Équilibre du produit intérieur brut

Data - INSEE

Author

François Geerolf

Info

source dataset Title .html .rData
insee CNT-2020-PIB-EQB-RF Équilibre du produit intérieur brut 2025-10-10 2025-10-09

Info

  • Méthodologie des comptes trimestriels, Base 2005. Insee Méthodes n° 126 - mai 2012. html / pdf

Données sur la macroéconomie en France

source dataset Title .html .rData
bdf CFT Comptes Financiers Trimestriels 2025-08-28 2025-03-09
insee CNA-2014-CONSO-SI Dépenses de consommation finale par secteur institutionnel 2025-10-10 2025-10-09
insee CNA-2014-CSI Comptes des secteurs institutionnels 2025-10-10 2025-10-09
insee CNA-2014-FBCF-BRANCHE Formation brute de capital fixe (FBCF) par branche 2025-10-10 2025-10-09
insee CNA-2014-FBCF-SI Formation brute de capital fixe (FBCF) par secteur institutionnel 2025-10-10 2025-10-09
insee CNA-2014-RDB Revenu et pouvoir d’achat des ménages 2025-10-10 2025-10-09
insee CNA-2020-CONSO-MEN Consommation des ménages 2025-10-10 2025-09-30
insee CNA-2020-PIB Produit intérieur brut (PIB) et ses composantes 2025-10-10 2025-05-28
insee CNT-2014-CB Comptes des branches 2025-10-10 2025-10-09
insee CNT-2014-CSI Comptes de secteurs institutionnels 2025-10-10 2025-10-09
insee CNT-2014-OPERATIONS Opérations sur biens et services 2025-10-10 2025-10-09
insee CNT-2014-PIB-EQB-RF Équilibre du produit intérieur brut 2025-10-10 2025-10-09
insee CONSO-MENAGES-2020 Consommation des ménages en biens 2025-10-10 2025-10-09
insee ICA-2015-IND-CONS Indices de chiffre d'affaires dans l'industrie et la construction 2025-10-10 2025-10-09
insee conso-mensuelle Consommation de biens, données mensuelles 2025-10-10 2023-07-04
insee t_1101 1.101 – Le produit intérieur brut et ses composantes à prix courants (En milliards d'euros) 2025-10-10 2022-01-02
insee t_1102 1.102 – Le produit intérieur brut et ses composantes en volume aux prix de l'année précédente chaînés (En milliards d'euros 2014) 2025-10-10 2020-10-30
insee t_1105 1.105 – Produit intérieur brut - les trois approches à prix courants (En milliards d'euros) - t_1105 2025-10-10 2020-10-30

LAST_UPDATE

Code 
`CNT-2020-PIB-EQB-RF` %>%
  group_by(LAST_UPDATE) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(LAST_UPDATE)) %>%
  print_table_conditional()
LAST_UPDATE Nobs
2025-08-29 20368

LAST_COMPILE

LAST_COMPILE
2025-10-11

Last

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(TIME_PERIOD == max(TIME_PERIOD)) %>%
  select(TIME_PERIOD, TITLE_FR, OBS_VALUE) %>%
  print_table_conditional()

Nobs

Code 
`CNT-2020-PIB-EQB-RF` %>%
  left_join(OPERATION,  by = "OPERATION") %>%
  group_by(OPERATION, Operation, CNA_PRODUIT) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

TITLE_FR

Code 
`CNT-2020-PIB-EQB-RF` %>%
  group_by(IDBANK, TITLE_FR) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

VALORISATION

Code 
`CNT-2020-PIB-EQB-RF` %>%
  left_join(VALORISATION,  by = "VALORISATION") %>%
  group_by(VALORISATION, Valorisation) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
VALORISATION Valorisation Nobs
L Volumes aux prix de l'année précédente chaînés 10882
V Valeurs aux prix courants 7650
SO Sans objet 1836

