ylab("Indice de Volume") +
  scale_x_date(breaks = seq(1960, 2020, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  scale_y_log10(breaks = seq(0, 500, 20),
                labels = dollar_format(accuracy = 1, prefix = ""),
                limits = c(40, 350)) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.5, 0.8),
        legend.title = element_blank())

Textile, Habillement

`COM-EXT` %>%
  filter(COM_EXT_NAF2 %in% c("13", "14"),
         ZONE_ECO == "190",
         NATURE == "INDICE_VOL") %>%
  
  
  month_to_date() %>%
  select(COM_EXT_NAF2, Com_ext_naf2, INDICATEUR, Indicateur, date, OBS_VALUE) %>%
  arrange(date) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Com_ext_naf2, linetype = Indicateur)) + 
  theme_minimal() + xlab("") + ylab("Indice de Volume") +
  scale_x_date(breaks = seq(1960, 2020, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  scale_y_log10(breaks = seq(0, 500, 20),
                labels = dollar_format(accuracy = 1, prefix = ""),
                limits = c(40, 350)) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.5, 0.8),
        legend.title = element_blank())

Table

`COM-EXT` %>%
  filter(ZONE_ECO == "190") %>%
  
  mutate(series = paste0(INDICATEUR, "-", NATURE)) %>%
  group_by(COM_EXT_NAF2, Com_ext_naf2, series) %>%
  summarise(change =  round(100*(OBS_VALUE[TIME_PERIOD == "2020-01"]/OBS_VALUE[TIME_PERIOD == "2019-01"]-1), 1)) %>%
  spread(series, change) %>%
  print_table_conditional()