Budget de famille 2017

Data - INSEE

Author

François Geerolf

Info

source dataset .html .RData
insee bdf2017 2024-11-05 2023-11-21

Données sur l’inflation en France

source dataset .html .RData
insee bdf2017 2024-11-05 2023-11-21
insee ILC-ILAT-ICC 2024-11-05 2024-11-09
insee INDICES_LOYERS 2024-11-05 2024-11-09
insee IPC-1970-1980 2024-11-05 2024-11-09
insee IPC-1990 2024-11-05 2024-11-09
insee IPC-2015 2024-11-05 2024-11-09
insee IPC-PM-2015 2024-11-05 2024-11-09
insee IPCH-2015 2024-11-05 2024-11-09
insee IPGD-2015 2024-08-22 2024-10-26
insee IPLA-IPLNA-2015 2024-11-05 2024-11-09
insee IPPI-2015 2024-11-05 2024-11-09
insee IRL 2024-11-05 2024-11-09
insee SERIES_LOYERS 2024-11-05 2024-11-09
insee T_CONSO_EFF_FONCTION 2024-11-05 2024-07-18

LAST_COMPILE

LAST_COMPILE
2024-11-09

Versions

  • Budget de famille 2017. FM / FE / html
  • Budget de famille 2011. FM / Dom
  • Budget de famille 2006. FM

Bibliographie

  • Les comportements de consommation en 2017. Le transport pèse plus en milieu rural, le logement en milieu urbain, Insee Première n°1749. html / insee / pdf
  • Les ménages les plus modestes dépensent davantage pour leur logement et les plus aisés pour les transports, Insee Focus n°203. html / insee

Info

  • AGPR : Âge de la personne de référence

  • CSPR : Catégorie socioprofessionnelle de la personne de référence

  • DECUC : Niveau de vie par décile

  • EQUIP : Taux d’équipement

  • NOMENCLATURE : Nomenclature des produits

  • NOMENCLATURE2 : Nomenclature agrégée

  • STATUT : Statut d’occupation du logement selon le niveau de vie par quintile

  • STRATE : Catégorie de commune de résidence

  • TYPMEN : Type de ménage

  • ZEAT : Zone d’étude et d’aménagement du territoire

  • ZEAT2 : Zone d’étude et d’aménagement du territoire

MENAGES_IPC_CAT

Code 
bdf2017 %>%
  left_join(MENAGES_IPC_CAT, by = "MENAGES_IPC_CAT") %>%
  group_by(MENAGES_IPC_CAT, Menages_ipc_cat) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
MENAGES_IPC_CAT Menages_ipc_cat Nobs
STATUT Statut d'occupation du logement selon le niveau de vie par quintile 8476
DECUC Niveau de vie par décile 7172
CSPR Catégorie socioprofessionnelle de la personne de référence 5868
AGPR Âge de la personne de référence 5216
STRATE Catégorie de commune de résidence 3912
TYPMEN Type de ménage 3912
ZEAT Zone d'étude et d'aménagement du territoire 3260
ZEAT2 Zone d'étude et d'aménagement du territoire 2934

MENAGES_IPC_CAT, MENAGES_IPC

Code 
bdf2017 %>%
  group_by(MENAGES_IPC_CAT, MENAGES_IPC) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

REF_AREA

Code 
bdf2017 %>%
  group_by(REF_AREA) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
REF_AREA Nobs
FE 20538
FM 20212

NOMENCLATURE - bdf2017

Tous

Code 
bdf2017 %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  group_by(NOMENCLATURE, Nomenclature) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

NOMENCLATURE - tm106

Tous

Code 
tm106 %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  group_by(NOMENCLATURE, Nomenclature) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

2-digit

Code 
tm106 %>%
  filter(nchar(NOMENCLATURE) == 2) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  group_by(NOMENCLATURE, Nomenclature) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
NOMENCLATURE Nomenclature Nobs
01 01 - PRODUITS ALIMENTAIRES ET BOISSONS NON-ALCOOLISEES 11
02 02 - BOISSONS ALCOOLISEES ET TABAC 11
03 03 - ARTICLES D’HABILLEMENT ET CHAUSSURES 11
04 04 - LOGEMENT, EAU, GAZ, ELECTRICITE ET AUTRES COMBUSTIBLES 11
05 05 - MEUBLES, ARTICLES DE MENAGE ET ENTRETIEN COURANT DE LA MAISON 11
06 06 - SANTE 11
07 07 - TRANSPORTS 11
08 08 - COMMUNICATIONS 11
09 09 - LOISIRS ET CULTURE 11
10 10 - ENSEIGNEMENT 11
11 11 - RESTAURATION ET HÔTELS 11
12 12 - BIENS ET SERVICES DIVERS 11
13 13 - HORS CHAMP DE LA CONSOMMATION (IMPOTS ET TAXES, GROS TRAVAUX, REMBOURSEMENT PRET, CADEAUX) 11

3-digit

Code 
tm106 %>%
  filter(nchar(NOMENCLATURE) == 3) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  group_by(NOMENCLATURE, Nomenclature) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

4-digit

Code 
tm106 %>%
  filter(nchar(NOMENCLATURE) == 4) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  group_by(NOMENCLATURE, Nomenclature) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

4-digit

Code 
tm106 %>%
  filter(nchar(NOMENCLATURE) == 4) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  group_by(NOMENCLATURE, Nomenclature) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

5-digit

Code 
tm106 %>%
  filter(nchar(NOMENCLATURE) == 5) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  group_by(NOMENCLATURE, Nomenclature) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

