| source | dataset | .html | .RData |
|---|---|---|---|
| insee | T_CONSO_EFF_FONCTION | 2024-11-05 | 2024-07-18 |
Consommation effective des ménages par fonction
Data - INSEE
Info
Données sur le pouvoir d’achat
| source | dataset | .html | .RData |
|---|---|---|---|
| insee | CNA-2014-RDB | 2024-11-09 | 2024-11-09 |
| insee | CNT-2014-CSI | 2024-11-09 | 2024-11-09 |
| insee | conso-eff-fonction | 2024-11-09 | 2022-06-14 |
| insee | econ-gen-revenu-dispo-pouv-achat-2 | 2024-11-09 | 2024-07-05 |
| insee | reve-conso-evo-dep-pa | 2024-11-09 | 2024-09-05 |
| insee | reve-niv-vie-individu-activite | 2024-11-09 | NA |
| insee | reve-niv-vie-pouv-achat-trim | 2024-11-09 | 2024-09-05 |
| insee | T_7401 | 2024-10-18 | 2024-10-18 |
| insee | t_men_val | 2024-11-05 | 2024-09-02 |
| insee | t_pouvachat_val | 2024-11-05 | 2024-09-04 |
| insee | t_recapAgent_val | 2024-11-05 | 2024-09-02 |
| insee | t_salaire_val | 2024-11-05 | 2024-09-02 |
| oecd | HH_DASH | 2024-09-15 | 2023-09-09 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-11-09 |
Dernière
Code
T_CONSO_EFF_FONCTION %>%
group_by(year) %>%
summarise(Nobs = n()) %>%
arrange(desc(year)) %>%
head(2) %>%
print_table_conditional()| year | Nobs |
|---|---|
| 2023 | 1074 |
| 2022 | 1074 |
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
Code
T_CONSO_EFF_FONCTION %>%
group_by(variable) %>%
summarise(Nobs = n()) %>%
print_table_conditional()| variable | Nobs |
|---|---|
| COEFFCOUR | 11605 |
| IPRIX2020 | 11605 |
| IVAL | 11426 |
| IVOL | 11426 |
| MEUR2020 | 11605 |
| MEURcour | 11605 |
fonction, Fonction
Tous
Code
T_CONSO_EFF_FONCTION %>%
group_by(fonction, Fonction) %>%
summarise(Nobs = n()) %>%
print_table_conditional()2-digit
Code
T_CONSO_EFF_FONCTION %>%
filter(nchar(fonction) == 2) %>%
group_by(fonction, Fonction) %>%
summarise(Nobs = n()) %>%
print_table_conditional()| fonction | Fonction | Nobs |
|---|---|---|
| _Z | Total de la consommation effective des ménages | 388 |
3-digit
Code
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 | 388 |
| CP02 | Boissons alcoolisées, tabac et stupéfiants | 388 |
| CP03 | Articles d’habillement et chaussures | 388 |
| CP04 | Logement, eau, gaz, électricité et autres combustibles | 388 |
| CP05 | Meubles, articles de ménage et entretien courant du foyer | 388 |
| CP06 | Santé | 388 |
| CP07 | Transports | 388 |
| CP08 | Information et communication | 388 |
| CP09 | Loisirs, sport et culture | 388 |
| CP10 | Services de l’enseignement | 388 |
| CP11 | Restaurants et services d’hébergement | 388 |
| CP12 | Assurance et services financiers | 388 |
| CP13 | Soins corporels, protection sociale et biens et services divers | 388 |
| CP14 | Dépenses de consommation individuelle à la charge des institutions sans but lucratif au service des ménages (ISBLSM) | 388 |
| CP15 | Dépenses de consommation individuelle à la charge des administrations publiques | 388 |
| CP16 | Solde territorial | 388 |
4-digit
Code
T_CONSO_EFF_FONCTION %>%
filter(nchar(fonction) == 6) %>%
group_by(fonction, Fonction) %>%
summarise(Nobs = n()) %>%
print_table_conditional()5-digit
Code
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 | 388 |
Autres
Code
T_CONSO_EFF_FONCTION %>%
filter(!(nchar(fonction) %in% c(2, 4, 6))) %>%
group_by(fonction, Fonction) %>%
summarise(Nobs = n()) %>%
print_table_conditional()year
Code
T_CONSO_EFF_FONCTION %>%
group_by(year) %>%
summarise(Nobs = n()) %>%
arrange(desc(year)) %>%
print_table_conditional()2020
Désordonné
Code
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
Code
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 €
Code
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
Code
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
Code
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
Code
T_CONSO_EFF_FONCTION %>%
filter(variable == "IPRIX2020",
year %in% c("1990", "2020")) %>%
select(-variable) %>%
spread(year, value) %>%
mutate(`% / an` = round(100*((`2020`/`1990`)^(1/30)-1), 2)) %>%
arrange(`% / an`) %>%
print_table_conditional()Déflateurs
Code
T_CONSO_EFF_FONCTION %>%
filter(variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(variable == "IPRIX2020") %>%
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-
Code
T_CONSO_EFF_FONCTION %>%
filter(variable == "IPRIX2020") %>%
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-
Code
T_CONSO_EFF_FONCTION %>%
filter(variable == "IPRIX2020") %>%
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-
Code
T_CONSO_EFF_FONCTION %>%
filter(variable == "IPRIX2020") %>%
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-
Code
T_CONSO_EFF_FONCTION %>%
filter(variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(variable == "COEFFCOUR",
year %in% c("1960", "1990", "2020")) %>%
select(-variable) %>%
spread(year, value) %>%
arrange(-`2020`) %>%
print_table_conditional()CP091 (Matériel audiovisuel, photographique et informatique), CP082 (Matériel de téléphonie et de télécopie)
Code
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
Code
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
Code
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
Code
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
Code
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
Code
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
Code
T_CONSO_EFF_FONCTION %>%
filter(variable == "COEFFCOUR",
year %in% c("1960", "1990", "2020")) %>%
select(-variable) %>%
group_by(year) %>%
arrange(fonction) %>%
mutate(value = round(100*value/value[fonction =="CPDEP"],2)) %>%
spread(year, value) %>%
print_table_conditional()2-digit
Code
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 | 23.15 | 13.75 | 13.63 |
| CP02 | Boissons alcoolisées, tabac et stupéfiants | 7.91 | 3.50 | 4.28 |
| CP03 | Articles d’habillement et chaussures | 12.19 | 7.14 | 3.20 |
| CP04 | Logement, eau, gaz, électricité et autres combustibles | 12.76 | 21.81 | 30.19 |
| CP05 | Meubles, articles de ménage et entretien courant du foyer | 8.48 | 5.63 | 4.51 |
| CP06 | Santé | 2.37 | 3.30 | 3.94 |
| CP07 | Transports | 10.39 | 14.38 | 10.91 |
| CP08 | Information et communication | 1.43 | 3.40 | 4.16 |
| CP09 | Loisirs, sport et culture | 6.48 | 7.39 | 6.29 |
| CP10 | Services de l’enseignement | 0.55 | 0.63 | 0.78 |
| CP11 | Restaurants et services d’hébergement | 5.83 | 6.04 | 5.66 |
| CP12 | Assurance et services financiers | 2.83 | 7.47 | 6.44 |
| CP13 | Soins corporels, protection sociale et biens et services divers | 4.80 | 6.22 | 6.08 |
