The Annual Regional Database of the European Commission (ARDECO) is maintained by the Joint Research Centre in close coordination with the Directorate General for Regional and Urban Policy. Its purpose is to provide consistent and harmonised time-series data on demographic and socio-economic variables at the regional and sub-regional levels.
ARDECO primarily relies on official data from sources like Eurostat’s ‘Regional Accounts’ ( https://ec.europa.eu/eurostat/web/national-accounts/methodology/european-accounts/regional-accounts ) and national or regional statistical offices. However, it also incorporates data from supplementary sources (such as the European Regional Database from Cambridge Econometrics - httpS://www.camecon.com/wp-content/uploads/2019/01/ERD-manual.pdf) and estimates generated using various methodologies (such as data interpolation, regional shares of closest year, and proxy variables).
In cases where official data exhibits inconsistencies across the time-series due to breaks or provisional values, ARDECO replaces these values with estimates based on the most suitable methodology. Ensuring consistency across variables and over time, therefore, sometimes requires adjustments of official values
For most variables and countries, ARDECO extends its time-series back to 1980 (1960 for population data). Additionally, short-term projections based on AMECO (https://economy-finance.ec.europa.eu/economic-research-and-databases/economic-databases/ameco-database_en) forecasts are included whenever possible.
More detailed information about ARDECO can be found in the methodological note:
European Commission, Joint Research Centre, Auteri, D., Attardo, C., Berzi, M., Dorati, C., Albinola, F., Baggio, L., Bucciarelli, G., Bussolari, I. and Dijkstra, L., The Annual Regional Database of the European Commission (ARDECO) - Methodological Note, European Commission, Ispra, 2024, JRC138212)
Within ARDECO, both variables and indicators are provided. Variables represent core data expressed as ‘volumes’ and are computed from primary sources. Indicators, on the other hand, are expressed as ‘ratios’ and result from dividing one variable (e.g., GDP) by another (e.g., average population).
For a convention adopted in ARDECO, variables have a code composed by 4 o 5 characters (for example: “GDP at current market prices” is coded SUVGD), while the indicators are coded with 6 or more characters (for example: “GDP per capita at current prices” is coded SUVGDP)
Currenlty ARDECO provides a set of variables and indicators related to the following domains:
The complete set of available variables and indicators is provided by the function ardeco_get_variable_list():
print(ardeco_get_variable_list(), n=1000)
#> # A tibble: 68 × 2
#> code description
#> <chr> <chr>
#> 1 PVGD "GDP price index (implicit deflator, national, 2015=100, euro)"
#> 2 PVGE "GVA price index (implicit deflator, national, 2015=100, euro)"
#> 3 SNPCN "Total population change"
#> 4 SNMTN "Net migration"
#> 5 SNPBN "Live births"
#> 6 SNPDN "Deaths"
#> 7 SNPNN "Natural change of population"
#> 8 SUVGDP "GDP per capita at current prices"
#> 9 SNPTN "Population on 1st January by broad age group and sex"
#> 10 SNPTD "Average annual population"
#> 11 SUVGD "GDP at current market prices"
#> 12 SUVGE "GVA at basic prices"
#> 13 SUVGZ "GVA at basic prices by industry (10 NACE sectors)"
#> 14 SNWTD "Wage and salary earners (workplace based, employees)"
#> 15 SNETZ "Employment by industry (10 NACE sectors)"
#> 16 SNETD "Total Employment (workplace based, employed persons)"
#> 17 SOVGD "GDP at constant prices"
#> 18 SOVGDP "GDP per capita at constant prices"
#> 19 SOVGE "GVA at constant prices"
#> 20 SOVGZ "GVA at constant prices by industry (10 NACE sectors)"
#> 21 SPVGD "GDP price index (chain-type index, 2015=100)"
#> 22 SPVGE "GVA price index (chain-type index, 2015=100)"
#> 23 PVGZ "GVA price indices by industry (implicit deflators, national, 2015=1…
#> 24 RNECN "Residence-based employment"
#> 25 RNUTN "Unemployment (LFS)"
#> 26 RNLHT "Hours Worked (employed persons)"
#> 27 RNLHTP "Hours worked per capita"
#> 28 RNLHTE "Hours worked per employed person"
#> 29 RNLHZ "Hours Worked by industry (10 NACE sectors)"
#> 30 RUWCZ "Compensation of employees at current prices by industry (10 NACE se…
#> 31 PPP "Purchasing power parities (PPPs) - EU=100"
#> 32 RNLHW "Hours Worked (employees)"
#> 33 RNLCN "Civilian Labour Force"
#> 34 RNLTN "Total Labour Force"
#> 35 SNETDP "Employment per capita"
#> 36 RUWCD "Compensation of employees at current prices"
#> 37 RUWCDH "Nominal compensation per hour worked"
#> 38 RUWCDW "Nominal compensation per employee"
#> 39 ROWCD "Compensation of Employees at constant prices"
#> 40 ROWCDH "Real compensation per hour worked"
#> 41 ROWCDW "Real compensation per employee"
#> 42 ROWCZ "Compensation of employees at constant prices by industry (10 NACE s…
