Formation brute de capital fixe (FBCF) par secteur institutionnel
Data - Insee
François Geerolf
Info
| source | dataset | .html | .RData |
|---|---|---|---|
| insee | CNA-2014-FBCF-SI | 2024-05-30 | 2024-06-07 |
| insee | CNA-2014-PIB | 2024-05-30 | 2024-06-07 |
Données sur la macroéconomie en France
| source | dataset | .html | .RData |
|---|---|---|---|
| bdf | CFT | 2024-05-24 | 2024-06-06 |
| insee | CNA-2014-CONSO-MEN | 2024-06-07 | 2024-04-09 |
| insee | CNA-2014-CONSO-SI | 2024-06-07 | 2024-06-07 |
| insee | CNA-2014-CSI | 2024-06-07 | 2024-06-07 |
| insee | CNA-2014-FBCF-BRANCHE | 2024-06-07 | 2024-06-07 |
| insee | CNA-2014-FBCF-SI | 2024-05-30 | 2024-06-07 |
| insee | CNA-2014-PIB | 2024-05-30 | 2024-06-07 |
| insee | CNA-2014-RDB | 2024-05-30 | 2024-06-07 |
| insee | CNT-2014-CB | 2024-05-30 | 2024-06-07 |
| insee | CNT-2014-CSI | 2024-05-30 | 2024-06-07 |
| insee | CNT-2014-OPERATIONS | 2024-05-30 | 2024-06-07 |
| insee | CNT-2014-PIB-EQB-RF | 2024-05-31 | 2024-06-07 |
| insee | CONSO-MENAGES-2014 | 2024-06-07 | 2024-06-07 |
| insee | conso-mensuelle | 2024-05-30 | 2023-07-04 |
| insee | ICA-2015-IND-CONS | 2024-05-30 | 2024-06-07 |
| insee | t_1101 | 2024-05-30 | 2022-01-02 |
| insee | t_1102 | 2024-05-30 | 2020-10-30 |
| insee | t_1105 | 2024-05-30 | 2020-10-30 |
LAST_UPDATE
`CNA-2014-FBCF-SI` %>%
group_by(LAST_UPDATE) %>%
summarise(Nobs = n()) %>%
arrange(desc(LAST_UPDATE)) %>%
print_table_conditional()| LAST_UPDATE | Nobs |
|---|---|
| 2023-05-31 | 9039 |
| 2022-06-14 | 22505 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-06-08 |
Last
`CNA-2014-FBCF-SI` %>%
group_by(TIME_PERIOD) %>%
summarise(Nobs = n()) %>%
arrange(desc(TIME_PERIOD)) %>%
head(1) %>%
print_table_conditional()| TIME_PERIOD | Nobs |
|---|---|
| 2022 | 15 |
SECT-INST
`CNA-2014-FBCF-SI` %>%
left_join(`SECT-INST`, by = "SECT-INST") %>%
group_by(`SECT-INST`, `Sect-Inst`) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| SECT-INST | Sect-Inst | Nobs |
|---|---|---|
| S11ES14AA | S11ES14AA - Sociétés et entreprises individuelles non financières | 9131 |
| S13 | S13 - Administrations publiques (APU) | 7242 |
| S12ES14AF | S12ES14AF - Sociétés et entreprises individuelles financières | 7206 |
| S15 | S15 - Institutions sans but lucratif au service des ménages | 5008 |
| S14B | S14B - Ménages hors entrepreneurs individuels | 2957 |
UNIT_MEASURE
`CNA-2014-FBCF-SI` %>%
group_by(UNIT_MEASURE) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| UNIT_MEASURE | Nobs |
|---|---|
| EUROS_COURANTS | 10590 |
| EUR2014 | 10477 |
| SO | 10477 |
TIME_PERIOD
CNA_PRODUIT
TIME_PERIOD
Decomposition
Table
`CNA-2014-FBCF-SI` %>%
filter(TIME_PERIOD == "2021",
UNIT_MEASURE == "EUROS_COURANTS",
`SECT-INST` == "S11ES14AA") %>%
select_if(~ n_distinct(.) > 1) %>%
left_join(CNA_PRODUIT, by = "CNA_PRODUIT") %>%
transmute(CNA_PRODUIT, Cna_produit, OBS_VALUE) %>%
arrange(-OBS_VALUE) %>%