| CP14 | Dépenses de consommation individuelle à la charge des institutions sans but lucratif au service des ménages (ISBLSM) | 3.39 | 2.85 | 4.38 |
| CP15 | Dépenses de consommation individuelle à la charge des administrations publiques | 14.31 | 23.85 | 31.74 |
| CP16 | Solde territorial | 0.84 | -0.66 | -0.08 |
| CPDEP | Dépense de consommation des ménages | 100.00 | 100.00 | 100.00 |
3-digit
Code
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
Code
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
Code
deflateur <- T_CONSO_EFF_FONCTION %>%
filter(variable == "IPRIX2020",
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
Code
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
Code
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
Code
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
Code
deflateur %>%
inner_join(IPC, by = "fonction") %>%
mutate(Difference = `Déflateur (%)`-`IPC (%)`) %>%
arrange(Difference) %>%
print_table_conditional()| fonction | Fonction.x | Déflateur (%) | Fonction.y | IPC (%) | Difference |
|---|---|---|---|---|---|
| NA | NA | NA | NA | NA | NA |
| :--------: | :----------: | :-------------: | :----------: | :-------: | :----------: |
2-digit
Code
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 |
|---|---|---|---|---|---|
| NA | NA | NA | NA | NA | NA |
| :--------: | :----------: | :-------------: | :----------: | :-------: | :----------: |
3-digit
Code
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 |
|---|---|---|---|---|---|
| NA | NA | NA | NA | NA | NA |
| :--------: | :----------: | :-------------: | :----------: | :-------: | :----------: |
4-digit
Code
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 |
|---|---|---|---|---|---|
| NA | NA | NA | NA | NA | NA |
| :--------: | :----------: | :-------------: | :----------: | :-------: | :----------: |
Comparer Poids Déflateur VS IPC
Code
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 |
|---|---|---|---|---|---|
| NA | NA | NA | NA | NA | NA |
| :--------: | :----------: | :---------------: | :----------: | :---------: | :----------: |
2-digit
00 - Tous
1996-
Code
T_CONSO_EFF_FONCTION %>%
filter(Fonction %in% c("Dépense de consommation des ménages"),
variable == "IPRIX2020") %>%
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 == "A",
PRIX_CONSO == "SO",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
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 == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
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(0.35, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
1996-
Code
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 == "IPRIX2020") %>%
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 == "A",
PRIX_CONSO == "SO",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
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 == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
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(0.35, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
2000-
Code
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 == "IPRIX2020") %>%
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-
Code
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 == "IPRIX2020") %>%
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-
Code
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 == "IPRIX2020") %>%
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-
Code
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 == "IPRIX2020") %>%
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 == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
filter(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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP01",
variable == "IPRIX2020") %>%
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 == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
bind_rows(`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("01"),
FREQ == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation Harmonisé")) %>%
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, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP02 - Boissons alcoolisées et tabac
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP02",
variable == "IPRIX2020") %>%
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 == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
bind_rows(`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("02"),
FREQ == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation Harmonisé")) %>%
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, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP03 - Articles d’habillement et chaussures
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP03",
variable == "IPRIX2020") %>%
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 == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
bind_rows(`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("03"),
FREQ == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation Harmonisé")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP04",
variable == "IPRIX2020") %>%
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 == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
bind_rows(`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("04"),
FREQ == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation Harmonisé")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP05",
variable == "IPRIX2020") %>%
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 == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
bind_rows(`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("05"),
FREQ == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation Harmonisé")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP06",
variable == "IPRIX2020") %>%
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 == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
bind_rows(`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("06"),
FREQ == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation Harmonisé")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
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(0.3, 0.85),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
CP07 - Transports
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP07",
variable == "IPRIX2020") %>%
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 == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
