Salaire moyen par tête - SMPT (données CVS)
Data - INSEE
Info
Données sur les salaires
source | dataset | Title | .html | .rData |
---|---|---|---|---|
insee | t_salaire_val | Salaire moyen par tête - SMPT (données CVS) | 2025-10-10 | 2025-08-29 |
dares | les-indices-de-salaire-de-base | Les indices de salaire de base | 2025-10-04 | 2025-10-04 |
insee | CNA-2014-RDB | Revenu et pouvoir d’achat des ménages | 2025-10-10 | 2025-10-09 |
insee | CNT-2014-CSI | Comptes de secteurs institutionnels | 2025-10-10 | 2025-10-09 |
insee | ECRT2023 | Emploi, chômage, revenus du travail - Edition 2023 | 2025-10-10 | 2023-06-30 |
insee | INDICE-TRAITEMENT-FP | Indice de traitement brut dans la fonction publique de l'État | 2025-10-10 | 2025-10-09 |
insee | SALAIRES-ACEMO | Indices trimestriels de salaires dans le secteur privé - Résultats par secteur d’activité | 2025-10-10 | 2025-10-09 |
insee | SALAIRES-ACEMO-2017 | Indices trimestriels de salaires dans le secteur privé | 2025-10-10 | 2025-10-09 |
insee | SALAIRES-ANNUELS | Salaires annuels | 2025-10-10 | 2025-10-10 |
insee | if230 | Séries longues sur les salaires dans le secteur privé | 2025-10-10 | 2021-12-04 |
insee | ir_salaires_SL_23_csv | NA | NA | NA |
insee | ir_salaires_SL_csv | NA | NA | NA |
insee | t_7401 | NA | NA | NA |
Structure
Bibliographie
Français
“Mesurer”le” pouvoir d’achat”, F. Geerolf, Document de travail, Juillet 2024. [pdf]
“Inflation en France: IPC ou IPCH ?”, F. Geerolf, Document de travail, Juillet 2024. [pdf]
“La taxe inflationniste, le pouvoir d’achat, le taux d’épargne et le déficit public”, F. Geerolf, Document de travail, Juillet 2024. [pdf]
variable
Code
%>%
t_salaire_val group_by(variable) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
variable | Nobs |
---|---|
(DE) à (C5) | 306 |
(DE) à (MN), (RU) | 306 |
(GZ) à (MN), (RU) | 306 |
AZ | 306 |
C | 306 |
C1 | 306 |
C2 | 306 |
C3 | 306 |
C4 | 306 |
C5 | 306 |
DE | 306 |
FZ | 306 |
GZ | 306 |
HZ | 306 |
IZ | 306 |
JZ | 306 |
KZ | 306 |
LZ | 306 |
MN | 306 |
OQ | 306 |
RU | 306 |
TOTAL | 306 |
date
Code
%>%
t_salaire_val group_by(date) %>%
summarise(Nobs = n()) %>%
arrange(desc(date)) %>%
print_table_conditional()
2021, Trimestre 3
Code
%>%
t_salaire_val filter(date %in% c(as.Date("2023-01-01"), as.Date("2011-07-01"))) %>%
spread(date, value) %>%
mutate(evolution = 100*((`2023-01-01`/`2011-07-01`)^(1/10)-1)) %>%
arrange(-`2023-01-01`) %>%
print_table_conditional()
variable | 2011-07-01 | 2023-01-01 | evolution |
---|---|---|---|
JZ | 12600.62 | 16504.17 | 2.7354145 |
C2 | 13343.24 | 15034.28 | 1.2003778 |
C4 | 11463.32 | 15030.37 | 2.7462371 |
KZ | 11148.96 | 14845.66 | 2.9050091 |
C3 | 10571.57 | 12629.61 | 1.7946715 |
DE | 10228.78 | 12156.64 | 1.7416957 |
LZ | 10001.07 | 11792.35 | 1.6612369 |
C5 | 9068.12 | 11264.20 | 2.1923319 |
(DE) à (C5) | 9034.87 | 11101.61 | 2.0813502 |
MN | 8808.78 | 11048.28 | 2.2911099 |
C | 8917.24 | 10987.44 | 2.1096073 |
FZ | 8486.31 | 10505.82 | 2.1576999 |
(DE) à (MN), (RU) | 8410.79 | 10483.04 | 2.2268657 |
(GZ) à (MN), (RU) | 8249.88 | 10351.70 | 2.2954706 |
TOTAL | 7851.71 | 9813.32 | 2.2551455 |
HZ | 8202.73 | 9445.25 | 1.4204432 |
GZ | 7305.70 | 9092.03 | 2.2115331 |
OQ | 6745.88 | 8473.93 | 2.3068295 |
C1 | 6522.17 | 8206.44 | 2.3237077 |
RU | 6267.23 | 7699.99 | 2.0801860 |
IZ | 5848.43 | 7154.65 | 2.0363488 |
AZ | 6218.37 | 5965.74 | -0.4138882 |
Valeurs en Euros
KZ, JZ, C4
All
Code
%>%
t_salaire_val filter(variable %in% c("KZ", "JZ", "C4")) %>%
group_by(variable) %>%
ggplot() + ylab("Salaire moyen par tête - SMPT") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = 1000*seq(1, 300, 1),
labels = dollar_format(accuracy = 1, su = "€", pre = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
1990-
Code
%>%
t_salaire_val filter(variable %in% c("KZ", "JZ", "C4"),
>= as.Date("1990-01-01")) %>%
date group_by(variable) %>%
ggplot() + ylab("Salaire moyen par tête - SMPT") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = 1000*seq(1, 300, 1),
