Emploi intérieur, durée effective travaillée et productivité horaire
Data - Insee
François Geerolf
Info
| source | dataset | .html | .RData |
|---|---|---|---|
| insee | CNA-2014-EMPLOI | 2024-05-30 | 2024-06-07 |
Données sur l’emploi
| source | dataset | .html | .RData |
|---|---|---|---|
| insee | CHOMAGE-TRIM-NATIONAL | 2024-06-07 | 2024-06-07 |
| insee | CNA-2014-EMPLOI | 2024-05-30 | 2024-06-07 |
| insee | DEMANDES-EMPLOIS-NATIONALES | 2024-05-30 | 2024-04-09 |
| insee | EMPLOI-BIT-TRIM | 2024-05-30 | 2024-06-07 |
| insee | EMPLOI-SALARIE-TRIM-NATIONAL | 2024-05-30 | 2024-06-07 |
| insee | TAUX-CHOMAGE | 2024-05-30 | 2024-06-07 |
| insee | TCRED-EMPLOI-SALARIE-TRIM | 2024-05-30 | 2024-06-07 |
LAST_UPDATE
`CNA-2014-EMPLOI` %>%
group_by(LAST_UPDATE) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| LAST_UPDATE | Nobs |
|---|---|
| 2023-05-31 | 47203 |
| 2022-06-14 | 1288 |
Champ
- 1949-
TITLE_FR
INDICATEUR
`CNA-2014-EMPLOI` %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR, Indicateur) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| INDICATEUR | Indicateur | Nobs |
|---|---|---|
| CNA_EMPLOI_INTERIEUR | Emploi intérieur | 29030 |
| CNA_VOLUME_HEURES_TRAV | Volume d’heures travaillées | 9765 |
| CNA_DUREE_TRAVAILLEE | Durée annuelle effective travaillée | 4883 |
| CNA_PRODUCTIVITE_HORAIRE | Productivité horaire | 4813 |
CNA_ACTIVITE
CNA_TYPE_EMP
`CNA-2014-EMPLOI` %>%
left_join(CNA_TYPE_EMP, by = "CNA_TYPE_EMP") %>%
group_by(CNA_TYPE_EMP, Cna_type_emp) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| CNA_TYPE_EMP | Cna_type_emp | Nobs |
|---|---|---|
| E10 | E10 - Emploi intérieur total | 19924 |
| E20 | E20 - Emploi intérieur salarié | 9778 |
| E21 | E21 - Emploi intérieur salarié déclaré | 9765 |
| E30 | E30 - Emploi intérieur non salarié | 9024 |
TIME_PERIOD
Durée du travail
Durée annuelle effective travaillée
`CNA-2014-EMPLOI` %>%
filter(CNA_ACTIVITE %in% c("NNTOTAL"),
INDICATEUR == "CNA_DUREE_TRAVAILLEE") %>%
year_to_date %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE)) +
theme_minimal() +
ylab("") + xlab("") +
scale_x_date(breaks = seq(1920, 2100,10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(100, 3000, 100),
labels = dollar_format(prefix = "", suffix = " h / an", acc = 1)) +
theme(legend.position = c(0.7, 0.9),
legend.title = element_blank())
Heures travaillées
`CNA-2014-EMPLOI` %>%
filter(CNA_ACTIVITE %in% c("NNTOTAL"),
#INDICATEUR == "CNA_EMPLOI_INTERIEUR",
#UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP",
CNA_TYPE_EMP == "E10",
`SECT-INST` == "S10") %>%
year_to_date %>%
select(date, UNIT_MEASURE, OBS_VALUE) %>%
spread(UNIT_MEASURE, OBS_VALUE) %>%
transmute(date,
HEURES_TRAVAILLEES_PP = HEURES_TRAVAILLEES/(MILLIERS_ACTIFS_OCCUPES_PP*10^3),
HEURES_TRAVAILLEES_ETP = HEURES_TRAVAILLEES/(MILLIERS_ACTIFS_OCCUPES_ETP*10^3)) %>%
gather(variable, OBS_VALUE, -date) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) +
theme_minimal() +
ylab("") + xlab("") +
scale_x_date(breaks = seq(1920, 2100,10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(100, 3000, 100),
labels = dollar_format(prefix = "", suffix = " h / an", acc = 1)) +
theme(legend.position = c(0.7, 0.9),
legend.title = element_blank())
Emploi Total
1950-
Secteurs
`CNA-2014-EMPLOI` %>%
filter(CNA_ACTIVITE %in% c("NNTOTAL"),
INDICATEUR == "CNA_EMPLOI_INTERIEUR",
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP",
CNA_TYPE_EMP == "E10") %>%
left_join(`SECT-INST`, by = "SECT-INST") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = `Sect-Inst`)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 40, 1), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"))
CNA_TYPE_EMP
`CNA-2014-EMPLOI` %>%
filter(CNA_ACTIVITE %in% c("NNTOTAL"),
