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# BFS <img src="man/figures/logo.png" align="right" height="138" />

> Search and download data from the Swiss Federal Statistical Office

The `BFS` package allows to search and download public data from the
<a href="https://www.bfs.admin.ch/bfs/en/home/statistics/catalogue.html"
target="_blank">Swiss Federal Statistical Office</a> (BFS stands for
*Bundesamt für Statistik* in German) APIs in a dynamic and reproducible
way.

## Installation

``` r
install.packages("BFS")
```

You can also install the development version from Github.

``` r
devtools::install_github("lgnbhl/BFS")
```

## Usage

``` r
library(BFS)
```

## Access Swiss Stats Exporer API

To get data from the [Swiss Stats Explorer API](https://stats.swiss/)
(SSE) you first create a query using the metadata codelist from SSE
using the `bfs_get_sse_metadata()` function. First get the codelist of a
BFS dataset:

``` r
codelist <- bfs_get_sse_metadata("DF_LWZ_1", language = "en")

codelist
```

    ## # A tibble: 2,321 × 5
    ##    code      text                             value valueText position_dimension
    ##    <chr>     <chr>                            <chr> <chr>                  <int>
    ##  1 GR_KT_GDE Cantons, districts and municipa… 1     Aeugst a…                  1
    ##  2 GR_KT_GDE Cantons, districts and municipa… 10    Obfelden                   1
    ##  3 GR_KT_GDE Cantons, districts and municipa… 100   Stadel                     1
    ##  4 GR_KT_GDE Cantons, districts and municipa… 1001  Dopplesc…                  1
    ##  5 GR_KT_GDE Cantons, districts and municipa… 1002  Entlebuch                  1
    ##  6 GR_KT_GDE Cantons, districts and municipa… 1004  Flühli                     1
    ##  7 GR_KT_GDE Cantons, districts and municipa… 1005  Hasle (L…                  1
    ##  8 GR_KT_GDE Cantons, districts and municipa… 1007  Romoos                     1
    ##  9 GR_KT_GDE Cantons, districts and municipa… 1008  Schüpfhe…                  1
    ## 10 GR_KT_GDE Cantons, districts and municipa… 1009  Werthens…                  1
    ## # ℹ 2,311 more rows

Then filter the codelist to build your query:

``` r
querydat <- codelist %>% 
  dplyr::filter((code == "GR_KT_GDE" & valueText %in% c("Aarau", "Olten")) |
                  (code == "WOHN_ANZAHL" & valueText == "Total") |
                  (code == "LEERWOHN_TYP" & valueText == "Vacant dwelling for rent") |
                  (code == "MEASURE_DIMENSION" & valueText == "Value"))
query <- tapply(querydat$value, querydat$code, c)

query
```

    ## $GR_KT_GDE
    ## [1] "2581" "4001"
    ## 
    ## $LEERWOHN_TYP
    ## [1] "4"
    ## 
    ## $MEASURE_DIMENSION
    ## [1] "V"
    ## 
    ## $WOHN_ANZAHL
    ## [1] "_T"

This query can be used in the new `bfs_get_sse_data()` function to
import the data.

``` r
bfs_get_sse_data(
  number_bfs = "DF_LWZ_1", 
  language = "en", 
  query =  query,
  start_period = "2020",
  end_period = "2023"
)
```

    ## # A tibble: 8 × 7
    ##   TIME_PERIOD GR_KT_GDE WOHN_ANZAHL LEERWOHN_TYP   MEASURE_DIMENSION FREQ  value
    ##   <chr>       <chr>     <chr>       <chr>          <chr>             <chr> <dbl>
    ## 1 2020        Olten     Total       Vacant dwelli… Value             Annu…   350
    ## 2 2021        Olten     Total       Vacant dwelli… Value             Annu…   393
    ## 3 2022        Olten     Total       Vacant dwelli… Value             Annu…   418
    ## 4 2023        Olten     Total       Vacant dwelli… Value             Annu…   266
    ## 5 2020        Aarau     Total       Vacant dwelli… Value             Annu…   110
    ## 6 2021        Aarau     Total       Vacant dwelli… Value             Annu…    85
    ## 7 2022        Aarau     Total       Vacant dwelli… Value             Annu…    61
    ## 8 2023        Aarau     Total       Vacant dwelli… Value             Annu…    49

### Get the data catalog

Before downloading a BFS dataset, you need to get its related BFS number
(FSO number) in the [official data
catalog](https://www.bfs.admin.ch/bfs/en/home/statistics/catalogue.html).
You can search in the catalog directly from R using the
`bfs_get_catalog_data()` function in any language (“de”, “fr”, “it” or
“en”):

``` r
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… 34248852     2025-03-27       https…
    ## 2 University of applie… en       px-x-1502… 34248849     2025-03-27       https…
    ## 3 University students … en       px-x-1502… 34248664     2025-03-27       https…
    ## 4 University students … en       px-x-1502… 34248666     2025-03-27       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`:

