source | dataset | .html | .RData |
---|---|---|---|
2024-08-09 | 2024-06-22 | ||
2024-09-07 | NA |
Les indices de salaire de base - résultats provisoires
Data - DARES
Info
Données sur les salaires
source | dataset | .html | .RData |
---|---|---|---|
2024-08-09 | 2024-06-22 | ||
2024-09-06 | 2024-09-05 | ||
2024-09-06 | 2024-09-05 | ||
2024-09-06 | 2023-06-30 | ||
2024-08-21 | 2021-12-04 | ||
2024-08-21 | 2024-09-05 | ||
2024-08-21 | NA | ||
2024-08-21 | NA | ||
2024-08-21 | 2024-09-05 | ||
2024-08-21 | 2024-09-05 | ||
2024-08-27 | 2024-09-05 | ||
2024-08-09 | 2023-12-23 | ||
2024-09-06 | 2024-09-02 |
LAST_COMPILE
LAST_COMPILE |
---|
2024-09-08 |
Last
Code
`les-indices-de-salaire-de-base` %>%
group_by(TIME_PERIOD) %>%
summarise(Nobs = n()) %>%
arrange(desc(TIME_PERIOD)) %>%
head(3) %>%
print_table_conditional()
TIME_PERIOD | Nobs |
---|---|
2024T2 | 1 |
2024T1 | 1 |
2023T4 | 1 |
sheets
Code
`les-indices-de-salaire-de-base` %>%
group_by(sheets) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
sheets | Nobs |
---|---|
Sal. mens. ensemble | 28 |
TIME_PERIOD
Code
`les-indices-de-salaire-de-base` %>%
group_by(TIME_PERIOD) %>%
summarise(Nobs = n()) %>%
arrange(desc(TIME_PERIOD)) %>%
print_table_conditional()
TIME_PERIOD | Nobs |
---|---|
2024T2 | 1 |
2024T1 | 1 |
2023T4 | 1 |
2023T3 | 1 |
2023T2 | 1 |
2023T1 | 1 |
2022T4 | 1 |
2022T3 | 1 |
2022T2 | 1 |
2022T1 | 1 |
2021T4 | 1 |
2021T3 | 1 |
2021T2 | 1 |
2021T1 | 1 |
2020T4 | 1 |
2020T3 | 1 |
2020T2 | 1 |
2019T4 | 1 |
2019T3 | 1 |
2019T2 | 1 |
2019T1 | 1 |
2018T4 | 1 |
2018T3 | 1 |
2018T2 | 1 |
2018T1 | 1 |
2017T4 | 1 |
2017T3 | 1 |
2017T2 | 1 |
Salaires, Ensemble, Ensemble
2017T2-
Salaires
Code
<- `les-indices-de-salaire-de-base` %>%
data1 filter(secteur == "ENS",
== "Sal. mens. ensemble") %>%
sheets 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("") +
::scale_x_yearqtr(labels = date_format("%YT%q"),
zoobreaks = 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)))
Prix
Code
load_data("insee/IPCH-IPC-2015-ensemble.RData")
<- `IPCH-IPC-2015-ensemble` %>%
data2 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 + geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"),
zoobreaks = 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)))
Bind
Code
%>%
data2 bind_rows(data1) %>%
+ geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("Augmentation depuis le 2017-T2") +
::scale_x_yearqtr(labels = date_format("%YT%q"),
zoobreaks = 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),
labels = percent(seq(100, 200, 2)/100-1)) +
theme(legend.position = c(0.3, 0.8),
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("2024 Q2"))),
aes(x = date, y = value, label = percent(value/100-1, acc = 0.1), color = variable)) +
ggtitle("Augmentation des prix et des salaires depuis le 2ème trimestre 2017")
2017T4-
Salaires
Code
<- `les-indices-de-salaire-de-base` %>%
data1 filter(secteur == "ENS",
== "Sal. mens. ensemble") %>%
sheets 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 + geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"),
zoobreaks = 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)))
Prix
Code
load_data("insee/IPCH-IPC-2015-ensemble.RData")
<- `IPCH-IPC-2015-ensemble` %>%
data2 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 + geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"),
zoobreaks = 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)))
Bind
Code
%>%
data2 bind_rows(data1) %>%
+ geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"),
zoobreaks = 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)))
2019T4-
Salaires
Code
<- `les-indices-de-salaire-de-base` %>%
data1 filter(secteur == "ENS",
== "Sal. mens. ensemble") %>%
sheets 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 + geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
zooscale_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)))
Prix
Code
load_data("insee/IPCH-IPC-2015-ensemble.RData")
<- `IPCH-IPC-2015-ensemble` %>%
data2 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 + geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
zooscale_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)))
Bind
Code
%>%
data2 bind_rows(data1) %>%
+ geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
zooscale_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)))
2020T4-
Salaires
Code
<- `les-indices-de-salaire-de-base` %>%
data1 filter(secteur == "ENS",
== "Sal. mens. ensemble") %>%
sheets 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 + geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
zooscale_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)))
Prix
Code
load_data("insee/IPCH-IPC-2015-ensemble.RData")
