Indice des prix à la consommation - Base 2015
Data - INSEE
François Geerolf
## [1] "fr_CA.UTF-8"Info
| source | dataset | .html | .RData |
|---|---|---|---|
| insee | IPC-2015 | 2024-04-18 | 2024-04-09 |
| insee | IPCH-2015 | 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 | 2024-05-09 | 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 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-05-09 |
LAST_UPDATE
Last
`IPC-2015` %>%
group_by(TIME_PERIOD, FREQ) %>%
summarise(Nobs = n()) %>%
group_by(FREQ) %>%
filter(TIME_PERIOD == max(TIME_PERIOD)) %>%
print_table_conditional()| TIME_PERIOD | FREQ | Nobs |
|---|---|---|
| 2024 | A | 1420 |
| 2024-03 | M | 17 |
Definitions
Moyenne annuelle: l’évolution en moyenne annuelle compare les prix d’une année donnée à ceux de l’année précédente.
Glissement annuel: l’évolution en glissement annuel compare les prix d’un seul mois d’une année donnée à ceux du même mois de l’année précédente.
Méthodo
- Indice des prix à la consommation (base 100=1998) (pdf, fr, 44 Ko, 01/02/2013)
- Indice des prix à la consommation des ménages du premier quintile de la distribution des niveaux de vie (pdf, fr, 119 Ko, 18/02/2013)
- Indice des prix à la consommation (base 100=2015) (pdf, fr, 145 Ko, 29/01/2016)
- Indice des prix à la consommation des ménages - base 100 en 2015 (pdf, fr, 955 Ko, 29/01/2016)
- Pour comprendre l’indice des prix - Édition 1998 (pdf, fr, 1 Mo, 01/01/1999)
- Indice des prix à la consommation des ménages - base 100 en 1998 (pdf, fr, 50 Ko, 01/01/1999)
- Dossier d’information méthodologique. Indice des prix, pouvoir d’achat (pdf, fr, 229 Ko, 01/02/2004)
Documents de travail
- L’IPC, miroir de l’évolution du coût de la vie en France ? - Ce qu’apporte l’analyse des courbes d’Engel. (pdf, fr, 615 Ko, 02/04/2010)
- Calcul d’un indice des prix des produits de grande consommation dans la grande distribution (pdf, fr, 204 Ko, 01/01/2014)
- Ce qui change pour l’IPC à partir du 29 janvier 2016 (pdf, fr, 129 Ko, 29/01/2016)
- L’expérience française des indices de prix a la consommation (pdf, fr, 107 Ko, 01/01/1996) Indice des prix à la consommation CVS et indice hors tarifs publics et produits à prix volatils corrigé des mesures fiscales, CVS (pdf, fr, 71 Ko, 27/03/1996)
- Indices mensuels des prix dans la grande distribution (pdf, fr, 126 Ko, 18/02/2016)
- Contenu des groupes avec les fonctions, les regroupements et les groupes publiés dans la nouvelle base 1998 (pdf, fr, 42 Ko, 01/01/1999)
- Comparaisons spatiales de prix au sein du territoire français (pdf, fr, 91 Ko, 01/12/2000)
- Les indices à utilité constante : une référence pour mesurer l’évolution des prix (pdf, fr, 219 Ko, 01/12/2000)
- La mesure des prix dans les domaines de la santé et de l’action sociale : quelques problèmes méthodologiques (pdf, fr, 212 Ko, 01/06/2003)
- Indice des prix à la consommation en base 100 en 1998 - Séries longues rétropolées, de 1990 à 2002 (pdf, fr, 201 Ko, 01/09/2003)
- Impact des ajustements de qualité dans le calcul de l’indice des prix à la consommation (pdf, fr, 53 Ko, 01/05/2004)
- Introduction à la pratique des indices statistiques (pdf, fr, 426 Ko, 01/11/2005)
- Note additionnelle sur les changements de l’année 2017 (pdf, fr, 228 Ko, 23/02/2017)
INDICATEUR
`IPC-2015` %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR, Indicateur) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| INDICATEUR | Indicateur | Nobs |
|---|---|---|
| IPC | Indice des prix à la consommation (IPC) | 534728 |
| ISJ | Indice d’inflation sous-jacente (ISJ) | 4956 |
PRIX_CONSO
CORRECTION
`IPC-2015` %>%
left_join(CORRECTION, by = "CORRECTION") %>%
group_by(CORRECTION, Correction) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| CORRECTION | Correction | Nobs |
|---|---|---|
| BRUT | Non corrigé | 533486 |
| CVS-FISC | Corrigé des mesures fiscales et des variations saisonnières | 4956 |
| CVS | Corrigé des variations saisonnières | 1242 |
NATURE
`IPC-2015` %>%
left_join(NATURE, by = "NATURE") %>%
group_by(NATURE, Nature) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| NATURE | Nature | Nobs |
|---|---|---|
| INDICE | Indice | 445720 |
| POND | Pondérations d’indice | 45859 |
| VARIATIONS_M | Variations mensuelles | 23812 |
| GLISSEMENT_ANNUEL | Glissement annuel | 23104 |
| VARIATIONS_A | Variations annuelles | 1189 |
FREQ
`IPC-2015` %>%
left_join(FREQ, by = "FREQ") %>%
group_by(FREQ, Freq) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| FREQ | Freq | Nobs |
|---|---|---|
| M | Monthly | 448702 |
| A | Annual | 90982 |
MENAGES_IPC
COICOP2016
All
2-digit
`IPC-2015` %>%
filter(nchar(COICOP2016) == 2) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016, Coicop2016) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| COICOP2016 | Coicop2016 | Nobs |
|---|---|---|
| SO | Sans objet | 109017 |
| 00 | 00 - Ensemble | 2758 |
| 01 | 01 - Produits alimentaires et boissons non alcoolisées | 1437 |
| 02 | 02 - Boissons alcoolisées, tabac et stupéfiants | 1437 |
| 03 | 03 - Articles d’habillement et chaussures | 1437 |
| 04 | 04 - Logement, eau, gaz, électricité et autres combustibles | 1437 |
| 05 | 05 - Meubles, articles de ménage et entretien courant du foyer | 1437 |
| 06 | 06 - Santé | 1437 |
| 07 | 07 - Transports | 1437 |
| 08 | 08 - Communications | 1437 |
| 09 | 09 - Loisirs et culture | 1437 |
| 10 | 10 - Enseignement | 1437 |
| 11 | 11 - Restaurants et hôtels | 1437 |
| 12 | 12 - Biens et services divers | 1437 |
3-digit
4-digit
5-digit
TITLE_FR
REF_AREA
`IPC-2015` %>%
group_by(REF_AREA) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional| REF_AREA | Nobs |
|---|---|
| FE | 279615 |
| FM | 225689 |
| D973 | 8226 |
| D971 | 8204 |
| D972 | 8204 |
| D974 | 8204 |
| D976 | 1542 |
TIME_PERIOD
France Entiere (FE), France Métropolitaine (FM)
Mensuel
