What about danstat?
Last updated on 2025-12-08 | Edit this page
Overview
Questions
- Is there an easier way to access Statistics Denmark?
Objectives
- Use a package to do the API-calls to Statistics Denmark
- Connect to Statistics Denmark, and extract data
- Create a list of lists to control the variables to be extracted
- Using the danstat package
Please note: These pages are autogenerated. Some of the API-calls may fail during that process. We are figuring out what to do about it, but please excuse us for any red errors on the pages for the time being.
Is there an easier way?
Many larger online services provide packages for easier access to their APIs.
Popular services might not have to do this, because enthusiasts write packages themselves.
A package called danstat is available, and makes it
easier to extract data from Statistics Denmark.
The danstat package/library
Previously we retrieved at table with demographic data from Statistics Denmark.
How can we get that table using the danstat package?
Before using the library, we will need to install it:
R
install.packages("danstat")
Some installations of R may have problems installing it. In that case, try this:
R
install.packages("remotes")
library(remotes)
remotes:install_github("cran/danstat")
After installation, we load the library using the library function. And then we can access the functions included in the library:
The danstat package contain four functions, equivalent to the four endpoints we discussed earlier.
The get_subjects() function sends a request to the
Statistics Denmark API, asking for a list of the subjects. The
information is returned to our script, and the
get_subjects() function presents us with a dataframe
containing the information.
R
library(danstat)
subjects <- get_subjects()
subjects
OUTPUT
id description active hasSubjects subjects
1 1 People TRUE TRUE NULL
2 2 Labour and income TRUE TRUE NULL
3 3 Economy TRUE TRUE NULL
4 4 Social conditions TRUE TRUE NULL
5 5 Education and research TRUE TRUE NULL
6 6 Business TRUE TRUE NULL
7 7 Transport TRUE TRUE NULL
8 8 Culture and leisure TRUE TRUE NULL
9 9 Environment and energy TRUE TRUE NULL
10 19 About Statistics Denmark TRUE TRUE NULLWe get the 10 major subjects from Statistics Denmark we have seen before. As before, each of them have sub-subjects.
If we want to take a closer look at the subdivisions of a given
subject, we use the get_subjects() function again, this
time specifying which subject we are interested in:
Let us try to get the sub-subjects from the subject 1 - containing information about populations and elections:
R
sub_subjects <- get_subjects(subjects = 1)
sub_subjects
OUTPUT
id description active hasSubjects
1 1 People TRUE TRUE
subjects
1 3401, 3407, 3410, 3415, 3412, 3411, 3428, 3409, Population, Households and family matters , Migration, Housing, Health, Democracy, National church, Names, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUEThe result is a bit complicated. The column “subjects” in the resulting dataframe contains another dataframe. We access it like we normally would access a column in a dataframe:
R
sub_subjects$subjects
OUTPUT
[[1]]
id description active hasSubjects subjects
1 3401 Population TRUE TRUE NULL
2 3407 Households and family matters TRUE TRUE NULL
3 3410 Migration TRUE TRUE NULL
4 3415 Housing TRUE TRUE NULL
5 3412 Health TRUE TRUE NULL
6 3411 Democracy TRUE TRUE NULL
7 3428 National church TRUE TRUE NULL
8 3409 Names TRUE TRUE NULLWe can continue diving into this, and will end up with subject “20021 Population figures”.
Which datatables exists?
