Indice des prix à la consommation - Base 2015

Data - INSEE

Author

[1] "fr_CA.UTF-8"

Info

source	dataset	.html	.RData
insee	IPC-2015	2024-11-17	2024-11-17
insee	IPCH-2015	2024-11-17	2024-11-17

Données sur l’inflation en France

Title	source	dataset	.html	.RData
Budget de famille 2017	insee	bdf2017	2024-11-09	2023-11-21
Indices pour la révision d’un bail commercial ou professionnel	insee	ILC-ILAT-ICC	2024-11-09	2024-11-09
Indices des loyers - Base 2019	insee	INDICES_LOYERS	2024-11-09	2024-11-09
Indice des prix à la consommation - Base 1970, 1980	insee	IPC-1970-1980	2024-11-09	2024-11-09
Indices des prix à la consommation - Base 1990	insee	IPC-1990	2024-11-09	2024-11-09
Indice des prix à la consommation - Base 2015	insee	IPC-2015	2024-11-17	2024-11-17
Prix moyens de vente de détail	insee	IPC-PM-2015	2024-11-17	2024-11-17
Indices des prix à la consommation harmonisés	insee	IPCH-2015	2024-11-17	2024-11-17
Indice des prix dans la grande distribution	insee	IPGD-2015	2024-11-17	2024-11-17
Indices des prix des logements neufs et Indices Notaires-Insee des prix des logements anciens	insee	IPLA-IPLNA-2015	2024-11-09	2024-11-09
Indices de prix de production et d'importation dans l'industrie	insee	IPPI-2015	2024-11-17	2024-11-17
Indice pour la révision d’un loyer d’habitation	insee	IRL	2024-11-09	2024-11-09
Variation des loyers	insee	SERIES_LOYERS	2024-11-09	2024-11-09
Consommation effective des ménages par fonction	insee	T_CONSO_EFF_FONCTION	2024-11-09	2024-07-18

LAST_COMPILE

LAST_COMPILE
2024-11-17

LAST_UPDATE

Code

`IPC-2015` %>%
  group_by(LAST_UPDATE) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(LAST_UPDATE)) %>%
  print_table_conditional()

LAST_UPDATE	Nobs
2024-11-15	419533
2024-10-24	29112
2024-08-30	1502
2024-08-29	24
2024-08-13	3
2024-02-16	44989
2024-01-12	42839
2023-01-23	1158
2021-02-19	155
2021-01-15	837
2020-02-20	88
2020-01-30	825
2020-01-22	275
2020-01-15	2202
2019-02-28	66
2019-02-22	510
2019-02-21	22
2019-01-15	812
2017-01-12	378
2016-04-13	3648
2016-02-22	78
2016-02-18	456

Last

Code

`IPC-2015` %>%
  group_by(TIME_PERIOD, FREQ) %>%
  summarise(Nobs = n()) %>%
  group_by(FREQ) %>%
  filter(TIME_PERIOD == max(TIME_PERIOD)) %>%
  print_table_conditional()

TIME_PERIOD	FREQ	Nobs
2024	A	1420
2024-10	M	1059

Definitions

Moyenne annuelle: l’évolution en moyenne annuelle compare les prix d’une année donnée à ceux de l’année précédente.
Glissement annuel: l’évolution en glissement annuel compare les prix d’un seul mois d’une année donnée à ceux du même mois de l’année précédente.

Méthodo

Indice des prix à la consommation (base 100=1998) (pdf, fr, 44 Ko, 01/02/2013)
Indice des prix à la consommation des ménages du premier quintile de la distribution des niveaux de vie (pdf, fr, 119 Ko, 18/02/2013)
Indice des prix à la consommation (base 100=2015) (pdf, fr, 145 Ko, 29/01/2016)
Indice des prix à la consommation des ménages - base 100 en 2015 (pdf, fr, 955 Ko, 29/01/2016)
Pour comprendre l’indice des prix - Édition 1998 (pdf, fr, 1 Mo, 01/01/1999)
Indice des prix à la consommation des ménages - base 100 en 1998 (pdf, fr, 50 Ko, 01/01/1999)
Dossier d’information méthodologique. Indice des prix, pouvoir d’achat (pdf, fr, 229 Ko, 01/02/2004)

Documents de travail

L’IPC, miroir de l’évolution du coût de la vie en France ? - Ce qu’apporte l’analyse des courbes d’Engel. (pdf, fr, 615 Ko, 02/04/2010)
Calcul d’un indice des prix des produits de grande consommation dans la grande distribution (pdf, fr, 204 Ko, 01/01/2014)
Ce qui change pour l’IPC à partir du 29 janvier 2016 (pdf, fr, 129 Ko, 29/01/2016)
L’expérience française des indices de prix a la consommation (pdf, fr, 107 Ko, 01/01/1996) Indice des prix à la consommation CVS et indice hors tarifs publics et produits à prix volatils corrigé des mesures fiscales, CVS (pdf, fr, 71 Ko, 27/03/1996)
Indices mensuels des prix dans la grande distribution (pdf, fr, 126 Ko, 18/02/2016)
Contenu des groupes avec les fonctions, les regroupements et les groupes publiés dans la nouvelle base 1998 (pdf, fr, 42 Ko, 01/01/1999)
Comparaisons spatiales de prix au sein du territoire français (pdf, fr, 91 Ko, 01/12/2000)
Les indices à utilité constante : une référence pour mesurer l’évolution des prix (pdf, fr, 219 Ko, 01/12/2000)
La mesure des prix dans les domaines de la santé et de l’action sociale : quelques problèmes méthodologiques (pdf, fr, 212 Ko, 01/06/2003)
Indice des prix à la consommation en base 100 en 1998 - Séries longues rétropolées, de 1990 à 2002 (pdf, fr, 201 Ko, 01/09/2003)
Impact des ajustements de qualité dans le calcul de l’indice des prix à la consommation (pdf, fr, 53 Ko, 01/05/2004)
Introduction à la pratique des indices statistiques (pdf, fr, 426 Ko, 01/11/2005)
Note additionnelle sur les changements de l’année 2017 (pdf, fr, 228 Ko, 23/02/2017)

