Prix moyens de vente de détail
Data - INSEE
François Geerolf
Info
| source | dataset | .html | .RData |
|---|---|---|---|
| insee | IPC-PM-2015 | 2024-04-18 | 2024-05-09 |
| insee | t_5203 | 2024-04-18 | 2021-12-02 |
Données sur l’inflation en France
| source | dataset | .html | .RData |
|---|---|---|---|
| insee | bdf2017 | 2024-05-09 | 2023-11-21 |
| insee | ILC-ILAT-ICC | 2024-05-09 | 2024-05-09 |
| insee | INDICES_LOYERS | 2024-05-09 | 2024-05-09 |
| insee | IPC-1970-1980 | 2024-05-09 | 2024-05-09 |
| insee | IPC-1990 | 2024-05-09 | 2024-05-09 |
| insee | IPC-2015 | 2024-05-09 | 2024-04-09 |
| insee | IPC-PM-2015 | 2024-04-18 | 2024-05-09 |
| insee | IPCH-2015 | 2024-04-18 | 2024-05-09 |
| insee | IPGD-2015 | 2024-04-18 | 2024-03-20 |
| insee | IPLA-IPLNA-2015 | 2024-04-18 | 2024-05-09 |
| insee | IPPI-2015 | 2024-04-18 | 2024-03-30 |
| insee | IRL | 2024-04-18 | 2024-05-09 |
| insee | SERIES_LOYERS | 2024-04-18 | 2024-05-09 |
| insee | T_CONSO_EFF_FONCTION | 2024-04-18 | 2024-04-01 |
Data on inflation
| source | dataset | .html | .RData |
|---|---|---|---|
| bis | CPI | 2024-04-19 | 2022-01-20 |
| ecb | CES | 2024-04-19 | 2024-01-12 |
| eurostat | nama_10_co3_p3 | 2024-04-18 | 2024-04-18 |
| eurostat | prc_hicp_cow | 2024-04-18 | 2024-04-18 |
| eurostat | prc_hicp_ctrb | 2024-04-18 | 2024-04-18 |
| eurostat | prc_hicp_inw | 2024-04-18 | 2024-04-18 |
| eurostat | prc_hicp_manr | 2024-04-18 | 2024-04-18 |
| eurostat | prc_hicp_midx | 2024-04-18 | 2024-04-18 |
| eurostat | prc_hicp_mmor | 2024-04-18 | 2024-04-18 |
| eurostat | prc_ppp_ind | 2024-04-18 | 2024-04-18 |
| eurostat | sts_inpp_m | 2024-04-18 | 2024-04-18 |
| eurostat | sts_inppd_m | 2024-04-18 | 2024-04-18 |
| eurostat | sts_inppnd_m | 2024-04-18 | 2024-04-18 |
| fred | cpi | 2024-04-19 | 2024-04-19 |
| fred | inflation | 2024-05-07 | 2024-05-07 |
| imf | CPI | 2024-01-06 | 2020-03-13 |
| oecd | MEI_PRICES_PPI | 2024-04-16 | 2024-04-15 |
| oecd | PPP2017 | 2024-04-16 | 2023-07-25 |
| oecd | PRICES_CPI | 2024-04-16 | 2024-04-15 |
| wdi | FP.CPI.TOTL.ZG | 2023-01-15 | 2024-04-14 |
| wdi | NY.GDP.DEFL.KD.ZG | 2024-04-14 | 2024-04-14 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-05-09 |
LAST_UPDATE
Last
`IPC-PM-2015` %>%
group_by(TIME_PERIOD) %>%
summarise(Nobs = n()) %>%
arrange(desc(TIME_PERIOD)) %>%
head(1) %>%
print_table_conditional()| TIME_PERIOD | Nobs |
|---|---|
| 2024-03 | 63 |
PRIX_CONSO
FREQ
`IPC-PM-2015` %>%
left_join(FREQ, by = "FREQ") %>%
group_by(FREQ, Freq) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional| FREQ | Freq | Nobs |
|---|---|---|
| M | Monthly | 38495 |
| A | Annual | 3152 |
TITLE_FR
TIME_PERIOD
Table - Last
`IPC-PM-2015` %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
filter(is.finite(OBS_VALUE)) %>%
group_by(PRIX_CONSO, Prix_conso) %>%
arrange(TIME_PERIOD) %>%
summarise(Nobs = n(),
TIME_PERIOD = last(TIME_PERIOD),
OBS_VALUE = last(OBS_VALUE)) %>%
arrange(desc(TIME_PERIOD), OBS_VALUE) %>%
print_table_conditionalTable
`IPC-PM-2015` %>%
filter(FREQ == "M") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
group_by(PRIX_CONSO, Prix_conso) %>%
arrange(TIME_PERIOD) %>%
summarise(`1996-01` = OBS_VALUE[TIME_PERIOD == "1996-01"],
`2022-01` = OBS_VALUE[TIME_PERIOD == "2022-01"]) %>%
mutate(`%` = (`2022-01`/`1996-01`)^(1/26)- 1) %>%
arrange(- `%`) %>%
print_table_conditional()| PRIX_CONSO | Prix_conso | 1996-01 | 2022-01 | % |
|---|---|---|---|---|
| 3790 | Non alimentaire : Fioul domestique : 1.000 litres (livré à domicile) | 315.91 | 1086.32 | 0.0486500 |
| 3863 | Gazole (1 litre) | 0.62 | 1.63 | 0.0378773 |
| 1873 | Non alimentaire : Gaz butane comprimé (13 kg), sans consigne | 14.82 | 36.86 | 0.0356655 |
| 3078 | Services : Travaux de plomberie (heure de M.O. TTC) | 29.23 | 59.80 | 0.0279136 |
| 3077 | Services : Travaux d’électricité (heure de M.O.), TTC | 28.20 | 56.58 | 0.0271439 |
| 3860 | Essence super sans plomb : octane 98 (1 litre) | 0.89 | 1.77 | 0.0267955 |
| 1188 | Viandes : Boeuf : entrecôte (kg) | 13.39 | 26.13 | 0.0260479 |
| 1932 | Services : Shampooing supérieur, coloration tenace et brushing pour femme | 30.48 | 55.86 | 0.0235727 |
| 3792 | Viandes : Boeuf : bifteck dans la bavette (kg) | 13.69 | 24.84 | 0.0231796 |
| 1185 | Viandes : Boeuf : filet (kg) | 23.69 | 42.71 | 0.0229273 |
| 1187 | Viandes : Boeuf : rumsteck (kg) | 14.63 | 25.58 | 0.0217224 |
| 1339 | Viandes : Lapin entier (kg) | 6.59 | 11.14 | 0.0203971 |
| 1186 | Viandes : Boeuf : faux-filet (kg) | 14.96 | 25.23 | 0.0203055 |
| 1266 | Viandes : Porc : échine avec os (kg) | 5.93 | 9.61 | 0.0187419 |
| 1223 | Produits alimentaires divers : Pain baguette (kg) | 2.36 | 3.64 | 0.0168059 |
| 1244 | Viandes : Veau : escalope (kg) | 16.19 | 24.80 | 0.0165372 |
| 1264 | Viandes : Porc : rôti dans le filet (kg) | 7.56 | 11.35 | 0.0157515 |
| 1139 | Viandes : Foie de veau frais (kg) | 21.71 | 30.89 | 0.0136562 |
| 1192 | Viandes : Boeuf : côte avec os (kg) | 12.00 | NaN | NaN |
Prix Baguette
Tous
Indice
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("1223", "1227"),
FREQ == "M") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("1223", "1227"),
FREQ == "M") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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
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
