Indices des prix à la consommation - Base 1990
Data - INSEE
François Geerolf
Info
| source | dataset | .html | .RData |
|---|---|---|---|
| insee | IPC-1990 | 2024-04-18 | 2024-05-09 |
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 | 2024-04-18 | 2024-05-09 |
| insee | IPC-2015 | 2024-04-18 | 2024-04-09 |
| insee | IPC-PM-2015 | 2024-04-18 | 2024-05-09 |
| insee | IPCH-2015 | 2024-04-18 | 2024-05-09 |
| insee | IPGD-2015 | 2024-04-18 | 2024-03-20 |
| insee | IPLA-IPLNA-2015 | 2024-04-18 | 2024-05-09 |
| insee | IPPI-2015 | 2024-04-18 | 2024-03-30 |
| insee | IRL | 2024-04-18 | 2024-05-09 |
| insee | SERIES_LOYERS | 2024-04-18 | 2024-05-09 |
| insee | T_CONSO_EFF_FONCTION | 2024-04-18 | 2024-04-01 |
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
`IPC-1990` %>%
group_by(LAST_UPDATE) %>%
summarise(Nobs = n()) %>%
arrange(desc(LAST_UPDATE)) %>%
print_table_conditional()| LAST_UPDATE | Nobs |
|---|---|
| 2018-02-12 | 69239 |
Last TIME_PERIOD
`IPC-1990` %>%
group_by(TIME_PERIOD) %>%
summarise(Nobs = n()) %>%
arrange(desc(TIME_PERIOD)) %>%
head(1) %>%
print_table_conditional()| TIME_PERIOD | Nobs |
|---|---|
| 1998-12 | 587 |
NATURE
`IPC-1990` %>%
left_join(NATURE, by = "NATURE") %>%
group_by(NATURE, Nature) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional| NATURE | Nature | Nobs |
|---|---|---|
| INDICE | Indice | 60671 |
| POND | Pondérations d’indice | 3168 |
| VARIATIONS_M | Variations mensuelles | 2758 |
| GLISSEMENT_ANNUEL | Glissement annuel | 2627 |
| VARIATIONS_A | Variations annuelles | 15 |
MENAGES_IPC
`IPC-1990` %>%
left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
group_by(MENAGES_IPC, Menages_ipc) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional| MENAGES_IPC | Menages_ipc | Nobs |
|---|---|---|
| POPULATION-TOTALE | Population totale | 62343 |
| MENAGES-URBAINS | Ménages urbains employés ou ouvriers | 3918 |
| MENAGES-PARISIENS | Ménages parisiens employés ou ouvriers | 2049 |
| ENSEMBLE | Ensemble des ménages | 929 |
COICOP_1990
PRODUITS_1990
TITLE_FR
REF_AREA
`IPC-1990` %>%
group_by(REF_AREA) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional| REF_AREA | Nobs |
|---|---|
| FE | 62343 |
| FM | 4847 |
| D75 | 2049 |
TIME_PERIOD
Pondérations d’indice
1992, 1994, 1996, 1998
`IPC-1990` %>%
filter(NATURE == "POND",
TIME_PERIOD %in% c("1998", "1996", "1994", "1992", "1990"),
MENAGES_IPC == "POPULATION-TOTALE") %>%
select_if(function(col) length(unique(col)) > 1) %>%
select(-IDBANK, -TITLE_FR, -TITLE_EN, -OBS_STATUS, -OBS_TYPE) %>%
left_join(PRODUITS_1990, by = "PRODUITS_1990") %>%
left_join(COICOP_1990, by = "COICOP_1990") %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
print_table_conditionalTabac
`IPC-1990` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "POPULATION-TOTALE",
COICOP_1990 %in% c("14"),
NATURE == "POND") %>%
year_to_date %>%
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_color_manual(values = viridis(3)[1:2]) +
scale_x_date(breaks = seq(1920, 2025, 1) %>% 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))
Santé
`IPC-1990` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "POPULATION-TOTALE",
COICOP_1990 %in% c("5"),
NATURE == "POND") %>%
year_to_date %>%
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_color_manual(values = viridis(3)[1:2]) +
scale_x_date(breaks = seq(1920, 2025, 1) %>% 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))
2-digit
Boissons Alcoolisées, Logement, Restaurants et hôtels
`IPC-1990` %>%
filter(COICOP_1990 %in% c("0", "2", "11", "4"),
REF_AREA == "FE",
NATURE == "INDICE",
FREQ == "M") %>%
month_to_date %>%
left_join(COICOP_1990, by = "COICOP_1990") %>%
group_by(COICOP_1990) %>%
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 = Coicop_1990)) +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1920, 2025, 1) %>% 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, 200, 1),
labels = dollar_format(accuracy = 1, prefix = ""))
Transports, Enseignement, B&S Divers
`IPC-1990` %>%
filter(COICOP_1990 %in% c("00", "1", "7", "12"),
REF_AREA == "FE",
NATURE == "INDICE",
FREQ == "M") %>%
month_to_date %>%
left_join(COICOP_1990, by = "COICOP_1990") %>%
group_by(COICOP_1990) %>%
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 = Coicop_1990)) +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1920, 2025, 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, 1),
labels = dollar_format(accuracy = 1, prefix = ""))
Alimentation, Habillement, Meubles
`IPC-1990` %>%
filter(COICOP_1990 %in% c("00", "5", "1", "3"),
REF_AREA == "FE",
NATURE == "INDICE",
FREQ == "M") %>%
month_to_date %>%
left_join(COICOP_1990, by = "COICOP_1990") %>%
group_by(COICOP_1990) %>%
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 = Coicop_1990)) +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1920, 2025, 1) %>% 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, 1),
labels = dollar_format(accuracy = 1, prefix = ""))
Santé, Communications, Loisirs
`IPC-1990` %>%
filter(COICOP_1990 %in% c("00", "6", "9", "8"),
REF_AREA == "FE",
NATURE == "INDICE",
FREQ == "M") %>%
month_to_date %>%
left_join(COICOP_1990, by = "COICOP_1990") %>%
group_by(COICOP_1990) %>%
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 = Coicop_1990)) +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.2, 0.85),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 10),
labels = dollar_format(accuracy = 1, prefix = ""))