Indices des prix à la consommation harmonisés - IPCH-2015

Name: Indices des prix à la consommation harmonisés
Creator: François Geerolf
License: https://www.etalab.gouv.fr/licence-ouverte-open-licence/

Données - INSEE

Auteur

François Geerolf

Structure

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.

00 - Ensemble

Glissement annuel

Code

`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

Code

`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	159.1418	2025-12-01
IPCH	100	1996-01-01	167.9217	2025-12-01

5 dernières années

Code

`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(6)) |>
  
  group_by(INDICATEUR) |>
  arrange(date) |>
  mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE)-1) |>
  select(date, OBS_VALUE, INDICATEUR) |>
  na.omit() |>
  ggplot() + ylab("Inflation annuelle") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = INDICATEUR)) +
  #
  theme(legend.position = c(0.6, 0.2),
        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_label(aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1), color = INDICATEUR), 
                  fontface ="plain", size = 3)

10 dernières années

1 Digit

Code

`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)) |>
  
  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), color = INDICATEUR), 
                  fontface ="plain", size = 3)

3 Digits (2011-2021)

Code

`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("2010-01-01"),
         date <= as.Date("2021-01-01")) |>
  
  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(0.5, 0.9),
        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 = 0.001), color = INDICATEUR), 
                  fontface ="plain", size = 3, show.legend = F)

2000-

Code

`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")) |>
  
  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), color = INDICATEUR), 
                  fontface ="plain", size = 3)

15 dernières années

Code

`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)) |>
  
  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), color = INDICATEUR), 
                  fontface ="plain", size = 3)

2017-

Code

`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() |>
  
  group_by(INDICATEUR) |>
  arrange(date) |>
  mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE)-1) |>
  filter(date >= as.Date("2017-01-01")) |>
  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), color = INDICATEUR), 
                  fontface ="plain", size = 3, show.legend = F)

2017-

Monthly

Code

`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

Code

`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)))

2021

Monthly

Code

`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)))

5 years before

Monthly

Code

`IPCH-IPC-2015-ensemble` |>
  filter(date >= as.Date("2018-11-01")) |>
  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), color = Indicateur))

3 years before

Monthly

Code

`IPCH-IPC-2015-ensemble` |>
  filter(date >= as.Date("2021-06-01")) |>
  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)))

décembre 2014- décembre 2024

Monthly - IPC and IPCH

Code

`IPCH-IPC-2015-ensemble` |>
  filter(date >= as.Date("2014-12-01"),
         year(date) <= 2024) |>
  group_by(Indicateur) |>
  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(2000, 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), color = Indicateur))

Annual

Code

`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"),
         date <= as.Date("2024-12-01")) |>
  
  group_by(INDICATEUR) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2000-01-01")]) |>
  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, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>% filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = INDICATEUR))

Début 2000- Fin 2024

Monthly - IPC and IPCH

Code

`IPCH-IPC-2015-ensemble` |>
  filter(year(date) >= 2000,
         year(date) <= 2024) |>
  group_by(Indicateur) |>
  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(2000, 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), color = Indicateur))

Annual

Code

`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"),
         date <= as.Date("2024-12-01")) |>
  
  group_by(INDICATEUR) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2000-01-01")]) |>
  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, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>% filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = INDICATEUR))

1999-

Monthly - IPC and IPCH

Code

`IPCH-IPC-2015-ensemble` |>
  filter(year(date) >= 1999) |>
  group_by(Indicateur) |>
  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(1999, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(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), color = Indicateur))

2016-

Monthly - IPC and IPCH

Code

`IPCH-IPC-2015-ensemble` |>
  filter(date >= as.Date("2016-01-01")) |>
  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, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>%
               filter(date == max(date)), aes(date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = Indicateur))

2008-

Monthly - IPC and IPCH

Code

`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

Code

`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), color = Indicateur))

With labels

Code

`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-

Code

`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-

Code

`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-

Code

`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-

Code

`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-

Code

`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")) |>
  
  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_label(data = . %>% filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = INDICATEUR))

