source | dataset | .html | .RData |
---|---|---|---|
notaires | arrdt | 2023-10-03 | 2024-04-08 |
notaires | dep | 2023-10-03 | 2023-10-03 |
source | dataset | .html | .RData |
---|---|---|---|
acpr | as151 | 2024-04-05 | 2024-04-05 |
bdf | BSI1 | 2024-04-04 | 2024-04-04 |
bdf | CPP | 2024-04-01 | 2024-04-01 |
bdf | FM | 2024-04-04 | 2024-04-01 |
bdf | immobilier | 2024-04-04 | 2024-04-04 |
bdf | MIR | 2024-04-03 | 2024-04-01 |
bdf | MIR1 | 2024-04-03 | 2024-04-01 |
bdf | RPP | 2024-04-01 | 2024-04-01 |
insee | CONSTRUCTION-LOGEMENTS | 2024-03-30 | 2024-03-30 |
insee | ENQ-CONJ-ART-BAT | 2024-03-30 | 2023-10-25 |
insee | ENQ-CONJ-IND-BAT | 2024-03-30 | 2024-03-30 |
insee | ENQ-CONJ-PROMO-IMMO | 2024-03-30 | 2024-03-30 |
insee | ENQ-CONJ-TP | 2024-03-30 | 2024-03-30 |
insee | ILC-ILAT-ICC | 2024-03-30 | 2024-03-30 |
insee | INDICES_LOYERS | 2024-03-30 | 2024-03-30 |
insee | IPLA-IPLNA-2015 | 2024-03-30 | 2024-03-30 |
insee | IRL | 2024-03-30 | 2024-03-30 |
insee | PARC-LOGEMENTS | 2024-03-30 | 2023-12-03 |
insee | SERIES_LOYERS | 2024-03-30 | 2024-03-30 |
insee | t_dpe_val | 2024-03-30 | 2024-03-04 |
notaires | arrdt | 2023-10-03 | 2024-04-08 |
notaires | dep | 2023-10-03 | 2023-10-03 |
source | dataset | .html | .RData |
---|---|---|---|
bdf | RPP | 2024-04-01 | 2024-04-01 |
bis | LONG_PP | 2023-06-01 | 2023-06-01 |
bis | SELECTED_PP | 2024-02-11 | 2023-10-19 |
ecb | RPP | 2024-04-01 | 2024-04-01 |
eurostat | ei_hppi_q | 2024-03-30 | 2024-03-30 |
eurostat | hbs_str_t223 | 2024-03-30 | 2024-03-30 |
eurostat | prc_hicp_midx | 2024-04-03 | 2024-04-03 |
eurostat | prc_hpi_q | 2024-03-30 | 2024-03-30 |
fred | housing | 2024-04-03 | 2024-04-03 |
insee | IPLA-IPLNA-2015 | 2024-03-30 | 2024-03-30 |
oecd | housing | 2024-03-30 | 2020-01-18 |
oecd | SNA_TABLE5 | 2024-03-30 | 2023-10-19 |
LAST_COMPILE |
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2024-04-08 |
arrdt %>%
filter(date %in% c(as.Date("2008-01-01"), max(date))) %>%
spread(date, value) %>%
#setNames(c("Arrondissement", "2021T3", "2023T2")) %>%
mutate(`Croissance (%)` = round(100*(.[[3]]/.[[2]]-1), 1),
`Croissance (€)` = round(.[[3]]-.[[2]])) %>%
arrange(-`Croissance (%)`) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}
arrdt %>%
filter(date %in% c(as.Date("2009-04-01"), max(date))) %>%
mutate(date = paste0("Prix m2 ", date)) %>%
spread(date, value) %>%
#setNames(c("Arrondissement", "2021T3", "2023T2")) %>%
mutate(`Croissance (%)` = round(100*(.[[3]]/.[[2]]-1), 1),
`Croissance (€)` = round(.[[3]]-.[[2]])) %>%
arrange(-`Croissance (%)`) %>%
rename(arrdt = Location) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}
arrdt %>%
filter(date %in% c(as.Date("2008-01-01"), as.Date("2020-01-01"))) %>%
spread(date, value) %>%
#setNames(c("Arrondissement", "2021T3", "2023T2")) %>%
mutate(`Croissance (%)` = round(100*(.[[3]]/.[[2]]-1), 1),
`Croissance (€)` = round(.[[3]]-.[[2]])) %>%
arrange(-`Croissance (%)`) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}
arrdt %>%
filter(date %in% c(as.Date("2022-04-01"), as.Date("2023-04-01"))) %>%
spread(date, value) %>%
#setNames(c("Arrondissement", "2021T3", "2023T2")) %>%
mutate(`Croissance (%)` = round(100*(.[[3]]/.[[2]]-1), 1),
`Croissance (€)` = round(.[[3]]-.[[2]])) %>%
arrange(-`Croissance (%)`) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}
arrdt %>%
filter(date %in% c(as.Date("2021-07-01"), as.Date("2023-04-01"))) %>%
spread(date, value) %>%
#setNames(c("Arrondissement", "2021T3", "2023T2")) %>%
mutate(`Croissance (%)` = round(100*(.[[3]]/.[[2]]-1), 1),
`Croissance (€)` = round(.[[3]]-.[[2]])) %>%
arrange(-`Croissance (%)`) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}
arrdt %>%
filter(Location %in% c("Centre", "6e", "10e")) %>%
group_by(Location) %>%
