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Read input-output tables from temporary directory

Source: R/iotables_read_tempdir.R
iotables_read_tempdir.Rd

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

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.

Details

Possible source parameters:

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

See also

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

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: 102 × 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 92 more rows
#> # ℹ 2 more variables: year <dbl>, data <list>

iotables_read_tempdir (source = "naio_10_pyp1750")
#> # A tibble: 102 × 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 92 more rows
#> # ℹ 2 more variables: year <dbl>, data <list>
# }