README.md
In rOpenGov/dkstat: API connection to the StatBank from Statistics Denmark.
dkstat 
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.
A short message in Danish
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.
Installation
You can only install the development version from github, using Hadley Wickham's devtools package:
if(!require("devtools")) install.packages("devtools")
library("devtools")
install_github("rOpenGov/dkstat")
Examples
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.
Four basic function
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
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
## 396 CFABNP
## 901 NAN1
## 905 NAHL2
## 906 NAHO2
## 907 NAHD21
## 955 NKN1
## 959 NKHO2
## 973 NRHP
## text
## 396 FoU udgifter i pct. af BNP
## 901 Forsyningsbalance, Bruttonationalprodukt (BNP), beskæftigelse mv.
## 905 1-2.1.1 Produktion, BNP og indkomstdannelse (hovedposter)
## 906 1-2.1.1 Produktion, BNP og indkomstdannelse (oversigt)
## 907 1 Produktion og BNP (detaljeret)
## 955 Forsyningsbalance, Bruttonationalprodukt (BNP), beskæftigelse mv.
## 959 1-2.1.1 Produktion, BNP og indkomstdannelse
## 973 1-2.1.1 Produktion, BNP og indkomstdannelse
## unit updated firstPeriod
## 396 Procent af bruttonationalprodukt (BNP) 2015-01-30T09:00:00 1997
## 901 - 2015-06-30T09:00:00 1966
## 905 Mio. kr. 2015-06-30T09:00:00 1966
## 906 Mio. kr. 2015-06-30T09:00:00 1995
## 907 Mio. kr. 2014-11-06T09:00:00 1995
## 955 - 2015-08-31T09:00:00 1990K1
## 959 Mio. kr. 2015-08-31T09:00:00 1990K1
## 973 - 2014-12-16T09:00:00 1993
## latestPeriod active variables
## 396 2013 TRUE pct af BNP, tid
## 901 2014 TRUE transaktion, prisenhed, tid
## 905 2014 TRUE transaktion, prisenhed, tid
## 906 2014 TRUE transaktion, prisenhed, tid
## 907 2013 TRUE transaktion, prisenhed, tid
## 955 2015K2 TRUE transaktion, prisenhed, sæsonkorrigering, tid
## 959 2015K2 TRUE transaktion, prisenhed, sæsonkorrigering, tid
## 973 2013 TRUE område, transaktion, prisenhed, tid
Download the tables
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
## 1 FOLK1 Folketal den 1. i kvartalet Antal
## 2 FOLK2 Folketal 1. januar Antal
## 3 FOLK3 Folketal 1. januar Antal
## 4 FT Folketal (summariske tal fra folketællinger) Antal
## 5 BEF5F Personer født på Færøerne og bosat i Danmark 1. januar Antal
## 6 BEF5G Personer født i Grønland og bosat i Danmark 1. januar Antal
## updated firstPeriod latestPeriod active
## 1 2015-08-11T09:00:00 2008K1 2015K3 TRUE
## 2 2015-02-10T09:00:00 1980 2015 TRUE
## 3 2015-02-10T09:00:00 2008 2015 TRUE
## 4 2015-02-10T09:00:00 1769 2015 TRUE
## 5 2015-02-10T09:00:00 2008 2015 TRUE
## 6 2015-02-10T09:00:00 2008 2015 TRUE
## variables
## 1 område,køn,alder,civilstand,herkomst,oprindelsesland,statsborgerskab,tid
## 2 alder,køn,herkomst,statsborgerskab,oprindelsesland,tid
## 3 fødselsdag,fødselsmåned,fødselsår,tid
## 4 hovedlandsdele,tid
## 5 køn,alder,forældrenes fødested,tid
## 6 køn,alder,forældrenes fødested,tid
Meta data
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
Basics
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"
##
## $updated
## [1] "2015-06-19T09:00:00"
There's a table id, a short description, a unit description and when the table was updated.