OPERATION

All

Code 
`CNT-2020-PIB-EQB-RF` %>%
  left_join(OPERATION,  by = "OPERATION") %>%
  group_by(OPERATION, Operation) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
OPERATION Operation Nobs
P3 P3 - Dépense de consommation finale 2438
PIB PIB - Produit intérieur brut 993
DINTF Demande intérieure totale finale 913
DINTFHS Demande intérieure totale finale hors stocks 913
P31 P31 - Dépense de consommation finale individuelle 913
P32 P32 - Dépense de consommation finale collective 913
P4 P4 - Consommation finale effective 913
P51 P51 - Formation brute de capital fixe 913
P51B P51B - FBCF des entreprises financières (y compris entreprises individuelles) 913
P51G P51G - Formation brute de capital fixe 913
P51M P51M - FBCF des ménages (hors entreprises individuelles) 913
P51P P51P - FBCF des ISBLSM 913
P51S P51S - FBCF des entreprises non financières (y compris entreprises individuelles) 913
P6 P6 - Exportations de biens et services 913
P7 P7 - Importations de biens et services 913
P54 P54 - Stocks et acquisitions moins cession d'objets de valeur 607
SOLDE SOLDE - Solde extérieur total 607
P52 P52 - Variation de stocks 488
D11 D11 - Salaires et traitements bruts 306
D121 D121 - Cotisations sociales effectives à la charge des employeurs 306
D122 D122 - Cotisations sociales imputées à la charge des employeurs 306
D211 D211 - Impôts de type 'Taxe à la Valeur Ajoutée' (TVA) 306
D212 D212 - Impôts sur les importations autres que la taxe à la valeur ajoutée 306
D214 D214 - Autres impôts sur les produits 306
D291 D291 - Impôts sur les salaires et la main-d'oeuvre 306
D292 D292 - Impôts divers sur la production 306
D319 D319 - Autres subventions sur les produits 306
D39 D39 - Subventions d'exploitation 306
P53 P53 - Acquisitions moins cession d'objets de valeur 306

Volume

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "L",
         NATURE == "VALEUR_ABSOLUE",
         `SECT-INST` == "SO") %>%
  left_join(OPERATION,  by = "OPERATION") %>%
  group_by(OPERATION, Operation) %>%
  summarise(Nobs = n(),
            Last = last(OBS_VALUE)) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
OPERATION Operation Nobs Last
DINTF Demande intérieure totale finale 306 70368.00
DINTFHS Demande intérieure totale finale hors stocks 306 68437.00
P3 P3 - Dépense de consommation finale 306 17988.00
P31 P31 - Dépense de consommation finale individuelle 306 8703.00
P32 P32 - Dépense de consommation finale collective 306 9353.00
P4 P4 - Consommation finale effective 306 56141.00
P6 P6 - Exportations de biens et services 306 3999.00
P7 P7 - Importations de biens et services 306 3951.00
PIB PIB - Produit intérieur brut 306 69933.00
P52 P52 - Variation de stocks 182 0.54

Valeur

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         NATURE == "VALEUR_ABSOLUE",
         `SECT-INST` == "SO") %>%
  left_join(OPERATION,  by = "OPERATION") %>%
  group_by(OPERATION, Operation) %>%
  summarise(Nobs = n(),
            Last = last(OBS_VALUE)) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
OPERATION Operation Nobs Last
D211 D211 - Impôts de type 'Taxe à la Valeur Ajoutée' (TVA) 306 231
D212 D212 - Impôts sur les importations autres que la taxe à la valeur ajoutée 306 4
D214 D214 - Autres impôts sur les produits 306 153
D319 D319 - Autres subventions sur les produits 306 -27
DINTF Demande intérieure totale finale 306 3151
DINTFHS Demande intérieure totale finale hors stocks 306 3030
P3 P3 - Dépense de consommation finale 306 483
P31 P31 - Dépense de consommation finale individuelle 306 243
P32 P32 - Dépense de consommation finale collective 306 239
P4 P4 - Consommation finale effective 306 2415
P52 P52 - Variation de stocks 306 120
P53 P53 - Acquisitions moins cession d'objets de valeur 306 2
P54 P54 - Stocks et acquisitions moins cession d'objets de valeur 306 122
P6 P6 - Exportations de biens et services 306 462
P7 P7 - Importations de biens et services 306 431
PIB PIB - Produit intérieur brut 306 3182
SOLDE SOLDE - Solde extérieur total 306 31

NATURE

Code 
`CNT-2020-PIB-EQB-RF` %>%
  left_join(NATURE, by = "NATURE") %>%
  group_by(NATURE, Nature) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
NATURE Nature Nobs
VALEUR_ABSOLUE Valeur absolue 14870
RATIO Ratio 5117
TAUX Taux 381

FREQ

Code 
`CNT-2020-PIB-EQB-RF` %>%
  left_join(FREQ, by = "FREQ") %>%
  group_by(FREQ, Freq) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
FREQ Freq Nobs
T NA 20292
A Annual 76

UNIT_MEASURE

Code 
`CNT-2020-PIB-EQB-RF` %>%
  group_by(UNIT_MEASURE) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()
UNIT_MEASURE Nobs
EUROS 14688
POURCENT 381
SO 5299

CNA_PRODUIT

Code 
`CNT-2020-PIB-EQB-RF` %>%
  left_join(CNA_PRODUIT, by = "CNA_PRODUIT") %>%
  group_by(CNA_PRODUIT, Cna_produit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
CNA_PRODUIT Cna_produit Nobs
D-CNT Ensemble des biens et services 11934
SO Sans objet 8434

TIME_PERIOD

Code 
`CNT-2020-PIB-EQB-RF` %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  print_table_conditional()