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
PRIX_CONSO %in% c("SO"),
FREQ == "M",
COICOP2016 %in% c("00"),
REF_AREA %in% c("FE", "FM"),
NATURE == "INDICE") %>%
month_to_date %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(desc(date)) %>%
group_by(REF_AREA) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = REF_AREA)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 10),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
Alimentation, loyers, chauffage, Ensemble
Hausse sur 2 ans
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
COICOP2016 %in% c("041", "00", "01", "045"),
FREQ == "M",
REF_AREA == "FM",
NATURE == "INDICE") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
filter(date >= max(date) - years(2)) %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
theme(legend.position = c(0.28, 0.87),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
scale_y_log10(breaks = seq(100, 130, 2),
labels = paste0(seq(0, 30, 2), "%")) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
Par type de ménages
3 déciles
`IPC-2015` %>%
filter(MENAGES_IPC %in% c("D6-D7", "INF-D1", "D9-PLUS")) %>%
left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
year_to_date %>%
group_by(MENAGES_IPC) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Menages_ipc)) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
10 dixièmes
1998-
`IPC-2015` %>%
filter(MENAGES_IPC %in% c("INF-D1", "D1-D2", "D2-D3", "D3-D4", "D4-D5",
"D5-D6", "D6-D7", "D7-D8", "D8-D9", "D9-PLUS")) %>%
left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
year_to_date %>%
group_by(MENAGES_IPC) %>%
mutate(MENAGES_IPC = factor(MENAGES_IPC, levels = c("INF-D1", "D1-D2", "D2-D3", "D3-D4", "D4-D5",
"D5-D6", "D6-D7", "D7-D8", "D8-D9", "D9-PLUS"))) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = MENAGES_IPC)) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.7),
legend.title = element_blank()) +
guides(color = guide_legend(ncol = 3)) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
2008-
`IPC-2015` %>%
filter(MENAGES_IPC %in% c("INF-D1", "D1-D2", "D2-D3", "D3-D4", "D4-D5",
"D5-D6", "D6-D7", "D7-D8", "D8-D9", "D9-PLUS")) %>%
left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
year_to_date %>%
group_by(MENAGES_IPC) %>%
mutate(MENAGES_IPC = factor(MENAGES_IPC,
levels = c("INF-D1", "D1-D2", "D2-D3", "D3-D4", "D4-D5",
"D5-D6", "D6-D7", "D7-D8", "D8-D9", "D9-PLUS"))) %>%
arrange(date) %>%
filter(date >= as.Date("2008-01-01")) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = MENAGES_IPC)) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.7),
legend.title = element_blank()) +
guides(color = guide_legend(ncol = 3)) +
scale_y_log10(breaks = seq(0, 200, 2),
labels = dollar_format(accuracy = 1, prefix = ""))
1er quintile, Tous
All
`IPC-2015` %>%
filter(MENAGES_IPC %in% c("PREMIERQUINTILE", "ENSEMBLE"),
FREQ == "M",
PRIX_CONSO == "4018",
REF_AREA == "FE",
COICOP2016 == "SO",
NATURE == "INDICE") %>%
left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
month_to_date %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Menages_ipc)) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
1998-
`IPC-2015` %>%
filter(MENAGES_IPC %in% c("PREMIERQUINTILE", "ENSEMBLE"),
FREQ == "M",
PRIX_CONSO == "4018",
REF_AREA == "FE",
COICOP2016 == "SO",
NATURE == "INDICE") %>%
left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
month_to_date %>%
filter(date >= as.Date("1998-01-01")) %>%
group_by(MENAGES_IPC) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Menages_ipc)) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.6, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
2012-
`IPC-2015` %>%
filter(MENAGES_IPC %in% c("PREMIERQUINTILE", "ENSEMBLE"),
FREQ == "M",
PRIX_CONSO == "4018",
REF_AREA == "FE",
COICOP2016 == "SO",
NATURE == "INDICE") %>%
left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
month_to_date %>%
filter(date >= as.Date("2012-01-01")) %>%
group_by(MENAGES_IPC) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Menages_ipc)) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.4, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
Glissement sur 2 ans
`IPC-2015` %>%
filter(MENAGES_IPC %in% c("PREMIERQUINTILE", "ENSEMBLE"),
FREQ == "M",
PRIX_CONSO == "4018",
REF_AREA == "FE",
COICOP2016 == "SO",
NATURE == "INDICE") %>%
left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
month_to_date %>%
filter(date >= as.Date("2021-09-01")) %>%
group_by(MENAGES_IPC) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Menages_ipc)) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
theme(legend.position = c(0.65, 0.2),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
scale_y_log10(breaks = seq(0, 200, 1),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)),
fontface ="plain", color = "black", size = 3)
Cadres, Ouvriers, Retraités
`IPC-2015` %>%
filter(MENAGES_IPC %in% c("CADRE", "OUVRIER", "RETRAITE", "ACTIF")) %>%
left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
year_to_date %>%
group_by(MENAGES_IPC) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Menages_ipc)) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
Inflation - Tous
Table - COICOP2016
All
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
REF_AREA == "FE",
NATURE == "INDICE",
PRIX_CONSO == "SO",
TIME_PERIOD %in% c("1990", "2000", "2010", "2020")) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
mutate(`% 1990-2019` = (100*((`2020`/`1990`)^(1/29)-1)) %>% round(., 2)) %>%
print_table_conditional2-digit
Javascript
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
REF_AREA == "FE",
NATURE == "INDICE",
PRIX_CONSO == "SO",
TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
nchar(COICOP2016) == 2) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
mutate(`% 1990-2019` = (100*((`2020`/`1990`)^(1/29)-1)) %>% round(., 2)) %>%
