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.
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>
# }