Last - 2022-Q1

Tous

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(TIME_PERIOD == "2022-Q1") %>%
  select_if(~ n_distinct(.) > 1) %>%
  select(-IDBANK, -TITLE_EN) %>%
  arrange(-OBS_VALUE) %>%
  print_table_conditional

% du PIB

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         TIME_PERIOD == "2022-Q1") %>%
  select_if(~ n_distinct(.) > 1) %>%
  select(-IDBANK, -TITLE_EN) %>%
  arrange(-OBS_VALUE) %>%
  mutate(`% of GDP` = round(100*OBS_VALUE/OBS_VALUE[OPERATION == "PIB"], 1)) %>%
  print_table_conditional
SECT-INST OPERATION CNA_PRODUIT TITLE_FR OBS_VALUE OBS_REV % of GDP
SO DINTF SO Demande intérieure totale finale - Valeur aux prix courants - Série CVS-CJO 658092 1 101.4
SO DINTFHS SO Demande intérieure totale finale hors stocks - Valeur aux prix courants - Série CVS-CJO 657687 NA 101.4
SO PIB SO Produit intérieur brut total - Valeur aux prix courants - Série CVS-CJO 648791 1 100.0
SO P4 D-CNT Dépenses de consommation totales - Valeur aux prix courants - Série CVS-CJO 506704 1 78.1
S14 P3 D-CNT Dépenses de consommation des ménages - Total - Valeur aux prix courants - Série CVS-CJO 331396 1 51.1
SO P7 D-CNT Importations - Total - Valeur aux prix courants - Série CVS-CJO 240048 1 37.0
SO P6 D-CNT Exportations - Total - Valeur aux prix courants - Série CVS-CJO 230747 1 35.6
SO P3 SO Dépenses de consommation des APU - Total - Valeur aux prix courants - Série CVS-CJO 160476 1 24.7
S0 P51 D-CNT FBCF de l'ensemble des secteurs institutionnels - Total - Valeur aux prix courants - Série CVS-CJO 150983 1 23.3
SO P31 D-CNT Dépenses de consommation individualisable des APU - Total - Valeur aux prix courants - Série CVS-CJO 104605 NA 16.1
S11 P51S D-CNT Investissement des entreprises non financières - Total - Valeur aux prix courants - Série CVS-CJO 78467 1 12.1
SO P32 D-CNT Dépenses de consommation collective des APU - Total - Valeur aux prix courants - Série CVS-CJO 55870 NA 8.6
SO D211 D-CNT TVA - Total - Valeur aux prix courants - Série CVS-CJO 48184 1 7.4
S14 P51M D-CNT FBCF des ménages - Total - Valeur aux prix courants - Série CVS-CJO 38093 1 5.9
SO D214 D-CNT Autres impôts sur les produits - Total - Valeur aux prix courants - Série CVS-CJO 29936 1 4.6
S13 P51G D-CNT FBCF des administrations publiques - Total - Valeur aux prix courants - Série CVS-CJO 26590 1 4.1
S15 P3 D-CNT Dépenses de consommation des ISBLSM - Total - Valeur aux prix courants - Série CVS-CJO 14832 NA 2.3
S12 P51B D-CNT FBCF des sociétés financières - Total - Valeur aux prix courants - Série CVS-CJO 6467 1 1.0
S15 P51P D-CNT FBCF des ISBLSM - Total - Valeur aux prix courants - Série CVS-CJO 1367 NA 0.2
SO D212 D-CNT Impôts sur importations - Total - Valeur aux prix courants - Série CVS-CJO 827 NA 0.1
SO P54 D-CNT Stocks et acquisitions moins cessions d'objets de valeur - Total - Valeur aux prix courants - Série CVS-CJO 405 1 0.1
SO P53 D-CNT Acquisitions moins cessions d'objets de valeur - Total - Valeur aux prix courants - Série CVS-CJO 252 NA 0.0
SO P52 D-CNT Variation des stocks - Total - Valeur aux prix courants - Série CVS-CJO 153 1 0.0
SO D319 D-CNT Subventions - Total - Valeur aux prix courants - Série CVS-CJO -5907 NA -0.9
SO SOLDE SO Solde extérieur total - Valeur aux prix courants - Série CVS-CJO -9301 1 -1.4

Deflators

All

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P4"),
         VALORISATION %in% c("V", "L"),
         NATURE == "VALEUR_ABSOLUE") %>%
  select_if(~ n_distinct(.) > 1) %>%
  select(-IDBANK, -TITLE_EN) %>%
  rowwise() %>%
  mutate(date = TIME_PERIOD_to_date(TIME_PERIOD)) %>%
  arrange(desc(date)) %>%
  group_by(OPERATION, `SECT-INST`, date) %>%
  summarise(deflator = 100*OBS_VALUE[VALORISATION == "V"]/OBS_VALUE[VALORISATION == "L"]) %>%
  ungroup %>%
  left_join(OPERATION, by = "OPERATION") %>%
  left_join(`SECT-INST`, by = "SECT-INST") %>%
  mutate(variable = paste0(OPERATION, " - ", `Sect-Inst`)) %>%
  ggplot + geom_line(aes(x = date, y = deflator, color = variable)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10() +
  theme(legend.position = c(0.5, 0.8),
        legend.title = element_blank())