print_table_conditional()| COICOP2016 | Coicop2016 | 1990 | 2000 | 2010 | 2020 | % 1990-2019 |
|---|---|---|---|---|---|---|
| 00 | 00 - Ensemble | 67.4 | 79.9 | 94.71 | 104.73 | 1.53 |
| 01 | 01 - Produits alimentaires et boissons non alcoolisées | 68.7 | 77.6 | 94.63 | 108.33 | 1.58 |
| 02 | 02 - Boissons alcoolisées, tabac et stupéfiants | 34.9 | 55.2 | 83.63 | 126.07 | 4.53 |
| 03 | 03 - Articles d’habillement et chaussures | 85.7 | 93.2 | 97.60 | 99.71 | 0.52 |
| 04 | 04 - Logement, eau, gaz, électricité et autres combustibles | 53.6 | 66.9 | 88.46 | 105.24 | 2.35 |
| 05 | 05 - Meubles, articles de ménage et entretien courant du foyer | 74.6 | 85.1 | 96.11 | 100.67 | 1.04 |
| 06 | 06 - Santé | 92.9 | 101.9 | 104.31 | 96.67 | 0.14 |
| 07 | 07 - Transports | 58.6 | 74.4 | 93.57 | 105.24 | 2.04 |
| 08 | 08 - Communications | 158.4 | 142.1 | 124.87 | 91.96 | -1.86 |
| 09 | 09 - Loisirs et culture | 101.5 | 109.2 | 101.30 | 103.29 | 0.06 |
| 10 | 10 - Enseignement | 55.3 | 70.0 | 92.09 | 107.42 | 2.32 |
| 11 | 11 - Restaurants et hôtels | 53.3 | 69.9 | 89.63 | 108.04 | 2.47 |
| 12 | 12 - Biens et services divers | 60.8 | 71.7 | 91.54 | 105.68 | 1.92 |
3-digit
Javascript
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
REF_AREA == "FE",
NATURE == "INDICE",
PRIX_CONSO == "SO",
TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
nchar(COICOP2016) == 3) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
mutate(`% 1990-2019` = (100*((`2020`/`1990`)^(1/29)-1)) %>% round(., 2)) %>%
print_table_conditional()
4-digit
Javascript
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
REF_AREA == "FE",
NATURE == "INDICE",
PRIX_CONSO == "SO",
TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
nchar(COICOP2016) == 4) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
mutate(`% 1990-2019` = (100*((`2020`/`1990`)^(1/29)-1)) %>% round(., 2)) %>%
print_table_conditional()
5-digit
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
REF_AREA == "FE",
NATURE == "INDICE",
PRIX_CONSO == "SO",
TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
nchar(COICOP2016) == 5) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
mutate(`% 1990-2019` = (100*((`2020`/`1990`)^(1/29)-1)) %>% round(., 2)) %>%
print_table_conditional()6-digit
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
REF_AREA == "FE",
NATURE == "INDICE",
PRIX_CONSO == "SO",
TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
nchar(COICOP2016) == 6) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
mutate(`% 1990-2019` = (100*((`2020`/`1990`)^(1/29)-1)) %>% round(., 2)) %>%
print_table_conditional()Table - PRIX_CONSO
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
REF_AREA == "FE",
NATURE == "INDICE",
COICOP2016 == "SO",
TIME_PERIOD %in% c("1990", "2000", "2010", "2020")) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
select(PRIX_CONSO, Prix_conso, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
print_table_conditionalInflation par catégorie
Tabac, Loyers, Ensemble, Carburants
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "045", "022", "0722"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
filter(date >= max(date) - years(2)) %>%
select(date, OBS_VALUE, Coicop2016) %>%
na.omit %>%
ggplot() + ylab("Inflation sur un an (IPC, IPCH)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
theme(legend.position = c(0.72, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 5),
labels = percent_format(accuracy = 1, prefix = "")) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
Tabac, Loyers, Ensemble
Glissement 1 an
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "01", "022"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
filter(date >= max(date) - years(2)) %>%
select(date, OBS_VALUE, Coicop2016) %>%
na.omit %>%
ggplot() + ylab("Inflation sur un an (IPC, IPCH)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 1),
labels = percent_format(accuracy = 1, prefix = "")) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
Glissement 2 ans
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "01", "022"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 24)-1) %>%
filter(date >= max(date) - years(2)) %>%
select(date, OBS_VALUE, Coicop2016) %>%
na.omit %>%
ggplot() + ylab("Glissement sur 2 ans (IPC, IPCH)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 1),
labels = percent_format(accuracy = 1, prefix = "")) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
Alimentation, Loyers, Ensemble
Glissement 1 an
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "01", "041"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
filter(date >= max(date) - years(2)) %>%
select(date, OBS_VALUE, Coicop2016) %>%
na.omit %>%
ggplot() + ylab("Glissement sur 1 an (IPC, IPCH)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 1),
labels = percent_format(accuracy = 1, prefix = "")) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
Glissement 2 ans
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "01", "041"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 24)-1) %>%
filter(date >= max(date) - years(2)) %>%
select(date, OBS_VALUE, Coicop2016) %>%
na.omit %>%
ggplot() + ylab("Glissement sur 2 ans (IPC, IPCH)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 1),
labels = percent_format(accuracy = 1, prefix = "")) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
Chauffage du logement, Loyers, Ensemble
Glissement 1 an
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "041", "045"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
filter(date >= max(date) - years(2)) %>%
select(date, OBS_VALUE, Coicop2016) %>%
na.omit %>%
ggplot() + ylab("Inflation sur un an (IPC, IPCH)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
theme(legend.position = c(0.4, 0.45),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 2),