``` r
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$orderNr links
    ##   <chr>              <int> <chr>       <chr>                  <chr>        <lis>
    ## 1 1e58cdb7-01b…    2301224 2025-03-27… University of applied… px-x-150204… <df> 
    ## 2 fa6d167c-6b6…    2301215 2025-03-27… University of applied… px-x-150204… <df> 
    ## 3 dfd53d00-f5c…    2301207 2025-03-27… University students b… px-x-150204… <df> 
    ## 4 8f4fb90d-b6a…    2301195 2025-03-27… University students b… px-x-150204… <df> 
    ## # ℹ abbreviated name: ¹​description$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:

``` r
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]>           1997-2024           This dataset pre…
    ## 2 University of ap… <df [2 × 4]>           1997-2024           This dataset pre…
    ## 3 University stude… <df [2 × 4]>           1990-2024           This dataset pre…
    ## 4 University stude… <df [2 × 4]>           1980-2024           This dataset pre…
    ## # ℹ abbreviated name: ¹​categorization$collection
    ## # ℹ 15 more variables: categorization$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):

``` r
# 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, return_raw = TRUE),
  .f = bfs_get_catalog_data,
)

catalog_all
```

    ## # A tibble: 768 × 5
    ##    ids$uuid     $contentId bfs$embargo description$titles$m…¹ shop$orderNr links
    ##    <chr>             <int> <chr>       <chr>                  <chr>        <lis>
    ##  1 c001d7d3-c8…     325772 2025-09-25… Heiraten und Heiratsh… px-x-010202… <df> 
    ##  2 be1f72da-ab…     189095 2025-09-25… Lebendgeburten nach M… px-x-010202… <df> 
    ##  3 595d7bde-97…     325776 2025-09-25… Scheidungen und Schei… px-x-010202… <df> 
    ##  4 cbc06e96-d8…     189065 2025-09-25… Todesfälle nach Monat… px-x-010202… <df> 
    ##  5 ed95f4e3-4a…   13807205 2025-08-28… Männliche Vornamen de… px-x-010405… <df> 
    ##  6 dcefda9c-b6…   13807212 2025-08-28… Weibliche Vornamen de… px-x-010405… <df> 
    ##  7 4d53b847-9d…     189124 2025-08-27… Auswanderung der stän… px-x-010302… <df> 
    ##  8 010ce6b9-38…     189120 2025-08-27… Auswanderung der stän… px-x-010302… <df> 
    ##  9 baf1b850-e1…     189087 2025-08-27… Auswanderung der stän… px-x-010302… <df> 
    ## 10 a3460776-11…     325764 2025-08-27… Auswanderung der stän… px-x-010302… <df> 
    ## # ℹ 758 more rows
    ## # ℹ abbreviated name: ¹​description$titles$main
    ## # ℹ 16 more variables: ids$gnp <chr>, $damId <int>, $languageCopyId <int>,
    ## #   bfs$lifecycle <df[,4]>, $lifecycleGroup <chr>, $provisional <lgl>,
    ## #   $articleModel <df[,4]>, $articleModelGroup <df[,4]>,
    ## #   $lastUpdatedVersion <chr>, description$titles$sub <chr>,
    ## #   description$categorization <df[,13]>, $bibliography <df[,2]>, …

``` r
# 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](https://www.bfs.admin.ch/bfs/en/home/statistics/catalogue.html)
(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.

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

    ## # A tibble: 18,900 × 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 18,890 more rows

### “Too Many Requests” error message

When running the `bfs_get_data()` function you may get the following
error message (issue [\#7](https://github.com/lgnbhl/BFS/issues/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](https://www.pxweb.bfs.admin.ch/api/v1/de/?config)). 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.

``` r
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:

``` r
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:

``` r
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.

``` r
# 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.

``` r
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](https://www.bfs.admin.ch/bfs/en/home/statistics/catalogue.html)
as “tables” in the “Article Type” dropdown of the BFS website. You can
search for specific tables using `bfs_get_catalog_tables()`.

