Indices des prix à la consommation harmonisés
Data - INSEE
François Geerolf
Info
| source | dataset | .html | .RData |
|---|---|---|---|
| insee | IPC-2015 | 2024-05-09 | 2024-04-09 |
| insee | IPCH-2015 | 2024-05-09 | 2024-05-16 |
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 | 2024-05-09 | 2024-05-09 |
| insee | IPCH-2015 | 2024-05-09 | 2024-05-16 |
| insee | IPGD-2015 | 2024-05-09 | 2024-03-20 |
| insee | IPLA-IPLNA-2015 | 2024-05-09 | 2024-05-09 |
| insee | IPPI-2015 | 2024-05-09 | 2024-03-30 |
| insee | IRL | 2024-05-09 | 2024-05-09 |
| insee | SERIES_LOYERS | 2024-05-09 | 2024-05-09 |
| insee | T_CONSO_EFF_FONCTION | 2024-05-09 | 2024-04-01 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-05-16 |
LAST_UPDATE
`IPCH-2015` %>%
group_by(LAST_UPDATE) %>%
summarise(Nobs = n()) %>%
arrange(desc(LAST_UPDATE)) %>%
print_table_conditional()| LAST_UPDATE | Nobs |
|---|---|
| 2024-05-15 | 132756 |
| 2024-02-16 | 11354 |
| 2024-01-12 | 10896 |
| 2021-02-19 | 23 |
| 2021-01-15 | 325 |
| 2019-02-22 | 48 |
| 2019-01-15 | 299 |
| 2017-01-12 | 273 |
| 2016-02-22 | 520 |
| 2016-02-18 | 1211 |
Last
`IPCH-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 | 417 |
| 2024-04 | M | 417 |
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.
TITLE_FR
COICOP2016
All
2-digit
`IPCH-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 |
|---|---|---|
| 00 | 00 - Ensemble | 725 |
| 01 | 01 - Produits alimentaires et boissons non alcoolisées | 397 |
| 02 | 02 - Boissons alcoolisées, tabac et stupéfiants | 397 |
| 03 | 03 - Articles d’habillement et chaussures | 397 |
| 04 | 04 - Logement, eau, gaz, électricité et autres combustibles | 397 |
| 05 | 05 - Meubles, articles de ménage et entretien courant du foyer | 397 |
| 06 | 06 - Santé | 397 |
| 07 | 07 - Transports | 397 |
| 08 | 08 - Communications | 397 |
| 09 | 09 - Loisirs et culture | 397 |
| 10 | 10 - Enseignement | 397 |
| 11 | 11 - Restaurants et hôtels | 397 |
| 12 | 12 - Biens et services divers | 397 |
INDICATEUR
`IPCH-2015` %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR, Indicateur) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional| INDICATEUR | Indicateur | Nobs |
|---|---|---|
| IPCH | Indice des prix à la consommation harmonisé (IPCH) | 157705 |
FREQ
`IPCH-2015` %>%
left_join(FREQ, by = "FREQ") %>%
group_by(FREQ, Freq) %>%
summarise(Nobs = n()) %>%
print_table_conditional| FREQ | Freq | Nobs |
|---|---|---|
| A | Annual | 22621 |
| M | Monthly | 135084 |
NATURE
`IPCH-2015` %>%
left_join(NATURE, by = "NATURE") %>%
group_by(NATURE, Nature) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional| NATURE | Nature | Nobs |
|---|---|---|
| INDICE | Indice | 145846 |
| POND | Pondérations d’indice | 11531 |
| GLISSEMENT_ANNUEL | Glissement annuel | 328 |
REF_AREA
`IPCH-2015` %>%
left_join(REF_AREA, by = "REF_AREA") %>%
group_by(REF_AREA, Ref_area) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional| REF_AREA | Ref_area | Nobs |
|---|---|---|
| FE | France | 157705 |
TITLE_FR
TIME_PERIOD
00 - Ensemble
Glissement annuel
`IPCH-2015` %>%
filter(NATURE == "GLISSEMENT_ANNUEL") %>%
month_to_date %>%
ggplot(.) + ylab("Glissement annuel (%)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/100)) +
theme(legend.position = c(0.3, 0.8),
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(-10, 10, 1),
labels = percent_format())
IPCH Ensemble
IPCH-IPC-2015-ensemble
`IPCH-IPC-2015-ensemble` <- `IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
arrange(desc(TIME_PERIOD)) %>%
filter(COICOP2016 %in% c("00"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
#filter(zoo::as.yearmon(TIME_PERIOD) >= zoo::as.yearmon("1995-12")) %>%
select(INDICATEUR, TIME_PERIOD, OBS_VALUE)
do.call(save, list("IPCH-IPC-2015-ensemble", file = "IPCH-IPC-2015-ensemble2.RData"))
`IPCH-IPC-2015-ensemble` <- `IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
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(INDICATEUR, by = "INDICATEUR") %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
group_by(INDICATEUR) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ungroup %>%
select(INDICATEUR, Indicateur, date, OBS_VALUE)
do.call(save, list("IPCH-IPC-2015-ensemble", file = "IPCH-IPC-2015-ensemble.RData"))
`IPCH-IPC-2015-ensemble` %>%
group_by(INDICATEUR) %>%
summarise(First_value = first(OBS_VALUE),
First_date = first(date),
Last_value = last(OBS_VALUE),
Last_date = last(date)) %>%
print_table_conditional()| INDICATEUR | First_value | First_date | Last_value | Last_date |
|---|---|---|---|---|
| IPC | 100 | 1996-01-01 | 157.2594 | 2024-03-01 |
| IPCH | 100 | 1996-01-01 | 166.5452 | 2024-04-01 |
5 dernières années
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "A",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
year_to_date %>%
filter(date >= Sys.Date() - years(7)) %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE)-1) %>%
select(date, OBS_VALUE, Indicateur) %>%
na.omit %>%
ggplot(.) + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
#
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 10, 1),
labels = percent_format()) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
10 dernières années
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "A",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
year_to_date %>%
filter(date >= Sys.Date() - years(12)) %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE)-1) %>%
select(date, OBS_VALUE, Indicateur) %>%
na.omit %>%
ggplot(.) + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
#
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 10, 1),
labels = percent_format()) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
2000-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "A",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
year_to_date %>%
filter(date >= as.Date("2000-01-01")) %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE)-1) %>%
select(date, OBS_VALUE, Indicateur) %>%
na.omit %>%
ggplot(.) + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
#
theme(legend.position = c(0.3, 0.8),
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(-10, 10, 1),
labels = percent_format()) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
15 dernières années
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "A",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
year_to_date %>%
filter(date >= Sys.Date() - years(17)) %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE)-1) %>%
select(date, OBS_VALUE, Indicateur) %>%
na.omit %>%
ggplot(.) + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
#
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 10, 1),
labels = percent_format()) +
