Prix moyens de vente de détail

Données - INSEE

Auteur

François Geerolf

Info

Last observation: 2026-06

First observation: 1992

Number of observations: 43 472

Last data update: 23 jul 2026, 22:50. Last compile: 24 jul 2026, 06:06

Structure

Données sur l’inflation en France

source	dataset	Title	.html	.rData
insee	ILC-ILAT-ICC	Indices pour la révision d’un bail commercial ou professionnel	2026-07-23	2026-07-23
insee	INDICES_LOYERS	Indices des loyers d'habitation (ILH)	2026-07-23	2026-07-23
insee	IPC-1970-1980	Indice des prix à la consommation - Base 1970, 1980	2026-07-23	2026-07-23
insee	IPC-1990	Indices des prix à la consommation - Base 1990	2026-07-23	2026-07-23
insee	IPC-2015	Indice des prix à la consommation - Base 2015	2026-07-23	2026-07-23
insee	IPC-PM-2015	Prix moyens de vente de détail	2026-07-23	2026-07-23
insee	IPCH-2015	Indices des prix à la consommation harmonisés	2026-07-23	2026-07-23
insee	IPCH-IPC-2015-ensemble	Indices des prix à la consommation harmonisés	2026-07-23	2026-07-23
insee	IPGD-2015	Indice des prix dans la grande distribution	2026-07-23	2026-07-23
insee	IPLA-IPLNA-2015	Indices des prix des logements neufs et Indices Notaires-Insee des prix des logements anciens	2026-07-23	2026-07-23
insee	IPPI-2015	Indices de prix de production et d'importation dans l'industrie	2026-07-23	2026-07-23
insee	IRL	Indice pour la révision d’un loyer d’habitation	2026-07-23	2026-07-23
insee	SERIES_LOYERS	Variation des loyers	2026-07-23	2026-07-23
insee	T_CONSO_EFF_FONCTION	Consommation effective des ménages par fonction	2026-07-23	2025-12-22
insee	bdf2017	Budget de famille 2017	2026-07-23	2023-11-21
insee	echantillon-agglomerations-IPC-2024	Échantillon d’agglomérations enquêtées de l’IPC en 2024	2026-07-23	2026-01-27
insee	echantillon-agglomerations-IPC-2025	Échantillon d’agglomérations enquêtées de l’IPC en 2025	2026-07-23	2026-01-27
insee	liste-varietes-IPC-2024	Liste des variétés pour la mesure de l'IPC en 2024	2026-07-23	2025-04-02
insee	liste-varietes-IPC-2025	Liste des variétés pour la mesure de l'IPC en 2025	2026-07-23	2026-01-27
insee	ponderations-elementaires-IPC-2024	Pondérations élémentaires 2024 intervenant dans le calcul de l’IPC	2026-07-23	2025-04-02
insee	ponderations-elementaires-IPC-2025	Pondérations élémentaires 2025 intervenant dans le calcul de l’IPC	2026-07-23	2026-01-27
insee	table_conso_moyenne_par_categorie_menages	Montants de consommation selon différentes catégories de ménages	2026-07-23	2026-01-27
insee	table_poste_au_sein_sous_classe_ecoicopv2_france_entiere_	Ventilation de chaque sous-classe (niveau 4 de la COICOP v2) en postes et leurs pondérations	2026-07-23	2026-01-27
insee	tranches_unitesurbaines	Poids de chaque tranche d’unités urbaines dans la consommation	2026-07-23	2026-01-27