TEF 2020

Code

ig_b("insee", "TEF2020", "114-IPC-poids")

INDICATEUR

Code

`IPC-2015` %>%
  left_join(INDICATEUR,  by = "INDICATEUR") %>%
  group_by(INDICATEUR, Indicateur) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

INDICATEUR	Indicateur	Nobs
IPC	Indice des prix à la consommation (IPC)	544460
ISJ	Indice d'inflation sous-jacente (ISJ)	5052

PRIX_CONSO

Code

`IPC-2015` %>%
  left_join(PRIX_CONSO,  by = "PRIX_CONSO") %>%
  group_by(PRIX_CONSO, Prix_conso) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

CORRECTION

Code

`IPC-2015` %>%
  left_join(CORRECTION,  by = "CORRECTION") %>%
  group_by(CORRECTION, Correction) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

CORRECTION	Correction	Nobs
BRUT	Non corrigé	543194
CVS-FISC	Corrigé des mesures fiscales et des variations saisonnières	5052
CVS	Corrigé des variations saisonnières	1266

NATURE

Code

`IPC-2015` %>%
  left_join(NATURE,  by = "NATURE") %>%
  group_by(NATURE, Nature) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

NATURE	Nature	Nobs
INDICE	Indice	454629
POND	Pondérations d'indice	45859
VARIATIONS_M	Variations mensuelles	24275
GLISSEMENT_ANNUEL	Glissement annuel	23560
VARIATIONS_A	Variations annuelles	1189

FREQ

Code

`IPC-2015` %>%
  left_join(FREQ,  by = "FREQ") %>%
  group_by(FREQ, Freq) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

FREQ	Freq	Nobs
M	Monthly	458407
A	Annual	91105

MENAGES_IPC

Code

`IPC-2015` %>%
  left_join(MENAGES_IPC,  by = "MENAGES_IPC") %>%
  group_by(MENAGES_IPC, Menages_ipc) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

COICOP2016

All

Code

`IPC-2015` %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  group_by(COICOP2016, Coicop2016) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

2-digit

Code

`IPC-2015` %>%
  filter(nchar(COICOP2016) == 2) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  group_by(COICOP2016, Coicop2016) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

COICOP2016	Coicop2016	Nobs
SO	Sans objet	111763
00	00 - Ensemble	2805
01	01 - Produits alimentaires et boissons non alcoolisées	1461
02	02 - Boissons alcoolisées, tabac et stupéfiants	1461
03	03 - Articles d'habillement et chaussures	1461
04	04 - Logement, eau, gaz, électricité et autres combustibles	1461
05	05 - Meubles, articles de ménage et entretien courant du foyer	1461
06	06 - Santé	1461
07	07 - Transports	1461
08	08 - Communications	1461
09	09 - Loisirs et culture	1461
10	10 - Enseignement	1461
11	11 - Restaurants et hôtels	1461
12	12 - Biens et services divers	1461

3-digit

Code

`IPC-2015` %>%
  filter(nchar(COICOP2016) == 3) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  group_by(COICOP2016, Coicop2016) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

4-digit

Code

`IPC-2015` %>%
  filter(nchar(COICOP2016) == 4) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  group_by(COICOP2016, Coicop2016) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

5-digit

Code

`IPC-2015` %>%
  filter(nchar(COICOP2016) == 5) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  group_by(COICOP2016, Coicop2016) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

6-digit

Code

`IPC-2015` %>%
  filter(nchar(COICOP2016) == 6) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  group_by(COICOP2016, Coicop2016) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

TITLE_FR

Code

`IPC-2015` %>%
  group_by(TITLE_FR, IDBANK) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional

REF_AREA

Code

`IPC-2015` %>%
  group_by(REF_AREA) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional

REF_AREA	Nobs
FE	284530
FM	229642
D973	8418
D971	8396
D972	8396
D974	8396
D976	1734

TIME_PERIOD

Code

`IPC-2015` %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  print_table_conditional

France Entiere (FE), France Métropolitaine (FM)

Mensuel

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         PRIX_CONSO %in% c("SO"),
         FREQ == "M",
         COICOP2016 %in% c("00"),
         REF_AREA %in% c("FE", "FM"),
         NATURE == "INDICE") %>%
  month_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  arrange(desc(date)) %>%
  group_by(REF_AREA) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = REF_AREA)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.75, 0.3),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

Alimentation, loyers, chauffage, Ensemble

Hausse sur 2 ans

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         COICOP2016 %in% c("041", "00", "01", "045"),
         FREQ == "M",
         REF_AREA == "FM",
         NATURE == "INDICE") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  filter(date >= max(date) - years(2)) %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.28, 0.87),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  scale_y_log10(breaks = seq(100, 130, 2),
                labels = paste0(seq(0, 30, 2), "%")) +
  geom_text_repel(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

Par type de ménages

3 déciles

Code

`IPC-2015` %>%
  filter(MENAGES_IPC %in% c("D6-D7", "INF-D1", "D9-PLUS")) %>%
  left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
  year_to_date %>%
  group_by(MENAGES_IPC) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Menages_ipc)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

10 dixièmes

1998-

Code

`IPC-2015` %>%
  filter(MENAGES_IPC %in% c("INF-D1", "D1-D2", "D2-D3", "D3-D4", "D4-D5",
                            "D5-D6", "D6-D7", "D7-D8", "D8-D9", "D9-PLUS")) %>%
  left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
  year_to_date %>%
  group_by(MENAGES_IPC) %>%
  mutate(MENAGES_IPC = factor(MENAGES_IPC, levels = c("INF-D1", "D1-D2", "D2-D3", "D3-D4", "D4-D5",
                            "D5-D6", "D6-D7", "D7-D8", "D8-D9", "D9-PLUS"))) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = MENAGES_IPC)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.7),
        legend.title = element_blank()) +
  guides(color = guide_legend(ncol = 3)) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

