This code written with the 'Rcpp' package replaces NA's values of numeric/integer columns by their mean. It's designed to work with big dataframe with thousand of columns and rows.

impute.cpp

#include <Rcpp.h>
using namespace Rcpp;

// [[Rcpp::export]]

List modiframe(DataFrame& df   ) {
    //nrow and ncol of the dataframe:
    int nrow = df.nrows(), ncol= df.size() ;
    double moy(0);
    //define an empty list~dataframe 
    List output(ncol) ;
  //loop through columns and copy particularities:
  ///and through rows of each of them then
  //compute the MEAN for non-missing elts:
    for( int i=0; i<ncol; i++) {
        output[i] = df[i] ;
        //names[i] = outputnames[i] ;
        NumericVector tmp=df[i];
        for (int j=0; j<nrow; j++) {
           if ( R_IsNA(tmp[j])==false ) {
              moy=moy+tmp[j];
              }    
        }
      moy=moy/nrow;
      //loop through columns again and impute missing values::
      for (int j=0; j<nrow; j++) {
        if ( R_IsNA(tmp[j]) ) {
          //Rcout << "impute missing value at position " << j + 1  << " of column f"<< i + 1 <<std::endl;
          tmp[j]=moy;
          output[i]=tmp;
        }  
      }
    }
    //to render the ouptut dataframe:
    output.attr("names")=df.attr("names");
    output.attr("class") = df.attr("class") ;
    output.attr("row.names") = df.attr("row.names") ;
    return wrap(output) ;
}