geom_text_repel(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
2017-
Monthly
`IPCH-IPC-2015-ensemble` %>%
filter(date >= as.Date("2017-01-01")) %>%
group_by(Indicateur) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% 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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label(data = . %>% tail(1), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)))
2020-
Monthly
`IPCH-IPC-2015-ensemble` %>%
filter(date >= as.Date("2019-05-01")) %>%
group_by(Indicateur) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 1),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label(data = . %>%
filter(date == max(date)), aes(date, y = OBS_VALUE, label = round(OBS_VALUE, 1)))
3 years
Monthly
`IPCH-IPC-2015-ensemble` %>%
filter(date >= as.Date("2021-01-01")) %>%
group_by(Indicateur) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 1),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label(data = . %>%
filter(date == max(date)), aes(date, y = OBS_VALUE, label = round(OBS_VALUE, 1)))
2008-
Monthly - IPC and IPCH
`IPCH-IPC-2015-ensemble` %>%
filter(date >+ as.Date("2008-01-01")) %>%
group_by(Indicateur) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label(data = . %>%
filter(date == max(date)), aes(date, y = OBS_VALUE, label = round(OBS_VALUE, 1)))
1996-
Monthly - IPC and IPCH
`IPCH-IPC-2015-ensemble` %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label(data = . %>%
filter(date == max(date)), aes(date, y = OBS_VALUE, label = round(OBS_VALUE, 1)))
With labels
`IPCH-IPC-2015-ensemble` %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_text_repel(data = . %>% filter(month(date) == 12),
aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)),
fontface ="plain", color = "black", size = 3)
Monthly - IPCH / IPC
Normal
1999-
`IPCH-IPC-2015-ensemble` %>%
select(date, INDICATEUR, OBS_VALUE) %>%
spread(INDICATEUR, OBS_VALUE) %>%
mutate(OBS_VALUE = 100*IPCH/IPC) %>%
filter(date >= as.Date("1999-01-01")) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Ratio IPCH/IPC") + 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(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 1),
labels = dollar_format(accuracy = 1, prefix = ""))
1996-
`IPCH-IPC-2015-ensemble` %>%
select(date, INDICATEUR, OBS_VALUE) %>%
spread(INDICATEUR, OBS_VALUE) %>%
mutate(OBS_VALUE = 100*IPCH/IPC) %>%
ggplot() + ylab("Ratio IPCH/IPC") + 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(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 1),
labels = dollar_format(accuracy = 1, prefix = ""))
Inverted
1996-
`IPCH-IPC-2015-ensemble` %>%
select(date, INDICATEUR, OBS_VALUE) %>%
spread(INDICATEUR, OBS_VALUE) %>%
mutate(OBS_VALUE = 100*IPC/IPCH) %>%
ggplot() + ylab("Ratio IPC/IPCH") + 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(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 1),
labels = dollar_format(accuracy = 1, prefix = ""))
1999-
`IPCH-IPC-2015-ensemble` %>%
select(date, INDICATEUR, OBS_VALUE) %>%
spread(INDICATEUR, OBS_VALUE) %>%
mutate(OBS_VALUE = 100*IPC/IPCH) %>%
filter(date >= as.Date("1999-01-01")) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Ratio IPC/IPCH") + 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(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 1),
labels = dollar_format(accuracy = 1, prefix = ""))
Annual
No labels
1996-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "A",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
year_to_date %>%
filter(date >= as.Date("1996-01-01")) %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
1999-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "A",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
year_to_date %>%
filter(date >= as.Date("1999-01-01")) %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1999-01-01")]) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1999, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
labels
France Entière
1996-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "A",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
year_to_date %>%
filter(date >= as.Date("1996-01-01")) %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 300, 5),
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)
1999-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "A",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
year_to_date %>%
filter(date >= as.Date("1999-01-01")) %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1999-01-01")]) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1999, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 300, 5),
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)
France Métropolitaine
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
NATURE == "INDICE",
REF_AREA == "FM",
FREQ == "A",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
year_to_date %>%
filter(date >= as.Date("1996-01-01")) %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 300, 5),
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)
Avec hors tabac
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00", "SO"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "A",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO" | PRIX_CONSO == "4018") %>%
year_to_date %>%
filter(date >= as.Date("1996-01-01")) %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur, linetype = COICOP2016)) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 300, 5),
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)
France Métropolitaine
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
NATURE == "INDICE",
REF_AREA == "FM",
FREQ == "A",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO" | PRIX_CONSO == "4018") %>%
year_to_date %>%
filter(date >= as.Date("1996-01-01")) %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 300, 5),
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)
Annual - Growth
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "A",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
year_to_date %>%
filter(date >= as.Date("1996-01-01")) %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")],
OBS_VALUE = OBS_VALUE/lag(OBS_VALUE)-1) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
#
theme(legend.position = c(0.3, 0.8),
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(-10, 10, 1),
labels = percent_format())
1990-
Quarterly
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
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(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
Annual
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "A",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE",