2008-

Code

`IPC-2015` %>%
  filter(MENAGES_IPC %in% c("INF-D1", "D1-D2", "D2-D3", "D3-D4", "D4-D5",
                            "D5-D6", "D6-D7", "D7-D8", "D8-D9", "D9-PLUS")) %>%
  left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
  year_to_date %>%
  group_by(MENAGES_IPC) %>%
  mutate(MENAGES_IPC = factor(MENAGES_IPC,
                              levels = c("INF-D1", "D1-D2", "D2-D3", "D3-D4", "D4-D5",
                                         "D5-D6", "D6-D7", "D7-D8", "D8-D9", "D9-PLUS"))) %>%
  arrange(date) %>%
  filter(date >= as.Date("2008-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = MENAGES_IPC)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.7),
        legend.title = element_blank()) +
  guides(color = guide_legend(ncol = 3)) +
  scale_y_log10(breaks = seq(0, 200, 2),
                     labels = dollar_format(accuracy = 1, prefix = ""))

1er quintile, Tous

All

Code

`IPC-2015` %>%
  filter(MENAGES_IPC %in% c("PREMIERQUINTILE", "ENSEMBLE"),
         FREQ == "M",
         PRIX_CONSO == "4018",
         REF_AREA == "FE",
         COICOP2016 == "SO",
         NATURE == "INDICE") %>%
  left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
  month_to_date %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Menages_ipc)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

1998-

Code

`IPC-2015` %>%
  filter(MENAGES_IPC %in% c("PREMIERQUINTILE", "ENSEMBLE"),
         FREQ == "M",
         PRIX_CONSO == "4018",
         REF_AREA == "FE",
         COICOP2016 == "SO",
         NATURE == "INDICE") %>%
  left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
  month_to_date %>%
  filter(date >= as.Date("1998-01-01")) %>%
  group_by(MENAGES_IPC) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Menages_ipc)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

2012-

Code

`IPC-2015` %>%
  filter(MENAGES_IPC %in% c("PREMIERQUINTILE", "ENSEMBLE"),
         FREQ == "M",
         PRIX_CONSO == "4018",
         REF_AREA == "FE",
         COICOP2016 == "SO",
         NATURE == "INDICE") %>%
  left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
  month_to_date %>%
  filter(date >= as.Date("2012-01-01")) %>%
  group_by(MENAGES_IPC) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Menages_ipc)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

Glissement sur 2 ans

Code

`IPC-2015` %>%
  filter(MENAGES_IPC %in% c("PREMIERQUINTILE", "ENSEMBLE"),
         FREQ == "M",
         PRIX_CONSO == "4018",
         REF_AREA == "FE",
         COICOP2016 == "SO",
         NATURE == "INDICE") %>%
  left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
  month_to_date %>%
  filter(date >= as.Date("2021-09-01")) %>%
  group_by(MENAGES_IPC) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Menages_ipc)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.65, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

Cadres, Ouvriers, Retraités

Code

`IPC-2015` %>%
  filter(MENAGES_IPC %in% c("CADRE", "OUVRIER", "RETRAITE", "ACTIF")) %>%
  left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
  year_to_date %>%
  group_by(MENAGES_IPC) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Menages_ipc)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

Inflation - Tous

Table - COICOP2016

All

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         REF_AREA == "FE",
         NATURE == "INDICE",
         PRIX_CONSO == "SO",
         TIME_PERIOD %in% c("1990", "2000", "2010", "2020")) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  mutate(`% 1990-2019` = (100*((`2020`/`1990`)^(1/29)-1)) %>% round(., 2)) %>%
  print_table_conditional

2-digit

Javascript

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         REF_AREA == "FE",
         NATURE == "INDICE",
         PRIX_CONSO == "SO",
         TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
         nchar(COICOP2016) == 2) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  mutate(`% 1990-2019` = (100*((`2020`/`1990`)^(1/29)-1)) %>% round(., 2)) %>%
  print_table_conditional()

COICOP2016	Coicop2016	1990	2000	2010	2020	% 1990-2019
00	00 - Ensemble	67.4	79.9	94.71	104.73	1.53
01	01 - Produits alimentaires et boissons non alcoolisées	68.7	77.6	94.63	108.33	1.58
02	02 - Boissons alcoolisées, tabac et stupéfiants	34.9	55.2	83.63	126.07	4.53
03	03 - Articles d'habillement et chaussures	85.7	93.2	97.60	99.71	0.52
04	04 - Logement, eau, gaz, électricité et autres combustibles	53.6	66.9	88.46	105.24	2.35
05	05 - Meubles, articles de ménage et entretien courant du foyer	74.6	85.1	96.11	100.67	1.04
06	06 - Santé	92.9	101.9	104.31	96.67	0.14
07	07 - Transports	58.6	74.4	93.57	105.24	2.04
08	08 - Communications	158.4	142.1	124.87	91.96	-1.86
09	09 - Loisirs et culture	101.5	109.2	101.30	103.29	0.06
10	10 - Enseignement	55.3	70.0	92.09	107.42	2.32
11	11 - Restaurants et hôtels	53.3	69.9	89.63	108.04	2.47
12	12 - Biens et services divers	60.8	71.7	91.54	105.68	1.92

png

Code

i_g("bib/insee/IPC-2015_2digit.png")