Data on inflation

source	dataset	Title	.html	.rData
bis	CPI	Consumer Price Index	2026-07-22	2026-07-22
ecb	CES	Consumer Expectations Survey	2026-07-23	2026-07-19
eurostat	nama_10_co3_p3	Final consumption expenditure of households by consumption purpose (COICOP 3 digit)	2026-07-18	2026-07-23
eurostat	prc_hicp_cow	HICP - country weights	2026-07-23	2026-07-23
eurostat	prc_hicp_ctrb	Contributions to euro area annual inflation (in percentage points)	2026-07-23	2026-07-23
eurostat	prc_hicp_inw	HICP - item weights	2026-07-23	2026-07-23
eurostat	prc_hicp_manr	HICP (2015 = 100) - monthly data (annual rate of change)	2026-07-23	2026-07-23
eurostat	prc_hicp_midx	HICP (2015 = 100) - monthly data (index)	2026-07-23	2026-07-23
eurostat	prc_hicp_mmor	HICP (2015 = 100) - monthly data (monthly rate of change)	2026-07-23	2026-07-23
eurostat	prc_ppp_ind	Purchasing power parities (PPPs), price level indices and real expenditures for ESA 2010 aggregates	2026-07-22	2026-07-23
eurostat	sts_inpp_m	Producer prices in industry, total - monthly data	2026-07-21	2026-07-23
eurostat	sts_inppd_m	Producer prices in industry, domestic market - monthly data	2026-07-21	2026-07-23
eurostat	sts_inppnd_m	Producer prices in industry, non domestic market - monthly data	2026-07-21	2026-07-23
fred	cpi	Consumer Price Index	2026-07-22	2026-07-22
fred	inflation	Inflation	2026-07-22	2026-07-22
imf	CPI	Consumer Price Index (CPI) 2026 February - CPI_2026_FEB_VINTAGE	2026-07-22	2026-04-13
oecd	MEI_PRICES_PPI	Producer Prices - MEI_PRICES_PPI	2026-07-23	2024-04-15
oecd	PPP2017	2017 PPP Benchmark results	2024-04-16	2023-07-25
oecd	PRICES_CPI	Consumer price indices (CPIs)	2024-04-16	2024-04-15
wdi	FP.CPI.TOTL.ZG	Inflation, consumer prices (annual %)	2026-07-22	2026-07-22
wdi	NY.GDP.DEFL.KD.ZG	Inflation, GDP deflator (annual %)	2026-07-22	2026-07-22

LAST_COMPILE

LAST_COMPILE
2026-07-24

Last

Code

`IPC-PM-2015` %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  head(1) %>%
  print_table_conditional()

TIME_PERIOD	Nobs
2026-06	62

Prix Baguette

Tous

Indice

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("1223", "1227"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  ggplot + theme_minimal() + xlab("") + ylab("Prix en €") +
  geom_line(aes(x = date, y = OBS_VALUE/4, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 3, 0.1),
                     labels = dollar_format(accuracy = .1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.7, 0.15),
        legend.title = element_blank())

Glissement Annuel

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("1223", "1227"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  group_by(Prix_conso) %>%
  arrange(date) %>%
  mutate(inflation = OBS_VALUE/lag(OBS_VALUE,12)-1) %>%
  ggplot + theme_minimal() + xlab("") + ylab("Glissement annuel") +
  geom_line(aes(x = date, y = inflation, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 30, 1),
                     labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

Immobilier, Pain Baguette

Code

t_5203_extract <- t_5203 %>%
  year_to_date2 %>%
  filter(sector %in% c("A10.LZ", "TOTAL")) %>%
  filter(date >= as.Date("1992-01-01")) %>%
  group_by(sector) %>%
  mutate(value = 100*value/value[date == as.Date("1992-01-01")]) %>%
  select(date, Variable = Sector, value)


data_extract <- `IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("1223"),
         FREQ == "M") %>%
  left_join(PRIX_CONSO,  by = "PRIX_CONSO") %>%
  month_to_date() %>%
  mutate(Variable = "Prix de la baguette") %>%
  select(date, Variable, value = OBS_VALUE) %>%
  mutate(value = 100*value/value[date == as.Date("1992-01-01")])


croissant <- `IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("1241"),
         FREQ == "M") %>%
  left_join(PRIX_CONSO,  by = "PRIX_CONSO") %>%
  month_to_date() %>%
  mutate(Variable = "Prix du croissant") %>%
  select(date, Variable, value = OBS_VALUE) %>%
  mutate(value = 100*value/value[date == as.Date("1992-01-01")])

t_5203_extract %>%
  bind_rows(data_extract) %>%
  bind_rows(croissant) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = value, color = Variable)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1950, 2022, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  
  scale_y_log10(breaks = seq(0, 200, 10))