is.na(PRIX_CONSO) | PRIX_CONSO == "SO") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ungroup %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
Inflation sur un an
3 dernières années
# [1] "fr_CA.UTF-8"`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
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(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
filter(date >= Sys.Date() - years(3)) %>%
select(date, OBS_VALUE, Indicateur) %>%
na.omit %>%
ggplot() + ylab("Inflation sur un an (IPC, IPCH)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = "2 months",
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 = 1, hjust = 1)) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
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)
2 dernières années
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
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(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
filter(date >= Sys.Date() - years(2)) %>%
select(date, OBS_VALUE, Indicateur) %>%
na.omit %>%
ggplot() + ylab("Inflation sur un an (IPC, IPCH)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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 = 1, hjust = 1)) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
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)
2020-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
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(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("2021-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
select(date, OBS_VALUE, Indicateur) %>%
na.omit %>%
ggplot() + ylab("Inflation sur un an (IPC, IPCH)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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 = 1, hjust = 1)) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
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)
2011-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
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(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("2011-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
select(date, OBS_VALUE, Indicateur) %>%
na.omit %>%
ggplot() + ylab("Inflation sur un an (IPC, IPCH)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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_continuous(breaks = 0.01*seq(-100, 300, 0.5),
labels = percent_format(accuracy = .1, prefix = ""))
1996-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
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(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
ggplot() + ylab("Inflation sur un an (IPC, IPCH)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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_continuous(breaks = 0.01*seq(-100, 300, 0.5),
labels = percent_format(accuracy = .1, prefix = ""))
1990-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
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(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1990-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
ggplot() + ylab("Inflation sur un an (IPC, IPCH)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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_continuous(breaks = 0.01*seq(-100, 300, 0.5),
labels = percent_format(accuracy = .1, prefix = ""))
Inflation sur 2 ans
Par an
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
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(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = (OBS_VALUE/lag(OBS_VALUE, 24))^(1/2)-1) %>%
ggplot() + ylab("Inflation annuelle sur 2 ans (IPC, IPCH)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
labels = percent_format(accuracy = .1, prefix = ""))
Sur 2 ans
Tous
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
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(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = (OBS_VALUE/lag(OBS_VALUE, 24))-1) %>%
ggplot() + ylab("Inflation annuelle sur 2 ans (IPC, IPCH)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 1),
labels = percent_format(accuracy = .1, prefix = ""))
2020-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
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(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = (OBS_VALUE/lag(OBS_VALUE, 24))-1) %>%
filter(date >= as.Date("2021-01-01")) %>%
ggplot() + ylab("Inflation sur 2 ans (IPC, IPCH)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.28, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 1),
labels = percent_format(accuracy = .1, prefix = "")) +
geom_text(data = . %>%
filter(month(date) %in% c(1, 4, 7, 10)), aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)))
2020- (3 ans )
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("00"),
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(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
mutate(OBS_VALUE = (OBS_VALUE/lag(OBS_VALUE, 36))-1) %>%
filter(date >= as.Date("2021-01-01")) %>%
ggplot() + ylab("Inflation sur 3 ans (IPC, IPCH)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.28, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 1),
labels = percent_format(accuracy = .1, prefix = "")) +
geom_text(data = . %>%
filter(month(date) %in% c(1, 4, 7, 10)), aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)))
041 Loyers effectifs, 00 Ensemble
1996-
`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("041", "00"),
FREQ == "M",
NATURE == "INDICE") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
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, 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 = ""))
1999-
`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("041", "00"),
FREQ == "M",
NATURE == "INDICE") %>%
month_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(Coicop2016) %>%
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 = Coicop2016)) +
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, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
Glissement sur 2 ans
`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("041", "00"),
FREQ == "M",
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(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, loyers, chauffage, Ensemble
Glissement sur 2 ans
`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("041", "00", "01", "045"),
FREQ == "M",
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(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)
Pondérations d’indice
Table - COICOP2016
All
2-digit
`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
REF_AREA == "FE",
NATURE == "POND",
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 | 2000 | 2010 | 2020 |
|---|---|---|---|---|
| 00 | 00 - Ensemble | 10000 | 10000 | 10000 |
