Salaires annuels

Data - INSEE

Author

François Geerolf

Info

source dataset .html .RData
insee IPC-2015 2025-05-24 2025-05-24
insee IPCH-2015 2025-05-24 2025-05-24
insee SALAIRES-ANNUELS 2025-05-24 2025-05-24

Données sur les salaires

source dataset .html .RData
dares les-indices-de-salaire-de-base 2025-01-17 2024-12-15
insee CNA-2014-RDB 2025-05-24 2025-05-24
insee CNT-2014-CSI 2025-05-24 2025-05-24
insee ECRT2023 2025-05-24 2023-06-30
insee if230 2025-05-24 2021-12-04
insee INDICE-TRAITEMENT-FP 2025-05-24 2025-05-24
insee ir_salaires_SL_23_csv 2025-05-24 NA
insee ir_salaires_SL_csv 2025-05-24 NA
insee SALAIRES-ACEMO 2025-05-24 2025-05-24
insee SALAIRES-ACEMO-2017 2025-05-24 2025-05-24
insee SALAIRES-ANNUELS 2025-05-24 2025-05-24
insee t_7401 2025-05-24 2024-10-18
insee t_salaire_val 2025-05-30 2025-05-28

LAST_UPDATE

Code 
`SALAIRES-ANNUELS` %>%
  group_by(LAST_UPDATE) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(LAST_UPDATE)) %>%
  print_table_conditional()
LAST_UPDATE Nobs
2025-01-10 64
2024-11-05 4750
2017-11-07 3981
2013-06-24 555

LAST_COMPILE

LAST_COMPILE
2025-05-30

Last

Last

Code 
`SALAIRES-ANNUELS` %>%
  filter(TIME_PERIOD == max(TIME_PERIOD)) %>%
  head(200) %>%
  select(TITLE_FR, TIME_PERIOD, OBS_VALUE) %>%
  print_table_conditional
TITLE_FR TIME_PERIOD OBS_VALUE
SMIC brut mensuel (pour 151,67 heures par mois) - En moyenne annuelle 2024 1772.73
SMIC net de CSG et CRDS mensuel (pour 151,67 heures par mois) - En moyenne annuelle 2024 1403.29
SMIC brut (en euros par heure) - En moyenne annuelle 2024 11.69

First

Code 
`SALAIRES-ANNUELS` %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  tail(1) %>%
  print_table_conditional()
TIME_PERIOD Nobs
1950 50

TITLE_FR

Tous

Code 
`SALAIRES-ANNUELS` %>%
  group_by(IDBANK, TITLE_FR) %>%
  summarise(Nobs = n(),
            date1 = first(TIME_PERIOD),
            date2 = last(TIME_PERIOD)) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

Ensemble

Code 
`SALAIRES-ANNUELS` %>%
  filter(SEXE == "0") %>%
  group_by(IDBANK, TITLE_FR) %>%
  summarise(Nobs = n(),
            date1 = first(TIME_PERIOD),
            date2 = last(TIME_PERIOD)) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

SEXE

Code 
`SALAIRES-ANNUELS` %>%
  left_join(SEXE, by = "SEXE") %>%
  group_by(SEXE, Sexe) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
SEXE Sexe Nobs
0 Ensemble 3404
1 Hommes 2988
2 Femmes 2958

BASIND

Code 
`SALAIRES-ANNUELS` %>%
  left_join(BASIND, by = "BASIND") %>%
  group_by(BASIND, Basind) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
BASIND Basind Nobs
SO Sans objet 8584
1951 1951 414
2000 2000 352

VALORISATION

Code 
`SALAIRES-ANNUELS` %>%
  left_join(VALORISATION, by = "VALORISATION") %>%
  group_by(VALORISATION, Valorisation) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
VALORISATION Valorisation Nobs
SO Sans objet 8998
EUROS_CONST Euros constants 184
EUROS_COUR Euros courants 168

SERIES_ARRETEE

Code 
`SALAIRES-ANNUELS` %>%
  left_join(SERIE_ARRETEE, by = "SERIE_ARRETEE") %>%
  group_by(SERIE_ARRETEE, Serie_arretee) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
SERIE_ARRETEE Serie_arretee Nobs
FALSE non 4814
TRUE oui 4536

METIER

Code 
`SALAIRES-ANNUELS` %>%
  left_join(METIER, by = "METIER") %>%
  group_by(METIER, Metier) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
METIER Metier Nobs
SO Sans objet 7025
5 Employés 462
6 Ouvriers 462
4 Professions intermédiaires 363
CCE Cadres, y compris les chefs d'entreprise salariés 204
3 Cadres et professions intellectuelles supérieures 159
AS Apprentis et stagiaires 159
CE Chefs d'entreprise 159
MOY Cadres moyens 99
SUP Cadres supérieurs 99
PSA Personnel de service 96
CONT Contremaîtres 63

