Institut National de la Statistique et des Etudes Economiques’ API
Data - INSEE
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List of APIs
LAST_COMPILE
| LAST_COMPILE |
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| 2024-06-18 |
List Datasets
Français
Code
"https://bdm.insee.fr/series/sdmx/dataflow" %>%
readSDMX %>%
as_tibble %>%
select(id, Name.fr) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}English
Code
"https://bdm.insee.fr/series/sdmx/dataflow" %>%
readSDMX %>%
as_tibble %>%
select(id, Name.en) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}Using IDBANKS
France, Paris, Lyon, Marseille
Code
"010567006+010567010+010567012+010567056" %>%
paste0("https://www.bdm.insee.fr/series/sdmx/data/SERIES_BDM/", .) %>%
readSDMX %>%
as_tibble %>%
quarter_to_date %>%
mutate(OBS_VALUE = as.numeric(OBS_VALUE)) %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015", "", TITLE_FR),
TITLE_FR = gsub(" - Série brute", "", TITLE_FR)) %>%
group_by(REF_AREA) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("1998-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 50))
0612 - Indice des prix des produits médicaux divers
Code
"001763064+001763624" %>%
paste0("https://www.bdm.insee.fr/series/sdmx/data/SERIES_BDM/", .) %>%
readSDMX %>%
as_tibble %>%
month_to_date %>%
mutate(OBS_VALUE = as.numeric(OBS_VALUE)) %>%
filter(date >= as.Date("2018-01-01")) %>%
arrange(date) %>%
mutate(TITLE_FR = gsub("- Base 2015 - Ensemble des ménages ", "", TITLE_FR),
TITLE_FR = gsub("- France - Nomenclature Coicop : ", "", TITLE_FR)) %>%
group_by(TITLE_FR) %>%
mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[date == as.Date("2018-01-01")]) %>%
ggplot() + ylab("") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% 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 = seq(10, 300, 5),
labels = dollar_format(accuracy = 1, prefix = ""))
Biens, Biens et Services
Code
library("rsdmx")
library("tidyverse")
library("viridis")
"010565588+010565630+010565590+010565632+010565592+010565634+010565707" %>%
paste0("https://www.bdm.insee.fr/series/sdmx/data/SERIES_BDM/", .) %>%
readSDMX %>%
as_tibble %>%
mutate(OBS_VALUE = OBS_VALUE %>% as.numeric,
year = TIME_PERIOD %>% substr(1, 4),
month = (TIME_PERIOD %>% substr(7, 7) %>% as.numeric - 1)*3 + 1,
month = month %>% str_pad(., 2, pad = "0"),
date = paste0(year, "-", month, "-01") %>% as.Date) %>%
select(IDBANK, date, OBS_VALUE) %>%
spread(IDBANK, OBS_VALUE) %>%
transmute(date,
`Biens` = (`010565588` - `010565630`) / `010565707`,
`Biens manufacturés ` = (`010565590` - `010565632`) / `010565707`,
`Biens industriels` = (`010565592` - `010565634`) / `010565707`) %>%
gather(Cna_produit, OBS_VALUE, -date) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Cna_produit)) +
theme_minimal() + xlab("") + ylab("Exportations Nettes (% du PIB)") +
scale_x_date(breaks = seq(1940, 2100, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 500, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.75, 0.9),
legend.title = element_blank()) +
geom_hline(yintercept = 0, linetype = "dashed")
Emploi trimestriel
Code
library("rsdmx")
library("tidyverse")
library("zoo")
library("scales")
library("viridis")
"001791539+001791541+010599703+010600319" %>%
paste0("https://www.bdm.insee.fr/series/sdmx/data/SERIES_BDM/", ., "?startPeriod=2016") %>%
readSDMX %>%
as_tibble %>%
mutate(OBS_VALUE = OBS_VALUE %>% as.numeric,
TIME_PERIOD = TIME_PERIOD %>% as.yearqtr(format = "%Y-Q%q") %>% as.Date) %>%
group_by(TITLE_FR) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[TIME_PERIOD == as.Date("2016-01-01")]) %>%
ggplot + geom_line(aes(x = TIME_PERIOD, y = OBS_VALUE, color = TITLE_FR)) +
scale_x_date(breaks = seq(1960, 2023, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(0, 120, 1)) +
xlab("") + ylab("Emploi Trimestriel (100 = 2016-Q1)") + theme_minimal() +
theme(legend.position = c(0.2, 0.9),
legend.title = element_blank())
Calcul IRL
Code
"001763862" %>%
paste0("https://www.bdm.insee.fr/series/sdmx/data/SERIES_BDM/", ., "?startPeriod=2006") %>%
readSDMX %>%
as_tibble %>%
mutate(OBS_VALUE = OBS_VALUE %>% as.numeric,
date = TIME_PERIOD %>% paste0("-01") %>% as.Date) %>%
arrange(date) %>%
transmute(date,
