Extract data from energyRt objects

Generic accessor. Dispatches on the object class:

scenario (or a list of scenarios): interpolated/solved data from modInp/modOut – see the scenario method below.
model objects (technology, commodity, storage, supply, demand, trade, ...): the object's own raw input slot data (pre-interpolation).
model / repository: raw input slots stacked across all contained objects.

Model-object methods (technology, commodity, storage, supply, demand, trade) return the object's raw input slot data. model/repository stack that across all contained objects. Use name to select slot(s) (e.g. "invcost"), ... to filter (region=, year=, comm=, or col_=regex), merge=TRUE for one long tidy frame (param/value), and interpolate=TRUE to expand year-bearing slots over years (or the observed year range) with each parameter's default interpolation rule.

Usage

getData(scen, ...)

# S3 method for class 'scenario'
getData(
  scen,
  name = NULL,
  ...,
  merge = FALSE,
  timeframe = c("lowest", "highest", "all"),
  geolevel = c("finest", "coarsest", "all"),
  process = FALSE,
  parameters = TRUE,
  variables = TRUE,
  sets = FALSE,
  maps = FALSE,
  ignore.case = TRUE,
  newNames = NULL,
  newValues = NULL,
  na.rm = FALSE,
  digits = NULL,
  drop.zeros = FALSE,
  add_weights = "auto",
  add_period_length = "auto",
  apply_weights = FALSE,
  apply_period_length = FALSE,
  asTibble = TRUE,
  as_data_table = FALSE,
  stringsAsFactors = FALSE,
  yearsAsFactors = FALSE,
  drop_duplicated_scenarios = TRUE,
  scenNameInList = as.logical(length(scen) - 1),
  unfold = TRUE,
  variants = TRUE,
  verbose = FALSE
)

# S3 method for class 'list'
getData(
  scen,
  name = NULL,
  ...,
  merge = FALSE,
  timeframe = c("lowest", "highest", "all"),
  geolevel = c("finest", "coarsest", "all"),
  process = FALSE,
  parameters = TRUE,
  variables = TRUE,
  sets = FALSE,
  maps = FALSE,
  ignore.case = TRUE,
  newNames = NULL,
  newValues = NULL,
  na.rm = FALSE,
  digits = NULL,
  drop.zeros = FALSE,
  add_weights = "auto",
  add_period_length = "auto",
  apply_weights = FALSE,
  apply_period_length = FALSE,
  asTibble = TRUE,
  as_data_table = FALSE,
  stringsAsFactors = FALSE,
  yearsAsFactors = FALSE,
  drop_duplicated_scenarios = TRUE,
  scenNameInList = as.logical(length(scen) - 1),
  unfold = TRUE,
  variants = TRUE,
  verbose = FALSE
)

# Default S3 method
getData(scen, ...)

get_data(scen, ...)

# S3 method for class 'technology'
getData(
  obj,
  name = NULL,
  ...,
  merge = FALSE,
  interpolate = FALSE,
  years = NULL,
  process = FALSE,
  asTibble = TRUE,
  ignore.case = TRUE,
  verbose = FALSE
)

# S3 method for class 'commodity'
getData(
  obj,
  name = NULL,
  ...,
  merge = FALSE,
  interpolate = FALSE,
  years = NULL,
  process = FALSE,
  asTibble = TRUE,
  ignore.case = TRUE,
  verbose = FALSE
)

# S3 method for class 'storage'
getData(
  obj,
  name = NULL,
  ...,
  merge = FALSE,
  interpolate = FALSE,
  years = NULL,
  process = FALSE,
  asTibble = TRUE,
  ignore.case = TRUE,
  verbose = FALSE
)

# S3 method for class 'supply'
getData(
  obj,
  name = NULL,
  ...,
  merge = FALSE,
  interpolate = FALSE,
  years = NULL,
  process = FALSE,
  asTibble = TRUE,
  ignore.case = TRUE,
  verbose = FALSE
)

# S3 method for class 'demand'
getData(
  obj,
  name = NULL,
  ...,
  merge = FALSE,
  interpolate = FALSE,
  years = NULL,
  process = FALSE,
  asTibble = TRUE,
  ignore.case = TRUE,
  verbose = FALSE
)

# S3 method for class 'trade'
getData(
  obj,
  name = NULL,
  ...,
  merge = FALSE,
  interpolate = FALSE,
  years = NULL,
  process = FALSE,
  asTibble = TRUE,
  ignore.case = TRUE,
  verbose = FALSE
)

# S3 method for class 'import'
getData(
  obj,
  name = NULL,
  ...,
  merge = FALSE,
  interpolate = FALSE,
  years = NULL,
  process = FALSE,
  asTibble = TRUE,
  ignore.case = TRUE,
  verbose = FALSE
)

