| source | dataset | .html | .RData |
|---|---|---|---|
| insee | INDICE-TRAITEMENT-FP | 2024-11-05 | 2024-11-08 |
| insee | IPC-2015 | 2024-11-05 | 2024-11-05 |
| insee | IPCH-2015 | 2024-11-05 | 2024-11-05 |
| ipp | revalorisation_pension | 2024-11-09 | 2024-11-09 |
Revalorisation du Régime général de la caisse nationale d’assurance vieillesse (CNAV)
Data - IPP
Info
Données sur le pouvoir d’achat
| source | dataset | .html | .RData |
|---|---|---|---|
| insee | CNA-2014-RDB | 2024-11-05 | 2024-11-08 |
| insee | CNT-2014-CSI | 2024-11-05 | 2024-11-08 |
| insee | conso-eff-fonction | 2024-11-05 | 2022-06-14 |
| insee | econ-gen-revenu-dispo-pouv-achat-2 | 2024-11-05 | 2024-07-05 |
| insee | reve-conso-evo-dep-pa | 2024-11-05 | 2024-09-05 |
| insee | reve-niv-vie-individu-activite | 2024-11-05 | NA |
| insee | reve-niv-vie-pouv-achat-trim | 2024-11-05 | 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 |
Données sur les retraites
| source | dataset | .html | .RData |
|---|---|---|---|
| insee | DECES-MORTALITE | 2024-11-05 | 2024-11-08 |
| ipp | revalorisation_pension | 2024-11-09 | 2024-11-09 |
Base 100
All
Code
revalorisation_pension %>%
select(date, index) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
mutate(value = 100*value/value[1]) %>%
ggplot() + geom_line(aes(x = date, y = value)) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 5) %>% 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, 3000, 100)) +
ylab("Point Indice Fonction Publique") + xlab("")
1996-
nominal
Code
revalorisation_pension %>%
select(date, index) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
filter(date >= as.Date("1996-01-01")) %>%
mutate(value = 100*value/value[1]) %>%
ggplot() + geom_line(aes(x = date, y = value)) +
theme_minimal() +
scale_x_date(breaks = seq(1999, 2100, 5) %>% 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, 3000, 5)) +
ylab("Pensions") + xlab("")
reel
Code
revalorisation_pension %>%
select(date, index) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
ungroup %>%
left_join(cpi2_m, by = "date") %>%
filter(day(date) == 1) %>%
filter(date >= as.Date("1996-01-01")) %>%
transmute(date,
`Retraites vs. 1999 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)-1,
`Retraites vs. 1999 (IPC, INSEE)` = (value/value[1])*(cpi[1]/cpi)-1) %>%
gather(variable, OBS_VALUE, -date) %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
scale_x_date(breaks = seq(1996, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.43, 0.12),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-200, 200, 1),
labels = percent_format(acc = 1)) +
ylab("Retraites vs. 1999") + xlab("") +
geom_text(data = . %>% filter(year(date) %in% seq(1996, 2040, 2),
month(date) == 1),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 1)),
fontface ="bold", color = "black", size = 3)
1999-
nominal
Code
revalorisation_pension %>%
select(date, index) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
filter(date >= as.Date("1999-01-01")) %>%
mutate(value = 100*value/value[1]) %>%
ggplot() + geom_line(aes(x = date, y = value)) +
theme_minimal() +
scale_x_date(breaks = seq(1999, 2100, 5) %>% 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, 3000, 5)) +
ylab("Pensions") + xlab("")
reel
Code
revalorisation_pension %>%
select(date, index) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
filter(date >= as.Date("1999-01-01")) %>%
ungroup %>%
left_join(cpi2_m, by = "date") %>%
filter(day(date) == 1) %>%
filter(date >= as.Date("1999-01-01")) %>%
transmute(date,
`Retraites vs. 1999 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)-1,
`Retraites vs. 1999 (IPC, INSEE)` = (value/value[1])*(cpi[1]/cpi)-1) %>%
gather(variable, OBS_VALUE, -date) %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
scale_x_date(breaks = seq(1999, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.43, 0.12),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-200, 200, 2),
labels = percent_format(acc = 1)) +
ylab("Retraites vs. 1999") + xlab("") +