#> 43 RUWCDHH "Nominal unit labour cost based on hours worked"
#> 44 RUWCDWE " Nominal unit labour cost based on persons"
#> 45 SUVGDH "Nominal labour productivity per hour worked"
#> 46 SUVGDE "Nominal labour productivity per person employed"
#> 47 SOVGDH " Real labour productivity per hour worked"
#> 48 SOVGDE "Real labour productivity per person employed"
#> 49 RPDTN "Population by educational attainment level"
#> 50 RPDEN "Early leavers from education and training (18-24 years)"
#> 51 RPDNN "Young people neither in employment nor in education and training (1…
#> 52 RUVNH "Households net disposable income"
#> 53 RUYNH "Net property income"
#> 54 RUONH "Net operating Surplus and Mixed Income"
#> 55 RUTYH "Current taxes on income and wealth"
#> 56 ROIGT "Gross Fixed Capital Formation at constant prices"
#> 57 ROIGZ "Gross Fixed Capital Formation by industry (10 NACE sectors) at cons…
#> 58 SOKCT "Consumption of fixed capital at constant prices"
#> 59 SOKCZ "Consumption of fixed capital by industry (10 NACE sectors) at const…
#> 60 PIGT "GFCF price index (implicit deflator, national, 2015=100, euro)"
#> 61 PIGZ "GFCF price indices by industry (implicit deflators, national, 2015=…
#> 62 ROKND "Capital Stock at constant prices"
#> 63 EDGAR "Emissions Database for Global Atmospheric Research"
#> 64 EDGARP "GHG emissions per capita"
#> 65 RUIGZ "Gross Fixed Capital Formation by industry (10 NACE sectors) at curr…
#> 66 RUIGT "Gross Fixed Capital Formation at current prices"
#> 67 SUKCT "Consumption of fixed capital at current prices"
#> 68 SUKCZ "Consumption of fixed capital by industry (10 NACE sectors) at curr…Variables and indicators provide data for each EU country and its regions, according to the territorial classification provided by the Nomenclature of Territorial Units for Statistics as defined by Eurostat (NUTS nomenclature: https://ec.europa.eu/eurostat/web/nuts/overview).
The NUTS classification is composed of territorial units organised in four hierarchical and nested levels, i.e. where the upper level corresponds to aggregates of the lower level – from NUTS0 (corresponding to national level) to NUTS3 (corresponding to sub-regional level).
Each three years a revision of the NUTS codes is performed, replacing some NUTS codes with new ones due to updating of statistical reference territory. Each review generates a new NUTS version
NUTS coding follows these rules:
More details about NUTS codeing are available in https://ec.europa.eu/eurostat/web/nuts/overview
According to EUROSTAT definition, territorial typologies (tercet) are classification systems that categorize regions and areas within the European Union based on specific geographical, socio-economic, and administrative criteria. These typologies are used to facilitate regional analysis and policy-making by providing a standardized framework for comparing different territories. Key territorial typologies include classifications such as urban-rural typologies, coastal and non-coastal regions, mountain areas, and metropolitan regions. By using these typologies, Eurostat aims to enhance the understanding of regional disparities and development patterns across the EU, supporting more effective regional policy interventions.
Currently, in ARDECO, for the variables which have data at NUTS3 level, it’s possible to require aggregated data at NUTS0 for Urban-rural typologies (URT). URT classifies each NUTS3 into one of three URT classes:
A more detailed URT classification is also available: Urban-rural typologies with remoteness (URT with remoteness). URT with remoteness classifies each NUTS3 in 5 classes:
The computation of the tercet classes for each NUTS0 is done by the sum of its NUTS3 belonging in each class.
For each variable and for each territorial unit ARDECO provides the yearly value.
In general, for each variable values from 2000 to the current year are provided.
Depending by the variable, data before 2000 are provided according to the available information recovered from different sources of data. For example, Total population data are provided from 1960, for many variables data start from 1980, for some specific and more detailed data (economic sectorial data, age class for population) data starts from 1990 or 1995.
In addition, provisional data are provided for 2/3 years from the current year according to the forecasted yearly data provided by AMECO (Annual Macro-economic database of the European COmmission’s Directorate General for Economic and Financial Affairs, https://economy-finance.ec.europa.eu/economic-research-and-databases/economic-databases/ameco-database_en).
In ARDECO, each variable have at least two dimension: ‘Unit’ and ‘Version’.
‘Unit’ is the unit of measure related to the value associated to a nutscode in a specific year.
Example: the unit for variable ‘Total Population’ is ‘Persons’.
‘Version’ refers to the NUTS version for which the values of a variables are provided.
