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.packages("BFS")You can also install the development version 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 the official RSS feed. Unfortunately, it seems that not the all public datasets are in the RSS feed, but only the most recently udpated. 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: 180 x 5
## title langu~1 publi~2 url_bfs url_px
## <chr> <chr> <chr> <chr> <chr>
## 1 Businesses by difficulties in recruiting staf~ en Busine~ https:~ https~
## 2 Businesses by difficulties in recruiting staf~ en Busine~ https:~ https~
## 3 Businesses by employment prospects and econom~ en Busine~ https:~ https~
## 4 Businesses by employment prospects and major ~ en Busine~ https:~ https~
## 5 Job vacancies by economic divisions (selectio~ en Job va~ https:~ https~
## 6 Job vacancies by major region en Job va~ https:~ https~
## 7 Jobs by economic division, employment rate an~ en Jobs b~ https:~ https~
## 8 Jobs by major region, economic sector, employ~ en Jobs b~ https:~ https~
## 9 Retail Trade Turnover Statistics - monthly se~ en Retail~ https:~ https~
## 10 Retail Trade Turnover Statistics - quarterly ~ en Retail~ https:~ https~
## # ... with 170 more rows, and abbreviated variable names 1: language,
## # 2: publishedTo 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 langu~1 publi~2 url_bfs url_px
## <chr> <chr> <chr> <chr> <chr>
## 1 University students by year, ISCED field, sex ~ en Univer~ https:~ https~
## # ... with abbreviated variable names 1: language, 2: publishedTo 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: 17,640 x 5
## Year `ISCED Field` Sex `Level of study` Unive~1
## <chr> <chr> <chr> <chr> <dbl>
## 1 1980/81 Education science Male First university degree or diploma 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, advanced studies~ 13
## 6 1980/81 Education science Female First university degree or diploma 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, advanced studies~ 52
## # ... with 17,630 more rows, and abbreviated variable name
## # 1: `University students`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 or CSV files. Note again that only a part
of all the public tables accessible are in the RSS feed (the most
recently updated datasets).
catalog_tables_en <- bfs_get_catalog_tables(language = "en")
catalog_tables_en## # A tibble: 350 x 5
## title langu~1 publi~2 url_bfs url_t~3
## <chr> <chr> <chr> <chr> <chr>
## 1 "Difficulties in recruiting staff with educa~ en "Diffi~ https:~ https:~
## 2 "Difficulties in recruiting staff with educa~ en "Diffi~ https:~ https:~
## 3 "Difficulties in recruiting staff with highe~ en "Diffi~ https:~ https:~
## 4 "Difficulties in recruiting staff with unive~ en "Diffi~ https:~ https:~
## 5 "Full-time job equivalent per sector" en "Full-~ https:~ https:~
## 6 "Full-time job per sector and gender" en "Full-~ https:~ https:~
## 7 "Index of employment evolution prospects per~ en "Index~ https:~ https:~
## 8 "Job vacancy per branch of economic activity~ en "Job v~ https:~ https:~
## 9 "Jobs per sector and gender, gross and seaso~ en "Jobs ~ https:~ https:~
## 10 "Jobs per sector and main region" en "Jobs ~ https:~ https:~
## # ... with 340 more rows, and abbreviated variable names 1: language,
## # 2: published, 3: url_tablelibrary(dplyr)
library(openxlsx)
index_table_url <- catalog_tables_en %>%
filter(grepl("index", title)) %>% # search table
slice(1) %>%
pull(url_table)
df <- tryCatch(expr = openxlsx::read.xlsx(index_table_url, startRow = 1),
error = function(e) "Failed reading table") %>%
as_tibble()
df## # A tibble: 466 x 19
## Landesind~1 X2 X3 X4 X5 ©.Bun~2 X7 Indic~3 X9 Indic~4 X11
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Warenkorbs~ <NA> <NA> <NA> <NA> "Ausku~ <NA> "Panie~ <NA> "Panie~ <NA>
## 2 Basis Deze~ <NA> <NA> <NA> <NA> "http:~ <NA> "Base ~ <NA> "Base ~ <NA>
## 3 Code PosNo PosT~ Level COIC~ "Posit~ PosT~ "Posit~ PosT~ "Posiz~ PosT~
## 4 100_100 100 1 1 <NA> "Total" Total "Total" Total "Total~ Tota~
## 5 100_1 1 2 2 '01 " N~ Nahr~ " A~ Alim~ " P~ Prod~
## 6 100_1001 1001 3 3 '01.1 " ~ Nahr~ " ~ Alim~ " ~ Prod~
## 7 100_1002 1002 3 4 '01.~ " ~ Brot~ " ~ Pain~ " ~ Pane~
## 8 100_1003 1003 4 5 '01.~ " ~ Reis " ~ Riz " ~ Riso
## 9 100_1008 1008 4 5 '01.~ " ~ Mehl~ " ~ Fari~ " ~ Fari~
## 10 100_1014 1014 3 5 <NA> " ~ Brot~ " ~ Pain~ " ~ Pane~
## # ... with 456 more rows, 8 more variables: Swiss.Consumer.Price.Index <chr>,
## # X13 <chr>, Gewicht <chr>, X15 <chr>, X16 <chr>, X17 <chr>, X18 <chr>,
## # X19 <chr>, and abbreviated variable names
## # 1: Landesindex.der.Konsumentenpreise,
## # 2: `©.Bundesamt.für.Statistik,.Espace.de.l'Europe.10,.CH-2010.Neuchâtel`,
## # 3: Indice.des.prix.à.la.consommation,
## # 4: Indice.nazionale.dei.prezzi.al.consumoA 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 and tidyRSS to scrap the official BFS RSS feeds.
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.