1. Introduction
Do you have a Norwegian data set with codes for Standard Industrial Classification that you want to find out what they mean? Or data with Norwegian municipality numbers and no names? Or perhaps you want to convert English standard occupations into Ny Norsk for a figure. These are tasks which the R package klassR can help you with.
Statistics Norway’s KLASS is a central database of classifications and code lists. An API makes it easy to fetch these standards in different computing environments. klassR provides an easy interface to fetch and apply these in R.
For Statistic Norway employees, the package is installed on most of our platforms. For others, it can be installed from CRAN with:
install.packages("klassR")CRAN is R’s central repository for thousands of useful packages. More information on the requirements for klassR can be found on CRAN
To use the function in klassR the package must be called each time a new R session is started. This can be done using:
2. Search for a classification
To fetch a classification from KLASS you need the unique classification number. This can be found in the URL of the KLASS website or you can search for it in R using one of the following functions.
List all classifications
The function list_klass will fetch a list of all
classifications. It returns the classification name
(klass_name), number (klass_nr) and the
classification family it belongs to (klass_family). The
classification type (klass_type) is also shown which
indicates whether it is a classification or code list.
| klass_name | klass_nr | klass_family | klass_type |
|---|---|---|---|
| Standard for yrkesklassifisering | 7 | 1 | Klassifikasjon |
| Standard for skadeforsikring bransje | 155 | 2 | Klassifikasjon |
| Standard for kjønn | 2 | 3 | Klassifikasjon |
| Standard for gruppering av familier | 17 | 3 | Klassifikasjon |
| Standard for sivilstand | 19 | 3 | Klassifikasjon |
| Standard for gruppering av husholdninger | 37 | 3 | Klassifikasjon |
Code lists are classifications that used for national and internal
(Statistics Norway) publications. These can be included in the list
using the codelist parameter
list_klass(codelists = TRUE)| klass_name | klass_nr | klass_family | klass_type |
|---|---|---|---|
| Standard for yrkesklassifisering | 7 | 1 | Klassifikasjon |
| Kodeliste for yrkeskatalogen, basert på STYRK 98 | 145 | 1 | Kodeliste |
| Kodeliste for arbeidstid (hel-/deltid) | 149 | 1 | Kodeliste |
| Kodeliste for arbeidsmarkedsstatus | 161 | 1 | Kodeliste |
| Kodeliste for arbeidsgiveravgiftstype | 162 | 1 | Kodeliste |
| Kodeliste for delpopulasjon for lønn og sysselsetting | 163 | 1 | Kodeliste |
Search for a classification using a keyword
You can also search for a classification by a keyword using the
search_klass function. The first parameter here is the
query to search for.
search_klass(query = "ARENA")| klass_name | klass_nr |
|---|---|
| Classification of type of building /cadastre | 31 |
| Classification of land use and land cover | 118 |
Again, to include code lists in the search this should be specified
search_klass(query = "ARENA", codelists = TRUE)| klass_name | klass_nr |
|---|---|
| Kodeliste for ARENA as_ytelse | 394 |
| Kodeliste for ARENA Tiltak | 386 |
| Kodeliste for ARENA as_f (arbeidssøkerstatus_fingruppe ) | 396 |
| Kodeliste for ARENA as_gr (arbeidssøkerstatus grovgruppe) | 395 |
| Kodeliste for ARENA as_stat (arbeidssøkerstatus, aktivitet og ytelse) | 393 |
| Classification of type of building /cadastre | 31 |
| Classification of land use and land cover | 118 |
| Kodeliste for Interkommunale selskap | 625 |
| Codelist for Intermunicipal companies | 625 |
Sometimes a classification or code list will appear several times. This is due to that it occurs several times in different langauges in the database.