Some variables can have more unit of measure. For example, GDP is provided in ‘EUR’ and in ‘PPS’.
A variable can also have additional dimensions.
Example: In ‘Total Population’ it has been defined two additional dimension: ‘sex’ and ‘age group’. Each of this dimensions have a set of possible values (domain).
The domain for dimension ‘sex’ is: ‘Total’, ‘Male’, ‘Female’. The domain for dimension ‘age group’ is: ‘Total’, ‘Less than 15’, ‘from 15 to 64’, ‘65 and over’.
The variable ‘Total Population’ can collect values for each nutcode, for each year and for each combination of values of its dimensions.
Each combination of values of the dimensions of a variable identifies a dataset for that variable.
Example: the variable ‘Total Population’ as defined above, with dimension ‘Unit’, ‘sex’ and ‘age group’ have the following datasets:
unit=‘Persons’ version= 2021 sex=‘Total’ ‘age group’=‘Total’ represent the total population at NUTS version 2021
unit=‘Persons’ version= 2021 sex=‘Male’ ‘age group’=‘Total’ represent the total male population at NUTS version 2021
unit=‘Persons’ version= 2021 sex=‘Female’ ‘age group’=‘Total’ represent the total female population at NUTS version 2021
unit=‘Persons’ version= 2021 sex=‘Total’ ‘age group’=‘Less than 15’ represent the total population have less than 15 years at NUTS version 2021
…an so on…
To retrieve the number of datasets defined for a variable, use the function ardeco_get_dataset_list():
print(ardeco_get_dataset_list('SNPTN'), n=100)
#> # A tibble: 12 × 5
#> var sex age unit vers
#> <chr> <chr> <chr> <chr> <chr>
#> 1 SNPTN Females 65 years and over Persons 2016, 2021, 2024
#> 2 SNPTN Males 65 years and over Persons 2016, 2021, 2024
#> 3 SNPTN Total 65 years and over Persons 2016, 2021, 2024
#> 4 SNPTN Females From 15 to 64 years Persons 2016, 2021, 2024
#> 5 SNPTN Males From 15 to 64 years Persons 2016, 2021, 2024
#> 6 SNPTN Total From 15 to 64 years Persons 2016, 2021, 2024
#> 7 SNPTN Females Less than 15 years Persons 2016, 2021, 2024
#> 8 SNPTN Males Less than 15 years Persons 2016, 2021, 2024
#> 9 SNPTN Total Less than 15 years Persons 2016, 2021, 2024
#> 10 SNPTN Females Total Persons 2016, 2021, 2024
#> 11 SNPTN Males Total Persons 2016, 2021, 2024
#> 12 SNPTN Total Total Persons 2016, 2021, 2024ARDECO R package provide a set of functions permitting to retrive the data of the ARDECO variables into a R dataframe. The data retrival is performed through the exploitation of the ARDECO APIs. These functions are an interface to the ARDECO APIs to simplify R users in data retrieval.
The core function is ardeco_get_dataset_data which call ARDECO APIs to retrive data. This functions need the variable code and, optionally, a set of parameters enabling the user to filter data at the request level, minimizing the time for the
In order to retrieve data for a variable, if the user doesn’t know the structure of the desired variable, the package provides a set fo functions in order to:
The unique mandatory information to retrieve data is the variable code. To retrieve the available ARDECO variables code, use
ardeco_get_variable_list()
The function returns a tibble with the following columns:
code is the mandatory code requested by the other ARDECO R package functions (ardeco_get_dataset_list, ardeco_get_dataset_data, ardeco_get_tercet_list) to retrieve information and data.
description explains the variable represented by the variable code
Example:
mytb <- ardeco_get_variable_list()
print(mytb, max=50)
#> # A tibble: 68 × 2
#> code description
#> <chr> <chr>
#> 1 PVGD GDP price index (implicit deflator, national, 2015=100, euro)
#> 2 PVGE GVA price index (implicit deflator, national, 2015=100, euro)
#> 3 SNPCN Total population change
#> 4 SNMTN Net migration
#> 5 SNPBN Live births
#> 6 SNPDN Deaths
#> 7 SNPNN Natural change of population
#> 8 SUVGDP GDP per capita at current prices
#> 9 SNPTN Population on 1st January by broad age group and sex
#> 10 SNPTD Average annual population
#> # ℹ 58 more rowsAs explained in ‘Variable as a set of datasets’ each variable have one or more datasets depending by its dimensions.