labels = dollar_format(accuracy = 1, su = "€", pre = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
2017T2-
Code
%>%
t_salaire_val filter(variable %in% c("KZ", "JZ", "C4"),
>= as.Date("2017-04-01")) %>%
date group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("2017-04-01")]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
C2, C3, C4, C5
All
Code
%>%
t_salaire_val filter(variable %in% c("C2", "C3", "C4", "C5")) %>%
group_by(variable) %>%
ggplot() + ylab("Salaire moyen par tête - SMPT") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = 1000*seq(1, 300, 1),
labels = dollar_format(accuracy = 1, su = "€", pre = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
1990-
Code
%>%
t_salaire_val filter(variable %in% c("C2", "C3", "C4", "C5"),
>= as.Date("1990-01-01")) %>%
date group_by(variable) %>%
ggplot() + ylab("Salaire moyen par tête - SMPT") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = 1000*seq(1, 300, 1),
labels = dollar_format(accuracy = 1, su = "€", pre = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
2017T2-
Code
%>%
t_salaire_val filter(variable %in% c("C2", "C3", "C4", "C5"),
>= as.Date("2017-04-01")) %>%
date group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("2017-04-01")]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
Agriculture, Industrie, Total
Value
Code
%>%
t_salaire_val filter(variable %in% c("TOTAL", "AZ", "C"),
>= as.Date("1990-01-01")) %>%
date group_by(variable) %>%
ggplot() + ylab("Salaire moyen par tête - SMPT") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = 1000*seq(1, 300, 1),
labels = dollar_format(accuracy = 1, su = "€", pre = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
1990-
Code
%>%
t_salaire_val filter(variable %in% c("TOTAL", "AZ", "C"),
>= as.Date("1990-01-01")) %>%
date group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 10),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
1996-
Code
%>%
t_salaire_val filter(variable %in% c("TOTAL", "AZ", "C"),
>= as.Date("1996-01-01")) %>%
date group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 10),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
2012-
Code
%>%
t_salaire_val filter(variable %in% c("TOTAL", "AZ", "C"),
>= as.Date("2012-01-01")) %>%
date group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("2012-01-01")]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
2017-
Code
%>%
t_salaire_val filter(variable %in% c("TOTAL", "AZ", "C"),
>= as.Date("2017-01-01")) %>%
date group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("2017-01-01")]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
2017T2-
Code
%>%
t_salaire_val filter(variable %in% c("TOTAL", "AZ", "C"),
>= as.Date("2017-04-01")) %>%
date group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("2017-04-01")]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
2019T4-
Code
%>%
t_salaire_val filter(variable %in% c("TOTAL", "AZ", "C"),
>= as.Date("2019-10-01")) %>%
date group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("2019-10-01")]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
2021T3-
Code
<- t_salaire_val %>%
df filter(variable %in% c("TOTAL", "AZ", "C"),
>= as.Date("2021-07-01")) %>%
date group_by(variable) %>%
arrange(date) %>%
mutate(value = 100*value/value[1]) %>%
date_to_yearqtr()
ggplot(data = df) + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_yearqtr(labels = date_format("%Y T%q"),
breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_y_log10(breaks = seq(10, 300, 1),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
2021T4-
Code
<- t_salaire_val %>%
df filter(variable %in% c("TOTAL", "AZ", "C"),
>= as.Date("2021-10-01")) %>%
date group_by(variable) %>%
arrange(date) %>%
mutate(value = 100*value/value[1]) %>%
date_to_yearqtr()
ggplot(data = df) + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_yearqtr(labels = date_format("%Y T%q"),
breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_y_log10(breaks = seq(10, 300, 1),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
Services Marchands, Total, Non Marchands
1990-
Code
%>%
