The Maximalist Trap in Scientific Computing

There is a tendency in data science communities to stretch their favorite programming languages well beyond their optimal use cases. Maybe this can be equated to a "comfort zone" problem, or it's the depth vs. breadth tendency as one climbs the STEM degree ladder. I think both the R and Python data science and/or bioinformatics communities fall into maximalist patterns that can compromise performance and maintainability. When your preferred tool is R/Python, every problem can look like a dataframe (I started here too!). When problems are consistently distilled through the lens of a tabular dataset, or packages abstract everything away, you fundamentally miss learning where to apply more efficient data structures.

Data Science Duality: Where R and Python Legitimately Excel

I'll admit, I learned R before Python. My training in quantitative ecology and bioinformatics necessitated a moderate amount of shell scripting with a heavy dose of R + tabular count data. I too was an R maximalist for a while. Exploratory data analysis is still easier for me in R due to familiarity, but as I collaborated on python codebases I began to appreciate the python ecosystem more. Python does not really follow a quasi-functional programming paradigm like R's tidyverse. R does not use object class methods as heavily as python's ecosystem, which I personally found to be the most difficult concept to master since R end users rarely build object classes with methods.

R in the Statistical Domain

R's power lies in its statistical distribution simulation, Bayesian inference ecosystem, and one line linear regression capabilities. The Bioconductor packages use all these great traits for bioinformatics applications. For example, simulating a negative binomial distribution in python requires an external package, numpy and looks like this:

import numpy as np

# set seed
np.random.seed(42)

# distribution parameters
n = 10 # number of successes
p = 0.3 # probability of success

# generate sample distribution with 1000 samples
samples = np.random.negative_binomial(n, p, size=1000) 

While we can do the same thing in base R without any external package imports

# set seed
set.seed(42)

# parameters
s <- 10 # successes (changed for clarity in the function that follows)
p <- 0.3 # probability of success 

# Generate distribution with our parameters
# Note: in this case n = 1000 samples, hence the variable assignment change
samples <- rnbinom(n = 1000, size = s, prob = p)

If we want to extend this to counts data, like in RNAseq analyses, we'll require scipy.stats in python and MASS in R. Instead of successes (n or s) and probability of success (p), the counts case changes our simulation parameters to mean (μ) and dispersion (r). I digress--the point being, a lot of statistical research ends up in R first. ggplot2 is also very robust for creating figures quickly and iteratively through functional programming paradigms.

Python's versatility sweet spots

Admittedly, if you talk to me lately I sound like a shill for "Big Python". I think I appreciate it for general purpose scripting and automation of tasks. argparse is delightful to work with and I made my first "real" command-line interface (CLI) program with it. Python also gets the most depth in terms of machine learning and AI extensibility (ex. scikit-learn, torch, TensorFlow, and much more). Application Program Interface (API) development and web services are made significantly easier. I found working with R shiny to be a miserable experience, but your mileage may vary.

The Hidden Costs of Maximalism

Specialization is great until it isn't. Consider each programming language as an island community. We can say that each programming language community yields its own paradigms and best practices on their islands. Maybe on each "programming language island" there are special needs or the language randomly asserts itself. As a trained ecologist, this reminds me of niche theory vs. neutral theory. Briefly, niche theory asserts that species have unique and defined ecological niches, while neutral theory allows for randomized functional equivalency and similar chances of survival. Consider language maximalism representative of niche theory--hyperspecialized, and adept at bending a particular language to one's goals. The polyglot approach to data science coding allows for functional equivalency and robust problem solving.

The R and Python Overlap

There is a lot of functional overlap between R and Python--differing only in syntax. Some examples: Data manipulation (tidyverse vs pandas), Visualization (ggplot2 vs matplotlib/seaborn) In my experience the choice then becomes driven by preference rather than necessity. Sometimes using both can produce interesting results see this blogpost.

Performance penalties at scale

Let's consider a bioinformatics use case where both R and python have performance bottlenecks--parsing sequencing alignment files (ex. BAM, SAM, CRAM)

Memory management in R for large alignment file datasets can be surprisingly inefficient. R's copy-on-write semantics and vector-based memory allocation create overhead that becomes problematic with alignment data. For instance, working with BAM files in R through Bioconductor's GenomicRanges requires loading entire genomic positions into memory, and converting between specialized objects like GPos and standard GRanges can inflate memory usage by thousands of times.