3-digit

Javascript

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         REF_AREA == "FE",
         NATURE == "INDICE",
         PRIX_CONSO == "SO",
         TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
         nchar(COICOP2016) == 3) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  mutate(`% 1990-2019` = (100*((`2020`/`1990`)^(1/29)-1)) %>% round(., 2)) %>%
  print_table_conditional()

png

Code

i_g("bib/insee/IPC-2015_3digit-0.png")

Code

i_g("bib/insee/IPC-2015_3digit-1.png")

4-digit

Javascript

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         REF_AREA == "FE",
         NATURE == "INDICE",
         PRIX_CONSO == "SO",
         TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
         nchar(COICOP2016) == 4) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  mutate(`% 1990-2019` = (100*((`2020`/`1990`)^(1/29)-1)) %>% round(., 2)) %>%
  print_table_conditional()

png

Code

i_g("bib/insee/IPC-2015_4digit-0.png")

Code

i_g("bib/insee/IPC-2015_4digit-1.png")

Code

i_g("bib/insee/IPC-2015_4digit-2.png")

Code

i_g("bib/insee/IPC-2015_4digit-3.png")

5-digit

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         REF_AREA == "FE",
         NATURE == "INDICE",
         PRIX_CONSO == "SO",
         TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
         nchar(COICOP2016) == 5) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  mutate(`% 1990-2019` = (100*((`2020`/`1990`)^(1/29)-1)) %>% round(., 2)) %>%
  print_table_conditional()

6-digit

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         REF_AREA == "FE",
         NATURE == "INDICE",
         PRIX_CONSO == "SO",
         TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
         nchar(COICOP2016) == 6) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  mutate(`% 1990-2019` = (100*((`2020`/`1990`)^(1/29)-1)) %>% round(., 2)) %>%
  print_table_conditional()

Table - PRIX_CONSO

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         REF_AREA == "FE",
         NATURE == "INDICE",
         COICOP2016 == "SO",
         TIME_PERIOD %in% c("1990", "2000", "2010", "2020")) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  left_join(PRIX_CONSO,  by = "PRIX_CONSO") %>%
  select(PRIX_CONSO, Prix_conso, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  print_table_conditional

PRIX_CONSO	Prix_conso	1990	2000	2010	2020
4000	Alimentation	NA	NA	94.14	108.10
4001	Produits frais	NA	NA	91.27	126.16
4002	Autres produits alimentaires	NA	NA	94.57	105.27
4003	Produits manufacturés	NA	NA	101.42	97.95
4004	Habillement, chaussures	NA	NA	97.80	99.42
4005	Produits de santé	NA	NA	114.95	88.35
4006	Autres produits manufacturés	NA	NA	99.31	99.98
4007	Énergie	NA	NA	88.98	108.34
4008	Produits pétroliers	NA	NA	97.83	106.18
4009	Services	NA	NA	92.90	105.22
4010	Services : Loyers, eau et enlèvement des ordures ménagères	NA	NA	92.41	101.88
4011	Services : Services de santé	NA	NA	96.55	102.75
4012	Services : Transports et communications	NA	NA	106.69	98.90
4013	Autres services	NA	NA	89.91	107.75
4014	Alimentation, y compris tabac	NA	NA	92.35	111.91
4015	Produits manufacturés, y compris énergie	NA	NA	98.65	100.37
4016	Produits manufacturés, y compris services liés, hors habillement et chaussures	NA	NA	102.07	NA
4017	Ensemble hors énergie	NA	NA	95.24	104.42
4018	Ensemble hors tabac	NA	NA	95.06	103.98
4023	Ensemble hors tabac et alcool	NA	NA	95.16	103.94
4024	Ensemble non alimentaire, y compris tabac	NA	NA	94.82	104.09
4025	Ensemble hors produits frais	NA	NA	94.80	104.26
4026	Alimentation, y compris restaurants, cantines, cafés	NA	NA	92.97	107.88
4037	Biens durables	NA	NA	102.00	99.95
4038	Produits manufacturés, hors habillement, biens durables et produits de santé	NA	NA	96.65	100.09
4566	Services : Transports, communications, hôtellerie	NA	NA	95.54	104.09
4600	Ensemble hors loyers et hors tabac	NA	NA	95.12	104.18
5272	Services : Transports	60.7	76.6	93.18	100.27
5273	Services : Communications	144.8	131.4	121.85	97.39
5329	Produits manufacturés, hors habillement et chaussures	NA	NA	NA	97.54

Inflation par catégorie

Tabac, Loyers, Ensemble, Carburants

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "045", "022", "0722"),
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M",
         is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
         is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
  filter(date >= max(date) - years(2)) %>%
  select(date, OBS_VALUE, Coicop2016) %>%
  na.omit %>%
  ggplot() + ylab("Inflation sur un an (IPC, IPCH)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.72, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 5),
                     labels = percent_format(accuracy = 1, prefix = "")) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3)

Tabac, Loyers, Ensemble

Glissement 1 an

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "01", "022"),
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M",
         is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
         is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
  filter(date >= max(date) - years(2)) %>%
  select(date, OBS_VALUE, Coicop2016) %>%
  na.omit %>%
  ggplot() + ylab("Inflation sur un an (IPC, IPCH)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 1),
                     labels = percent_format(accuracy = 1, prefix = "")) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3)

Glissement 2 ans

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "01", "022"),
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M",
         is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
         is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 24)-1) %>%
  filter(date >= max(date) - years(2)) %>%
  select(date, OBS_VALUE, Coicop2016) %>%
  na.omit %>%
  ggplot() + ylab("Glissement sur 2 ans (IPC, IPCH)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 1),
                     labels = percent_format(accuracy = 1, prefix = "")) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3)

Alimentation, Loyers, Ensemble

Glissement 1 an

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "01", "041"),
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M",
         is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
         is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
  filter(date >= max(date) - years(2)) %>%
  select(date, OBS_VALUE, Coicop2016) %>%
  na.omit %>%
  ggplot() + ylab("Glissement sur 1 an (IPC, IPCH)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 1),
                     labels = percent_format(accuracy = 1, prefix = "")) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3)

