Converting Matlab Code to R Code Using Tidyverse and Matconv

Learn how to efficiently convert Matlab code to R using Tidyverse and Matconv with a practical approach.
---
This video is based on the question https://stackoverflow.com/q/67546524/ asked by the user 'MYaseen208' ( https://stackoverflow.com/u/707145/ ) and on the answer https://stackoverflow.com/a/67546633/ provided by the user 'Ronak Shah' ( https://stackoverflow.com/u/3962914/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Using tidyverse and matconv to convert Matlab code to R code

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Efficiently Converting Matlab Code to R Using Tidyverse and Matconv

Transitioning from Matlab to R can be challenging due to the different syntax and functionalities. However, with the right tools and methods, this process can be simplified significantly. In this guide, we'll explore how to leverage the power of the tidyverse and the matconv package to automate the conversion of Matlab files to R code. Let's dive in!

The Problem: Automated Conversion of Matlab Files

When you have several .m Matlab files to convert to R, doing it manually for each one can be tedious. An initial approach might look something like this:

[[See Video to Reveal this Text or Code Snippet]]

While functional, this method is not efficient, especially when dealing with a large number of files. An attempt was made to streamline this with the purrr package:

[[See Video to Reveal this Text or Code Snippet]]

But as noted, this implementation doesn't work as intended.

The Solution: Efficient Batch Conversion

Step-by-step Code Explanation

To automate the conversion process effectively, you can make a small alteration to the purrr implementation. Here’s the refined code:

[[See Video to Reveal this Text or Code Snippet]]

Breakdown of the Code

Identify .m Files: The dir() function lists all the files in the current directory that match the pattern *.m. This allows you to target all Matlab files at once.

Mapping with purrr: The map() function from the purrr package is used to apply a function to each of these identified files efficiently.

Conversion with mat2r:

The argument inMat = .x takes each individual .m file.

The output file name for the R file is created using sub('m$', "R", .x), which replaces the .m extension with .R.

funcConverters and dataConverters remain set to NULL, and verbose = 1 can be helpful for debugging.

Automated Execution: This entire operation is executed in a single line, thereby automating the process efficiently without the need for redundant code.

Conclusion

Converting Matlab files to R doesn’t have to be a daunting task. By using the functionality of the tidyverse and matconv, you can handle batch conversions quickly and efficiently. The implementation we discussed not only streamlines the process but also allows for easy adjustments should you require additional features in your conversions.

Feel free to reach out if you have any questions or need further assistance with your Matlab to R conversions!