t_salaire_val filter(variable %in% c("TOTAL", "OQ", "(GZ) à (MN), (RU)"),
>= as.Date("1990-01-01")) %>%
date group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1990-01-01")]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
1996-
Code
%>%
t_salaire_val filter(variable %in% c("TOTAL", "OQ", "(GZ) à (MN), (RU)"),
>= as.Date("1996-01-01")) %>%
date group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
2012-
Code
%>%
t_salaire_val filter(variable %in% c("TOTAL", "OQ", "(GZ) à (MN), (RU)"),
>= as.Date("2012-01-01")) %>%
date group_by(variable) %>%
mutate(value = 100*value/value[date == as.Date("2012-01-01")]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(10, 300, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
2017-
Code
<- t_salaire_val %>%
df filter(variable %in% c("TOTAL", "OQ", "(GZ) à (MN), (RU)"),
>= as.Date("2017-01-01")) %>%
date group_by(variable) %>%
arrange(date) %>%
mutate(value = 100*value/value[1]) %>%
%>%
date_to_yearqtr
ungroup
ggplot(data = df) + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_yearqtr(labels = date_format("%Y T%q"),
breaks = seq(from = min(df$date), to = max(df$date), by = 0.5)) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_y_log10(breaks = seq(10, 300, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
2017T2-
Code
<- t_salaire_val %>%
df filter(variable %in% c("TOTAL", "OQ", "(GZ) à (MN), (RU)"),
>= as.Date("2017-04-01")) %>%
date group_by(variable) %>%
arrange(date) %>%
mutate(value = 100*value/value[1]) %>%
%>%
date_to_yearqtr
ungroup
ggplot(data = df) + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_yearqtr(labels = date_format("%Y T%q"),
breaks = seq(from = min(df$date), to = max(df$date), by = 0.5)) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_y_log10(breaks = seq(10, 300, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
2019T4-
Code
<- t_salaire_val %>%
df filter(variable %in% c("TOTAL", "OQ", "(GZ) à (MN), (RU)"),
>= as.Date("2019-10-01")) %>%
date group_by(variable) %>%
arrange(date) %>%
mutate(value = 100*value/value[1]) %>%
%>%
date_to_yearqtr
ungroup
ggplot(data = df) + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_yearqtr(labels = date_format("%Y T%q"),
breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_y_log10(breaks = seq(10, 300, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
2021T2-
Code
<- t_salaire_val %>%
df filter(variable %in% c("TOTAL", "OQ", "(GZ) à (MN), (RU)"),
>= as.Date("2021-04-01")) %>%
date group_by(variable) %>%
arrange(date) %>%
mutate(value = 100*value/value[1]) %>%
%>%
date_to_yearqtr
ungroup
ggplot(data = df) + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_yearqtr(labels = date_format("%Y T%q"),
breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_y_log10(breaks = seq(10, 300, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
2021T3-
Code
<- t_salaire_val %>%
df filter(variable %in% c("TOTAL", "OQ", "(GZ) à (MN), (RU)"),
>= as.Date("2021-07-01")) %>%
date group_by(variable) %>%
arrange(date) %>%
mutate(value = 100*value/value[1]) %>%
%>%
date_to_yearqtr
ungroup
ggplot(data = df) + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_yearqtr(labels = date_format("%Y T%q"),
breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_y_log10(breaks = seq(10, 300, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))
2021T4-
Nominal
Code
<- t_salaire_val %>%
df filter(variable %in% c("TOTAL", "OQ", "(GZ) à (MN), (RU)"),
>= as.Date("2021-10-01")) %>%
date group_by(variable) %>%
arrange(date) %>%
mutate(value = 100*value/value[1]) %>%
%>%
date_to_yearqtr
ungroup
ggplot(data = df) + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value, color = variable)) +
scale_x_yearqtr(labels = date_format("%Y T%q"),
breaks = seq(from = min(df$date), to = max(df$date), by = 0.25)) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_y_log10(breaks = seq(10, 300, 2),
labels = dollar_format(accuracy = 1, prefix = "")) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = value, color = variable, label = round(value, 1)))