1999-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P4"),
         VALORISATION %in% c("V", "L"),
         NATURE == "VALEUR_ABSOLUE") %>%
  select_if(~ n_distinct(.) > 1) %>%
  select(-IDBANK, -TITLE_EN) %>%
  rowwise() %>%
  mutate(date = TIME_PERIOD_to_date(TIME_PERIOD)) %>%
  arrange(desc(date)) %>%
  group_by(OPERATION, `SECT-INST`, date) %>%
  summarise(OBS_VALUE = 100*OBS_VALUE[VALORISATION == "V"]/OBS_VALUE[VALORISATION == "L"]) %>%
  ungroup %>%
  left_join(OPERATION, by = "OPERATION") %>%
  left_join(`SECT-INST`, by = "SECT-INST") %>%
  mutate(variable = paste0(OPERATION, " - ", `Sect-Inst`)) %>%
  filter(date >= as.Date("1999-01-01")) %>%
  
  group_by(variable) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE  , color = variable)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1999, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5)) +
  theme(legend.position = c(0.5, 0.8),
        legend.title = element_blank())+
  geom_label_repel(data = . %>%
               filter(date == max(date)), aes(date, y = OBS_VALUE, label = round(OBS_VALUE, 1),color = variable))

2014-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P4"),
         VALORISATION %in% c("V", "L"),
         NATURE == "VALEUR_ABSOLUE") %>%
  select_if(~ n_distinct(.) > 1) %>%
  select(-IDBANK, -TITLE_EN) %>%
  rowwise() %>%
  mutate(date = TIME_PERIOD_to_date(TIME_PERIOD)) %>%
  arrange(desc(date)) %>%
  group_by(OPERATION, `SECT-INST`, date) %>%
  summarise(OBS_VALUE = 100*OBS_VALUE[VALORISATION == "V"]/OBS_VALUE[VALORISATION == "L"]) %>%
  ungroup %>%
  left_join(OPERATION, by = "OPERATION") %>%
  left_join(`SECT-INST`, by = "SECT-INST") %>%
  filter(date >= as.Date("2014-01-01")) %>%
  mutate(variable = paste0(OPERATION, " - ", `Sect-Inst`)) %>%
  group_by(variable) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE  , color = variable)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10() +
  theme(legend.position = c(0.5, 0.8),
        legend.title = element_blank())

2019-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P4"),
         VALORISATION %in% c("V", "L"),
         NATURE == "VALEUR_ABSOLUE") %>%
  select_if(~ n_distinct(.) > 1) %>%
  select(-IDBANK, -TITLE_EN) %>%
  rowwise() %>%
  mutate(date = TIME_PERIOD_to_date(TIME_PERIOD)) %>%
  arrange(desc(date)) %>%
  group_by(OPERATION, `SECT-INST`, date) %>%
  summarise(deflator = 100*OBS_VALUE[VALORISATION == "V"]/OBS_VALUE[VALORISATION == "L"]) %>%
  ungroup %>%
  left_join(OPERATION, by = "OPERATION") %>%
  left_join(`SECT-INST`, by = "SECT-INST") %>%
  filter(date >= as.Date("2019-01-01")) %>%
  mutate(variable = paste0(OPERATION, " - ", `Sect-Inst`)) %>%
  ggplot + geom_line(aes(x = date, y = deflator, color = variable)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10() +
  theme(legend.position = c(0.5, 0.8),
        legend.title = element_blank())

2017T2-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P4"),
         VALORISATION %in% c("V", "L"),
         NATURE == "VALEUR_ABSOLUE") %>%
  select_if(~ n_distinct(.) > 1) %>%
  select(-IDBANK, -TITLE_EN) %>%
  rowwise() %>%
  mutate(date = TIME_PERIOD_to_date(TIME_PERIOD)) %>%
  arrange(desc(date)) %>%
  group_by(OPERATION, `SECT-INST`, date) %>%
  summarise(OBS_VALUE = 100*OBS_VALUE[VALORISATION == "V"]/OBS_VALUE[VALORISATION == "L"]) %>%
  ungroup %>%
  left_join(OPERATION, by = "OPERATION") %>%
  left_join(`SECT-INST`, by = "SECT-INST") %>%
  mutate(variable = paste0(OPERATION, " - ", `Sect-Inst`)) %>%
  filter(date >= as.Date("2017-04-01")) %>%
  
  group_by(variable) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE  , color = variable)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1999, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5)) +
  theme(legend.position = c(0.5, 0.8),
        legend.title = element_blank())+
  geom_label_repel(data = . %>%
               filter(date == max(date)), aes(date, y = OBS_VALUE, label = round(OBS_VALUE, 1),color = variable))