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("1873"),
FREQ == "M") %>%
mutate(OBS_VALUE = ifelse(PRIX_CONSO == "3790", OBS_VALUE/1000, OBS_VALUE)) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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
`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)) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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
`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)) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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
`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)) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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
`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)) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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
`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)) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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
`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)) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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
`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)) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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
`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)) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("3860", "3861", "3863"),
FREQ == "M") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("3860", "3861", "3863"),
FREQ == "M") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("3860", "3863"),
FREQ == "M") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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-
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("3860", "3861", "3863"),
FREQ == "M") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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-
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("3860", "3861", "3863"),
FREQ == "M") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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-
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("1188", "1244", "1264", "1334"),
FREQ == "M") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("1188", "1244", "1264", "1334"),
FREQ == "M") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("1188", "1244", "1264", "1334"),
FREQ == "M") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("1180", "1181"),
FREQ == "M") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("1180", "1181"),
FREQ == "M") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("1180", "1181"),
FREQ == "M") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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())
Services - Heures de M.O
Electricite, Plomberie
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("3077", "3078"),
FREQ == "M") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("2849", "2848"),
FREQ == "M") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("1932", "2326", "1930"),
FREQ == "M") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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, 2026, 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())
Bar / Restaurant
Café
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("2782", "2126"),
FREQ == "M") %>%
month_to_date() %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
ggplot + theme_minimal() + xlab("") + ylab("") +
geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
scale_x_date(breaks = seq(1960, 2026, 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
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("2433", "2768"),
FREQ == "M") %>%
month_to_date() %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
ggplot + theme_minimal() + xlab("") + ylab("") +
geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
scale_x_date(breaks = seq(1960, 2026, 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
`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")) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
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
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("3840", "3903", "3841"),
FREQ == "M") %>%
month_to_date() %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
ggplot + theme_minimal() + xlab("") + ylab("") +
geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
scale_x_date(breaks = seq(1960, 2026, 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
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("8959", "8932", "8948", "8942"),
FREQ == "M") %>%
month_to_date() %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
ggplot + theme_minimal() + xlab("") + ylab("") +
geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
scale_x_date(breaks = seq(1960, 2026, 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
`IPC-PM-2015` %>%
filter(PRIX_CONSO %in% c("8953", "8944", "8955", "8951"),
FREQ == "M") %>%
month_to_date() %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
ggplot + theme_minimal() + xlab("") + ylab("") +
geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
scale_x_date(breaks = seq(1960, 2026, 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())