‘R’-package: Broadcasted Array Operations Like ‘NumPy’

 

🗺️Overview

‘broadcast’ is an efficient ‘C’/‘C++’ - based ‘R’ package that, as the name suggests, performs “broadcasting” (similar to broadcasting in the ‘Numpy’ module for ‘Python’).

In the context of operations involving 2 (or more) arrays, “broadcasting” refers to recycling array dimensions without allocating additional memory, which is considerably faster and more memory-efficient than R’s regular dimensions replication mechanism.

At its core, the ‘broadcast’ package provides the following functionalities, all related to “broadcasting”:

1. Broadcasted Infix Operators.
   They support a large set of relational-, arithmetic-, Boolean-, string-, and bit-wise operations.
2. The bind_array() function for binding arrays along any arbitrary dimension. Similar to the fantastic abind::abind() function, but with a few key differences:
   • bind_array() is faster and more memory efficient;
   • bind_array() supports broadcasting;
   • bind_array() supports both atomic and recursive arrays (abind() only supports atomic arrays).
3. ‘broadcast’ provides several generic functions for broadcasting, namely bcapply() (broadcasted apply-like function) and bc_ifelse() (broadcasted version of ifelse()).
4. casting functions, that cast subset-groups of an array to a new dimension, cast nested lists to dimensional lists, and vice-versa.
   These functions are useful for facilitating complex broadcasted operations, though they also have much merit beside broadcasting.

Additionally, ‘broadcast’ comes with a few linear algebra functions for statistics.

The Quick-Start Guide can be found here.

 

🚀Quick Example

Consider the matrices x and y:

x <- array(1:20, c(4, 5))
y <- array(1:5 * 100, c(1, 5))
print(x)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    1    5    9   13   17
#> [2,]    2    6   10   14   18
#> [3,]    3    7   11   15   19
#> [4,]    4    8   12   16   20
print(y)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]  100  200  300  400  500

Suppose one wishes to compute the element-wise addition of these 2 arrays.

This won’t work in base ‘R’:

x + y
Error in x + y : non-conformable arrays

You could do the following….

x + y[rep(1L, 4L),]
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]  101  205  309  413  517
#> [2,]  102  206  310  414  518
#> [3,]  103  207  311  415  519
#> [4,]  104  208  312  416  520

… but if x and/or y is very large, it will be slow and may even lead to an error:

Error: cannot allocate vector of size

The ‘broadcast’ package performs “broadcasting”, which can do the above, but faster, without unnecessary copies, and scalable to arrays of any size (up to 16 dimensions).

Like so:

broadcaster(x) <- TRUE
broadcaster(y) <- TRUE

x + y
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]  101  205  309  413  517
#> [2,]  102  206  310  414  518
#> [3,]  103  207  311  415  519
#> [4,]  104  208  312  416  520
#> broadcaster

 

🤷🏽Why use ‘broadcast’

Efficiency

Broadcasting as implemented in the ‘broadcasting’ package is about as fast as - and sometimes even faster than - NUmPy.
The implementations in the ‘broadcast’ package are also much faster and much more memory efficient than using base ‘R’ solutions like sweep().
Efficient programs use less energy and resources, and is thus better for the environment.
Benchmarks can be found in the “About” section on the website.

 

Convenience

Have you ever been bothered by any of the following while programming in ‘R’:

• Receiving the “non-conformable arrays” error message in a simple array operation, when it intuitively should work?
• Receiving the “cannot allocate vector of size…” error message because ‘R’ unnecessarily allocated too much memory in array operations?
• abind::abind() being too slow, or ruining the structure of recursive arrays?
• The sweep() and outer() functions being too slow or too limiting?
• that there is no array analogy to data.table::dcast()?
• difficulties in handling nested lists?
• that certain ‘Numpy’ operations have no equivalent operation in ‘R’?

If you answered “YES” to any of the above, ‘broadcast’ may be the ‘R’ - package for you.

 

Minimal Dependencies

Besides linking to ‘Rcpp’, ‘broadcast’ does not depend on, vendor, link to, include, or otherwise use any external libraries; ‘broadcast’ was essentially made from scratch and can be installed out-of-the-box.

Not using external libraries brings a number of advantages:

• Avoid dependency hell.
• Avoid wasting time, memory and computing resources for translating between language structures.
• Ensure consistent behaviour with the rest of ‘R’.

 

Tested

The ‘broadcast’ package is frequently checked using a large suite of unit tests via the tinytest package. These tests have a coverage of over 90%. So the chance of a function from this package breaking completely is relatively low.

‘broadcast’ is still relatively new package, however, so (small) bugs are still very much possible. I encourage users who find bugs to report them promptly to the issues tab on the GitHub page, and I will fix them as soon as time permits.

 

📊Status

‘broadcast’ is now available on CRAN! 🎉

If you have any suggestions or feedback on the package, its documentation, or even the benchmarks, I encourage you to let me know (either as an Issue or a Discussion).
I’m eager to read your input!

 

📖Documentation

The documentation in the ‘broadcast’ website is divided into 3 main parts:

• Guides and Vignettes: contains the topic-oriented guides in the form of a few Vignettes.
• Reference Manual: contains the function-oriented reference manual.
• About: Contains the Acknowledgements, Change logs and License file. Here you’ll also find some information regarding the relationship between ‘broadcast’ and other packages/modules. Benchmarks can also be found here.