print_table_conditional()Change 2010-2021
`CNA-2014-FBCF-SI` %>%
filter(TIME_PERIOD %in% c("2021", "2010"),
UNIT_MEASURE == "EUROS_COURANTS",
`SECT-INST` == "S11ES14AA") %>%
left_join(CNA_PRODUIT, by = "CNA_PRODUIT") %>%
transmute(CNA_PRODUIT, Cna_produit, TIME_PERIOD, OBS_VALUE) %>%
arrange(-OBS_VALUE) %>%
spread(TIME_PERIOD, OBS_VALUE) %>%
mutate(Change = `2021` - `2010`) %>%
arrange(-Change) %>%
print_table_conditional() GU, BE, JZ
`CNA-2014-FBCF-SI` %>%
filter(CNA_PRODUIT %in% c("A5-GU", "A10-BE", "A17-JZ"),
UNIT_MEASURE == "EUROS_COURANTS",
`SECT-INST` == "S11ES14AA") %>%
year_to_date %>%
left_join(CNA_PRODUIT, by = "CNA_PRODUIT") %>%
select(date, CNA_PRODUIT, Cna_produit, OBS_VALUE) %>%
left_join(gdp, by = "date") %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE/gdp, color = Cna_produit)) +
theme_minimal() + xlab("") + ylab("") +
theme(legend.title = element_blank(),
legend.position = c(0.3, 0.91)) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(0, 100, 0.5),
labels = scales::percent_format(accuracy = 0.1))
NNTOTAL, BE, JZ
`CNA-2014-FBCF-SI` %>%
filter(CNA_PRODUIT %in% c("NNTOTAL", "A10-BE", "A17-JZ"),
UNIT_MEASURE == "EUROS_COURANTS",
`SECT-INST` == "S11ES14AA") %>%
year_to_date %>%
left_join(CNA_PRODUIT, by = "CNA_PRODUIT") %>%
select(date, CNA_PRODUIT, Cna_produit, OBS_VALUE) %>%
left_join(gdp, by = "date") %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE/gdp, color = Cna_produit)) +
theme_minimal() + xlab("") + ylab("") +
theme(legend.title = element_blank(),
legend.position = c(0.3, 0.91)) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(0, 100, 0.5),
labels = scales::percent_format(accuracy = 0.1))
NNTOTAL, BE, JZ, FZ
`CNA-2014-FBCF-SI` %>%
filter(CNA_PRODUIT %in% c("NNTOTAL", "A10-BE", "A17-JZ", "A17-FZ"),
UNIT_MEASURE == "EUROS_COURANTS",
`SECT-INST` == "S11ES14AA") %>%
year_to_date %>%
left_join(CNA_PRODUIT, by = "CNA_PRODUIT") %>%
select(date, CNA_PRODUIT, Cna_produit, OBS_VALUE) %>%
left_join(gdp, by = "date") %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE/gdp, color = Cna_produit)) +
theme_minimal() + xlab("") + ylab("") +
theme(legend.title = element_blank(),
legend.position = c(0.3, 0.91)) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
labels = scales::percent_format(accuracy = 1))
MN, C4, C3, MB
`CNA-2014-FBCF-SI` %>%
filter(CNA_PRODUIT %in% c("A17-MN", "A17-C4", "A17-C3", "A38-MB"),
UNIT_MEASURE == "EUROS_COURANTS",
`SECT-INST` == "S11ES14AA") %>%
year_to_date %>%
left_join(CNA_PRODUIT, by = "CNA_PRODUIT") %>%
select(date, CNA_PRODUIT, Cna_produit, OBS_VALUE) %>%
left_join(gdp, by = "date") %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE/gdp, color = Cna_produit)) +
theme_minimal() + xlab("") + ylab("") +
theme(legend.title = element_blank(),
legend.position = c(0.5, 0.85)) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(0, 100, 0.2),
labels = scales::percent_format(accuracy = .1))