2008-2022

Code

`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("2008-01-01"),
         date <= as.Date("2022-01-01")) |>
  
  group_by(INDICATEUR) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2008-01-01")]) |>
  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_label(data = . %>% filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = INDICATEUR))

2008-2021

Code

`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("2008-01-01"),
         date <= as.Date("2021-01-01")) |>
  
  group_by(INDICATEUR) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2008-01-01")]) |>
  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, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 300, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>% filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = INDICATEUR))

2009-2021

Code

`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("2008-01-01"),
         date <= as.Date("2021-01-01")) |>
  
  group_by(INDICATEUR) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2008-01-01")]) |>
  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, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 300, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>% filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 2), color = INDICATEUR))

1999-

Code

`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")) |>
  
  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-

Code

`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")) |>
  
  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)

2003-2022

Code

`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("2003-01-01"),
         date <= as.Date("2022-01-01")) |>
  
  group_by(INDICATEUR) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2003-01-01")]) |>
  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-

Code

`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")) |>
  
  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

Code

`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")) |>
  
  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

Code

`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")) |>
  
  
  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

Code

`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")) |>
  
  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

Code

`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")) |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

invisible(Sys.setlocale("LC_TIME", "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() |>

  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(0.5, 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

Code

`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() |>
  
  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-

Code

`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() |>
  
  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-

Code

`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() |>
  
  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-

Code

`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() |>
  
  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-

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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-

Code

`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() |>
  
  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 )

Code

`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() |>
  
  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-

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c("041", "00"),
         FREQ == "M",
         NATURE == "INDICE") |>
  month_to_date() |>
  
  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-

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c("041", "00"),
         FREQ == "M",
         NATURE == "INDICE") |>
  month_to_date() |>
  
  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

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c("041", "00"),
         FREQ == "M",
         NATURE == "INDICE") |>
  month_to_date() |>
  
  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, ensemble, Tabac

Janvier 2021

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c( "00", "01", "022"),
         FREQ == "M",
         NATURE == "INDICE") |>
  month_to_date() |>
  
  filter(date >= as.Date("2021-01-01")) |>
  group_by(COICOP2016) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) |>
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = COICOP2016)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               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_label(data = . %>% filter(date == max(date)),
                  aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = COICOP2016), 
                  fontface ="plain", size = 3)

Alimentation, ensemble

Janvier 2021

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c( "00", "01"),
         FREQ == "M",
         NATURE == "INDICE") |>
  month_to_date() |>
  
  filter(date >= as.Date("2021-01-01")) |>
  group_by(COICOP2016) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) |>
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = COICOP2016)) +
  theme_minimal() + xlab("") + ylab("100 = Janvier 2021") +
  scale_x_date(breaks = "2 months",
               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_label(data = . %>% filter(date == max(date)),
                  aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = COICOP2016), 
                  fontface ="plain", size = 3)

Alimentation, loyers, chauffage, Ensemble

Janvier 2021

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c("041", "00", "01", "045"),
         FREQ == "M",
         NATURE == "INDICE") |>
  month_to_date() |>
  
  filter(date >= as.Date("2021-01-01")) |>
  group_by(COICOP2016) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) |>
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               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(data = . %>% filter(date == max(date)),
                  aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

Juillet 2021

Code

`IPC-2015` |>
  filter(INDICATEUR == "IPC",
         COICOP2016 %in% c("00", "01", "045"),
         FREQ == "M",
         NATURE == "INDICE",
         PRIX_CONSO == "SO",
         MENAGES_IPC == "ENSEMBLE",
         REF_AREA == "FE") |>
  month_to_date() |>
  arrange(date) |>
  
  filter(date >= as.Date("2019-07-01")) |>
  group_by(COICOP2016) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) |>
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "2 months",
               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, 5),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>% filter(date == max(date)),
                  aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = Coicop2016), 
                  fontface ="plain", size = 3)

Glissement sur 2 ans

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c("041", "00", "01", "045"),
         FREQ == "M",
         NATURE == "INDICE") |>
  month_to_date() |>
  
  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)