2019T4-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P4"),
         VALORISATION %in% c("V", "L"),
         NATURE == "VALEUR_ABSOLUE") %>%
  select_if(~ n_distinct(.) > 1) %>%
  select(-IDBANK, -TITLE_EN) %>%
  rowwise() %>%
  mutate(date = TIME_PERIOD_to_date(TIME_PERIOD)) %>%
  arrange(desc(date)) %>%
  group_by(OPERATION, `SECT-INST`, date) %>%
  summarise(OBS_VALUE = 100*OBS_VALUE[VALORISATION == "V"]/OBS_VALUE[VALORISATION == "L"]) %>%
  ungroup %>%
  left_join(OPERATION, by = "OPERATION") %>%
  left_join(`SECT-INST`, by = "SECT-INST") %>%
  mutate(variable = paste0(OPERATION, " - ", `Sect-Inst`)) %>%
  filter(date >= as.Date("2019-10-01")) %>%
  
  group_by(variable) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE  , color = variable)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1999, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5)) +
  theme(legend.position = c(0.5, 0.8),
        legend.title = element_blank())+
  geom_label_repel(data = . %>%
               filter(date == max(date)), aes(date, y = OBS_VALUE, label = round(OBS_VALUE, 1),color = variable))

2020-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P4"),
         VALORISATION %in% c("V", "L"),
         NATURE == "VALEUR_ABSOLUE") %>%
  select_if(~ n_distinct(.) > 1) %>%
  select(-IDBANK, -TITLE_EN) %>%
  rowwise() %>%
  mutate(date = TIME_PERIOD_to_date(TIME_PERIOD)) %>%
  arrange(desc(date)) %>%
  group_by(OPERATION, `SECT-INST`, date) %>%
  summarise(deflator = 100*OBS_VALUE[VALORISATION == "V"]/OBS_VALUE[VALORISATION == "L"]) %>%
  ungroup %>%
  left_join(OPERATION, by = "OPERATION") %>%
  left_join(`SECT-INST`, by = "SECT-INST") %>%
  filter(date >= as.Date("2020-01-01")) %>%
  mutate(variable = paste0(OPERATION, " - ", `Sect-Inst`)) %>%
  ggplot + geom_line(aes(x = date, y = deflator, color = variable)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10() +
  theme(legend.position = c(0.5, 0.8),
        legend.title = element_blank())

2021-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P4"),
         VALORISATION %in% c("V", "L"),
         NATURE == "VALEUR_ABSOLUE") %>%
  select_if(~ n_distinct(.) > 1) %>%
  select(-IDBANK, -TITLE_EN) %>%
  rowwise() %>%
  mutate(date = TIME_PERIOD_to_date(TIME_PERIOD)) %>%
  arrange(desc(date)) %>%
  group_by(OPERATION, `SECT-INST`, date) %>%
  summarise(deflator = 100*OBS_VALUE[VALORISATION == "V"]/OBS_VALUE[VALORISATION == "L"]) %>%
  ungroup %>%
  left_join(OPERATION, by = "OPERATION") %>%
  left_join(`SECT-INST`, by = "SECT-INST") %>%
  filter(date >= as.Date("2021-01-01")) %>%
  mutate(variable = paste0(OPERATION, " - ", `Sect-Inst`)) %>%
  group_by(OPERATION, `SECT-INST`) %>%
  mutate(deflator = 100*deflator/deflator[1]) %>%
  ggplot + geom_line(aes(x = date, y = deflator, color = variable)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10() +
  theme(legend.position = c(0.5, 0.8),
        legend.title = element_blank())

Agrégats

consommation nominale

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("P3", "PIB")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2021-10-01")) %>%
  group_by(OPERATION, `SECT-INST`) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[1]) %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.4),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 300, 5),
                     labels = percent_format(accuracy = 1))

Consommation: P4, P3

All

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("P4", "P3", "PIB")) %>%
  quarter_to_date %>%
  group_by(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[OPERATION == "PIB"]) %>%
  filter(OPERATION != "PIB") %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.4),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 300, 5),
                     labels = percent_format(accuracy = 1))

1980-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("P4", "P3", "PIB")) %>%
  quarter_to_date %>%
  group_by(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[OPERATION == "PIB"]) %>%
  filter(OPERATION != "PIB") %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  filter(date >= as.Date("1980-01-01")) %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.4),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 300, 5),
                     labels = percent_format(accuracy = 1))

1995-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("P4", "P3", "PIB")) %>%
  quarter_to_date %>%
  group_by(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[OPERATION == "PIB"]) %>%
  filter(OPERATION != "PIB") %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.4),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 300, 5),
                     labels = percent_format(accuracy = 1))