`SECT-INST` %in% c("S10"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_TYPE_EMP, by = "CNA_TYPE_EMP") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_type_emp)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 40, 1), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"))
1995-
`CNA-2014-EMPLOI` %>%
filter(CNA_ACTIVITE %in% c("NNTOTAL"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP",
`SECT-INST` %in% c("S10"),
CNA_TYPE_EMP == "E10") %>%
left_join(CNA_TYPE_EMP, by = "CNA_TYPE_EMP") %>%
year_to_date %>%
filter(date >= as.Date("1995-01-01")) %>%
mutate(OBS_VALUE = as.numeric(OBS_VALUE)) %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = `SECT-INST`, linetype = `SECT-INST`)) +
scale_x_date(breaks = seq(1920, 2100,2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.5, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 40, 1), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 1, prefix = "", suffix = "M"))
Heures travaillées
A5
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
TIME_PERIOD == "2018",
grepl("A5", CNA_ACTIVITE)) %>%
arrange(CNA_ACTIVITE) %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
select(Cna_activite, UNIT_MEASURE, OBS_VALUE) %>%
spread(UNIT_MEASURE, OBS_VALUE) %>%
setNames(c("Secteur", "Heures", "ETP", "PP", "%")) %>%
{if (is_html_output()) print_table(.) else .}| Secteur | Heures | ETP | PP | % |
|---|---|---|---|---|
| A5-AZ - Agriculture, sylviculture et pêche | 1676549970 | 788.7 | 754.1 | 4.2 |
| A5-BE - Industrie manufacturière, industries extractives et autres | 4513166760 | 2784.9 | 2876.9 | 1.4 |
| A5-FZ - Construction | 3003677122 | 1750.0 | 1743.3 | -1.4 |
| A5-GU - Services principalement marchands | 22362627335 | 13472.3 | 14433.0 | -0.2 |
| A5-OQ - Services principalement non marchands | 11086745049 | 7722.5 | 8350.7 | 1.2 |
A10
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
TIME_PERIOD == "2018",
grepl("A10", CNA_ACTIVITE)) %>%
arrange(CNA_ACTIVITE) %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
select(Cna_activite, UNIT_MEASURE, OBS_VALUE) %>%
spread(UNIT_MEASURE, OBS_VALUE) %>%
setNames(c("Secteur", "Heures", "ETP", "PP", "%")) %>%
{if (is_html_output()) print_table(.) else .}| Secteur | Heures | ETP | PP | % |
|---|---|---|---|---|
| A10-AZ - Agriculture, sylviculture et pêche | 1676549970 | 788.7 | 754.1 | 4.2 |
| A10-BE - Industrie manufacturière, industries extractives et autres | 4513166760 | 2784.9 | 2876.9 | 1.4 |
| A10-FZ - Construction | 3003677122 | 1750.0 | 1743.3 | -1.4 |
| A10-GI - Commerce de gros et de détail, transports, hébergement et restauration | 10108707570 | 5988.4 | 6453.0 | -1.7 |
| A10-JZ - Information et communication | 1467543035 | 867.4 | 890.3 | -0.2 |
| A10-KZ - Activités financières et d’assurance | 1236254630 | 767.1 | 787.4 | 5.9 |
| A10-LZ - Activités immobilières | 590932478 | 362.2 | 382.6 | 0.4 |
| A10-MN - Activités spécialisées, scientifiques et techniques et activités de services administratifs et de soutien | 6872417655 | 4145.8 | 4421.6 | -0.6 |
| A10-OQ - Administration publique, enseignement, santé humaine et action sociale | 11086745049 | 7722.5 | 8350.7 | 1.2 |
| A10-RU - Autres activités de services | 2086771967 | 1341.4 | 1498.1 | 2.0 |
A17
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
TIME_PERIOD == "2018",
grepl("A17", CNA_ACTIVITE)) %>%
arrange(CNA_ACTIVITE) %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
select(CNA_ACTIVITE, Cna_activite, UNIT_MEASURE, OBS_VALUE) %>%
spread(UNIT_MEASURE, OBS_VALUE) %>%
setNames(c("CNA_ACTIVITE", "Secteur", "Heures", "ETP", "PP", "%")) %>%
{if (is_html_output()) print_table(.) else .}| CNA_ACTIVITE | Secteur | Heures | ETP | PP | % |
|---|---|---|---|---|---|
| A17-AZ | A17-AZ - Agriculture, sylviculture et pêche | 1676549970 | 788.7 | 754.1 | 4.2 |
| A17-C1 | A17-C1 - Fabrication de denrées alimentaires, de boissons et de produits à base de tabac | 971685139 | 593.9 | 618.8 | 0.5 |