labels = percent_format(accuracy = 1, prefix = "")) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
Glissement 2 ans
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "041", "045"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 24)-1) %>%
filter(date >= max(date) - years(2)) %>%
select(date, OBS_VALUE, Coicop2016) %>%
na.omit %>%
ggplot() + ylab("Glissement sur 2 ans (IPC, IPCH)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
theme(legend.position = c(0.4, 0.45),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 2),
labels = percent_format(accuracy = 1, prefix = "")) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
Pondérations d’indice
Table - COICOP2016
All
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
REF_AREA == "FE",
NATURE == "POND",
PRIX_CONSO == "SO",
CORRECTION == "BRUT",
TIME_PERIOD %in% c("1990", "2000", "2010", "2020")) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
print_table_conditional2-digit
Javascript
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
REF_AREA == "FE",
NATURE == "POND",
PRIX_CONSO == "SO",
TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
nchar(COICOP2016) == 2) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
print_table_conditional| COICOP2016 | Coicop2016 | 1990 | 2000 | 2010 | 2020 |
|---|---|---|---|---|---|
| 00 | 00 - Ensemble | 10000 | 10000 | 10000 | 10000 |
| 01 | 01 - Produits alimentaires et boissons non alcoolisées | 1999 | 1558 | 1474 | 1423 |
| 02 | 02 - Boissons alcoolisées, tabac et stupéfiants | 378 | 382 | 329 | 392 |
| 03 | 03 - Articles d’habillement et chaussures | 844 | 552 | 487 | 394 |
| 04 | 04 - Logement, eau, gaz, électricité et autres combustibles | 1233 | 1364 | 1348 | 1399 |
| 05 | 05 - Meubles, articles de ménage et entretien courant du foyer | 736 | 644 | 617 | 495 |
| 06 | 06 - Santé | 774 | 896 | 1005 | 1050 |
| 07 | 07 - Transports | 1659 | 1669 | 1634 | 1581 |
| 08 | 08 - Communications | 188 | 254 | 303 | 248 |
| 09 | 09 - Loisirs et culture | 851 | 859 | 916 | 854 |
| 10 | 10 - Enseignement | 33 | 23 | 25 | 5 |
| 11 | 11 - Restaurants et hôtels | 818 | 805 | 685 | 810 |
| 12 | 12 - Biens et services divers | 487 | 994 | 1177 | 1349 |
3-digit
Javascript
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
REF_AREA == "FE",
NATURE == "POND",
PRIX_CONSO == "SO",
CORRECTION == "BRUT",
TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
nchar(COICOP2016) == 3) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
print_table_conditional
4-digit
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
REF_AREA == "FE",
NATURE == "POND",
PRIX_CONSO == "SO",
CORRECTION == "BRUT",
TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
nchar(COICOP2016) == 4) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
print_table_conditional5-digit
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
REF_AREA == "FE",
NATURE == "POND",
PRIX_CONSO == "SO",
CORRECTION == "BRUT",
TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
nchar(COICOP2016) == 5) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
print_table_conditionalTable - PRIX_CONSO
1990, 2000, 2010, 2020
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
REF_AREA == "FE",
NATURE == "POND",
CORRECTION == "BRUT",
COICOP2016 == "SO",
TIME_PERIOD %in% c("1990", "2000", "2010", "2020")) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
select(PRIX_CONSO, Prix_conso, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
print_table_conditional2016, 2017, 2018, 2019, 2020
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
REF_AREA == "FE",
NATURE == "POND",
CORRECTION == "BRUT",
COICOP2016 == "SO",
TIME_PERIOD %in% c("2016", "2017", "2018", "2019", "2020")) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
select(PRIX_CONSO, Prix_conso, TIME_PERIOD, OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
print_table_conditional()Alimentation, Produits alimentaires
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("01", "011"),
REF_AREA == "FE",
NATURE == "POND") %>%
year_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
mutate(OBS_VALUE = OBS_VALUE/10000) %>%
ggplot() + ylab("Poids de l'alimentation dans l'indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.65, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 40, 0.5),
labels = percent_format(accuracy = .1))
Tabac
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("022"),
REF_AREA == "FE",
NATURE == "POND") %>%
year_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
mutate(OBS_VALUE = OBS_VALUE/10000) %>%
ggplot() + ylab("Poids du tabac dans l'indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(0, 10, 0.1),
labels = percent_format(accuracy = .1))
1253 - Assurance Santé Complémentaire
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("1253"),
REF_AREA == "FE",
NATURE == "POND") %>%
year_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
mutate(OBS_VALUE = OBS_VALUE/10000) %>%
ggplot() + ylab("Poids de l'assurance santé dans l'indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 10, 0.1),
labels = percent_format(accuracy = .1))
Santé
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("06"),
REF_AREA == "FE",
NATURE == "POND") %>%
year_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
mutate(OBS_VALUE = OBS_VALUE/10000) %>%
ggplot() + ylab("Poids de la santé dans l'indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(0, 20, 0.5),
labels = percent_format(accuracy = .1))
Santé, Assurance santé complémentaire
All
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("06", "1253"),
REF_AREA == "FE",
NATURE == "POND") %>%
year_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
mutate(OBS_VALUE = OBS_VALUE/10000) %>%
ggplot() + ylab("Poids de la santé dans l'indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_color_manual(values = viridis(2)[1:2]) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 20, 0.5),