| 01 | 01 - Produits alimentaires et boissons non alcoolisées | 1695 | 1603 | 1592 |
| 02 | 02 - Boissons alcoolisées, tabac et stupéfiants | 412 | 357 | 442 |
| 03 | 03 - Articles d’habillement et chaussures | 588 | 531 | 439 |
| 04 | 04 - Logement, eau, gaz, électricité et autres combustibles | 1481 | 1467 | 1567 |
| 05 | 05 - Meubles, articles de ménage et entretien courant du foyer | 705 | 674 | 545 |
| 06 | 06 - Santé | 317 | 445 | 430 |
| 07 | 07 - Transports | 1802 | 1759 | 1735 |
| 08 | 08 - Communications | 273 | 330 | 276 |
| 09 | 09 - Loisirs et culture | 931 | 992 | 815 |
| 10 | 10 - Enseignement | 57 | 58 | 41 |
| 11 | 11 - Restaurants et hôtels | 870 | 740 | 899 |
| 12 | 12 - Biens et services divers | 869 | 1044 | 1219 |
Santé, Assurance santé complémentaire
All
`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
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'IPCH") + 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_continuous(breaks = 0.01*seq(0, 20, 0.5),
labels = percent_format(accuracy = .1))
Creche, cantine
All
`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("1112", "12401"),
REF_AREA == "FE",
NATURE == "POND") %>%
year_to_date %>%
left_join(COICOP2016, by = "COICOP2016") %>%
mutate(OBS_VALUE = OBS_VALUE/10000) %>%
ggplot() + ylab("Poids dans l'IPCH") + 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_continuous(breaks = 0.01*seq(0, 20, 0.5),
labels = percent_format(accuracy = .1))
IPC VS IPCH
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("1112", "12401"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération - IPC vs. IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Coicop2016, linetype = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.55),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .1),
labels = percent_format(accuracy = .1))
2-digit
Boissons Alcoolisées, Logement, Restaurants et hôtels
`IPCH-2015` %>%
filter(COICOP2016 %in% c("00", "02", "11", "04"),
NATURE == "INDICE",
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 = ""))
Transports, Enseignement, B&S Divers
`IPCH-2015` %>%
filter(COICOP2016 %in% c("00", "10", "07", "12"),
NATURE == "INDICE",
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 = ""))
Alimentation, Habillement, Meubles
`IPCH-2015` %>%
filter(COICOP2016 %in% c("00", "05", "01", "03"),
NATURE == "INDICE",
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 = ""))
Santé, Communications, Loisirs
`IPCH-2015` %>%
filter(COICOP2016 %in% c("00", "06", "09", "08"),
NATURE == "INDICE",
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, 0.85),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
Comparatif Pondérations IPC et IPCH
Santé
06 - Santé et 1253 - Assurances santé
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("06", "1253"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération - Santé") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Coicop2016, linetype = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.6),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, 1),
labels = percent_format(accuracy = 1))
Santé
06 - Santé
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("06"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération, 06 - Santé") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.55),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, 1),
labels = percent_format(accuracy = 1))
061 - Produits médicaux
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("061"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération, 061 - Produits médicaux") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.55),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, 1),
labels = percent_format(accuracy = 1))
062 - Services médicaux
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("062"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération, 062 - Services médicaux") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.55),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, 1),
labels = percent_format(accuracy = 1))
0611 - Produits pharmaceutiques
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0611"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération, 0611 - Produits pharmaceutiques") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.55),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, 1),
labels = percent_format(accuracy = 1))
0612 - Produits médicaux divers
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0612"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération des produits médicaux divers l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .1),
labels = percent_format(accuracy = .1))
0613 - Appareils et matériel thérapeutiques
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0613"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération des Appareils et matériel thérapeutiques l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.55),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .1),
labels = percent_format(accuracy = .1))
0621 - Services médicaux
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0621"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération des Appareils et matériel thérapeutiques l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.55),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .1),
labels = percent_format(accuracy = .1))
0622 - Services médicaux
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0622"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération des Appareils et matériel thérapeutiques l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.55),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .1),
labels = percent_format(accuracy = .1))
0623 - Services paramédicaux
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0623"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération des Appareils et matériel thérapeutiques l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.55),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .1),
labels = percent_format(accuracy = .1))
Enseignement
10,
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("10", "101", "102", "104"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
left_join(COICOP2016, by = "COICOP2016") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération - Santé") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Coicop2016, linetype = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .1),
labels = percent_format(accuracy = .1))
10 - Enseignement
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("10"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération de l'Enseignement dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.28, 0.95),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .1),