INDICATEUR

Code 
`SALAIRES-ANNUELS` %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  group_by(INDICATEUR, Indicateur) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
INDICATEUR Indicateur Nobs
SAL_ANN_MOY Salaire net annuel moyen 2892
SAL_ANN_D5 Médiane (D5) du salaire net annuel 567
SAL_ANN_D9 Neuvième décile (D9) du salaire net annuel 567
SAL_ANN_D9-D5 Rapport interdécile D9/D5 du salaire net annuel 567
SAL_ANN_D1 Premier décile (D1) du salaire net annuel 557
SAL_ANN_D5-D1 Rapport interdécile D5/D1 du salaire net annuel 557
SAL_ANN_D9-D1 Rapport interdécile D9/D1 du salaire net annuel 557
SAL_ANN_PVRACH-EVO Évolution du pouvoir d'achat du salaire net annuel moyen 414
SAL_ANN_PVRACH-IND Indice du pouvoir d'achat du salaire net annuel moyen 414
SAL_ANN_SNAM-EVO Évolution du salaire net annuel moyen 414
SAL_ANN_Q1 Premier quartile (Q1) du salaire net annuel 411
SAL_ANN_Q3 Troisième quartile (Q3) du salaire net annuel 411
SAL_ANN_C95 Quatre-vingt-quinzième centile (C95) du salaire net annuel 303
SAL_ANN_C99 Quatre-vingt-dix-neuvième centile (C99) du salaire net annuel 303
SAL_ANN_SNMMTC Indice du salaire net mensuel moyen d'un temps complet 64
SAL_ANN_PLAFSSB Indice du plafond de la Sécurité sociale (sur les salaires bruts) 48
SAL_ANN_PLAFSSN Indice du plafond de la Sécurité sociale (sur les salaires nets) 48
SAL_ANN_SHBO Indice du salaire horaire brut de base des ouvriers (SHBO) 48
SAL_ANN_SMICBH Indice du SMIC brut horaire 48
SAL_ANN_SMICBM Indice du SMIC brut mensuel 48
SAL_ANN_SMICNM Indice du SMIC net mensuel 48
SAL_ANN_SMICH Montant horaire brut du SMIC 24
SAL_ANN_SMICMB-35H Montant mensuel brut du SMIC 20
SAL_ANN_SMICMNN-35H Montant mensuel net du SMIC 20

NATURE

Code 
`SALAIRES-ANNUELS` %>%
  left_join(NATURE, by = "NATURE") %>%
  group_by(NATURE, Nature) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
NATURE Nature Nobs
VALEUR_ABSOLUE Valeur absolue 6075
RATIO Ratio 1681
TAUX Taux 828
INDICE Indice 766

QUOTITE-TRAV

Code 
`SALAIRES-ANNUELS` %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  group_by(`QUOTITE-TRAV`, `Quotite-Trav`) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
QUOTITE-TRAV Quotite-Trav Nobs
TC Temps complet 3489
TCP Temps complet dans le secteur privé 3178
ETPP Équivalent temps plein dans le secteur privé 1260
ETP Équivalent temps plein 1071
SO Sans objet 352

TIME_PERIOD

Code 
`SALAIRES-ANNUELS` %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  print_table_conditional()

Catégories socioprofessionnelles, séries longues

Salaire net annuel moyen des postes à temps complet

1950-1982

Tous

Code 
`SALAIRES-ANNUELS` %>%
  group_by(IDBANK, TITLE_FR) %>%
  filter(first(TIME_PERIOD) == "1982",
         SEXE == "0") %>%
  year_to_date %>%
  arrange(date) %>%
  left_join(METIER, by = "METIER") %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/1000, color = Metier))  +
  theme_minimal() + ylab("") + xlab("")+
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = ""))

Quelques

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("001665183", "001665186", "001665189", "001665192")) %>%
  year_to_date %>%
  arrange(date) %>%
  left_join(METIER, by = "METIER") %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/1000, color = Metier))  +
  theme_minimal() + ylab("") + xlab("")+
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = ""))

1983-

TC

Code 
`SALAIRES-ANNUELS` %>%
  group_by(IDBANK, TITLE_FR) %>%
  filter(SEXE == "0",
         `QUOTITE-TRAV` == "TC",
         INDICATEUR == "SAL_ANN_MOY") %>%
  year_to_date %>%
  arrange(date) %>%
  left_join(METIER, by = "METIER") %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/1000, color = Metier))  +
  theme_minimal() + ylab("") + xlab("")+
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.6)) +
  scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = ""))

TCP

Code 
`SALAIRES-ANNUELS` %>%
  group_by(IDBANK, TITLE_FR) %>%
  filter(SEXE == "0",
         `QUOTITE-TRAV` == "TCP",
         INDICATEUR == "SAL_ANN_MOY") %>%
  year_to_date %>%
  arrange(date) %>%
  left_join(METIER, by = "METIER") %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/1000, color = Metier))  +
  theme_minimal() + ylab("") + xlab("")+
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.6)) +
  scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = ""))

SAL_ANN_MOY - moyenne

Cadres

1984-

Value

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         METIER %in% c("3", "CCE")) %>%
  left_join(METIER, by = "METIER") %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  year_to_date %>%
  arrange(date) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE/1000, color = paste(Metier, "-", `Quotite-Trav`))) +
  theme(legend.title = element_blank(),
        legend.position = c(0.5, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = ""))