geom_text(data = . %>% filter(year(date) %in% seq(1999, 2040, 5),
month(date) == 1),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 1)),
fontface ="bold", color = "black", size = 3)
Décembre 2007-
nominal
Code
revalorisation_pension %>%
select(date, index) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
filter(date >= as.Date("2007-12-01")) %>%
mutate(value = 100*value/value[1]) %>%
ggplot() + geom_line(aes(x = date, y = value)) +
theme_minimal() +
scale_x_date(breaks = seq(2008, 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, 3000, 5)) +
ylab("Pensions") + xlab("") +
geom_label(data = . %>% tail(1), aes(x = date, y = value, label = round(value, 1)))
2008-
nominal
Code
revalorisation_pension %>%
select(date, index) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
filter(date >= as.Date("2008-01-01")) %>%
mutate(value = 100*value/value[1]) %>%
ggplot() + geom_line(aes(x = date, y = value)) +
theme_minimal() +
scale_x_date(breaks = seq(2008, 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, 3000, 5)) +
ylab("Pensions") + xlab("") +
geom_label(data = . %>% tail(1), aes(x = date, y = value, label = round(value, 1)))
Réel
Code
revalorisation_pension %>%
select(date, index) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
filter(date >= as.Date("2007-12-01")) %>%
ungroup %>%
left_join(cpi2_m, by = "date") %>%
filter(day(date) == 1) %>%
filter(date >= as.Date("2007-12-01")) %>%
transmute(date,
`Retraites vs. 2008 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)-1,
`Retraites vs. 2008 (IPC, INSEE)` = (value/value[1])*(cpi[1]/cpi)-1) %>%
gather(variable, OBS_VALUE, -date) %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
scale_x_date(breaks = seq(2008, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.43, 0.12),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-200, 200, 1),
labels = percent_format(acc = 1)) +
ylab("Retraites vs. 1999") + xlab("") +
geom_text(data = . %>% filter(date %in% c(as.Date("2014-01-01"),
as.Date("2017-04-01"),
max(date))),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)),
fontface ="bold", color = "black", size = 3)
2017-
nominal
Code
revalorisation_pension %>%
select(date, index) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
filter(date >= as.Date("2017-01-01")) %>%
mutate(value = 100*value/value[1]) %>%
ggplot() + geom_line(aes(x = date, y = value)) +
theme_minimal() +
scale_x_date(breaks = seq(2017, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 3000, 5)) +
ylab("Pensions") + xlab("") +
geom_label(data = . %>% tail(1), aes(x = date, y = value, label = round(value, 1)))
Réel
Code
revalorisation_pension %>%
select(date, index) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
filter(date >= as.Date("2017-01-01")) %>%
ungroup %>%
left_join(cpi2_m, by = "date") %>%
filter(day(date) == 1) %>%
filter(date >= as.Date("2007-01-01")) %>%
transmute(date,
`Retraites vs. 2017 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)-1,
`Retraites vs. 2017 (IPC, INSEE)` = (value/value[1])*(cpi[1]/cpi)-1) %>%
gather(variable, OBS_VALUE, -date) %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
scale_x_date(breaks = seq(2008, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.43, 0.12),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-200, 200, 1),
labels = percent_format(acc = 1)) +
ylab("Retraites vs. 1999") + xlab("") +
geom_text(data = . %>% filter(date %in% c(as.Date("2014-01-01"),
as.Date("2017-04-01"),
max(date))),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)),
fontface ="bold", color = "black", size = 3)
2021-
nominal
Code
revalorisation_pension %>%
select(date, index) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
filter(date >= as.Date("2021-01-01")) %>%
mutate(value = 100*value/value[1]) %>%
ggplot() + geom_line(aes(x = date, y = value)) +
theme_minimal() +
scale_x_date(breaks = seq(2017, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 3000, 2)) +
ylab("Pensions") + xlab("") +
geom_label(data = . %>% tail(1), aes(x = date, y = value, label = round(value, 1)))
