# setup chunk NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")),"true") knitr::opts_chunk$set(purl = NOT_CRAN) library(insee) library(tidyverse)
library(kableExtra) library(magrittr) library(htmltools) library(prettydoc)
The insee package gathers tools to easily download data and metadata from insee BDM database.
It uses SDMX queries under the hood. Have a look at the detailed SDMX webservice page.
The first version of the package was published on CRAN 2020-07-29.
In order for someone working behind a proxy server to be able to use insee, it is necessary to modify system variables as follow.
Sys.setenv(http_proxy = "my_proxy_server") Sys.setenv(https_proxy = "my_proxy_server")
You can easily install insee with the following code :
install.packages("insee")
You can easily load insee with the following code :
library(insee)
This vignette uses the tidyverse package to exemplify the insee package functionalities with hands-on examples.
library(tidyverse)
This section will give you an overview of what you can do with insee.
Series have two identifiers the SDMX identifier and the so called idbank. Both can be used to download data.
INSEE BDM database offers more than 200 Datasets. The get_dataset_list()
function returns the datasets catalogue :
insee_dataset <- get_dataset_list()
rownames(insee_dataset) <- NULL insee_dataset %>% select(id, Name.en, Name.fr, url, n_series) %>% slice(1:10) %>% kable(row.names=NA) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
INSEE BDM database currently offers more than 140 000 Series. The get_idbank_list
function returns the series catalogue.
idbank_list = get_idbank_list()
rownames(idbank_list) <- NULL idbank_list %>% select(nomflow, idbank, cleFlow) %>% group_by(nomflow) %>% slice(1) %>% ungroup() %>% head(10) %>% kable(row.names=NA) %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
The best way to download data is to find the right series key (idbank), but how ? Indeed, in some cases it is not easy to understand what are the differences among series, especially for non-French speakers.
To make the search easier, the insee package provides the function get_insee_title
to get the title related to an idbank, either in English or in French. It is not advised to use the get_insee_title
function on the whole idbank dataset, as each SDMX query has 400-idbank limit. Then, get_insee_title
function splits the input into several lists of 400 idbanks each and is consequently quite slow. Thus, the user should filter the idbank dataset before using the get_insee_title
function to avoid as much as possible this bottleneck as the following example shows.
idbank_list = get_idbank_list() idbank_list_selected = idbank_list %>% filter(nomflow == "IPI-2015") %>% #industrial production index dataset filter(dim1 == "M") %>% #monthly filter(dim5 == "INDICE") %>% #index filter(dim8 == "CVS-CJO") %>% #Working day and seasonally adjusted SA-WDA #automotive industry and overall industrial production filter(str_detect(dim4,"^29$|A10-BE")) %>% mutate(title = get_insee_title(idbank)) idbank_list_selected
The get_insee_idbank
function should handle up to 400 idbanks but not more.
It is then advised to narrow down the idbanks list used as argument of the function.
library(tidyverse) # the user can make a manual list of idbanks to get the data # example 1 data = get_insee_idbank("001558315", "010540726") # using a list of idbanks extracted from the insee idbank dataset # example 2 : household's confidence survey idbank_dataset = get_idbank_list() df_idbank = idbank_dataset %>% filter(nomflow == "ENQ-CONJ-MENAGES") %>% #monthly households' confidence survey mutate(title = get_insee_title(idbank)) %>% separate(title, sep = " - ", into = paste0("title", 1:4), fill = "right", remove = FALSE) %>% filter(dim7 == "CVS") #seasonally adjusted # dim2: IND_SYNT_CONF - Summary indicator of households' confidence list_idbank = df_idbank %>% pull(idbank) data = get_insee_idbank(list_idbank)
For some datasets as IPC-2015 (inflation), the filter is necessary.