Temps complet

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         METIER %in% c("3", "CCE"),
         grepl(" temps complet", TITLE_FR)) %>%
  left_join(METIER, by = "METIER") %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  year_to_date %>%
  arrange(date) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE/1000, color = paste(Metier, "-", `Quotite-Trav`))) +
  theme(legend.title = element_blank(),
        legend.position = c(0.5, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = ""))

Base 100

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         METIER %in% c("3", "CCE"),
         grepl(" temps complet", TITLE_FR)) %>%
  left_join(METIER, by = "METIER") %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  year_to_date %>%
  arrange(date) %>%
  group_by(Metier) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = paste(Metier, "-", `Quotite-Trav`))) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.95)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5))

1990-

Value

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         METIER %in% c("3", "CCE")) %>%
  left_join(METIER, by = "METIER") %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  year_to_date %>%
  filter(date >= as.Date("1990-01-01")) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE/1000, color = paste(Metier, "-", `Quotite-Trav`))) +
  theme(legend.title = element_blank(),
        legend.position = c(0.5, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = ""))

Temps complet

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         METIER %in% c("3", "CCE"),
         grepl(" temps complet", TITLE_FR)) %>%
  left_join(METIER, by = "METIER") %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  year_to_date %>%
  filter(date >= as.Date("1990-01-01")) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE/1000, color = paste(Metier, "-", `Quotite-Trav`))) +
  theme(legend.title = element_blank(),
        legend.position = c(0.5, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = ""))

Base 100

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         METIER %in% c("3", "CCE"),
         grepl(" temps complet", TITLE_FR)) %>%
  left_join(METIER, by = "METIER") %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  year_to_date %>%
  filter(date >= as.Date("1990-01-01")) %>%
  arrange(date) %>%
  group_by(Metier) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = paste(Metier, "-", `Quotite-Trav`))) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.95)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5))

1995-

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         METIER %in% c("3", "CCE")) %>%
  left_join(METIER, by = "METIER") %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE/1000, color = paste(Metier, "-", `Quotite-Trav`))) +
  theme(legend.title = element_blank(),
        legend.position = c(0.5, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = ""))

1996-

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         METIER %in% c("3", "CCE")) %>%
  left_join(METIER, by = "METIER") %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  year_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE/1000, color = paste(Metier, "-", `Quotite-Trav`))) +
  theme(legend.title = element_blank(),
        legend.position = c(0.5, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = ""))

2000-

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         METIER %in% c("3", "CCE")) %>%
  left_join(METIER, by = "METIER") %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  year_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE/1000, color = paste(Metier, "-", `Quotite-Trav`))) +
  theme(legend.title = element_blank(),
        legend.position = c(0.5, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = ""))

Base 100

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         METIER %in% c("3", "CCE"),
         grepl(" temps complet", TITLE_FR)) %>%
  left_join(METIER, by = "METIER") %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  year_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  arrange(date) %>%
  group_by(Metier) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = paste(Metier, "-", `Quotite-Trav`))) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.95)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5))

Salaires annuels moyens, ETPP

Cadres, Employés, Ouvriers, Prof. intermédiaires, SO

Values

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP") %>%
  left_join(METIER, by = "METIER") %>%
  year_to_date %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE/1000, color = Metier)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.7)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = ""))

Indice

Tous

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP") %>%
  left_join(METIER, by = "METIER") %>%
  year_to_date %>%
  group_by(Metier) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Metier)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5))

2008-

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP") %>%
  left_join(METIER, by = "METIER") %>%
  year_to_date %>%
  group_by(Metier) %>%
  filter(date >= as.Date("2008-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2008-01-01")]) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Metier)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5))

Cadres , Professions intermédiaires

Indice - Nominal, Réel

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP",
         METIER %in% c("CCE", "4")) %>%
  left_join(METIER, by = "METIER") %>%
  year_to_date %>%
  group_by(Metier) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
  ungroup %>%
  left_join(`inflation-A`, by = "date") %>%
  arrange(date) %>%
  transmute(date, Metier,
            `Nominal` = OBS_VALUE,
            `Réel (inflation IPCH)` = IPCH[1]*OBS_VALUE/IPCH,
            `Réel (inflation IPC)` = IPC[1]*OBS_VALUE/IPC) %>%
  gather(variable, value, -date, -Metier) %>%
  ggplot() + geom_line(aes(x = date, y = value,  color = variable, linetype = Metier)) +
  theme_minimal() + ylab("") + xlab("") +
  scale_color_manual(values = c("darkgrey", "#F8766D", "#619CFF")) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5))

Indice - Réel

1996-

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP",
         METIER %in% c("CCE", "4")) %>%
  left_join(METIER, by = "METIER") %>%
  year_to_date %>%
  group_by(Metier) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
  ungroup %>%
  left_join(`inflation-A`, by = "date") %>%
  arrange(date) %>%
  transmute(date, Metier,
            `Réel (inflation IPCH)` = IPCH[1]*OBS_VALUE/IPCH,
            `Réel (inflation IPC)` = IPC[1]*OBS_VALUE/IPC) %>%
  gather(variable, value, -date, -Metier) %>%
  ggplot() + geom_line(aes(x = date, y = value,  color = variable, linetype = Metier)) +
  theme_minimal() + ylab("Salaire (100 = 1996)") + xlab("") +
  #scale_color_manual(values = c("darkgrey", "#F8766D", "#619CFF")) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.4, 0.15),
        legend.title = element_blank(),
        legend.direction = "horizontal") +
  scale_y_log10(breaks = seq(0, 200, 1))

