Données - Dares
Last observation: 2026T1 (N = 793)
First observation: 2017T2 (N = 792)
Last data update: 30 août 2026, 17:44
Last compile: 02 sept. 2026, 22:45
`les-indices-de-salaire-de-base` |>
group_by(sheets) |>
summarise(Nobs = n()) |>
arrange(-Nobs) |>
print_table_conditional()| sheets | Nobs |
|---|---|
| Sal. mens. ensemble | 4549 |
| Sal. mens. PI | 4235 |
| Sal. mens. emp. | 4060 |
| Sal. mens. cadres | 3955 |
| Sal. mens. ouv. | 3675 |
| Sal. horaire ouv. | 3640 |
| Sal. horaire ouv. emp. | 3640 |
`les-indices-de-salaire-de-base` |>
group_by(secteur, Secteur) |>
summarise(Nobs = n()) |>
arrange(-Nobs) |>
print_table_conditional()`les-indices-de-salaire-de-base` |>
group_by(TIME_PERIOD) |>
summarise(Nobs = n()) |>
arrange(desc(TIME_PERIOD)) |>
print_table_conditional()`les-indices-de-salaire-de-base` |>
filter(secteur == "24") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
ggplot() + geom_line(aes(x = date, y = value, color = sheets)) +
theme_minimal() + xlab("") + ylab("Croissance vs. 2017T2") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
scale_y_log10(breaks = seq(100, 200, 2),
labels = percent(seq(100, 200, 2)/100-1, acc = 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
`les-indices-de-salaire-de-base` |>
filter(secteur == "24") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2019 Q4")) |>
group_by(sheets) |>
mutate(value = 100*value/value[1]) |>
ggplot() + geom_line(aes(x = date, y = value, color = sheets)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
`les-indices-de-salaire-de-base` |>
filter(secteur == "24") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2021 Q3")) |>
group_by(sheets) |>
mutate(value = 100*value/value[1]) |>
ggplot() + geom_line(aes(x = date, y = value, color = sheets)) +
theme_minimal() + xlab("") + ylab("Metallurgie") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = expand.grid(2017:2100, c(1, 3)) |>
mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
pull(breaks)) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
data1 <- `les-indices-de-salaire-de-base` |>
filter(secteur == "ENS",
sheets == "Sal. mens. ensemble") |>
select(TIME_PERIOD, value) |>
mutate(variable = "Salaire mensuel du secteur privé") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2014 Q4"),
date <= zoo::as.yearqtr("2024 Q4")) |>
mutate(value = 100*value/value[1]) |>
ungroup()
data1 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = expand.grid(2017:2100, c(2, 4)) |>
mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
pull(breaks)) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>%
filter(quarter(as.Date(date)) %in% c(2, 4)),
aes(x = date, y = value, label = round(value, 1)))
`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
filter(date >= as.Date("2014-12-01"),
month(date) %in% c(3, 6, 9, 12),
date <= as.Date("2024-12-01")) |>
group_by(INDICATEUR) |>
mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
value = 100*OBS_VALUE/OBS_VALUE[1],
VARIABLE = INDICATEUR) |>
mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
ungroup()
data2 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = expand.grid(2017:2100, c(2, 4)) |>
mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
pull(breaks)) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>%
filter(quarter(as.Date(date)) %in% c(2, 4)),
aes(x = date, y = value, label = round(value, 1)))
data2 |>
bind_rows(data1) |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = expand.grid(2017:2100, c(2, 4)) |>
mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
pull(breaks)) +
scale_y_log10(breaks = seq(100, 200, 2)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>%
filter(date %in% zoo::as.yearqtr(c("2017 Q2", "2020 Q4", "2022 Q4", "2024 Q2"))),
aes(x = date, y = value, label = round(value, 1)))
data1 <- `les-indices-de-salaire-de-base` |>
filter(secteur == "ENS",
sheets == "Sal. mens. ensemble") |>
select(TIME_PERIOD, value) |>
mutate(variable = "Salaire mensuel du secteur privé") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2017 Q2")) |>
mutate(value = 100*value/value[1]) |>
ungroup()
data1 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = expand.grid(2017:2100, c(2, 4)) |>
mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
pull(breaks)) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>%
filter(quarter(as.Date(date)) %in% c(2, 4)),
aes(x = date, y = value, label = round(value, 1)))
`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
filter(date >= as.Date("2017-06-01"),
month(date) %in% c(3, 6, 9, 12)) |>
group_by(INDICATEUR) |>
mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
value = 100*OBS_VALUE/OBS_VALUE[1],
VARIABLE = INDICATEUR) |>
mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
ungroup()
data2 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = expand.grid(2017:2100, c(2, 4)) |>
mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
