~/data/insee/

Info

source	dataset	.html	.RData
insee	bdf2017	2024-04-18	2023-11-21

Données sur l’inflation en France

source	dataset	.html	.RData
insee	bdf2017	2024-04-18	2023-11-21
insee	ILC-ILAT-ICC	2024-04-18	2024-05-09
insee	INDICES_LOYERS	2024-04-18	2024-05-09
insee	IPC-1970-1980	2024-04-18	2024-05-09
insee	IPC-1990	2024-04-18	2024-05-09
insee	IPC-2015	2024-04-18	2024-04-09
insee	IPC-PM-2015	2024-04-18	2024-05-09
insee	IPCH-2015	2024-04-18	2024-05-09
insee	IPGD-2015	2024-04-18	2024-03-20
insee	IPLA-IPLNA-2015	2024-04-18	2024-05-09
insee	IPPI-2015	2024-04-18	2024-03-30
insee	IRL	2024-04-18	2024-05-09
insee	SERIES_LOYERS	2024-04-18	2024-05-09
insee	T_CONSO_EFF_FONCTION	2024-04-18	2024-04-01

LAST_COMPILE

LAST_COMPILE
2024-05-09

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

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

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

REF_AREA

bdf2017 %>%
  group_by(REF_AREA) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

REF_AREA	Nobs
FE	20538
FM	20212

NOMENCLATURE - bdf2017

Tous

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

NOMENCLATURE - tm106

Tous

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

2-digit

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

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

4-digit

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

4-digit

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

5-digit

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

ZEAT2

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

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

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

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

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

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

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

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

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)

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()

Alimentation - Tous

DECUC

% de la consommation

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

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

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

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

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

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

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

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

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

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

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

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

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

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())

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

CSPR

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

%

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

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

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

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

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

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

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

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

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

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

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

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

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

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)

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

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

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

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

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

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

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

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

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)

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

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

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

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 (045), Pains et Céréales (0111)

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

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

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

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

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

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 ?

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

Budget de famille 2017

Data - INSEE

François Geerolf

Info

Données sur l’inflation en France

LAST_COMPILE

Versions

Bibliographie

Info

MENAGES_IPC_CAT

MENAGES_IPC_CAT, MENAGES_IPC

REF_AREA

NOMENCLATURE - bdf2017

Tous

NOMENCLATURE - tm106

Tous

2-digit

3-digit

4-digit

4-digit

5-digit

ZEAT2

AGPR

CSPR

STRATE

TYPMEN

DECUC

Alimentation - Focus

Décile 1 vs Décile 10

Somme des poids

Par décile

Par âge

Par strate (type ville)

Alimentation - Tous

DECUC

% de la consommation

% du revenu

Alimentation

DECUC

% de la consommation

% du revenu

Pains et Céréales (0111), Pain (01112)

CSPR

AGPR

DECUC

STRATE

TYPMEN

ZEAT2

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

DECUC

% de la consommation

All

All

Remboursements de prêts immobiliers, Loyers

DECUC

% de la consommation

Remboursements de prêts immobiliers, Loyers

DECUC

% de la consommation

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

CSPR

AGPR

% de la consommation

DECUC

% de la consommation

% du revenu

STRATE

TYPMEN

ZEAT2

Communications (081), Services de téléphone (0813)

CSPR

AGPR

DECUC

STRATE

TYPMEN

ZEAT2

Pâtes

DECUC

% du revenu

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