BFS

Search and download data from the Swiss Federal Statistical Office

The BFS package allows to search and download public data from the Swiss Federal Statistical Office (BFS stands for Bundesamt für Statistik in German) APIs in a dynamic and reproducible way.

Installation

install.packages("BFS")

You can also install the development version from Github.

devtools::install_github("lgnbhl/BFS")

Usage

library(BFS)

Get the data catalog

Before downloading a BFS dataset, you need to get its related BFS number (FSO number) in the official data catalog. You can search in the catalog directly from R using the bfs_get_catalog_data() function in any language (“de”, “fr”, “it” or “en”):

bfs_get_catalog_data(language = "en", extended_search = "student")

## # A tibble: 4 × 6
##   title                 language number_bfs number_asset publication_date url_px
##   <chr>                 <chr>    <chr>      <chr>        <date>           <chr> 
## 1 University of applie… en       px-x-1502… 31306033     2024-03-28       https…
## 2 University of applie… en       px-x-1502… 31306029     2024-03-28       https…
## 3 University students … en       px-x-1502… 31305852     2024-03-28       https…
## 4 University students … en       px-x-1502… 31305854     2024-03-28       https…

You can search in the data catalog using the following arguments:

language: The language of a BFS catalog, i.e. “de”, “fr”, “it” or “en”.
title: to search in title, subtitle and supertitle.
extended_search: extended search in (sub/super)title, orderNr, summary, shortSummary, shortTextGNP.
spatial_division: choose between “Switzerland”, “Cantons”, “Districts”, “Communes”, “Other spatial divisions” or “International”.
prodima: by specific BFS themes using a unique prodima number.
inquiry: by inquiry number.
institution: by institution.
publishing_year_start: by publishing year start.
publishing_year_end: by publishing year end.
order_nr: by BFS Number (FSO number).
limit: limit of query results (API limit seems to be 350)
article_model_group: article model group
article_model: article model

Note that English (“en”) and Italian (“it”) data catalogs offer a limited list of datasets. For the full list please get the French (“fr”) or German (“de”) data catalogs (see language_available column).

To return all the catalog metadata in the raw (uncleaned) structure, you can add return_raw = TRUE:

catalog_raw <- bfs_get_catalog_data(
  language = "en", 
  extended_search = "student", 
  return_raw = TRUE
)

catalog_raw

## # A tibble: 4 × 5
##   ids$uuid      $contentId bfs$embargo description$titles$m…¹ shop$orderNr links
##   <chr>              <int> <chr>       <chr>                  <chr>        <lis>
## 1 9cb3291f-425…    2301224 2024-03-28… University of applied… px-x-150204… <df> 
## 2 8f65763b-907…    2301215 2024-03-28… University of applied… px-x-150204… <df> 
## 3 4fd81856-d35…    2301207 2024-03-28… University students b… px-x-150204… <df> 
## 4 7d7f1b9e-0c6…    2301195 2024-03-28… University students b… px-x-150204… <df> 
## # ℹ abbreviated name: ¹description$titles$main
## # ℹ 14 more variables: ids$gnp <chr>, $damId <int>, $languageCopyId <int>,
## #   bfs$lifecycle <df[,4]>, $lifecycleGroup <chr>, $provisional <lgl>,
## #   $articleModel <df[,4]>, $articleModelGroup <df[,4]>,
## #   description$categorization <df[,13]>, $bibliography <df[,1]>,
## #   $shortSummary <df[,2]>, $language <chr>, $abstractShort <chr>,
## #   shop$stock <lgl>

The data catalog in a raw structure returns a data.frame containing nested data.frames in some columns. Here an example to get the description nested data.frame as a tibble:

library(dplyr)

as_tibble(catalog_raw$description)