pull(breaks)) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>%
filter(quarter(as.Date(date)) %in% c(2, 4)),
aes(x = date, y = value, label = round(value, 1)))
data2 |>
bind_rows(data1) |>
#filter(variable != "Prix Insee (IPC)") %>%
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = expand.grid(2017:2100, c(2, 4)) |>
mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
pull(breaks)) +
scale_y_log10(breaks = seq(100, 200, 2)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text_repel(data = . %>%
filter(date %in% zoo::as.yearqtr(c(max(date), "2017 Q2", "2020 Q4", "2022 Q4", "2024 Q3"))),
aes(x = date, y = value, label = round(value, 1), color = variable))
data1 <- `les-indices-de-salaire-de-base` |>
filter(secteur == "ENS",
sheets == "Sal. mens. ensemble") |>
select(TIME_PERIOD, value) |>
mutate(variable = "Salaire mensuel du secteur privé") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2017 Q2"),
date <= zoo::as.yearqtr("2024 Q2")) |>
mutate(value = 100*value/value[1]) |>
ungroup()
data1 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = expand.grid(2017:2100, c(2, 4)) |>
mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
pull(breaks)) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>%
filter(quarter(as.Date(date)) %in% c(2, 4)),
aes(x = date, y = value, label = round(value, 1)))
`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
filter(date >= as.Date("2017-06-01"),
month(date) %in% c(3, 6, 9, 12),
date <= as.Date("2024-06-01")) |>
group_by(INDICATEUR) |>
mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
value = 100*OBS_VALUE/OBS_VALUE[1],
VARIABLE = INDICATEUR) |>
mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
ungroup()
data2 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = expand.grid(2017:2100, c(2, 4)) |>
mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
pull(breaks)) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>%
filter(quarter(as.Date(date)) %in% c(2, 4)),
aes(x = date, y = value, label = round(value, 1)))
data2 |>
bind_rows(data1) |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = expand.grid(2017:2100, c(2, 4)) |>
mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
pull(breaks)) +
scale_y_log10(breaks = seq(100, 200, 2)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>%
filter(date %in% zoo::as.yearqtr(c(max(date), "2017 Q2", "2020 Q4", "2022 Q4", "2024 Q2"))),
aes(x = date, y = value, label = round(value, 1)))
data1 <- `les-indices-de-salaire-de-base` |>
filter(secteur == "ENS",
sheets == "Sal. mens. ensemble") |>
select(TIME_PERIOD, value) |>
mutate(variable = "Salaire mensuel du secteur privé") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2017 Q4")) |>
mutate(value = 100*value/value[1]) |>
ungroup()
data1 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = expand.grid(2017:2100, c(2, 4)) |>
mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
pull(breaks)) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>%
filter(quarter(as.Date(date)) %in% c(2, 4)),
aes(x = date, y = value, label = round(value, 1)))
`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
filter(date >= as.Date("2017-12-01"),
month(date) %in% c(3, 6, 9, 12)) |>
group_by(INDICATEUR) |>
mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
value = 100*OBS_VALUE/OBS_VALUE[1],
VARIABLE = INDICATEUR) |>
mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
ungroup()
data2 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = expand.grid(2017:2100, c(2, 4)) |>
mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
pull(breaks)) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>%
filter(quarter(as.Date(date)) %in% c(2, 4)),
aes(x = date, y = value, label = round(value, 1)))
data2 |>
bind_rows(data1) |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = expand.grid(2017:2100, c(2, 4)) |>
mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
pull(breaks)) +
scale_y_log10(breaks = seq(100, 200, 2)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>%
filter(date %in% zoo::as.yearqtr(c("2017 Q2", "2020 Q4", "2022 Q4", "2023 Q4"))),
aes(x = date, y = value, label = round(value, 1)))
data1 <- `les-indices-de-salaire-de-base` |>
filter(secteur == "ENS",
sheets == "Sal. mens. ensemble") |>
select(TIME_PERIOD, value) |>
mutate(variable = "Salaire mensuel du secteur privé") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2019 Q4")) |>
mutate(value = 100*value/value[1])
data1 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(aes(x = date, y = value, label = round(value, 1)))
`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
filter(date >= as.Date("2019-12-01"),
month(date) %in% c(3, 6, 9, 12)) |>
group_by(INDICATEUR) |>
mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
value = 100*OBS_VALUE/OBS_VALUE[1],
VARIABLE = INDICATEUR) |>
mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q"))
