
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.
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' pricesnaio_10_cp16
Use table at purchasers' pricesnaio_10_cp1610
Use table at basic pricesnaio_10_pyp1610
Use table at basic prices (previous years prices) (naio_10_pyp1610)naio_10_cp1620
Table of trade and transport margins at basic pricesnaio_10_pyp1620
Table of trade and transport margins at previous years' pricesnaio_10_cp1630
Table of taxes less subsidies on products at basic pricesnaio_10_pyp1630
Table of taxes less subsidies on products at previous years' pricesuk_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: 96 × 10
#> unit stk_flow geo time unit_lab stk_f…¹ geo_lab time_lab year
#> <chr> <chr> <chr> <date> <chr> <chr> <chr> <date> <dbl>
#> 1 MIO_EUR DOM DK 2018-01-01 Million e… Domest… Denmark 2018-01-01 2018
#> 2 MIO_EUR IMP DK 2018-01-01 Million e… Imports Denmark 2018-01-01 2018
#> 3 MIO_EUR TOTAL DK 2018-01-01 Million e… Total Denmark 2018-01-01 2018
#> 4 MIO_NAC DOM DK 2018-01-01 Million u… Domest… Denmark 2018-01-01 2018
#> 5 MIO_NAC IMP DK 2018-01-01 Million u… Imports Denmark 2018-01-01 2018
#> 6 MIO_NAC TOTAL DK 2018-01-01 Million u… Total Denmark 2018-01-01 2018
#> 7 MIO_EUR DOM DK 2017-01-01 Million e… Domest… Denmark 2017-01-01 2017
#> 8 MIO_EUR IMP DK 2017-01-01 Million e… Imports Denmark 2017-01-01 2017
#> 9 MIO_EUR TOTAL DK 2017-01-01 Million e… Total Denmark 2017-01-01 2017
#> 10 MIO_NAC DOM DK 2017-01-01 Million u… Domest… Denmark 2017-01-01 2017
#> # … with 86 more rows, 1 more variable: data <list>, and abbreviated variable
#> # name ¹stk_flow_lab
iotables_read_tempdir (source = "naio_10_pyp1750")
#> # A tibble: 96 × 10
#> unit stk_flow geo time unit_lab stk_f…¹ geo_lab time_lab year
#> <chr> <chr> <chr> <date> <chr> <chr> <chr> <date> <dbl>
#> 1 MIO_EUR DOM DK 2018-01-01 Million e… Domest… Denmark 2018-01-01 2018
#> 2 MIO_EUR IMP DK 2018-01-01 Million e… Imports Denmark 2018-01-01 2018
#> 3 MIO_EUR TOTAL DK 2018-01-01 Million e… Total Denmark 2018-01-01 2018
#> 4 MIO_NAC DOM DK 2018-01-01 Million u… Domest… Denmark 2018-01-01 2018
#> 5 MIO_NAC IMP DK 2018-01-01 Million u… Imports Denmark 2018-01-01 2018
#> 6 MIO_NAC TOTAL DK 2018-01-01 Million u… Total Denmark 2018-01-01 2018
#> 7 MIO_EUR DOM DK 2017-01-01 Million e… Domest… Denmark 2017-01-01 2017
#> 8 MIO_EUR IMP DK 2017-01-01 Million e… Imports Denmark 2017-01-01 2017
#> 9 MIO_EUR TOTAL DK 2017-01-01 Million e… Total Denmark 2017-01-01 2017
#> 10 MIO_NAC DOM DK 2017-01-01 Million u… Domest… Denmark 2017-01-01 2017
#> # … with 86 more rows, 1 more variable: data <list>, and abbreviated variable
#> # name ¹stk_flow_lab
# }