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Read input-output tables from temporary directory

Validate the source input parameter and try to load the table from the current sessions' temporary directory.

  • naio_10_cp1700Symmetric input-output table at basic prices (product by product)

  • naio_10_pyp1700Symmetric input-output table at basic prices (product by product) (previous years prices)

  • naio_10_cp1750Symmetric input-output table at basic prices (industry by industry)

  • naio_10_pyp1750Symmetric input-output table at basic prices (industry by industry) (previous years prices)

  • naio_10_cp15Supply table at basic prices incl. transformation into purchasers' prices

  • naio_10_cp16Use table at purchasers' prices

  • naio_10_cp1610Use table at basic prices

  • naio_10_pyp1610Use table at basic prices (previous years prices) (naio_10_pyp1610)

  • naio_10_cp1620Table of trade and transport margins at basic prices

  • naio_10_pyp1620Table of trade and transport margins at previous years' prices

  • naio_10_cp1630Table of taxes less subsidies on products at basic prices

  • naio_10_pyp1630Table of taxes less subsidies on products at previous years' prices

  • uk_2010_siotUnited Kingdom Input-Output Analytical Tables data

Usage

iotables_read_tempdir(source = "naio_10_cp1700")

Arguments

source

See the available list of sources above in the Description. Defaults to source = "naio_10_cp1700".

Value

A nested data frame. Each input-output table is in a separate row of the nested output, where all the metadata are in columns, and the actual, tidy, ordered input-output table is in the data data column.

See also

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>

iotables_read_tempdir (source = "naio_10_pyp1750")
#> # 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>
# }