``` r
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       35008068     2025-03-27       ts-x-15…
    ## 2 Students at universities and … en       34707698     2025-03-27       su-e-15…
    ## 3 Students at universities of a… en       35008067     2025-03-27       ts-x-15…
    ## 4 Students at universities of a… en       34707700     2025-03-27       su-e-15…
    ## 5 Students at universities of t… en       34707695     2025-03-27       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`.

``` r
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`:

``` r
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$orderNr links
    ##   <chr>              <int> <chr>       <chr>                  <chr>        <lis>
    ## 1 2f0d3744-3b1…   14876281 2025-10-27… Student mobility with… su-e-15.02.… <df> 
    ## 2 bf5e392a-e95…   20044168 2025-03-27… Students at universit… ts-x-15.02.… <df> 
    ## 3 c9eb6b70-43f…     528179 2025-03-27… Students at universit… su-e-15.02.… <df> 
    ## 4 36a042c8-b94…   20044200 2025-03-27… Students at universit… ts-x-15.02.… <df> 
    ## 5 28ce8307-668…     528173 2025-03-27… Students at universit… su-e-15.02.… <df> 
    ## 6 d3bc2d74-119…     528176 2025-03-27… Students at universit… su-e-15.02.… <df> 
    ## # ℹ abbreviated name: ¹​description$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]>,
    ## #   $language <chr>, $shortSummary <df[,2]>, $abstractShort <chr>,
    ## #   shop$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:

``` r
library(dplyr)

as_tibble(catalog_tables_raw$description)
```

    ## # A tibble: 6 × 6
    ##   titles$main                categorization$colle…¹ bibliography$period language
    ##   <chr>                      <list>                 <chr>               <chr>   
    ## 1 Student mobility within S… <df [2 × 4]>           2023                EN      
    ## 2 Students at universities … <df [3 × 4]>           1980-2024           DE/EN/F…
    ## 3 Students at universities … <df [3 × 4]>           1990-2024           EN      
    ## 4 Students at universities … <df [3 × 4]>           2000-2024           DE/EN/F…
    ## 5 Students at universities … <df [3 × 4]>           1997-2024           EN      
    ## 6 Students at universities … <df [2 × 4]>           2005-2024           EN      
    ## # ℹ abbreviated name: ¹​categorization$collection
    ## # ℹ 14 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 <df[,2]>, abstractShort <chr>

## Access geodata catalog

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

``` r
library(rstac) # install.packages("rstac")

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”.

``` r
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.

``` r
# 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):

``` r
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")
```

<img style="border:1px solid black;" src="https://raw.githubusercontent.com/lgnbhl/BFS/master/man/figures/leaflet_g1.png" align="center" />

### Cartographic base maps

You can get [cartographic base
maps](https://www.bfs.admin.ch/bfs/en/home/statistics/regional-statistics/base-maps/cartographic-bases.assetdetail.30566934.html)
from the ThemaKart project using `bfs_get_base_maps()`. The list of
available geometries in the [official
documentation](https://www.bfs.admin.ch/bfs/en/home/statistics/regional-statistics/base-maps/cartographic-bases.html).

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

``` r
library(sf) install.packages("sf")

# 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 ThemaKart geodata as updated every year
)
```

A typical base maps ThemaKart file looks like this:

<img style="border:0px solid black;" src="https://raw.githubusercontent.com/lgnbhl/BFS/master/man/figures/base_maps_file.png" align="center" />

All available geometry files in ThemaKart asset can be listed using
`return_sf = FALSE`:

``` r
all_themakart_files <- bfs_get_base_maps(
  return_sf = FALSE, # do NOT return sf object
  asset_number = "30566934", # ThemaKart asset of 2024
  geom = "", 
  category = "", 
  type = "", 
  format = "",
  date = ""
)

length(all_themakart_files) # number of files available
```

    ## [1] 701

For example, all available river files can be found with:

``` r
all_river_files <- bfs_get_base_maps(
    return_sf = FALSE, # do NOT return sf object
    asset_number = "30566934", # ThemaKart asset of 2024
    geom = "flus", # "flus" for river related files
    category = "", 
    type = "", 
    format = "shp",
    date = ""
)
```

The function `bfs_get_base_maps()` eases file selection with arguments
and returns an sf object by default.

``` r
switzerland_sf <- bfs_get_base_maps(geom = "suis")
communes_sf <- bfs_get_base_maps(geom = "polg")
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")
# for some reason rivers don't have a "type" in their file names, so add type = ""
rivers_sf <- bfs_get_base_maps(geom = "flus", type = "", category = "22")

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 = rivers_sf, color = "lightblue2", lwd = 1) +
  geom_sf(data = cantons_capitals_sf, shape = 18, size = 3) +
  theme_minimal() +
  theme(axis.text = element_blank()) +
  labs(caption = "Source: ThemaKart, © BFS")
```

<img style="border:1px solid black;" src="https://raw.githubusercontent.com/lgnbhl/BFS/master/man/figures/base_maps.png" align="center" />

Note that the geometries are available for different date of data
release. By default, `bfs_get_base_maps()` tries to get the most recent
date. You can specify the date using the “date” argument.

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