labels = percent_format(accuracy = .1))
1996-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("06", "1253"),
REF_AREA == "FE",
NATURE == "POND") %>%
year_to_date %>%
filter(date >= as.Date("1996-01-01")) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
mutate(OBS_VALUE = OBS_VALUE/10000) %>%
ggplot() + ylab("Poids de la santé dans l'indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_color_manual(values = viridis(2)[1:2]) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 20, 0.5),
labels = percent_format(accuracy = .1))
Santé
Santé, Assurance Santé vs. Ensemble
1990-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
PRIX_CONSO == "SO",
COICOP2016 %in% c("06", "1253", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = c(100, 164, 200, 400, 816, seq(100, 180, 10)),
labels = dollar_format(accuracy = 1, prefix = ""))
1996-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
PRIX_CONSO == "SO",
COICOP2016 %in% c("06", "1253", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(date >= as.Date("1996-01-01")) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix, IPC") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = c(100, 164, 200, 400, 816, seq(100, 180, 10)),
labels = dollar_format(accuracy = 1, prefix = ""))
Péages
Péages vs. Ensemble
1990-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
PRIX_CONSO == "SO",
COICOP2016 %in% c("07242", "0724", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.35, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(100, 400, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
1996-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
PRIX_CONSO == "SO",
COICOP2016 %in% c("07242", "0724", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
filter(date >= as.Date("1996-01-01")) %>%
group_by(Coicop2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.35, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(100, 400, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
Pondération des péages, services divers dans l’IPC
1990-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("07242", "0724"),
REF_AREA == "FE",
NATURE == "POND") %>%
year_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
mutate(OBS_VALUE = OBS_VALUE/10000) %>%
ggplot() + ylab("Poids des péages dans l'indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
theme(legend.position = c(0.35, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(0, 10, 0.1),
labels = percent_format(accuracy = .1),
limits = c(0, 0.016))
1996-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("07242", "0724"),
REF_AREA == "FE",
NATURE == "POND") %>%
year_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
mutate(OBS_VALUE = OBS_VALUE/10000) %>%
filter(date >= as.Date("1996-01-01")) %>%
ggplot() + ylab("Poids des péages dans l'indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
theme(legend.position = c(0.35, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(0, 10, 0.1),
labels = percent_format(accuracy = .1),
limits = c(0, 0.016))
Services à domicile
Services à domicile vs. Ensemble
1998-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
PRIX_CONSO == "SO",
COICOP2016 %in% c("05621", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(date >= as.Date("1998-01-01")) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.35, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(100, 200, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
Tabac
Tabac vs. Ensemble
1990-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
PRIX_CONSO == "SO",
COICOP2016 %in% c("022", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = c(100, 164, 200, 400, 600, 800, 1000),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
1992-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("022", "00"),
FREQ == "M",
PRIX_CONSO == "SO",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(date >= as.Date("1992-01-01")) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = c(100, 164, 200, 400, 816),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
1996-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("022", "00"),
FREQ == "M",
PRIX_CONSO == "SO",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(date >= as.Date("1996-01-01")) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(100, 1000, 100),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
2000-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("022", "00"),
FREQ == "M",
PRIX_CONSO == "SO",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(date >= as.Date("2000-01-01")) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 100, 20), seq(0, 1000, 50)),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
2012-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("022", "00"),
FREQ == "M",
PRIX_CONSO == "SO",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(date >= as.Date("2012-01-01")) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 100, 10), seq(0, 1000, 10)),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
Ensemble avec Tabac (IdBank: 001763852, 001759970)
1990-
`IPC-2015` %>%
filter(IDBANK %in% c("001759970", "001763852")) %>%
month_to_date %>%
arrange(date) %>%
filter(date >= as.Date("1990-01-01")) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
group_by(Prix_conso) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 10),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
1992-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
PRIX_CONSO %in% c("4035", "4018", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(date >= as.Date("1992-01-01")) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
group_by(Prix_conso) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 10),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