<- `IPCH-IPC-2015-ensemble` %>%
data2 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 + geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
zooscale_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)))
Bind
Code
<- data2 %>%
data_bind bind_rows(data1) %>%
%>%
ungroup select(date, value, variable) %>%
arrange(date)
# write_excel_csv(data_bind, file = "alter_eco_20230213.csv")
%>%
data_bind + geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("Salaire de base") +
::scale_x_yearqtr(labels = date_format("%YT%q"),
zoobreaks = 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)))
2021T1-
Salaires
Code
<- `les-indices-de-salaire-de-base` %>%
data1 filter(secteur == "ENS",
== "Sal. mens. ensemble") %>%
sheets 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 + geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
zooscale_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)))
Prix
Code
load_data("insee/IPCH-IPC-2015-ensemble.RData")
<- `IPCH-IPC-2015-ensemble` %>%
data2 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 + geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
zooscale_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)))
Bind
Code
<- data2 %>%
data_bind bind_rows(data1) %>%
%>%
ungroup select(date, value, variable) %>%
arrange(date)
# write_excel_csv(data_bind, file = "alter_eco_20230213.csv")
%>%
data_bind + geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
zooscale_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)))
2021T2-
Salaires
Code
<- `les-indices-de-salaire-de-base` %>%
data1 filter(secteur == "ENS",
== "Sal. mens. ensemble") %>%
sheets 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 + geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
zooscale_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)))
Prix
Code
load_data("insee/IPCH-IPC-2015-ensemble.RData")
<- `IPCH-IPC-2015-ensemble` %>%
data2 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 + geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
zooscale_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)))
Bind
Code
<- data2 %>%
data_bind bind_rows(data1) %>%
%>%
ungroup select(date, value, variable) %>%
arrange(date)
# write_excel_csv(data_bind, file = "alter_eco_20230213.csv")
%>%
data_bind + geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("Hausse vs. 2ème trimestre 2021") +
::scale_x_yearqtr(labels = date_format("%YT%q"),
zoobreaks = 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_label(data = . %>% filter(date == max(date)), aes(x = date, y = value, label = round(value, 1), color = variable))
2021T4-
Salaires
Code
<- `les-indices-de-salaire-de-base` %>%
data1 filter(secteur == "ENS",
== "Sal. mens. ensemble") %>%
sheets 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 + geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
zooscale_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)))
Prix
Code
load_data("insee/IPCH-IPC-2015-ensemble.RData")
<- `IPCH-IPC-2015-ensemble` %>%
data2 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 + geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
zooscale_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)))
Bind
Code
<- data2 %>%
data_bind bind_rows(data1) %>%
%>%
ungroup select(date, value, variable) %>%
arrange(date)
# write_excel_csv(data_bind, file = "alter_eco_20230213.csv")
%>%
data_bind + geom_line(aes(x = date, y = value, color = variable)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"),
zoobreaks = 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(aes(x = date, y = value, label = round(value, 1)))
Salaires, Ensemble
Tous
Code
`les-indices-de-salaire-de-base` %>%
filter(secteur == "ENS") %>%
mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) %>%
+ geom_line(aes(x = date, y = value, color = sheets)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
zooscale_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))
2019T4-
Code
`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]) %>%
+ geom_line(aes(x = date, y = value, color = sheets)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
zooscale_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))
2021T3-
Code
`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]) %>%
+ geom_line(aes(x = date, y = value, color = sheets)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
zooscale_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))
2020T4-
Code
`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]) %>%
+ geom_line(aes(x = date, y = value, color = sheets)) +
ggplot theme_minimal() + xlab("") + ylab("") +
::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
zooscale_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))