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"),
  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,
  verbose = FALSE
)

# S3 method for class 'list'
getData(
  scen,
  name = NULL,
  ...,
  merge = FALSE,
  timeframe = c("lowest", "highest", "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,
  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.
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?
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