Gaz butane comprimé

Valeur

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("1873"),
         FREQ == "M") %>%
  mutate(OBS_VALUE = ifelse(PRIX_CONSO == "3790", OBS_VALUE/1000, OBS_VALUE)) %>%
  
  month_to_date() %>%
  mutate(Prix_conso = gsub("Non alimentaire : ", "", Prix_conso)) %>%
  mutate(Prix_conso = gsub(": 1.000 litres \\(livré à domicile\\)", 
                           "\\(1 litre, livré à domicile\\)", Prix_conso)) %>%
  ggplot + theme_minimal() + xlab("") + ylab("Gaz butane comprimé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1992, 2022, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 40, 2),
                     labels = dollar_format(accuracy = 1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

Base 100

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("1873", "3863", "3860"),
         FREQ == "M") %>%
  mutate(OBS_VALUE = ifelse(PRIX_CONSO == "3790", OBS_VALUE/1000, OBS_VALUE)) %>%
  
  month_to_date() %>%
  mutate(Prix_conso = gsub("Non alimentaire : ", "", Prix_conso)) %>%
  mutate(Prix_conso = gsub(": 1.000 litres \\(livré à domicile\\)", 
                           "\\(1 litre, livré à domicile\\)", Prix_conso)) %>%
  group_by(PRIX_CONSO) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1992, 2022, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 850, 20)) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

Essence, Fioul, Gazole

Tous

Value - Linear

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("3860", "3790", "3863"),
         FREQ == "M") %>%
  mutate(OBS_VALUE = ifelse(PRIX_CONSO == "3790", OBS_VALUE/1000, OBS_VALUE)) %>%
  
  month_to_date() %>%
  mutate(Prix_conso = gsub("Non alimentaire : ", "", Prix_conso)) %>%
  mutate(Prix_conso = gsub(": 1.000 litres \\(livré à domicile\\)", 
                           "\\(1 litre, livré à domicile\\)", Prix_conso)) %>%
  ggplot + theme_minimal() + xlab("") + ylab("1 litre en €") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1992, 2022, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 3, 0.1),
                     labels = dollar_format(accuracy = .1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

Value - Log

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("3860", "3790", "3863"),
         FREQ == "M") %>%
  mutate(OBS_VALUE = ifelse(PRIX_CONSO == "3790", OBS_VALUE/1000, OBS_VALUE)) %>%
  
  month_to_date() %>%
  mutate(Prix_conso = gsub("Non alimentaire : ", "", Prix_conso)) %>%
  mutate(Prix_conso = gsub(": 1.000 litres \\(livré à domicile\\)", 
                           "\\(1 litre, livré à domicile\\)", Prix_conso)) %>%
  ggplot + theme_minimal() + xlab("") + ylab("1 litre en €") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1992, 2022, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 3, 0.1),
                     labels = dollar_format(accuracy = .1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank())

Indice

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("3860", "3790", "3863"),
         FREQ == "M") %>%
  mutate(OBS_VALUE = ifelse(PRIX_CONSO == "3790", OBS_VALUE/1000, OBS_VALUE)) %>%
  
  month_to_date() %>%
  mutate(Prix_conso = gsub("Non alimentaire : ", "", Prix_conso)) %>%
  mutate(Prix_conso = gsub(": 1.000 litres \\(livré à domicile\\)", 
                           "\\(1 litre, livré à domicile\\)", Prix_conso)) %>%
  group_by(PRIX_CONSO) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1992, 2022, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 850, 50)) +
  
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank())