Python fares no better in this use case. While pysam provides a wrapper around the C-based htslib library, naïve usage patterns can still lead to memory bloat. For example, when users store entire dictionaries of reads as Python objects rather than streaming files through their analysis code. The iterative BAM file processing approach that works well in pure C becomes memory-intensive when mediated through Python's object overhead.

Python was designed for single-core CPUs and therefore has many limitations for parallel processing. This is hampered by the Global Interpreter Lock (GIL), which prevents multiple Python threads from executing bytecode simultaneously. The GIL's purpose in modern Python is hotly debated, since working around it requires complex module management and a deeper understanding of concurrency vs. parallelism. These process scaling concepts push many data scientists beyond their comfort zones into multiprocessing or distributed computing frameworks (not that this is a bad thing, but you might not really need it).

Don't let performance entirely dictate your development, sometimes it's faster to try something in a scripting language first before investing more time into building a tool with a more performant language.

Maintenance and technical debt

Sometimes you need bits of many languages to build something interesting and the programmatic link between languages might be more common than you think. A lot of scripting languages like R and Python are syntactic sugar that make calls behind the scenes to other languages via foreign function interfaces. For example, a lot of R packages call C++ via Rcpp and FORTRAN subroutines via the .Fortran() base R function. In Python, we can call Rust code via pyo3 or use R code via rpy2. These interoperations are complex in my personal experience, but if you've read this far you're probably curious enough to continue.

Innovation bottlenecks

Language maximalism means missing out on emerging tools and paradigms. There are complex workarounds for language limitations and difficulty onboarding team members when the codebase spans many languages. For example, Polars is gaining traction in both R and Python's ecosystems due to its Rust-based performance and memory safety. Polars alleviates a lot of performance pain points, with a small time investment in learning the API for either language's interface. However, many languages wrap others for performance purposes as I previously described. If you don't know python, it's hard to stay on the bleeding edge of LLMs, AI, etc., since academic research is often siloed here. Reinventing wheels that exist in other ecosystems is a huge time cost. Consider making an R library to do something that already exists in python--by the time CRAN approves it, you could have spent that time learning python!!

Complexity as a Case Against Polyglot Programming

Doesn't polyglot programming add to cognitive overhead and maintenance complexity? Yes, but sometimes learning other approaches/languages makes you appreciate the features of your favorite languages more. Learning another language might inspire you to mix and match like Ben Roston's article above. What data scientists and bioinformaticians do is complex in nature and growing your skills is paramount to longevity in a rapidly changing field.

The Polyglot Advantage: Strategic Language Selection

My Dad always joked that you could nail a screw into a wall with a hammer if you tried hard enough, but it was going to be a long and messy process. The more familiar one becomes with programming languages' particular strengths and weaknesses, the more diverse the toolbox becomes. Once we venture out of the safe confines of scripting languages like python or R, suddenly we need to worry about memory management, static type systems, compilers, etc., as we wade deeper into the computer's physical structure. Let's consider a few different pieces of our analyses, and in general what we can learn from microservice architectures.

Performance-critical components

C/C++ are historically the languages of choice for performance critical components, like the sequence alignment example from before, while Rust adds memory-safety to our programming toolbox. Go can be useful for concurrent processing development and has a significantly easier learning curve than the aforementioned languages. GPU languages (CUDA/OpenCL) or language wrappers for massively parallel tasks can speed up parallel tasks like linear algebra computations (AI is after all, a bunch of linear algebra operations in a trenchcoat). And may FORTRAN, an array-first language that inspired much of mathematical operations on the computer, never die!

Data engineering layer

Maybe you're already using shell scripting for simple file operations, pipelining, etc., but part of the art of data engineering is the organization and database schemas. Data engineering might be the least cool sounding discipline, but its skillset is a fantastic addition to any data practitioner's toolset. I've learned Groovy while working with Jenkins and Nextflow as well as a little Scala/Java for Spark clusters like Databricks. SQL databases allow you to not force everything through disparate dataframes and effectively do SQL joins from CSV files with pandas or tidyverse. Data warehousing/lakehousing/whatever-buzz-word is a great way of connecting databases, especially with the complexity of a biotech or engineering organization. I've personally deployed Redash and worked with an already deployed Databricks instance, which really lets you bring together databases with proper SQL or SparkSQL syntax.