1996-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
PRIX_CONSO %in% c("4035", "4018", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(date >= as.Date("1996-01-01")) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
group_by(Prix_conso) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 10),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
2000-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
PRIX_CONSO %in% c("4035", "4018", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(date >= as.Date("2000-01-01")) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
group_by(Prix_conso) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
2012-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
PRIX_CONSO %in% c("4035", "4018", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(date >= as.Date("2012-01-01")) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
group_by(Prix_conso) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.35, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 1),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
2017-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
PRIX_CONSO %in% c("4035", "4018", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(date >= as.Date("2017-01-01")) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
group_by(Prix_conso) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.35, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 1),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
Glissement sur 2 ans
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
PRIX_CONSO %in% c("4035", "4018", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
month_to_date %>%
filter(date >= max(date) - years(2)) %>%
group_by(Prix_conso) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
theme(legend.position = c(0.65, 0.2),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
scale_y_log10(breaks = seq(0, 200, 1),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)),
fontface ="plain", color = "black", size = 3)
Glissement sur 1 an
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
PRIX_CONSO %in% c("4035", "4018", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
month_to_date %>%
filter(date >= max(date) - years(1)) %>%
group_by(Prix_conso) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
theme(legend.position = c(0.65, 0.2),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
scale_y_log10(breaks = seq(0, 200, 1),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)),
fontface ="plain", color = "black", size = 3)
Immobilier
4003, 4009, 4034
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
PRIX_CONSO %in% c("4003", "4009", "4034"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso, linetype = Prix_conso)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
041, 043, 044
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("041", "043", "044"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE",
#OBS_STATUS == "A"
) %>%
month_to_date %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
left_join(COICOP2016, by = "COICOP2016") %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.6, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
041 Loyers effectifs, 00 Ensemble
1990-
All
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("041", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016, Prix_conso) %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1990-01-01")]) %>%
mutate(Variable = paste0(Coicop2016, " - ", Prix_conso),
Variable = gsub(" - Sans objet", "", Variable)) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
Loyers, Loyers Réels, IPC
`IPC-2015-2020` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("041", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE",
#OBS_STATUS == "A"
) %>%
#select(-OBS_VALUE, - TIME_PERIOD) %>%
#distinct
month_to_date %>%
select(date, COICOP2016, OBS_VALUE) %>%
spread(COICOP2016, OBS_VALUE) %>%
mutate(`real_rents` = `041`/`00`) %>%
gather(COICOP2016, OBS_VALUE, -date) %>%
left_join(tibble(COICOP2016 = c("041", "00", "real_rents"),
Coicop2016 = c("Loyers", "IPC", "Loyers Réels")),
by = "COICOP2016") %>%
group_by(COICOP2016) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
1992-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("041", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016, Prix_conso) %>%
filter(date >= as.Date("1992-01-01")) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1992-01-01")]) %>%
mutate(Variable = paste0(Coicop2016, " - ", Prix_conso),
Variable = gsub(" - Sans objet", "", Variable)) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Variable)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
1992- (fictifs)
- Blog Loyers. Lien
`IPC-2015-2020` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("041", "00", "4035"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE",
#OBS_STATUS == "A"
) %>%
#select(-OBS_VALUE, - TIME_PERIOD) %>%
#distinct
month_to_date %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016) %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1992-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2022, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
1990-
`IPC-2015-2020` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("041", "00", "4035"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE",
#OBS_STATUS == "A"
) %>%
#select(-OBS_VALUE, - TIME_PERIOD) %>%
#distinct
month_to_date %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016) %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_color_manual(values = viridis(3)[1:2]) +
scale_x_date(breaks = seq(1920, 2022, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
1996-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("041", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016, Prix_conso) %>%