2000-

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP",
         METIER %in% c("CCE", "4")) %>%
  left_join(METIER, by = "METIER") %>%
  year_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  group_by(Metier) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2000-01-01")]) %>%
  ungroup %>%
  left_join(`inflation-A`, by = "date") %>%
  arrange(date) %>%
  transmute(date, Metier,
            `Réel (inflation IPCH)` = IPCH[1]*OBS_VALUE/IPCH,
            `Réel (inflation IPC)` = IPC[1]*OBS_VALUE/IPC) %>%
  gather(variable, value, -date, -Metier) %>%
  ggplot() + geom_line(aes(x = date, y = value,  color = variable, linetype = Metier)) +
  theme_minimal() + ylab("") + xlab("") +
  #scale_color_manual(values = c("darkgrey", "#F8766D", "#619CFF")) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.3),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 1))

2000-

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP",
         METIER %in% c("CCE")) %>%
  left_join(METIER, by = "METIER") %>%
  year_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  group_by(Metier) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2000-01-01")]) %>%
  ungroup %>%
  left_join(`inflation-A`, by = "date") %>%
  arrange(date) %>%
  transmute(date, Metier,
            `Réel (inflation IPCH)` = IPCH[1]*OBS_VALUE/IPCH,
            `Réel (inflation IPC)` = IPC[1]*OBS_VALUE/IPC) %>%
  gather(variable, value, -date, -Metier) %>%
  ggplot() + geom_line(aes(x = date, y = value,  color = variable, linetype = Metier)) +
  theme_minimal() + ylab("Base 100 = 2000") + xlab("") +
  #scale_color_manual(values = c("darkgrey", "#F8766D", "#619CFF")) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.3),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 1)) +
  geom_hline(yintercept = 100, linetype = "dashed")

Cadres, Ouvriers, Prof. Intermédiaires, Tous

Indice - Réel

All

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP",
         METIER %in% c("CCE", "4", "6", "SO")) %>%
  left_join(METIER, by = "METIER") %>%
  year_to_date %>%
  group_by(Metier) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
  ungroup %>%
  left_join(`inflation-A`, by = "date") %>%
  arrange(date) %>%
  transmute(date, Metier,
            `Réel (inflation IPCH)` = IPCH[1]*OBS_VALUE/IPCH,
            `Réel (inflation IPC)` = IPC[1]*OBS_VALUE/IPC) %>%
  gather(variable, value, -date, -Metier) %>%
  ggplot() + geom_line(aes(x = date, y = value,  color = variable, linetype = Metier)) +
  theme_minimal() + ylab("") + xlab("") +
  #scale_color_manual(values = c("darkgrey", "#F8766D", "#619CFF")) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 1)) +
  geom_hline(yintercept = 100, linetype = "dashed")

Indice - Réel

All

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP",
         METIER %in% c("CCE", "4", "6")) %>%
  left_join(METIER, by = "METIER") %>%
  year_to_date %>%
  group_by(Metier) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
  ungroup %>%
  left_join(`inflation-A`, by = "date") %>%
  arrange(date) %>%
  transmute(date, Metier,
            `Réel (inflation IPCH)` = IPCH[1]*OBS_VALUE/IPCH,
            `Réel (inflation IPC)` = IPC[1]*OBS_VALUE/IPC) %>%
  gather(variable, value, -date, -Metier) %>%
  ggplot() + geom_line(aes(x = date, y = value,  color = variable, linetype = Metier)) +
  theme_minimal() + ylab("") + xlab("") +
  #scale_color_manual(values = c("darkgrey", "#F8766D", "#619CFF")) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 1)) +
  geom_hline(yintercept = 100, linetype = "dashed")

1999-

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP",
         METIER %in% c("CCE", "4", "6")) %>%
  left_join(METIER, by = "METIER") %>%
  year_to_date %>%
  filter(date >= as.Date("1999-01-01")) %>%
  group_by(Metier) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1999-01-01")]) %>%
  ungroup %>%
  left_join(`inflation-A`, by = "date") %>%
  arrange(date) %>%
  transmute(date, Metier,
            `Réel (inflation IPCH)` = IPCH[1]*OBS_VALUE/IPCH,
            `Réel (inflation IPC)` = IPC[1]*OBS_VALUE/IPC) %>%
  gather(variable, value, -date, -Metier) %>%
  ggplot() + geom_line(aes(x = date, y = value,  color = variable, linetype = Metier)) +
  theme_minimal() + ylab("") + xlab("") +
  #scale_color_manual(values = c("darkgrey", "#F8766D", "#619CFF")) +
  scale_x_date(breaks = seq(1999, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 1)) +
  geom_hline(yintercept = 100, linetype = "dashed")

