Les indices de salaire de base - résultats provisoires

Data - DARES

Author

François Geerolf

Info

source dataset Title .html .rData
dares les-indices-de-salaire-de-base Les indices de salaire de base 2025-09-17 2025-09-17
dares les-indices-de-salaire-de-base-provisoires NA NA NA
insee IPCH-IPC-2015-ensemble Indices des prix à la consommation harmonisés 2025-09-30 2025-10-02

Données sur les salaires

source dataset Title .html .rData
insee SALAIRES-ANNUELS Salaires annuels 2025-09-30 2025-09-26
dares les-indices-de-salaire-de-base Les indices de salaire de base 2025-09-17 2025-09-17
insee CNA-2014-RDB Revenu et pouvoir d’achat des ménages 2025-09-30 2025-09-26
insee CNT-2014-CSI Comptes de secteurs institutionnels 2025-09-30 2025-09-26
insee ECRT2023 Emploi, chômage, revenus du travail - Edition 2023 2025-09-30 2023-06-30
insee INDICE-TRAITEMENT-FP Indice de traitement brut dans la fonction publique de l'État 2025-09-30 2025-09-26
insee SALAIRES-ACEMO Indices trimestriels de salaires dans le secteur privé - Résultats par secteur d’activité 2025-09-30 2025-09-26
insee SALAIRES-ACEMO-2017 Indices trimestriels de salaires dans le secteur privé 2025-09-30 2025-09-26
insee if230 Séries longues sur les salaires dans le secteur privé 2025-09-30 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
insee t_salaire_val Salaire moyen par tête - SMPT (données CVS) 2025-09-30 2025-08-29

LAST_COMPILE

LAST_COMPILE
2025-10-04

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
2025T2 2
2025T1 1
2024T4 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 33

TIME_PERIOD

Code 
`les-indices-de-salaire-de-base` %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  print_table_conditional()

Salaires, Ensemble, Ensemble

2017T2-

Salaires

Code 
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)))

Prix

Code 
load_data("insee/IPCH-IPC-2015-ensemble.RData")
data2 <- `IPCH-IPC-2015-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)))

Bind

Code 
invisible(Sys.setlocale("LC_TIME", "fr_FR.UTF-8"))
data2 %>%
  bind_rows(data1) %>%
  ggplot + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("Augmentation depuis le 2017-T2") +
  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),
                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 == max(date)),
            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")

Juste-IPCH

Code 
invisible(Sys.setlocale("LC_TIME", "fr_FR.UTF-8"))
data2 %>%
  bind_rows(data1) %>%
  filter(variable != "Prix Insee (IPC)") %>%
  ggplot + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("Augmentation depuis le 2017-T2") +
  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),
                labels = paste0("+", 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_label(data = . %>%
              filter(date == max(date)),
            aes(x = date, y = value, label = paste0("+", percent(value/100-1, acc = 0.1)), color = variable)) +
  ggtitle("Augmentation des prix et des salaires depuis le 2ème trimestre 2017")

2017T4-

Salaires

Code 
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)))

Prix

Code 
load_data("insee/IPCH-IPC-2015-ensemble.RData")
data2 <- `IPCH-IPC-2015-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)))

Bind

Code 
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)))

2019T4-

Salaires

Code 
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)))

Prix

Code 
load_data("insee/IPCH-IPC-2015-ensemble.RData")

data2 <- `IPCH-IPC-2015-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)))

Bind

Code 
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_repel(data = . %>% filter(date == max(date)), aes(x = date, y = value, label = round(value, 1), color = variable))

Juste IPCH

Code 
invisible(Sys.setlocale("LC_TIME", "fr_FR.UTF-8"))
data2 %>%
  bind_rows(data1) %>%
  filter(variable != "Prix Insee (IPC)") %>%
  ggplot + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("Augmentation depuis le 2019-T4") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2019:2100, c(2, 4)) %>%
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) %>%
                         pull(breaks)) +
  scale_y_log10(breaks = seq(100, 200, 2),
                labels = paste0("+", 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_label(data = . %>%
              filter(date == max(date)),
            aes(x = date, y = value, label = paste0("+", percent(value/100-1, acc = 0.1)), color = variable)) +
  ggtitle("Augmentation des prix et des salaires depuis le 4ème trimestre 2019")

2020T4-

Salaires

Code 
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)))

Prix

Code 
load_data("insee/IPCH-IPC-2015-ensemble.RData")
data2 <- `IPCH-IPC-2015-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)))

Bind

Code 
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)))

2021T1-

Salaires

Code 
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)))

Prix

Code 
load_data("insee/IPCH-IPC-2015-ensemble.RData")
data2 <- `IPCH-IPC-2015-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)))

Bind

Code 
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(aes(x = date, y = value, label = round(value, 1)))

2021T2-

Salaires

Code 
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)))

Prix

Code 
load_data("insee/IPCH-IPC-2015-ensemble.RData")
data2 <- `IPCH-IPC-2015-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)))

Bind

Code 
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("Hausse vs. 2ème trimestre 2021") +
  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_label(data = . %>% filter(date == max(date)), aes(x = date, y = value, label = round(value, 1), color = variable))

2021T4-

Salaires

Code 
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)))

Prix

Code 
load_data("insee/IPCH-IPC-2015-ensemble.RData")
data2 <- `IPCH-IPC-2015-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)))

Bind

All ticks

Code 
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(aes(x = date, y = value, label = round(value, 1)))

All ticks

Code 
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_label_repel(data = . %>%
              filter(date == max(date)),
            aes(x = date, y = value, label = sprintf("%+.1f%%", value-100), color = variable)) +
  ggtitle("Augmentation des prix et des salaires sur 3 ans depuis le 4ème trimestre 2021")

Salaires, Ensemble

Tous

Code 
`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))

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]) %>%
  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))

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]) %>%
  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))

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]) %>%
  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))