Variables
The variables in the list has a short description of each variable as well as the id. You might want to make sure that you have supplied all the ID's where the elimination columns is equal to FALSE. The IDs where eliminnation is equal FALSE are mandatory.
aulaar_meta$variables
## id text elimination
## 1 KØN køn TRUE
## 2 PERPCT personer/pct. FALSE
## 3 Tid tid FALSE
Values
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': 36 obs. of 2 variables:
## ..$ id : chr [1:36] "1979" "1980" "1981" "1982" ...
## ..$ text: chr [1:36] "1979" "1980" "1981" "1982" ...
Get data
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.
folk1_meta <- dst_meta("folk1", lang = "da")
str(dst_get_data(table = "folk1",
OMRÅDE = c("Hele landet", "København", "Frederiksberg", "Odense"),
STATSB = "Danmark",
TID = "*",
lang = "da",
meta_data = folk1_meta))
## 'data.frame': 124 obs. of 4 variables:
## $ OMRÅDE: chr "Hele landet" "Hele landet" "Hele landet" "Hele landet" ...
## $ STATSB: chr "Danmark" "Danmark" "Danmark" "Danmark" ...
## $ TID : POSIXct, format: "2008-01-01" "2008-04-01" ...
## $ value : int 5177301 5180007 5185500 5190271 5191263 5192575 5198180 5202378 5204798 5205473 ...
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"),
STATSB = "Danmark",
TID = "*")
str(dst_get_data(table = "folk1", query = my_query, lang = "da"))
## 'data.frame': 124 obs. of 4 variables:
## $ OMRÅDE: chr "Hele landet" "Hele landet" "Hele landet" "Hele landet" ...
## $ STATSB: chr "Danmark" "Danmark" "Danmark" "Danmark" ...
## $ TID : POSIXct, format: "2008-01-01" "2008-04-01" ...
## $ value : int 5177301 5180007 5185500 5190271 5191263 5192575 5198180 5202378 5204798 5205473 ...
str(dst_get_data(table = "AUP01", OMRÅDE = c("Hele landet"), TID = "*", lang = "da"))
## 'data.frame': 103 obs. of 3 variables:
## $ OMRÅDE: chr "Hele landet" "Hele landet" "Hele landet" "Hele landet" ...
## $ TID : POSIXct, format: "2007-01-01" "2007-02-01" ...
## $ value : num 4.6 4.5 4.3 4 3.7 3.5 3.2 3 3.1 3 ...
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.
rOpenGov/dkstat documentation built on Oct. 16, 2021, 5:22 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
dkstat
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.
A short message in Danish
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.
Installation
You can only install the development version from github, using Hadley Wickham's devtools package:
if(!require("devtools")) install.packages("devtools")
library("devtools")
install_github("rOpenGov/dkstat")
Examples
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.
