What is it about?
In data science, researchers often need to share large datasets—especially matrices—between different programming languages like R and Python. A common file format for this is the "Matrix Market" (.mtx) format. However, the existing tools in R for handling these files were slow, had bugs, and couldn't handle all data types. I created fastMatMR, an R package designed to read and write these Matrix Market files at extremely high speeds. It works by creating a lightweight R "wrapper" around a powerful, state-of-the-art C++ library. Crucially, this is the same C++ library used by major Python tools like SciPy. This makes fastMatMR a seamless, high-performance bridge, ensuring that data written in R can be read perfectly in Python and vice-versa, solving a major interoperability headache.
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Why is it important?
fastMatMR solves several key problems for data scientists. First, it dramatically speeds up the most basic step of any analysis: loading and saving data. Second, it breaks down the language silos between R and Python, allowing researchers to use the best tool for each part of their job without fighting with file formats. Finally, by fixing subtle but important bugs in how missing values are handled, it improves the reliability and correctness of scientific data pipelines. As a package that has passed the rigorous peer-review process of rOpenSci, it also serves as a piece of high-quality, sustainable infrastructure for the entire R community.
Perspectives
This package was a tangential part of my doctoral thesis work, and it gets to the heart of what I love to do: building high-performance, interoperable tools that make science easier. I saw a clear need within the R community. R is fantastic for statistics, but it was being held back by slow and buggy I/O for a file format that's fundamental to scientific computing. It created an unnecessary barrier for R users who needed to interact with data from other sources. I tend to use a lot of Bayesian methods, and those are best expressed in R, so it was only natural for me to work on. My solution was to build a better bridge from R to the best-in-class tools available. I took the high-performance C++ library used in other ecosystems and built a clean, robust R interface for it. This gives the R community top-tier performance and, as a huge bonus, perfect interoperability. Getting fastMatMR through the rOpenSci peer-review process was a major milestone. It was about contributing a piece of high-quality, reliable infrastructure to the scientific community, and a concrete example of my passion for binding languages and high-performance code together. Plus it was a gateway drug to eventually working as a JOSS editor as well.
Rohit Goswami
University of Iceland
Read the Original
This page is a summary of: fastMatMR: High-Performance Matrix Market File Operations, November 2023, The R Foundation,
DOI: 10.32614/cran.package.fastmatmr.
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