To retrieve the list of the datasets defined for a variable, use
ardeco_get_dataset_list(‘varcode’)
The function returns a tibble with the following columns:
Example:
mytb <- ardeco_get_dataset_list('SNETZ')
print(mytb)
#> # A tibble: 13 × 4
#> var unit sector vers
#> <chr> <chr> <chr> <chr>
#> 1 SNETZ Thousands Persons A 2016, 2021, 2024
#> 2 SNETZ Thousands Persons B-E 2016, 2021, 2024
#> 3 SNETZ Thousands Persons F 2016, 2021, 2024
#> 4 SNETZ Thousands Persons G-I 2016, 2021, 2024
#> 5 SNETZ Thousands Persons G-J 2016, 2021, 2024
#> 6 SNETZ Thousands Persons J 2016, 2021, 2024
#> 7 SNETZ Thousands Persons K 2016, 2021, 2024
#> 8 SNETZ Thousands Persons K-N 2016, 2021, 2024
#> 9 SNETZ Thousands Persons L 2016, 2021, 2024
#> 10 SNETZ Thousands Persons M-N 2016, 2021, 2024
#> 11 SNETZ Thousands Persons O-Q 2016, 2021, 2024
#> 12 SNETZ Thousands Persons O-U 2016, 2021, 2024
#> 13 SNETZ Thousands Persons R-U 2016, 2021, 2024ARDECO R package provides the possibility to require data for a variable at available tercet (see ‘Territorial typologies: aggregated data by tercet’). To check which tercet are currently available in ARDECO database, use:
ardeco_get_tercet_list()
This function return the available tercet and related tercet class. Each tercet and related tercet classes) are identified by codes: tercet_code and tercet_class_code.
The function returns a tibble with the following columns:
Example:
mytb <- ardeco_get_tercet_list()
print(mytb)
#> tercet_code tercet_name tercet_class_code
#> 1 1 by Urban-Rural Typology 0
#> 2 1 by Urban-Rural Typology 2
#> 3 1 by Urban-Rural Typology 1
#> 4 2 by Urban-Rural Typology with Remoteness 0
#> 5 2 by Urban-Rural Typology with Remoteness 3
#> 6 2 by Urban-Rural Typology with Remoteness 4
#> 7 2 by Urban-Rural Typology with Remoteness 5
#> 8 2 by Urban-Rural Typology with Remoteness 6
#> tercet_class_name
#> 1 Predominantly urban
#> 2 Predominantly rural
#> 3 Intermediate
#> 4 Predominantly urban
#> 5 Intermediate, close to a city
#> 6 Intermediate, remote
#> 7 Predominantly rural, close to a city
#> 8 Predominantly rural, remoteIt’s no possible require tercet data for all variable but for only those variables for which exist data at NUTS3 level. To check if it’s possible to require tercet data for a variable, use
ardeco_get_tercet_list(‘varcode’)
If the variable haven’t the NUTS3 level, the function return a warning highlighting the impossibility to aggregate data at tercet classes.
Example:
The core function to retrieve data is
ardeco_get_dataset_data(‘varcode’)
This function return a dataframe where any row expose the value for a specific nuts, year, nutsversion, unit, dimensions. The dataframe include the following columns:
Following an example to retrieve the complete set of ‘Average annual population’ data:
mydf <- ardeco_get_dataset_data('SNPTD')
print(mydf, max=50)
#> VARIABLE VERSIONS LEVEL NUTSCODE YEAR UNIT VALUE
#> 1 SNPTD 2016 0 AT 1980 Persons 7549428
#> 2 SNPTD 2016 0 BE 1980 Persons 9859002
#> 3 SNPTD 2016 0 CY 1980 Persons 509050
#> 4 SNPTD 2016 0 DE 1980 Persons 61566000
#> 5 SNPTD 2016 0 DK 1980 Persons 5124001
#> 6 SNPTD 2016 0 EL 1980 Persons 9642501
#> 7 SNPTD 2016 0 ES 1980 Persons 37491173
#> [ reached 'max' / getOption("max.print") -- omitted 228621 rows ]Additional columns will be added if for the variable it has been defined additional dimensions.
In order to avoid problems due to the big amount of data which can be requested by user, ardeco_get_dataset_data works sending a set of requests, one for any dataset defined into the variable, joining the results of every call into the resulting dataframe. In this way each call ask a relative small set of data, avoiding timeout constrain imposed by the network infrastructure.
