Consommation effective des ménages par fonction
Data - INSEE
François Geerolf
Info
| source | dataset | .html | .RData |
|---|---|---|---|
| insee | T_CONSO_EFF_FONCTION | 2024-04-18 | 2024-04-01 |
Données sur le pouvoir d’achat
| source | dataset | .html | .RData |
|---|---|---|---|
| insee | CNA-2014-RDB | 2024-05-09 | 2024-05-09 |
| insee | CNT-2014-CSI | 2024-05-09 | 2024-05-09 |
| insee | conso-eff-fonction | 2024-05-09 | 2022-06-14 |
| insee | reve-niv-vie-individu-activite | 2024-05-09 | NA |
| insee | t_7401 | 2024-05-09 | 2023-12-23 |
| insee | t_men_val | 2024-04-18 | 2024-03-04 |
| insee | t_pouvachat_val | 2024-04-18 | 2024-03-04 |
| insee | t_recapAgent_val | 2024-04-18 | 2024-04-02 |
| insee | t_salaire_val | 2024-04-18 | 2024-03-04 |
| oecd | HH_DASH | 2024-04-16 | 2023-09-09 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-05-09 |
Dernière
T_CONSO_EFF_FONCTION %>%
group_by(year) %>%
summarise(Nobs = n()) %>%
arrange(desc(year)) %>%
head(2) %>%
print_table_conditional()| year | Nobs |
|---|---|
| 2022 | 828 |
| 2021 | 882 |
Données reliées
Sources
Info
Les tableaux détaillés présentent la consommation effective des ménages depuis 1959 jusqu’à l’année du compte provisoire, déclinée aux niveaux diffusables les plus fins des nomenclatures de produits (Nomenclature agrégée), de fonction (COICOP) et de durabilité.
Pour chaque nomenclature (produit, fonction durabilité), les résultats détaillés ont le format suivant :
- Séries en niveau :
- Consommation aux prix courants (onglet MEURcour), que l’on appelle aussi “en valeur” ou “en euros courants”
- Consommation en volume aux prix de l’année précédente chaînés (onglet M€2014), que l’on appelle aussi "“en volume”" ou en "“euros 2014”". Les consommations en volume au prix de l’année précédente chaînée ne sont pas sommables. En conséquence, la somme des consommations en volume aux prix de l’année précédente chaîné des séries élémentaires constituant un niveau diffère de la consommation pour le niveau total de l’agrégat.
- Indices de prix base 100 en 2014 (onglet IPRIX2014)
- Séries en évolution n/n-1 :
- Indices de valeur base 100 l’année précédente (onglet Ival)
- Indices de volume base 100 l’année précédente (onglet Ivol)
- Indices de prix base 100 l’année précédente (onglet Iprix)
- Structure des séries :
- Coefficients budgétaires aux prix courants en % (onglet COEFFCOUR)
variable
| variable | Nobs |
|---|---|
| COEFFCOUR | 9374 |
| IPRIX2014 | 9374 |
| IVAL | 9227 |
| IVOL | 9227 |
| MEUR2014 | 9374 |
| MEURcour | 9374 |
fonction, Fonction
Tous
2-digit
T_CONSO_EFF_FONCTION %>%
filter(nchar(fonction) == 2) %>%
group_by(fonction, Fonction) %>%
summarise(Nobs = n()) %>%
print_table_conditional()| fonction | Fonction | Nobs |
|---|---|---|
| _Z | Total | 382 |
3-digit
T_CONSO_EFF_FONCTION %>%
filter(nchar(fonction) == 4) %>%
group_by(fonction, Fonction) %>%
summarise(Nobs = n()) %>%
print_table_conditional()| fonction | Fonction | Nobs |
|---|---|---|
| CP01 | Produits alimentaires et boissons non alcoolisées | 382 |
| CP02 | Boissons alcoolisées, tabac et stupéfiants | 382 |
| CP03 | Articles d’habillement et chaussures | 382 |
| CP04 | Logement, eau, gaz, électricité et autres combustibles | 382 |
| CP05 | Meubles, articles de ménage et entretien courant du foyer | 382 |
| CP06 | Santé | 382 |
| CP07 | Transports | 382 |
| CP08 | Communications | 382 |
| CP09 | Loisirs et culture | 382 |
| CP10 | Enseignement | 382 |
| CP11 | Restaurants et hôtels | 382 |
| CP12 | Biens et services divers | 382 |
| CP13 | Dépenses de consommation individuelle à la charge des institutions sans but lucratif au service des ménages (ISBLSM) | 382 |
| CP14 | Dépenses de consommation individuelle à la charge des administrations publiques | 382 |
| CP15 | Correction territoriale | 382 |
4-digit
5-digit
T_CONSO_EFF_FONCTION %>%
filter(nchar(fonction) == 8) %>%
group_by(fonction, Fonction) %>%
summarise(Nobs = n()) %>%
print_table_conditional()| fonction | Fonction | Nobs |
|---|---|---|
| CPDEPHSI | Dépense de consommation des ménages hors SIFIM | 382 |
year
2020
Désordonné
T_CONSO_EFF_FONCTION %>%
filter(variable == "MEURcour") %>%
year_to_date2 %>%
left_join(gdp, by = "date") %>%
filter(date == as.Date("2020-01-01")) %>%
select(-date) %>%
mutate(`% du PIB` = (100*value/(gdp)) %>% round(., digits = 2),
value = round(value) %>% paste0(" Mds€")) %>%
select(-gdp) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}Ordonné
T_CONSO_EFF_FONCTION %>%
filter(variable == "MEURcour") %>%
year_to_date2 %>%
left_join(gdp, by = "date") %>%
filter(date == as.Date("2020-01-01")) %>%
select(-date) %>%
arrange(-value) %>%
mutate(`% du PIB` = (100*value/(gdp)) %>% round(., digits = 2),