filter(date >= as.Date("1996-01-01")) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
mutate(Variable = paste0(Coicop2016, " - ", Prix_conso),
Variable = gsub(" - Sans objet", "", Variable)) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Variable)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
1990-1999
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("041", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016, Prix_conso) %>%
filter(date >= as.Date("1990-01-01"),
date <= as.Date("1999-01-01")) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1990-01-01")]) %>%
mutate(Variable = paste0(Coicop2016, " - ", Prix_conso),
Variable = gsub(" - Sans objet", "", Variable)) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Variable)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.35, 0.85),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
1999-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("041", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016, Prix_conso) %>%
filter(date >= as.Date("1999-01-01")) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1999-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = paste0(Coicop2016, " - ", Prix_conso))) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
2000-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("041", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016, Prix_conso) %>%
filter(date >= as.Date("2000-01-01")) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2000-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = paste0(Coicop2016, " - ", Prix_conso))) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
Glissement sur 2 ans
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("041", "00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(date >= max(date) - years(2)) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = paste0(Coicop2016, " - ", Prix_conso))) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
theme(legend.position = c(0.28, 0.87),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
scale_y_log10(breaks = seq(0, 200, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)),
fontface ="plain", color = "black", size = 3)
Insee calcule y compris loyers fictifs
1990-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
REF_AREA == "FE",
PRIX_CONSO %in% c("00", "4035"),
#PRIX_CONSO %in% c("00"),
NATURE == "INDICE",
FREQ == "M") %>%
month_to_date %>%
group_by(PRIX_CONSO) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("1990-01-01")]) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 10),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
1992-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
REF_AREA == "FE",
PRIX_CONSO %in% c("00", "4035"),
#PRIX_CONSO %in% c("00"),
NATURE == "INDICE",
FREQ == "M") %>%
month_to_date %>%
group_by(PRIX_CONSO) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("1992-01-01")]) %>%
filter(date >= as.Date("1992-01-01")) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
1995-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
REF_AREA == "FE",
PRIX_CONSO %in% c("00", "4035"),
#PRIX_CONSO %in% c("00"),
NATURE == "INDICE",
FREQ == "M") %>%
month_to_date %>%
filter(date >= as.Date("1995-01-01")) %>%
group_by(PRIX_CONSO) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("1995-01-01")]) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 10),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
2008-
`IPC-2015` %>%
filter(INDICATEUR == "IPC",
REF_AREA == "FE",
PRIX_CONSO %in% c("00", "4035"),
#PRIX_CONSO %in% c("00"),
NATURE == "INDICE",
FREQ == "M") %>%
month_to_date %>%
group_by(PRIX_CONSO) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("2008-01-01")]) %>%
filter(date >= as.Date("2008-01-01")) %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
Categories
Par Deciles - INF-D1, D5-D6, D9-PLUS
`IPC-2015` %>%
filter(MENAGES_IPC %in% c("INF-D1", "D5-D6", "D9-PLUS")) %>%
year_to_date %>%
left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
group_by(MENAGES_IPC) %>%
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 = Menages_ipc)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.35, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
Par CSP - Actif, Retraité, Cadre, Ouvrier
`IPC-2015` %>%
filter(MENAGES_IPC %in% c("ACTIF", "RETRAITE", "CADRE", "OUVRIER")) %>%
year_to_date %>%
left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
group_by(MENAGES_IPC) %>%
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 = Menages_ipc)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.35, 0.85),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(data = . %>%
filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), size = 3)
Par Age - 30-44, 45-59, 60-74
All
`IPC-2015` %>%
filter(MENAGES_IPC %in% c("MOINS-29", "30-44", "45-59", "60-74", "PLUS-75")) %>%
year_to_date %>%
left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
group_by(MENAGES_IPC) %>%
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 = Menages_ipc)) +
scale_color_manual(values = viridis(6)[1:5]) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
2008-
`IPC-2015` %>%
filter(MENAGES_IPC %in% c("MOINS-29", "ENSEMBLE", "45-59", "PLUS-75"),
INDICATEUR == "IPC",
PRIX_CONSO == "4035",
NATURE == "INDICE",
REF_AREA == "FM",
FREQ == "A",
COICOP2016 == "SO") %>%
year_to_date %>%
group_by(MENAGES_IPC) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("2008-01-01")]) %>%
filter(date >= as.Date("2008-01-01")) %>%
left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Menages_ipc)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 1),
labels = dollar_format(accuracy = 1, prefix = ""))
Locataire VS Propriétaire VS Accédant
`IPC-2015` %>%
filter(MENAGES_IPC %in% c("ACCES-PROPRIETE", "LOCATAIRE", "PROPRIETAIRE")) %>%
year_to_date %>%
left_join(MENAGES_IPC, by = "MENAGES_IPC") %>%