ZEAT2

Code 
tm101 %>%
  left_join(ZEAT2, by = "ZEAT2") %>%
  group_by(ZEAT2, Zeat2) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
ZEAT2 Zeat2 Nobs
1 REGION PARISIENNE 326
2 BASSIN PARISIEN 326
3 NORD 326
4 EST 326
5 OUEST 326
7 SUD OUEST 326
8 CENTRE EST 326
9 MEDITERRANEE 326
TOT Ensemble 326

AGPR

Code 
tm102 %>%
  left_join(AGPR, by = "AGPR") %>%
  group_by(AGPR, Agpr) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
AGPR Agpr Nobs
1 Moins de 25 ans 326
2 De 25 à 34 ans 326
3 De 35 à 44 ans 326
4 De 45 à 54 ans 326
5 De 55 à 64 ans 326
6 De 65 à 74 ans 326
7 75 ans et plus 326
TOT Ensemble 326

CSPR

Code 
tm103 %>%
  left_join(CSPR, by = "CSPR") %>%
  group_by(CSPR, Cspr) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
CSPR Cspr Nobs
1 Agriculteurs 326
2 Artisans, commerçants et chefs d entreprise 326
3 Cadres 326
4 Professions intermédiaires 326
5 Employés 326
6 Ouvriers 326
7 Retraités 326
8 Autres inactifs 326
TOT Ensemble 326

STRATE

Code 
tm104 %>%
  left_join(STRATE, by = "STRATE") %>%
  group_by(STRATE, Strate) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
STRATE Strate Nobs
0 Rural 326
1 Petites villes (- de 20 000 hab.) 326
2 Villes moyennes (de 20 000 à 100 000 hab.) 326
3 Grandes villes (+ de 100 000 hab.) 326
4 Complexe agglo Paris 326
TOT Ensemble 326

TYPMEN

Code 
tm105 %>%
  left_join(TYPMEN, by = "TYPMEN") %>%
  group_by(TYPMEN, Typmen) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
TYPMEN Typmen Nobs
1 Personnes seules 326
2 Familles monoparentales 326
3 Couples sans enfant 326
4 Couples avec enfants 326
5 Autres ménages 326
TOT Ensemble 326

DECUC

Code 
tm106 %>%
  left_join(DECUC, by = "DECUC") %>%
  group_by(DECUC, Decuc) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
DECUC Decuc Nobs
1 Décile 1 326
10 Décile 10 326
2 Décile 2 326
3 Décile 3 326
4 Décile 4 326
5 Décile 5 326
6 Décile 6 326
7 Décile 7 326
8 Décile 8 326
9 Décile 9 326
TOT Ensemble 326

Alimentation - Focus

Décile 1 vs Décile 10

Code 
weights <- bdf2017 %>%
  filter(MENAGES_IPC_CAT == "DECUC",
         MENAGES_IPC %in% c("1", "10"),
         substr(NOMENCLATURE, 1, 2) == "01" | NOMENCLATURE == "CTOTALE",
         REF_AREA == "FE") %>%
  select_if(~ n_distinct(.) > 1) %>%
  group_by(MENAGES_IPC) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE", NOMENCLATURE != "01") %>%
  transmute(COICOP2016 = NOMENCLATURE, MENAGES_IPC, weights = OBS_VALUE) %>%
  ungroup

inflation <- `IPC-2015` %>%
  filter(NATURE == "INDICE",
         INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 != "SO",
         substr(COICOP2016, 1, 2) == "01",
         PRIX_CONSO == "SO",
         REF_AREA %in% c("FE")) %>%
  month_to_date %>%
  select_if(~ n_distinct(.) > 1) %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(inflation = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
  select(date, COICOP2016, inflation) %>%
  ungroup %>%
  filter(date >= as.Date("2021-01-01"))

weights %>%
  mutate(COICOP2016_nchar = nchar(COICOP2016)) %>%
  full_join(inflation) %>%
  group_by(COICOP2016_nchar, MENAGES_IPC, date) %>%
  summarise(inflation_sum = sum(inflation*weights/sum(weights))) %>%
  ggplot + geom_line(aes(x = date, y = inflation_sum, color = factor(COICOP2016_nchar), linetype = MENAGES_IPC))

Somme des poids

Par décile

Code 
bdf2017 %>%
  filter(MENAGES_IPC_CAT == "DECUC",
         REF_AREA == "FE") %>%
  group_by(MENAGES_IPC) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[NOMENCLATURE == "CTOTALE"]) %>%
  ungroup %>%
  select(NOMENCLATURE, DECUC = MENAGES_IPC, OBS_VALUE) %>%
  left_join(DECUC, by = "DECUC") %>%
  # 13: hors champ de la consommation -----
  filter(NOMENCLATURE != "CTOTALE",
         substr(NOMENCLATURE, 1, 2) != "13") %>%
  mutate(NOMENCLATURE_DIGIT = nchar(NOMENCLATURE)) %>%
  group_by(NOMENCLATURE_DIGIT, DECUC2) %>%
  summarise(OBS_VALUE = sum(OBS_VALUE),
            Nobs = n()) %>%
  arrange(NOMENCLATURE_DIGIT, DECUC2) %>%
  print_table_conditional()

Par âge

Code 
bdf2017 %>%
  filter(MENAGES_IPC_CAT == "AGPR",
         REF_AREA == "FE") %>%
  group_by(MENAGES_IPC) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[NOMENCLATURE == "CTOTALE"]) %>%
  ungroup %>%
  select(NOMENCLATURE, AGPR = MENAGES_IPC, OBS_VALUE) %>%
  left_join(AGPR, by = "AGPR") %>%
  # 13: hors champ de la consommation -----
  filter(NOMENCLATURE != "CTOTALE",
         substr(NOMENCLATURE, 1, 2) != "13") %>%
  mutate(NOMENCLATURE_DIGIT = nchar(NOMENCLATURE)) %>%
  group_by(NOMENCLATURE_DIGIT, AGPR2) %>%
  summarise(OBS_VALUE = sum(OBS_VALUE),
            Nobs = n()) %>%
  arrange(NOMENCLATURE_DIGIT, AGPR2) %>%
  print_table_conditional()