Réel
Code
revalorisation_pension %>%
select(date, index) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
filter(date >= as.Date("2021-01-01")) %>%
ungroup %>%
left_join(cpi2_m, by = "date") %>%
filter(day(date) == 1) %>%
filter(date >= as.Date("2021-01-01")) %>%
transmute(date,
`Retraites vs. 2017 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)-1,
`Retraites vs. 2017 (IPC, INSEE)` = (value/value[1])*(cpi[1]/cpi)-1) %>%
gather(variable, OBS_VALUE, -date) %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
scale_x_date(breaks = seq(2008, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.43, 0.12),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-200, 200, 1),
labels = percent_format(acc = 1)) +
ylab("Retraites vs. 2021") + xlab("") +
geom_text(data = . %>% filter(date %in% c(as.Date("2014-01-01"),
as.Date("2017-04-01"),
max(date))),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)),
fontface ="bold", color = "black", size = 3)
1999-
Retraites
Code
data1 <- revalorisation_pension %>%
select(date, index) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
filter(date >= as.Date("1999-01-01")) %>%
ungroup %>%
left_join(cpi2_m, by = "date") %>%
filter(day(date) == 1) %>%
filter(date >= as.Date("1999-01-01")) %>%
transmute(date,
`IPCH, Eurostat` = (value/value[1])*(cpih[1]/cpih),
`IPC, INSEE` = (value/value[1])*(cpi[1]/cpi)) %>%
gather(type, OBS_VALUE, -date) %>%
mutate(variable = "Retraites vs. 1999")
data1 %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, linetype = type)) + theme_minimal() +
scale_x_date(breaks = seq(1999, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.43, 0.12),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(1, 0.02, -0.02),
labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) +
ylab("Retraites vs. 1999") + xlab("") +
geom_text(data = . %>% filter(year(date) %in% seq(1999, 2040, 5),
month(date) == 1),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)),
fontface ="bold", color = "black", size = 3)
Indice Fonction Publique
Code
load_data("insee/INDICE-TRAITEMENT-FP-net-brut-mensuel.RData")
data2 <- indicefp %>%
select(date, point_indice_en_euros) %>%
arrange(desc(date)) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
ungroup %>%
left_join(cpi2_m, by = "date") %>%
filter(day(date) == 1) %>%
left_join(net_brut_mensuel, by = "date") %>%
filter(date >= as.Date("1999-01-01")) %>%
transmute(date,
`IPCH, Eurostat` = (value/value[1])*(cpih[1]/cpih)*net_brut,
`IPC, INSEE` = (value/value[1])*(cpi[1]/cpi)*net_brut) %>%
gather(type, OBS_VALUE, -date) %>%
mutate(variable = "Point d'Indice de la Fonction Publique vs. 1999")
data2 %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, linetype = type)) + theme_minimal() +
scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.43, 0.12),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(1, 0.02, -0.02),
labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) +
ylab("Point Indice Fonction Publique vs. 1999") + xlab("") +
geom_text(data = . %>% filter(year(date) %in% seq(1999, 2040, 5),
month(date) == 1),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)),
fontface ="bold", color = "black", size = 3)
Merge
Code
data2 %>%
bind_rows(data1) %>%
mutate(variable = factor(variable, levels = c("Retraites vs. 1999",
"Point d'Indice de la Fonction Publique vs. 1999"))) %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable, linetype = type)) + theme_minimal() +
scale_linetype_manual(values = c("dashed", "solid")) +
scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.32, 0.25),
legend.title = element_blank(),
legend.key.size = unit(0.5, "cm")) +
scale_y_log10(breaks = seq(1, 0.02, -0.02),
labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) +
ylab("Valeur vs. 1999") + xlab("") +
geom_text(data = . %>% filter(date == max(date)),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 1), color = variable),
fontface ="bold", size = 3)
2007-12-
Retraites
Code
data1 <- revalorisation_pension %>%
