# New package RcppCNPy with release 0.1.0 (and 0.0.1 earlier last week)

**Thinking inside the box**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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A few days ago I had

blogged about getting NumPy data in R

by using a simple converter script. That works fine, but it is a little

annoying to have to write an entire file only to read from it again. So I

kept looking around for a better solution—and soon found

the cnpy library by Carl Rogers

which provides simple C++ functions to read and write NumPy files.

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:

RCPP_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"); }

which give us at the R prompt

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>

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…

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:

## 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.

I will follow up with a little usage example later.

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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