2000-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("P4", "P3", "PIB")) %>%
  quarter_to_date %>%
  group_by(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[OPERATION == "PIB"]) %>%
  filter(OPERATION != "PIB") %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  filter(date >= as.Date("2000-01-01")) %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.4),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 300, 5),
                     labels = percent_format(accuracy = 1))

All

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("P31", "P32", "PIB")) %>%
  quarter_to_date %>%
  group_by(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[OPERATION == "PIB"]) %>%
  filter(OPERATION != "PIB") %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 300, 1),
                     labels = percent_format(accuracy = 1))

1980-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("P31", "P32", "PIB")) %>%
  quarter_to_date %>%
  group_by(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[OPERATION == "PIB"]) %>%
  filter(OPERATION != "PIB") %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  filter(date >= as.Date("1980-01-01")) %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 300, 1),
                     labels = percent_format(accuracy = 1))

1995-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("P31", "P32", "PIB")) %>%
  quarter_to_date %>%
  group_by(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[OPERATION == "PIB"]) %>%
  filter(OPERATION != "PIB") %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 300, 1),
                     labels = percent_format(accuracy = 1))

Exports, Imports

All

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("P6", "P7", "PIB")) %>%
  quarter_to_date %>%
  group_by(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[OPERATION == "PIB"]) %>%
  filter(OPERATION != "PIB") %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 300, 2),
                     labels = percent_format(accuracy = 1))

1980-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("P6", "P7", "PIB")) %>%
  quarter_to_date %>%
  group_by(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[OPERATION == "PIB"]) %>%
  filter(OPERATION != "PIB") %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  filter(date >= as.Date("1980-01-01")) %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 300, 2),
                     labels = percent_format(accuracy = 1))

1995-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("P6", "P7", "PIB")) %>%
  quarter_to_date %>%
  group_by(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[OPERATION == "PIB"]) %>%
  filter(OPERATION != "PIB") %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 300, 2),
                     labels = percent_format(accuracy = 1))

Solde Extérieur

All

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("SOLDE", "PIB")) %>%
  quarter_to_date %>%
  group_by(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[OPERATION == "PIB"]) %>%
  filter(OPERATION != "PIB") %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  na.omit %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 1),
                     labels = percent_format(accuracy = 1))

1980-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("SOLDE", "PIB")) %>%
  quarter_to_date %>%
  group_by(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[OPERATION == "PIB"]) %>%
  filter(OPERATION != "PIB") %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  na.omit %>%
  filter(date >= as.Date("1980-01-01")) %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 1),
                     labels = percent_format(accuracy = 1))

1995-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("SOLDE", "PIB")) %>%
  quarter_to_date %>%
  group_by(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[OPERATION == "PIB"]) %>%
  filter(OPERATION != "PIB") %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  na.omit %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 1),
                     labels = percent_format(accuracy = 1))

Investissement

All

Tous

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("P51", "P51B", "P51G", "P51M", "P51P", "P51S", "PIB")) %>%
  quarter_to_date %>%
  group_by(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[OPERATION == "PIB"]) %>%
  filter(OPERATION != "PIB") %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = paste(OPERATION, "-", TITLE_FR))) +
  #
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.78),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 300, 2),
                     labels = percent_format(accuracy = 1))

Tous sauf un

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("P51B", "P51G", "P51M", "P51P", "P51S", "PIB")) %>%
  quarter_to_date %>%
  group_by(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[OPERATION == "PIB"]) %>%
  filter(OPERATION != "PIB") %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = paste(OPERATION, "-", TITLE_FR))) +
  #
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.78),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 300, 2),
                     labels = percent_format(accuracy = 1))

1980-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("P51", "P51B", "P51G", "P51M", "P51P", "P51S", "PIB")) %>%
  quarter_to_date %>%
  group_by(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[OPERATION == "PIB"]) %>%
  filter(OPERATION != "PIB") %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  filter(date >= as.Date("1980-01-01")) %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  #
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 300, 2),
                     labels = percent_format(accuracy = 1))

1995-

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(FREQ == "T",
         VALORISATION == "V",
         OPERATION %in% c("P51", "P51B", "P51G", "P51M", "P51P", "P51S", "PIB")) %>%
  quarter_to_date %>%
  group_by(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[OPERATION == "PIB"]) %>%
  filter(OPERATION != "PIB") %>%
  mutate(TITLE_FR = gsub("- Valeur aux prix courants - Série CVS-CJO", "", TITLE_FR)) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot() + ylab("% du PIB") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  #
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 300, 2),
                     labels = percent_format(accuracy = 1))