Glissement 2 ans

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "01", "041"),
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M",
         is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
         is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 24)-1) %>%
  filter(date >= max(date) - years(2)) %>%
  select(date, OBS_VALUE, Coicop2016) %>%
  na.omit %>%
  ggplot() + ylab("Glissement sur 2 ans (IPC, IPCH)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 1),
                     labels = percent_format(accuracy = 1, prefix = "")) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3)

Chauffage du logement, Loyers, Ensemble

Glissement 1 an

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "041", "045"),
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M",
         is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
         is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
  filter(date >= max(date) - years(2)) %>%
  select(date, OBS_VALUE, Coicop2016) %>%
  na.omit %>%
  ggplot() + ylab("Inflation sur un an (IPC, IPCH)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.4, 0.45),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 2),
                     labels = percent_format(accuracy = 1, prefix = "")) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3)

Glissement 2 ans

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "041", "045"),
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M",
         is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
         is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 24)-1) %>%
  filter(date >= max(date) - years(2)) %>%
  select(date, OBS_VALUE, Coicop2016) %>%
  na.omit %>%
  ggplot() + ylab("Glissement sur 2 ans (IPC, IPCH)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.4, 0.45),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 2),
                     labels = percent_format(accuracy = 1, prefix = "")) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3)

Pondérations d’indice

Table - COICOP2016

All

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         REF_AREA == "FE",
         NATURE == "POND",
         PRIX_CONSO == "SO",
         CORRECTION == "BRUT",
         TIME_PERIOD %in% c("1990", "2000", "2010", "2020")) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  print_table_conditional

2-digit

Javascript

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         REF_AREA == "FE",
         NATURE == "POND",
         PRIX_CONSO == "SO",
         TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
         nchar(COICOP2016) == 2) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  print_table_conditional

COICOP2016	Coicop2016	1990	2000	2010	2020
00	00 - Ensemble	10000	10000	10000	10000
01	01 - Produits alimentaires et boissons non alcoolisées	1999	1558	1474	1423
02	02 - Boissons alcoolisées, tabac et stupéfiants	378	382	329	392
03	03 - Articles d'habillement et chaussures	844	552	487	394
04	04 - Logement, eau, gaz, électricité et autres combustibles	1233	1364	1348	1399
05	05 - Meubles, articles de ménage et entretien courant du foyer	736	644	617	495
06	06 - Santé	774	896	1005	1050
07	07 - Transports	1659	1669	1634	1581
08	08 - Communications	188	254	303	248
09	09 - Loisirs et culture	851	859	916	854
10	10 - Enseignement	33	23	25	5
11	11 - Restaurants et hôtels	818	805	685	810
12	12 - Biens et services divers	487	994	1177	1349

png

Code

i_g("bib/insee/IPC-2015_2digit_weights.png")

3-digit

Javascript

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         REF_AREA == "FE",
         NATURE == "POND",
         PRIX_CONSO == "SO",
         CORRECTION == "BRUT",
         TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
         nchar(COICOP2016) == 3) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  print_table_conditional

png

Code

i_g("bib/insee/IPC-2015_3digit_weights-0.png")

Code

i_g("bib/insee/IPC-2015_3digit_weights-1.png")

4-digit

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         REF_AREA == "FE",
         NATURE == "POND",
         PRIX_CONSO == "SO",
         CORRECTION == "BRUT",
         TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
         nchar(COICOP2016) == 4) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  print_table_conditional

5-digit

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         REF_AREA == "FE",
         NATURE == "POND",
         PRIX_CONSO == "SO",
         CORRECTION == "BRUT",
         TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
         nchar(COICOP2016) == 5) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  print_table_conditional

Table - PRIX_CONSO

1990, 2000, 2010, 2020

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         REF_AREA == "FE",
         NATURE == "POND",
         CORRECTION == "BRUT",
         COICOP2016 == "SO",
         TIME_PERIOD %in% c("1990", "2000", "2010", "2020")) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  left_join(PRIX_CONSO,  by = "PRIX_CONSO") %>%
  select(PRIX_CONSO, Prix_conso, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  print_table_conditional

2016, 2017, 2018, 2019, 2020

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         REF_AREA == "FE",
         NATURE == "POND",
         CORRECTION == "BRUT",
         COICOP2016 == "SO",
         TIME_PERIOD %in% c("2016", "2017", "2018", "2019", "2020")) %>%
  left_join(COICOP2016,  by = "COICOP2016") %>%
  left_join(PRIX_CONSO,  by = "PRIX_CONSO") %>%
  select(PRIX_CONSO, Prix_conso, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  print_table_conditional()

PRIX_CONSO	Prix_conso	2016	2017	2018	2019	2020
4000	Alimentation	1615	1627	1627	1619	1610
4001	Produits frais	217	235	243	244	230
4002	Autres produits alimentaires	1398	1392	1384	1375	1380
4003	Produits manufacturés	2651	2617	2594	2556	2491
4004	Habillement, chaussures	414	433	416	400	380
4005	Produits de santé	466	433	425	416	412
4006	Autres produits manufacturés	1771	1751	1753	1740	1699
4007	Énergie	773	748	777	804	808
4008	Produits pétroliers	419	378	408	425	439
4009	Services	4766	4820	4809	4830	4886
4010	Services : Loyers, eau et enlèvement des ordures ménagères	768	779	764	746	752
4011	Services : Services de santé	598	600	617	604	604
4012	Services : Transports et communications	524	524	505	504	515
4013	Autres services	2876	2917	2923	2976	3015
4014	Alimentation, y compris tabac	1810	1815	1820	1810	1815
4015	Produits manufacturés, y compris énergie	3434	3375	3382	3371	3311
4016	Produits manufacturés, y compris services liés, hors habillement et chaussures	2247	2194	2189	2167	2123
4017	Ensemble hors énergie	9227	9252	9223	9196	9192
4018	Ensemble hors tabac	9805	9812	9807	9809	9795
4024	Ensemble non alimentaire, y compris tabac	8385	8373	8373	8381	8390
4025	Ensemble hors produits frais	9798	9785	9776	9777	9790
4026	Alimentation, y compris restaurants, cantines, cafés	2185	2214	2229	2238	2239
4034	Tabac	195	188	193	191	205
4037	Biens durables	745	739	753	755	730
4038	Produits manufacturés, hors habillement, biens durables et produits de santé	1033	1019	1005	990	975
4566	Services : Transports, communications, hôtellerie	2304	2289	2359	2403	2440
4600	Ensemble hors loyers et hors tabac	9183	9183	9192	9208	9185
5272	Services : Transports	279	282	282	285	300
5273	Services : Communications	245	242	223	219	215
5329	Produits manufacturés, hors habillement et chaussures	2237	2184	2178	2156	2111