Par strate (type ville)

Code 
bdf2017 %>%
  filter(MENAGES_IPC_CAT == "STRATE",
         REF_AREA == "FE") %>%
  group_by(MENAGES_IPC) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[NOMENCLATURE == "CTOTALE"]) %>%
  ungroup %>%
  select(NOMENCLATURE, STRATE = MENAGES_IPC, OBS_VALUE) %>%
  left_join(STRATE, by = "STRATE") %>%
  # 13: hors champ de la consommation -----
  filter(NOMENCLATURE != "CTOTALE",
         substr(NOMENCLATURE, 1, 2) != "13") %>%
  mutate(NOMENCLATURE_DIGIT = nchar(NOMENCLATURE)) %>%
  group_by(NOMENCLATURE_DIGIT, Strate) %>%
  summarise(OBS_VALUE = sum(OBS_VALUE),
            Nobs = n()) %>%
  arrange(NOMENCLATURE_DIGIT, Strate) %>%
  print_table_conditional()
NOMENCLATURE_DIGIT Strate OBS_VALUE Nobs
2 Complexe agglo Paris 1.0000000 12
2 Ensemble 1.0000365 12
2 Grandes villes (+ de 100 000 hab.) 0.9999627 12
2 Petites villes (- de 20 000 hab.) 1.0000000 12
2 Rural 1.0000000 12
2 Villes moyennes (de 20 000 à 100 000 hab.) 1.0000000 12
3 Complexe agglo Paris 1.0000000 48
3 Ensemble 1.0000000 48
3 Grandes villes (+ de 100 000 hab.) 1.0001118 48
3 Petites villes (- de 20 000 hab.) 1.0000376 48
3 Rural 1.0001479 48
3 Villes moyennes (de 20 000 à 100 000 hab.) 0.9999607 48
4 Complexe agglo Paris 0.9999371 105
4 Ensemble 1.0000000 105
4 Grandes villes (+ de 100 000 hab.) 1.0002237 105
4 Petites villes (- de 20 000 hab.) 1.0001129 105
4 Rural 1.0001479 105
4 Villes moyennes (de 20 000 à 100 000 hab.) 0.9999607 105
5 Complexe agglo Paris 0.9999685 138
5 Ensemble 1.0000365 138
5 Grandes villes (+ de 100 000 hab.) 1.0002237 138
5 Petites villes (- de 20 000 hab.) 1.0001129 138
5 Rural 1.0001109 138
5 Villes moyennes (de 20 000 à 100 000 hab.) 0.9998820 138

Alimentation - Tous

DECUC

% de la consommation

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("011", "0111", "0112", "0113", "CTOTALE"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  #
  theme(legend.position = c(0.4, 0.15),
        legend.title = element_blank())

% du revenu

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("011", "0111", "0112", "0113"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(revenu_dispo_decile, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO/revenu_dispo_decile, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% du revenu") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 2),
                     labels = percent_format(accuracy = 1)) +
  #
  theme(legend.position = c(0.4, 0.15),
        legend.title = element_blank())

Alimentation

DECUC

% de la consommation

Code 
bdf2017 %>%
  filter(NOMENCLATURE %in% c("0111", "0112", "0113", "0114", "0115",
                             "0116", "0117", "0118", "0119", "CTOTALE"),
         MENAGES_IPC != "TOT",
         MENAGES_IPC_CAT == "DECUC",
         REF_AREA == "FE") %>%
  rename(DECUC = MENAGES_IPC) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(OBS_VALUE = OBS_VALUE/OBS_VALUE[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC2, y = OBS_VALUE, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.4, 0.6),
        legend.title = element_blank())

% du revenu

Code 
bdf2017 %>%
  filter(NOMENCLATURE %in% c("0111", "0112", "0113", "0114", "0115",
                             "0116", "0117", "0118", "0119", "CTOTALE"),
         MENAGES_IPC != "TOT",
         MENAGES_IPC_CAT == "DECUC",
         REF_AREA == "FE") %>%
  rename(DECUC = MENAGES_IPC) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(revenu_dispo_decile, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC2, y = OBS_VALUE/revenu_dispo_decile, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  #
  theme(legend.position = c(0.4, 0.6),
        legend.title = element_blank())

Pains et Céréales (0111), Pain (01112)

CSPR

Code 
tm103 %>%
  filter(NOMENCLATURE %in% c("0111", "01112", "CTOTALE"),
         CSPR != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(CSPR, by = "CSPR") %>%
  group_by(CSPR) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  mutate(Cspr = gsub(" ", " \n", Cspr),
         Cspr = ifelse(CSPR == "2", "Artisans, \ncommerçants \net chefs \nd'entreprise", Cspr)) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = paste0(CSPR, " - ", Cspr), y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale (Budget de famille 2017)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

AGPR

Code 
tm102 %>%
  filter(NOMENCLATURE %in% c("0111", "01112", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(AGPR, by = "AGPR") %>%
  filter(AGPR != "TOT") %>%
  group_by(AGPR) %>%
  mutate(AGPR2 = gsub(" ", " \n", AGPR2)) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = AGPR2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale (Budget de famille 2017)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.2),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.55, 0.9),
        legend.title = element_blank())

