Thell

Community Imposed Answer Acceptance

(For Abandoned Questions)

The Unanswered questions listings are a great place to find questions with some meat on their bones that take some time, thought, research and self-learning to answer, but the quantity of questions that build up which actually do have an answer makes finding those fun gems not so fun to find.

Sure, we have advanced search syntax, data.stackexchange queries and filtered questions but none of these stop the build up of abandoned and answered but unaccepted questions.

In the blog post Why Can’t I Accept My Own Answer? Jeff Atwood gives a great explanation of the spirit of accepting answers. Two pertinent quotes from that posting are...

Thell

---
title: "Pandoc MathTest"
author: "Thell"
date: "04/09/2014"
output:
  html_document: default
  pdf_document:
    pandoc_args: --tex-escape-outer-math
---

Thell

---
title: "SO22524822"
author: "Thell"
date: "03/21/2014"
output: html_document
---

```{r setup}
# A killPrefix hook.
default_output_hook <- knitr::knit_hooks$get("output")

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clang++ -I/usr/share/R/include -DNDEBUG -I../inst/include/  -I"/home/thell/R/x86_64-pc-linux-gnu-library/3.0/Rcpp/include"   -fpic  -Wall -O3 -c RcppExports.cpp -o RcppExports.o
14		
** libs
15		
clang++ -I/usr/share/R/include -DNDEBUG -I../inst/include/  -I"/home/thell/R/x86_64-pc-linux-gnu-library/3.0/Rcpp/include"   -fpic  -Wall -O3 -c RcppMultiPrecision.cpp -o RcppMultiPrecision.o
16		
clang++ -shared -o RcppMultiPrecision.so RcppExports.o RcppMultiPrecision.o -lmpfr -lgmp -L/usr/lib/R/lib -lR

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/***
 * 
 * Scenario:
 *
 *  You desire to use RCPP_EXPOSE_CLASS on a class and it needs to store
 *  a pointer to somewhere in a constructor initialized container either
 *  using the default initializer list or from within the constructor.
 * 
 * Problem:
 * 

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_Why_ it is so slow? A little research located a mail group posting from a year ago this month where @hadley, the package author, [states][2] 

> This is a drawback of the way that ddply always works with data
frames.  It will be a bit faster if you use summarise instead of
data.frame (because data.frame is very slow), but I'm still thinking
about how to overcome this fundamental limitation of the ddply
approach.

---

Thell

// [[Rcpp::plugins("cpp11")]]

#include <RcppCommon.h>

class preVector;
RCPP_EXPOSED_CLASS(preVector)

class postVector;
RCPP_EXPOSED_CLASS(postVector)

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// [[Rcpp::plugins("cpp11")]]

#include <ostream>
#include <iterator>
#include <RcppCommon.h>

class BadIter;
RCPP_EXPOSED_CLASS(BadIter)

#include <Rcpp.h>

Thell

myread.csv <- function( file, header=TRUE, sep=",", quote="\"", dec=".", fill=TRUE, comment.char="", skip.footer=0, ... ) {
 file <- readLines(file)
 if( skip.footer > 0 ) file <- file[-(skip.footer)]
 read.csv( file, header, sep, quote, dec, fill, comment.char, ... )
}

Thell

```{r setup, echo=FALSE, results='hide'}
chunkref <- local({
  function(chunklabel) {
    sprintf('[%s](#%s)', chunklabel, chunklabel )
  }  
})

secref <- local({
  function(seclabel) {
    sprintf('[%s](#%s)', seclabel, seclabel )