Modern Infrastructure: Workflows, Containers, and IaC

Workflow management is always context dependent. I've worked with something as simple as a shell script, a hacked together Jenkins/Python job scheduler, to my own implementation of a serverless Nextflow workflow manager on AWS. Shell scripts are shell scripts, they kind of work, as long as all the software stack is installed on a system. Whenever my colleagues and I had to touch the Jenkins/Python workflow manager, it was terrifying to write Groovy in the control pane to reset stuck clusters. All of these workflow management systems got the job done, with varying levels of complexity and maintainability. I very infrequently use HPC schedulers like PBS or SLURM anymore, since I don't generally use university HPC systems. They are conceptually the same as local or cloud schedulers--have workflow, will travel across compute resources.

While at CyVerse I was heavily involved in teaching academics how to use software containers like Docker or Singularity to create reproducible environments to solve the "it runs on my machine" problem. I think it's easy to view these as monolithic, bring-your-analysis, to jam into a container. Once I started working with Kubernetes and serverless deployments like AWS Fargate, I started to see the value in microservices. In the context of containers within data science and bioinformatics, this means each step in a workflow gets its own discrete container. This allows you to swap out bits and pieces as packages become deprecated or workflow bottlenecks are identified.

When I was building workflows, I got exposed to infrastructure-as-code (IaC) tools like Vagrant and Terraform. This really helped with the IT side of things and allowed me to securely and safely store security credentials without publishing them to a public repository. Not to mention, this allows you to integrate CI/CD into your workflows and expands that thinking into the coding processes too.

Breaking Free: Practical Polyglot Strategies

You will always work with people of varying experience levels (outside of being a solo developer). This situation is advantageous, since you can learn while doing. I learned a lot of my C++/CUDA from collaborating on refactoring python proof-of-concepts into production code free of GIL bottlenecks.

Identifying maximalist anti-patterns

Performance profiling languages and scripts reveals their breaking points. For example, within a Nextflow pipeline I was able to see very clearly that one python script was looping through a large tabular file--line-by-line. This script was also recalculating the same values every time it was run, when that particular calculation only needed to be done once! I broke it up into two scripts, one to calculate my target values, and another to parallel process the lines in the file with concurrent futures.

In other cases, team velocity on a project can slow due to language-fighting or when "We need a package for that" becomes too frequent. There is always a trade-off between mixing languages and getting work done in a timely manner.

Gradual adoption patterns

As mentioned before, the microservices approach allows room for each analysis or service to be written and deployed in its optimal language. Pipelining lets you glue your microservices together and iteratively build toward the right tool for each step of an analysis pipeline.

Team and cultural considerations

Sometimes it's difficult to overcome the "not invented here" syndrome--if your team consistently reinvents the wheel, that's a huge time sink. In one role I worked, it was important to build bridges between the R and Python camps within the department. It was often good to allow people to do their data analyses in whatever scripting language was fastest and familiar for them. However, when it came time to work on prototyping code for physical devices, we agreed on a python → C++ workflow with adherence to particular styles of python docstrings and PEP8. I think this environment was great for establishing polyglot best practices.

Recognizing language biases in ourselves

I think it's important, since I'm still writing about it in 2025, to realize the R vs. Python debates miss the plot entirely. Each language is a tool, not an identity. As I mentioned before, the time spent reinventing a package in your favorite language clutters the package ecosystem, contributes to the growth of abandonware, and is time that could be spent broadening your skillset.

Building complementary skill sets

Learn other programming languages that fill performance or implementation gaps, while not duplicating capabilities for the sake of language aestheticism. Focus on paradigms, not just syntax. Like I said before, if you're a statistician you probably can get away with an R heavy workflow for a long time. However, it's important to realize when you've "walked to the edge of the map".

Cross-pollination of ideas between ecosystems benefits all communities greatly. I've found this to be particularly true on my journey learning Rust. Better collaboration transpires across teams with different backgrounds. Treat everything as a learning opportunity and a way to cultivate a growth mindset.

Resources and Further Reading

• The R Inferno - Performance pitfalls in R
• Rcpp documentation - R and C++ integration
• rpy2 documentation - Python and R integration
• PyO3 User Guide - Rust bindings for Python
• Top Community forums that embrace programming language diversity

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Note: This post was written by me based on my own experiences and perspectives. In all transparency, I used Claude (Anthropic's AI assistant) to help with link population, grammar checking, and spelling corrections, but not for content generation or overall structure.