DECUC

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("0111", "01112", "01113", "CTOTALE"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  #
  theme(legend.position = c(0.4, 0.3),
        legend.title = element_blank())

STRATE

Code 
tm104 %>%
  filter(NOMENCLATURE %in% c("0111", "01112", "CTOTALE"),
         STRATE != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(STRATE, by = "STRATE") %>%
  group_by(STRATE) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  mutate(Strate = gsub("\\(", "\n\\(", Strate)) %>%
  ggplot + geom_line(aes(x = paste0(STRATE, " - ", Strate), y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1),
                     limits = c(0, 0.032)) +
  
  theme(legend.position = c(0.4, 0.15),
        legend.title = element_blank())

TYPMEN

Code 
tm105 %>%
  filter(NOMENCLATURE %in% c("0111", "01112", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(TYPMEN, by = "TYPMEN") %>%
  filter(TYPMEN != "TOT") %>%
  group_by(TYPMEN) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  mutate(Typmen = gsub(" ", " \n", Typmen)) %>%
  ggplot + geom_line(aes(x = paste0(TYPMEN, " - ", Typmen), y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale (Budget de famille 2017)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.2),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

ZEAT2

Code 
tm101 %>%
  filter(NOMENCLATURE %in% c("0111", "01112", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(ZEAT2, by = "ZEAT2") %>%
  filter(ZEAT2 != "TOT") %>%
  group_by(ZEAT2) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  mutate(Zeat2 = gsub(" ", " \n", Zeat2)) %>%
  ggplot + geom_line(aes(x = Zeat2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale (Budget de famille 2017)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.4, 0.2),
        legend.title = element_blank())

HORS CHAMP DE LA CONSOMMATION (IMPOTS ET TAXES, GROS TRAVAUX, REMBOURSEMENT PRET, CADEAUX)

DECUC

% de la consommation

All

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("131", "132", "133", "134", "135", "CTOTALE"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 1),
                     labels = percent_format(accuracy = .1)) +
  #
  theme(legend.position = c(0.4, 0.8),
        legend.title = element_blank())

All

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("132", "133", "134", "135", "CTOTALE"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 1),
                     labels = percent_format(accuracy = .1)) +
  #
  theme(legend.position = c(0.4, 0.8),
        legend.title = element_blank())

Remboursements de prêts immobiliers, Loyers

DECUC

% de la consommation

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("132Z", "0411", "0412", "CTOTALE"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  #
  theme(legend.position = c(0.4, 0.15),
        legend.title = element_blank())

Remboursements de prêts immobiliers, Loyers

DECUC

% de la consommation

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("132Z", "0411", "CTOTALE"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  #
  theme(legend.position = c(0.4, 0.8),
        legend.title = element_blank())

Electricité, Gaz, Electricite + gaz

DECUC

% de la consommation

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("0451", "045", "0452","CTOTALE"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1),
                     limits = c(0, 0.065)) +
  #
  theme(legend.position = c(0.4, 0.15),
        legend.title = element_blank())

% du revenu

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("0451", "045", "0452","CTOTALE"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(revenu_dispo_decile, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO/revenu_dispo_decile, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% du revenu") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1),
                     limits = c(0, 0.09)) +
  theme(legend.position = c(0.6, 0.8),
        legend.title = element_blank())

Carburants et lubrifiants, antigel (0722), Electricité (045)

CSPR

Code 
tm103 %>%
  filter(NOMENCLATURE %in% c("0722", "045", "CTOTALE"),
         CSPR != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(CSPR, by = "CSPR") %>%
  group_by(CSPR) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  mutate(Cspr = gsub(" ", " \n", Cspr),
         Cspr = ifelse(CSPR == "2", "Artisans, \ncommerçants \net chefs \nd'entreprise", Cspr)) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = paste0(CSPR, " - ", Cspr), y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale (Budget de famille 2017)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  #
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

AGPR

% de la consommation

Code 
tm102 %>%
  filter(NOMENCLATURE %in% c("0722", "045", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(AGPR, by = "AGPR") %>%
  filter(AGPR != "TOT") %>%
  group_by(AGPR) %>%
  mutate(AGPR2 = gsub(" ", " \n", AGPR2)) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = AGPR2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 1),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 0.08)) +
  #
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank())

DECUC

% de la consommation

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("0722", "045", "CTOTALE"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1),
                     limits = c(0, 0.065)) +
  #
  theme(legend.position = c(0.4, 0.15),
        legend.title = element_blank())

% du revenu

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("0722", "045"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(revenu_dispo_decile, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO/revenu_dispo_decile, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% du revenu") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1),
                     limits = c(0, 0.09)) +
  #
  theme(legend.position = c(0.4, 0.15),
        legend.title = element_blank())

STRATE

Code 
tm104 %>%
  filter(NOMENCLATURE %in% c("0722", "045", "CTOTALE"),
         STRATE != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(STRATE, by = "STRATE") %>%
  group_by(STRATE) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  mutate(Strate = gsub("\\(", "\n\\(", Strate)) %>%
  ggplot + geom_line(aes(x = paste0(STRATE, " - ", Strate), y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1),
                     limits = c(0, 0.065)) +
  #
  theme(legend.position = c(0.4, 0.15),
        legend.title = element_blank())

TYPMEN

Code 
tm105 %>%
  filter(NOMENCLATURE %in% c("0722", "045", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(TYPMEN, by = "TYPMEN") %>%
  filter(TYPMEN != "TOT") %>%
  group_by(TYPMEN) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  mutate(Typmen = gsub(" ", " \n", Typmen)) %>%
  ggplot + geom_line(aes(x = paste0(TYPMEN, " - ", Typmen), y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale (Budget de famille 2017)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.2),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