| A17-C2 | A17-C2 - Cokéfaction et raffinage | 12718435 | 8.5 | 8.8 | 19.7 |
| A17-C3 | A17-C3 - Fabrication d’équipements électriques, électroniques, informatiques ; fabrication de machines | 487614952 | 301.1 | 309.1 | -0.4 |
| A17-C4 | A17-C4 - Fabrication de matériels de transport | 295014688 | 183.7 | 188.8 | 5.7 |
| A17-C5 | A17-C5 - Fabrication d’autres produits industriels | 2275694360 | 1397.5 | 1443.2 | -0.5 |
| A17-DE | A17-DE - Industries extractives, énergie, eau, gestion des déchets et dépollution | 470439187 | 300.2 | 308.2 | 3.6 |
| A17-FZ | A17-FZ - Construction | 3003677122 | 1750.0 | 1743.3 | -1.4 |
| A17-GZ | A17-GZ - Commerce ; réparation d’automobiles et de motocycles | 5890323015 | 3522.4 | 3785.9 | -0.6 |
| A17-HZ | A17-HZ - Transports et entreposage | 2164789823 | 1341.8 | 1408.8 | -4.3 |
| A17-IZ | A17-IZ - Hébergement et restauration | 2053594732 | 1124.2 | 1258.3 | -1.2 |
| A17-JZ | A17-JZ - Information et communication | 1467543035 | 867.4 | 890.3 | -0.2 |
| A17-KZ | A17-KZ - Activités financières et d’assurance | 1236254630 | 767.1 | 787.4 | 5.9 |
| A17-LZ | A17-LZ - Activités immobilières | 590932478 | 362.2 | 382.6 | 0.4 |
| A17-MN | A17-MN - Activités scientifiques et techniques ; services administratifs et de soutien | 6872417655 | 4145.8 | 4421.6 | -0.6 |
| A17-OQ | A17-OQ - Administration publique, enseignement, santé humaine et action sociale | 11086745049 | 7722.5 | 8350.7 | 1.2 |
| A17-RU | A17-RU - Autres activités de services | 2086771967 | 1341.4 | 1498.1 | 2.0 |
A38
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
TIME_PERIOD == "2017",
grepl("A38", CNA_ACTIVITE)) %>%
arrange(CNA_ACTIVITE) %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
select(CNA_ACTIVITE, Cna_activite, UNIT_MEASURE, OBS_VALUE) %>%
spread(UNIT_MEASURE, OBS_VALUE) %>%
setNames(c("CNA_ACTIVITE", "Secteur", "Heures", "ETP", "PP", "%")) %>%
{if (is_html_output()) print_table(.) else .}| CNA_ACTIVITE | Secteur | Heures | ETP | PP | % |
|---|---|---|---|---|---|
| A38-AZ | A38-AZ - Agriculture, sylviculture et pêche | 1681125349 | 793.2 | 752.4 | 10.3 |
| A38-BZ | A38-BZ - Industries extractives | 24043805 | 15.1 | 15.4 | 4.6 |
| A38-CA | A38-CA - Fabrication de denrées alimentaires, de boissons et de produits à base de tabac | 980991568 | 602.1 | 630.1 | 3.1 |
| A38-CB | A38-CB - Fabrication de textiles, industries de l’habillement, industrie du cuir et de la chaussure | 152250237 | 94.6 | 97.7 | 4.7 |
| A38-CC | A38-CC - Travail du bois, industries du papier et imprimerie | 283499198 | 174.0 | 179.9 | 4.9 |
| A38-CD | A38-CD - Cokéfaction et raffinage | 12795620 | 8.4 | 8.6 | 2.1 |
| A38-CE | A38-CE - Industrie chimique | 167404769 | 107.7 | 111.4 | 3.6 |
| A38-CF | A38-CF - Industrie pharmaceutique | 67645545 | 44.1 | 46.3 | 4.5 |
| A38-CG | A38-CG - Fabrication de produits en caoutchouc et en plastique ainsi que d’autres produits minéraux non métalliques | 375938270 | 238.6 | 245.5 | 2.4 |
| A38-CH | A38-CH - Métallurgie et fabrication de produits métalliques à l’exception des machines et des équipements | 597517010 | 368.3 | 378.7 | 4.3 |
| A38-CI | A38-CI - Fabrication de produits informatiques, électroniques et optiques | 131934142 | 82.2 | 84.9 | 6.2 |
| A38-CJ | A38-CJ - Fabrication d’équipements électriques | 121485896 | 76.6 | 78.8 | 5.0 |
| A38-CK | A38-CK - Fabrication de machines et équipements n.c.a. | 222904756 | 138.3 | 141.8 | 4.0 |
| A38-CL | A38-CL - Fabrication de matériels de transport | 290347352 | 181.7 | 186.7 | 3.7 |
| A38-CM | A38-CM - Autres industries manufacturières ; réparation et installation de machines et d’équipements | 608099845 | 368.4 | 379.0 | 3.0 |
| A38-DZ | A38-DZ - Production et distribution d’électricité, de gaz, de vapeur et d’air conditionné | 197076762 | 127.2 | 131.9 | -4.8 |