insee_dataset <- get_dataset_list() # example 1 : full dataset data = get_insee_dataset("CLIMAT-AFFAIRES") # example 2 : filtered dataset # the user can filter the data data = get_insee_dataset("IPC-2015", filter = "M+A.........CVS.", startPeriod = "2015-03") # in the filter, the + is used to select several values in one dimension, like an "and" statement # the void means "all" values available # example 3 : only one series # by filtering with the full SDMX series key, the user will get only one series data = get_insee_dataset("CNA-2014-CPEB", filter = "A.CNA_CPEB.A38-CB.VAL.D39.VALEUR_ABSOLUE.FE.EUROS_COURANTS.BRUT", lastNObservations = 10)
library(tidyverse) idbank_list = get_idbank_list() df_idbank_list_selected = idbank_list %>% filter(nomflow == "CNT-2014-PIB-EQB-RF") %>% # Gross domestic product balance filter(dim1 == "T") %>% #quarter filter(dim4 == "PIB") %>% #GDP filter(dim6 == "TAUX") %>% #rate filter(dim10 == "CVS-CJO") #SA-WDA, seasonally adjusted, working day adjusted idbank = df_idbank_list_selected %>% pull(idbank) data = get_insee_idbank(idbank) #plot 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$TIME_PERIOD[1]))
library(tidyverse) library(lubridate) idbank_list = get_idbank_list() df_idbank_list_selected = idbank_list %>% filter(nomflow == "IPC-2015") %>% #Inflation dataset filter(dim1 == "M") %>% # monthly filter(str_detect(dim4, "^[0-9]{2}$")) %>% # coicop aggregation level filter(dim6 == "INDICE") %>% # index filter(dim7 == "ENSEMBLE") %>% # all kinds of household filter(dim8 == "FE") %>% # all France including overseas departements mutate(title = get_insee_title(idbank)) list_idbank = df_idbank_list_selected %>% pull(idbank) data = get_insee_idbank(list_idbank, startPeriod = "2010-01") n_sep = str_count(data$TITLE_FR[1], " - ") + 1 data_plot = data %>% separate(TITLE_EN, into = paste0("title", 1:n_sep), sep = " - ", remove = FALSE, fill = "right") %>% mutate(title6 = case_when(is.na(title6) ~ title5, TRUE ~ as.character(title6))) %>% mutate(title6 = substr(title6, 1 , 22)) %>% mutate(month = month(DATE)) %>% arrange(DATE) %>% group_by(title6, month) %>% mutate(growth = 100 * (OBS_VALUE / dplyr::lag(OBS_VALUE) - 1)) ggplot(data_plot, aes(x = DATE, y = growth)) + geom_col() + facet_wrap(~title6, scales = "free") + ggtitle("French inflation, by product categories, year-on-year") + labs(subtitle = sprintf("Last updated : %s", data_plot$TIME_PERIOD[nrow(data_plot)]))
library(tidyverse) dataset_list = get_dataset_list() idbank_list = get_idbank_list() df_idbank_list_selected = idbank_list %>% filter(nomflow == "CHOMAGE-TRIM-NATIONAL") %>% #Unemployment dataset mutate(title = get_insee_title(idbank)) %>% filter(dim2 == "CTTXC") %>% #unemployment rate based on ILO standards filter(dim4 == "FE") %>% # all France including overseas departements filter(dim5 == 0) # men and women list_idbank = df_idbank_list_selected %>% pull(idbank) data = get_insee_idbank(list_idbank, startPeriod = "2000-01") n_sep = str_count(data$TITLE_FR[1], " - ") + 1 data_plot = data %>% separate(TITLE_EN, into = paste0("title", 1:n_sep), sep = " - ", remove = FALSE, fill = "right") ggplot(data_plot, aes(x = DATE, y = OBS_VALUE, colour = title2)) + geom_line() + geom_point() + ggtitle("French unemployment rate, by age") + labs(subtitle = sprintf("Last updated : %s", data_plot$TIME_PERIOD[1]))
library(tidyverse) dataset_list = get_dataset_list() idbank_list = get_idbank_list() df_idbank_list_selected = idbank_list %>% filter(nomflow == "POPULATION-STRUCTURE") %>% #population dataset mutate(title = get_insee_title(idbank)) %>% filter(dim2 == "POPULATION_1ER_JANVIER") %>% #population at the beginning of the year filter(dim5 == "FE") %>% # all France including overseas departements filter(dim6 == 0) %>% # men and women filter(dim7 %in% c("00-19", "20-59", "60-")) #age ranges list_idbank = df_idbank_list_selected %>% pull(idbank) data = get_insee_idbank(list_idbank) n_sep = str_count(data$TITLE_FR[1], " - ") + 1 data_plot = data %>% separate(TITLE_EN, into = paste0("title", 1:n_sep), sep = " - ", remove = FALSE, fill = "right") %>% mutate(OBS_VALUE = OBS_VALUE / 10^6) ggplot(data_plot, aes(x = DATE, y = OBS_VALUE, fill = title3)) + geom_area() + ggtitle("French population in millions, by age") + labs(subtitle = sprintf("Last updated : %s", data_plot$TIME_PERIOD[1]))
library(insee) library(tidyverse) library(raster) library(rgdal) library(broom) library(viridis) idbank_list = get_idbank_list() dataset_list = get_dataset_list() list_idbank = idbank_list %>% filter(nomflow == "TCRED-ESTIMATIONS-POPULATION") %>% filter(dim6 == "00-") %>% #all population filter(dim5 == 0) %>% #men and women filter(str_detect(dim4, "^D")) %>% #departement mutate(title = get_insee_title(idbank)) list_idbank_selected = list_idbank %>% pull(idbank) # get population data by departement pop = get_insee_idbank(list_idbank_selected) #get departement 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$pop = vec_pop # extract the departement limits from the spatial object FranceMap_tidy <- broom::tidy(FranceMap) # mapping table dptm_df = data.frame(dptm = FranceMap@data$CC_2, dptm_name = FranceMap@data$NAME_2, pop = FranceMap@data$pop, id = rownames(FranceMap@data)) # add population data to departement dataframe FranceMap_tidy_final = FranceMap_tidy %>% left_join(dptm_df, by = "id") %>% select(long, lat, pop, group, id) ggplot() + geom_polygon(data = FranceMap_tidy_final, aes(fill = pop, x = long, y = lat, group = group) , size = 0, alpha = 0.9) + coord_map() + theme_void() + scale_fill_viridis() + ggtitle("Distribution of the population on French territory in 2020")
Feel free to contact me with any question about this package using this e-mail address.
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