3. Fetch a classification
To fetch a complete classification, use the get_klass
function together with the unique identifier. For example, to fetch the
Standard Industrial Classifications (KLASS number 6) we
run:
| code | parentCode | level | name |
|---|---|---|---|
| 01 | A | 2 | Jordbruk og tjenester tilknyttet jordbruk, jakt og viltstell |
| 01.1 | 01 | 3 | Dyrking av ettårige vekster |
| 01.11 | 01.1 | 4 | Dyrking av korn (unntatt ris), belgvekster og oljeholdige vekster |
| 01.110 | 01.11 | 5 | Dyrking av korn (unntatt ris), belgvekster og oljeholdige vekster |
| 01.12 | 01.1 | 4 | Dyrking av ris |
| 01.120 | 01.12 | 5 | Dyrking av ris |
Level
Classifications are often organised in a heirachical way. In the
example above, the Standard Industrial Classifications have different
values for level. To fetch a specific level, use the
output_level parameter. For example, to fetch only the top
level Standard Industrial Classification codes we use:
| code | parentCode | level | name |
|---|---|---|---|
| A | NA | 1 | Jordbruk, skogbruk og fiske |
| B | NA | 1 | Bergverksdrift og utvinning |
| C | NA | 1 | Industri |
| D | NA | 1 | Elektrisitets-, gass-, damp- og varmtvannsforsyning |
| E | NA | 1 | Vannforsyning, avløps- og renovasjonsvirksomhet |
| F | NA | 1 | Bygge- og anleggsvirksomhet |
Language
In the above examples we have seen that the names are returned in
Norwegian (Bokmål). However, many of the classification in
KLASS are in multiple languages. The output language can be
specified as Bokmål (“nb”), Nynorsk (“nn”) or English (“en”) using the
language parameter. Note: all 3 languages are not
available for all classifcations.
| code | parentCode | level | name |
|---|---|---|---|
| A | NA | 1 | Agriculture, forestry and fishing |
| B | NA | 1 | Mining and quarrying |
| C | NA | 1 | Manufacturing |
| D | NA | 1 | Electricity, gas, steam and air conditioning supply |
| E | NA | 1 | Water supply; sewerage, waste management and remediation activities |
| F | NA | 1 | Construction |
Output format
The standard output style is ‘long’ where all levels of
classifications are listed down. An alternative format can be chosen
using the parameter output_style='wide'. This will give
only one row per detailed classification with the codes and names of the
higher/broader levels given as variables.
| code5 | name5 | code4 | name4 | code3 | name3 | code2 | name2 | code1 | name1 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 01.110 | Growing of cereals (except rice), leguminous crops and oil seeds | 01.11 | Growing of cereals (except rice), leguminous crops and oil seeds | 01.1 | Growing of non-perennial crops | 01 | Crop and animal production, hunting and related service activities | A | Agriculture, forestry and fishing |
| 6 | 01.120 | Growing of rice | 01.12 | Growing of rice | 01.1 | Growing of non-perennial crops | 01 | Crop and animal production, hunting and related service activities | A | Agriculture, forestry and fishing |
Notes
Some classifications have additional notes that can be fetched with
the classification. These can be included in the data using the option
notes = T.
| code | parentCode | level | name | notes |
|---|---|---|---|---|
| 01 | A | 2 | Jordbruk og tjenester tilknyttet jordbruk, jakt og viltstell | Inkluderer: Denne næringen omfatter to basisaktiviteter: produksjon av vegetabilske og animalske produkter, jordbruk, dyrking av genetisk modifiserte vekster og oppdrett av genetisk modifiserte dyr. Både dyrking av vekster på friland og i veksthus inngår Inkluderer også: Omfatter også tjenester tilknyttet jordbruk, jakt og fangst Ekskluderer: Grunnarbeid, f.eks. anlegg av jordterrasser, drenering o.l. grupperes under næringshovedområde: F Bygge- og anleggsvirksomhet. Kjøpere og andelslag engasjert i markedsføring av jordbruksprodukter grupperes under næringshovedområde: G Varehandel, reparasjon av motorvogner. Stell og vedlikehold av landskap grupperes under: 81.30 Beplantning av hager og parkanlegg |
| 01.1 | 01 | 3 | Dyrking av ettårige vekster | Inkluderer: Omfatter dyrking av ettårige vekster, dvs. planter som ikke varer i mer enn to vekstsesonger Inkluderer også: Omfatter også dyrking av ettårige vekster med henblikk på produksjon av såfrø og såkorn |
4. Applying a classification
If you have a data set and want to apply a classification to a
variable this is possible to do with apply_klass. This can
be used to get the name of a variable which is in code form for
example.
