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) in a dynamic and
reproducible way.
# Install the released version from CRAN
install.packages("BFS")To get a bug fix, or use a feature from the development version, you can install BFS from GitHub.
# install from Github
devtools::install_github("lgnbhl/BFS")library(BFS)To search and download data from the Swiss Federal Statistical Office, you first need to retrieve information about the available public datasets.
You can get the data catalog by language based on their official RSS feed. Unfortunately, it seems that not the all public datasets are in the RSS feed. Note also that Italian and English give access to less datasets.
catalog_data_en <- bfs_get_catalog_data(language = "en")
catalog_data_en## # A tibble: 170 x 5
## title language published url_bfs url_px
## <chr> <chr> <chr> <chr> <chr>
## 1 Permanent resid~ en Permanent resident~ https://www.bfs.~ https://www.~
## 2 Private househo~ en Private households~ https://www.bfs.~ https://www.~
## 3 Hotel accommoda~ en Hotel accommodatio~ https://www.bfs.~ https://www.~
## 4 Hotel accommoda~ en Hotel accommodatio~ https://www.bfs.~ https://www.~
## 5 Hotel accommoda~ en Hotel accommodatio~ https://www.bfs.~ https://www.~
## 6 Hotel sector: S~ en Hotel sector: Supp~ https://www.bfs.~ https://www.~
## 7 Hotel sector: S~ en Hotel sector: Supp~ https://www.bfs.~ https://www.~
## 8 Hotel sector: s~ en Hotel sector: supp~ https://www.bfs.~ https://www.~
## 9 Retail Trade Tu~ en Retail Trade Turno~ https://www.bfs.~ https://www.~
## 10 Retail Trade Tu~ en Retail Trade Turno~ https://www.bfs.~ https://www.~
## # ... with 160 more rows
To find older datasets, you can use the search bar in the official BFS website.
You could use for example dplyr to search for a given dataset.
library(dplyr)
catalog_data_uni <- catalog_data_en %>%
filter(title == "University students by year, ISCED field, sex and level of study")
catalog_data_uni## # A tibble: 1 x 5
## title language published url_bfs url_px
## <chr> <chr> <chr> <chr> <chr>
## 1 University stu~ en University studen~ https://www.bfs.ad~ https://www.b~
To download a BFS dataset, you have two options. You can add the
official BFS URL webpage to the url_bfs argument to the
bfs_get_data(). For example, you can use the URL of a given dataset
you found using bfs_get_catalog_data().
# https://www.bfs.admin.ch/content/bfs/en/home/statistiken/kataloge-datenbanken/daten.assetdetail.16324907.html
df_uni <- bfs_get_data(url_bfs = catalog_data_uni$url_bfs, language = "en")## Downloading large query (in 4 batches):
## | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100%
df_uni## # A tibble: 17,220 x 5
## Year `ISCED Field` Sex `Level of study` `University stud~
## <chr> <chr> <chr> <chr> <dbl>
## 1 1980/81 Education science Man First university degree or~ 545
## 2 1980/81 Education science Man Bachelor 0
## 3 1980/81 Education science Man Master 0
## 4 1980/81 Education science Man Doctorate 93
## 5 1980/81 Education science Man Further education, advance~ 13
## 6 1980/81 Education science Woman First university degree or~ 946
## 7 1980/81 Education science Woman Bachelor 0
## 8 1980/81 Education science Woman Master 0
## 9 1980/81 Education science Woman Doctorate 70
## 10 1980/81 Education science Woman Further education, advance~ 52
## # ... with 17,210 more rows
Note that some datasets are only accessible in German and French.
In case the data is not accessible using bfs_get_catalog_data(), you
can manually add the BFS number in the bfs_get_data() function using
the number_bfs argument.
# open webpage
browseURL("https://www.bfs.admin.ch/content/bfs/en/home/statistiken/kataloge-datenbanken/daten.assetdetail.16324907.html")Use again bfs_get_data() but this time with the number_bfs argument.
bfs_get_data(number_bfs = "px-x-1502040100_131", language = "en")Please privilege the number_bfs argument of the bfs_get_data() if
you want more stable and reproducible code.
You can access additional information about the dataset by running
bfs_get_data_comments().
bfs_get_data_comments(number_bfs = "px-x-1502040100_131", language = "en")## Downloading large query (in 4 batches):
## | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100%
## # A tibble: 1 x 4
## row_no col_no comment_type comment
## <int> <int> <chr> <chr>
## 1 NA 4 column_comment "To ensure that the presentations from cubes con~
A lot of tables are not accessible through the official API, but they
are still present in the official BFS website. You can access the RSS
feed tables
catalog
using bfs_get_catalog_tables(). Most of these tables are Excel files.
