SEXP summarise_not_grouped(DataFrame df, const LazyDots& dots){
int nexpr = dots.size() ;
if( nexpr == 0) return DataFrame() ;
LazySubsets subsets( df ) ;
NamedListAccumulator<DataFrame> accumulator ;
List results(nexpr) ;
for( int i=0; i<nexpr; i++){
Rcpp::checkUserInterrupt() ;
const Lazy& lazy = dots[i] ;
Environment env = lazy.env() ;
Shield<SEXP> expr_(lazy.expr()) ; SEXP expr = expr_ ;
boost::scoped_ptr<Result> res( get_handler( expr, subsets, env ) ) ;
SEXP result ;
if(res) {
result = results[i] = res->process( FullDataFrame(df) ) ;
} else {
result = results[i] = CallProxy( lazy.expr(), subsets, env).eval() ;
}
if( Rf_length(result) != 1 ){
stop( "expecting result of length one, got : %d", Rf_length(result) ) ;
}
accumulator.set(lazy.name(), result );
subsets.input( lazy.name(), result ) ;
}
List data = accumulator ;
copy_most_attributes(data, df) ;
data.names() = accumulator.names() ;
set_rownames(data, 1 ) ;
return data ;
}
SEXP summarise_grouped(const DataFrame& df, const LazyDots& dots){
Data gdf(df) ;
int nexpr = dots.size() ;
int nvars = gdf.nvars() ;
check_not_groups(dots, gdf);
NamedListAccumulator<Data> accumulator ;
int i=0;
List results(nvars + nexpr) ;
for( ; i<nvars; i++){
results[i] = shared_SEXP(gdf.label(i)) ;
accumulator.set( PRINTNAME(gdf.symbol(i)), results[i] ) ;
}
Subsets subsets(gdf) ;
for( int k=0; k<nexpr; k++, i++ ){
Rcpp::checkUserInterrupt() ;
const Lazy& lazy = dots[k] ;
const Environment& env = lazy.env() ;
Shield<SEXP> expr_(lazy.expr()) ; SEXP expr = expr_ ;
boost::scoped_ptr<Result> res( get_handler( expr, subsets, env ) );
// if we could not find a direct Result
// we can use a GroupedCallReducer which will callback to R
if( !res ) {
res.reset( new GroupedCallReducer<Data, Subsets>( lazy.expr(), subsets, env) );
}
RObject result = res->process(gdf) ;
results[i] = result ;
accumulator.set( lazy.name(), result );
subsets.input( lazy.name(), SummarisedVariable(result) ) ;
}
List out = accumulator ;
copy_most_attributes( out, df) ;
out.names() = accumulator.names() ;
int nr = gdf.ngroups() ;
set_rownames(out, nr ) ;
if( gdf.nvars() > 1){
out.attr( "class" ) = classes_grouped<Data>() ;
List vars = gdf.data().attr("vars") ;
vars.erase( gdf.nvars() - 1) ;
out.attr( "vars") = vars ;
out.attr( "labels") = R_NilValue ;
out.attr( "indices") = R_NilValue ;
out.attr( "group_sizes") = R_NilValue ;
out.attr( "biggest_group_size") = R_NilValue ;
out.attr( "drop" ) = true ;
} else {
out.attr( "class" ) = classes_not_grouped() ;
SET_ATTRIB( out, strip_group_attributes(out) ) ;
}
return out ;
}
