Get univariate or bivariate data for a specific source

Fetches univariate or bivariate data for a given source, year, NUTS level, and selected filters.

Usage

mi_data(
  x_source,
  y_source = NULL,
  year,
  level,
  x_filters = list(),
  y_filters = NULL,
  limit = 2500
)

Arguments

x_source: A character string specifying the source name for the x variable.
y_source: (Optional) A character string specifying the source name for the y variable.
year: A character or integer specifying the year.
level: A character string specifying the NUTS level ("0", "1", "2", or "3").
x_filters: A named list where the names are the filter fields for the x variable and the values are the selected values for those fields. Default is an empty list. To find out which filters to use, use mi_source_filters with the desired source_name.
y_filters: (Optional) A named list where the names are the filter fields for the y variable and the values are the selected values for those fields. Default is NULL. To find out which filters to use, use mi_source_filters with the desired source_name.
limit: An integer specifying the maximum number of results to return. Default is 2500. This default should be enough for most uses, as it is well above the number of NUTS 3 regions in the EU. The maximum limited by the API is 10000.

Value

A tibble with the following columns:

geo: code for the (NUTS) region at the requested level.
geo_name: name of the (NUTS) region at the requested level.
geo_source: source (type) of the spatial units at the requested level.
geo_year: year of the (NUTS) region at the requested level.
x_year: The year of the predictor variable (X), included in bivariate requests.
y_year (optional): The year of the outcome variable (Y), included in bivariate requests (only included when y_source is provided).
x: the value of the univariate variable.
y (optional): the value of the y variable (only included when y_source is provided).

Examples

# \donttest{
# Univariate example
mi_data(
  x_source = "TGS00010",
  year = 2020,
  level = "2",
  x_filters = list(isced11 = "TOTAL", sex = "F")
)
#> # A tibble: 334 × 6
#>    geo   geo_name         geo_source geo_year x_year     x
#>    <chr> <chr>            <chr>      <chr>    <chr>  <dbl>
#>  1 AL01  Veri             NUTS       2021     2020    NA  
#>  2 AL02  Qender           NUTS       2021     2020    NA  
#>  3 AL03  Jug              NUTS       2021     2020    NA  
#>  4 AT11  Burgenland       NUTS       2021     2020    NA  
#>  5 AT12  Niederösterreich NUTS       2021     2020     4  
#>  6 AT13  Wien             NUTS       2021     2020    10  
#>  7 AT21  Kärnten          NUTS       2021     2020     5  
#>  8 AT22  Steiermark       NUTS       2021     2020     4.4
#>  9 AT31  Oberösterreich   NUTS       2021     2020     3.7
#> 10 AT32  Salzburg         NUTS       2021     2020     3  
#> # ℹ 324 more rows

# Bivariate example
mi_data(
  x_source = "TGS00010",
  y_source = "DEMO_R_MLIFEXP",
  year = 2020,
  level = "2",
  x_filters = list(isced11 = "TOTAL", sex = "F"),
  y_filters = list(age = "Y2", sex = "F")
)
#> # A tibble: 332 × 8
#>    geo   geo_name         geo_source geo_year x_year y_year     x     y
#>    <chr> <chr>            <chr>      <chr>    <chr>  <chr>  <dbl> <dbl>
#>  1 AL01  Veri             NUTS       2018     2020   2020    NA    78.3
#>  2 AL02  Qender           NUTS       2018     2020   2020    NA    80.4
#>  3 AL03  Jug              NUTS       2018     2020   2020    NA    76.9
#>  4 AT11  Burgenland       NUTS       2018     2020   2020    NA    81.8
#>  5 AT12  Niederösterreich NUTS       2018     2020   2020     4    81.8
#>  6 AT13  Wien             NUTS       2018     2020   2020    10    80.9
#>  7 AT21  Kärnten          NUTS       2018     2020   2020     5    82.2
#>  8 AT22  Steiermark       NUTS       2018     2020   2020     4.4  81.9
#>  9 AT31  Oberösterreich   NUTS       2018     2020   2020     3.7  82.3
#> 10 AT32  Salzburg         NUTS       2018     2020   2020     3    82.7
#> # ℹ 322 more rows
# }