2017

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c("041", "00", "01", "045"),
         FREQ == "M",
         NATURE == "INDICE") |>
  month_to_date() |>
  
  filter(date >= as.Date("2017-01-01")) |>
  group_by(COICOP2016) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) |>
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = "6 months",
               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, 5),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>%
              filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = Coicop2016), size = 3)

Electricité, Gaz, Electricité + gaz

30 ans

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c("0452", "0451", "045"),
         FREQ == "M",
         NATURE == "INDICE") |>
  month_to_date() |>
  
  filter(date >= max(date) - years(30)) |>
  group_by(COICOP2016) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) |>
  ggplot() + ylab("Indice des prix (J - 30 ans = 100)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.87),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  scale_y_log10(breaks = seq(0, 10000, 20),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>% filter(date == max(date)),
                  aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = Coicop2016), 
                  fontface ="plain", size = 3)

20 ans

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c("0452", "0451", "045"),
         FREQ == "M",
         NATURE == "INDICE") |>
  month_to_date() |>
  
  filter(date >= max(date) - years(20)) |>
  group_by(COICOP2016) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) |>
  ggplot() + ylab("Indice des prix (J - 20 ans = 100)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.87),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  scale_y_log10(breaks = seq(0, 10000, 20),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>% filter(date == max(date)),
                  aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = Coicop2016), 
                  fontface ="plain", size = 3)

10 ans

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c("0452", "0451", "045"),
         FREQ == "M",
         NATURE == "INDICE") |>
  month_to_date() |>
  
  filter(date >= max(date) - years(10)) |>
  group_by(COICOP2016) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) |>
  ggplot() + ylab("Indice des prix (J - 10 ans = 100)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.87),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  scale_y_log10(breaks = seq(0, 10000, 20),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>% filter(date == max(date)),
                  aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = Coicop2016), 
                  fontface ="plain", size = 3)

Janvier 2008

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c("0452", "0451", "045"),
         FREQ == "M",
         NATURE == "INDICE") |>
  month_to_date() |>
  
  filter(date >= as.Date("2008-01-01")) |>
  group_by(COICOP2016) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) |>
  ggplot() + ylab("Indice des prix (Janvier 2008 = 100)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.87),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  scale_y_log10(breaks = seq(0, 10000, 20),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>% filter(date == max(date)),
                  aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = Coicop2016), 
                  fontface ="plain", size = 3)

Janvier 2017

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c("0452", "0451", "045"),
         FREQ == "M",
         NATURE == "INDICE") |>
  month_to_date() |>
  
  filter(date >= as.Date("2017-01-01")) |>
  group_by(COICOP2016) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) |>
  ggplot() + ylab("Indice des prix (Janvier 2017 = 100)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  scale_x_date(breaks = "3 months",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.25, 0.87),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  scale_y_log10(breaks = seq(0, 10000, 5),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>% filter(date == max(date)),
                  aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = Coicop2016), 
                  fontface ="plain", size = 3)

Mai 2017

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c("0452", "0451", "045"),
         FREQ == "M",
         NATURE == "INDICE") |>
  month_to_date() |>
  
  filter(date >= as.Date("2017-05-01")) |>
  group_by(COICOP2016) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) |>
  ggplot() + ylab("Indice des prix (Mai 2017 = 100)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  scale_x_date(breaks = "3 months",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.25, 0.87),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  scale_y_log10(breaks = seq(0, 10000, 5),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>% filter(date == max(date)),
                  aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = Coicop2016), 
                  fontface ="plain", size = 3)

Janvier 2020

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c("0452", "0451", "045"),
         FREQ == "M",
         NATURE == "INDICE") |>
  month_to_date() |>
  
  filter(date >= as.Date("2020-01-01")) |>
  group_by(COICOP2016) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) |>
  ggplot() + ylab("Indice des prix (Janvier 2020 = 100)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.25, 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, 5),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>% filter(date == max(date)),
                  aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = Coicop2016), 
                  fontface ="plain", size = 3)

Juillet 2021

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c("0452", "0451", "045"),
         FREQ == "M",
         NATURE == "INDICE") |>
  month_to_date() |>
  
  filter(date >= as.Date("2021-07-01")) |>
  group_by(COICOP2016) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) |>
  ggplot() + ylab("Indice des prix (Juillet 2021 = 100)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.25, 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, 5),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>% filter(date == max(date)),
                  aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = Coicop2016), 
                  fontface ="plain", size = 3)