bind_rows(`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("07"),
FREQ == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation Harmonisé")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP08",
variable == "IPRIX2020") %>%
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 == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation")) %>%
bind_rows(`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("08"),
FREQ == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation Harmonisé")) %>%
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, 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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP08",
variable == "IPRIX2020") %>%
year_to_date2 %>%
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 == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation (IPC)")) %>%
bind_rows(`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("08"),
FREQ == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation Harmonisé (IPCH)")) %>%
group_by(variable) %>%
filter(date >= as.Date("1996-01-01")) %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP09",
variable == "IPRIX2020") %>%
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 == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation (IPC)")) %>%
bind_rows(`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("09"),
FREQ == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation Harmonisé (IPCH)")) %>%
group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP09",
variable == "IPRIX2020") %>%
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 == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation (IPC)")) %>%
bind_rows(`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("09"),
FREQ == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation Harmonisé (IPCH)")) %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP10",
variable == "IPRIX2020") %>%
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 == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation (IPC)")) %>%
bind_rows(`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("10"),
FREQ == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation Harmonisé (IPCH)")) %>%
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, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP11 - Hôtels, cafés et restaurants
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP11",
variable == "IPRIX2020") %>%
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 == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation (IPC)")) %>%
bind_rows(`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("11"),
FREQ == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation Harmonisé (IPCH)")) %>%
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, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 500, 10),
labels = dollar_format(accuracy = 1, prefix = ""))
CP12 - Biens et services divers
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP12",
variable == "IPRIX2020") %>%
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 == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation (IPC)")) %>%
bind_rows(`IPCH-2015` %>%
filter(INDICATEUR == "IPCH",
COICOP2016 %in% c("12"),
FREQ == "A",
REF_AREA == "FE",
NATURE == "INDICE") %>%
year_to_date %>%
select(date, value = OBS_VALUE) %>%
mutate(variable = "Indice des Prix à la Consommation Harmonisé (IPCH)")) %>%
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(0.25, 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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP13",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP14",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP15",
variable == "IPRIX2020") %>%
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
CP023 - Tabac
All
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP023",
variable == "IPRIX2020") %>%
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-
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP023",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP0213",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP041",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP043",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP044",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP051",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP052",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP053",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP054",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP055",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP056",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP072",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP081",
variable == "IPRIX2020") %>%
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-
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP082",
variable == "IPRIX2020") %>%
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-
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP082",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP083",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP091",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP092",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP093",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP094",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP095",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP096",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP126",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP126",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP141",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP142",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP143",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP144",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP0562",
variable == "IPRIX2020") %>%
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 -
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP0912",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP0914",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP0913",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP0915",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP0931",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP0954",
variable == "IPRIX2020") %>%
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
Code
T_CONSO_EFF_FONCTION %>%
filter(fonction =="CP1261",
variable == "IPRIX2020") %>%
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…