We ended up with a specific subject,
20021 Population figures
And can use the get_tables() function to get information
about the tables available:
R
tables <- get_tables(subjects="20021")
tables |> head()
OUTPUT
id text unit
1 FOLK1A Population at the first day of the quarter Number
2 FOLK1AM Population at the first day of the month Number
3 BEFOLK1 Population 1. January Number
4 BEFOLK2 Population 1. January Number
5 FOLK3 Population 1. January Number
6 FOLK3FOD Population 1. January Number
updated firstPeriod latestPeriod active
1 2025-11-10T08:00:00 2008Q1 2025Q4 TRUE
2 2025-11-10T08:00:00 2021M10 2025M10 TRUE
3 2025-02-11T08:00:00 1971 2025 TRUE
4 2025-02-11T08:00:00 1901 2025 TRUE
5 2025-02-11T08:00:00 2008 2025 TRUE
6 2025-02-11T08:00:00 2008 2025 TRUE
variables
1 region, sex, age, marital status, time
2 region, sex, age, time
3 sex, age, marital status, time
4 sex, age, time
5 day of birth, birth month, year of birth, time
6 day of birth, birth month, country of birth, timeWe have seen this information before, and can now use the
get_table_metadata() function to extract metadata on
specific tables:
R
metadata <- get_table_metadata("FOLK1A", variables_only = TRUE)
metadata
OUTPUT
id text elimination time map
1 OMRÅDE region TRUE FALSE denmark_municipality_07
2 KØN sex TRUE FALSE <NA>
3 ALDER age TRUE FALSE <NA>
4 CIVILSTAND marital status TRUE FALSE <NA>
5 Tid time FALSE TRUE <NA>
values
1 000, 084, 101, 147, 155, 185, 165, 151, 153, 157, 159, 161, 163, 167, 169, 183, 173, 175, 187, 201, 240, 210, 250, 190, 270, 260, 217, 219, 223, 230, 400, 411, 085, 253, 259, 350, 265, 269, 320, 376, 316, 326, 360, 370, 306, 329, 330, 340, 336, 390, 083, 420, 430, 440, 482, 410, 480, 450, 461, 479, 492, 530, 561, 563, 607, 510, 621, 540, 550, 573, 575, 630, 580, 082, 710, 766, 615, 707, 727, 730, 741, 740, 746, 706, 751, 657, 661, 756, 665, 760, 779, 671, 791, 081, 810, 813, 860, 849, 825, 846, 773, 840, 787, 820, 851, All Denmark, Region Hovedstaden, Copenhagen, Frederiksberg, Dragør, Tårnby, Albertslund, Ballerup, Brøndby, Gentofte, Gladsaxe, Glostrup, Herlev, Hvidovre, Høje-Taastrup, Ishøj, Lyngby-Taarbæk, Rødovre, Vallensbæk, Allerød, Egedal, Fredensborg, Frederikssund, Furesø, Gribskov, Halsnæs, Helsingør, Hillerød, Hørsholm, Rudersdal, Bornholm, Christiansø, Region Sjælland, Greve, Køge, Lejre, Roskilde, Solrød, Faxe, Guldborgsund, Holbæk, Kalundborg, Lolland, Næstved, Odsherred, Ringsted, Slagelse, Sorø, Stevns, Vordingborg, Region Syddanmark, Assens, Faaborg-Midtfyn, Kerteminde, Langeland, Middelfart, Nordfyns, Nyborg, Odense, Svendborg, Ærø, Billund, Esbjerg, Fanø, Fredericia, Haderslev, Kolding, Sønderborg, Tønder, Varde, Vejen, Vejle, Aabenraa, Region Midtjylland, Favrskov, Hedensted, Horsens, Norddjurs, Odder, Randers, Samsø, Silkeborg, Skanderborg, Syddjurs, Aarhus, Herning, Holstebro, Ikast-Brande, Lemvig, Ringkøbing-Skjern, Skive, Struer, Viborg, Region Nordjylland, Brønderslev, Frederikshavn, Hjørring, Jammerbugt, Læsø, Mariagerfjord, Morsø, Rebild, Thisted, Vesthimmerlands, Aalborg
2 TOT, 1, 2, Total, Men, Women
3 IALT, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, Age, total, 0 years, 1 year, 2 years, 3 years, 4 years, 5 years, 6 years, 7 years, 8 years, 9 years, 10 years, 11 years, 12 years, 13 years, 14 years, 15 years, 16 years, 17 years, 18 years, 19 years, 20 years, 21 years, 22 years, 23 years, 24 years, 25 years, 26 years, 27 years, 28 years, 29 years, 30 years, 31 years, 32 years, 33 years, 34 years, 35 years, 36 years, 37 years, 38 years, 39 years, 40 years, 41 years, 42 years, 43 years, 44 years, 45 years, 46 years, 47 years, 48 years, 49 years, 50 years, 51 years, 52 years, 53 years, 54 years, 55 years, 56 years, 57 years, 58 years, 59 years, 60 years, 61 years, 62 years, 63 years, 64 years, 65 years, 66 years, 67 years, 68 years, 