select(date, index) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
filter(date >= as.Date("2007-12-01")) %>%
ungroup %>%
left_join(cpi2_m, by = "date") %>%
filter(day(date) == 1) %>%
filter(date >= as.Date("2007-12-01")) %>%
transmute(date,
`IPCH, Eurostat` = (value/value[1])*(cpih[1]/cpih),
`IPC, INSEE` = (value/value[1])*(cpi[1]/cpi)) %>%
gather(type, OBS_VALUE, -date) %>%
mutate(variable = "Retraites vs. 2008")
data1 %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, linetype = type)) + theme_minimal() +
scale_x_date(breaks = seq(1999, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.43, 0.12),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(1, 0.02, -0.02),
labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) +
ylab("Retraites vs. 2008") + xlab("") +
geom_text(data = . %>% filter(year(date) %in% seq(1999, 2040, 5),
month(date) == 1),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)),
fontface ="bold", color = "black", size = 3)
Indice Fonction Publique
Code
load_data("insee/INDICE-TRAITEMENT-FP-net-brut-mensuel.RData")
data2 <- indicefp %>%
select(date, point_indice_en_euros) %>%
arrange(desc(date)) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
ungroup %>%
left_join(cpi2_m, by = "date") %>%
filter(day(date) == 1) %>%
left_join(net_brut_mensuel, by = "date") %>%
filter(date >= as.Date("2007-12-01")) %>%
transmute(date,
`IPCH, Eurostat` = (value/value[1])*(cpih[1]/cpih)*net_brut,
`IPC, INSEE` = (value/value[1])*(cpi[1]/cpi)*net_brut) %>%
gather(type, OBS_VALUE, -date) %>%
mutate(variable = "Point d'Indice de la Fonction Publique vs. 2008")
data2 %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, linetype = type)) + theme_minimal() +
scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.43, 0.12),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(1, 0.02, -0.02),
labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) +
ylab("Point Indice Fonction Publique vs. 2008") + xlab("") +
geom_text(data = . %>% filter(year(date) %in% seq(1999, 2040, 5),
month(date) == 1),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)),
fontface ="bold", color = "black", size = 3)
Merge
Code
data2 %>%
bind_rows(data1) %>%
mutate(variable = factor(variable, levels = c("Retraites vs. 2008",
"Point d'Indice de la Fonction Publique vs. 2008"))) %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable, linetype = type)) + theme_minimal() +
scale_linetype_manual(values = c("dashed", "solid")) +
scale_x_date(breaks = c(seq(1998, 2100, 2), seq(2008, 2100, 2)) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.32, 0.25),
legend.title = element_blank(),
legend.key.size = unit(0.5, "cm")) +
scale_y_log10(breaks = seq(1, 0.02, -0.02),
labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) +
ylab("Valeur vs. 2008") + xlab("") +
geom_text(data = . %>% filter(date == max(date)),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)),
fontface ="bold", color = "black", size = 3)
2012-
Retraites
Code
data1 <- revalorisation_pension %>%
select(date, index) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
filter(date >= as.Date("2012-01-01")) %>%
ungroup %>%
left_join(cpi2_m, by = "date") %>%
filter(day(date) == 1) %>%
filter(date >= as.Date("2012-01-01")) %>%
transmute(date,
`IPCH, Eurostat` = (value/value[1])*(cpih[1]/cpih),
`IPC, INSEE` = (value/value[1])*(cpi[1]/cpi)) %>%
gather(type, OBS_VALUE, -date) %>%
mutate(variable = "Retraites vs. 2012")
data1 %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, linetype = type)) + theme_minimal() +
scale_x_date(breaks = seq(1999, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.43, 0.12),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(1, 0.02, -0.02),
labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) +
ylab("Retraites vs. 2012") + xlab("") +
geom_text(data = . %>% filter(year(date) %in% seq(1999, 2040, 5),
month(date) == 1),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)),
fontface ="bold", color = "black", size = 3)
Indice Fonction Publique
Code