1996-

Value

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("3860", "3790", "3863"),
         FREQ == "M") %>%
  mutate(OBS_VALUE = ifelse(PRIX_CONSO == "3790", OBS_VALUE/1000, OBS_VALUE)) %>%
  
  month_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  mutate(Prix_conso = gsub("Non alimentaire : ", "", Prix_conso)) %>%
  mutate(Prix_conso = gsub(": 1.000 litres \\(livré à domicile\\)", 
                           "\\(1 litre, livré à domicile\\)", Prix_conso)) %>%
  ggplot + theme_minimal() + xlab("") + ylab("1 litre en €") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 3, 0.1),
                     labels = dollar_format(accuracy = .1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank())

Indice

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("3860", "3790", "3863"),
         FREQ == "M") %>%
  mutate(OBS_VALUE = ifelse(PRIX_CONSO == "3790", OBS_VALUE/1000, OBS_VALUE)) %>%
  
  month_to_date() %>%
  arrange(date) %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(PRIX_CONSO) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  mutate(Prix_conso = gsub(": 1.000 litres \\(livré à domicile\\)", 
                           "\\(1 litre, livré à domicile\\)", Prix_conso)) %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 850, 50)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

2015-

Value

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("3860", "3790", "3863"),
         FREQ == "M") %>%
  mutate(OBS_VALUE = ifelse(PRIX_CONSO == "3790", OBS_VALUE/1000, OBS_VALUE)) %>%
  
  month_to_date() %>%
  filter(date >= as.Date("2015-01-01")) %>%
  mutate(Prix_conso = gsub("Non alimentaire : ", "", Prix_conso)) %>%
  mutate(Prix_conso = gsub(": 1.000 litres \\(livré à domicile\\)", 
                           "\\(1 litre, livré à domicile\\)", Prix_conso)) %>%
  ggplot + theme_minimal() + xlab("") + ylab("1 litre en €") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 3, 0.1),
                     labels = dollar_format(accuracy = .1, prefix = "", su = " €")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

Indice

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("3860", "3790", "3863"),
         FREQ == "M") %>%
  mutate(OBS_VALUE = ifelse(PRIX_CONSO == "3790", OBS_VALUE/1000, OBS_VALUE)) %>%
  
  month_to_date() %>%
  arrange(date) %>%
  filter(date >= as.Date("2015-01-01")) %>%
  group_by(PRIX_CONSO) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  mutate(Prix_conso = gsub(": 1.000 litres \\(livré à domicile\\)", 
                           "\\(1 litre, livré à domicile\\)", Prix_conso)) %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 850, 50)) +
  
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

Essence, Gazole

Tous

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("3860", "3861", "3863"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  ggplot + theme_minimal() + xlab("") + ylab("1 litre en €") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 3, 0.1),
                     labels = dollar_format(accuracy = .1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

1996-

Value

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("3860", "3861", "3863"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("1 litre en €") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 3, 0.1),
                     labels = dollar_format(accuracy = .1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank())

Indice

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("3860", "3863"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  arrange(date) %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(PRIX_CONSO) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 850, 10)) +
  
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank())

2002-

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("3860", "3861", "3863"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  filter(date >= as.Date("2002-01-01")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("1 litre en €") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 3, 0.1),
                     labels = dollar_format(accuracy = .1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank())

2015-

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("3860", "3861", "3863"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  filter(date >= as.Date("2015-01-01")) %>%
  arrange(desc(date)) %>%
  select(date, OBS_VALUE, Prix_conso, everything()) %>%
  ggplot + theme_minimal() + xlab("") + ylab("1 litre en €") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 3, 0.1),
                     labels = dollar_format(accuracy = .1, prefix = "", su = " €")) +
  scale_color_manual(values = c("blue", "red", "black")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank())

Viandes, Boissons

Entrecôte de boeuf, Porc, Veau, Poulet

1992-

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("1188", "1244", "1264", "1334"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  ggplot + theme_minimal() + xlab("") + ylab("1 kg en €") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 100, 1),
                     labels = dollar_format(accuracy = 1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

1996-

Value

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("1188", "1244", "1264", "1334"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("1 kg en €") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 100, 1),
                     labels = dollar_format(accuracy = 1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

100

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("1188", "1244", "1264", "1334"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  arrange(date) %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(PRIX_CONSO) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 850, 10)) +
  