## # A tibble: 4 × 6
##   titles$main       categorization$colle…¹ bibliography$period shortSummary$html
##   <chr>             <list>                 <chr>               <chr>            
## 1 University of ap… <df [2 × 4]>           1997-2023           This dataset pre…
## 2 University of ap… <df [2 × 4]>           1997-2023           This dataset pre…
## 3 University stude… <df [2 × 4]>           1990-2023           This dataset pre…
## 4 University stude… <df [2 × 4]>           1980-2023           This dataset pre…
## # ℹ abbreviated name: ¹categorization$collection
## # ℹ 15 more variables: categorization$prodima <list>, $inquiry <list>,
## #   $spatialdivision <list>, $classification <list>, $institution <list>,
## #   $publisher <list>, $tags <list>, $dataSource <list>, $copyrights <list>,
## #   $termsOfUse <list>, $serie <list>, $periodicity <list>,
## #   shortSummary$raw <chr>, language <chr>, abstractShort <chr>

As the API limit is 350 results, you can get the full data catalog by looping on specific parameters. For example, you can loop over all prodima numbers (equivalent to BFS themes):

# themes_names <- c("Statistical basis and overviews 00", "Population 01", "Territory and environment 02", "Work and income 03", "National economy 04", "Prices 05", "Industry and services 06", "Agriculture and forestry 07", "Energy 08", "Construction and housing 09", "Tourism 10", "Mobility and transport 11", "Money, banks and insurance 12", "Social security 13", "Health 14", "Education and science 15", "Culture, media, information society, sports 16", "Politics 17", "General Government and finance 18", "Crime and criminal justice 19", "Economic and social situation of the population 20", "Sustainable development, regional and international disparities 21")
themes_prodima <- c(900001, 900010, 900035, 900051, 900075, 900084, 900092, 900104, 900127, 900140, 900160, 900169, 900191, 900198, 900210, 900212, 900214, 900226, 900239, 900257, 900269, 900276)

library(purrr)

catalog_all <- purrr::pmap_dfr(
  .l = list(language = "de", prodima = themes_prodima),
  .f = bfs_get_catalog_data,
)

catalog_all

## # A tibble: 760 × 8
##    title                language number_bfs number_asset publication_date url_px
##    <chr>                <chr>    <chr>      <chr>        <date>           <chr> 
##  1 Heiraten und Heirat… de       px-x-0102… 32506838     2024-09-26       https…
##  2 Lebendgeburten nach… de       px-x-0102… 32506840     2024-09-26       https…
##  3 Scheidungen und Sch… de       px-x-0102… 32506841     2024-09-26       https…
##  4 Todesfälle nach Mon… de       px-x-0102… 32506839     2024-09-26       https…
##  5 Männliche Vornamen … de       px-x-0104… 32187356     2024-08-23       https…
##  6 Weibliche Vornamen … de       px-x-0104… 32187357     2024-08-23       https…
##  7 Auswanderung der st… de       px-x-0103… 32208056     2024-08-22       https…
##  8 Auswanderung der st… de       px-x-0103… 32208055     2024-08-22       https…
##  9 Auswanderung der st… de       px-x-0103… 32208061     2024-08-22       https…
## 10 Auswanderung der st… de       px-x-0103… 32208057     2024-08-22       https…
## # ℹ 750 more rows
## # ℹ 2 more variables: url_structure_json <chr>, damId <int>

# to not overload the server, please save the data frame locally
# readr::write_csv(catalog_all, "catalog_all.csv") 
# catalog_all <- readr::read_csv("catalog_all.csv")

Please use this loop moderately to not overload BFS server unnecessarily (just run it when needed and save the result locally).

Download data in any language

The function bfs_get_data() allows you to download any dataset from the BFS catalog (equivalent to selecting “data” in the “Article Type” dropdown of the BFS website) using its BFS number (FSO number).

Using the number_bfs argument (FSO number), you can get BFS data in a given language (“en”, “de”, “fr” or “it”) from the official PXWeb API of the Swiss Federal Statistical Office.