labels = percent_format(accuracy = .1))
101 - Enseignement Primaire
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("101"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération de l'Enseignement Primaire dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.28, 0.95),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .01),
labels = percent_format(accuracy = .01))
102 - Enseignement Secondaire
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("102"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération de l'Enseignement Secondaire dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.28, 0.95),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .02),
labels = percent_format(accuracy = .01))
104 - Enseignement Supérieur
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("104"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération de l'Enseignement Supérieur dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.28, 0.95),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .02),
labels = percent_format(accuracy = .01))
09 - Loisirs et culture
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("09"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération de la Santé dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .5),
labels = percent_format(accuracy = .1))
05 - Meubles en entretien
All
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("05"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération de 05 dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .5),
labels = percent_format(accuracy = .1))
05.6.2 - Services domestiques et services ménagers
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0562"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération de 05 dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .1),
labels = percent_format(accuracy = .1))
01 - Produits alimentaires
All
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("01"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
arrange(date) %>%
ggplot() + ylab("Pondération de l'Alimentation dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .5),
labels = percent_format(accuracy = .1)) +
geom_text_repel(aes(x = date, y = OBS_VALUE/10000, label = percent(OBS_VALUE/10000, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
1996-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("01"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération de l'Alimentation dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .5),
labels = percent_format(accuracy = .1)) +
geom_text_repel(aes(x = date, y = OBS_VALUE/10000, label = percent(OBS_VALUE/10000, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
2010-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("01"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("2010-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération de l'Alimentation dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .5),
labels = percent_format(accuracy = .1)) +
geom_text_repel(aes(x = date, y = OBS_VALUE/10000, label = percent(OBS_VALUE/10000, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
02 - Boissons
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("02"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération de l'Alimentation dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .5),
labels = percent_format(accuracy = .1))
11 - Restaurants
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("11"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.4, 0.15),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .5),
labels = percent_format(accuracy = .1))
1112 - Cantines
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("1112"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.4, 0.15),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .1),
labels = percent_format(accuracy = .1))
03 - Habillement
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("03"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération de l'Habillement dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .5),
labels = percent_format(accuracy = .1))
04 - Logement
04 - Logement
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("04"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération du Logement dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
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_continuous(breaks = 0.01*seq(0, 300, 1),
labels = percent_format(accuracy = 1)) +
geom_text_repel(aes(x = date, y = OBS_VALUE/10000, label = percent(OBS_VALUE/10000, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
041 - Loyers
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("041"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération des Loyers dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
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_continuous(breaks = 0.01*seq(0, 300, .2),
labels = percent_format(accuracy = .1)) +
geom_text_repel(aes(x = date, y = OBS_VALUE/10000, label = percent(OBS_VALUE/10000, acc = 0.1)),
fontface ="plain", color = "black", size = 3)
07 - Transports
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("07"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération des Transports dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .5),
labels = percent_format(accuracy = .1))
08 - Communications
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("08"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération des Communications dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
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_continuous(breaks = 0.01*seq(0, 300, .2),
labels = percent_format(accuracy = .1))
12
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("12"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondérationdans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
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_continuous(breaks = 0.01*seq(0, 300, .2),
labels = percent_format(accuracy = .1))
12.4 - Protection sociale
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("124"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondérationdans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
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_continuous(breaks = 0.01*seq(0, 300, .2),
labels = percent_format(accuracy = .1))
12.4.0.1 - Services de garde d’enfants
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("12401"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondérationdans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
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_continuous(breaks = 0.01*seq(0, 300, .2),