1998-

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP",
         METIER %in% c("CCE", "4", "6")) %>%
  left_join(METIER, by = "METIER") %>%
  year_to_date %>%
  filter(date >= as.Date("1998-01-01")) %>%
  group_by(Metier) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1998-01-01")]) %>%
  ungroup %>%
  left_join(`inflation-A`, by = "date") %>%
  arrange(date) %>%
  transmute(date, Metier,
            `Salaire déflaté par l'indice IPCH harmonisé par Eurostat` = IPCH[1]*OBS_VALUE/IPCH,
            `Salaire déflaté par l'indice IPC` = IPC[1]*OBS_VALUE/IPC) %>%
  gather(variable, value, -date, -Metier) %>%
  ggplot() + geom_line(aes(x = date, y = value,  linetype = variable, color = Metier)) +
  theme_minimal() + ylab("Pouvoir d'achat du salaire (Indice 100 = 1998)") + xlab("") +
  scale_linetype_manual(values = c("dashed", "solid")) +
  scale_x_date(breaks = seq(1998, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.28, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 1)) +
  geom_hline(yintercept = 100, linetype = "dashed") +
  geom_label(data = . %>% filter(date == max(date)), aes(x = date, y = value, color = Metier, label = percent(value/100-1, style_positive = "plus")))

2000-

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP",
         METIER %in% c("CCE", "4", "6")) %>%
  left_join(METIER, by = "METIER") %>%
  year_to_date %>%
  filter(date >= as.Date("1999-01-01")) %>%
  group_by(Metier) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1999-01-01")]) %>%
  ungroup %>%
  left_join(`inflation-A`, by = "date") %>%
  arrange(date) %>%
  transmute(date, Metier,
            `Réel (inflation IPCH)` = IPCH[1]*OBS_VALUE/IPCH,
            `Réel (inflation IPC)` = IPC[1]*OBS_VALUE/IPC) %>%
  gather(variable, value, -date, -Metier) %>%
  ggplot() + geom_line(aes(x = date, y = value,  linetype = variable, color = Metier)) +
  theme_minimal() + ylab("Pouvoir d'achat du salaire") + xlab("") +
  scale_linetype_manual(values = c("dashed", "solid")) +
  scale_x_date(breaks = seq(1999, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 1)) +
  geom_hline(yintercept = 100, linetype = "dashed")

2007-

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR == "SAL_ANN_MOY",
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP",
         METIER %in% c("CCE", "4", "6")) %>%
  left_join(METIER, by = "METIER") %>%
  year_to_date %>%
  filter(date >= as.Date("2007-01-01")) %>%
  group_by(Metier) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2007-01-01")]) %>%
  ungroup %>%
  left_join(`inflation-A`, by = "date") %>%
  arrange(date) %>%
  transmute(date, Metier,
            `Réel (inflation IPCH)` = IPCH[1]*OBS_VALUE/IPCH,
            `Réel (inflation IPC)` = IPC[1]*OBS_VALUE/IPC) %>%
  gather(variable, value, -date, -Metier) %>%
  ggplot() + geom_line(aes(x = date, y = value,  color = variable, linetype = Metier)) +
  theme_minimal() + ylab("") + xlab("") +
  #scale_color_manual(values = c("darkgrey", "#F8766D", "#619CFF")) +
  scale_x_date(breaks = seq(1999, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 1)) +
  geom_hline(yintercept = 100, linetype = "dashed")

Médiane, 9ème décile, 1er décile

Values

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR %in% c("SAL_ANN_D1", "SAL_ANN_D5", "SAL_ANN_D9"),
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP") %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  year_to_date %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicateur)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.7)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = ""))

Indice

1996-

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR %in% c("SAL_ANN_D1", "SAL_ANN_D5", "SAL_ANN_D9"),
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP") %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  year_to_date %>%
  group_by(Indicateur) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5))

2002-

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR %in% c("SAL_ANN_D1", "SAL_ANN_D5", "SAL_ANN_D9"),
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP") %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  year_to_date %>%
  filter(date >= as.Date("2002-01-01")) %>%
  group_by(Indicateur) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2002-01-01")]) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5))

Médiane, 1er quartile, 3ème quartile

Values

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR %in% c("SAL_ANN_Q1", "SAL_ANN_D5", "SAL_ANN_Q3"),
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP") %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  year_to_date %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicateur)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.7)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = ""))

Indice

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR %in% c("SAL_ANN_Q1", "SAL_ANN_D5", "SAL_ANN_Q3"),
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP") %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  year_to_date %>%
  group_by(Indicateur) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5))

Médiane, 9ème décile, 1er décile, 95ème centile, 99ème centile

Values - Log

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR %in% c("SAL_ANN_D1", "SAL_ANN_D5", "SAL_ANN_D9", "SAL_ANN_C95", "SAL_ANN_C99"),
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP") %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  year_to_date %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicateur)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.7)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = ""))