data2 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(aes(x = date, y = value, label = round(value, 1)))
data2 |>
bind_rows(data1) |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>% filter(date == max(date)), aes(x = date, y = value, label = round(value, 1)))
data1 <- `les-indices-de-salaire-de-base` |>
filter(secteur == "ENS",
sheets == "Sal. mens. ensemble") |>
select(TIME_PERIOD, value) |>
mutate(variable = "Salaire mensuel du secteur privé") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2020 Q4")) |>
mutate(value = 100*value/value[1])
data1 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(aes(x = date, y = value, label = round(value, 1)))
`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
filter(date >= as.Date("2020-12-01"),
month(date) %in% c(3, 6, 9, 12)) |>
group_by(INDICATEUR) |>
mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
value = 100*OBS_VALUE/OBS_VALUE[1],
VARIABLE = INDICATEUR) |>
mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q"))
data2 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(aes(x = date, y = value, label = round(value, 1)))
data_bind <- data2 |>
bind_rows(data1) |>
ungroup() |>
select(date, value, variable) |>
arrange(date)
# write_excel_csv(data_bind, file = "alter_eco_20230213.csv")
data_bind |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("Salaire de base") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = seq(min(data_bind$date), max(data_bind$date), by = 0.25)) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_label(data = . %>%
filter(date == max(date)),
aes(x = date, y = value, label = round(value, 1)))
data1 <- `les-indices-de-salaire-de-base` |>
filter(secteur == "ENS",
sheets == "Sal. mens. ensemble") |>
select(TIME_PERIOD, value) |>
mutate(variable = "Salaire mensuel du secteur privé") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2021 Q1")) |>
mutate(value = 100*value/value[1])
data1 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(aes(x = date, y = value, label = round(value, 1)))
`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
filter(date >= as.Date("2021-03-01"),
month(date) %in% c(3, 6, 9, 12)) |>
group_by(INDICATEUR) |>
mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
value = 100*OBS_VALUE/OBS_VALUE[1],
VARIABLE = INDICATEUR) |>
mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q"))
data2 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(aes(x = date, y = value, label = round(value, 1)))
data_bind <- data2 |>
bind_rows(data1) |>
ungroup() |>
select(date, value, variable) |>
arrange(date)
# write_excel_csv(data_bind, file = "alter_eco_20230213.csv")
data_bind |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>% filter(date == max(date)), aes(x = date, y = value, label = round(value, 1)))
data1 <- `les-indices-de-salaire-de-base` |>
filter(secteur == "ENS",
sheets == "Sal. mens. ensemble") |>
select(TIME_PERIOD, value) |>
mutate(variable = "Salaire mensuel du secteur privé") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2021 Q2")) |>
mutate(value = 100*value/value[1])
data1 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(aes(x = date, y = value, label = round(value, 1)))
`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
filter(date >= as.Date("2021-06-01"),
month(date) %in% c(3, 6, 9, 12)) |>
group_by(INDICATEUR) |>
mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
value = 100*OBS_VALUE/OBS_VALUE[1],
VARIABLE = INDICATEUR) |>
mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q"))
data2 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(aes(x = date, y = value, label = round(value, 1)))
data_bind <- data2 |>
bind_rows(data1) |>
ungroup() |>
select(date, value, variable) |>
arrange(date)
write_excel_csv(data_bind, file = "nathalie_silbert_20240923.csv")
data_bind |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>% filter(date == max(date)), aes(x = date, y = value, label = round(value, 1)))
data1 <- `les-indices-de-salaire-de-base` |>
filter(secteur == "ENS",
sheets == "Sal. mens. ensemble") |>
select(TIME_PERIOD, value) |>
mutate(variable = "Salaire mensuel du secteur privé") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2021 Q4")) |>
mutate(value = 100*value/value[1])
data1 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(aes(x = date, y = value, label = round(value, 1)))
`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
filter(date >= as.Date("2021-12-01"),
month(date) %in% c(3, 6, 9, 12)) |>
group_by(INDICATEUR) |>
mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
value = 100*OBS_VALUE/OBS_VALUE[1],
VARIABLE = INDICATEUR) |>
mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q"))
data2 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(aes(x = date, y = value, label = round(value, 1)))
data_bind <- data2 |>
bind_rows(data1) |>
ungroup() |>
select(date, value, variable) |>
arrange(date)