``` r
library(mapview)

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

<img style="border:1px solid black;" src="https://raw.githubusercontent.com/lgnbhl/BFS/master/man/figures/mapview.png" align="center" />

### Get official list of Swiss municipalities

You can also get the historicized list of Swiss municipalities from the
official BFS API using the new **swissMunicipalities** R package. The
documentation is
[here](https://github.com/SwissStatsR/swissMunicipalities).

``` r
# remotes::install_github("SwissStatsR/swissMunicipalities")
library(swissMunicipalities)
library(dplyr) # just for data wrangling

# snapshot of today list of Swiss municipalites/districts/cantons
snapshot <- swissMunicipalities::get_snapshots(hist_id = TRUE)

municipalities <- snapshot |> 
  filter(Level == 3) |>
  rename_with(~ paste0(.x, "_municipality", recycle0 = TRUE)) |>
  select(-Level_municipality)

districts <- snapshot |> 
  filter(Level == 2) |>
  rename_with(~ paste0(.x, "_district", recycle0 = TRUE)) |>
  select(-Level_district)

cantons <- snapshot |> 
  filter(Level == 1) |>
  rename_with(~ paste0(.x, "_canton", recycle0 = TRUE)) |>
  select(-Level_canton)

# consolidate municipality data with districts and cantons levels
municipalities_consolidated <- municipalities |>
  left_join(districts, by = join_by(Parent_municipality == Identifier_district)) |>
  left_join(cantons, by = join_by(Parent_district == Identifier_canton)) |>
  rename(Identifier_district = Parent_municipality, Identifier_canton = Parent_district) |>
  select(starts_with(c("Identifier", "Name", "ABBREV", "Valid")), everything()) |>
  arrange(Identifier_municipality, Identifier_district)

municipalities_consolidated
```

    # A tibble: 2,131 × 82
       Identifier_municipality Identifier_district Identifier_canton Name_en_municipality Name_fr_municipality
                         <dbl>               <dbl>             <dbl> <chr>                <chr>               
     1                       1                 101                 1 Aeugst am Albis      Aeugst am Albis     
     2                       2                 101                 1 Affoltern am Albis   Affoltern am Albis  
     3                       3                 101                 1 Bonstetten           Bonstetten          
     4                       4                 101                 1 Hausen am Albis      Hausen am Albis     
     5                       5                 101                 1 Hedingen             Hedingen            
     6                       6                 101                 1 Kappel am Albis      Kappel am Albis     
     7                       7                 101                 1 Knonau               Knonau              
     8                       8                 101                 1 Maschwanden          Maschwanden         
     9                       9                 101                 1 Mettmenstetten       Mettmenstetten      
    10                      10                 101                 1 Obfelden             Obfelden            
    # ℹ 2,121 more rows
    # ℹ 77 more variables: Name_de_municipality <chr>, Name_it_municipality <chr>, Name_en_district <chr>,
    #   Name_fr_district <chr>, Name_de_district <chr>, Name_it_district <chr>, Name_en_canton <chr>,
    #   Name_fr_canton <chr>, Name_de_canton <chr>, Name_it_canton <chr>, ABBREV_1_Text_en_municipality <chr>,
    #   ABBREV_1_Text_fr_municipality <chr>, ABBREV_1_Text_de_municipality <chr>, ABBREV_1_Text_it_municipality <chr>,
    #   ABBREV_1_Text_municipality <chr>, ABBREV_1_Text_en_district <chr>, ABBREV_1_Text_fr_district <chr>,
    #   ABBREV_1_Text_de_district <chr>, ABBREV_1_Text_it_district <chr>, ABBREV_1_Text_district <chr>, …
    # ℹ Use `print(n = ...)` to see more rows

You can now use the consolidated list of Swiss municipalities to ease
geodata analysis.

``` r
library(sf)
library(ggplot2)

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

communes_ge <- communes_sf |>
  inner_join(municipalities_consolidated |>
               filter(Name_de_canton == "Genève"), 
             by = c("id" = "Identifier_municipality"))

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")
```

<img style="border:1px solid black;" src="https://raw.githubusercontent.com/lgnbhl/BFS/master/man/figures/base_maps_ge.png" align="center" />

## Main dependencies of the package

Under the hood, this package is using the
<a href="https://ropengov.github.io/pxweb/index.html"
target="_blank">pxweb</a> 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](https://brazil-data-cube.github.io/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](https://github.com/lgnbhl/BFS/issues).
