code: def combine_vars(
      data: list[pd.DataFrame],
      environment: Environment,
      vars: list[str],
      drop: bool = True,
  ) -> pd.DataFrame:
      """
      Generate all combinations of data needed for facetting
  
      The first data frame in the list should be the default data
      for the plot. Other data frames in the list are ones that are
      added to the layers.
      """
      if len(vars) == 0:
          return pd.DataFrame()
  
      # For each layer, compute the facet values
      values = [
          eval_facet_vars(df, vars, environment)
          for df in data
          if df is not None
      ]
  
      # Form the base data frame which contains all combinations
      # of facetting variables that appear in the data
      has_all = [x.shape[1] == len(vars) for x in values]
      if not any(has_all):
          raise PlotnineError(
              "At least one layer must contain all variables "
              "used for facetting"
          )
      base = pd.concat(
          [x for i, x in enumerate(values) if has_all[i]], axis=0
      )
      base = base.drop_duplicates()
  
      if not drop:
          base = unique_combs(base)
  
      # sorts according to order of factor levels
      base = base.sort_values(base.columns.tolist())
  
      # Systematically add on missing combinations
      for i, value in enumerate(values):
          if has_all[i] or len(value.columns) == 0:
              continue
          old = base.loc[
              :, list(base.columns.difference(value.columns))
          ]
          new = value.loc[
              :, list(base.columns.intersection(value.columns))
          ].drop_duplicates()
  
          if not drop:
              new = unique_combs(new)
  
          base = pd.concat(
              [base, cross_join(old, new)], ignore_index=True
          )
  
      if len(base) == 0:
          raise PlotnineError(
              "Faceting variables must have at least one value"
          )
  
      base = base.reset_index(drop=True)
      return base
  
imports: from __future__ import annotations
  import itertools
  import types
  import typing
  from copy import copy, deepcopy
  import numpy
  import pandas
  import pandas.api.types
  from .._utils import cross_join, match
  from ..exceptions import PlotnineError
  from ..scales.scales import Scales
  from .strips import Strips
label_desc: Use image node_id 1 for calling a global function with example usage: combine_vars(data, environment, vars, drop) and returns: base, pd