#catalog_student$number_bfs[1] # px-x-1502040100_131
bfs_get_data(number_bfs = "px-x-1502040100_131", language = "en")

## # A tibble: 18,480 × 5
##    Year    `ISCED Field`     Sex    `Level of study`       `University students`
##    <chr>   <chr>             <chr>  <chr>                                  <dbl>
##  1 1980/81 Education science Male   First university degr…                   545
##  2 1980/81 Education science Male   Bachelor                                   0
##  3 1980/81 Education science Male   Master                                     0
##  4 1980/81 Education science Male   Doctorate                                 93
##  5 1980/81 Education science Male   Further education, ad…                    13
##  6 1980/81 Education science Female First university degr…                   946
##  7 1980/81 Education science Female Bachelor                                   0
##  8 1980/81 Education science Female Master                                     0
##  9 1980/81 Education science Female Doctorate                                 70
## 10 1980/81 Education science Female Further education, ad…                    52
## # ℹ 18,470 more rows

“Too Many Requests” error message

When running the bfs_get_data() function you may get the following error message (issue #7).

Error in pxweb_advanced_get(url = url, query = query, verbose = verbose) : 
  Too Many Requests (RFC 6585) (HTTP 429).

This could happen because you ran too many times a bfs_get_*() function (API config is here). A solution is to wait a few seconds before running the next bfs_get_*() function. You can add a delay in your R code using the delay argument.

bfs_get_data(
  number_bfs = "px-x-1502040100_131", 
  language = "en", 
  delay = 10
)

If the error message remains, it could be because you are querying a very large BFS dataset. Two workarounds exist: a) download the BFS file using bfs_download_asset() to read it locally or b) query only specific elements of the data to reduce the API call (see next section).

Here an example using the bfs_download_asset() function:

BFS::bfs_download_asset(
  number_bfs = "px-x-1502040100_131", #number_asset also possible
  destfile = "px-x-1502040100_131.px"
)

library(pxR) # install.packages("pxR")
large_dataset <- pxR::read.px(filename = "px-x-1502040100_131.px") |>
  as.data.frame()

Note that reading a PX file using pxR::read.px() gives access only to the German version.

Query specific elements

First you want to get the metadata of your dataset, i.e. the variables (code and text) and dimensions (values and valueTexts). For example:

metadata <- bfs_get_metadata(number_bfs = "px-x-1502040100_131", language = "en")

# tidy metadata
library(dplyr)
library(tidyr) # for unnest_longer

metadata_tidy <- metadata |>
  unnest_longer(c(values, valueTexts))

metadata_tidy

## # A tibble: 92 × 7
##    code  text  values valueTexts time  elimination
##    <chr> <chr> <chr>  <chr>      <lgl> <lgl>      
##  1 Jahr  Year  0      1980/81    TRUE  NA         
##  2 Jahr  Year  1      1981/82    TRUE  NA         
##  3 Jahr  Year  2      1982/83    TRUE  NA         
##  4 Jahr  Year  3      1983/84    TRUE  NA         
##  5 Jahr  Year  4      1984/85    TRUE  NA         
##  6 Jahr  Year  5      1985/86    TRUE  NA         
##  7 Jahr  Year  6      1986/87    TRUE  NA         
##  8 Jahr  Year  7      1987/88    TRUE  NA         
##  9 Jahr  Year  8      1988/89    TRUE  NA         
## 10 Jahr  Year  9      1989/90    TRUE  NA         
## # ℹ 82 more rows
## # ℹ 1 more variable: title <chr>

Then you can filter the dimensions you want to query using the text and valueTexts variables and build the query dimension object with the code and values variables.

# select dimensions
dim1 <- metadata_tidy |>
  filter(text == "Year" & valueTexts %in% c("2020/21", "2021/22"))
dim2 <- metadata_tidy |>
  filter(text == "Level of study" & valueTexts %in% c("Master", "Doctorate"))
dim3 <- metadata_tidy |>
  filter(text == "ISCED Field" & valueTexts %in% c("Education science"))
dim4 <- metadata_tidy |>
  filter(text == "Sex") # all valueTexts dimensions

# build dimensions list object
dimensions <- list(
  dim1$values,
  dim2$values,
  dim3$values,
  dim4$values
)

names(dimensions) <- c(
  unique(dim1$code), 
  unique(dim2$code), 
  unique(dim3$code), 
  unique(dim4$code)
)

dimensions

## $Jahr
## [1] "40" "41"
## 
## $Studienstufe
## [1] "2" "3"
## 
## $`ISCED Fach`
## [1] "0"
## 
## $Geschlecht
## [1] "0" "1"

Finally you can query BFS data with specific dimensions.