Alimentation, Produits alimentaires

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("01", "011"),
         REF_AREA == "FE",
         NATURE == "POND") %>%
  year_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  mutate(OBS_VALUE = OBS_VALUE/10000) %>%
  ggplot() + ylab("Poids de l'alimentation dans l'indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.65, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 40, 0.5),
                     labels = percent_format(accuracy = .1))

Tabac

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("022"),
         REF_AREA == "FE",
         NATURE == "POND") %>%
  year_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  mutate(OBS_VALUE = OBS_VALUE/10000) %>%
  ggplot() + ylab("Poids du tabac dans l'indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 10, 0.1),
                     labels = percent_format(accuracy = .1))

1253 - Assurance Santé Complémentaire

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("1253"),
         REF_AREA == "FE",
         NATURE == "POND") %>%
  year_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  mutate(OBS_VALUE = OBS_VALUE/10000) %>%
  ggplot() + ylab("Poids de l'assurance santé dans l'indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 10, 0.1),
                     labels = percent_format(accuracy = .1))

Santé

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("06"),
         REF_AREA == "FE",
         NATURE == "POND") %>%
  year_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  mutate(OBS_VALUE = OBS_VALUE/10000) %>%
  ggplot() + ylab("Poids de la santé dans l'indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 20, 0.5),
                     labels = percent_format(accuracy = .1))

Santé, Assurance santé complémentaire

All

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("06", "1253"),
         REF_AREA == "FE",
         NATURE == "POND") %>%
  year_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  mutate(OBS_VALUE = OBS_VALUE/10000) %>%
  ggplot() + ylab("Poids de la santé dans l'indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  scale_color_manual(values = viridis(2)[1:2]) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 20, 0.5),
                     labels = percent_format(accuracy = .1))

1996-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("06", "1253"),
         REF_AREA == "FE",
         NATURE == "POND") %>%
  year_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  mutate(OBS_VALUE = OBS_VALUE/10000) %>%
  ggplot() + ylab("Poids de la santé dans l'indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  scale_color_manual(values = viridis(2)[1:2]) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 20, 0.5),
                     labels = percent_format(accuracy = .1))

Santé

Santé, Assurance Santé vs. Ensemble

1990-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         PRIX_CONSO == "SO",
         COICOP2016 %in% c("06", "1253", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Coicop2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = c(100, 164, 200, 400, 816, seq(100, 180, 10)),
                     labels = dollar_format(accuracy = 1, prefix = ""))

1996-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         PRIX_CONSO == "SO",
         COICOP2016 %in% c("06", "1253", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Coicop2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix, IPC") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = c(100, 164, 200, 400, 816, seq(100, 180, 10)),
                     labels = dollar_format(accuracy = 1, prefix = ""))

Péages

Péages vs. Ensemble

1990-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         PRIX_CONSO == "SO",
         COICOP2016 %in% c("07242", "0724", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Coicop2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(100, 400, 10),
                     labels = dollar_format(accuracy = 1, prefix = ""))

1996-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         PRIX_CONSO == "SO",
         COICOP2016 %in% c("07242", "0724", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(Coicop2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(100, 400, 10),
                     labels = dollar_format(accuracy = 1, prefix = ""))

Pondération des péages, services divers dans l’IPC

1990-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("07242", "0724"),
         REF_AREA == "FE",
         NATURE == "POND") %>%
  year_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  mutate(OBS_VALUE = OBS_VALUE/10000) %>%
  ggplot() + ylab("Poids des péages dans l'indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank()) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 10, 0.1),
                     labels = percent_format(accuracy = .1),
                     limits = c(0, 0.016))

1996-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("07242", "0724"),
         REF_AREA == "FE",
         NATURE == "POND") %>%
  year_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  mutate(OBS_VALUE = OBS_VALUE/10000) %>%
  filter(date >= as.Date("1996-01-01")) %>%
  ggplot() + ylab("Poids des péages dans l'indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank()) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 10, 0.1),
                     labels = percent_format(accuracy = .1),
                     limits = c(0, 0.016))

Services à domicile

Services à domicile vs. Ensemble

1998-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         PRIX_CONSO == "SO",
         COICOP2016 %in% c("05621", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  filter(date >= as.Date("1998-01-01")) %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Coicop2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(100, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = ""))

Tabac

Tabac vs. Ensemble

1990-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         PRIX_CONSO == "SO",
         COICOP2016 %in% c("022", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  arrange(date) %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Coicop2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = c(100, 164, 200, 400, 600, 800, 1000),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

1992-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("022", "00"),
         FREQ == "M",
         PRIX_CONSO == "SO",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  filter(date >= as.Date("1992-01-01")) %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Coicop2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = c(100, 164, 200, 400, 816),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

1996-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("022", "00"),
         FREQ == "M",
         PRIX_CONSO == "SO",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Coicop2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(100, 1000, 100),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