ZEAT2

Code 
tm101 %>%
  filter(NOMENCLATURE %in% c("0722", "045", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(ZEAT2, by = "ZEAT2") %>%
  filter(ZEAT2 != "TOT") %>%
  group_by(ZEAT2) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  mutate(Zeat2 = gsub(" ", " \n", Zeat2)) %>%
  ggplot + geom_line(aes(x = Zeat2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale (Budget de famille 2017)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.4, 0.2),
        legend.title = element_blank())

Communications (081), Services de téléphone (0813)

CSPR

Code 
tm103 %>%
  filter(NOMENCLATURE %in% c("081", "0813", "CTOTALE"),
         CSPR != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(CSPR, by = "CSPR") %>%
  group_by(CSPR) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  mutate(Cspr = gsub(" ", " \n", Cspr),
         Cspr = ifelse(CSPR == "2", "Artisans, \ncommerçants \net chefs \nd'entreprise", Cspr)) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = paste0(CSPR, " - ", Cspr), y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale (Budget de famille 2017)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

AGPR

Code 
tm102 %>%
  filter(NOMENCLATURE %in% c("081", "0813", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(AGPR, by = "AGPR") %>%
  filter(AGPR != "TOT") %>%
  group_by(AGPR) %>%
  mutate(AGPR2 = gsub(" ", " \n", AGPR2)) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = AGPR2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale (Budget de famille 2017)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.2),
                     labels = percent_format(accuracy = .1),
                     limits = c(0, 0.037)) +
  
  theme(legend.position = c(0.55, 0.9),
        legend.title = element_blank())

DECUC

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("081", "0813", "CTOTALE"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1),
                     limits = c(0, 0.039)) +
  
  theme(legend.position = c(0.4, 0.15),
        legend.title = element_blank())

STRATE

Code 
tm104 %>%
  filter(NOMENCLATURE %in% c("081", "0813", "CTOTALE"),
         STRATE != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(STRATE, by = "STRATE") %>%
  group_by(STRATE) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  mutate(Strate = gsub("\\(", "\n\\(", Strate)) %>%
  ggplot + geom_line(aes(x = paste0(STRATE, " - ", Strate), y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.1),
                     labels = percent_format(accuracy = .1),
                     limits = c(0, 0.029)) +
  
  theme(legend.position = c(0.4, 0.15),
        legend.title = element_blank())

TYPMEN

Code 
tm105 %>%
  filter(NOMENCLATURE %in% c("081", "0813", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(TYPMEN, by = "TYPMEN") %>%
  filter(TYPMEN != "TOT") %>%
  group_by(TYPMEN) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  mutate(Typmen = gsub(" ", " \n", Typmen)) %>%
  ggplot + geom_line(aes(x = paste0(TYPMEN, " - ", Typmen), y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale (Budget de famille 2017)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.1),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

ZEAT2

Code 
tm101 %>%
  filter(NOMENCLATURE %in% c("081", "0813", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(ZEAT2, by = "ZEAT2") %>%
  filter(ZEAT2 != "TOT") %>%
  group_by(ZEAT2) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  mutate(Zeat2 = gsub(" ", " \n", Zeat2)) %>%
  ggplot + geom_line(aes(x = Zeat2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale (Budget de famille 2017)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.1),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.4, 0.7),
        legend.title = element_blank())

Pâtes

DECUC

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("01113", "CTOTALE"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.01),
                     labels = percent_format(accuracy = .01)) +
  #
  theme(legend.position = c(0.4, 0.3),
        legend.title = element_blank())

% du revenu

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("01113"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(revenu_dispo_decile, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO/revenu_dispo_decile, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% du revenu") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.02),
                     labels = percent_format(accuracy = .01)) +
  #
  theme(legend.position = c(0.4, 0.15),
        legend.title = element_blank())

Santé (06), Assurances liées à la santé (1253)

CSPR

Code 
tm103 %>%
  filter(NOMENCLATURE %in% c("1253", "06", "CTOTALE"),
         CSPR != "TOT", CSPR != "8") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(CSPR, by = "CSPR") %>%
  group_by(CSPR) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  mutate(Cspr = gsub(" ", " \n", Cspr),
         Cspr = ifelse(CSPR == "2", "Artisans, \ncommerçants \net chefs \nd'entreprise", Cspr)) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = paste0(CSPR, " - ", Cspr), y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale (Budget de famille 2017)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1),
                     limits = c(0, 0.07)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

AGPR

Code 
tm102 %>%
  filter(NOMENCLATURE %in% c("1253", "06", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(AGPR, by = "AGPR") %>%
  filter(AGPR != "TOT") %>%
  group_by(AGPR) %>%
  mutate(AGPR2 = gsub(" ", " \n", AGPR2)) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = AGPR2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale (Budget de famille 2017)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1),
                     limits = c(0, 0.085)) +
  
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())

DECUC

% de la consommation

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("1253", "06", "CTOTALE"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

% du revenu

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("1253", "06"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(revenu_dispo_decile, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO/revenu_dispo_decile, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% du revenu") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1),
                     limits = c(0, 0.05)) +
  
  theme(legend.position = c(0.4, 0.15),
        legend.title = element_blank())

STRATE

Code 
tm104 %>%
  filter(NOMENCLATURE %in% c("1253", "06", "CTOTALE"),
         STRATE != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(STRATE, by = "STRATE") %>%
  group_by(STRATE) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  mutate(Strate = gsub("\\(", "\n\\(", Strate)) %>%
  ggplot + geom_line(aes(x = paste0(STRATE, " - ", Strate), y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