There is a built in test data set in klassR called
klassdata. It contains fictitious persons with sex,
education level, municipality numbers, industry classification for
workplace and occupation.
| ID | sex | education | kommune | kommune2 | nace5 | occupation |
|---|---|---|---|---|---|---|
| 1 | 2 | 2799 | 0706 | 706 | 47710 | 5132 |
| 2 | 2 | 5620 | 1567 | 1567 | 86902 | NA |
| 3 | 1 | 4010 | 1903 | 1903 | 41200 | 4177 |
| 4 | 1 | 1799 | 1003 | 1003 | 84120 | 3114 |
| 5 | 2 | NA | 0806 | 806 | 87102 | 2411 |
| 6 | 1 | 5621 | 0301 | 301 | 88911 | 8141 |
We can use apply_klass to create a variable for the
municipality names (classification number 131) for the persons based on
the codes. We specify the vector of codes as the first parameter
followed by the unique classification number.
klassdata$kommune_names <- apply_klass(klassdata$kommune,
classification = 131
)
head(klassdata)| ID | sex | education | kommune | kommune2 | nace5 | occupation | kommune_names |
|---|---|---|---|---|---|---|---|
| 1 | 2 | 2799 | 0706 | 706 | 47710 | 5132 | Sandefjord |
| 2 | 2 | 5620 | 1567 | 1567 | 86902 | NA | Rindal |
| 3 | 1 | 4010 | 1903 | 1903 | 41200 | 4177 | Harstad |
| 4 | 1 | 1799 | 1003 | 1003 | 84120 | 3114 | Farsund |
| 5 | 2 | NA | 0806 | 806 | 87102 | 2411 | Skien |
| 6 | 1 | 5621 | 0301 | 301 | 88911 | 8141 | Oslo |
Again, the language and output_level can be
specified.
5. Working with dates
Classifications will often change over time. The KLASS
database considers this and older classifications can be fetched using
the date parameter.
Specify a specific date
Fetching or using a classification at a specific time point can be
done using the date parameter and specifying the date for
which the version of classification applies. The date format should be
in the form “yyyy-mm-dd”, for example “2022-05-27” for the 27th May,
2022.
There have been many changes to the regions in Norway (classification number 106) over the past few years. We can see this by fetching the classifications for these at different times
get_klass(106, date = "2019-01-01")| code | parentCode | level | name |
|---|---|---|---|
| 1 | NA | 1 | Oslo og Akershus |
| 2 | NA | 1 | Hedmark og Oppland |
| 3 | NA | 1 | Sør-Østlandet |
| 4 | NA | 1 | Agder og Rogaland |
| 5 | NA | 1 | Vestlandet |
| 6 | NA | 1 | Trøndelag |
| 7 | NA | 1 | Nord-Norge |
| 9 | NA | 1 | Uoppgitt |
get_klass(106, date = "2020-01-01")| code | parentCode | level | name |
|---|---|---|---|
| 1 | NA | 1 | Oslo og Viken |
| 2 | NA | 1 | Innlandet |
| 3 | NA | 1 | Agder og Sør-Østlandet |
| 4 | NA | 1 | Vestlandet |
| 5 | NA | 1 | Trøndelag |
| 6 | NA | 1 | Nord-Norge |
| 9 | NA | 1 | Uoppgitt |
Time intervals
Sometime it may be useful to fetch all codes over a period of time.
We can do this by specifing two dates as a vector in the
date paramter.
The following code fetched Norwegian regional codes between 1st January 2019 to the 1st January 2020. There are 26 different codes that show both old and newer names.
| code | parentCode | level | name | validFromInRequestedRange | validToInRequestedRange |
|---|---|---|---|---|---|
| 1 | NA | 1 | Oslo og Akershus | 2018-01-01 | 2020-01-01 |
| 1 | NA | 1 | Oslo og Viken | 2020-01-01 | 2020-01-02 |
| 2 | NA | 1 | Innlandet | 2020-01-01 | 2020-01-02 |
| 2 | NA | 1 | Hedmark og Oppland | 2018-01-01 | 2020-01-01 |
| 3 | NA | 1 | Agder og Sør-Østlandet | 2020-01-01 | 2020-01-02 |
| 3 | NA | 1 | Sør-Østlandet | 2018-01-01 | 2020-01-01 |
| 4 | NA | 1 | Vestlandet | 2020-01-01 | 2020-01-02 |
| 4 | NA | 1 | Agder og Rogaland | 2018-01-01 | 2020-01-01 |
| 5 | NA | 1 | Vestlandet | 2018-01-01 | 2020-01-01 |
| 5 | NA | 1 | Trøndelag | 2020-01-01 | 2020-01-02 |
| 6 | NA | 1 | Trøndelag | 2018-01-01 | 2020-01-01 |
| 6 | NA | 1 | Nord-Norge | 2020-01-01 | 2020-01-02 |
| 7 | NA | 1 | Nord-Norge | 2018-01-01 | 2020-01-01 |
| 9 | NA | 1 | Uoppgitt | 2018-01-01 | 2020-01-02 |
Changes in time
To fetch only the changes in a time period rather than all codes we
can specify correspond=TRUE allong with the time interval
we are interested in.