| A38-EZ | A38-EZ - Production et distribution d’eau ; assainissement, gestion des déchets et dépollution | 245547469 | 156.0 | 158.8 | 4.6 |
| A38-FZ | A38-FZ - Construction | 2930430016 | 1718.8 | 1717.9 | 3.6 |
| A38-GZ | A38-GZ - Commerce; réparation d’automobiles et de motocycles | 5797265772 | 3484.9 | 3754.4 | 1.5 |
| A38-HZ | A38-HZ - Transports et entreposage | 2126939576 | 1323.6 | 1387.1 | 2.2 |
| A38-IZ | A38-IZ - Hébergement et restauration | 1991643556 | 1087.6 | 1217.7 | -0.7 |
| A38-JA | A38-JA - Édition, audiovisuel et diffusion | 334823674 | 200.8 | 208.2 | 9.5 |
| A38-JB | A38-JB - Télécommunications | 150768265 | 100.8 | 107.6 | 10.1 |
| A38-JC | A38-JC - Activités informatiques et services d’information | 902547811 | 531.9 | 540.5 | 3.9 |
| A38-KZ | A38-KZ - Activités financières et d’assurance | 1221471311 | 759.7 | 781.1 | 2.1 |
| A38-LZ | A38-LZ - Activités immobilières | 585537321 | 357.2 | 376.4 | -1.6 |
| A38-MA | A38-MA - Activités juridiques, comptables, de gestion, d’architecture, d’ingénierie, de contrôle et d’analyses techniques | 2084958737 | 1217.1 | 1294.4 | 3.1 |
| A38-MB | A38-MB - Recherche-développement scientifique | 692692871 | 429.1 | 450.9 | 4.3 |
| A38-MC | A38-MC - Autres activités spécialisées, scientifiques et techniques | 439726261 | 253.9 | 279.1 | 4.2 |
| A38-NZ | A38-NZ - Activités de services administratifs et de soutien | 3383029956 | 2097.4 | 2270.3 | -1.2 |
| A38-OZ | A38-OZ - Administration publique | 3364512366 | 2292.9 | 2452.0 | 0.7 |
| A38-PZ | A38-PZ - Enseignement | 2312532354 | 1903.3 | 1999.5 | 2.1 |
| A38-QA | A38-QA - Activités pour la santé humaine | 2926753725 | 1802.1 | 1938.2 | 2.9 |
| A38-QB | A38-QB - Hébergement médico-social et social et action sociale sans hébergement | 2536151112 | 1775.4 | 1971.9 | 2.0 |
| A38-RZ | A38-RZ - Arts, spectacles et activités récréatives | 800992393 | 520.8 | 606.1 | 2.1 |
| A38-SZ | A38-SZ - Autres activités de services | 977689028 | 623.5 | 738.1 | 2.9 |
| A38-TZ | A38-TZ - Activités des ménages en tant qu’employeurs ; activités indifférenciées des ménages en tant que producteurs de biens et services pour usage propre | 312456676 | 202.6 | 162.0 | 2.5 |
Emploi salarié, non salarié
`CNA-2014-EMPLOI` %>%
filter(CNA_ACTIVITE %in% c("NNTOTAL"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP",
`SECT-INST` == "S10") %>%
left_join(CNA_TYPE_EMP, by = "CNA_TYPE_EMP") %>%
year_to_date %>%
filter(date >= as.Date("2016-01-01")) %>%
group_by(Cna_type_emp) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE-OBS_VALUE[1]) %>%
ggplot() + theme_minimal() + ylab("") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE, color = Cna_type_emp)) +
scale_x_date(breaks = seq(1920, 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_continuous(breaks = seq(-200, 100000, 100))
Emploi Industrie, Manufacturier
BE: Manuf
`CNA-2014-EMPLOI` %>%
filter(CNA_ACTIVITE %in% c("A10-BE"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_TYPE_EMP, by = "CNA_TYPE_EMP") %>%
year_to_date %>%
filter(date >= as.Date("2016-01-01")) %>%
group_by(Cna_type_emp) %>%
arrange(date) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
ggplot() + theme_minimal() + ylab("Base 100 = 1990") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE, color = Cna_type_emp)) +
scale_x_date(breaks = seq(1920, 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(10, 200, 1))
Nombre d’emplois
`CNA-2014-EMPLOI` %>%
filter(CNA_ACTIVITE %in% c("A10-BE"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_TYPE_EMP, by = "CNA_TYPE_EMP") %>%
year_to_date %>%
filter(date >= as.Date("2016-01-01")) %>%
group_by(Cna_type_emp) %>%
arrange(date) %>%
mutate(OBS_VALUE = OBS_VALUE-OBS_VALUE[1]) %>%