`Inflation Indice de Référence des Loyers (IRL):\nVariation sur 12 mois de la moyenne sur 12 mois de l'IPC hors loyers, hors tabac` = zoo::rollmean(OBS_VALUE, 12, fill = NA, align = "right"),
`Inflation Indice des Prix à la Consommation (IPC-) hors loyers, hors tabac: \nVariation sur 12 mois de l'IPC hors loyers, hors tabac` = OBS_VALUE) %>%
filter(month(date) %in% c(12, 3, 6, 9)) %>%
gather(variable, value, -date) %>%
group_by(variable) %>%
mutate(value_d12 = value/lag(value, 4) - 1) %>%
na.omit %>%
filter(date >= as.Date("2008-01-01")) %>%
ggplot() + ylab("Inflation") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = value_d12, color = variable)) +
scale_color_manual(values = c("blue", "red")) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.45, 0.9),
legend.title = element_blank(),
legend.key.size= unit(1.0, 'cm')) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
labels = percent_format(accuracy = .1, prefix = ""))
Calcul inflation annuelle pour pension de retraite
formule: croissance de la moyenne entre novembre Y-2 et octobre Y-1 pour la revalorisation du 1er janvier 2023 (001763852)
Code
"001763852" %>%
paste0("https://www.bdm.insee.fr/series/sdmx/data/SERIES_BDM/", ., "?startPeriod=2006") %>%
readSDMX %>%
as_tibble %>%
mutate(OBS_VALUE = OBS_VALUE %>% as.numeric,
date = TIME_PERIOD %>% paste0("-01") %>% as.Date) %>%
select(date, OBS_VALUE) %>%
mutate(date2 = date + months(2),
year = year(date2)) %>%
group_by(year) %>%
summarise(Nobs = n(),
avg = mean(OBS_VALUE)) %>%
mutate(date = as.Date(paste0(year, "-01-01")),
OBS_VALUE = avg/lag(avg)-1) %>%
ggplot() + ylab("croissance de la moyenne entre novembre Y-2 et octobre Y-1\n et novembre Y-1 et octobre Y") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE)) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
labels = percent_format(accuracy = .1, prefix = ""))
Glissement annuel inflation
Code
inflation_ga <- "001761313" %>%
paste0("https://www.bdm.insee.fr/series/sdmx/data/SERIES_BDM/", ., "?startPeriod=2008") %>%
readSDMX %>%
as_tibble %>%
mutate(OBS_VALUE = OBS_VALUE %>% as.numeric,
date = TIME_PERIOD %>% paste0("-01") %>% as.Date) %>%
transmute(date = TIME_PERIOD %>% paste0("-01") %>% as.Date,
OBS_VALUE = OBS_VALUE %>% as.numeric/100,
variable = "Glissement annuel (inflation)") %>%
filter(month(date) == 1)
inflation_ga %>%
ggplot() + ylab("Inflation") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = variable)) +
scale_color_manual(values = c("blue", "red")) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.45, 0.9),
legend.title = element_blank(),
legend.key.size= unit(1.0, 'cm')) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
labels = percent_format(accuracy = .1, prefix = ""))
INSEE Package
Idbank list
Code
get_idbank_list() %>%
head(100) %>%
print_table_conditionalDataset list
Code
get_dataset_list() %>%
print_table_conditionalFrench GDP growth rate, quarter-on-quarter, sa-wda
Code
dataset_list = get_dataset_list()
df_idbank_list_selected =
get_idbank_list("CNT-2014-PIB-EQB-RF") %>% # Gross domestic product balance
filter(FREQ == "T") %>% #quarter
add_insee_title() %>% #add titles
filter(OPERATION == "PIB") %>% #GDP
filter(NATURE == "TAUX") %>% #rate
filter(CORRECTION == "CVS-CJO") #SA-WDA, seasonally adjusted, working day adjusted#
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idbank = df_idbank_list_selected %>% pull(idbank)
data =
get_insee_idbank(idbank)#
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ggplot(data, aes(x = DATE, y = OBS_VALUE)) +
geom_col() +
ggtitle("French GDP growth rate, quarter-on-quarter, sa-wda") +
labs(subtitle = sprintf("Last updated : %s", data$TIME_PERIOD[1]))
Deaths and Births in France since 1948
Code
insee_dataset = get_dataset_list()
list_idbank_selected =
get_idbank_list("DECES-MORTALITE", "NAISSANCES-FECONDITE") %>%
filter(FREQ == "M") %>% #monthly
filter(REF_AREA == "FM") %>% #metropolitan territory
filter(DEMOGRAPHIE %in% c("NAISS", "DECES"))
idbank_selected = list_idbank_selected %>% pull(idbank)
data =
get_insee_idbank(idbank_selected) %>%
split_title() %>%
mutate(period = case_when(DATE < "1975-01-01" ~ "1948 - 1974",
DATE >= "1975-01-01" & DATE < "2000-01-01" ~ "1975 - 1999",