2000-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("022", "00"),
         FREQ == "M",
         PRIX_CONSO == "SO",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Coicop2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = c(seq(0, 100, 20), seq(0, 1000, 50)),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

2012-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("022", "00"),
         FREQ == "M",
         PRIX_CONSO == "SO",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  filter(date >= as.Date("2012-01-01")) %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Coicop2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = c(seq(0, 100, 10), seq(0, 1000, 10)),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

Ensemble avec Tabac (IdBank: 001763852, 001759970)

1990-

Code

`IPC-2015` %>%
  filter(IDBANK %in% c("001759970", "001763852")) %>%
  month_to_date %>%
  arrange(date) %>%
  filter(date >= as.Date("1990-01-01")) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  group_by(Prix_conso) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.75, 0.3),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

1992-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         PRIX_CONSO %in% c("4035", "4018", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  filter(date >= as.Date("1992-01-01")) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  group_by(Prix_conso) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.75, 0.3),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

1996-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         PRIX_CONSO %in% c("4035", "4018", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  group_by(Prix_conso) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.75, 0.3),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

2000-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         PRIX_CONSO %in% c("4035", "4018", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  group_by(Prix_conso) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.75, 0.3),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

2012-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         PRIX_CONSO %in% c("4035", "4018", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  filter(date >= as.Date("2012-01-01")) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  group_by(Prix_conso) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

2017-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         PRIX_CONSO %in% c("4035", "4018", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  group_by(Prix_conso) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

Glissement sur 3 ans

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         PRIX_CONSO %in% c("4035", "4018"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  month_to_date %>%
  filter(date >= max(date) - years(3)) %>%
  group_by(Prix_conso) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.65, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

Glissement sur 2 ans

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         PRIX_CONSO %in% c("4035", "4018", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  month_to_date %>%
  filter(date >= max(date) - years(2)) %>%
  group_by(Prix_conso) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.65, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

Glissement sur 1 an

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         PRIX_CONSO %in% c("4035", "4018", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  month_to_date %>%
  filter(date >= max(date) - years(1)) %>%
  group_by(Prix_conso) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.65, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

Immobilier

4003, 4009, 4034

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         PRIX_CONSO %in% c("4003", "4009", "4034"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso, linetype = Prix_conso)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.75, 0.3),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = ""))

041, 043, 044

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("041", "043", "044"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE",
         #OBS_STATUS == "A"
         ) %>%
  month_to_date %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.3),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = ""))

041 Loyers effectifs, 00 Ensemble

1990-

All

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("041", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Coicop2016, Prix_conso) %>%
  filter(date >= as.Date("1990-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1990-01-01")]) %>%
  mutate(Variable = paste0(Coicop2016, " - ", Prix_conso),
         Variable = gsub(" - Sans objet", "", Variable)) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Variable)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

Loyers, Loyers Réels, IPC

Code

`IPC-2015-2020` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("041", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE",
         #OBS_STATUS == "A"
         ) %>%
  #select(-OBS_VALUE, - TIME_PERIOD) %>%
  #distinct
  month_to_date %>%
  select(date, COICOP2016, OBS_VALUE) %>%
  spread(COICOP2016, OBS_VALUE) %>%
  mutate(`real_rents` = `041`/`00`) %>%
  gather(COICOP2016, OBS_VALUE, -date) %>%
  left_join(tibble(COICOP2016 = c("041", "00", "real_rents"),
                   Coicop2016 = c("Loyers", "IPC", "Loyers Réels")), 
            by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1990-01-01")]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = ""))

1992-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("041", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Coicop2016, Prix_conso) %>%
  filter(date >= as.Date("1992-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1992-01-01")]) %>%
  mutate(Variable = paste0(Coicop2016, " - ", Prix_conso),
         Variable = gsub(" - Sans objet", "", Variable)) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Variable)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

1992- (fictifs)

Blog Loyers. Lien

Code

`IPC-2015-2020` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("041", "00", "4035"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE",
         #OBS_STATUS == "A"
         ) %>%
  #select(-OBS_VALUE, - TIME_PERIOD) %>%
  #distinct
  month_to_date %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Coicop2016) %>%
  filter(date >= as.Date("1990-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1992-01-01")]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2022, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.75, 0.3),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = ""))

1990-

Blog Loyers. [pdf] [html]

Code

`IPC-2015-2020` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("041", "00", "4035"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE",
         #OBS_STATUS == "A"
         ) %>%
  #select(-OBS_VALUE, - TIME_PERIOD) %>%
  #distinct
  month_to_date %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Coicop2016) %>%
  filter(date >= as.Date("1990-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1990-01-01")]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_x_date(breaks = seq(1920, 2022, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.75, 0.3),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = ""))

1996-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("041", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Coicop2016, Prix_conso) %>%
  filter(date >= as.Date("1996-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
  mutate(Variable = paste0(Coicop2016, " - ", Prix_conso),
         Variable = gsub(" - Sans objet", "", Variable)) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Variable)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

1990-1999

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("041", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Coicop2016, Prix_conso) %>%
  filter(date >= as.Date("1990-01-01"),
         date <= as.Date("1999-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1990-01-01")]) %>%
  mutate(Variable = paste0(Coicop2016, " - ", Prix_conso),
         Variable = gsub(" - Sans objet", "", Variable)) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Variable)) +
  
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

1999-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("041", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Coicop2016, Prix_conso) %>%
  filter(date >= as.Date("1999-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1999-01-01")]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = paste0(Coicop2016, " - ", Prix_conso))) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

2000-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("041", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Coicop2016, Prix_conso) %>%
  filter(date >= as.Date("2000-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2000-01-01")]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = paste0(Coicop2016, " - ", Prix_conso))) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