3 ans

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c("0452", "0451", "045"),
         FREQ == "M",
         NATURE == "INDICE") |>
  month_to_date() |>
  
  filter(date >= max(date) - years(3)) |>
  group_by(COICOP2016) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) |>
  ggplot() + ylab("Indice des prix (Il y a 3 ans = 100)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.25, 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, 5),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>% filter(date == max(date)),
                  aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = Coicop2016), 
                  fontface ="plain", size = 3)

Pondérations d’indice

Table - COICOP2016

All

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         REF_AREA == "FE",
         NATURE == "POND",
         TIME_PERIOD %in% c("1990", "2000", "2010", "2020")) |>
  
  select(COICOP2016, Coicop2016, TIME_PERIOD, OBS_VALUE) |>
  spread(TIME_PERIOD, OBS_VALUE) |>
  print_table_conditional()

2-digit

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         REF_AREA == "FE",
         NATURE == "POND",
         TIME_PERIOD %in% c("1990", "2000", "2010", "2020"),
         nchar(COICOP2016) == 2) |>
  
  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

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c("06", "1253"),
         REF_AREA == "FE",
         NATURE == "POND") |>
  year_to_date() |>
  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

Code

`IPCH-2015` |>
  filter(INDICATEUR == "IPCH",
         COICOP2016 %in% c("1112", "12401"),
         REF_AREA == "FE",
         NATURE == "POND") |>
  year_to_date() |>
  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

Code

`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() |>
  
  
  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

Code

`IPCH-2015` |>
  filter(COICOP2016 %in% c("00", "02", "11", "04"),
         NATURE == "INDICE",
         FREQ == "M") |>
  month_to_date() |>
  
  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

Code

`IPCH-2015` |>
  filter(COICOP2016 %in% c("00", "10", "07", "12"),
         NATURE == "INDICE",
         FREQ == "M") |>
  month_to_date() |>
  
  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

Code

`IPCH-2015` |>
  filter(COICOP2016 %in% c("00", "05", "01", "03"),
         NATURE == "INDICE",
         FREQ == "M") |>
  month_to_date() |>
  
  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

Code

`IPCH-2015` |>
  filter(COICOP2016 %in% c("00", "06", "09", "08"),
         NATURE == "INDICE",
         FREQ == "M") |>
  month_to_date() |>
  
  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

Code

`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() |>
  
  
  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

Code

plot_sante_ponderation <- `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() |>
  
  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(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(0, 300, 1),
                     labels = percent_format(accuracy = 1))

plot_sante_ponderation

061 - Produits médicaux

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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,

Code

`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() |>
  
  
  group_by(INDICATEUR) |>
  filter(date >= as.Date("1996-01-01")) |>
  arrange(date) |>
  ggplot() + ylab("") + 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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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-

Code

`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() |>
  
  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-

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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))

Transports de passager par Train (SNCF ?)

Code

`IPCH-2015` |>
  bind_rows(`IPC-2015`) |>
  filter(COICOP2016 %in% c("07311"),
         NATURE == "POND",
         REF_AREA == "FE",
         is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") |>
  year_to_date() |>
  
  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, .1),
                     labels = percent_format(accuracy = .1))

Voitures neuves

Code

`IPCH-2015` |>
  bind_rows(`IPC-2015`) |>
  filter(COICOP2016 %in% c("0711"),
         NATURE == "POND",
         REF_AREA == "FE",
         is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") |>
  year_to_date() |>
  
  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, .1),
                     labels = percent_format(accuracy = .1))

Voitures neuves

Code

`IPCH-2015` |>
  bind_rows(`IPC-2015`) |>
  filter(COICOP2016 %in% c("0712"),
         NATURE == "POND",
         REF_AREA == "FE",
         is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") |>
  year_to_date() |>
  
  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, .1),
                     labels = percent_format(accuracy = .1))