GDP Updates

gdp_quarterly2

Code 
gdp_quarterly <- `CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION == "PIB",
         FREQ == "T",
         VALORISATION == "V") %>%
  quarter_to_date %>%
  arrange(date) %>%
  mutate(date = date + months(3) - days(1)) %>%
  select(date, gdp = OBS_VALUE) %>%
  mutate(gdp = gdp/1000)

save(gdp_quarterly, file = "gdp_quarterly2.RData")

gdp_quarterly %>%
  tail(5) %>%
  print_table_conditional()
date gdp
2024-06-30 725.198
2024-09-30 735.960
2024-12-31 735.637
2025-03-31 739.602
2025-06-30 743.500

gdp_quarterly3

Code 
gdp_quarterly <- `CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION == "PIB",
         FREQ == "T",
         VALORISATION == "V") %>%
  quarter_to_date %>%
  arrange(date) %>%
  select(date, gdp = OBS_VALUE) %>%
  mutate(gdp = gdp/1000)

save(gdp_quarterly, file = "gdp_quarterly3.RData")

gdp_quarterly %>%
  tail(5) %>%
  print_table_conditional()
date gdp
2024-04-01 725.198
2024-07-01 735.960
2024-10-01 735.637
2025-01-01 739.602
2025-04-01 743.500

gdp_quarterly4: IDBANK 010565707

Code 
gdp_quarterly <- `CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION == "PIB",
         FREQ == "T",
         VALORISATION == "V") %>%
  quarter_to_date %>%
  arrange(date) %>%
  select(date, gdp = OBS_VALUE)

save(gdp_quarterly, file = "gdp_quarterly4.RData")

gdp_quarterly %>%
  tail(5) %>%
  print_table_conditional()
date gdp
2024-04-01 725198
2024-07-01 735960
2024-10-01 735637
2025-01-01 739602
2025-04-01 743500

Depuis le Covid-19

PIB valeur

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P31", "P32", "P4"),
         FREQ == "T",
         VALORISATION == "V",
         NATURE == "VALEUR_ABSOLUE",
         `SECT-INST` == "SO") %>%
  left_join(OPERATION,  by = "OPERATION") %>%
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%Y-Q%q")) %>%
  filter(date >= zoo::as.yearqtr("2019 Q4")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  arrange(date) %>%
  group_by(OPERATION) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == zoo::as.yearqtr("2019 Q4")]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Operation)) +
  xlab("") + ylab("") +  theme_minimal() +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) %>%
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) %>%
                         pull(breaks)) +
  scale_y_log10(breaks = seq(0, 200, 5)) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

PIB volume

2019-Q4

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P31", "P32", "P4"),
         FREQ == "T",
         VALORISATION == "L",
         NATURE == "VALEUR_ABSOLUE",
         `SECT-INST` == "SO") %>%
  left_join(OPERATION,  by = "OPERATION") %>%
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%Y-Q%q")) %>%
  filter(date >= zoo::as.yearqtr("2019 Q4")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  arrange(date) %>%
  group_by(OPERATION) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == zoo::as.yearqtr("2019 Q4")]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Operation)) +
  xlab("") + ylab("") +  theme_minimal() +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) %>%
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) %>%
                         pull(breaks)) +
  scale_y_log10(breaks = seq(0, 200, 5)) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Demande, Demande hors stocks

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "DINTF", "DINTFHS"),
         FREQ == "T",
         VALORISATION == "L",
         NATURE == "VALEUR_ABSOLUE",
         `SECT-INST` == "SO") %>%
  left_join(OPERATION,  by = "OPERATION") %>%
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%Y-Q%q")) %>%
  filter(date >= zoo::as.yearqtr("2019 Q4")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  arrange(date) %>%
  group_by(OPERATION) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == zoo::as.yearqtr("2019 Q4")]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Operation)) +
  xlab("") + ylab("") +  theme_minimal() +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) %>%
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) %>%
                         pull(breaks)) +
  scale_y_log10(breaks = seq(0, 200, 5)) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2017-Q2 -

PIB valeur

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P31", "P32", "P4"),
         FREQ == "T",
         VALORISATION == "V",
         NATURE == "VALEUR_ABSOLUE",
         `SECT-INST` == "SO") %>%
  left_join(OPERATION,  by = "OPERATION") %>%
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%Y-Q%q")) %>%
  filter(date >= zoo::as.yearqtr("2017 Q2")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  arrange(date) %>%
  group_by(OPERATION) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == zoo::as.yearqtr("2017 Q2")]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Operation)) +
  xlab("") + ylab("") +  theme_minimal() +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) %>%
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) %>%
                         pull(breaks)) +
  scale_y_log10(breaks = seq(0, 200, 5)) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

PIB volume

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P31", "P32", "P4"),
         FREQ == "T",
         VALORISATION == "L",
         NATURE == "VALEUR_ABSOLUE",
         `SECT-INST` == "SO") %>%
  left_join(OPERATION,  by = "OPERATION") %>%
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%Y-Q%q")) %>%
  filter(date >= zoo::as.yearqtr("2017 Q2")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  arrange(date) %>%
  group_by(OPERATION) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == zoo::as.yearqtr("2017 Q2")]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Operation)) +
  xlab("") + ylab("") +  theme_minimal() +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) %>%
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) %>%
                         pull(breaks)) +
  scale_y_log10(breaks = seq(0, 200, 5)) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Demande, Demande hors stocks