Glissement sur 2 ans

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "ENSEMBLE",
         COICOP2016 %in% c("041", "00"),
         FREQ == "M",
         REF_AREA == "FE",
         NATURE == "INDICE") %>%
  month_to_date %>%
  filter(date >= max(date) - years(2)) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = paste0(Coicop2016, " - ", Prix_conso))) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.28, 0.87),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  scale_y_log10(breaks = seq(0, 200, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

Insee calcule y compris loyers fictifs

1990-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         REF_AREA == "FE",
         PRIX_CONSO %in% c("00", "4035"),
         #PRIX_CONSO %in% c("00"),
         NATURE == "INDICE",
         FREQ == "M") %>%
  month_to_date %>%
  group_by(PRIX_CONSO) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("1990-01-01")]) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.75, 0.3),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

1992-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         REF_AREA == "FE",
         PRIX_CONSO %in% c("00", "4035"),
         #PRIX_CONSO %in% c("00"),
         NATURE == "INDICE",
         FREQ == "M") %>%
  month_to_date %>%
  group_by(PRIX_CONSO) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("1992-01-01")]) %>%
  filter(date >= as.Date("1992-01-01")) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.75, 0.3),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

1995-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         REF_AREA == "FE",
         PRIX_CONSO %in% c("00", "4035"),
         #PRIX_CONSO %in% c("00"),
         NATURE == "INDICE",
         FREQ == "M") %>%
  month_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  group_by(PRIX_CONSO) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("1995-01-01")]) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.75, 0.3),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

2008-

Code

`IPC-2015` %>%
  filter(INDICATEUR == "IPC",
         REF_AREA == "FE",
         PRIX_CONSO %in% c("00", "4035"),
         #PRIX_CONSO %in% c("00"),
         NATURE == "INDICE",
         FREQ == "M") %>%
  month_to_date %>%
  group_by(PRIX_CONSO) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("2008-01-01")]) %>%
  filter(date >= as.Date("2008-01-01")) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.75, 0.3),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)

Cantines

1990-

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "1112"),
         REF_AREA == "FM",
         FREQ == "M") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  filter(date >= as.Date("1990-01-01")) %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

2000-

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "1112"),
         REF_AREA == "FM",
         FREQ == "M") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  filter(date >= as.Date("2000-01-01")) %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

2010-

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "1112"),
         REF_AREA == "FM",
         FREQ == "M") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  filter(date >= as.Date("2010-01-01")) %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

Scolaire, Universitaire VS Entreprise ou Admin.

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("111202", "111201"),
         NATURE == "INDICE") %>%
  year_to_date %>%
  arrange(desc(date)) %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

Assurance santé

1990-

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "12532", "1253"),
         REF_AREA == "FM",
         FREQ == "M") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  filter(date >= as.Date("1990-01-01")) %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

2000-

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "12532", "1253"),
         REF_AREA == "FM",
         FREQ == "M") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  filter(date >= as.Date("2000-01-01")) %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

2010-

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "12532"),
         REF_AREA == "FM",
         FREQ == "M") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  filter(date >= as.Date("2010-01-01")) %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

2-digit

Boissons Alcoolisées, Logement, Restaurants et hôtels

All

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "02", "11", "04"),
         REF_AREA == "FM",
         FREQ == "M") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = c(100, 120, 150, 200, 220, 250, 300, 400, 500),
                     labels = dollar_format(accuracy = 1, prefix = ""))

Glissement sur 2 ans

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "02", "11", "04"),
         REF_AREA == "FM",
         FREQ == "M") %>%
  month_to_date %>%
  filter(date >= max(date) - years(2)) %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.28, 0.87),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

Transports, Enseignement, B&S Divers

Tous

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "10", "07", "12"),
         REF_AREA == "FM",
         FREQ == "M") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(100, 300, 10),
                     labels = dollar_format(accuracy = 1, prefix = ""))

Glissement sur 2 ans

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "10", "07", "12"),
         REF_AREA == "FM",
         FREQ == "M") %>%
  month_to_date %>%
  filter(date >= max(date) - years(2)) %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.18, 0.87),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  scale_y_log10(breaks = seq(0, 200, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

Alimentation, Habillement, Meubles

Tous

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "05", "01", "03"),
         REF_AREA == "FM",
         FREQ == "M") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(100, 300, 10),
                     labels = dollar_format(accuracy = 1, prefix = ""))

Glissement sur 2 ans

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "05", "01", "03"),
         REF_AREA == "FM",
         FREQ == "M") %>%
  month_to_date %>%
  filter(date >= max(date) - years(2)) %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.28, 0.87),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  scale_y_log10(breaks = seq(0, 200, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

Santé, Communications, Loisirs

Tous

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "06", "09", "08"),
         REF_AREA == "FM",
         FREQ == "M") %>%
  month_to_date %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 10),
                     labels = dollar_format(accuracy = 1, prefix = ""))

1996-

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "06", "09", "08"),
         REF_AREA == "FM",
         FREQ == "M") %>%
  month_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 10),
                     labels = dollar_format(accuracy = 1, prefix = ""))

2000-

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "06", "09", "08"),
         REF_AREA == "FM",
         FREQ == "M") %>%
  month_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.18, 0.22),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 10),
                     labels = dollar_format(accuracy = 1, prefix = ""))

Glissement sur 2 ans

Code

`IPC-2015` %>%
  filter(COICOP2016 %in% c("00", "06", "09", "08"),
         REF_AREA == "FM",
         FREQ == "M") %>%
  month_to_date %>%
  filter(date >= max(date) - years(2)) %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(COICOP2016) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "1 month",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.28, 0.87),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  scale_y_log10(breaks = seq(0, 200, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

Comparer l’IPC et le déflateur de la consommation

Revenu primaire au RDB

Code

ig_b("insee", "FPS2021", "revenu-primaire-RDB")

Déflateur consommation finale des ménages

Code

ig_b("insee", "FPS2021", "depense-consommation-finale-menages")