Values - Linear

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR %in% c("SAL_ANN_D1", "SAL_ANN_D5", "SAL_ANN_D9", "SAL_ANN_C95", "SAL_ANN_C99"),
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP") %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  year_to_date %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE/1000, color = Indicateur)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.7)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 200, 5),
                     labels = scales::dollar_format(accuracy = 1, suffix = " k€", prefix = ""))

Indice

Nominal

Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR %in% c("SAL_ANN_D1", "SAL_ANN_D5", "SAL_ANN_D9", "SAL_ANN_C95", "SAL_ANN_C99"),
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP") %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  year_to_date %>%
  group_by(Indicateur) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5))

Réel

Tous
Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR %in% c("SAL_ANN_D1", "SAL_ANN_D5", "SAL_ANN_D9", "SAL_ANN_C95", "SAL_ANN_C99"),
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP") %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  year_to_date %>%
  group_by(Indicateur) %>%
  left_join(`inflation-A`, by = "date") %>%
  mutate(OBS_VALUE = OBS_VALUE/IPCH) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 2)) +
  geom_hline(yintercept = 100, linetype = "dashed")

2008-
Code 
`SALAIRES-ANNUELS` %>%
  filter(INDICATEUR %in% c("SAL_ANN_D1", "SAL_ANN_D5", "SAL_ANN_D9", "SAL_ANN_C95", "SAL_ANN_C99"),
         SEXE == "0",
         `QUOTITE-TRAV` == "ETPP") %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  year_to_date %>%
  group_by(Indicateur) %>%
  left_join(`inflation-A`, by = "date") %>%
  mutate(OBS_VALUE = OBS_VALUE/IPCH) %>%
  filter(date >= as.Date("2008-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2008-01-01")]) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(80, 200, 2))

Euros constants vs courants

Euros constants

Code 
`SALAIRES-ANNUELS` %>%
  filter(VALORISATION == "EUROS_CONST",
         `QUOTITE-TRAV` == "SO") %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  year_to_date %>%
  group_by(Indicateur) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2000-01-01")]) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.7)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 2))

Euros courants

Code 
`SALAIRES-ANNUELS` %>%
  filter(VALORISATION == "EUROS_COUR",
         `QUOTITE-TRAV` == "SO") %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  year_to_date %>%
  group_by(Indicateur) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2000-01-01")]) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5))

Series

En 1980

Code 
`SALAIRES-ANNUELS` %>%
  filter(TIME_PERIOD == "1980") %>%
  select(IDBANK, TITLE_FR, METIER, INDICATEUR, SEXE, OBS_VALUE) %>%
  print_table_conditional()

Salaire moyen dans le secteur privé

Annuel, temps complet

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK == "010752364") %>%
  select(TIME_PERIOD, OBS_VALUE, TITLE_FR) %>%
  year_to_date() %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE / 1000, color = TITLE_FR)) + 
  scale_color_manual(values = viridis(3)[1:2]) +
  theme(legend.title = element_blank(),
        legend.position = c(0.8, 0.2)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 200, 5))

Smic

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("000883657", "000883658")) %>%
  year_to_date() %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE / 1000, color = TITLE_FR)) + 
  scale_color_manual(values = viridis(3)[1:2]) +
  theme(legend.title = element_blank(),
        legend.position = c(0.8, 0.2)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 200, 1))

Ratio D9/D1

Code 
`SALAIRES-ANNUELS` %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  filter(IDBANK %in% c("010752411", "001665221")) %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  year_to_date() %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = `Quotite-Trav`)) + 
  scale_color_manual(values = viridis(3)[1:2]) +
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(2, 5, 0.2))

Séries longues, Temps complets dans le privé

1951

Code 
`SALAIRES-ANNUELS` %>%
  filter(TIME_PERIOD %in% c("1951", "2010"),
         is.finite(OBS_VALUE)) %>%
  select_if(~ n_distinct(.) > 1) %>%
  select(TITLE_FR, TIME_PERIOD, OBS_VALUE) %>%
  group_by(TITLE_FR) %>%
  filter(n() == 2) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  print_table_conditional

Les Deux - 010752375, 010752366

Tous

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752366")) %>%
  year_to_date() %>%
  group_by(TITLE_FR) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(100, 200, 500, 800, 1000, 2000, 5000, 10000, 20000))

1975-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1975-01-01")) %>%
  group_by(TITLE_FR) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 5000, 100))

1984-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1984-01-01")) %>%
  group_by(TITLE_FR) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 5000, 10))

1990-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1990-01-01")) %>%
  group_by(TITLE_FR) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.6, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 5000, 10))

1993-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1993-01-01")) %>%
  group_by(TITLE_FR) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.6, 0.8)) +
  scale_x_date(breaks = seq(1993, 2100, 2) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 5000, 10)) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

1996-

Hommes

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752373", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(TITLE_FR) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.6, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 5000, 10))

Femmes

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752374", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(TITLE_FR) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.6, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 5000, 10))

Tous

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(TITLE_FR) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.6, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 5000, 10))

1999-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1999-01-01")) %>%
  group_by(TITLE_FR) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.6, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 5000, 10))

2008-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("2008-01-01")) %>%
  group_by(TITLE_FR) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.6, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 5000, 2))