If a variable have a huge number of datasets, it’s possible that the retrieval process of the entire variable needs long time (minutes) before receiving the final result. To check the status and the progress of the data retrieval, it’s possible to use an optional parameter which display the progress of the data retrieval process. This parameter is verbose which by default is set to FALSE
ardeco_get_dataset_data(‘varcode’, verbose=TRUE)
Example:
mydf <- ardeco_get_dataset_data('SNETZ', verbose=TRUE)
#> [1] "Fetching... [unit]=Thousands Persons [sector]=A"
#> [1] "Fetching... [unit]=Thousands Persons [sector]=B-E"
#> [1] "Fetching... [unit]=Thousands Persons [sector]=F"
#> [1] "Fetching... [unit]=Thousands Persons [sector]=G-I"
#> [1] "Fetching... [unit]=Thousands Persons [sector]=G-J"
#> [1] "Fetching... [unit]=Thousands Persons [sector]=J"
#> [1] "Fetching... [unit]=Thousands Persons [sector]=K"
#> [1] "Fetching... [unit]=Thousands Persons [sector]=K-N"
#> [1] "Fetching... [unit]=Thousands Persons [sector]=L"
#> [1] "Fetching... [unit]=Thousands Persons [sector]=M-N"
#> [1] "Fetching... [unit]=Thousands Persons [sector]=O-Q"
#> [1] "Fetching... [unit]=Thousands Persons [sector]=O-U"
#> [1] "Fetching... [unit]=Thousands Persons [sector]=R-U"The function ardeco_get_dataset_data permits to send a request for a subset of data filtered according to the following parameters:
The filter is composed by a set of additional parameters to add to the call of the function ardeco_get_dataset_data according to the following syntax:
ardeco_get_dataset_data(‘varcode’, parameter-1 = par-value-1, …, parameter-n = par-value-n)
Where
Example:
# Request data for Italy at level 1, at nuts version=2021,
# with unit of measure = 'Million EUR' and related to year 2015
mydf <- ardeco_get_dataset_data('SUVGD', version=2021, unit='Million PPS', year=2015, nutscode='IT', level=1)
print(mydf, max=50)
#> VARIABLE VERSIONS LEVEL NUTSCODE YEAR UNIT VALUE
#> 1 SUVGD 2021 1 ITC 2015 Million PPS 526625.2
#> 2 SUVGD 2021 1 ITF 2015 Million PPS 251691.9
#> 3 SUVGD 2021 1 ITG 2015 Million PPS 116697.4
#> 4 SUVGD 2021 1 ITH 2015 Million PPS 367591.2
#> 5 SUVGD 2021 1 ITI 2015 Million PPS 347427.1
#> 6 SUVGD 2021 1 ITZ 2015 Million PPS 1245.9To request data at a specific nuts version, use:
ardeco_get_dataset_data(‘varcode’, version=nutsversion)
where
To check the available versions for the requested variable, use:
ardeco_get_dataset_list(‘varcode’) (see ‘List of the available datasets’)
Example:
# Check the available versions for variable SNPTD
ardeco_get_dataset_list('SNPTD')
#> # A tibble: 1 × 3
#> var unit vers
#> <chr> <chr> <chr>
#> 1 SNPTD Persons 2016, 2021, 2024
mydf <- ardeco_get_dataset_data('SNPTD', version=2021)
print(mydf, max=50)
#> VARIABLE VERSIONS LEVEL NUTSCODE YEAR UNIT VALUE
#> 1 SNPTD 2021 0 AL 1960 Persons 1608801
#> 2 SNPTD 2021 0 AT 1960 Persons 7047440
#> 3 SNPTD 2021 0 BE 1960 Persons 9153499
#> 4 SNPTD 2021 0 BG 1960 Persons 7867374
#> 5 SNPTD 2021 0 CH 1960 Persons 5328000
#> 6 SNPTD 2021 0 CY 1960 Persons 441788
#> 7 SNPTD 2021 0 CZ 1960 Persons 9602011
#> [ reached 'max' / getOption("max.print") -- omitted 157081 rows ]Filtering by nuts-code returns data for all territories coded with nutscode starting with the value passed to the function. The filter syntax is the following:
ardeco_get_dataset_data(‘varcode’, nutscode=val-nutscode)
where
Example:
# Retrive data with nutscode starting with 'IT'
mydf <- ardeco_get_dataset_data('SUVGD', nutscode='IT')
print(mydf, max=50)
#> VARIABLE VERSIONS LEVEL NUTSCODE YEAR UNIT VALUE
#> 1 SUVGD 2016 0 IT 1980 Million EUR 341455