mutate(value = 100*value/value[date == as.Date("2008-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = value, color = Location)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix au mètre carré (2008 = 100") +
scale_y_log10(breaks = seq(0, 7000, 10))
arrdt %>%
filter(Location %in% c("13e", "14e", "15e")) %>%
group_by(Location) %>%
ggplot + geom_line(aes(x = date, y = value, color = gsub("e", "ème arrondissement", Location))) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Prix au mètre carré") +
scale_y_log10(breaks = seq(0, 17000, 1000),
labels = dollar_format(p = "", su = "€", a = 1))
arrdt %>%
filter(Location %in% c("13e", "14e", "15e")) %>%
group_by(Location) %>%
filter(date >= as.Date("2014-01-01")) %>%
ggplot + geom_line(aes(x = date, y = value, color = gsub("e", "ème arrondissement", Location))) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 1), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Prix au mètre carré") +
scale_y_log10(breaks = seq(0, 17000, 1000),
labels = dollar_format(p = "", su = "€", a = 1))
arrdt %>%
filter(Location %in% c("13e", "14e", "15e")) %>%
group_by(Location) %>%
filter(date >= as.Date("2018-01-01")) %>%
ggplot + geom_line(aes(x = date, y = value, color = gsub("e", "ème arrondissement", Location))) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 1), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Prix au mètre carré") +
scale_y_log10(breaks = seq(0, 17000, 200),
labels = dollar_format(p = "", su = "€", a = 1))
arrdt %>%
filter(Location %in% c("16e", "6e", "7e")) %>%
group_by(Location) %>%
ggplot + geom_line(aes(x = date, y = value, color = gsub("e", "ème arrondissement", Location))) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Prix au mètre carré") +
scale_y_log10(breaks = seq(0, 17000, 1000),
labels = dollar_format(p = "", su = "€", a = 1))
arrdt %>%
filter(Location %in% c("16e", "6e", "7e")) %>%
group_by(Location) %>%
mutate(value = 100*value/value[date == as.Date("2008-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = value, color = gsub("e", "ème arrondissement", Location))) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix au mètre carré (2008 = 100)") +
scale_y_log10(breaks = seq(0, 7000, 10))
arrdt %>%
filter(Location %in% c("6e", "5e", "14e")) %>%
group_by(Location) %>%
ggplot + geom_line(aes(x = date, y = value, color = gsub("e", "ème arrondissement", Location))) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Prix au mètre carré") +
scale_y_log10(breaks = seq(0, 17000, 1000),
labels = dollar_format(p = "", su = "€", a = 1))
arrdt %>%
filter(Location %in% c("6e", "5e", "14e")) %>%
group_by(Location) %>%
mutate(value = 100*value/value[date == as.Date("2008-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = value, color = gsub("e", "ème arrondissement", Location))) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix au mètre carré (2008 = 100)") +
scale_y_log10(breaks = seq(0, 7000, 10))
arrdt %>%
filter(Location %in% c("1er", "2e", "3e")) %>%
mutate(Location = case_when(Location == "1er" ~ "1er arrondissement",
Location == "2e" ~ "2ème arrondissement",
Location == "3e" ~ "3ème arrondissement")) %>%
group_by(Location) %>%
ggplot + geom_line(aes(x = date, y = value, color = Location)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Prix au mètre carré") +
scale_y_log10(breaks = seq(0, 17000, 1000),
labels = dollar_format(p = "", su = "€", a = 1))
arrdt %>%
filter(Location %in% c("1er", "2e", "3e")) %>%
mutate(Location = case_when(Location == "1er" ~ "1er arrondissement",
Location == "2e" ~ "2ème arrondissement",
Location == "3e" ~ "3ème arrondissement")) %>%
group_by(Location) %>%
mutate(value = 100*value/value[date == as.Date("2008-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = value, color = Location)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix au mètre carré (2008 = 100)") +
scale_y_log10(breaks = seq(0, 7000, 10))