69 years, 70 years, 71 years, 72 years, 73 years, 74 years, 75 years, 76 years, 77 years, 78 years, 79 years, 80 years, 81 years, 82 years, 83 years, 84 years, 85 years, 86 years, 87 years, 88 years, 89 years, 90 years, 91 years, 92 years, 93 years, 94 years, 95 years, 96 years, 97 years, 98 years, 99 years, 100 years, 101 years, 102 years, 103 years, 104 years, 105 years, 106 years, 107 years, 108 years, 109 years, 110 years, 111 years, 112 years, 113 years, 114 years, 115 years, 116 years, 117 years, 118 years, 119 years, 120 years, 121 years, 122 years, 123 years, 124 years, 125 years
4 TOT, U, G, E, F, Total, Never married, Married/separated, Widowed, Divorced
5 2008K1, 2008K2, 2008K3, 2008K4, 2009K1, 2009K2, 2009K3, 2009K4, 2010K1, 2010K2, 2010K3, 2010K4, 2011K1, 2011K2, 2011K3, 2011K4, 2012K1, 2012K2, 2012K3, 2012K4, 2013K1, 2013K2, 2013K3, 2013K4, 2014K1, 2014K2, 2014K3, 2014K4, 2015K1, 2015K2, 2015K3, 2015K4, 2016K1, 2016K2, 2016K3, 2016K4, 2017K1, 2017K2, 2017K3, 2017K4, 2018K1, 2018K2, 2018K3, 2018K4, 2019K1, 2019K2, 2019K3, 2019K4, 2020K1, 2020K2, 2020K3, 2020K4, 2021K1, 2021K2, 2021K3, 2021K4, 2022K1, 2022K2, 2022K3, 2022K4, 2023K1, 2023K2, 2023K3, 2023K4, 2024K1, 2024K2, 2024K3, 2024K4, 2025K1, 2025K2, 2025K3, 2025K4, 2008Q1, 2008Q2, 2008Q3, 2008Q4, 2009Q1, 2009Q2, 2009Q3, 2009Q4, 2010Q1, 2010Q2, 2010Q3, 2010Q4, 2011Q1, 2011Q2, 2011Q3, 2011Q4, 2012Q1, 2012Q2, 2012Q3, 2012Q4, 2013Q1, 2013Q2, 2013Q3, 2013Q4, 2014Q1, 2014Q2, 2014Q3, 2014Q4, 2015Q1, 2015Q2, 2015Q3, 2015Q4, 2016Q1, 2016Q2, 2016Q3, 2016Q4, 2017Q1, 2017Q2, 2017Q3, 2017Q4, 2018Q1, 2018Q2, 2018Q3, 2018Q4, 2019Q1, 2019Q2, 2019Q3, 2019Q4, 2020Q1, 2020Q2, 2020Q3, 2020Q4, 2021Q1, 2021Q2, 2021Q3, 2021Q4, 2022Q1, 2022Q2, 2022Q3, 2022Q4, 2023Q1, 2023Q2, 2023Q3, 2023Q4, 2024Q1, 2024Q2, 2024Q3, 2024Q4, 2025Q1, 2025Q2, 2025Q3, 2025Q4We use the variables_only = TRUE to remove eg. contact
information to Statistics Denmark.
What kind of values can the individual datapoints take?
R
metadata |>
slice(4) |>
pull(values)
OUTPUT
[[1]]
id text
1 TOT Total
2 U Never married
3 G Married/separated
4 E Widowed
5 F DivorcedWe use the slice function from tidyverse to pull out the fourth row of the dataframe, and the pull-function to pull out the values in the values column.
The same trick can be done for the other fields in the table:
R
metadata |>
slice(1) |>
pull(values) |>
pluck(1) |>
head()
OUTPUT
id text
1 000 All Denmark
2 084 Region Hovedstaden
3 101 Copenhagen
4 147 Frederiksberg
5 155 Dragør
6 185 TårnbyHere we see the individual municipalities in Denmark.
Which variables do we want?
As before we need to specify the variables we want in our answer.
These variables, and the values of them, need to be specified when we pull the data from Statistics Denmark.
We have seen how to do that using the POST() function,
it is done similarly using the danstat package:
R
variables <- list(list(code = "OMRÅDE", values = NA),
list(code = "CIVILSTAND", values = c("U", "G", "E", "F")),
list(code = "Tid", values = NA)
)
And now we can call the get_data() function and retrieve
data:
R
data <- get_data(table_id = "FOLK1A", variables = variables)
OUTPUT
Rows: 30240 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ";"
chr (3): OMRÅDE, CIVILSTAND, TID
dbl (1): INDHOLD
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.It takes a short moment. But now we have a dataframe containing the data we requested:
R
head(data)
OUTPUT
# A tibble: 6 × 4
OMRÅDE CIVILSTAND TID INDHOLD
<chr> <chr> <chr> <dbl>
1 All Denmark Never married 2008Q1 2552700
2 All Denmark Never married 2008Q2 2563134
3 All Denmark Never married 2008Q3 2564705
4 All Denmark Never married 2008Q4 2568255
5 All Denmark Never married 2009Q1 2575185
6 All Denmark Never married 2009Q2 2584993- Larger services often provide packages to make it easier to use their API.