BFS::bfs_get_data(
  number_bfs = "px-x-1502040100_131",
  language = "en",
  query = dimensions
  )

## # A tibble: 8 × 5
##   Year    `ISCED Field`     Sex    `Level of study` `University students`
##   <chr>   <chr>             <chr>  <chr>                            <dbl>
## 1 2020/21 Education science Male   Master                             151
## 2 2020/21 Education science Male   Doctorate                          121
## 3 2020/21 Education science Female Master                             555
## 4 2020/21 Education science Female Doctorate                          306
## 5 2021/22 Education science Male   Master                             143
## 6 2021/22 Education science Male   Doctorate                          115
## 7 2021/22 Education science Female Master                             599
## 8 2021/22 Education science Female Doctorate                          318

Catalog of tables

A lot of datasets are not accessible through the official PXWeb API. They are listed in the data catalog as “tables” in the “Article Type” dropdown of the BFS website. You can search for specific tables using bfs_get_catalog_tables().

catalog_tables_en_students <- bfs_get_catalog_tables(language = "en", extended_search = "students")

catalog_tables_en_students

## # A tibble: 5 × 5
##   title                          language number_asset publication_date order_nr
##   <chr>                          <chr>    <chr>        <date>           <chr>   
## 1 Students at universities and … en       31826381     2024-05-01       ts-x-15…
## 2 Students at universities of a… en       31826380     2024-05-01       ts-x-15…
## 3 Students at universities and … en       31185431     2024-03-28       su-e-15…
## 4 Students at universities of a… en       31185438     2024-03-28       su-e-15…
## 5 Students at universities of t… en       31185427     2024-03-28       su-e-15…

Most of the BFS tables are Excel or CSV files. You can download an table with bfs_download_asset() using the number asset.

library(dplyr)

tables_asset_number_students <- catalog_tables_en_students |>
  dplyr::filter(title == "Students at universities and institutes of technology: Basistables") |>
  dplyr::pull(number_asset)

file_path <- BFS::bfs_download_asset(
  number_asset = tables_asset_number_students,
  destfile = "su-e-15.02.04.01.xlsx"
)

To return all the catalog metadata in the raw (uncleaned) structure, you can add return_raw = TRUE:

catalog_tables_raw <- bfs_get_catalog_tables(
  language = "en", 
  extended_search = "student", 
  return_raw = TRUE
)

catalog_tables_raw

## # A tibble: 6 × 5
##   ids$uuid      $contentId bfs$embargo description$titles$m…¹ shop$orderNr links
##   <chr>              <int> <chr>       <chr>                  <chr>        <lis>
## 1 7a604831-d27…   20044168 2024-05-01… Students at universit… ts-x-15.02.… <df> 
## 2 ac4e3021-db4…   20044200 2024-05-01… Students at universit… ts-x-15.02.… <df> 
## 3 5e328530-77f…     528179 2024-03-28… Students at universit… su-e-15.02.… <df> 
## 4 6e27402b-8dc…     528173 2024-03-28… Students at universit… su-e-15.02.… <df> 
## 5 1e86c267-5f9…     528176 2024-03-28… Students at universit… su-e-15.02.… <df> 
## 6 ab482495-fc9…   14876281 2023-11-02… Student mobility with… su-e-15.02.… <df> 
## # ℹ abbreviated name: ¹description$titles$main
## # ℹ 14 more variables: ids$gnp <chr>, $damId <int>, $languageCopyId <int>,
## #   bfs$lifecycle <df[,4]>, $lifecycleGroup <chr>, $provisional <lgl>,
## #   $articleModel <df[,4]>, $articleModelGroup <df[,4]>,
## #   description$categorization <df[,13]>, $bibliography <df[,1]>,
## #   $shortSummary <df[,2]>, $language <chr>, $abstractShort <chr>,
## #   shop$stock <lgl>