group_by(MENAGES_IPC) %>%
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 = Menages_ipc, linetype = Menages_ipc)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
Cantines
1990-
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "1112"),
REF_AREA == "FM",
FREQ == "M") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
filter(date >= as.Date("1990-01-01")) %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
2000-
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "1112"),
REF_AREA == "FM",
FREQ == "M") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
filter(date >= as.Date("2000-01-01")) %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
2010-
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "1112"),
REF_AREA == "FM",
FREQ == "M") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
filter(date >= as.Date("2010-01-01")) %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
Scolaire, Universitaire VS Entreprise ou Admin.
`IPC-2015` %>%
filter(COICOP2016 %in% c("111202", "111201"),
NATURE == "INDICE") %>%
year_to_date %>%
arrange(desc(date)) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.35, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
Assurance santé
1990-
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "12532", "1253"),
REF_AREA == "FM",
FREQ == "M") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
filter(date >= as.Date("1990-01-01")) %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
2000-
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "12532", "1253"),
REF_AREA == "FM",
FREQ == "M") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
filter(date >= as.Date("2000-01-01")) %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
2010-
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "12532"),
REF_AREA == "FM",
FREQ == "M") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
filter(date >= as.Date("2010-01-01")) %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
2-digit
Boissons Alcoolisées, Logement, Restaurants et hôtels
All
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "02", "11", "04"),
REF_AREA == "FM",
FREQ == "M") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.85),
legend.title = element_blank()) +
scale_y_log10(breaks = c(100, 120, 150, 200, 220, 250, 300, 400, 500),
labels = dollar_format(accuracy = 1, prefix = ""))
Glissement sur 2 ans
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "02", "11", "04"),
REF_AREA == "FM",
FREQ == "M") %>%
month_to_date %>%
filter(date >= max(date) - years(2)) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
theme(legend.position = c(0.28, 0.87),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
scale_y_log10(breaks = seq(0, 200, 1),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)),
fontface ="plain", color = "black", size = 3)
Transports, Enseignement, B&S Divers
Tous
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "10", "07", "12"),
REF_AREA == "FM",
FREQ == "M") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.85),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(100, 300, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
Glissement sur 2 ans
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "10", "07", "12"),
REF_AREA == "FM",
FREQ == "M") %>%
month_to_date %>%
filter(date >= max(date) - years(2)) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
theme(legend.position = c(0.18, 0.87),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
scale_y_log10(breaks = seq(0, 200, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)),
fontface ="plain", color = "black", size = 3)
Alimentation, Habillement, Meubles
Tous
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "05", "01", "03"),
REF_AREA == "FM",
FREQ == "M") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.35, 0.85),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(100, 300, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
Glissement sur 2 ans
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "05", "01", "03"),
REF_AREA == "FM",
FREQ == "M") %>%
month_to_date %>%
filter(date >= max(date) - years(2)) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
theme(legend.position = c(0.28, 0.87),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
scale_y_log10(breaks = seq(0, 200, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)),
fontface ="plain", color = "black", size = 3)
Santé, Communications, Loisirs
Tous
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "06", "09", "08"),
REF_AREA == "FM",
FREQ == "M") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.2, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
1996-
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "06", "09", "08"),
REF_AREA == "FM",
FREQ == "M") %>%
month_to_date %>%
filter(date >= as.Date("1996-01-01")) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.2, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
2000-
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "06", "09", "08"),
REF_AREA == "FM",
FREQ == "M") %>%
month_to_date %>%
filter(date >= as.Date("2000-01-01")) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.18, 0.22),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
Glissement sur 2 ans
`IPC-2015` %>%
filter(COICOP2016 %in% c("00", "06", "09", "08"),
REF_AREA == "FM",
FREQ == "M") %>%
month_to_date %>%
filter(date >= max(date) - years(2)) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(COICOP2016) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
theme(legend.position = c(0.28, 0.87),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
scale_y_log10(breaks = seq(0, 200, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)),
fontface ="plain", color = "black", size = 3)
Comparer l’IPC et le déflateur de la consommation
Déflateur consommation finale des ménages