value = round(value) %>% paste0(" Mds€")) %>%
select(-gdp) %>%
print_table_conditional()Loyers réels, loyers imputés
Mds €
T_CONSO_EFF_FONCTION %>%
filter(variable == "MEURcour") %>%
year_to_date2 %>%
filter(fonction %in% c("CP041", "CP042", "CP045")) %>%
left_join(gdp, by = "date") %>%
ggplot(.) + theme_minimal() + ylab("Consommation (Milliards€)") + xlab("") +
geom_line(aes(x = date, y = value/1000, color = Fonction)) +
theme(legend.title = element_blank(),
legend.position = c(0.3, 0.91)) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 500, 20),
labels = dollar_format(acc = 1, pre = "", su = " Mds€"))
% de la consommation
T_CONSO_EFF_FONCTION %>%
filter(variable == "MEURcour") %>%
year_to_date2 %>%
filter(fonction %in% c("CP041", "CP042", "CP045")) %>%
left_join(gdp, by = "date") %>%
ggplot(.) + theme_minimal() + ylab("Consommation (% du PIB)") + xlab("") +
geom_line(aes(x = date, y = value/(gdp), color = Fonction)) +
theme(legend.title = element_blank(),
legend.position = c(0.3, 0.91)) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(0, 100, 0.5),
labels = scales::percent_format(accuracy = 0.1))
Entretien et réparation des logements
T_CONSO_EFF_FONCTION %>%
filter(variable == "MEURcour") %>%
year_to_date2 %>%
filter(fonction %in% c("CP043", "CP044")) %>%
left_join(gdp, by = "date") %>%
ggplot(.) + theme_minimal() + ylab("Consommation (% du PIB)") + xlab("") +
geom_line(aes(x = date, y = value/(gdp), color = Fonction)) +
theme(legend.title = element_blank(),
legend.position = c(0.6, 0.2)) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(0, 100, 0.1),
labels = scales::percent_format(accuracy = 0.1))
Indices de Prix
Tous
Déflateurs
T_CONSO_EFF_FONCTION %>%
filter(variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(Fonction %in% c("Dépense de consommation des ménages",
"Dépense de consommation des ménages hors SIFIM",
"Consommation effective des ménages")) %>%
filter(date >= as.Date("1990-01-01")) %>%
group_by(fonction) %>%
arrange(date) %>%
mutate(value = 100*value/value[1]) %>%
ggplot + theme_minimal() + xlab("") + ylab("") +
geom_line(aes(x = date, y = value, color = Fonction)) +
scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(100, 300, 10)) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank())
Loyers effectifs, imputés
All
T_CONSO_EFF_FONCTION %>%
filter(variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(fonction %in% c("CP041", "CP042")) %>%
filter(date >= as.Date("1990-01-01")) %>%
group_by(fonction) %>%
arrange(date) %>%
mutate(value = 100*value/value[1]) %>%
ggplot + theme_minimal() + xlab("") + ylab("") +
geom_line(aes(x = date, y = value, color = Fonction)) +
scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(100, 300, 10)) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank())
1996-
T_CONSO_EFF_FONCTION %>%
filter(variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(fonction %in% c("CP041", "CP042")) %>%
filter(date >= as.Date("1996-01-01")) %>%
group_by(fonction) %>%
arrange(date) %>%
mutate(value = 100*value/value[1]) %>%
ggplot + theme_minimal() + xlab("") + ylab("") +
geom_line(aes(x = date, y = value, color = Fonction)) +
scale_x_date(breaks = seq(1996, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(100, 300, 10)) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank())
Forts effets qualité
1972-
T_CONSO_EFF_FONCTION %>%
filter(variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(fonction %in% c("CP082", "CP0913", "CP0912", "CP0911", "CP1261")) %>%
filter(date >= as.Date("1972-01-01")) %>%
group_by(fonction) %>%
arrange(date) %>%
mutate(value = 100*value/value[1]) %>%
ggplot + theme_minimal() + xlab("") + ylab("") +
geom_line(aes(x = date, y = value, color = Fonction)) +
scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = c(0.1, 1, 2, 3, 5, 10, 20, 30, 50, 100, 200, 400, 800, 1600)) +
theme(legend.position = c(0.35, 0.2),
legend.title = element_blank())
1990-
T_CONSO_EFF_FONCTION %>%
filter(variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(fonction %in% c("CP082", "CP0913", "CP0912", "CP0911", "CP1261")) %>%
filter(date >= as.Date("1990-01-01")) %>%
group_by(fonction) %>%
arrange(date) %>%
mutate(value = 100*value/value[1]) %>%
ggplot + theme_minimal() + xlab("") + ylab("") +
geom_line(aes(x = date, y = value, color = Fonction)) +
scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = c(0.1, 1, 2, 3, 5, 10, 20, 30, 50, 100)) +
theme(legend.position = c(0.35, 0.2),
legend.title = element_blank())
1996-
T_CONSO_EFF_FONCTION %>%
filter(variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(fonction %in% c("CP082", "CP0913", "CP0912", "CP0911", "CP1261")) %>%
filter(date >= as.Date("1996-01-01")) %>%
group_by(fonction) %>%
arrange(date) %>%