The data catalog in a raw structure returns a data.frame containing nested data.frames in some columns. Here an example to get the description nested data.frame as a tibble:

library(dplyr)

as_tibble(catalog_tables_raw$description)

## # A tibble: 6 × 6
##   titles$main       categorization$colle…¹ bibliography$period shortSummary$html
##   <chr>             <list>                 <chr>               <chr>            
## 1 Students at univ… <df [3 × 4]>           1980-2023           <p>Descriptions …
## 2 Students at univ… <df [3 × 4]>           2000-2023           <p>Descriptions …
## 3 Students at univ… <df [3 × 4]>           1990-2023           <NA>             
## 4 Students at univ… <df [3 × 4]>           1997-2023           <NA>             
## 5 Students at univ… <df [2 × 4]>           2005-2023           <NA>             
## 6 Student mobility… <df [2 × 4]>           2021                <NA>             
## # ℹ abbreviated name: ¹categorization$collection
## # ℹ 15 more variables: categorization$prodima <list>, $inquiry <list>,
## #   $spatialdivision <list>, $classification <list>, $institution <list>,
## #   $publisher <list>, $tags <list>, $dataSource <list>, $copyrights <list>,
## #   $termsOfUse <list>, $serie <list>, $periodicity <list>,
## #   shortSummary$raw <chr>, language <chr>, abstractShort <chr>

Access geodata catalog

Display geo-information catalog of the Swiss Official STAC API using bfs_get_catalog_geodata().

catalog_geodata <- bfs_get_catalog_geodata(include_metadata = TRUE)

catalog_geodata

## # A tibble: 281 × 12
##    collection_id     type  href  title description created updated crs   license
##    <chr>             <chr> <chr> <chr> <chr>       <chr>   <chr>   <chr> <chr>  
##  1 ch.are.agglomera… API   http… Citi… "The list … 2021-1… 2023-0… http… propri…
##  2 ch.are.alpenkonv… API   http… Alpi… "The perim… 2021-1… 2022-0… http… propri…
##  3 ch.are.belastung… API   http… Load… "Passenger… 2021-1… 2022-0… http… propri…
##  4 ch.are.belastung… API   http… Load… "Passenger… 2021-1… 2022-0… http… propri…
##  5 ch.are.belastung… API   http… Load… "Vehicles … 2021-1… 2022-0… http… propri…
##  6 ch.are.belastung… API   http… Load… "Vehicles … 2021-1… 2022-0… http… propri…
##  7 ch.are.erreichba… API   http… Acce… "Accessibi… 2021-1… 2022-0… http… propri…
##  8 ch.are.erreichba… API   http… Acce… "Accessibi… 2021-1… 2022-0… http… propri…
##  9 ch.are.gemeindet… API   http… Typo… "The typol… 2021-1… 2022-0… http… propri…
## 10 ch.are.gueteklas… API   http… Publ… "The publi… 2021-1… 2023-0… http… propri…
## # ℹ 271 more rows
## # ℹ 3 more variables: provider_name <chr>, bbox <list>, inverval <list>

Download geodata

For example you can get information about the dataset “Generalised borders G1 and area with urban character”.

library(dplyr)

geodata_g1 <- catalog_geodata |>
  filter(title == "Generalised borders G1 and area with urban character")
  
geodata_g1

## # A tibble: 1 × 12
##   collection_id      type  href  title description created updated crs   license
##   <chr>              <chr> <chr> <chr> <chr>       <chr>   <chr>   <chr> <chr>  
## 1 ch.bfs.generalisi… API   http… Gene… Administra… 2022-0… 2023-0… http… propri…
## # ℹ 3 more variables: provider_name <chr>, bbox <list>, inverval <list>

Download dataset by collection id with bfs_download_geodata() and unzip file if needed.