# write_excel_csv(data_bind, file = "alter_eco_20230213.csv")
data_bind |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = expand.grid(2021:2100, c(1, 2, 3, 4)) |>
mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
pull(breaks)) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>% filter(date == max(date)), aes(x = date, y = value, label = round(value, 1)))
data1 <- `les-indices-de-salaire-de-base` |>
filter(secteur == "ENS",
sheets == "Sal. mens. ensemble") |>
select(TIME_PERIOD, value) |>
mutate(variable = "Salaire mensuel du secteur privé") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2022 Q1")) |>
mutate(value = 100*value/value[1])
data1 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(aes(x = date, y = value, label = round(value, 1)))
`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
filter(date >= as.Date("2022-03-01"),
month(date) %in% c(3, 6, 9, 12)) |>
group_by(INDICATEUR) |>
mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
value = 100*OBS_VALUE/OBS_VALUE[1],
VARIABLE = INDICATEUR) |>
mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q"))
data2 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(aes(x = date, y = value, label = round(value, 1)))
data_bind <- data2 |>
bind_rows(data1) |>
ungroup() |>
select(date, value, variable) |>
arrange(date)
# write_excel_csv(data_bind, file = "alter_eco_20230213.csv")
data_bind |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = expand.grid(2021:2100, c(1, 2, 3, 4)) |>
mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
pull(breaks)) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(data = . %>% filter(date == max(date)), aes(x = date, y = value, label = round(value, 1)))
`les-indices-de-salaire-de-base` |>
filter(secteur == "ENS") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
ggplot() + geom_line(aes(x = date, y = value, color = sheets)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
scale_y_log10(breaks = seq(100, 200, 2)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
`les-indices-de-salaire-de-base` |>
filter(secteur == "ENS") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2019 Q4")) |>
group_by(sheets) |>
mutate(value = 100*value/value[1]) |>
ggplot() + geom_line(aes(x = date, y = value, color = sheets)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
`les-indices-de-salaire-de-base` |>
filter(secteur == "ENS") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2021 Q3")) |>
group_by(sheets) |>
mutate(value = 100*value/value[1]) |>
ggplot() + geom_line(aes(x = date, y = value, color = sheets)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
`les-indices-de-salaire-de-base` |>
filter(secteur == "ENS") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2020 Q4")) |>
group_by(sheets) |>
mutate(value = 100*value/value[1]) |>
ggplot() + geom_line(aes(x = date, y = value, color = sheets)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
`les-indices-de-salaire-de-base` |>
filter(secteur %in% c("ENS", "ET", "EU", "EV"),
sheets == "Sal. mens. cadres") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
ggplot() + geom_line(aes(x = date, y = value, color = Secteur)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
scale_y_log10(breaks = seq(100, 200, 2)) +
theme(legend.position = c(0.35, 0.8),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
data1 <- `les-indices-de-salaire-de-base` |>
filter(secteur == "ENS",
sheets == "Sal. mens. cadres") |>
select(TIME_PERIOD, value) |>
mutate(variable = "Salaire mensuel des cadres du secteur privé") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2017 Q1")) |>
mutate(value = 100*value/value[1])
data1 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(aes(x = date, y = value, label = round(value, 1)))
`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
filter(date >= as.Date("2017-03-01"),
month(date) %in% c(3, 6, 9, 12)) |>
group_by(INDICATEUR) |>
mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
value = 100*OBS_VALUE/OBS_VALUE[1],
VARIABLE = INDICATEUR) |>
mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q"))
data2 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(aes(x = date, y = value, label = round(value, 1)))
data_bind <- data2 |>
bind_rows(data1) |>
ungroup() |>
select(date, value, variable) |>
arrange(date)
# write_excel_csv(data_bind, file = "alter_eco_20230213.csv")
data_bind |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = seq(min(data_bind$date), max(data_bind$date), by = 0.25)) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_label_repel(data = . %>%
filter(date == max(date)),
aes(x = date, y = value, label = round(value, 1), color = variable))
`les-indices-de-salaire-de-base` |>
filter(secteur %in% c("ENS", "ET", "EU", "EV"),
sheets == "Sal. mens. cadres") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2019 Q4")) |>
group_by(Secteur) |>
arrange(date) |>