#> 2 SUVGD 2016 0 IT 1981 Million EUR 385319
#> 3 SUVGD 2016 0 IT 1982 Million EUR 434131
#> 4 SUVGD 2016 0 IT 1983 Million EUR 496161
#> 5 SUVGD 2016 0 IT 1984 Million EUR 554663
#> 6 SUVGD 2016 0 IT 1985 Million EUR 594280
#> 7 SUVGD 2016 0 IT 1986 Million EUR 651995
#> [ reached 'max' / getOption("max.print") -- omitted 27633 rows ]
# Retrive data with nutscode starting with 'IT,FR'
mydf <- ardeco_get_dataset_data('SUVGD', nutscode='IT,FR')
print(mydf, max=50)
#> VARIABLE VERSIONS LEVEL NUTSCODE YEAR UNIT VALUE
#> 1 SUVGD 2016 0 FR 1980 Million EUR 504931
#> 2 SUVGD 2016 0 IT 1980 Million EUR 341455
#> 3 SUVGD 2016 0 FR 1981 Million EUR 553861
#> 4 SUVGD 2016 0 IT 1981 Million EUR 385319
#> 5 SUVGD 2016 0 FR 1982 Million EUR 597688
#> 6 SUVGD 2016 0 IT 1982 Million EUR 434131
#> 7 SUVGD 2016 0 FR 1983 Million EUR 630220
#> [ reached 'max' / getOption("max.print") -- omitted 57881 rows ]This filter can be refined using the parameter ‘level’. The parameter level filter the data by nuts level. The filter syntax is the following:
ardeco_get_dataset_data(‘varcode’, level=val-nutslevel)
where
Example:
# Retrive SUVGD data for Italy at country level (level 0) for the year 2015
mydf <- ardeco_get_dataset_data('SUVGD', level=0, nutscode='IT', year=2015, version=2021)
print(mydf, max=50)
#> VARIABLE VERSIONS LEVEL NUTSCODE YEAR UNIT VALUE
#> 1 SUVGD 2021 0 IT 2015 Million EUR 1655355
#> 2 SUVGD 2021 0 IT 2015 Million PPS 1611279
# Retrive SUVGD data for Italy at country and regional level (level 0 and 2) for the year 2015
mydf <- ardeco_get_dataset_data('SUVGD', level='0,2', nutscode='IT', year=2015, version=2021)
print(mydf, max=50)
#> VARIABLE VERSIONS LEVEL NUTSCODE YEAR UNIT VALUE
#> 1 SUVGD 2021 0 IT 2015 Million EUR 1655355.0
#> 2 SUVGD 2021 2 ITC1 2015 Million EUR 126868.3
#> 3 SUVGD 2021 2 ITC2 2015 Million EUR 4653.4
#> 4 SUVGD 2021 2 ITC3 2015 Million EUR 47179.5
#> 5 SUVGD 2021 2 ITC4 2015 Million EUR 362329.6
#> 6 SUVGD 2021 2 ITF1 2015 Million EUR 31863.8
#> 7 SUVGD 2021 2 ITF2 2015 Million EUR 6128.4
#> [ reached 'max' / getOption("max.print") -- omitted 39 rows ]
# Retrive SUVGD data for Italy at level from 0 to 2 for the year 2015
mydf <- ardeco_get_dataset_data('SUVGD', level='0-2', nutscode='IT', year=2015, version=2021)
print(mydf, max=100)
#> VARIABLE VERSIONS LEVEL NUTSCODE YEAR UNIT VALUE
#> 1 SUVGD 2021 0 IT 2015 Million EUR 1655355.0
#> 2 SUVGD 2021 1 ITC 2015 Million EUR 541030.9
#> 3 SUVGD 2021 1 ITF 2015 Million EUR 258576.9
#> 4 SUVGD 2021 1 ITG 2015 Million EUR 119889.6
#> 5 SUVGD 2021 1 ITH 2015 Million EUR 377646.6
#> 6 SUVGD 2021 1 ITI 2015 Million EUR 356930.8
#> 7 SUVGD 2021 1 ITZ 2015 Million EUR 1280.3
#> 8 SUVGD 2021 2 ITC1 2015 Million EUR 126868.3
#> 9 SUVGD 2021 2 ITC2 2015 Million EUR 4653.4
#> 10 SUVGD 2021 2 ITC3 2015 Million EUR 47179.5
#> 11 SUVGD 2021 2 ITC4 2015 Million EUR 362329.6
#> 12 SUVGD 2021 2 ITF1 2015 Million EUR 31863.8
#> 13 SUVGD 2021 2 ITF2 2015 Million EUR 6128.4
#> 14 SUVGD 2021 2 ITF3 2015 Million EUR 103763.8
#> [ reached 'max' / getOption("max.print") -- omitted 44 rows ]To request data for a specific unit of measure, use:
ardeco_get_dataset_data(‘varcode’, unit=‘unit’)
where
To check the available unit of measures for the requested variable, use:
ardeco_get_dataset_list(‘varcode’) (see ‘List of the available datasets’)
Example:
# Check the available units for variable SUVGD
ardeco_get_dataset_list('SUVGD')
#> # A tibble: 2 × 3
#> var unit vers
#> <chr> <chr> <chr>
#> 1 SUVGD Million EUR 2016, 2021, 2024
#> 2 SUVGD Million PPS 2016, 2021, 2024