ggplot() + theme_minimal() + ylab("") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE, color = Cna_type_emp)) +
scale_x_date(breaks = seq(1920, 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_continuous(breaks = seq(-200, 200, 10))
Emploi 10 Branches (2010-)
AZ-BE-FZ: Construction - Manuf - Agriculture
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A10-FZ", "A10-BE", "A10-AZ"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
filter(date >= as.Date("2010-01-01")) %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.4, 0.1),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1)),
labels = dollar_format(accuracy = 0.1, prefix = "", suffix = "M"))
GI-MN-OQ-RU: Administration publique (OQ) - Commerce de Gros (GI) - Activités spécialisées (MN) - RU
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A10-OQ", "A10-GI", "A10-MN", "A10-RU"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
filter(date >= as.Date("2010-01-01")) %>%
ggplot() +
geom_line(aes(x = date, y = OBS_VALUE/1000000, color = Cna_activite, linetype = Cna_activite)) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 10, 1),
labels = dollar_format(accuracy = 0.1, prefix = "", suffix = "M"),
limits = c(0.5, 15)) +
ylab("Nombre d'actifs occupés") + xlab("")
JZ-KZ-LZ: Information-Communication - Activités Financières - Immobilières
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A10-JZ", "A10-KZ", "A10-LZ"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
filter(date >= as.Date("2010-01-01")) %>%
ggplot() +
geom_line(aes(x = date, y = OBS_VALUE/1000000, color = Cna_activite, linetype = Cna_activite)) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2100,1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 6, 0.1),
labels = dollar_format(accuracy = 0.1, prefix = "", suffix = "M")) +
ylab("Nombre d'actifs occupés") + xlab("")
Emploi 10 Branches
AZ-BE-FZ: Construction - Manuf - Agriculture
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A10-FZ", "A10-BE", "A10-AZ"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.4, 0.1),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1)),
labels = dollar_format(accuracy = 0.1, prefix = "", suffix = "M"))
GI-MN-OQ-RU: Administration publique (OQ) - Commerce de Gros (GI) - Activités spécialisées (MN) - RU
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A10-OQ", "A10-GI", "A10-MN", "A10-RU"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() +
geom_line(aes(x = date, y = OBS_VALUE/1000000, color = Cna_activite, linetype = Cna_activite)) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 10, 1),
labels = dollar_format(accuracy = 0.1, prefix = "", suffix = "M"),
limits = c(0.5, 15)) +
ylab("Nombre d'actifs occupés") + xlab("")
JZ-KZ-LZ: Information-Communication - Activités Financières - Immobilières
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A10-JZ", "A10-KZ", "A10-LZ"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() +
geom_line(aes(x = date, y = OBS_VALUE/1000000, color = Cna_activite, linetype = Cna_activite)) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(0, 6, 0.1),
labels = dollar_format(accuracy = 0.1, prefix = "", suffix = "M")) +
ylab("Nombre d'actifs occupés") + xlab("")
Emploi 17 Branches
Transport, Matériel électrique
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A17-C4", "A17-C3"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
filter(date >= as.Date("1990-01-01")) %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.5, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.1, 0.7))
C1-C3-C4: Fabrication denrées alimentaires, équipements électriques, matériels de transport
Tous
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A17-C1", "A17-C4", "A17-C3"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.5, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.1, 0.7))
1990-