mutate(value = 100*value/value[1]) |>
ggplot() + geom_line(aes(x = date, y = value, color = Secteur)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.35, 0.8),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
data1 <- `les-indices-de-salaire-de-base` |>
filter(secteur == "ENS",
sheets == "Sal. mens. cadres") |>
select(TIME_PERIOD, value) |>
mutate(variable = "Salaire mensuel des cadres du secteur privé") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2020 Q4")) |>
mutate(value = 100*value/value[1])
data1 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.2, 0.7),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(aes(x = date, y = value, label = round(value, 1)))
`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
filter(date >= as.Date("2020-12-01"),
month(date) %in% c(3, 6, 9, 12)) |>
group_by(INDICATEUR) |>
mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
value = 100*OBS_VALUE/OBS_VALUE[1],
VARIABLE = INDICATEUR) |>
mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q"))
data2 |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_text(aes(x = date, y = value, label = round(value, 1)))
data_bind <- data2 |>
bind_rows(data1) |>
ungroup() |>
select(date, value, variable) |>
arrange(date)
# write_excel_csv(data_bind, file = "alter_eco_20230213.csv")
data_bind |>
ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
breaks = seq(min(data_bind$date), max(data_bind$date), by = 0.25)) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
geom_label(data = . %>%
filter(date == max(date)),
aes(x = date, y = value, label = round(value, 1)))
`les-indices-de-salaire-de-base` |>
filter(secteur %in% c("ENS", "ET", "EU", "EV"),
sheets == "Sal. mens. cadres") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2021 Q3")) |>
group_by(Secteur) |>
arrange(date) |>
mutate(value = 100*value/value[1]) |>
ggplot() + geom_line(aes(x = date, y = value, color = Secteur)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.35, 0.8),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
`les-indices-de-salaire-de-base` |>
filter(secteur %in% c("ENS", "ET", "EU", "EV"),
sheets == "Sal. mens. ouv.") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
ggplot() + geom_line(aes(x = date, y = value, color = Secteur)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
scale_y_log10(breaks = seq(100, 200, 2)) +
theme(legend.position = c(0.35, 0.8),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
`les-indices-de-salaire-de-base` |>
filter(secteur %in% c("ENS", "ET", "EU", "EV"),
sheets == "Sal. mens. ouv.") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2019 Q4")) |>
group_by(Secteur) |>
arrange(date) |>
mutate(value = 100*value/value[1]) |>
ggplot() + geom_line(aes(x = date, y = value, color = Secteur)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.35, 0.8),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
`les-indices-de-salaire-de-base` |>
filter(secteur %in% c("ENS", "ET", "EU", "EV"),
sheets == "Sal. mens. ouv.") |>
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
filter(date >= zoo::as.yearqtr("2021 Q3")) |>
group_by(Secteur) |>
arrange(date) |>
mutate(value = 100*value/value[1]) |>
ggplot() + geom_line(aes(x = date, y = value, color = Secteur)) +
theme_minimal() + xlab("") + ylab("") +
zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
scale_y_log10(breaks = seq(100, 200, 1)) +
theme(legend.position = c(0.35, 0.8),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
| source | dataset | Title | Updated |
|---|---|---|---|
 insee |
SALAIRES-ANNUELS | Salaires annuels - SALAIRES-ANNUELS | 2026-09-01 |
| dares |
les-indices-de-salaire-de-base | Les indices de salaire de base - les-indices-de-salaire-de-base | 2026-08-30 |
| insee |
CNA-2014-RDB | Revenu et pouvoir d’achat des ménages - CNA-2014-RDB | 2026-09-01 |
| insee |
CNT-2014-CSI | Comptes de secteurs institutionnels - CNT-2014-CSI | 2026-09-01 |
| insee |
ECRT2023 | Emploi, chômage, revenus du travail - Edition 2023 - ECRT2023 | 2026-08-02 |
| insee |
INDICE-TRAITEMENT-FP | Indice de traitement brut dans la fonction publique de l'État - INDICE-TRAITEMENT-FP | 2026-09-01 |
| insee |
SALAIRES-ACEMO | Indices trimestriels de salaires dans le secteur privé - Résultats par secteur d’activité - SALAIRES-ACEMO | 2026-09-01 |
| insee |
SALAIRES-ACEMO-2017 | Indices trimestriels de salaires dans le secteur privé - SALAIRES-ACEMO-2017 | 2026-09-01 |
| insee |
T_2101 | 2.101 - Revenu disponible brut des ménages et évolution du pouvoir d'achat - t_2101 | 2026-08-29 |
| insee |
T_7401 | 7.401 - Compte des ménages (S14) - T_7401 | 2026-08-29 |
| insee |
if230 | Séries longues sur les salaires dans le secteur privé - if230 | 2026-08-02 |
| insee |
ir_salaires_SL_23_csv | NA | NA |
| insee |
ir_salaires_SL_csv | NA | NA |
| insee |
t_salaire_val | Salaire moyen par tête - SMPT (données CVS) - t_salaire_val | 2026-08-29 |