This package connects to the StatBank API from Statistics Denmark.
This package is in early BETA and new changes will most likely not have backward compatibility.
Denne R Statistics pakke indeholder funktioner til at give dig adgang til data gennem API’en fra Danmarks Statistik. Funktionerne henter data fra Statistikbanken og retunerer data.frame objekter med værdierne du spørger efter i dit funktionskald.
You can install {dkstat} from r-universe with:
install.packages(
"dkstat",
repos = c(
ropengov = "https://ropengov.r-universe.dev",
getOption("repos")
)
)You can install the latest development version of {dkstat} from
GitHub with:
# install.packages("devtools")
devtools::install_github("rOpenGov/dkstat")The default language is danish, but have got a lang parameter that you can change from “da” to “en” if you wan’t the data returned in English.
There are four basic functions to learn:
- dst_search() This function makes it possible to search through the different tables for a word or a phrase.
- dst_tables() This function downloads all the possible tables available.
- dst_meta() This function lets you download the meta data for a specific table, so you can see the description, unit, variables and values you can download data for.
- dst_get_data() lets you download the actual data you wan’t.
Here are a few simple examples that will go through the basics of requesting data from the StatBank and the structure of the output.
First, we’ll load the package:
library(dkstat)The search function let’s you.. OK, you might know this already.
Here I search for gdp in the text field of the tables.
dst_search(string = "bnp", field = "text")## id
## 584 NAN1
## 588 NKN1
## 593 NAHL2
## 596 NKHO2
## 599 NAHO2
## 602 NAHD21
## 669 NRHP
## 670 VNRHP
## 1318 CFABNP
## text
## 584 Forsyningsbalance, bruttonationalprodukt (BNP),økonomisk vækst, beskæftigelse mv.
## 588 Forsyningsbalance, Bruttonationalprodukt (BNP), beskæftigelse mv.
## 593 1-2.1.1 Produktion, BNP og indkomstdannelse (hovedposter)
## 596 1-2.1.1 Produktion, BNP og indkomstdannelse
## 599 1-2.1.1 Produktion, BNP og indkomstdannelse (oversigt)
## 602 1 Produktion og BNP (detaljeret)
## 669 1-2.1.1 Produktion, BNP og indkomstdannelse
## 670 Versionstabel NRHP - Produktion, BNP og indkomstdannelse
## 1318 FoU udgifter i pct. af BNP
## unit updated firstPeriod latestPeriod active
## 584 - 2024-10-03T08:00:00 1966 2023 TRUE
## 588 - 2024-11-20T08:00:00 1990K1 2024K3 TRUE
## 593 Mio. kr. 2024-06-28T08:00:00 1966 2023 TRUE
## 596 Mio. kr. 2024-11-20T08:00:00 1990K1 2024K3 TRUE
## 599 Mio. kr. 2024-06-28T08:00:00 1995 2023 TRUE
## 602 Mio. kr. 2024-06-28T08:00:00 1995 2023 TRUE
## 669 - 2024-10-28T08:00:00 1993 2023 TRUE
## 670 - 2024-10-28T08:00:00 1993 2023 TRUE
## 1318 Pct. af bnp 2023-12-14T08:00:00 1997 2022 TRUE
## variables
## 584 transaktion, prisenhed, tid
## 588 transaktion, prisenhed, sæsonkorrigering, tid
## 593 transaktion, prisenhed, tid
## 596 transaktion, prisenhed, sæsonkorrigering, tid
## 599 transaktion, prisenhed, tid
## 602 transaktion, prisenhed, tid
## 669 område, transaktion, prisenhed, tid
## 670 version, område, transaktion, prisenhed, tid
## 1318 pct af BNP, tid
The dst_get_tables function downloads all the available tables that the search function use when searching for a word or a phrase.
head(dst_get_tables(lang = "da"))## id text unit updated
## 1 FOLK1A Befolkningen den 1. i kvartalet Antal 2024-11-11T08:00:00
## 2 FOLK1AM Befolkningen den 1. i måneden Antal 2024-11-11T08:00:00
## 3 BEFOLK1 Befolkningen 1. januar Antal 2024-02-12T08:00:00
## 4 BEFOLK2 Befolkningen 1. januar Antal 2024-02-12T08:00:00
## 5 FOLK3 Befolkningen 1. januar Antal 2024-02-12T08:00:00
## 6 FOLK3FOD Befolkningen 1. januar Antal 2024-02-12T08:00:00
## firstPeriod latestPeriod active variables
## 1 2008K1 2024K4 TRUE område,køn,alder,civilstand,tid
## 2 2021M10 2024M10 TRUE område,køn,alder,tid
## 3 1971 2024 TRUE køn,alder,civilstand,tid
## 4 1901 2024 TRUE køn,alder,tid
## 5 2008 2024 TRUE fødselsdag,fødselsmåned,fødselsår,tid
## 6 2008 2024 TRUE fødselsdag,fødselsmåned,fødeland,tid
The dst_meta function retrieves meta data from the table you wan’t to take a closer look at. It can be used to create the final request, but if you can figure out the structure of the query you can define it yourself.