DATE >= "2000-01-01" ~ "2000 - today"
))#
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x_dates = seq.Date(from = as.Date("1940-01-01"), to = Sys.Date(), by = "5 years")
last_date = data %>% pull(DATE) %>% max()
ggplot(data, aes(x = DATE, y = OBS_VALUE, colour = TITLE_EN2)) +
facet_wrap(~period, scales = "free_x", ncol = 1) +
geom_line() +
geom_point(size = 0.9) +
ggtitle("Deaths and Births in France since 1948") +
labs(subtitle = sprintf("Last update : %s", last_date)) +
scale_x_date(breaks = x_dates, date_labels = "%Y") +
theme(
legend.position = "bottom",
legend.title = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
Population map
Code
dataset_list = get_dataset_list()
list_idbank =
get_idbank_list("TCRED-ESTIMATIONS-POPULATION") %>%
filter(AGE == "00-") %>% #all ages
filter(SEXE == 0) %>% #men and women
filter(str_detect(REF_AREA, "^D")) %>% #select only departements
add_insee_title()#
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list_idbank_selected = list_idbank %>% pull(idbank)
# get population data by departement
pop = get_insee_idbank(list_idbank_selected) #
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#get departements' geographical limits
FranceMap <- raster::getData(name = "GADM", country = "FRA", level = 2)
# extract the population by departement in 2020
pop_plot = pop %>%
group_by(TITLE_EN) %>%
filter(DATE == "2020-01-01") %>%
mutate(dptm = gsub("D", "", REF_AREA)) %>%
filter(dptm %in% FranceMap@data$CC_2) %>%
mutate(dptm = factor(dptm, levels = FranceMap@data$CC_2)) %>%
arrange(dptm) %>%
mutate(id = dptm)
vec_pop = pop_plot %>% pull(OBS_VALUE)
# add population data to the departement object map
FranceMap@data$pop = vec_pop
get_area = function(long, lat){
area = areaPolygon(data.frame(long = long, lat = lat)) / 1000000
return(data.frame(area = area))
}
library("geosphere")
# extract the departements' limits from the spatial object and compute the surface
FranceMap_tidy_area <-
broom::tidy(FranceMap) %>%
group_by(id) %>%
group_modify(~get_area(long = .x$long, lat = .x$lat))
FranceMap_tidy <-
broom::tidy(FranceMap) %>%
left_join(FranceMap_tidy_area)
# mapping table
dptm_df = data.frame(dptm = FranceMap@data$CC_2,
dptm_name = FranceMap@data$NAME_2,
pop = FranceMap@data$pop,
id = rownames(FranceMap@data))
FranceMap_tidy_final_all =
FranceMap_tidy %>%
left_join(dptm_df, by = "id") %>%
mutate(pop_density = pop/area) %>%
mutate(density_range = case_when(pop_density < 40 ~ "< 40",
pop_density >= 40 & pop_density < 50 ~ "[40, 50]",
pop_density >= 50 & pop_density < 70 ~ "[50, 70]",
pop_density >= 70 & pop_density < 100 ~ "[70, 100]",
pop_density >= 100 & pop_density < 120 ~ "[100, 120]",
pop_density >= 120 & pop_density < 160 ~ "[120, 160]",
pop_density >= 160 & pop_density < 200 ~ "[160, 200]",
pop_density >= 200 & pop_density < 240 ~ "[200, 240]",
pop_density >= 240 & pop_density < 260 ~ "[240, 260]",
pop_density >= 260 & pop_density < 410 ~ "[260, 410]",
pop_density >= 410 & pop_density < 600 ~ "[410, 600]",
pop_density >= 600 & pop_density < 1000 ~ "[600, 1000]",
pop_density >= 5000 & pop_density < 10000 ~ "[5000, 10000]",
pop_density >= 20000 ~ ">= 20000"
)) %>%
mutate(`people per square kilometer` = factor(density_range,
levels = c("< 40","[40, 50]", "[50, 70]","[70, 100]",
"[100, 120]", "[120, 160]", "[160, 200]",
"[200, 240]", "[240, 260]", "[260, 410]",
"[410, 600]", "[600, 1000]",
"[5000, 10000]", ">= 20000")))
ggplot(data = FranceMap_tidy_final_all,
aes(fill = `people per square kilometer`, x = long, y = lat, group = group) ,
size = 0, alpha = 0.9) +
geom_polygon() +
geom_path(colour = "white") +
coord_map() +
theme_void() +
scale_fill_viridis(discrete = T) +
ggtitle("Distribution of the population within French territory in 2020") +
labs(subtitle = "the density displayed here is an approximation, it should not be considered as an official statistics")
Information
INDICATEUR
Code
"https://www.bdm.insee.fr/series/sdmx/codelist/FR1/CL_INDICATEUR" %>%
readSDMX() %>%
as_tibble %>%
select(1, 3) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}Data Structure Definition (DSD)
Data Structure Definition: https://bdm.insee.fr/series/sdmx/ressource/agence/identifiant/version?parametresFacultatifs.