TYPMEN

Code 
tm105 %>%
  filter(NOMENCLATURE %in% c("1253", "06", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(TYPMEN, by = "TYPMEN") %>%
  filter(TYPMEN != "TOT") %>%
  group_by(TYPMEN) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  mutate(Typmen = gsub(" ", " \n", Typmen)) %>%
  ggplot + geom_line(aes(x = paste0(TYPMEN, " - ", Typmen), y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale (Budget de famille 2017)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())

ZEAT2

Code 
tm101 %>%
  filter(NOMENCLATURE %in% c("1253", "06", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(ZEAT2, by = "ZEAT2") %>%
  filter(ZEAT2 != "TOT") %>%
  group_by(ZEAT2) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  mutate(Zeat2 = gsub(" ", " \n", Zeat2)) %>%
  ggplot + geom_line(aes(x = Zeat2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale (Budget de famille 2017)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank())

Catégories Socioprofessionelles

%

Code 
tm103 %>%
  filter(CSPR %in% c("TOT", "3", "4", "5", "6")) %>%
  left_join(CSPR, by = "CSPR") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  select(NOMENCLATURE, Nomenclature, Cspr, CONSO) %>%
  spread(Cspr, CONSO) %>%
  mutate_at(vars(-1, -2), funs(round(100*./.[NOMENCLATURE == "CTOTALE"],2))) %>%
  print_table_conditional()

Tabac, Enseignement Supérieur, Communications

Code 
tm103 %>%
  filter(NOMENCLATURE %in% c("1013", "022", "081", "CTOTALE"),
         CSPR %in% c("3", "4", "5", "6", "7")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(CSPR, by = "CSPR") %>%
  group_by(CSPR) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = paste0(CSPR, " - ", Cspr), y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  #scale_x_continuous(breaks = seq(0, 10, 1)) +
  
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())

Enseignement, Achats de téléphones, Services

Code 
tm103 %>%
  filter(NOMENCLATURE %in% c("0813", "0812", "10", "CTOTALE"),
         CSPR %in% c("3", "4", "5", "6", "7")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(CSPR, by = "CSPR") %>%
  group_by(CSPR) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = paste0(CSPR, " - ", Cspr), y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  #scale_x_continuous(breaks = seq(0, 10, 1)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

Enseignement secondaire, Enseignement supérieur, Assurances liées à la santé

Code 
tm103 %>%
  filter(NOMENCLATURE %in% c("1253", "1013", "1012", "CTOTALE"),
         CSPR %in% c("3", "4", "5", "6", "7")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(CSPR, by = "CSPR") %>%
  group_by(CSPR) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = paste0(CSPR, " - ", Cspr), y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  #scale_x_continuous(breaks = seq(0, 10, 1)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

Santé, Assurances liées à la santé

Code 
tm103 %>%
  filter(NOMENCLATURE %in% c("1253", "06", "061", "CTOTALE"),
         CSPR %in% c("3", "4", "5", "6", "7")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(CSPR, by = "CSPR") %>%
  group_by(CSPR) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = paste0(CSPR, " - ", Cspr), y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  #scale_x_continuous(breaks = seq(0, 10, 1)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

Déciles

% du revenu disponible

Code 
tm106 %>%
  filter(DECUC %in% c("1", "4", "7", "10")) %>%
  left_join(revenu_dispo_decile, by = "DECUC") %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  transmute(NOMENCLATURE, Nomenclature, DECUC2,
            CONSO = round(100*CONSO/revenu_dispo_decile, 2)) %>%
  spread(DECUC2, CONSO) %>%
  print_table_conditional()

% de la Consommation

Tous

Code 
tm106 %>%
  filter(DECUC %in% c("1", "4", "7", "10", "TOT")) %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  select(NOMENCLATURE, Nomenclature, DECUC2, CONSO) %>%
  spread(DECUC2, CONSO) %>%
  mutate_at(vars(-1, -2), funs(round(100*./.[NOMENCLATURE == "CTOTALE"],2))) %>%
  print_table_conditional()

2-digit

Code 
tm106 %>%
  filter(nchar(NOMENCLATURE) == 2 | NOMENCLATURE == "CTOTALE") %>%
  filter(DECUC %in% c("1", "4", "7", "10", "TOT")) %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  select(NOMENCLATURE, Nomenclature, DECUC2, CONSO) %>%
  spread(DECUC2, CONSO) %>%
  mutate_at(vars(-1, -2), funs(round(100*./.[NOMENCLATURE == "CTOTALE"],2))) %>%
  print_table_conditional()
NOMENCLATURE Nomenclature D1- D3-D4 D6-D7 D9+ TOT
01 01 - PRODUITS ALIMENTAIRES ET BOISSONS NON-ALCOOLISEES 17.44 17.91 15.57 13.56 16.08
02 02 - BOISSONS ALCOOLISEES ET TABAC 3.57 3.42 2.85 2.25 2.89
03 03 - ARTICLES D’HABILLEMENT ET CHAUSSURES 5.45 4.61 4.91 4.86 5.00
04 04 - LOGEMENT, EAU, GAZ, ELECTRICITE ET AUTRES COMBUSTIBLES 22.75 19.65 15.49 12.01 16.29
05 05 - MEUBLES, ARTICLES DE MENAGE ET ENTRETIEN COURANT DE LA MAISON 3.60 4.32 5.76 8.02 5.67
06 06 - SANTE 1.60 1.87 1.99 1.59 1.85
07 07 - TRANSPORTS 13.61 15.11 17.33 18.11 16.28
08 08 - COMMUNICATIONS 3.84 3.09 2.65 1.82 2.66
09 09 - LOISIRS ET CULTURE 7.84 8.02 9.66 11.05 9.40
10 10 - ENSEIGNEMENT 1.88 0.69 0.54 0.98 0.78
11 11 - RESTAURATION ET HÔTELS 5.36 6.16 6.80 10.28 7.23
12 12 - BIENS ET SERVICES DIVERS 13.08 15.14 16.46 15.47 15.87
13 13 - HORS CHAMP DE LA CONSOMMATION (IMPOTS ET TAXES, GROS TRAVAUX, REMBOURSEMENT PRET, CADEAUX) 14.85 21.19 36.36 63.20 34.97
CTOTALE TOTAL DE LA CONSOMMATION - CHAMP COMPTABILITE NATIONALE 100.00 100.00 100.00 100.00 100.00