Indice des prix Implicite - 010752375, 010752366

Tous

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752366")) %>%
  year_to_date() %>%
  select(date, IDBANK, OBS_VALUE) %>%
  spread(IDBANK, OBS_VALUE) %>%
  mutate(OBS_VALUE = `010752366` / `010752375`) %>%
  na.omit %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Indice des prix implicite") +
  geom_line(aes(x = date, y = OBS_VALUE)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(100, 200, 500, 800, 1000, 1500, 2000, 5000, 10000, 20000))

1975-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1975-01-01")) %>%
  select(date, IDBANK, OBS_VALUE) %>%
  spread(IDBANK, OBS_VALUE) %>%
  mutate(OBS_VALUE = `010752366` / `010752375`) %>%
  na.omit %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Indice des prix implicite") +
  geom_line(aes(x = date, y = OBS_VALUE)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 5000, 100))

1984-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1984-01-01")) %>%
  select(date, IDBANK, OBS_VALUE) %>%
  spread(IDBANK, OBS_VALUE) %>%
  mutate(OBS_VALUE = `010752366` / `010752375`) %>%
  na.omit %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Indice des prix implicite") +
  geom_line(aes(x = date, y = OBS_VALUE)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 5000, 10))

1990-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1990-01-01")) %>%
  select(date, IDBANK, OBS_VALUE) %>%
  spread(IDBANK, OBS_VALUE) %>%
  mutate(OBS_VALUE = `010752366` / `010752375`) %>%
  na.omit %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Indice des prix implicite") +
  geom_line(aes(x = date, y = OBS_VALUE)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.6, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 5000, 5))

1993-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1993-01-01")) %>%
  select(date, IDBANK, OBS_VALUE) %>%
  spread(IDBANK, OBS_VALUE) %>%
  mutate(OBS_VALUE = `010752366` / `010752375`) %>%
  na.omit %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Indice des prix implicite") +
  geom_line(aes(x = date, y = OBS_VALUE)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.6, 0.8)) +
  scale_x_date(breaks = seq(1993, 2100, 2) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 5000, 5)) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

1996-

Exactement l’IPC !

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  select(date, IDBANK, OBS_VALUE) %>%
  spread(IDBANK, OBS_VALUE) %>%
  mutate(OBS_VALUE = `010752366` / `010752375`) %>%
  na.omit %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Indice des prix implicite") +
  geom_line(aes(x = date, y = OBS_VALUE)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.6, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 5000, 5)) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

1999-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1999-01-01")) %>%
  select(date, IDBANK, OBS_VALUE) %>%
  spread(IDBANK, OBS_VALUE) %>%
  mutate(OBS_VALUE = `010752366` / `010752375`) %>%
  na.omit %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Indice des prix implicite") +
  geom_line(aes(x = date, y = OBS_VALUE)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.6, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 5000, 5))

Indice du pouvoir d’achat du salaire net annuel moyen dans le secteur privé

Tous

Log

Code 
plot_log <- `SALAIRES-ANNUELS` %>%
  filter(IDBANK == "010752375") %>%
  year_to_date() %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 10) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 20))

plot_log

Linear

Code 
plot_linear <- plot_log + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  scale_y_continuous(breaks = seq(100, 1000, 20))

plot_linear

Both

Code 
ggpubr::ggarrange(plot_linear + ggtitle("Linear plot"), plot_log + ggtitle("Log plot"))

1965-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK == "010752375") %>%
  year_to_date() %>%
  filter(date >= as.Date("1965-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1965-01-01")]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 10))

1975-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK == "010752375") %>%
  year_to_date() %>%
  filter(date >= as.Date("1975-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1975-01-01")]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 2))

1984-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK == "010752375") %>%
  year_to_date() %>%
  filter(date >= as.Date("1984-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1984-01-01")]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 2))

1990-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK == "010752375") %>%
  year_to_date() %>%
  filter(date >= as.Date("1990-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1990-01-01")]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 2)) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

1993-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK == "010752375") %>%
  year_to_date() %>%
  filter(date >= as.Date("1993-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1993-01-01")]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.8)) +
  scale_x_date(breaks = seq(1993, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 2)) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

1996-

Salaire net moyen= + 10%

Salaire net moyen en EQTP =

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK == "010752375") %>%
  year_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 2)) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

1999-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK == "010752375") %>%
  year_to_date() %>%
  filter(date >= as.Date("1999-01-01")) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 2)) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

2008- (15 ans)

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK == "010752375") %>%
  year_to_date() %>%
  filter(date >= as.Date("2008-01-01")) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.7, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 1)) +
  geom_text_repel(aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1)), 
                  fontface ="plain", color = "black", size = 3)

Différences de concepts

Moyen vs. Médian

EQTP:

  • 010752333: Salaire net annuel moyen en équivalent temps plein dans le secteur privé - Ensemble des salariés

  • 010752342: Médiane (D5) du salaire net annuel en équivalent temps plein dans le secteur privé - Ensemble des salariés

Temps complets:

  • 010752366: Salaire net annuel moyen des postes à temps complet dans le secteur privé - Ensemble des salariés

