Salaires annuels

Data - INSEE

Author

François Geerolf

Info

source dataset .html .RData
insee IPC-2015 2024-11-22 2024-11-22
insee IPCH-2015 2024-11-22 2024-11-22
insee SALAIRES-ANNUELS 2024-11-22 2024-11-22

Données sur les salaires

source dataset .html .RData
dares les-indices-de-salaire-de-base 2024-11-04 2024-09-21
insee CNA-2014-RDB 2024-11-22 2024-11-22
insee CNT-2014-CSI 2024-11-22 2024-11-21
insee ECRT2023 2024-11-22 2023-06-30
insee if230 2024-11-22 2021-12-04
insee INDICE-TRAITEMENT-FP 2024-11-22 2024-11-22
insee ir_salaires_SL_23_csv 2024-11-22 NA
insee ir_salaires_SL_csv 2024-11-22 NA
insee SALAIRES-ACEMO 2024-11-22 2024-11-22
insee SALAIRES-ACEMO-2017 2024-11-22 2024-11-22
insee SALAIRES-ANNUELS 2024-11-22 2024-11-22
insee t_7401 2024-10-18 2024-10-18
insee t_salaire_val 2024-11-09 2024-09-02

LAST_UPDATE

Code 
`SALAIRES-ANNUELS` %>%
  group_by(LAST_UPDATE) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(LAST_UPDATE)) %>%
  print_table_conditional()
LAST_UPDATE Nobs
2024-11-05 4750
2024-01-08 61
2017-11-07 3981
2013-06-24 555

LAST_COMPILE

LAST_COMPILE
2024-11-22

Last

Last

Code 
`SALAIRES-ANNUELS` %>%
  filter(TIME_PERIOD == max(TIME_PERIOD)) %>%
  head(200) %>%
  select(TITLE_FR, TIME_PERIOD, OBS_VALUE) %>%
  print_table_conditional

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 3401
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 8581
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 8995
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 4811
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 7022
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 23
SAL_ANN_SMICMB-35H Montant mensuel brut du SMIC 19
SAL_ANN_SMICMNN-35H Montant mensuel net du SMIC 19

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 6072
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 349

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, 2022, 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, 2022, 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, 2022, 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, 2022, 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, 2022, 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, 2022, 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, 2022, 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, 2022, 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, 2022, 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, 2022, 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, 2022, 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, 2022, 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 = ""))

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, 2022, 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 == "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, 2022, 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, 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

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, 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))

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, 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))

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, 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))

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, 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))

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, 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))

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, 2022, 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, 2022, 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, 2022, 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, 2022, 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, 2022, 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, 2022, 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, 2022, 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

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, 2022, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5))

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, 2022, 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, 2022, 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-

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-

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))