up Rcpp dep to 0.12.0

DESCRIPTION

@@ -36,7 +36,7 @@ Suggests:
     methods
 VignetteBuilder: knitr
 LazyData: yes
-LinkingTo: Rcpp (>= 0.11.6),
+LinkingTo: Rcpp (>= 0.12.0),
     BH (>= 1.58.0-1)
 License: MIT + file LICENSE
 Collate:

NEWS.md

@@ -46,7 +46,9 @@
 * Introduced the SubsetVectorVisitor class and various implementations, and the
   DataFrameSubsetVisitors. Simplified the VectorVisitor and DataFrameVisitors.
   This deals with several performance regression problems.
-
+
+* up Rcpp dependency to 0.12.0, and remove the obsolete SHALLOW_COPY workaround
+
 # dplyr 0.4.2
 
 This is a minor release containing fixes for a number of crashes and issues identified by R CMD CHECK. There is one new "feature": dplyr no longer complains about unrecognised attributes, and instead just copies them over to the output.

inst/include/dplyr/workarounds.h

@@ -1,18 +1,6 @@
 #ifndef DPLYR_WORKAROUND_H
 #define DPLYR_WORKAROUND_H
 
-// Rcpp workaround - get rid of this once we have a released version
-//                   greater than 0.11.6
-#if RCPP_DEV_VERSION > RcppDevVersion(0,11,6,0)
-    #define SHALLOW_COPY(__NAME__,__SOURCE__) DataFrame __NAME__(shallow_copy(__SOURCE__))
-#else
-    #define SHALLOW_COPY(__NAME__,__SOURCE__)                       \
-        SEXP shallow_copied = PROTECT( shallow_copy(__SOURCE__) ) ; \
-        DataFrame __NAME__(shallow_copied) ;                        \
-        UNPROTECT(1)
-#endif
-
-// C/R API workaround
 // installChar was introduced in R 3.2.0
 #ifndef installChar
   #define installChar(x) Rf_install(CHAR(x))

src/dplyr.cpp

@@ -1449,7 +1449,7 @@ IntegerVector match_data_frame( DataFrame x, DataFrame y){
 // [[Rcpp::export]]
 DataFrame grouped_df_impl( DataFrame data, ListOf<Symbol> symbols, bool drop ){
     assert_all_white_list(data);
-    SHALLOW_COPY(copy,data) ;
+    DataFrame copy( shallow_copy(data));
     copy.attr("vars") = symbols ;
     copy.attr("drop") = drop ;
     if( !symbols.size() )
@@ -1558,7 +1558,7 @@ DataFrame build_index_adj(DataFrame df, ListOf<Symbol> symbols ){
 
 // [[Rcpp::export]]
 DataFrame grouped_df_adj_impl( DataFrame data, ListOf<Symbol> symbols, bool drop ){
-    SHALLOW_COPY(copy,data) ;
+    DataFrame copy( shallow_copy(data));
     copy.attr("vars") = symbols ;
     copy.attr("drop") = drop ;
     return build_index_adj(data, symbols) ;
@@ -2019,7 +2019,7 @@ SEXP n_distinct(SEXP x, bool na_rm = false){
 
 // [[Rcpp::export]]
 DataFrame as_regular_df(DataFrame df){
-  SHALLOW_COPY(copy,df) ;
+  DataFrame copy( shallow_copy(df));
   SET_ATTRIB(copy, strip_group_attributes(df)) ;
   SET_OBJECT(copy, OBJECT(df)) ;
   copy.attr("class") = CharacterVector::create("data.frame") ;
@@ -2028,7 +2028,7 @@ DataFrame as_regular_df(DataFrame df){
 
 // [[Rcpp::export]]
 DataFrame ungroup_grouped_df( DataFrame df){
-  SHALLOW_COPY(copy,df) ;
+  DataFrame copy( shallow_copy(df));
   SET_ATTRIB(copy, strip_group_attributes(df)) ;
   return copy ;
 }

src/select.cpp

@@ -5,62 +5,59 @@ using namespace dplyr ;
 
 SEXP select_not_grouped( const DataFrame& df, const CharacterVector& keep, CharacterVector new_names ){
   CharacterVector names = df.names() ;
-  IntegerVector positions = match( keep, names ); 
-  int n = keep.size() ; 
+  IntegerVector positions = match( keep, names );
+  int n = keep.size() ;
   List res(n) ;
   for( int i=0; i<n; i++){
       int pos = positions[i] ;
       if( pos < 1 || pos > df.size() ){
           std::stringstream s ;
-          if( pos == NA_INTEGER ){ 
+          if( pos == NA_INTEGER ){
               s << "NA" ;
           } else {
-              s << pos ;    
+              s << pos ;
           }
-          stop( "invalid column index : %d for variable: %s = %s", 
+          stop( "invalid column index : %d for variable: %s = %s",
               s.str(), CHAR((SEXP)new_names[i]), CHAR((SEXP)keep[i]) );
       }
-      res[i] = df[ pos-1 ] ;  
+      res[i] = df[ pos-1 ] ;
   }
   copy_most_attributes(res, df) ;
-  res.names() = new_names ; 
-  return res ; 
+  res.names() = new_names ;
+  return res ;
 }
 
 DataFrame select_grouped( GroupedDataFrame gdf, const CharacterVector& keep, CharacterVector new_names ){
   int n = keep.size() ;
   DataFrame copy = select_not_grouped( gdf.data(), keep, new_names );
-  
-  // handle vars  attribute : make a shallow copy of the list and alter 
+
+  // handle vars  attribute : make a shallow copy of the list and alter
   //   its names attribute
-  // 
-  // note: this does not need the SHALLOW_COPY macro because the 
-  //      Vector(SEXP) ctor is fine
-  List vars = shallow_copy( copy.attr("vars") ); 
-
+  List vars = shallow_copy( copy.attr("vars") );
+
   int nv = vars.size() ;
   for( int i=0; i<nv; i++){
     SEXP s = PRINTNAME(vars[i]) ;
-    int j = 0; 
+    int j = 0;
     for( ; j < n; j++){
       if( s == keep[j] ){
-        vars[i] = Rf_installChar( new_names[j] );  
+        vars[i] = Rf_installChar( new_names[j] );
       }
     }
   }
-  
+
   copy.attr("vars") = vars ;
-    
+
   // hangle labels attribute
   //   make a shallow copy of the data frame and alter its names attributes
   if( !Rf_isNull( copy.attr("labels" ) ) ){
-      
+
     DataFrame original_labels( copy.attr("labels" ) ) ;
-    
-    SHALLOW_COPY(labels,original_labels) ;
+
+    DataFrame labels( shallow_copy(original_labels));
     CharacterVector label_names = clone<CharacterVector>( labels.names() ) ;
-    
-    IntegerVector positions = match( label_names, keep ); 
+
+    IntegerVector positions = match( label_names, keep );
     int nl = label_names.size() ;
     for( int i=0; i<nl; i++){
       label_names[i] = new_names[ positions[i]-1 ] ;
@@ -76,9 +73,8 @@ DataFrame select_grouped( GroupedDataFrame gdf, const CharacterVector& keep, Cha
 DataFrame select_impl( DataFrame df, CharacterVector vars ){
   check_valid_colnames(df) ;
   if( is<GroupedDataFrame>(df) ){
-    return select_grouped( GroupedDataFrame(df), vars, vars.names() ) ;  
+    return select_grouped( GroupedDataFrame(df), vars, vars.names() ) ;
   } else {
-    return select_not_grouped(df, vars, vars.names() ) ;  
+    return select_not_grouped(df, vars, vars.names() ) ;
   }
 }
-