mutate(value = 100*value/value[1]) %>%
ggplot + theme_minimal() + xlab("") + ylab("") +
geom_line(aes(x = date, y = value, color = Fonction)) +
scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = c(0.1, 1, 2, 3, 5, 10, 20, 30, 50, 100)) +
theme(legend.position = c(0.35, 0.2),
legend.title = element_blank())
Poids
% de la dépense de consommation finale effective
CP091 (Matériel audiovisuel, photographique et informatique), CP082 (Matériel de téléphonie et de télécopie)
T_CONSO_EFF_FONCTION %>%
filter(variable == "COEFFCOUR",
fonction %in% c("CP091", "CP082", "CP126")) %>%
year_to_date2 %>%
mutate(value = value / 100) %>%
ggplot() + ylab("Pondération (% de la conso effective totale)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = paste0(fonction, " - ", Fonction))) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.28, 0.93),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .5),
labels = percent_format(accuracy = .1))
14, 01..12+15
T_CONSO_EFF_FONCTION %>%
filter(variable == "COEFFCOUR",
fonction %in% c("CP14", "CP01..CP12+CP15")) %>%
year_to_date2 %>%
mutate(value = value / 100) %>%
ggplot() + ylab("Pondération (% de la conso effective totale)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = paste0(fonction, " - ", Fonction))) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.4, 0.6),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
labels = percent_format(accuracy = 1))
CP141, CP142, CP143, CP144, CP145
T_CONSO_EFF_FONCTION %>%
filter(variable == "COEFFCOUR",
fonction %in% c("CP141", "CP142", "CP143", "CP144", "CP145")) %>%
year_to_date2 %>%
mutate(value = value / 100) %>%
ggplot() + ylab("Pondération (% de la conso effective totale)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = paste0(fonction, " - ", Fonction))) +
#
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.2, 0.88),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
labels = percent_format(accuracy = 1))
CP041, CP042, 04.5
% de la conso effective
T_CONSO_EFF_FONCTION %>%
filter(variable == "COEFFCOUR",
fonction %in% c("CP041", "CP042", "CP045")) %>%
year_to_date2 %>%
mutate(value = value / 100) %>%
ggplot() + ylab("Pondération (% de la conso effective totale)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = paste0(fonction, " - ", Fonction))) +
#
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.28, 0.93),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .5),
labels = percent_format(accuracy = .1))
% de la conso finale
T_CONSO_EFF_FONCTION %>%
filter(variable == "COEFFCOUR",
fonction %in% c("CP041", "CP042", "CP045", "CPDEP")) %>%
year_to_date2 %>%
group_by(date) %>%
mutate(value = value/value[fonction =="CPDEP"]) %>%
ungroup %>%
filter(fonction != "CPDEP") %>%
ggplot() + ylab("Pondération (% de la consommation finale)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = paste0(fonction, " - ", Fonction))) +
#
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.28, 0.93),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .5),
labels = percent_format(accuracy = .1))
Santé
T_CONSO_EFF_FONCTION %>%
filter(variable == "COEFFCOUR",
fonction %in% c("CP142", "06", "CP1253", "CPDEP")) %>%
year_to_date2 %>%
mutate(value = value / 100) %>%
ggplot() + ylab("Pondération (% de la conso effective totale)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = paste0(fonction, " - ", Fonction))) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.93),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 300, .5),
labels = percent_format(accuracy = .1))
% de la dépense de consommation finale
All
2-digit
T_CONSO_EFF_FONCTION %>%
filter(variable == "COEFFCOUR",
year %in% c("1960", "1990", "2020"),
nchar(fonction) == 4 | fonction =="CPDEP") %>%
select(-variable) %>%
group_by(year) %>%
arrange(fonction) %>%
mutate(value = round(100*value/value[fonction =="CPDEP"],2)) %>%
spread(year, value) %>%
print_table_conditional()| fonction | Fonction | 1960 | 1990 | 2020 |
|---|---|---|---|---|
| CP01 | Produits alimentaires et boissons non alcoolisées | 25.07 | 14.89 | 14.97 |
| CP02 | Boissons alcoolisées, tabac et stupéfiants | 7.13 | 3.42 | 4.39 |
| CP03 | Articles d’habillement et chaussures | 11.95 | 6.79 | 3.16 |
| CP04 | Logement, eau, gaz, électricité et autres combustibles | 11.45 | 20.13 | 28.41 |
| CP05 | Meubles, articles de ménage et entretien courant du foyer | 8.54 | 6.19 | 4.85 |
| CP06 | Santé | 2.41 | 3.23 | 4.01 |
| CP07 | Transports | 10.58 | 15.09 | 11.78 |
| CP08 | Communications | 0.60 | 2.10 | 2.56 |
| CP09 | Loisirs et culture | 7.09 | 8.58 | 7.55 |
| CP10 | Enseignement | 0.31 | 0.35 | 0.49 |
| CP11 | Restaurants et hôtels | 6.67 | 6.17 | 5.67 |
| CP12 | Biens et services divers | 7.41 | 13.74 | 12.82 |