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A17-C1", "A17-C4", "A17-C3"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
filter(date >= as.Date("1990-01-01")) %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.5, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.1, 0.7))
C2-C5-DE: Cokéfaction - Autres produits - Industries extractives
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A17-C2", "A17-C5", "A17-DE"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.5, 0.45),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"))
Emploi 38 Branches
QA-QB-GZ: Commerce; réparation; Santé; Hébergement médico-social
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-QA", "A38-QB", "A38-GZ"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.6, 0.15),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.3, 4))
OZ-NZ-PZ: Administration publique, Activités de service administratifs, Enseignement
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-OZ", "A38-NZ", "A38-c"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"))
FZ-HZ-MA: Construction; Transports et entreposage; Activités juridiques, comptables, de gestion, d’architecture, d’ingénierie, de contrôle et d’analyses techniques
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-FZ", "A38-HZ", "A38-MA"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.59, 0.14),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.1, 2.1))
IZ-AZ-KZ: Hébergement et restauration; Agriculture, sylviculture et pêche; Activités financières et d’assurance
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-IZ", "A38-AZ", "A38-KZ"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.59, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.1, 5))
SZ-CA-JC: Autres activités de services; Fabrication de denrées alimentaires, de boissons et de produits à base de tabac; Activités informatiques et services d’information
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-SZ", "A38-CA", "A38-JC"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.5, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.01, 1))
RZ-MB-CH: Arts, spectacles et activités récréatives ; Recherche-développement scientifique; Métallurgie et fabrication de produits métalliques à l’exception des machines et des équipements
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-RZ", "A38-MB", "A38-CH"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.5, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.1, 1))
CM-LZ-MC: Autres industries manufacturières ; réparation et installation de machines et d’équipements ; Activités immobilières; Autres activités spécialisées, scientifiques et techniques
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-CM", "A38-LZ", "A38-MC"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.5, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.06, 0.7))
CG-TZ-JA: Fabrication de produits en caoutchouc et en plastique ainsi que d’autres produits minéraux non métalliques ; Activités des ménages en tant qu’employeurs ; activités indifférenciées des ménages en tant que producteurs de biens et services pour usage propre ; Édition, audiovisuel et diffusion
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-CG", "A38-TZ", "A38-JA"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.15),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.03, 0.55))
CL-CC-EZ: Fabrication de matériels de transport ; Travail du bois, industries du papier et imprimerie ; Production et distribution d’eau ; assainissement, gestion des déchets et dépollution
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-CL", "A38-CC", "A38-EZ"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.5, 0.15),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.03, 0.5))
A38-CK - Fabrication de machines et équipements n.c.a.