load_data("insee/INDICE-TRAITEMENT-FP-net-brut-mensuel.RData")
data2 <- indicefp %>%
select(date, point_indice_en_euros) %>%
arrange(desc(date)) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
ungroup %>%
left_join(cpi2_m, by = "date") %>%
filter(day(date) == 1) %>%
left_join(net_brut_mensuel, by = "date") %>%
filter(date >= as.Date("2012-01-01")) %>%
transmute(date,
`IPCH, Eurostat` = (value/value[1])*(cpih[1]/cpih)*net_brut,
`IPC, INSEE` = (value/value[1])*(cpi[1]/cpi)*net_brut) %>%
gather(type, OBS_VALUE, -date) %>%
mutate(variable = "Point d'Indice de la Fonction Publique vs. 2012")
data2 %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, linetype = type)) + theme_minimal() +
scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.43, 0.12),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(1, 0.02, -0.02),
labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) +
ylab("Point Indice Fonction Publique vs. 2012") + xlab("") +
geom_text(data = . %>% filter(year(date) %in% seq(1999, 2040, 5),
month(date) == 1),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)),
fontface ="bold", color = "black", size = 3)
Merge
Code
data2 %>%
bind_rows(data1) %>%
mutate(variable = factor(variable, levels = c("Retraites vs. 2012",
"Point d'Indice de la Fonction Publique vs. 2012"))) %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable, linetype = type)) + theme_minimal() +
scale_linetype_manual(values = c("dashed", "solid")) +
scale_x_date(breaks = c(seq(1999, 2100, 1), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.32, 0.25),
legend.title = element_blank(),
legend.key.size = unit(0.5, "cm")) +
scale_y_log10(breaks = seq(2, 0.02, -0.02),
labels = percent(seq(2, 0.02, -0.02)-1, acc = 1)) +
ylab("Valeur vs. 2017") + xlab("") +
geom_text(data = . %>% filter(date == max(date)),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1), color = variable),
fontface ="bold", size = 3)
2017-
Retraites
Code
data1 <- revalorisation_pension %>%
select(date, index) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
filter(date >= as.Date("2017-01-01")) %>%
ungroup %>%
left_join(cpi2_m, by = "date") %>%
filter(day(date) == 1) %>%
filter(date >= as.Date("2017-01-01")) %>%
transmute(date,
`IPCH, Eurostat` = (value/value[1])*(cpih[1]/cpih),
`IPC, INSEE` = (value/value[1])*(cpi[1]/cpi)) %>%
gather(type, OBS_VALUE, -date) %>%
mutate(variable = "Retraites vs. 2017")
data1 %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, linetype = type)) + theme_minimal() +
scale_x_date(breaks = seq(1999, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.43, 0.12),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(1, 0.02, -0.02),
labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) +
ylab("Retraites vs. 1999") + xlab("") +
geom_text(data = . %>% filter(year(date) %in% seq(1999, 2040, 5),
month(date) == 1),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)),
fontface ="bold", color = "black", size = 3)
Indice Fonction Publique
Code
load_data("insee/INDICE-TRAITEMENT-FP-net-brut-mensuel.RData")
data2 <- indicefp %>%
select(date, point_indice_en_euros) %>%
arrange(desc(date)) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
ungroup %>%
left_join(cpi2_m, by = "date") %>%
filter(day(date) == 1) %>%
left_join(net_brut_mensuel, by = "date") %>%
filter(date >= as.Date("2017-01-01")) %>%
transmute(date,
`IPCH, Eurostat` = (value/value[1])*(cpih[1]/cpih)*net_brut,
`IPC, INSEE` = (value/value[1])*(cpi[1]/cpi)*net_brut) %>%
gather(type, OBS_VALUE, -date) %>%
mutate(variable = "Point d'Indice de la Fonction Publique vs. 2017")
data2 %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, linetype = type)) + theme_minimal() +
scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.43, 0.12),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(1, 0.02, -0.02),
labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) +
ylab("Point Indice Fonction Publique vs. 1999") + xlab("") +
geom_text(data = . %>% filter(year(date) %in% seq(1999, 2040, 5),