3-digit

Code 
tm106 %>%
  filter(nchar(NOMENCLATURE) == 3 | NOMENCLATURE == "CTOTALE") %>%
  filter(DECUC %in% c("1", "4", "7", "10", "TOT")) %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  select(NOMENCLATURE, Nomenclature, DECUC2, CONSO) %>%
  spread(DECUC2, CONSO) %>%
  mutate_at(vars(-1, -2), funs(round(100*./.[NOMENCLATURE == "CTOTALE"],2))) %>%
  print_table_conditional()

4-digit

Code 
tm106 %>%
  filter(nchar(NOMENCLATURE) == 4 | NOMENCLATURE == "CTOTALE") %>%
  filter(DECUC %in% c("1", "4", "7", "10", "TOT")) %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  select(NOMENCLATURE, Nomenclature, DECUC2, CONSO) %>%
  spread(DECUC2, CONSO) %>%
  mutate_at(vars(-1, -2), funs(round(100*./.[NOMENCLATURE == "CTOTALE"],2))) %>%
  print_table_conditional()

5-digit

Code 
tm106 %>%
  filter(nchar(NOMENCLATURE) == 5 | NOMENCLATURE == "CTOTALE") %>%
  filter(DECUC %in% c("1", "4", "7", "10", "TOT")) %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  select(NOMENCLATURE, Nomenclature, DECUC2, CONSO) %>%
  spread(DECUC2, CONSO) %>%
  mutate_at(vars(-1, -2), funs(round(100*./.[NOMENCLATURE == "CTOTALE"],2))) %>%
  print_table_conditional()

Euros

Code 
tm106 %>%
  filter(DECUC %in% c("TOT", "1", "4", "7", "10")) %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  select(NOMENCLATURE, Nomenclature, Decuc, CONSO) %>%
  spread(Decuc, CONSO) %>%
  print_table_conditional()

Biais du 1er décile

Code 
tm106 %>%
  filter(DECUC %in% c("TOT", "1", "10")) %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  select(NOMENCLATURE, Nomenclature, Decuc, CONSO) %>%
  spread(Decuc, CONSO) %>%
  mutate_at(vars(-1, -2), funs(round(100*./.[NOMENCLATURE == "CTOTALE"],2))) %>%
  mutate(`Décile 1 - Ensemble` = round(`Décile 1`/`Ensemble`-1,2)) %>%
  arrange(-`Décile 1 - Ensemble`) %>%
  print_table_conditional()

Biais du 10ème décile

Code 
tm106 %>%
  filter(DECUC %in% c("TOT", "1", "10")) %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  select(NOMENCLATURE, Nomenclature, Decuc, CONSO) %>%
  spread(Decuc, CONSO) %>%
  mutate_at(vars(-1, -2), funs(round(100*./.[NOMENCLATURE == "CTOTALE"],2))) %>%
  mutate(`Décile 10 - Ensemble` = round(`Décile 10`/`Ensemble`-1, 2)) %>%
  arrange(-`Décile 10 - Ensemble`) %>%
  print_table_conditional()

Communications (081), Services de téléphone (0813)

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("081", "0813", "CTOTALE"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

Tabac (022), Enseignement Supérieur (1013), Communications (081)

% de la consommation

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("1013", "022", "081", "CTOTALE"),
         DECUC != "TOT") %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(value = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  select(DECUC2, value, Nomenclature, everything()) %>%
  ggplot + geom_line(aes(x = DECUC2, y = value, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank())

% du revenu disponible

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("1013", "022", "081"),
         DECUC != "TOT") %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(revenu_dispo_decile, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  mutate(value = CONSO/revenu_dispo_decile) %>%
  select(DECUC2, value, Nomenclature, everything()) %>%
  ggplot + geom_line(aes(x = DECUC2, y = value, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank())

Tabac (022), Loyer (041)

% de la consommation

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("041", "022", "CTOTALE"),
         DECUC != "TOT") %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(value = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  select(DECUC2, value, Nomenclature, everything()) %>%
  ggplot + geom_line(aes(x = DECUC2, y = value, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank())

% du revenu disponible

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("041", "022"),
         DECUC != "TOT") %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(revenu_dispo_decile, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  mutate(value = CONSO/revenu_dispo_decile) %>%
  select(DECUC2, value, Nomenclature, everything()) %>%
  ggplot + geom_line(aes(x = DECUC2, y = value, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% ddu revenu disponible") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 1),
                     labels = percent_format(accuracy = 1)) +
  #
  theme(legend.position = c(0.5, 0.9),
        legend.title = element_blank())

Tabac (022), Loyer (041)

% de la consommation

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("011", "041", "022", "CTOTALE"),
         DECUC != "TOT") %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(value = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  select(DECUC2, value, Nomenclature, everything()) %>%
  ggplot + geom_line(aes(x = DECUC2, y = value, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