| CP13 | Dépenses de consommation individuelle à la charge des institutions sans but lucratif au service des ménages (ISBLSM) | 3.08 | 2.59 | 4.13 |
| CP14 | Dépenses de consommation individuelle à la charge des administrations publiques | 14.21 | 23.59 | 31.78 |
| CP15 | Correction territoriale | 0.78 | -0.67 | -0.66 |
| CPDEP | Dépense de consommation des ménages | 100.00 | 100.00 | 100.00 |
3-digit
T_CONSO_EFF_FONCTION %>%
filter(variable == "COEFFCOUR",
year %in% c("1960", "1990", "2020"),
nchar(fonction) == 5 | fonction =="CPDEP") %>%
select(-variable) %>%
group_by(year) %>%
arrange(fonction) %>%
mutate(value = round(100*value/value[fonction =="CPDEP"],2)) %>%
spread(year, value) %>%
print_table_conditional()4-digit
T_CONSO_EFF_FONCTION %>%
filter(variable == "COEFFCOUR",
year %in% c("1960", "1990", "2020"),
nchar(fonction) == 6 | fonction =="CPDEP") %>%
select(-variable) %>%
group_by(year) %>%
arrange(fonction) %>%
mutate(value = round(100*value/value[fonction =="CPDEP"],2)) %>%
spread(year, value) %>%
print_table_conditional()Comparer IPC, IPCH, Déflateur de la consommation
Table Déflateur
deflateur <- T_CONSO_EFF_FONCTION %>%
filter(variable == "IPRIX2014",
year %in% c("1990", "2020"),
nchar(fonction) %in% c(4, 5, 6)) %>%
mutate(fonction = gsub("\\.", "", fonction)) %>%
select(fonction, Fonction, year, value) %>%
spread(year, value) %>%
mutate(`Déflateur (%)` = round(100*((`2020`/`1990`)^(1/30)-1),2)) %>%
select(-`1990`, -`2020`)
deflateur %>%
print_table_conditionalTable Déflateur Poids
deflateur_poids <- T_CONSO_EFF_FONCTION %>%
filter(variable == "COEFFCOUR",
year %in% c("2020"),
nchar(fonction) %in% c(4, 5, 6)) %>%
mutate(fonction = gsub("\\.", "", fonction)) %>%
select(fonction, Fonction, year, value) %>%
spread(year, value) %>%
rename(`Déflateur Poids` = `2020`)
deflateur_poids %>%
print_table_conditionalTable IPC
IPC <- `IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE",
TIME_PERIOD %in% c("1990-01", "2020-01")) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
select(fonction = COICOP2016, Fonction = Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
filter(!(fonction %in% c("SO", "00"))) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
mutate(`IPC (%)` = round(100*((`2020-01`/`1990-01`)^(1/30)-1),2)) %>%
select(-`1990-01`, -`2020-01`)
IPC %>%
print_table_conditional()Table IPC Poids
IPC_poids <- `IPC-2015` %>%
filter(INDICATEUR == "IPC",
REF_AREA == "FE",
MENAGES_IPC == "ENSEMBLE",
NATURE == "POND",
TIME_PERIOD %in% c("2020")) %>%
left_join(COICOP2016, by = "COICOP2016") %>%
select(fonction = COICOP2016, Fonction = Coicop2016, TIME_PERIOD, OBS_VALUE) %>%
filter(!(fonction %in% c("SO", "00"))) %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
rename(`IPC Poids` = `2020`)
IPC_poids %>%
print_table_conditional()Comparer Table Déflateur VS IPC
Tous
deflateur %>%
inner_join(IPC, by = "fonction") %>%
mutate(Difference = `Déflateur (%)`-`IPC (%)`) %>%
arrange(Difference) %>%
print_table_conditional()| fonction | Fonction.x | Déflateur (%) | Fonction.y | IPC (%) | Difference |
|---|
2-digit
deflateur %>%
inner_join(IPC, by = "fonction") %>%
filter(nchar(fonction) == 2) %>%
mutate(Difference = `Déflateur (%)`-`IPC (%)`) %>%
arrange(Difference) %>%
print_table_conditional()| fonction | Fonction.x | Déflateur (%) | Fonction.y | IPC (%) | Difference |
|---|
3-digit
deflateur %>%
inner_join(IPC, by = "fonction") %>%
filter(nchar(fonction) == 3) %>%
mutate(Difference = `Déflateur (%)`-`IPC (%)`) %>%
arrange(Difference) %>%
print_table_conditional()| fonction | Fonction.x | Déflateur (%) | Fonction.y | IPC (%) | Difference |
|---|
4-digit
deflateur %>%
inner_join(IPC, by = "fonction") %>%
filter(nchar(fonction) == 4) %>%
mutate(Difference = `Déflateur (%)`-`IPC (%)`) %>%
arrange(Difference) %>%
print_table_conditional()| fonction | Fonction.x | Déflateur (%) | Fonction.y | IPC (%) | Difference |
|---|
Comparer Poids Déflateur VS IPC
deflateur_poids %>%
inner_join(IPC_poids, by = "fonction") %>%
mutate(Difference = `Déflateur Poids`-`IPC Poids`) %>%
arrange(Difference) %>%
print_table_conditional()| fonction | Fonction.x | Déflateur Poids | Fonction.y | IPC Poids | Difference |
|---|
2-digit
00 - Tous
1996-
T_CONSO_EFF_FONCTION %>%
filter(Fonction %in% c("Consommation effective des ménages",
"Dépense de consommation des ménages",
"Dépense de consommation des ménages hors SIFIM"),
variable == "IPRIX2014") %>%
mutate(Fonction = ifelse(fonction =="Consommation effective des ménages",
"Dépense de consommation effective des ménages",
Fonction)) %>%
year_to_date2 %>%
filter(date >= as.Date("1996-01-01")) %>%
select(variable = Fonction, date, value) %>%
mutate(variable = paste0("Déflateur de la ", variable)) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("00"),
