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Get Metadata from Nested iotables File

Source: R/iotables_metadata_get.R
iotables_metadata_get.Rd

Remove the data column and return only the metadata information of input-output (or related tables) from a source. If dat is not inputed as a nested data frame created by iotables_download, validate the source input parameter and try to load the table from the current sessions' temporary directory.

  • naio_10_cp1700 Symmetric input-output table at basic prices (product by product)

  • naio_10_pyp1700 Symmetric input-output table at basic prices (product by product) (previous years prices)

  • naio_10_cp1750 Symmetric input-output table at basic prices (industry by industry)

  • naio_10_pyp1750 Symmetric input-output table at basic prices (industry by industry) (previous years prices)

  • naio_10_cp15 Supply table at basic prices incl. transformation into purchasers' prices

  • 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_pyp1610)

  • naio_10_cp1620 Table of trade and transport margins at basic prices

  • naio_10_pyp1620 Table of trade and transport margins at previous years' prices

  • naio_10_cp1630 Table of taxes less subsidies on products at basic prices

  • naio_10_pyp1630 Table of taxes less subsidies on products at previous years' prices

  • uk_2010_siot United Kingdom Input-Output Analytical Tables data

Usage

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

Arguments

dat

A nested data file created by iotables_download. Defaults to NULL in which case an attempt is made to find and read in the nested data from the current R sessions' temporary directory.

source

See the available list of sources above in the Description.

Value

A data frame, which contains the metadata of all available input-output tables from a specific source.

See also

Other import functions: airpol_get(), employment_get(), iotables_download(), iotables_read_tempdir()

Examples

# \donttest{
# The table must be present in the sessions' temporary directory:
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: 96 x 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    2018-01-01 Million eu~ Domestic     Denmark 2018-01-01
#>  2 MIO_EUR IMP      DK    2018-01-01 Million eu~ Imports      Denmark 2018-01-01
#>  3 MIO_EUR TOTAL    DK    2018-01-01 Million eu~ Total        Denmark 2018-01-01
#>  4 MIO_NAC DOM      DK    2018-01-01 Million un~ Domestic     Denmark 2018-01-01
#>  5 MIO_NAC IMP      DK    2018-01-01 Million un~ Imports      Denmark 2018-01-01
#>  6 MIO_NAC TOTAL    DK    2018-01-01 Million un~ Total        Denmark 2018-01-01
#>  7 MIO_EUR DOM      DK    2017-01-01 Million eu~ Domestic     Denmark 2017-01-01
#>  8 MIO_EUR IMP      DK    2017-01-01 Million eu~ Imports      Denmark 2017-01-01
#>  9 MIO_EUR TOTAL    DK    2017-01-01 Million eu~ Total        Denmark 2017-01-01
#> 10 MIO_NAC DOM      DK    2017-01-01 Million un~ Domestic     Denmark 2017-01-01
#> # ... with 86 more rows, and 2 more variables: year <dbl>, data <list>

# Now you can get the metadata:
iotables_metadata_get(source = "naio_10_pyp1750")
#> # A tibble: 96 x 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    2018-01-01 Million eu~ Domestic     Denmark 2018-01-01
#>  2 MIO_EUR IMP      DK    2018-01-01 Million eu~ Imports      Denmark 2018-01-01
#>  3 MIO_EUR TOTAL    DK    2018-01-01 Million eu~ Total        Denmark 2018-01-01
#>  4 MIO_NAC DOM      DK    2018-01-01 Million un~ Domestic     Denmark 2018-01-01
#>  5 MIO_NAC IMP      DK    2018-01-01 Million un~ Imports      Denmark 2018-01-01
#>  6 MIO_NAC TOTAL    DK    2018-01-01 Million un~ Total        Denmark 2018-01-01
#>  7 MIO_EUR DOM      DK    2017-01-01 Million eu~ Domestic     Denmark 2017-01-01
#>  8 MIO_EUR IMP      DK    2017-01-01 Million eu~ Imports      Denmark 2017-01-01
#>  9 MIO_EUR TOTAL    DK    2017-01-01 Million eu~ Total        Denmark 2017-01-01
#> 10 MIO_NAC DOM      DK    2017-01-01 Million un~ Domestic     Denmark 2017-01-01
#> # ... with 86 more rows, and 1 more variable: year <dbl>
# }