On Sat, Sep 22, 2018 at 8:11 AM Dirk Eddelbuettel <e...@debian.org> wrote:
> > But for any non-trivial example the cost of that will be well under, say, > 1% > while still getting a "many times" speedup over alternative R solutions. > So > a win for most people. Hence 1444 packages on CRAN using it. Those people > aren't all fools ... > > I try to think of the bigger picture here too. My package relies on Rcpp (and FORTRAN) to simulate data ... potentially millions of rows in a data frame (or matrix). But we also need to do things with that data, like summarize it, make plots, generate summary tables in TeX and the like. If I simulated in a stand-alone C++ program, I *might* save some time ... maybe several seconds. But then I'd either need to generate the summaries, plots, tables in my C++ program (no thanks) or read all of that data into a program like ... R. Well, there goes any time savings I might have gained plus it's way less convenient. That's why we use Rcpp: inputs get generated and formatted in R, I get fast computation, and the results come back in memory and I never have to read a ton of data in to keep going with the analysis. So everyone has different problems to solve and whatever we might farm out to compiled code is just one piece of the puzzle / picture. For what I do, R/Rcpp is hands-down winner to get me from start to finish. Kyle
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