# Getting numpy data into R — Take Two

[This article was first published on

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

A couple of days ago, I had
posted a short Python script
to convert numpy files into a simple
binary format which R can read quickly. Nice, but still needing an extra
file. Shortly thereafter, I found Carl Rogers
cnpy library
which makes reading and writing numpy files from C++ a breeze, and I quickly
wrapped this up into a
new package RcppCNPy
which was released a few days ago.
**Thinking inside the box**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

This post will show a quick example, also summarized in the short pdf vignette describing the package, and provided as a demo within the package.

We first load the new package (as well as the rbenchmark package used for the benchmarking example) into R. We then create a large matrix of 100,000 rows and 50 columns. Not quiteR> library(RcppCNPy) Loading required package: Rcpp R> library(rbenchmark) R> R> n <- 1e5 R> k <- 50 R> R> M <- matrix(seq(1.0, n*k, by=1.0), n, k) R> R> txtfile <- tempfile(fileext=".txt") R> write.table(M, file=txtfile) R> R> pyfile <- tempfile(fileext=".py") R> npySave(pyfile, M) R> R> pygzfile <- tempfile(fileext=".py") R> npySave(pygzfile, M) R> system(paste("gzip -9", pygzfile)) R> pygzfile <- paste(pygzfile, ".gz", sep="") R> R>

*big data*by any stretch, but large enough for ascii reading to be painfully slow. We also write two npy files and compress the second one.

Next, we use the `benchmark`

function to time the three
approaches:

As shown by this example, loading a numpy file directly beats the pants off reading the data from ascii: it is about 78 times faster. Reading a compressed file is somewhat slower as the data stream has to be passed through the uncompressor provide by the zlib library. So instead of reading a binary blob in one go (once the file header has been parsed) we have to operate piecemeal—which is bound to be slower. It does however save in storage space (and users can make this tradeoff between speed and size) and is still orders of magnitude faster than parsing the ascii file. Finally, and not shown here, we unlink the temporary files.R> res <- benchmark(read.table(txtfile), + npyLoad(pyfile), + npyLoad(pygzfile), + order="relative", + columns=c("test", "replications", "elapsed", "relative"), + replications=10) R> print(res) test replications elapsed relative 2 npyLoad(pyfile) 10 1.241 1.00000 3 npyLoad(pygzfile) 10 3.098 2.49637 1 read.table(txtfile) 10 96.744 77.95649 R>

Summing up, this post demonstrated how the RcppCNPy package can be a useful to access data in numpy files (which may even be compressed). Data can also be written from R to be accessed later by numpy.

To

**leave a comment**for the author, please follow the link and comment on their blog:**Thinking inside the box**.R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.