Example for CNA-2010-FBCF-SI: https://www.bdm.insee.fr/series/sdmx/datastructure/FR1/CNA-2010-FBCF-SI.
CNA-2010-FBCF-SI Components in DSD:
Code
"https://www.bdm.insee.fr/series/sdmx/datastructure/FR1/CNA-2010-FBCF-SI" %>%
readSDMX() %>%
slot("datastructures") %>%
magrittr::extract2(1) %>%
slot("Components") %>%
as_tibble %>%
select(1:4, 7) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}- IPC-2015
Code
"https://www.bdm.insee.fr/series/sdmx/datastructure/FR1/IPC-2015" %>%
readSDMX() %>%
slot("datastructures") %>%
magrittr::extract2(1) %>%
slot("Components") %>%
as_tibble %>%
select(1:4, 7) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}Using IDBANKS and startPeriod
Example 1
Code
"001565183+001690224+000067677" %>%
paste0("https://www.bdm.insee.fr/series/sdmx/data/SERIES_BDM/", ., "?startPeriod=2010") %>%
readSDMX %>%
as_tibble %>%
select(1, 3, TIME_PERIOD, OBS_VALUE) %>%
head(10) %>%
{if (is_html_output()) print_table(.) else .}| IDBANK | TITLE_FR | TIME_PERIOD | OBS_VALUE |
|---|---|---|---|
| 001690224 | Produit intérieur brut total - Volume aux prix de l'année précédente chaînés - Série CVS-CJO - série arrêtée | 2018-Q1 | 547289 |
| 001690224 | Produit intérieur brut total - Volume aux prix de l'année précédente chaînés - Série CVS-CJO - série arrêtée | 2017-Q4 | 545905 |
| 001690224 | Produit intérieur brut total - Volume aux prix de l'année précédente chaînés - Série CVS-CJO - série arrêtée | 2017-Q3 | 542169 |
| 001690224 | Produit intérieur brut total - Volume aux prix de l'année précédente chaînés - Série CVS-CJO - série arrêtée | 2017-Q2 | 539312 |
| 001690224 | Produit intérieur brut total - Volume aux prix de l'année précédente chaînés - Série CVS-CJO - série arrêtée | 2017-Q1 | 535988 |
| 001690224 | Produit intérieur brut total - Volume aux prix de l'année précédente chaînés - Série CVS-CJO - série arrêtée | 2016-Q4 | 532264 |
| 001690224 | Produit intérieur brut total - Volume aux prix de l'année précédente chaînés - Série CVS-CJO - série arrêtée | 2016-Q3 | 529801 |
| 001690224 | Produit intérieur brut total - Volume aux prix de l'année précédente chaînés - Série CVS-CJO - série arrêtée | 2016-Q2 | 528970 |
| 001690224 | Produit intérieur brut total - Volume aux prix de l'année précédente chaînés - Série CVS-CJO - série arrêtée | 2016-Q1 | 529430 |
| 001690224 | Produit intérieur brut total - Volume aux prix de l'année précédente chaînés - Série CVS-CJO - série arrêtée | 2015-Q4 | 526173 |
Chômage Trimestriel
Code
`CHOMAGE-TRIM-NATIONAL` <- "CHOMAGE-TRIM-NATIONAL" %>%
paste0("https://bdm.insee.fr/series/sdmx/data/", .) %>%
readSDMX %>%
as_tibble %>%
select(IDBANK, TIME_PERIOD, OBS_VALUE)
`CHOMAGE-TRIM-NATIONAL` %>%
head(10) %>%
{if (is_html_output()) print_table(.) else .}| IDBANK | TIME_PERIOD | OBS_VALUE |
|---|---|---|
| 001688370 | 2019-Q2 | 300 |
| 001688370 | 2019-Q1 | 314 |
| 001688370 | 2018-Q4 | 291 |
| 001688370 | 2018-Q3 | 352 |
| 001688370 | 2018-Q2 | 325 |
| 001688370 | 2018-Q1 | 313 |
| 001688370 | 2017-Q4 | 323 |
| 001688370 | 2017-Q3 | 345 |
| 001688370 | 2017-Q2 | 348 |
| 001688370 | 2017-Q1 | 327 |