Note again that only a part of all the public tables accessible are in
the RSS feed.
catalog_tables_en <- bfs_get_catalog_tables(language = "en")
catalog_tables_en## # A tibble: 350 x 5
## title language published url_bfs url_table
## <chr> <chr> <chr> <chr> <chr>
## 1 Deaths per wee~ en Deaths per week by~ https://www.bfs~ https://www.bf~
## 2 Deaths per wee~ en Deaths per week by~ https://www.bfs~ https://www.bf~
## 3 Weekly number ~ en Weekly number of d~ https://www.bfs~ https://www.bf~
## 4 Permanent resi~ en Permanent resident~ https://www.bfs~ https://www.bf~
## 5 Permanent resi~ en Permanent resident~ https://www.bfs~ https://www.bf~
## 6 Private househ~ en Private households~ https://www.bfs~ https://www.bf~
## 7 Private househ~ en Private households~ https://www.bfs~ https://www.bf~
## 8 CPI (december ~ en CPI (december 2020~ https://www.bfs~ https://www.bf~
## 9 CPI (december ~ en CPI (december 2020~ https://www.bfs~ https://www.bf~
## 10 CPI, Global in~ en CPI, Global index ~ https://www.bfs~ https://www.bf~
## # ... with 340 more rows
library(dplyr)
library(openxlsx)
catalog_tables_en %>%
filter(title == "Admissions to universities of applied sciences at diploma and bachelor level by fields of specialisation") %>%
pull(url_table) %>%
openxlsx::read.xlsx(startRow = 2) %>%
as_tibble()## # A tibble: 20 x 73
## X1 `1997` X3 X4 `1998` X6 X7 `1999` X9 X10 `2000` X12
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Field ~ Women Men "Pro~ Women Men "Pro~ Women Men "Pro~ Women Men
## 2 Total 866 4010 "17.~ 1635 4857 "25.~ 1927 5267 "26.~ 2707 5772
## 3 Archit~ 99 606 "14.~ 90 590 "13.~ 91 532 "14.~ 112 578
## 4 Engine~ 45 1969 "2.2~ 64 2095 "2.9~ 90 2188 "3.9~ 86 2351
## 5 Chemis~ 43 184 "18.~ 71 248 "22.~ 67 193 "25.~ 68 172
## 6 Agricu~ 19 72 "20.~ 19 66 "22.~ 27 76 "26.~ 21 88
## 7 Econom~ 451 1046 "30.~ 544 1350 "28.~ 710 1566 "31.~ 838 1506
## 8 Design 148 101 "59.~ 221 137 "61.~ 226 189 "54.~ 277 196
## 9 Sports 0 0 "*" 0 0 "*" 7 28 "20" 0 0
## 10 Music,~ 0 0 "*" 276 178 "60.~ 269 298 "47.~ 804 675
## 11 Applie~ 0 0 "*" 0 0 "*" 58 X "X" 69 12
## 12 Social~ 61 32 "65.~ 285 171 "62.~ 313 168 "65.~ 390 185
## 13 Applie~ 0 0 "*" 39 21 "65" 39 16 "70.~ 42 9
## 14 Health 0 0 "*" 26 X "X" 30 9 "76.~ 0 0
## 15 Teache~ 0 0 "*" 0 0 "*" 0 0 "*" 0 0
## 16 * Not ~ <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 17 X Not ~ <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 18 Source~ <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 19 © FSO <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 20 Inform~ <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## # ... with 61 more variables: X13 <chr>, 2001 <chr>, X15 <chr>, X16 <chr>,
## # 2002 <chr>, X18 <chr>, X19 <chr>, 2003 <chr>, X21 <chr>, X22 <chr>,
## # 2004 <chr>, X24 <chr>, X25 <chr>, 2005 <chr>, X27 <chr>, X28 <chr>,
## # 2006 <chr>, X30 <chr>, X31 <chr>, 2007 <chr>, X33 <chr>, X34 <chr>,
## # 2008 <chr>, X36 <chr>, X37 <chr>, 2009 <chr>, X39 <chr>, X40 <chr>,
## # 2010 <chr>, X42 <chr>, X43 <chr>, 2011 <chr>, X45 <chr>, X46 <chr>,
## # 2012 <chr>, X48 <chr>, X49 <chr>, 2013 <chr>, X51 <chr>, X52 <chr>, ...
A blog article showing a concrete example about how to use the BFS package and to visualize the data in a Swiss map.
The BFS package is using the pxweb R package under the hood to access the Swiss Federal Statistical Office pxweb API.
This package is in no way officially related to or endorsed by the Swiss Federal Statistical Office (BFS).
Any contribution is strongly appreciated. Feel free to report a bug, ask any question or make a pull request for any remaining issue.