labels = percent_format(accuracy = .1))
12.4.0.2 - Maisons de retraite pour personnes âgées et foyers pour handicapés
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("12402"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondérationdans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
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_continuous(breaks = 0.01*seq(0, 300, .2),
labels = percent_format(accuracy = .1))
12.4.0.3 - Aides à domicile
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("12403"),
NATURE == "POND",
REF_AREA == "FE",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
year_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
ggplot() + ylab("Pondération dans l'IPC, IPCH") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE/10000, color = Indicateur)) +
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_continuous(breaks = 0.01*seq(0, 300, .2),
labels = percent_format(accuracy = .1))
Comparatif IPC et IPCH
Santé
La principale différence entre l’IPCH et l’IPC porte sur les dépenses de santé : l’IPCH suit des prix nets des remboursements de la sécurité sociale tandis que l’IPC suit des prix bruts.
06 - Santé
All
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("06"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("06 - Santé") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.55),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
2018-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("06"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("2018-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2018-01-01")]) %>%
ggplot() + ylab("06 - Santé") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.4, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 1),
labels = dollar_format(accuracy = 1, prefix = ""))
2020-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("06"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("2020-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2020-01-01")]) %>%
ggplot() + ylab("06 - Santé") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
labels = date_format("%b %y")) +
theme(legend.position = c(0.4, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 1),
labels = dollar_format(accuracy = 1, prefix = ""))
061 - Produits, appareils et matériels médicaux
All
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("061"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("061 - Produits, appareils et matériels médicaux") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.55),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
2018-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("061"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("2018-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2018-01-01")]) %>%
ggplot() + ylab("061 - Produits, appareils et matériels médicaux") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 1),
labels = dollar_format(accuracy = 1, prefix = ""))
2020-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("061"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("2020-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2020-01-01")]) %>%
ggplot() + ylab("061 - Produits, appareils et matériels médicaux") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
labels = date_format("%b %y")) +
theme(legend.position = c(0.3, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 1),
labels = dollar_format(accuracy = 1, prefix = ""))
062 - Services ambulatoires
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("062"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2000-01-01")]) %>%
ggplot() + ylab("Indice des prix de la Santé") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.55),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
0611 - Produits pharmaceutiques
All
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0611"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("0611 - Produits pharmaceutiques") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.55),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
2018-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0611"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("2018-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2018-01-01")]) %>%
ggplot() + ylab("0611 - Produits pharmaceutiques") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 1),
labels = dollar_format(accuracy = 1, prefix = ""))
2020-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0611"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("2020-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2020-01-01")]) %>%
ggplot() + ylab("0611 - Produits pharmaceutiques") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
labels = date_format("%b %y")) +
theme(legend.position = c(0.7, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 1),
labels = dollar_format(accuracy = 1, prefix = ""))
0612 - Produits médicaux divers
All
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0612"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("0612 - Indice des prix des produits médicaux divers") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = paste0(Indicateur, "- Produits médicaux divers"))) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.4, 0.25),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
2018-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0612"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("2018-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2018-01-01")]) %>%
ggplot() + ylab("0612 - Indice des prix des produits médicaux divers") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = paste0(Indicateur, " - Produits médicaux divers"))) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.4, 0.25),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
2020-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0612"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("2020-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2020-01-01")]) %>%
ggplot() + ylab("0612 - Indice des prix des produits médicaux divers") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = paste0(Indicateur, " - Produits médicaux divers"))) +
scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), as.Date("2024-01-01"), "3 months"),
labels = date_format("%b %y")) +
theme(legend.position = c(0.4, 0.25),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
0613 - Appareils et matériel thérapeutiques
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0613"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2000-01-01")]) %>%
ggplot() + ylab("Indice des prix des appareils et matériel thérapeutique") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