# S3 method for class 'export'
getData(
  obj,
  name = NULL,
  ...,
  merge = FALSE,
  interpolate = FALSE,
  years = NULL,
  process = FALSE,
  asTibble = TRUE,
  ignore.case = TRUE,
  verbose = FALSE
)

# S3 method for class 'weather'
getData(
  obj,
  name = NULL,
  ...,
  merge = FALSE,
  interpolate = FALSE,
  years = NULL,
  process = FALSE,
  asTibble = TRUE,
  ignore.case = TRUE,
  verbose = FALSE
)

# S3 method for class 'model'
getData(
  obj,
  name = NULL,
  ...,
  merge = FALSE,
  interpolate = FALSE,
  years = NULL,
  process = FALSE,
  asTibble = TRUE,
  ignore.case = TRUE,
  verbose = FALSE
)

# S3 method for class 'repository'
getData(
  obj,
  name = NULL,
  ...,
  merge = FALSE,
  interpolate = FALSE,
  years = NULL,
  process = FALSE,
  asTibble = TRUE,
  ignore.case = TRUE,
  verbose = FALSE
)

Arguments

scen: Object scenario or list of scenarios.
...: filters for various sets (setname = c(val1, val2) or setname_ = "matching pattern"), see details.
name: character vector with names of parameters and/or variables.
merge: if TRUE, the search results will be merged in one dataframe; the named list will be returned if FALSE. When TRUE, a data.frame (empty if nothing matched) is always returned, never NULL.
timeframe: controls sub-annual time aggregation of results that carry a slice column. One of "lowest" (default, aggregate/sum flows up to the coarsest level, normally ANNUAL), "highest" (native/finest, as stored), "all" (return every timeframe level stacked), or an explicit calendar level name (e.g. "SEASON", "YDAY") to aggregate to that level. Non-slice data, and state/level variables (e.g. vStorageStore) for which summing over slices is meaningless, are returned unchanged.
geolevel: controls spatial aggregation of results that carry a region column, the spatial twin of timeframe. One of "finest" (default, native resolution as stored), "coarsest" (aggregate up to the top geoscale level), "all" (return every level stacked), or an explicit geoscale level name (e.g. "zone", "nation"). Requires a geoscale on the model; without one this is inert. Inter-regional flow variables (e.g. vTradeIr) are returned unchanged, because summing a flow across regions double-counts it — the spatial counterpart of leaving state variables alone under timeframe.
process: if TRUE, dimensions "tech", "stg", "trade", "imp", "expp", "dem", and "sup" will be renamed with "process".
parameters: if TRUE, parameters will be included in the search and returned if found.
variables: if TRUE, variables will be included in the search and returned if found.
maps: if TRUE, map-type parameters (membership mappings, no value column) are also returned.
ignore.case: grepl parameter if regular expressions are used in '...' or 'name_'.
newNames: renaming sets, named character vector or list with new names as values, and old names as names - the input parameter to renameSets function. The operation is performed before merging the data (merge parameter).
newValues: revalue sets, named character vector or list with new values as values, and old values as names - the input parameter to revalueSets function. The operation is performed after merging the data (merge parameter).
na.rm: if TRUE, NA values will be dropped.
digits: if integer, indicates the number of decimal places for rounding, if NULL - no actions.
drop.zeros: logical, should rows containing zero values be filtered out.
asTibble: logical, if the data.frames should be converted into tibbles.
stringsAsFactors: logical, should the sets values be converted to factors?
yearsAsFactors: logical, should year be converted to factors? Set 'year' is integer by default.
scenNameInList: logical, should the name of the scenarios be used if not provided in the list with several scenarios?
variants: logical, default TRUE: attach the technology-variant provenance columns base, vintage and cluster (from scenario@modInp@sets$tech_variant) to the returned data, so results of a vintaged / clustered technology can be grouped or rolled up by those dimensions without parsing the variant names. Has no effect when the model has no variants, so the returned shape is unchanged for such models. Set FALSE to suppress the columns.
verbose: logical, print progress and diagnostic messages.
interpolate: if TRUE, expand year-bearing object slots over the target years using each parameter's default interpolation rule (for quick demonstration plots). Default FALSE (raw data as stored). Only meaningful for object/model/repository methods.
years: integer vector of target milestone years for interpolate (default: the yearly range observed in the data).

Examples

if (FALSE) { # \dontrun{
data("utopia_scen_BAU.RData")
getData(scen, name = "pDemand", year = 2015, merge = TRUE)
getData(scen, name = "vTechOut", comm = "ELC", merge = TRUE, year = 2015)
elc2050 <- getData(scen, parameters = FALSE, comm = "ELC", year = 2050)
names(elc2050)
elc2050$vBalance
} # }

Usage

Arguments

See also

Examples