Last observation: M: 1998-12 (N = 3)
First observation: M: 1971-01 (N = 2)
Last data update: 17 aoû 2026, 01:02. Last compile: 18 aoû 2026, 02:20
Données - INSEE
Last observation: M: 1998-12 (N = 3)
First observation: M: 1971-01 (N = 2)
Last data update: 17 aoû 2026, 01:02. Last compile: 18 aoû 2026, 02:20
| source | dataset | Title | Updated |
|---|---|---|---|
| insee | REVALORISATION-PENSIONS | IPC utilisés par la calculette de revalorisation des pensions alimentaires | 2026-08-16 |
`REVALORISATION-PENSIONS` |>
group_by(LAST_UPDATE) |>
summarise(Nobs = n()) |>
print_table_conditional()| LAST_UPDATE | Nobs |
|---|---|
| 1999-01-30 | 1020 |
`REVALORISATION-PENSIONS` |>
group_by(IDBANK, TITLE_FR) |>
summarise(Nobs = n(),
date1 = first(TIME_PERIOD),
date2 = last(TIME_PERIOD)) |>
arrange(-Nobs) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}`REVALORISATION-PENSIONS` |>
group_by(TIME_PERIOD) |>
summarise(Nobs = n()) |>
arrange(desc(TIME_PERIOD)) |>
print_table_conditional()`REVALORISATION-PENSIONS` |>
month_to_date() |>
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Ipc_revalorisation)) +
scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
`REVALORISATION-PENSIONS` |>
month_to_date() |>
group_by(IPC_REVALORISATION) |>
arrange(date) |>
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1981-01-01")]) |>
ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Ipc_revalorisation)) +
scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.3),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 200, 5),
labels = dollar_format(accuracy = 1, prefix = ""))