0621 - Services médicaux
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0621"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2000-01-01")]) %>%
ggplot() + ylab("Indice des prix des Services médicaux") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
0622 - Services dentaires
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0622"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2000-01-01")]) %>%
ggplot() + ylab("Indice des prix des Services médicaux") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
0623 - Services paramédicaux
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0623"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2000-01-01")]) %>%
ggplot() + ylab("Indice des prix des Services paramédicaux") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
Enseignement
10 - Enseignement
All
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("10"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix, Enseignement") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
2002-
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("10"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("2002-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix, Enseignement") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
101 - Enseignement primaire
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("101"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix, Enseignement") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
102 - Enseignement Secondaire
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("102"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix, Enseignement") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
104 - Enseignement Supérieur
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("104"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("Indice des prix, Enseignement") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
09 - Loisirs et culture
Tous
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("09"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix, Loisirs et Culture") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
094231
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("09423"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix, redevances (Redevance audiviosuelle)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
05 - Meubles, articles de ménage et entretien courant du foyer
All
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("05"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("05 - Meubles, articles de ménage et entretien courant du foyer") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
05.6.2 - Services domestiques et services ménagers
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("0562"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1998-01-01")]) %>%
ggplot() + ylab("05.6.2 - Services domestiques et services ménagers") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
05.6.2.1 - Services domestiques fournis par du personnel salarié
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("05621"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1998-01-01")]) %>%
ggplot() + ylab("05621") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
01 - Produits alimentaires
Indice
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("01"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
Glissement annuel
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("01"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-10, 40, 2),
labels = percent_format(accuracy = 1, prefix = ""))
02 - Boissons alcolisées
Indice
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("02"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix, Boissons") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
Glissement annuel
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("02"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-10, 40, 2),
labels = percent_format(accuracy = 1, prefix = ""))
11 - Restaurants
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("11"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix, Restaurants") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
Cantines
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("1112"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1998-01-01")]) %>%
ggplot() + ylab("Cantines") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
03 - Habillement et chaussures
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("03"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix, Habillement") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
04 - Logement
All
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("04"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix, Logement") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
041 - loyers
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("041"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix, Logement") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
07 - Transports
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("07"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix, Transports") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
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(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
08 - Communications
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("08"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix, Communications") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
12 - Biens et services divers
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("12"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
12.4 - Protection sociale
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("124"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
12401 - crèche (dans protection sociale)
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("12401"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
1253 - Assurance santé complémentaire
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("1253"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
12.4.0.2 - Maisons de retraite pour personnes âgées et foyers pour handicapés
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("12402"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
12.4.0.3 - Aides à domicile
`IPCH-2015` %>%
bind_rows(`IPC-2015`) %>%
filter(COICOP2016 %in% c("12403"),
NATURE == "INDICE",
REF_AREA == "FE",
FREQ == "M",
is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") %>%
month_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
filter(date >= as.Date("1996-01-01")) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))