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

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)

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 = "en")

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] "Net unemployed"
## 
## $description
## [1] "Net unemployed by sex, persons/pct. and time"
## 
## $unit
## [1] "Number"
## 
## $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          sex        TRUE
## 2 PERPCT persons/pct.       FALSE
## 3    Tid         time       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] "Total" "Men" "Women"
##  $ PERPCT:'data.frame':  2 obs. of  2 variables:
##   ..$ id  : chr [1:2] "L10" "L9"
##   ..$ text: chr [1:2] "Per cent of the labour force" "Unemployed (thousands)"
##  $ 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 ...