source | dataset | .html | .RData |
---|---|---|---|
insee | ENQ-CONJ-IND-BAT | 2024-10-29 | 2024-11-05 |
Conjoncture dans l’industrie du bâtiment - ENQ-CONJ-IND-BAT
Data - Insee
Info
Données sur l’immobilier
source | dataset | .html | .RData |
---|---|---|---|
acpr | as151 | 2024-06-19 | 2024-04-05 |
bdf | BSI1 | 2024-07-26 | 2024-06-14 |
bdf | CPP | 2024-07-26 | 2024-07-01 |
bdf | FM | 2024-07-26 | 2024-10-16 |
bdf | immobilier | 2024-07-26 | 2024-06-18 |
bdf | MIR | 2024-07-26 | 2024-07-01 |
bdf | MIR1 | 2024-10-16 | 2024-10-16 |
bdf | RPP | 2024-07-26 | 2024-07-01 |
cgedd | nombre-vente-maison-appartement-ancien | 2024-09-26 | 2024-09-26 |
insee | CONSTRUCTION-LOGEMENTS | 2024-11-05 | 2024-11-05 |
insee | ENQ-CONJ-ART-BAT | 2024-11-05 | 2024-11-05 |
insee | ENQ-CONJ-IND-BAT | 2024-10-29 | 2024-11-05 |
insee | ENQ-CONJ-PROMO-IMMO | 2024-10-29 | 2024-11-05 |
insee | ENQ-CONJ-TP | 2024-10-29 | 2024-11-05 |
insee | ILC-ILAT-ICC | 2024-10-29 | 2024-11-05 |
insee | INDICES_LOYERS | 2024-10-29 | 2024-11-05 |
insee | IPLA-IPLNA-2015 | 2024-10-29 | 2024-11-05 |
insee | IRL | 2024-10-29 | 2024-11-05 |
insee | PARC-LOGEMENTS | 2024-10-29 | 2023-12-03 |
insee | SERIES_LOYERS | 2024-10-29 | 2024-11-05 |
insee | t_dpe_val | 2024-10-29 | 2024-09-02 |
notaires | arrdt | 2024-06-30 | 2024-09-09 |
notaires | dep | 2024-06-30 | 2024-09-08 |
LAST_UPDATE
Code
`ENQ-CONJ-IND-BAT` %>%
group_by(LAST_UPDATE) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
LAST_UPDATE | Nobs |
---|---|
2024-10-24 | 69475 |
2016-10-25 | 1336 |
TITLE_FR
Code
`ENQ-CONJ-IND-BAT` %>%
group_by(IDBANK, TITLE_FR) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
INDICATEUR
Code
`ENQ-CONJ-IND-BAT` %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR, Indicateur) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
NATURE
Code
`ENQ-CONJ-IND-BAT` %>%
left_join(NATURE, by = "NATURE") %>%
group_by(NATURE, Nature) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
NATURE | Nature | Nobs |
---|---|---|
SOLDE_PROPORTION | Solde d'opinions | 62876 |
PROPORTION | Proportion | 7002 |
INDICE | Indice | 933 |
UNIT_MEASURE
Code
`ENQ-CONJ-IND-BAT` %>%
group_by(UNIT_MEASURE) %>%
summarise(Nobs = n()) %>%
if (is_html_output()) print_table(.) else .} {
UNIT_MEASURE | Nobs |
---|---|
MOIS | 3570 |
POURCENT | 66308 |
SO | 933 |
TIME_PERIOD / date
Code
`ENQ-CONJ-IND-BAT` %>%
group_by(TIME_PERIOD) %>%
summarise(Nobs = n()) %>%
arrange(desc(TIME_PERIOD)) %>%
print_table_conditional()
Difficultés de recrutement
ECB_DREC_ENT
Brut
Code
`ENQ-CONJ-IND-BAT` %>%
filter(INDICATEUR == "ECB_DREC_ENT",
== "BRUT") %>%
CORRECTION left_join(ACTIVITE_ECB, by = "ACTIVITE_ECB") %>%
quarter_to_date() %>%
arrange(desc(date)) %>%
mutate(OBS_VALUE = OBS_VALUE / 100) %>%
+ theme_minimal() + xlab("") + ylab("Difficultés de recrutement (%)") +
ggplot geom_line(aes(x = date, y = OBS_VALUE, color = Activite_ecb)) +
scale_x_date(breaks = seq(1960, 2022, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 5),
labels = percent_format(accuracy = 1))
CVS
Code
`ENQ-CONJ-IND-BAT` %>%
filter(INDICATEUR == "ECB_DREC_ENT",
== "CVS") %>%
CORRECTION left_join(ACTIVITE_ECB, by = "ACTIVITE_ECB") %>%
quarter_to_date() %>%
arrange(desc(date)) %>%
mutate(OBS_VALUE = OBS_VALUE / 100) %>%
+ theme_minimal() + xlab("") + ylab("Difficultés de recrutement (%)") +
ggplot geom_line(aes(x = date, y = OBS_VALUE, color = Activite_ecb)) +
scale_x_date(breaks = seq(1960, 2022, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 5),
labels = percent_format(accuracy = 1))
Par Type
Code
`ENQ-CONJ-IND-BAT` %>%
filter(INDICATEUR %in% c("ECB_DMAN", "ECB_DQUA", "ECB_DSPE", "ECB_DTEC")) %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
quarter_to_date() %>%
mutate(OBS_VALUE = OBS_VALUE / 100) %>%
+ theme_minimal() + xlab("") + ylab("Difficultés de recrutement (%)") +
ggplot geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
scale_x_date(breaks = seq(1960, 2022, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.65, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 5),
labels = percent_format(accuracy = 1))