  • 010752396: Médiane (D5) du salaire net annuel des postes à temps complet dans le secteur privé - Ensemble des salariés

EQTP

Tous

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752333", "010752342")) %>%
  year_to_date() %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  group_by(Indicateur) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1996, 2100, 2) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 10))

2008-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752333", "010752342")) %>%
  year_to_date() %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  group_by(Indicateur) %>%
  arrange(date) %>%
  filter(date >= as.Date("2008-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1996, 2100, 2) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 2))

Temps complets

Tous

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752366", "010752396")) %>%
  year_to_date() %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  group_by(Indicateur) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 10))

1996-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752366", "010752396")) %>%
  year_to_date() %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  group_by(Indicateur) %>%
  arrange(date) %>%
  filter(date >= as.Date("1996-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1996, 2100, 2) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 10))

2008-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752366", "010752396")) %>%
  year_to_date() %>%
  left_join(INDICATEUR, by = "INDICATEUR") %>%
  group_by(Indicateur) %>%
  arrange(date) %>%
  filter(date >= as.Date("2008-01-01")) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 2))

EQTP vs. temps complets

Preuve que temps plein existait avant

Tous

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752333", "010752366")) %>%
  year_to_date() %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  group_by(`Quotite-Trav`) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = `Quotite-Trav`)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 10))

1996-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752333", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  group_by(`Quotite-Trav`) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = `Quotite-Trav`)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 10)) +
  geom_label(data = . %>% filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = `Quotite-Trav`))

2008-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752333", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("2008-01-01")) %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  group_by(`Quotite-Trav`) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = `Quotite-Trav`)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 2)) +
  geom_label(data = . %>% filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = `Quotite-Trav`))

2008-2021

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752333", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("2008-01-01"),
         date <= as.Date("2021-01-01")) %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  group_by(`Quotite-Trav`) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = `Quotite-Trav`)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 2)) +
  geom_label(data = . %>% filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 1), color = `Quotite-Trav`))

2009-2021

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752333", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("2009-01-01"),
         date <= as.Date("2021-01-01")) %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  group_by(`Quotite-Trav`) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = `Quotite-Trav`)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 2)) +
  geom_label(data = . %>% filter(date == max(date)), aes(x = date, y = OBS_VALUE, label = round(OBS_VALUE, 2), color = `Quotite-Trav`))

Secteur privé vs. Tous

Tous

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("001665161", "010752375")) %>%
  year_to_date() %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  group_by(`Quotite-Trav`) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = `Quotite-Trav`)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 10))

1990-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("001665161", "010752375")) %>%
  year_to_date() %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  filter(date >= as.Date("1990-01-01")) %>%
  group_by(`Quotite-Trav`) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = `Quotite-Trav`)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 1000, 2))

1999-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("001665161", "010752375")) %>%
  year_to_date() %>%
  left_join(`QUOTITE-TRAV`, by = "QUOTITE-TRAV") %>%
  filter(date >= as.Date("1999-01-01")) %>%
  group_by(`Quotite-Trav`) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = `Quotite-Trav`)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1999, 2100, 2) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 1))

Hommes, Femmes, Tous

Tous

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752373", "010752374")) %>%
  year_to_date() %>%
  left_join(SEXE, by = "SEXE") %>%
  group_by(SEXE) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Sexe)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 10))

1965-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752373", "010752374")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1965-01-01")) %>%
  left_join(SEXE, by = "SEXE") %>%
  group_by(SEXE) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1965-01-01")]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Sexe)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 10))

1975-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752373", "010752374")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1975-01-01")) %>%
  left_join(SEXE, by = "SEXE") %>%
  group_by(SEXE) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1975-01-01")]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Sexe)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 10))

1996-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752373", "010752374")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  left_join(SEXE, by = "SEXE") %>%
  group_by(SEXE) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("1996-01-01")]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Sexe)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 2))

2008-

Tous

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752373", "010752374")) %>%
  year_to_date() %>%
  filter(date >= as.Date("2008-01-01")) %>%
  left_join(SEXE, by = "SEXE") %>%
  group_by(SEXE) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2008-01-01")]) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = OBS_VALUE, color = Sexe)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 2))

IPC, IPCH

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752375", "010752373", "010752374")) %>%
  year_to_date() %>%
  filter(date >= as.Date("2008-01-01")) %>%
  left_join(SEXE, by = "SEXE") %>%
  group_by(Sexe) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  left_join(`inflation-A`, by = "date") %>%
  arrange(date) %>%
  mutate(IPC = IPC/IPC[1],
         IPCH = IPCH/IPCH[1]) %>%
  transmute(date, `Pouvoir d'achat du salaire, IPC` = OBS_VALUE, `Pouvoir d'achat du salaire, IPCH` = OBS_VALUE*IPC/IPCH, Sexe) %>%
  gather(variable, value, -date, -Sexe) %>%
  ggplot(.) + theme_minimal() +
  xlab("") + ylab("Pouvoir d'achat du salaire net annuel moyen dans le privé") +
  geom_line(aes(x = date, y = value, color = Sexe, linetype = variable)) + 
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(90, 1000, 2))