08 - Communications

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

0711 - Automobiles

Tous

Code

`IPCH-2015` |>
  bind_rows(`IPC-2015`) |>
  filter(IDBANK %in% c("001763082", "001763083", "001763084")) |>
  month_to_date() |>
  
  group_by(COICOP2016) |>
  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("0711 - Voitures automobiles") + 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, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

2017-

Code

`IPCH-2015` |>
  bind_rows(`IPC-2015`) |>
  filter(IDBANK %in% c("001763082", "001763083", "001763084")) |>
  month_to_date() |>
  
  group_by(COICOP2016) |>
  filter(date >= as.Date("2017-01-01")) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2017-01-01")]) |>
  ggplot() + ylab("0711 - Voitures automobiles") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

2019-

Code

`IPCH-2015` |>
  bind_rows(`IPC-2015`) |>
  filter(IDBANK %in% c("001763082", "001763083", "001763084")) |>
  month_to_date() |>
  
  group_by(COICOP2016) |>
  filter(date >= as.Date("2019-01-01")) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2019-01-01")]) |>
  ggplot() + ylab("0711 - Voitures automobiles") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop2016)) +
  
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

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

Code

plot_sante <- `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() |>
  
  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 de prix - Santé (100 = 1996)") + 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(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(10, 300, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

plot_sante

2000-2024

Monthly

Code

`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() |>
  
  group_by(INDICATEUR) |>
  filter(date >= as.Date("2000-01-01"),
         date <= as.Date("2024-12-01")) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2000-01-01")]) |>
  ggplot() + ylab("Indice de prix - Santé (100 = 2000)") + 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(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(10, 300, 5),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>% filter(date == max(date)), aes(x = date, y = OBS_VALUE, color = Indicateur, label = round(OBS_VALUE, 1)))

Annual

Code

`IPCH-2015` |>
  bind_rows(`IPC-2015`) |>
  filter(COICOP2016 %in% c("06"),
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "A",
         is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") |>
  year_to_date() |>
  
  group_by(INDICATEUR) |>
  filter(date >= as.Date("2000-01-01"),
         date <= as.Date("2024-12-01")) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2000-01-01")]) |>
  ggplot() + ylab("Indice de prix - Santé (100 = 2000)") + 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(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(10, 300, 5),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label(data = . %>% filter(date == max(date)), aes(x = date, y = OBS_VALUE, color = Indicateur, label = round(OBS_VALUE, 1)))

Bind

Code

ggarrange(plot_sante_ponderation, plot_sante, common.legend = T)

2018-

Code

`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() |>
  
  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-

Code

`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() |>
  
  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"), Sys.Date(), "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

Code

`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() |>
  
  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-

Code

`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() |>
  
  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-

Code

`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() |>
  
  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"), Sys.Date(), "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

Code

`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() |>
  
  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

Code

`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() |>
  
  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-

Code

`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() |>
  
  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-

À compter du 15 mai 2024, le montant de la participation forfaitaire passe de 1€ à 2€

Code

`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() |>
  
  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"), Sys.Date(), "3 months"),
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.5, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(10, 300, 1),
                     labels = dollar_format(accuracy = 1, prefix = ""))

0612 - Produits médicaux divers

All

Code

`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() |>
  
  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-

Code

`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() |>
  
  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-

Code

`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() |>
  
  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"), Sys.Date(), "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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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-

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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 = ""))

Données

Jeux d’argent et de hasard: 13,4 milliards d’euros. Dépense de consommation finale des ménages s’établit à 1 262,5 Md€ toutes taxes comprises pour l’année 2019, soit 51,9 % du produit intérieur brut (PIB).