A38-DZ - Production et distribution d’électricité, de gaz, de vapeur et d’air conditionné
A38-CE - Industrie chimique
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-CK", "A38-DZ", "A38-CE"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.5, 0.15),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.07, 0.35))
A38-JB - Télécommunications A38-CB - Fabrication de textiles, industries de l’habillement, industrie du cuir et de la chaussure A38-CI - Fabrication de produits informatiques, électroniques et optiques
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-JB", "A38-CB", "A38-CI"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.5, 0.15),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.07, 1))
A38-CJ - Fabrication d’équipements électriques
A38-CF - Industrie pharmaceutique
A38-BZ - Industries extractives
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-CJ", "A38-CF", "A38-BZ"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.4, 0.25),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.01, 0.2))
CD - Cokéfaction et raffinage
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-CD"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.5, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.005, 0.06))
Emploi 38 Branches - Industrie
CA-CB-CC-CD
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-CA", "A38-CB", "A38-CC", "A38-CD"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.55, 0.4),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"))
CE-CF-CG-CH
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-CE", "A38-CF", "A38-CG", "A38-CH"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.55, 0.35),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"))
CI-CJ-CK-CL
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-CI", "A38-CJ", "A38-CK", "A38-CL"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.55, 0.15),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"))
CM-DZ-EZ-BZ
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-CM", "A38-DZ", "A38-EZ", "A38-BZ"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.45, 0.15),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.01, 0.7))
BZ-CD-CF-CJ: Fabrication de produits en caoutchouc et en plastique ainsi que d’autres produits minéraux non métalliques ; Activités des ménages en tant qu’employeurs ; activités indifférenciées des ménages en tant que producteurs de biens et services pour usage propre ; Édition, audiovisuel et diffusion ; Cokéfaction et raffinage
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-CJ", "A38-CF", "A38-BZ", "A38-CD"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.22, 0.18),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.01, 0.2))
CC-CL-CG-CM: Fabrication de produits en caoutchouc et en plastique ainsi que d’autres produits minéraux non métalliques ; Activités des ménages en tant qu’employeurs ; activités indifférenciées des ménages en tant que producteurs de biens et services pour usage propre ; Édition, audiovisuel et diffusion ; Cokéfaction et raffinage
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-CC", "A38-CL", "A38-CG", "A38-CM"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.5, 0.18),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.1, 0.7))
`CNA-2014-EMPLOI` %>%
filter(CNA_TYPE_EMP == "E10",
CNA_ACTIVITE %in% c("A38-CD"),
UNIT_MEASURE == "MILLIERS_ACTIFS_OCCUPES_PP") %>%
left_join(CNA_ACTIVITE, by = "CNA_ACTIVITE") %>%
year_to_date %>%
ggplot() + theme_minimal() + ylab("Nombre d'actifs occupés") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Cna_activite, linetype = Cna_activite)) +
scale_x_date(breaks = seq(1920, 2100,5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.5, 0.2),
legend.title = element_blank()) +
scale_y_log10(breaks = c(seq(0, 6, 0.5), seq(0, 1, 0.1), seq(0, 0.1, 0.01)),
labels = dollar_format(accuracy = 0.01, prefix = "", suffix = "M"),
limits = c(0.005, 0.06))