FREQ == "M",
PRIX_CONSO == "SO",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
mutate(variable = "Indice des Prix à la Consommation (IPC)") %>%
select(variable, date, value = OBS_VALUE)) %>%
bind_rows(`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
mutate(variable = "Indice des Prix à la Consommation Harmonisé (IPCH)") %>%
select(variable, date, value = OBS_VALUE)) %>%
group_by(variable) %>%
filter(date >= as.Date("1996-01-01")) %>%
mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
mutate(variable = gsub("Indice des prix à la consommation - Base 2015 - Ensemble des ménages - France - ", "", variable)) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
#
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
2000-
T_CONSO_EFF_FONCTION %>%
filter(Fonction %in% c("Consommation effective des ménages",
"Dépense de consommation des ménages",
"Dépense de consommation des ménages hors SIFIM"),
variable == "IPRIX2014") %>%
mutate(Fonction = ifelse(fonction =="Consommation effective des ménages",
"Dépense de consommation effective des ménages",
Fonction)) %>%
year_to_date2 %>%
filter(date >= as.Date("2000-01-01")) %>%
select(variable = Fonction, date, value) %>%
mutate(variable = paste0("Déflateur de la ", variable)) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("00"),
FREQ == "M",
PRIX_CONSO == "SO",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
mutate(variable = "Indice des Prix à la Consommation (IPC)") %>%
select(variable, date, value = OBS_VALUE)) %>%
bind_rows(`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
mutate(variable = "Indice des Prix à la Consommation Harmonisé (IPCH)") %>%
select(variable, date, value = OBS_VALUE)) %>%
group_by(variable) %>%
filter(date >= as.Date("2000-01-01")) %>%
mutate(value = 100*value/value[date == as.Date("2000-01-01")]) %>%
mutate(variable = gsub("Indice des prix à la consommation - Base 2015 - Ensemble des ménages - France - ", "", variable)) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
#
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
2017-
T_CONSO_EFF_FONCTION %>%
filter(Fonction %in% c("Consommation effective des ménages",
"Dépense de consommation des ménages",
"Dépense de consommation des ménages hors SIFIM"),
variable == "IPRIX2014") %>%
mutate(Fonction = ifelse(fonction =="Consommation effective des ménages",
"Dépense de consommation effective des ménages",
Fonction)) %>%
year_to_date2 %>%
filter(date >= as.Date("2017-01-01")) %>%
select(variable = Fonction, date, value) %>%
mutate(variable = paste0("Déflateur de la ", variable)) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("00"),
FREQ == "M",
PRIX_CONSO == "SO",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
mutate(variable = "Indice des Prix à la Consommation (IPC)") %>%
select(variable, date, value = OBS_VALUE)) %>%
bind_rows(`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
mutate(variable = "Indice des Prix à la Consommation Harmonisé (IPCH)") %>%
select(variable, date, value = OBS_VALUE)) %>%
group_by(variable) %>%
filter(date >= as.Date("2017-01-01")) %>%
mutate(value = 100*value/value[date == as.Date("2017-01-01")]) %>%
mutate(variable = gsub("Indice des prix à la consommation - Base 2015 - Ensemble des ménages - France - ", "", variable)) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
#
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.35, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 1),
labels = dollar_format(accuracy = 1, prefix = ""))
00 - Tous
All
2000-
T_CONSO_EFF_FONCTION %>%
filter(Fonction %in% c("Consommation effective des ménages",
"Dépense de consommation des ménages",
"Dépense de consommation des ménages hors SIFIM"),
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
select(variable = Fonction, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(variable = TITLE_FR, date, value = OBS_VALUE)
) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
mutate(variable = gsub("Indice des prix à la consommation - Base 2015 - Ensemble des ménages - France - ", "", variable)) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
#
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
1996-
T_CONSO_EFF_FONCTION %>%
filter(Fonction %in% c("Consommation effective des ménages",
"Dépense de consommation des ménages",
"Dépense de consommation des ménages hors SIFIM"),
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1996-01-01")) %>%
select(variable = Fonction, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("00"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1,
date >= as.Date("1996-01-01")) %>%
select(variable = TITLE_FR, date, value = OBS_VALUE)
) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