% du revenu disponible

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("011", "041", "022"),
         DECUC != "TOT") %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(revenu_dispo_decile, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  mutate(value = CONSO/revenu_dispo_decile) %>%
  select(DECUC2, value, Nomenclature, everything()) %>%
  ggplot + geom_line(aes(x = DECUC2, y = value, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% ddu revenu disponible") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 1),
                     labels = percent_format(accuracy = 1)) +
  #
  theme(legend.position = c(0.5, 0.9),
        legend.title = element_blank())

Tabac (022)

% de la consommation

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("022", "CTOTALE"),
         DECUC != "TOT") %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(value = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  select(DECUC2, value, Nomenclature, everything()) %>%
  ggplot + geom_line(aes(x = DECUC, y = value, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("Dixième de revenu") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.2),
                     labels = percent_format(accuracy = .1)) +
  scale_x_continuous(breaks = seq(1, 10, 1)) +
  
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank())

% du revenu disponible

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("022"),
         DECUC != "TOT") %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(revenu_dispo_decile, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  arrange(DECUC2) %>%
  ggplot + geom_line(aes(x = DECUC, y = CONSO/revenu_dispo_decile, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("Dixième de revenu") + ylab("% du revenu disponible") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1),
                     limits = c(0, 0.035)) +
  scale_x_continuous(breaks = seq(1, 10, 1)) +
  
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank())

Services de communications (081, 0811, 0812, 0813)

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("081", "0811", "0812", "0813", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC, y = CONSO, color = Nomenclature)) +
  theme_minimal() + xlab("Décile") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  scale_x_continuous(breaks = seq(0, 10, 1)) +
  
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank())

Communications, Services de téléphone

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("0811", "0812", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC, y = CONSO, color = Nomenclature)) +
  theme_minimal() + xlab("Décile") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50,0.05),
                     labels = percent_format(accuracy = .01)) +
  scale_x_continuous(breaks = seq(0, 10, 1)) +
  
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

Alimentation, Logement, Loyer

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("01", "04", "041", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC, y = CONSO, color = Nomenclature)) +
  theme_minimal() + xlab("Décile") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  scale_x_continuous(breaks = seq(0, 10, 1)) +
  
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank())

Alimentation, Electricité, Frais utilisation

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("01", "072", "045", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC, y = CONSO, color = Nomenclature)) +
  theme_minimal() + xlab("Décile") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  scale_x_continuous(breaks = seq(0, 10, 1)) +
  
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank())

Carburants et lubrifiants, antigel (0722), Electricité + gaz (045)

% de la consommation

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("045", "0722", "CTOTALE"),
         DECUC != "TOT") %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(value = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  select(DECUC2, value, Nomenclature, everything()) %>%
  ggplot + geom_line(aes(x = DECUC2, y = value, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank())

% du revenu disponible

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("045", "0722"),
         DECUC != "TOT") %>%
  left_join(DECUC, by = "DECUC") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(revenu_dispo_decile, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  mutate(value = CONSO/revenu_dispo_decile) %>%
  select(DECUC2, value, Nomenclature, everything()) %>%
  ggplot + geom_line(aes(x = DECUC2, y = value, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% ddu revenu disponible") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 1),
                     labels = percent_format(accuracy = 1)) +
  #
  theme(legend.position = c(0.5, 0.9),
        legend.title = element_blank())

Carburants et lubrifiants, antigel (0722), Electricité (045), Pains et Céréales (0111)

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("045", "0722", "0111", "CTOTALE"),
         DECUC != "TOT") %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1),
                     limits = c(0, 0.065)) +
  
  theme(legend.position = c(0.5, 0.2),
        legend.title = element_blank())

Electricité (045), Pains et Céréales (0111), Pâtes

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("045", "01113", "0111", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  left_join(DECUC, by = "DECUC") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC2, y = CONSO, color = Nomenclature, group = Nomenclature)) +
  theme_minimal() + xlab("Décile") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  
  theme(legend.position = c(0.5, 0.9),
        legend.title = element_blank())

Enseignement, Achats de téléphones, Services

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("0813", "0812", "10", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC, y = CONSO, color = Nomenclature)) +
  theme_minimal() + xlab("Décile") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  scale_x_continuous(breaks = seq(0, 10, 1)) +
  
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank())

Enseignement secondaire, Enseignement supérieur, Assurances liées à la santé

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("1011", "1013", "1012", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC, y = CONSO, color = Nomenclature)) +
  theme_minimal() + xlab("Décile") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  scale_x_continuous(breaks = seq(0, 10, 1)) +
  
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank())

Enseignement secondaire, Enseignement supérieur, Assurances liées à la santé

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("1253", "1013", "1012", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC, y = CONSO, color = Nomenclature)) +
  theme_minimal() + xlab("Décile") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  scale_x_continuous(breaks = seq(0, 10, 1)) +
  
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank())

Santé, Assurances liées à la santé

Code 
tm106 %>%
  filter(NOMENCLATURE %in% c("1253", "06", "061", "CTOTALE")) %>%
  left_join(NOMENCLATURE, by = "NOMENCLATURE") %>%
  mutate(DECUC = DECUC %>% as.numeric) %>%
  group_by(DECUC) %>%
  mutate(CONSO = CONSO/CONSO[NOMENCLATURE == "CTOTALE"]) %>%
  filter(NOMENCLATURE != "CTOTALE") %>%
  ggplot + geom_line(aes(x = DECUC, y = CONSO, color = Nomenclature)) +
  theme_minimal() + xlab("Décile") + ylab("% de la Consommation Totale") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  scale_x_continuous(breaks = seq(0, 10, 1)) +
  
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank())

IPC par déciles ?

Code 
load_data("insee/IPC-2015.RData")
load_data("insee/IPCH-2015.RData")