Prix moyens de vente de détail

Données - INSEE

Auteur

François Geerolf

Info

Last observation: Annuelle: 2025 (N = 63) · Mensuelle: 2026-06 (N = 62)

First observation: Annuelle: 1992 (N = 60) · Mensuelle: 1992-01 (N = 60)

Number of observations: 43 472

Last data update: 14 aoû 2026, 01:55. Last compile: 14 aoû 2026, 05:57

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	2026-08-13	2026-08-13
insee	INDICES_LOYERS	Indices des loyers d'habitation (ILH)	2026-08-13	2026-08-13
insee	IPC-1970-1980	Indice des prix à la consommation - Base 1970, 1980	2026-08-13	2026-08-13
insee	IPC-1990	Indices des prix à la consommation - Base 1990	2026-08-13	2026-08-13
insee	IPC-2015	Indice des prix à la consommation - Base 2015	2026-08-13	2026-08-13
insee	IPC-PM-2015	Prix moyens de vente de détail	2026-08-13	2026-08-13
insee	IPCH-2015	Indices des prix à la consommation harmonisés	2026-08-13	2026-08-13
insee	IPCH-IPC-2015-ensemble	Indices des prix à la consommation harmonisés	2026-08-13	2026-08-02
insee	IPGD-2015	Indice des prix dans la grande distribution	2026-08-13	2026-07-23
insee	IPLA-IPLNA-2015	Indices des prix des logements neufs et Indices Notaires-Insee des prix des logements anciens	2026-08-13	2026-08-13
insee	IPPI-2015	Indices de prix de production et d'importation dans l'industrie	2026-08-13	2026-08-13
insee	IRL	Indice pour la révision d’un loyer d’habitation	2026-08-13	2026-08-13
insee	SERIES_LOYERS	Variation des loyers	2026-08-13	2026-08-13
insee	T_CONSO_EFF_FONCTION	Consommation effective des ménages par fonction	2026-08-13	2026-08-02
insee	bdf2017	Budget de famille 2017	2026-08-13	2026-08-02
insee	echantillon-agglomerations-IPC-2024	Échantillon d’agglomérations enquêtées de l’IPC en 2024	2026-08-13	2026-08-02
insee	echantillon-agglomerations-IPC-2025	Échantillon d’agglomérations enquêtées de l’IPC en 2025	2026-08-13	2026-08-02
insee	liste-varietes-IPC-2024	Liste des variétés pour la mesure de l'IPC en 2024	2026-08-13	2026-08-02
insee	liste-varietes-IPC-2025	Liste des variétés pour la mesure de l'IPC en 2025	2026-08-13	2026-08-02
insee	ponderations-elementaires-IPC-2024	Pondérations élémentaires 2024 intervenant dans le calcul de l’IPC	2026-08-13	2026-08-02
insee	ponderations-elementaires-IPC-2025	Pondérations élémentaires 2025 intervenant dans le calcul de l’IPC	2026-08-13	2026-08-02
insee	table_conso_moyenne_par_categorie_menages	Montants de consommation selon différentes catégories de ménages	2026-08-13	2026-08-02
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	2026-08-13	2026-08-02
insee	tranches_unitesurbaines	Poids de chaque tranche d’unités urbaines dans la consommation	2026-08-13	2026-08-02

Data on inflation

source	dataset	Title	.html	.rData
bis	CPI	Consumer Price Index	2026-08-12	2026-08-12
ecb	CES	Consumer Expectations Survey	2026-08-12	2026-08-12
eurostat	nama_10_co3_p3	Final consumption expenditure of households by consumption purpose (COICOP 3 digit)	2026-08-13	2026-08-13
eurostat	prc_hicp_cow	HICP - country weights	2026-08-13	2026-08-12
eurostat	prc_hicp_ctrb	Contributions to euro area annual inflation (in percentage points)	2026-08-13	2026-08-12
eurostat	prc_hicp_inw	HICP - item weights	2026-08-13	2026-08-12
eurostat	prc_hicp_manr	HICP (2015 = 100) - monthly data (annual rate of change)	2026-08-13	2026-08-13
eurostat	prc_hicp_midx	HICP (2015 = 100) - monthly data (index)	2026-08-13	2026-08-13
eurostat	prc_hicp_mmor	HICP (2015 = 100) - monthly data (monthly rate of change)	2026-08-13	2026-08-13
eurostat	prc_ppp_ind	Purchasing power parities (PPPs), price level indices and real expenditures for ESA 2010 aggregates	2026-08-13	2026-08-12
eurostat	sts_inpp_m	Producer prices in industry, total - monthly data	2026-08-13	2026-08-12
eurostat	sts_inppd_m	Producer prices in industry, domestic market - monthly data	2026-08-13	2026-08-12
eurostat	sts_inppnd_m	Producer prices in industry, non domestic market - monthly data	2026-08-13	2026-08-13
fred	cpi	Consumer Price Index	2026-08-12	2026-08-12
fred	inflation	Inflation	2026-08-12	2026-08-12
imf	CPI	Consumer Price Index (CPI) 2026 February - CPI_2026_FEB_VINTAGE	2026-08-12	2026-08-12
oecd	MEI_PRICES_PPI	Producer Prices - MEI_PRICES_PPI	2026-08-13	2026-08-02
oecd	PPP2017	2017 PPP Benchmark results	2026-08-13	2026-08-02
oecd	PRICES_CPI	Consumer price indices (CPIs)	2026-08-13	2026-08-02
wdi	FP.CPI.TOTL.ZG	Inflation, consumer prices (annual %)	2026-08-12	2026-08-12
wdi	NY.GDP.DEFL.KD.ZG	Inflation, GDP deflator (annual %)	2026-08-12	2026-08-12

LAST_COMPILE

LAST_COMPILE
2026-08-14

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") |>
  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") |>
  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, 2100, 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, 2100, 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, 2100, 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, 2100, 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, 2100, 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, 2100, 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())