mutate(variable = gsub("Indice des prix à la consommation - Base 2015 - Ensemble des ménages - France - ", "", variable)) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
#
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP01 - Produits alimentaires et boissons non alcoolisées
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP01",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("01"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP02 - Boissons alcoolisées et tabac
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP02",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("02"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP03 - Articles d’habillement et chaussures
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP03",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("03"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix (03 - Habillement et chaussures)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 1),
labels = dollar_format(accuracy = 1, prefix = ""))
CP04 - Logement, eau, gaz, électricité et autres combustibles
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP04",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("04"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix (04 - Logement)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP05 - Meubles, articles de ménage et entretien courant de l’habitation
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP05",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("05"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix (05 - Meubles)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
CP06 - Santé
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP06",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("06"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix (06 - Santé)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 1),
labels = dollar_format(accuracy = 1, prefix = ""))
CP07 - Transports
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP07",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("07"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix (07 - Transports)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP08 - Communications
All
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP08",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("08"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix (08 - Communications)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
1996
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP08",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1996-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("08"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1,
date >= as.Date("1996-01-01")) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix (08 - Communications)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP09 - Loisirs et culture
All
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP09",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("09"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix (09 - Loisirs et culture)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
1996
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP09",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1996-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("09"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1,
date >= as.Date("1996-01-01")) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix (09)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP10 - Éducation
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP10",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("10"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP11 - Hôtels, cafés et restaurants
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP11",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("11"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP12 - Biens et services divers
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP12",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("12"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP13 - Dépense de consommation finale individualisable des ISBLSM
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP13",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP14 - Dépense de consommation finale individualisable des APU
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP14",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP15 - Solde territorial
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP15",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
3-digit
CP022 - Tabac
All