Four basic function
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
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
## 396 CFABNP
## 901 NAN1
## 905 NAHL2
## 906 NAHO2
## 907 NAHD21
## 955 NKN1
## 959 NKHO2
## 973 NRHP
## text
## 396 FoU udgifter i pct. af BNP
## 901 Forsyningsbalance, Bruttonationalprodukt (BNP), beskæftigelse mv.
## 905 1-2.1.1 Produktion, BNP og indkomstdannelse (hovedposter)
## 906 1-2.1.1 Produktion, BNP og indkomstdannelse (oversigt)
## 907 1 Produktion og BNP (detaljeret)
## 955 Forsyningsbalance, Bruttonationalprodukt (BNP), beskæftigelse mv.
## 959 1-2.1.1 Produktion, BNP og indkomstdannelse
## 973 1-2.1.1 Produktion, BNP og indkomstdannelse
## unit updated firstPeriod
## 396 Procent af bruttonationalprodukt (BNP) 2015-01-30T09:00:00 1997
## 901 - 2015-06-30T09:00:00 1966
## 905 Mio. kr. 2015-06-30T09:00:00 1966
## 906 Mio. kr. 2015-06-30T09:00:00 1995
## 907 Mio. kr. 2014-11-06T09:00:00 1995
## 955 - 2015-08-31T09:00:00 1990K1
## 959 Mio. kr. 2015-08-31T09:00:00 1990K1
## 973 - 2014-12-16T09:00:00 1993
## latestPeriod active variables
## 396 2013 TRUE pct af BNP, tid
## 901 2014 TRUE transaktion, prisenhed, tid
## 905 2014 TRUE transaktion, prisenhed, tid
## 906 2014 TRUE transaktion, prisenhed, tid
## 907 2013 TRUE transaktion, prisenhed, tid
## 955 2015K2 TRUE transaktion, prisenhed, sæsonkorrigering, tid
## 959 2015K2 TRUE transaktion, prisenhed, sæsonkorrigering, tid
## 973 2013 TRUE område, transaktion, prisenhed, tid
Download the tables
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
## 1 FOLK1 Folketal den 1. i kvartalet Antal
## 2 FOLK2 Folketal 1. januar Antal
## 3 FOLK3 Folketal 1. januar Antal
## 4 FT Folketal (summariske tal fra folketællinger) Antal
## 5 BEF5F Personer født på Færøerne og bosat i Danmark 1. januar Antal
## 6 BEF5G Personer født i Grønland og bosat i Danmark 1. januar Antal
## updated firstPeriod latestPeriod active
## 1 2015-08-11T09:00:00 2008K1 2015K3 TRUE
## 2 2015-02-10T09:00:00 1980 2015 TRUE
## 3 2015-02-10T09:00:00 2008 2015 TRUE
## 4 2015-02-10T09:00:00 1769 2015 TRUE
## 5 2015-02-10T09:00:00 2008 2015 TRUE
## 6 2015-02-10T09:00:00 2008 2015 TRUE
## variables
## 1 område,køn,alder,civilstand,herkomst,oprindelsesland,statsborgerskab,tid
## 2 alder,køn,herkomst,statsborgerskab,oprindelsesland,tid
## 3 fødselsdag,fødselsmåned,fødselsår,tid
## 4 hovedlandsdele,tid
## 5 køn,alder,forældrenes fødested,tid
## 6 køn,alder,forældrenes fødested,tid
Meta data
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
Basics
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"
##
## $updated
## [1] "2015-06-19T09:00:00"
There's a table id, a short description, a unit description and when the table was updated.
Variables
The variables in the list has a short description of each variable as well as the id. You might want to make sure that you have supplied all the ID's where the elimination columns is equal to FALSE. The IDs where eliminnation is equal FALSE are mandatory.
aulaar_meta$variables
## id text elimination
## 1 KØN køn TRUE
## 2 PERPCT personer/pct. FALSE
## 3 Tid tid FALSE
Values
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': 36 obs. of 2 variables:
## ..$ id : chr [1:36] "1979" "1980" "1981" "1982" ...
## ..$ text: chr [1:36] "1979" "1980" "1981" "1982" ...
Get data
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.
folk1_meta <- dst_meta("folk1", lang = "da")
str(dst_get_data(table = "folk1",
OMRÅDE = c("Hele landet", "København", "Frederiksberg", "Odense"),
STATSB = "Danmark",
TID = "*",
lang = "da",
meta_data = folk1_meta))
## 'data.frame': 124 obs. of 4 variables:
## $ OMRÅDE: chr "Hele landet" "Hele landet" "Hele landet" "Hele landet" ...
## $ STATSB: chr "Danmark" "Danmark" "Danmark" "Danmark" ...
## $ TID : POSIXct, format: "2008-01-01" "2008-04-01" ...
## $ value : int 5177301 5180007 5185500 5190271 5191263 5192575 5198180 5202378 5204798 5205473 ...
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"),
STATSB = "Danmark",
TID = "*")
str(dst_get_data(table = "folk1", query = my_query, lang = "da"))
## 'data.frame': 124 obs. of 4 variables:
## $ OMRÅDE: chr "Hele landet" "Hele landet" "Hele landet" "Hele landet" ...
## $ STATSB: chr "Danmark" "Danmark" "Danmark" "Danmark" ...
## $ TID : POSIXct, format: "2008-01-01" "2008-04-01" ...
## $ value : int 5177301 5180007 5185500 5190271 5191263 5192575 5198180 5202378 5204798 5205473 ...
str(dst_get_data(table = "AUP01", OMRÅDE = c("Hele landet"), TID = "*", lang = "da"))
## 'data.frame': 103 obs. of 3 variables:
## $ OMRÅDE: chr "Hele landet" "Hele landet" "Hele landet" "Hele landet" ...
## $ TID : POSIXct, format: "2007-01-01" "2007-02-01" ...
## $ value : num 4.6 4.5 4.3 4 3.7 3.5 3.2 3 3.1 3 ...
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.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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