# Access Generalised borders G1 and area with urban character
borders_g1_path <- bfs_download_geodata(
  collection_id = "ch.bfs.generalisierte-grenzen_agglomerationen_g1", 
  output_dir = tempdir() #  temporary directory
)

# you may need to unzip the file
unzip(borders_g1_path[4], exdir = "borders_G1")

By default, the files are downloaded in a temporary directory. You can specify the folder where saving the files using the output_dir argument.

Some layers are accessible using WMS (Web Map Service):

library(leaflet)

leaflet() %>% 
  setView(lng = 8, lat = 46.8, zoom = 8) %>%
  addWMSTiles(
    baseUrl = "https://wms.geo.admin.ch/?", 
    layers = "ch.bfs.generalisierte-grenzen_agglomerationen_g2",
    options = WMSTileOptions(format = "image/png", transparent = TRUE),
    attribution = "Generalised borders G1 © 2024 BFS")

Cartographic base maps

You can get cartographic base maps from the ThemaKart project using bfs_get_base_maps(). The list of available geometries in the official documentation.

The default arguments of bfs_get_base_maps() can be change to access specific files:

# default arguments
bfs_get_base_maps(
  geom = NULL,
  category = "gf", # "gf" for total area (i.e. "Gesamtflaeche")
  type = "Poly",
  date = NULL,
  most_recent = TRUE, #get most recent file by default
  format = "shp",
  asset_number = "24025646" #change to get older ThemaKart data
)

A typical base maps ThemaKart file looks like this:

# list of geometry names: https://www.bfs.admin.ch/asset/en/24025645
switzerland_sf <- bfs_get_base_maps(geom = "suis")
communes_sf <- bfs_get_base_maps(geom = "polg", date = "20230101")
districts_sf <- bfs_get_base_maps(geom = "bezk")
cantons_sf <- bfs_get_base_maps(geom = "kant")
cantons_capitals_sf <- bfs_get_base_maps(geom = "stkt", type = "Pnts", category = "kk")
lakes_sf <- bfs_get_base_maps(geom = "seen", category = "11")

library(ggplot2)

ggplot() + 
  geom_sf(data = communes_sf, fill = "snow", color = "grey45") + 
  geom_sf(data = lakes_sf, fill = "lightblue2", color = "black") +
  geom_sf(data = districts_sf, fill = "transparent", color = "grey65") + 
  geom_sf(data = cantons_sf, fill = "transparent", color = "black") +
  geom_sf(data = cantons_capitals_sf, shape = 18, size = 3) +
  theme_minimal() +
  theme(axis.text = element_blank()) +
  labs(caption = "Source: ThemaKart, © BFS")

You can create an interactive map easily with the mapview R package.

library(mapview)

BFS::bfs_get_base_maps(geom = "bezk") |>
  mapview(zcol = "name", legend = FALSE)

Swiss Official Commune Register

The package also contains the official Swiss official commune registers for different administrative levels:

register_gde
register_gde_other
register_bzn
register_kt
register_kt_seeanteile
register_dic

# commune register data
BFS::register_gde

## # A tibble: 2,136 × 8
##    GDEKT GDEBZNR GDENR GDENAME            GDENAMK      GDEBZNA GDEKTNA GDEMUTDAT
##    <chr>   <dbl> <dbl> <chr>              <chr>        <chr>   <chr>   <chr>    
##  1 ZH        101     1 Aeugst am Albis    Aeugst am A… Bezirk… Zürich  1976-11-…
##  2 ZH        101     2 Affoltern am Albis Affoltern a… Bezirk… Zürich  1848-09-…
##  3 ZH        101     3 Bonstetten         Bonstetten   Bezirk… Zürich  1848-09-…
##  4 ZH        101     4 Hausen am Albis    Hausen am A… Bezirk… Zürich  1911-01-…
##  5 ZH        101     5 Hedingen           Hedingen     Bezirk… Zürich  1848-09-…
##  6 ZH        101     6 Kappel am Albis    Kappel am A… Bezirk… Zürich  1911-01-…
##  7 ZH        101     7 Knonau             Knonau       Bezirk… Zürich  1848-09-…
##  8 ZH        101     8 Maschwanden        Maschwanden  Bezirk… Zürich  1848-09-…
##  9 ZH        101     9 Mettmenstetten     Mettmenstet… Bezirk… Zürich  1848-09-…
## 10 ZH        101    10 Obfelden           Obfelden     Bezirk… Zürich  1848-09-…
## # ℹ 2,126 more rows