All

Code

`IPCH-2015` |>
  bind_rows(`IPC-2015`) |>
  filter(COICOP2016 %in% c("09430"),
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M",
         is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") |>
  month_to_date() |>
  
  group_by(INDICATEUR) |>
  filter(date >= as.Date("1996-01-01")) |>
  arrange(desc(date)) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) |>
  ggplot() + ylab("Indice des prix, Tickets à gratter") + 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, 0.3),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 1),
                     labels = dollar_format(accuracy = 1, prefix = ""))

-2024

Code

`IPCH-2015` |>
  bind_rows(`IPC-2015`) |>
  filter(COICOP2016 %in% c("09430"),
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M",
         is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") |>
  month_to_date() |>
  
  group_by(INDICATEUR) |>
  filter(date >= as.Date("1996-01-01"),
         date <= as.Date("2024-05-01")) |>
  arrange(desc(date)) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) |>
  ggplot() + ylab("Indice des prix, Tickets à gratter") + 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, 0.3),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 1),
                     labels = dollar_format(accuracy = 1, prefix = ""))

094231

All

Code

`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() |>
  
  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 = ""))

2015-

Code

`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() |>
  
  group_by(INDICATEUR) |>
  filter(date >= as.Date("2015-01-01")) |>
  arrange(date) |>
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2015-01-01")]) |>
  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, 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, 5),
                     labels = dollar_format(accuracy = 1, prefix = ""))

05 - Meubles, articles de ménage et entretien courant du foyer

All

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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 = ""))

07311 - Transport de passagers par train

Code

`IPCH-2015` |>
  bind_rows(`IPC-2015`) |>
  filter(COICOP2016 %in% c("07311"),
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M",
         is.na(MENAGES_IPC) | MENAGES_IPC == "ENSEMBLE") |>
  month_to_date() |>
  
  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, Transport de passagers par train") + 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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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)

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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

Code

`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() |>
  
  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 = ""))

Related Data

Inflation in France: CPI or HICP ?

source	dataset	Title	Updated
insee	IPC-2015	Indice des prix à la consommation - Base 2015 - IPC-2015	2026-09-20
insee	IPCH-2015	Indices des prix à la consommation harmonisés - IPCH-2015	2026-09-20
insee	SALAIRES-ANNUELS	Salaires annuels - SALAIRES-ANNUELS	2026-09-20
insee	bdf2017	Budget de famille 2017 - bdf2017	2026-09-04
ipp	indicefp	Point d’indice de la fonction publique - indicefp	2026-09-20

Insights into Inflation Statistics

source	dataset	Title	Updated
ecb	CES	Consumer Expectations Survey - CES	2026-09-20
eurostat	namq_10_gdp	GDP and main components (output, expenditure and income) - namq_10_gdp	2026-09-20
eurostat	prc_hicp_midx	HICP (2015 = 100) - monthly data (index) - prc_hicp_midx	2026-09-09
eurostat	sts_inpp_m	Producer prices in industry, total - monthly data - sts_inpp_m	2026-09-20
eurostat	sts_inppnd_m	Producer prices in industry, non domestic market - monthly data - sts_inppnd_m	2026-09-20
fred	energy	Energy - energy	2026-09-20
fred	inflation	Inflation - inflation	2026-09-20
insee	IPCH-2015	Indices des prix à la consommation harmonisés - IPCH-2015	2026-09-20
oecd	PRICES_CPI	Consumer price indices (CPIs) - PRICES_CPI	2026-08-02

Évolution de l’inflation en France depuis 2017

source	dataset	Title	Updated
insee	INDICES_LOYERS	Indices des loyers d’habitation (ILH) - INDICES_LOYERS	2026-09-20
insee	IPC-2015	Indice des prix à la consommation - Base 2015 - IPC-2015	2026-09-20
insee	IPC-PM-2015	Prix moyens de vente de détail - IPC-PM-2015	2026-09-20
insee	IPCH-2015	Indices des prix à la consommation harmonisés - IPCH-2015	2026-09-20
insee	IPCH-IPC-2015-ensemble	Indices des prix à la consommation harmonisés - IPCH-IPC-2015-ensemble	2026-08-02
insee	IPGD-2015	Indice des prix dans la grande distribution - IPGD-2015	2026-09-04
insee	IPPI-2021	Indices de prix de production et d’importation dans l’industrie - IPPI-2021	2026-09-04
insee	IRL	Indice pour la révision d’un loyer d’habitation - IRL	2026-09-20
wdi	FP.CPI.TOTL.ZG	Inflation, consumer prices (annual %) - FP.CPI.TOTL.ZG	2026-09-20