**Thinking inside the box**, and kindly contributed to R-bloggers)

Bringing such a C++ library to R is done very easily via *Rcpp modules*.
The resulting package contains a single R file with a single line:
`loadModule("cnpy", TRUE)`

. And it relies on the following
module declarations in the a C++ file:

which give us at the R promptRCPP_MODULE(cnpy){ using namespace Rcpp; function("npyLoad", // name of the identifier at the R level &npyLoad, // function pointer to helper function defined above List::create( Named("filename"), // function arguments including default value Named("type") = "numeric"), "read an npy file into a numeric or integer vector or matrix"); function("npySave", // name of the identifier at the R level &npySave, // function pointer to helper function defined above List::create( Named("filename"), // function arguments including default value Named("object"), Named("mode") = "w"), "save an R object (vector or matrix of type integer or numeric) to an npy file"); }

these two functions (and their docstrings) defined above. That's all! Well there are about one hundred more lines dealing with whether we have integer or numeric data, and whether we use a vector or a matrix. But all in all pretty simple...R> library(RcppCNPy) Loading required package: Rcpp R> npyLoad internal C++ function <0x243af70> docstring : read an npy file into a numeric or integer vector or matrix signature : Rcpp::RObject npyLoad(std::string, std::string) R> npySave internal C++ function <0x23033e0> docstring : save an R object (vector or matrix of type integer or numeric) to an npy file signature : void npySave(std::string, Rcpp::RObject, std::string) R>

So version 0.1.0 of this new package RcppCNPy completes the initial release 0.0.1 from earlier in the week by adding

- the ability to load compressing NumPy files ending in
`.npy.gz`

- a simple regression test suite loading some data sets
- a demo script with a timing example comparing ascii reads to reading npy and compressed npy
- a short pdf vignette describing the package

The NEWS entry for this release (as well as the initial one) follow:

I will follow up with a little usage example later.## News for Package RcppCNPy

## Changes in version 0.1.0 (2012-07-07)

Added automatic use of transpose to automagically account for Fortran-vs-C major storage defaults between Python and R.

Support for integer types in dependent on the

`int64_t`

type which is available only when the`-std=c++0x`

switch is used at build-time (and CRAN still discourages use of it)Added support for reading gzip'ed files ending in ".npy.gz"

Added regression tests in directory

`tests/`

Added a vignette describing the package

Added a timing benchmark in demo/timings.R

## Changes in version 0.0.1 (2012-07-04)

Initial version, as a straightforward Rcpp modules wrap around the

`cpny`

library by Carl Rogers (on github under a MIT license).At present,

`npy`

files can be read and written for vectors and matrices of either`numeric`

or`integer`

type. Note however that matrices are currentlytransposedbecause of the default Fortran ordering done by numpy.

CRANberries also provides a diffstat report for 0.1.0 relative to 0.0.1. As always, feedback is welcome and the rcpp-devel mailing list off the R-Forge page for Rcpp is the best place to start a discussion.

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**Thinking inside the box**.

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