month(date) == 1),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)),
fontface ="bold", color = "black", size = 3)
Merge
Code
data2 %>%
bind_rows(data1) %>%
mutate(variable = factor(variable, levels = c("Retraites vs. 2017",
"Point d'Indice de la Fonction Publique vs. 2017"))) %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable, linetype = type)) + theme_minimal() +
scale_linetype_manual(values = c("dashed", "solid")) +
scale_x_date(breaks = c(seq(1999, 2100, 1), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.32, 0.25),
legend.title = element_blank(),
legend.key.size = unit(0.5, "cm")) +
scale_y_log10(breaks = seq(1, 0.02, -0.02),
labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) +
ylab("Valeur vs. 2017") + xlab("") +
geom_label(data = . %>% filter(date == max(date)),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1), color = variable),
fontface ="bold", size = 3)
Juin 2017
Retraites
Code
data1 <- revalorisation_pension %>%
select(date, index) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
filter(date >= as.Date("2017-06-01")) %>%
ungroup %>%
left_join(cpi2_m, by = "date") %>%
filter(day(date) == 1) %>%
filter(date >= as.Date("2017-06-01")) %>%
transmute(date,
`IPCH, Eurostat` = (value/value[1])*(cpih[1]/cpih),
`IPC, INSEE` = (value/value[1])*(cpi[1]/cpi)) %>%
gather(type, OBS_VALUE, -date) %>%
mutate(variable = "Retraites vs. juin 2017")
data1 %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, linetype = type)) + theme_minimal() +
scale_x_date(breaks = seq(1999, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.43, 0.12),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(1, 0.02, -0.02),
labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) +
ylab("Retraites vs. juin 2017") + xlab("") +
geom_text(data = . %>% filter(year(date) %in% seq(1999, 2040, 5),
month(date) == 1),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)),
fontface ="bold", color = "black", size = 3)
Indice Fonction Publique
Code
load_data("insee/INDICE-TRAITEMENT-FP-net-brut-mensuel.RData")
data2 <- indicefp %>%
select(date, point_indice_en_euros) %>%
arrange(desc(date)) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
complete(date = seq.Date(min(date), max(date), by = "day")) %>%
fill(value) %>%
ungroup %>%
left_join(cpi2_m, by = "date") %>%
filter(day(date) == 1) %>%
left_join(net_brut_mensuel, by = "date") %>%
filter(date >= as.Date("2017-06-01")) %>%
transmute(date,
`IPCH, Eurostat` = (value/value[1])*(cpih[1]/cpih)*(net_brut/net_brut[1]),
`IPC, INSEE` = (value/value[1])*(cpi[1]/cpi)*(net_brut/net_brut[1])) %>%
gather(type, OBS_VALUE, -date) %>%
mutate(variable = "Point d'Indice de la Fonction Publique vs. juin 2017")
data2 %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, linetype = type)) + theme_minimal() +
scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.43, 0.12),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(1, 0.02, -0.02),
labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) +
ylab("Point Indice Fonction Publique vs. juin 2017") + xlab("") +
geom_text(data = . %>% filter(year(date) %in% seq(1999, 2040, 5),
month(date) == 1),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)),
fontface ="bold", color = "black", size = 3)
Merge
Code
data2 %>%
bind_rows(data1) %>%
mutate(variable = factor(variable, levels = c("Retraites vs. juin 2017",
"Point d'Indice de la Fonction Publique vs. juin 2017"))) %>%
ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable, linetype = type)) + theme_minimal() +
scale_linetype_manual(values = c("dashed", "solid")) +
scale_x_date(breaks = c(seq(1999, 2100, 1), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.32, 0.25),
legend.title = element_blank(),
legend.key.size = unit(0.5, "cm")) +
scale_y_log10(breaks = seq(1, 0.02, -0.02),
labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) +
ylab("Valeur vs. 2017") + xlab("") +
geom_label(data = . %>% filter(date == max(date)),
aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1), color = variable),
fontface ="bold", size = 3)