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "DINTF", "DINTFHS"),
         FREQ == "T",
         VALORISATION == "L",
         NATURE == "VALEUR_ABSOLUE",
         `SECT-INST` == "SO") %>%
  left_join(OPERATION,  by = "OPERATION") %>%
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%Y-Q%q")) %>%
  filter(date >= zoo::as.yearqtr("2017 Q2")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  arrange(date) %>%
  group_by(OPERATION) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == zoo::as.yearqtr("2017 Q2")]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Operation)) +
  xlab("") + ylab("") +  theme_minimal() +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) %>%
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) %>%
                         pull(breaks)) +
  scale_y_log10(breaks = seq(0, 200, 2)) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2011-Q1

PIB valeur

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P31", "P32", "P4"),
         FREQ == "T",
         VALORISATION == "V",
         NATURE == "VALEUR_ABSOLUE",
         `SECT-INST` == "SO") %>%
  left_join(OPERATION,  by = "OPERATION") %>%
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%Y-Q%q")) %>%
  filter(date >= zoo::as.yearqtr("2011 Q1")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  arrange(date) %>%
  group_by(OPERATION) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == zoo::as.yearqtr("2011 Q1")]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Operation)) +
  xlab("") + ylab("") +  theme_minimal() +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2011:2100, c(2, 4)) %>%
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) %>%
                         pull(breaks)) +
  scale_y_log10(breaks = c(seq(0, 170, 5), 106, 107, 111, 112, 113)) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

PIB volume

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P31", "P32", "P4"),
         FREQ == "T",
         VALORISATION == "L",
         NATURE == "VALEUR_ABSOLUE",
         `SECT-INST` == "SO") %>%
  left_join(OPERATION,  by = "OPERATION") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2010-01-01")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  arrange(date) %>%
  group_by(OPERATION) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2010-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Operation)) +
  xlab("") + ylab("") +  theme_minimal() +
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(seq(0, 180, 5), 106, 107)) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank())

2010-2014

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P31", "P32", "P4"),
         FREQ == "T",
         VALORISATION == "L",
         NATURE == "VALEUR_ABSOLUE",
         `SECT-INST` == "SO") %>%
  left_join(OPERATION,  by = "OPERATION") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2010-01-01")) %>%
  filter(date <= as.Date("2014-01-01")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  arrange(date) %>%
  group_by(OPERATION) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2010-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Operation)) +
  xlab("") + ylab("") +  theme_minimal() +
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(seq(100, 180, 1), 106, 107)) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank())

2011-2014

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P31", "P32", "P4"),
         FREQ == "T",
         VALORISATION == "L",
         NATURE == "VALEUR_ABSOLUE",
         `SECT-INST` == "SO") %>%
  left_join(OPERATION,  by = "OPERATION") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2011-01-01")) %>%
  filter(date <= as.Date("2014-01-01")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  arrange(date) %>%
  group_by(OPERATION) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2011-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Operation)) +
  xlab("") + ylab("") +  theme_minimal() +
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(seq(100, 180, 1), 106, 107)) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank())

2011-14

PIB valeur

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P31", "P32", "P4"),
         FREQ == "T",
         VALORISATION == "V",
         NATURE == "VALEUR_ABSOLUE",
         `SECT-INST` == "SO") %>%
  left_join(OPERATION,  by = "OPERATION") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2011-01-01"),
         date <= as.Date("2014-01-01")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  arrange(date) %>%
  group_by(OPERATION) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2011-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Operation)) +
  xlab("") + ylab("") +  theme_minimal() +
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(seq(0, 170, 1), 106, 107, 111, 112, 113)) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank())

PIB volume

Code 
`CNT-2020-PIB-EQB-RF` %>%
  filter(OPERATION %in% c("PIB", "P3", "P31", "P32", "P4"),
         FREQ == "T",
         VALORISATION == "L",
         NATURE == "VALEUR_ABSOLUE",
         `SECT-INST` == "SO") %>%
  left_join(OPERATION,  by = "OPERATION") %>%
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%Y-Q%q")) %>%
  filter(date >= zoo::as.yearqtr("2011 Q1"),
         date <= zoo::as.yearqtr("2014 Q1")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  arrange(date) %>%
  group_by(OPERATION) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == zoo::as.yearqtr("2011 Q1")]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Operation)) +
  xlab("") + ylab("") +  theme_minimal() +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2011:2100, c(1, 2, 3, 4)) %>%
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) %>%
                         pull(breaks)) +
  scale_y_log10(breaks = c(seq(0, 170, 1), 106, 107, 111, 112, 113)) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))