You can use registers to ease geodata analysis.

library(dplyr)
library(sf)

communes_sf <- bfs_get_base_maps(geom = "polg", date = "20230101")

communes_ge <- communes_sf |>
  inner_join(BFS::register_gde |> 
               filter(GDEKTNA == "Genève"), 
             by = c("id" = "GDENR"))

bbox_ge <- sf::st_bbox(communes_ge)

lake_leman <- bfs_get_base_maps(geom = "seen", category = "11") |>
  filter(name == "Lac Léman")

communes_ge |> 
  ggplot() + 
  geom_sf(data = lake_leman, fill = "lightblue2", color = "grey65") +
  geom_sf(fill = "snow", color = "grey65") + 
  geom_sf_text(aes(label = name), size = 3, check_overlap = T) + 
  # bounding box
  coord_sf(
    xlim = c(bbox_ge$xmin, bbox_ge$xmax),
    ylim = c(bbox_ge$ymin, bbox_ge$ymax)
  ) +
  theme_minimal() +
  theme(axis.text = element_blank()) +
  labs(title = "Communes du canton de Genève",
       x = NULL, y = NULL, 
       caption = "Source: ThemaKart, © BFS")

Main dependencies of the package

Under the hood, this package is using the pxweb package to query the Swiss Federal Statistical Office PXWEB API. PXWEB is an API structure developed by Statistics Sweden and other national statistical institutions (NSI) to disseminate public statistics in a structured way. To query the Geo Admin STAC API, this package is using the rstac package. STAC is a specification of files and web services used to describe geospatial information assets.

You can clean the column names of the datasets automatically using janitor::clean_names() by adding the argument clean_names = TRUE in the bfs_get_data() function.

Other information

This package is in no way officially related to or endorsed by the Swiss Federal Statistical Office (BFS).

Contribute

Any contribution is strongly appreciated. Feel free to report a bug, ask any question or make a pull request for any remaining issue.

Name		Name	Last commit message	Last commit date
Latest commit History 371 Commits
.github		.github
R		R
data-raw/swiss_register		data-raw/swiss_register
data		data
docs		docs
man		man
pkgdown/favicon		pkgdown/favicon
tests		tests
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
BFS.Rproj		BFS.Rproj
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
DESCRIPTION		DESCRIPTION
LICENSE.md		LICENSE.md
NAMESPACE		NAMESPACE
NEWS.md		NEWS.md
README.Rmd		README.Rmd
README.md		README.md
_pkgdown.yml		_pkgdown.yml
codecov.yml		codecov.yml
codemeta.json		codemeta.json

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

BFS

Installation

Usage

Get the data catalog

Download data in any language

“Too Many Requests” error message

Query specific elements

Catalog of tables

Access geodata catalog

Download geodata

Cartographic base maps

Swiss Official Commune Register

Main dependencies of the package

Other information

Contribute

About

Releases 7

Packages

Contributors 6

Languages

License

lgnbhl/BFS

Folders and files

Latest commit

History

Repository files navigation

BFS

Installation

Usage

Get the data catalog

Download data in any language

“Too Many Requests” error message

Query specific elements

Catalog of tables

Access geodata catalog

Download geodata

Cartographic base maps

Swiss Official Commune Register

Main dependencies of the package

Other information

Contribute

About

Topics

Resources

License

Code of conduct

Stars

Watchers

Forks

Releases 7

Packages 0

Contributors 6

Languages

Packages