Salaires annuels

Data - INSEE

Author

François Geerolf

Info

source dataset .html .RData

insee

IPC-2015

 2024-07-03 2024-07-02

insee

IPCH-2015

 2024-07-03 2024-07-02

insee

SALAIRES-ANNUELS

 2024-07-02 2024-07-03

Données sur les salaires

source dataset .html .RData

dares

les-indices-de-salaire-de-base

 2024-06-23 2024-06-22

insee

CNA-2014-RDB

 2024-07-03 2024-07-03

insee

CNT-2014-CSI

 2024-07-03 2024-07-03

insee

ECRT2023

 2024-07-03 2023-06-30

insee

if230

 2024-07-03 2021-12-04

insee

INDICE-TRAITEMENT-FP

 2024-07-03 2024-07-02

insee

ir_salaires_SL_csv

 2024-07-04 NA

insee

SALAIRES-ACEMO

 2024-07-04 2024-07-03

insee

SALAIRES-ACEMO-2017

 2024-07-04 2024-07-03

insee

SALAIRES-ANNUELS

 2024-07-02 2024-07-03

insee

t_7401

 2024-07-02 2023-12-23

insee

t_salaire_val

 2024-06-20 2024-07-01

LAST_UPDATE

Code 
`SALAIRES-ANNUELS` %>%
  group_by(LAST_UPDATE) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(LAST_UPDATE)) %>%
  print_table_conditional()
LAST_UPDATE Nobs
2024-01-08 61
2023-12-13 4637
2017-11-07 3981
2013-06-24 555

LAST_COMPILE

LAST_COMPILE
2024-07-04

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 2023 1734.56
SMIC net de CSG et CRDS mensuel (pour 151,67 heures par mois) - En moyenne annuelle 2023 1373.07
SMIC brut (en euros par heure) - En moyenne annuelle 2023 11.44

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 3354
1 Hommes 2955
2 Femmes 2925

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 8485
1951 1951 411
2000 2000 338

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 8896
EUROS_CONST Euros constants 177
EUROS_COUR Euros courants 161

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 4698
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 6933
5 Employés 456
6 Ouvriers 456
4 Professions intermédiaires 357
CCE Cadres, y compris les chefs d'entreprise salariés 198
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 2862
SAL_ANN_D5 Médiane (D5) du salaire net annuel 561
SAL_ANN_D9 Neuvième décile (D9) du salaire net annuel 561
SAL_ANN_D9-D5 Rapport interdécile D9/D5 du salaire net annuel 561
SAL_ANN_D1 Premier décile (D1) du salaire net annuel 551
SAL_ANN_D5-D1 Rapport interdécile D5/D1 du salaire net annuel 551
SAL_ANN_D9-D1 Rapport interdécile D9/D1 du salaire net annuel 551
SAL_ANN_PVRACH-EVO Évolution du pouvoir d'achat du salaire net annuel moyen 411
SAL_ANN_PVRACH-IND Indice du pouvoir d'achat du salaire net annuel moyen 411
SAL_ANN_SNAM-EVO Évolution du salaire net annuel moyen 411
SAL_ANN_Q1 Premier quartile (Q1) du salaire net annuel 405
SAL_ANN_Q3 Troisième quartile (Q3) du salaire net annuel 405
SAL_ANN_C95 Quatre-vingt-quinzième centile (C95) du salaire net annuel 297
SAL_ANN_C99 Quatre-vingt-dix-neuvième centile (C99) du salaire net annuel 297
SAL_ANN_SNMMTC Indice du salaire net mensuel moyen d'un temps complet 62
SAL_ANN_PLAFSSB Indice du plafond de la Sécurité sociale (sur les salaires bruts) 46
SAL_ANN_PLAFSSN Indice du plafond de la Sécurité sociale (sur les salaires nets) 46
SAL_ANN_SHBO Indice du salaire horaire brut de base des ouvriers (SHBO) 46
SAL_ANN_SMICBH Indice du SMIC brut horaire 46
SAL_ANN_SMICBM Indice du SMIC brut mensuel 46
SAL_ANN_SMICNM Indice du SMIC net mensuel 46
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 6000
RATIO Ratio 1663
TAUX Taux 822
INDICE Indice 749

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 3488
TCP Temps complet dans le secteur privé 3123
ETPP Équivalent temps plein dans le secteur privé 1215
ETP Équivalent temps plein 1071
SO Sans objet 337

TIME_PERIOD

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

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

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

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, 0.8)) +
  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 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

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))

Annuel

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

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 - 010752373, 010752366

Tous

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752373", "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("010752373", "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("010752373", "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("010752373", "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("010752373", "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-

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))

1999-

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752373", "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))

Indice des prix Implicite - 010752373, 010752366

Tous

Code 
`SALAIRES-ANNUELS` %>%
  filter(IDBANK %in% c("010752373", "010752366")) %>%
  year_to_date() %>%
  select(date, IDBANK, OBS_VALUE) %>%
  spread(IDBANK, OBS_VALUE) %>%
  mutate(OBS_VALUE = `010752366` / `010752373`) %>%
  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("010752373", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1975-01-01")) %>%
  select(date, IDBANK, OBS_VALUE) %>%
  spread(IDBANK, OBS_VALUE) %>%
  mutate(OBS_VALUE = `010752366` / `010752373`) %>%
  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("010752373", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1984-01-01")) %>%
  select(date, IDBANK, OBS_VALUE) %>%
  spread(IDBANK, OBS_VALUE) %>%
  mutate(OBS_VALUE = `010752366` / `010752373`) %>%
  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("010752373", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1990-01-01")) %>%
  select(date, IDBANK, OBS_VALUE) %>%
  spread(IDBANK, OBS_VALUE) %>%
  mutate(OBS_VALUE = `010752366` / `010752373`) %>%
  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("010752373", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1993-01-01")) %>%
  select(date, IDBANK, OBS_VALUE) %>%
  spread(IDBANK, OBS_VALUE) %>%
  mutate(OBS_VALUE = `010752366` / `010752373`) %>%
  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("010752373", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  select(date, IDBANK, OBS_VALUE) %>%
  spread(IDBANK, OBS_VALUE) %>%
  mutate(OBS_VALUE = `010752366` / `010752373`) %>%
  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("010752373", "010752366")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1999-01-01")) %>%
  select(date, IDBANK, OBS_VALUE) %>%
  spread(IDBANK, OBS_VALUE) %>%
  mutate(OBS_VALUE = `010752366` / `010752373`) %>%
  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 == "010752373") %>%
  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 == "010752373") %>%
  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 == "010752373") %>%
  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 == "010752373") %>%
  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 == "010752373") %>%
  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 == "010752373") %>%
  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 == "010752373") %>%
  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 == "010752373") %>%
  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)