| sourceCode | sourceName | targetCode | targetName | changeOccurred |
|---|---|---|---|---|
| NA | NA | 1 | Oslo og Akershus | 2020-01-01 |
| NA | NA | 3 | Sør-Østlandet | 2020-01-01 |
| NA | NA | 4 | Agder og Rogaland | 2020-01-01 |
| NA | NA | 5 | Vestlandet | 2020-01-01 |
| 1 | Oslo og Viken | NA | NA | 2020-01-01 |
| 2 | Innlandet | 2 | Hedmark og Oppland | 2020-01-01 |
| 3 | Agder og Sør-Østlandet | NA | NA | 2020-01-01 |
| 4 | Vestlandet | NA | NA | 2020-01-01 |
| 5 | Trøndelag | 6 | Trøndelag | 2020-01-01 |
| 6 | Nord-Norge | 7 | Nord-Norge | 2020-01-01 |
The table returned is a correspondents in codes and/or names in the
time interval specified. The sourceCode and
sourceName refer to the original name and coding. The
targetCode and targetName refer to the newer
code and name. Notice there is not a simple 1:1 correspondence between
all of the regions. Here the municipality number would be needed to map
the changes more accurately.
6. Correspondence tables
In addition to small changes in time, some classifications will
change completely and a correspondence table is then defined within the
KLASS database. These can be fetched or applied using
get_klass and apply_klass functions together
with the correspond parameter which should give the unique
classification number to convert into.
To fetch a correspondence table between municipality codes (131) and greater regional codes (106) we can run:
get_klass(131, correspond = 106, date = "2020-01-01")| sourceCode | sourceName | targetCode | targetName |
|---|---|---|---|
| 0301 | Oslo | 1 | Oslo og Viken |
| 3001 | Halden | 1 | Oslo og Viken |
| 3002 | Moss | 1 | Oslo og Viken |
| 3003 | Sarpsborg | 1 | Oslo og Viken |
| 3004 | Fredrikstad | 1 | Oslo og Viken |
| 3005 | Drammen | 1 | Oslo og Viken |
We can apply this correspondence between municipality and region in
our example data set using apply_klass.
klassdata$region <- apply_klass(klassdata$kommune,
classification = 131,
correspond = 106,
date = "2020-01-01"
)
klassdata| ID | sex | education | kommune | kommune2 | nace5 | occupation | kommune_names | region |
|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 2799 | 0706 | 706 | 47710 | 5132 | Sandefjord | NA |
| 2 | 2 | 5620 | 1567 | 1567 | 86902 | NA | Rindal | NA |
| 3 | 1 | 4010 | 1903 | 1903 | 41200 | 4177 | Harstad | NA |
| 4 | 1 | 1799 | 1003 | 1003 | 84120 | 3114 | Farsund | NA |
| 5 | 2 | NA | 0806 | 806 | 87102 | 2411 | Skien | NA |
| 6 | 1 | 5621 | 0301 | 301 | 88911 | 8141 | Oslo | Oslo og Viken |
7. Variants
It is also possible to fetch a variant of a classification. You need to provide both the classification number and the variant number.
get_klass(6, variant = 1616, date = "2021-01-02")| code | parentCode | level | name |
|---|---|---|---|
| 01 | 01-03 | 2 | Jordbruk og tjenester tilknyttet jordbruk, jakt og viltstell |
| 01-03 | NA | 1 | Jordbruk, skogbruk og fiske |
| 01.1 | 01 | 3 | Dyrking av ettårige vekster |
| 01.11 | 01.1 | 4 | Dyrking av korn (unntatt ris), belgvekster og oljeholdige vekster |
| 01.110 | 01.11 | 5 | Dyrking av korn (unntatt ris), belgvekster og oljeholdige vekster |
| 01.12 | 01.1 | 4 | Dyrking av ris |