# Retrive data only for unit = 'Million PPS'
mydf <- ardeco_get_dataset_data('SUVGD', unit='Million PPS')
print(mydf, max=50)
#> VARIABLE VERSIONS LEVEL NUTSCODE YEAR UNIT VALUE
#> 1 SUVGD 2016 0 AT 1980 Million PPS 63886.9
#> 2 SUVGD 2016 0 BE 1980 Million PPS 81472.1
#> 3 SUVGD 2016 0 DE 1980 Million PPS NA
#> 4 SUVGD 2016 0 DK 1980 Million PPS 40713.8
#> 5 SUVGD 2016 0 EL 1980 Million PPS 67653.8
#> 6 SUVGD 2016 0 ES 1980 Million PPS 212593.0
#> 7 SUVGD 2016 0 FI 1980 Million PPS 35398.8
#> [ reached 'max' / getOption("max.print") -- omitted 176726 rows ]To request data for a year, use:
ardeco_get_dataset_data(‘varcode’, year=‘val-year’)
where
Example:
# Retrive data only for year = 2015
mydf <- ardeco_get_dataset_data('SNPTD', year=2015, version=2021, nutscode='ITF11')
print(mydf, max=50)
#> VARIABLE VERSIONS LEVEL NUTSCODE YEAR UNIT VALUE
#> 1 SNPTD 2021 3 ITF11 2015 Persons 303200
# Retrive data for years 2015 and 2018
mydf <- ardeco_get_dataset_data('SNPTD', year='2015,2018', version=2021, nutscode='ITF11')
print(mydf, max=50)
#> VARIABLE VERSIONS LEVEL NUTSCODE YEAR UNIT VALUE
#> 1 SNPTD 2021 3 ITF11 2015 Persons 303200
#> 2 SNPTD 2021 3 ITF11 2018 Persons 298200
# Retrive data for years from 2015 to 2018
mydf <- ardeco_get_dataset_data('SNPTD', year='2015-2018', version=2021, nutscode='ITF11')
print(mydf, max=50)
#> VARIABLE VERSIONS LEVEL NUTSCODE YEAR UNIT VALUE
#> 1 SNPTD 2021 3 ITF11 2015 Persons 303200
#> 2 SNPTD 2021 3 ITF11 2016 Persons 301500
#> 3 SNPTD 2021 3 ITF11 2017 Persons 299900
#> 4 SNPTD 2021 3 ITF11 2018 Persons 298200If a variable have also additional dimensions, it is possible to filter for these additional dimensions, using the following syntax (like the other dimensions):
ardeco_get_dataset_data(‘varcode’, dim-1 = dim-value-1, …, dim-n = dim-value-n)
Where
To check the existance of additional dimensions for the requested variable, use:
ardeco_get_dataset_list(‘varcode’) (see ‘List of the available datasets’)
Example:
# Check the available dimensions for variable SNPTN
ardeco_get_dataset_list('SNPTN')
#> # A tibble: 12 × 5
#> var sex age unit vers
#> <chr> <chr> <chr> <chr> <chr>
#> 1 SNPTN Females 65 years and over Persons 2016, 2021, 2024
#> 2 SNPTN Males 65 years and over Persons 2016, 2021, 2024
#> 3 SNPTN Total 65 years and over Persons 2016, 2021, 2024
#> 4 SNPTN Females From 15 to 64 years Persons 2016, 2021, 2024
#> 5 SNPTN Males From 15 to 64 years Persons 2016, 2021, 2024
#> 6 SNPTN Total From 15 to 64 years Persons 2016, 2021, 2024
#> 7 SNPTN Females Less than 15 years Persons 2016, 2021, 2024
#> 8 SNPTN Males Less than 15 years Persons 2016, 2021, 2024
#> 9 SNPTN Total Less than 15 years Persons 2016, 2021, 2024
#> 10 SNPTN Females Total Persons 2016, 2021, 2024
#> 11 SNPTN Males Total Persons 2016, 2021, 2024
#> 12 SNPTN Total Total Persons 2016, 2021, 2024
# Retrive data only for sex = 'Males' and age = 'Total'
mydf <- ardeco_get_dataset_data('SNPTN', sex='Males', age = 'Total')
print(mydf, max=50)
#> VARIABLE VERSIONS LEVEL NUTSCODE YEAR SEX AGE UNIT VALUE
#> 1 SNPTN 2021 0 AL 1960 Males Total Persons 804267
#> 2 SNPTN 2021 0 AT 1960 Males Total Persons 3357007
#> 3 SNPTN 2021 0 BE 1960 Males Total Persons 4448001
#> 4 SNPTN 2021 0 BG 1960 Males Total Persons 3815790
#> 5 SNPTN 2021 0 CH 1960 Males Total Persons 2553621
#> [ reached 'max' / getOption("max.print") -- omitted 149511 rows ]It’s possible to retrieve the data for a variable at tercel level or a single tercet class (see ‘List of tercet’). This function computes the tercet values at level 0 for any requested country and tercet class.
