convert_table returns a list containing a data.table where values are
converted from years into time periods and from one regional mapping to
another. The function is calling convert_time_period and
convert_region. More details about the parameters in these functions.
The input table might be a data.table or a data.frame and
should contain a column "value". All others columns are considered as
indices.
Usage
convert_table(
.x,
...,
options = list(),
time_mapping = NULL,
from_reg = NULL,
to_reg = NULL,
agg_weight = NULL,
regions = NULL,
do_time_period = TRUE,
do_region = TRUE,
info = FALSE
)Arguments
- .x
a well-formatted data.table.
- ...
parameters to send to convert_region or convert_time_period.
- options
a list of parameters to send to convert_region or convert_time_period.
- time_mapping
a time mapping data.table.
- from_reg
initial regional mapping name or a data.table with the mapping.
- to_reg
final regional mapping name or a data.table with the mapping.
- agg_weight
aggregation weight data.table
- regions
optional list of region mappings (see Details for format)
- do_time_period
logical indicating whether years should be converted.
- do_region
logical indicating whether region should be converted.
- info
logical indicating whether to include information, only necessary when the agg_operator is "sumby".
Value
a data.table or list containing a converted data.table and information about the coperture if available.
See also
convert_gdx for WITCH gdx files.
Other conversion functions:
convert_duckdb(),
convert_gdx(),
convert_region(),
convert_sqlite(),
convert_time_period()
Examples
# Convert a country/year table into WITCH regions and time periods.
# Note that agg_weight and regions have no default here and must be given.
dt <- data.table::data.table(
iso3 = rep(c("FRA", "DEU", "ITA"), 2),
year = rep(c(2005, 2010), each = 3),
value = as.numeric(1:6)
)
convert_table(dt,
to_reg = "witch17",
time_mapping = "t30",
agg_weight = default_weights[["gdp"]],
regions = region_mappings)
#> t witch17 value
#> <char> <char> <num>
#> 1: 1 europe 6
#> 2: 2 europe 15