We’ll get some meta data from the AULAAR table. The AULAAR table has net unemployment numbers.
aulaar_meta <- dst_meta(table = "AULAAR", lang = "da")The ‘dst_meta’ function returns a list with 4 objects: - basics - variables - values - basic_query
Let’s see what the basics contains:
aulaar_meta$basics## $id
## [1] "AULAAR"
##
## $text
## [1] "Fuldtidsledige (netto)"
##
## $description
## [1] "Fuldtidsledige (netto) efter køn, personer/pct. og tid"
##
## $unit
## [1] "Antal"
##
## $updated
## [1] "2024-04-16T08:00:00"
##
## $footnote
## NULL
There’s a table id, a short description, a unit description and when the table was updated.
The variables in the list has a short description of each variable as well as the id. You might want to make sure that you have supplied all the ID’s where the elimination columns is equal to FALSE. The IDs where eliminnation is equal FALSE are mandatory.
aulaar_meta$variables## id text elimination
## 1 KØN køn TRUE
## 2 PERPCT personer/pct. FALSE
## 3 Tid tid FALSE
The values is a list object of all the values in each variable. You use the text column to construct your final query:
str(aulaar_meta$values)## List of 3
## $ KØN :'data.frame': 3 obs. of 2 variables:
## ..$ id : chr [1:3] "TOT" "M" "K"
## ..$ text: chr [1:3] "I alt" "Mænd" "Kvinder"
## $ PERPCT:'data.frame': 2 obs. of 2 variables:
## ..$ id : chr [1:2] "L10" "L9"
## ..$ text: chr [1:2] "Procent af arbejdsstyrken" "Ledige (1000 personer)"
## $ Tid :'data.frame': 45 obs. of 2 variables:
## ..$ id : chr [1:45] "1979" "1980" "1981" "1982" ...
## ..$ text: chr [1:45] "1979" "1980" "1981" "1982" ...
You need to build your query based on the text column that each variable contains in the meta_data$values list.
aulaar <- dst_get_data(
table = "AULAAR", KØN = "Total", PERPCT = "Per cent of the labour force", Tid = 2013,
lang = "en"
)
str(aulaar)## 'data.frame': 1 obs. of 4 variables:
## $ KØN : chr "Total"
## $ PERPCT: chr "Per cent of the labour force"
## $ TID : POSIXct, format: "2013-01-01"
## $ value : num 4.4
In the request above I don’t supply the meta_data to the dst_get_data function, but this is possible as I will show below. It’s a good idea to supply the meta data to the dst_get_data function if you query the table more than once. If you don’t supply the meta data the dst_get_data function will request the meta data for the table and this will be very ineffecient.
Let’s query the statbank using more than one value for each variable.
folk1a_meta <- dst_meta("folk1a", lang = "da")
str(dst_get_data(
table = "folk1a",
Tid = "*",
CIVILSTAND = "*",
ALDER = "*",
OMRÅDE = c("Hele landet", "København", "Dragør", "Albertslund"),
lang = "da",
meta_data = folk1a_meta
))## 'data.frame': 172720 obs. of 5 variables:
## $ TID : POSIXct, format: "2008-01-01" "2008-01-01" ...
## $ CIVILSTAND: chr "I alt" "I alt" "I alt" "I alt" ...
## $ ALDER : chr "Alder i alt" "Alder i alt" "Alder i alt" "Alder i alt" ...
## $ OMRÅDE : chr "Hele landet" "København" "Dragør" "Albertslund" ...
## $ value : int 5475791 509861 13261 27602 64412 7986 121 295 65722 7097 ...
I can also build a query beforehand and then use the query in the query parameter. This might be a good way to split your script up into smaller pieces and make it more structured.
You might have noticed that I use the * as a value in the TID variable. You can use the star as a alternative to writing all the text values for the variable.
my_query <- list(
OMRÅDE = c("Hele landet", "København", "Frederiksberg", "Odense"),
CIVILSTAND = "Ugift",
TID = "*"
)
str(dst_get_data(table = "folk1a", query = my_query, lang = "da"))## 'data.frame': 272 obs. of 4 variables:
## $ OMRÅDE : chr "Hele landet" "Hele landet" "Hele landet" "Hele landet" ...
## $ CIVILSTAND: chr "Ugift" "Ugift" "Ugift" "Ugift" ...
## $ TID : POSIXct, format: "2008-01-01" "2008-04-01" ...
## $ value : int 2552700 2563134 2564705 2568255 2575185 2584993 2584560 2588198 2593172 2604129 ...
str(dst_get_data(table = "AUP01", OMRÅDE = c("Hele landet"), TID = "*", lang = "da"))## 'data.frame': 87 obs. of 3 variables:
## $ OMRÅDE: chr "Hele landet" "Hele landet" "Hele landet" "Hele landet" ...
## $ TID : POSIXct, format: "2017-07-01" "2017-08-01" ...
## $ value : num 4 4.1 3.9 4 4 4.1 4.2 4.2 4.1 3.7 ...
If you run into problems, then try to set the parse_dst_tid parameter to FALSE as there are few datasets with non-standard date formats.
Don’t hesitate to submit an issue or question on github and I’ll try to help as much as I can.