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank())

Apéritifs anisés, Whisky

1992

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("1180", "1181"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  ggplot + theme_minimal() + xlab("") + ylab("en €") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 100, 1),
                     labels = dollar_format(accuracy = 1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

1996-

Value

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("1180", "1181"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("1 kg en €") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 100, 1),
                     labels = dollar_format(accuracy = 1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

100

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("1180", "1181"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  arrange(date) %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(PRIX_CONSO) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 850, 10)) +
  
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank())

2017-

Value

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("1180", "1181"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("1 kg en €") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 100, 1),
                     labels = dollar_format(accuracy = 1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

100

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("1180", "1181"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  arrange(date) %>%
  filter(date >= as.Date("2017-01-01")) %>%
  group_by(PRIX_CONSO) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 850, 2)) +
  
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank())

Services - Heures de M.O

Electricite, Plomberie

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("3077", "3078"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  ggplot + theme_minimal() + xlab("") + ylab("1 kg en €") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 100, 5),
                     labels = dollar_format(accuracy = 1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

Carrosserie automobile, Mécanique automobile

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("2849", "2848"),
         FREQ == "M") %>%
  
  mutate(Prix_conso = gsub(" : une heure de main-d'oeuvre (y c. TVA)", "", Prix_conso)) %>%
  month_to_date() %>%
  ggplot + theme_minimal() + xlab("") + ylab("1 heure de M.O en €") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 100, 5),
                     labels = dollar_format(accuracy = 1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.65, 0.1),
        legend.title = element_blank())

Shampooing

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("1932", "2326", "1930"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 100, 5),
                     labels = dollar_format(accuracy = 1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank())

Services: Auto, Shampooing, Plomberie

2017-01

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("2849", "3078", "2848", "1932"),
         FREQ == "M") %>%
  
  month_to_date() %>%
  filter(date >= as.Date("2017-01-01")) %>%
  group_by(PRIX_CONSO) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot + theme_minimal() + xlab("") + ylab("Prix moyens, Services (100 = janvier 2017)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 300, 5)) +
  
  theme(legend.position = c(0.4, 0.85),
        legend.title = element_blank()) +
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = OBS_VALUE, color = Prix_conso, label = round(OBS_VALUE, 1)),
             show.legend = F)

Bar / Restaurant

Café

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("2782", "2126"),
         FREQ == "M") %>%
  month_to_date() %>%
  
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 3, 0.1),
                     labels = dollar_format(accuracy = .1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank())

Cola, Bière

All

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("2433", "2768"),
         FREQ == "M") %>%
  month_to_date() %>%
  
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 7, 0.1),
                     labels = dollar_format(accuracy = .1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank())

2000-2004

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("2433", "2768"),
         FREQ == "M") %>%
  month_to_date() %>%
  filter(date >= as.Date("2000-01-01"),
         date <= as.Date("2004-01-01")) %>%
  
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = "3 months",
               labels = date_format("%b %Y")) +
  scale_y_continuous(breaks = seq(0, 7, 0.1),
                     labels = dollar_format(accuracy = .1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Fruits et Légumes

Kiwi, Pamplemousses, Avocat

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("3840", "3903", "3841"),
         FREQ == "M") %>%
  month_to_date() %>%
  
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 3, 0.1),
                     labels = dollar_format(accuracy = .1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank())

Bananes, Oranges, Carottes, Pomme de terre

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("8959", "8932", "8948", "8942"),
         FREQ == "M") %>%
  month_to_date() %>%
  
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 5, 0.2),
                     labels = dollar_format(accuracy = .1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank())

Pommes, Courgettes, Oignons, Tomates

Code

`IPC-PM-2015` %>%
  filter(PRIX_CONSO %in% c("8953", "8944", "8955", "8951"),
         FREQ == "M") %>%
  month_to_date() %>%
  
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 5, 0.2),
                     labels = dollar_format(accuracy = .1, prefix = "", su = " €")) +
  
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank())