Extract metadata from a downloaded IO table

Return only the metadata information from a nested input–output (IO) table (or related table) created by iotables_download(). The data list-column is removed, leaving only metadata rows.

Usage

iotables_metadata_get(dat = NULL, source = "naio_10_cp1700")

Arguments

dat: A nested tibble created by iotables_download(). Defaults to NULL, in which case the function attempts to read the file from tempdir().
source: Character. A valid data source code (see Sources).

Value

A tibble with only metadata columns. The data list-column is removed and unnested.

Details

If dat is NULL, the function tries to load the file corresponding to source from the current session's tempdir().

Sources

Supported Eurostat/ONS products include:

"naio_10_cp1700" — Symmetric IO table, basic prices (product × product)
"naio_10_pyp1700" — Symmetric IO table, basic prices (product × product), previous years’ prices
"naio_10_cp1750" — Symmetric IO table, basic prices (industry × industry)
"naio_10_pyp1750" — Symmetric IO table, basic prices (industry × industry), previous years’ prices
"naio_10_cp15" — Supply table at basic prices incl. margins/taxes
"naio_10_cp16" — Use table at purchasers’ prices
"naio_10_cp1610" — Use table at basic prices
"naio_10_pyp1610" — Use table at basic prices (previous years’ prices)
"naio_10_cp1620" / "naio_10_pyp1620" — Trade & transport margins
"naio_10_cp1630" / "naio_10_pyp1630" — Taxes less subsidies on products
"uk_2010_siot" — United Kingdom IO Analytical Tables

Examples

# \donttest{
# Download data into tempdir()
iotables_download(source = "naio_10_pyp1750")
#> The naio_10_pyp1750_processed.rds is retrieved from the temporary directory.
#> Returning the processed SIOTs from tempdir. You can override this with force_download=TRUE.
#> # A tibble: 114 × 10
#>    unit    stk_flow geo   time       unit_lab    stk_flow_lab geo_lab time_lab  
#>    <chr>   <chr>    <chr> <date>     <chr>       <chr>        <chr>   <date>    
#>  1 MIO_EUR DOM      DK    2008-01-01 Million eu… Domestic     Denmark 2008-01-01
#>  2 MIO_EUR DOM      DK    2009-01-01 Million eu… Domestic     Denmark 2009-01-01
#>  3 MIO_EUR DOM      DK    2010-01-01 Million eu… Domestic     Denmark 2010-01-01
#>  4 MIO_EUR DOM      DK    2011-01-01 Million eu… Domestic     Denmark 2011-01-01
#>  5 MIO_EUR DOM      DK    2012-01-01 Million eu… Domestic     Denmark 2012-01-01
#>  6 MIO_EUR DOM      DK    2013-01-01 Million eu… Domestic     Denmark 2013-01-01
#>  7 MIO_EUR DOM      DK    2014-01-01 Million eu… Domestic     Denmark 2014-01-01
#>  8 MIO_EUR DOM      DK    2015-01-01 Million eu… Domestic     Denmark 2015-01-01
#>  9 MIO_EUR DOM      DK    2016-01-01 Million eu… Domestic     Denmark 2016-01-01
#> 10 MIO_EUR DOM      DK    2017-01-01 Million eu… Domestic     Denmark 2017-01-01
#> # ℹ 104 more rows
#> # ℹ 2 more variables: year <dbl>, data <list>

# Extract metadata only
iotables_metadata_get(source = "naio_10_pyp1750")
#> # A tibble: 114 × 9
#>    unit    stk_flow geo   time       unit_lab    stk_flow_lab geo_lab time_lab  
#>    <chr>   <chr>    <chr> <date>     <chr>       <chr>        <chr>   <date>    
#>  1 MIO_EUR DOM      DK    2008-01-01 Million eu… Domestic     Denmark 2008-01-01
#>  2 MIO_EUR DOM      DK    2009-01-01 Million eu… Domestic     Denmark 2009-01-01
#>  3 MIO_EUR DOM      DK    2010-01-01 Million eu… Domestic     Denmark 2010-01-01
#>  4 MIO_EUR DOM      DK    2011-01-01 Million eu… Domestic     Denmark 2011-01-01
#>  5 MIO_EUR DOM      DK    2012-01-01 Million eu… Domestic     Denmark 2012-01-01
#>  6 MIO_EUR DOM      DK    2013-01-01 Million eu… Domestic     Denmark 2013-01-01
#>  7 MIO_EUR DOM      DK    2014-01-01 Million eu… Domestic     Denmark 2014-01-01
#>  8 MIO_EUR DOM      DK    2015-01-01 Million eu… Domestic     Denmark 2015-01-01
#>  9 MIO_EUR DOM      DK    2016-01-01 Million eu… Domestic     Denmark 2016-01-01
#> 10 MIO_EUR DOM      DK    2017-01-01 Million eu… Domestic     Denmark 2017-01-01
#> # ℹ 104 more rows
#> # ℹ 1 more variable: year <dbl>
# }