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP022",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("022"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 800, 50),
labels = dollar_format(accuracy = 1, prefix = ""))
1996-
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP022",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1996-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("022"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1,
date >= as.Date("1996-01-01")) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 800, 50),
labels = dollar_format(accuracy = 1, prefix = ""))
02.3 - Bière
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP0213",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("0213"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 800, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP041 - Loyers effectifs
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP041",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("041"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP043 - Entretien et réparation des logements
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP043",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("043"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP044 - Autres services liés au logement
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP044",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("044"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP051 - Meubles, articles d’ameublement, tapis et autres revêtements de sol
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP051",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("051"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP052 - Articles de ménage en textile
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP052",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("052"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP053 - Appareils ménagers
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP053",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("053"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP054 - Verrerie, vaisselle et ustensiles de ménage
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP054",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("054"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP055 - Outillage et autre matériel pour la maison et le jardin
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP055",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("055"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP056 - Biens et services pour l’entretien courant du foyer
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP056",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("056"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP072 - Dépenses d’utilisation des véhicules
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP072",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("072"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP081 - Services postaux
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP081",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("081"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP082 - Matériel de téléphonie et de télécopie
1990-
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP082",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("082"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1993-01-01")]) %>%
ggplot() + ylab("Indice des prix (1993=100)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = c(1, 2, 3, 5, 8, 10, 20, 30, 50, 100),
labels = dollar_format(accuracy = 1, prefix = ""))
1996-
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP082",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1996-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("082"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1,
date >= as.Date("1996-01-01")) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix (1996=100)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = c(1, 2, 3, 5, 8, 10, 20, 30, 50, 100),
labels = dollar_format(accuracy = 1, prefix = ""))
CP083 - Services de télécommunications
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP083",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("083"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP091 - Matériel audiovisuel, photographique et informatique
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP091",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("091"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 500, 10), 15),
labels = dollar_format(accuracy = 1, prefix = ""))
CP092 - Autres biens durables culturels et récréatifs