The function permit to retrieve data at tercet level in two main ways:
The correct syntax to request data by tercet is the following:
ardeco_get_dataset_data(‘varcode’, tercet_code=val-tercet-code[, show_perc=TRUE])
where
Example:
# Check available tercet for variable SNPTD
ardeco_get_tercet_list('SNPTD')
#> tercet_code tercet_name tercet_class_code
#> 1 1 by Urban-Rural Typology 0
#> 2 1 by Urban-Rural Typology 2
#> 3 1 by Urban-Rural Typology 1
#> 4 2 by Urban-Rural Typology with Remoteness 0
#> 5 2 by Urban-Rural Typology with Remoteness 3
#> 6 2 by Urban-Rural Typology with Remoteness 4
#> 7 2 by Urban-Rural Typology with Remoteness 5
#> 8 2 by Urban-Rural Typology with Remoteness 6
#> tercet_class_name
#> 1 Predominantly urban
#> 2 Predominantly rural
#> 3 Intermediate
#> 4 Predominantly urban
#> 5 Intermediate, close to a city
#> 6 Intermediate, remote
#> 7 Predominantly rural, close to a city
#> 8 Predominantly rural, remote
# Retrieve absolute values for tercet 'Urban-Rural Typology' for variable 'SNPTD' for country 'IT', year 2020, nuts version 2021
mydf <- ardeco_get_dataset_data('SNPTD', tercet_code=1, nutscode='IT', year=2020, version=2021)
print(mydf)
#> VARIABLE VERSIONS LEVEL NUTSCODE TERCET_CLASS_CODE TERCET_CODE YEAR UNIT
#> 1 SNPTD 2021 0 IT 0 1 2020 Persons
#> 2 SNPTD 2021 0 IT 1 1 2020 Persons
#> 3 SNPTD 2021 0 IT 2 1 2020 Persons
#> TERCET_NAME TERCET_CLASS_NAME VALUE
#> 1 by Urban-Rural Typology Predominantly urban 28475100
#> 2 by Urban-Rural Typology Intermediate 24767700
#> 3 by Urban-Rural Typology Predominantly rural 6196100
# Retrieve share for tercet 'Urban-Rural Typology' for variable 'SNPTD' for country 'IT', year 2020, nuts version 2021
mydf <- ardeco_get_dataset_data('SNPTD', tercet_code=1, nutscode='IT', year=2020, version=2021, show_perc=TRUE)
print(mydf)
#> VARIABLE VERSIONS LEVEL NUTSCODE TERCET_CLASS_CODE TERCET_CODE YEAR UNIT
#> 1 SNPTD 2021 0 IT 0 1 2020 Persons
#> 2 SNPTD 2021 0 IT 1 1 2020 Persons
#> 3 SNPTD 2021 0 IT 2 1 2020 Persons
#> TERCET_NAME TERCET_CLASS_NAME VALUE
#> 1 by Urban-Rural Typology Predominantly urban 47.91
#> 2 by Urban-Rural Typology Intermediate 41.67
#> 3 by Urban-Rural Typology Predominantly rural 10.42The correct syntax to request data by tercet_class is the following:
ardeco_get_dataset_data(‘varcode’, tercet_class_code=val-tercet-calss-code[, show_perc=TRUE])
where
Example:
# Check available tercet classes for variable SNPTD
ardeco_get_tercet_list('SNPTD')
#> tercet_code tercet_name tercet_class_code
#> 1 1 by Urban-Rural Typology 0
#> 2 1 by Urban-Rural Typology 2
#> 3 1 by Urban-Rural Typology 1
#> 4 2 by Urban-Rural Typology with Remoteness 0
#> 5 2 by Urban-Rural Typology with Remoteness 3
#> 6 2 by Urban-Rural Typology with Remoteness 4
#> 7 2 by Urban-Rural Typology with Remoteness 5
#> 8 2 by Urban-Rural Typology with Remoteness 6
#> tercet_class_name
#> 1 Predominantly urban
#> 2 Predominantly rural
#> 3 Intermediate
#> 4 Predominantly urban
#> 5 Intermediate, close to a city
#> 6 Intermediate, remote
#> 7 Predominantly rural, close to a city
#> 8 Predominantly rural, remote
# Retrieve absolute value for tercet class 'Predominantly urban' for variable 'SNPTD' for country 'IT', year 2020, nuts version 2021
mydf <- ardeco_get_dataset_data('SNPTD', tercet_class_code=0, nutscode='IT', year=2020, version=2021)
print(mydf)
#> VARIABLE VERSIONS LEVEL NUTSCODE TERCET_CLASS_CODE YEAR UNIT
#> 1 SNPTD 2021 0 IT 0 2020 Persons
#> TERCET_CLASS_NAME VALUE
#> 1 Predominantly urban 28475100
# Retrieve share for tercet class 'Predominantly urban' for variable 'SNPTD' for country 'IT', year 2020, nuts version 2021
mydf <- ardeco_get_dataset_data('SNPTD', tercet_class_code=0, nutscode='IT', year=2020, version=2021, show_perc=TRUE)
print(mydf)
#> VARIABLE VERSIONS LEVEL NUTSCODE TERCET_CLASS_CODE YEAR UNIT
#> 1 SNPTD 2021 0 IT 0 2020 Persons
#> TERCET_CLASS_NAME VALUE
#> 1 Predominantly urban 47.91