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP092",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("092"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 500, 10), 15),
labels = dollar_format(accuracy = 1, prefix = ""))
CP093 - Autres articles et matériel de loisirs, de jardinage et animaux de compagnie
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP093",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("093"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 500, 10), 15),
labels = dollar_format(accuracy = 1, prefix = ""))
CP094 - Services récréatifs et culturels
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP094",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("094"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 500, 10), 15),
labels = dollar_format(accuracy = 1, prefix = ""))
CP095 - Journaux, livres et articles de papeterie
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP095",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("095"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 500, 10), 15),
labels = dollar_format(accuracy = 1, prefix = ""))
CP096 - Forfaits touristiques
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP096",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("096"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 500, 10), 15),
labels = dollar_format(accuracy = 1, prefix = ""))
CP126 - Services financiers
All
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP126",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("126"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.7),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
All
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP126",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1996-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("126"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.7),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP141 - Logement
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP141",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP142 - Santé
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP142",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP143 - Loisirs et culture
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP143",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP144 - Enseignement
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP144",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
4-digit
CP0562 - Services domestiques et services ménagers
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP0562",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("0562"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP0911 -
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP0912",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("0912"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP0914
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP0914",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("0914"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP0913
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP0913",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("0913"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP0915 - Réparation de matériels audiovisuel, photographique et informatique
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP0915",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("0914"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP0931 - Jeux, jouets et passe-temps
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP0931",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("0931"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP0954 - Papeterie et matériel de dessin
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP0954",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
bind_rows(`IPC-2015` %>%
filter(INDICATEUR == "IPC",
MENAGES_IPC == "ENSEMBLE",
COICOP2016 %in% c("0954"),
FREQ == "M",
REF_AREA == "FE",
NATURE == "INDICE") %>%
month_to_date %>%
filter(month(date) == 1) %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP1261 - Services financiers indirectement mesurés
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP1261",
variable == "IPRIX2014") %>%
year_to_date2 %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(variable = "Deflateur de la Consommation") %>%
select(variable, date, value) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.7),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
Différences
- Santé est en categorie 13 et 14 dans la COICOP lorsque c’est une dépense de consommation des APU ou des IBSLM, ie dans le déflateur de la consommation finale (pareil dans l’IPCH). Dans l’IPC en revanche, la santé est incluse dans la classification n°6 ce qui apparait contradictoire…