Why Do New Analysts Often Ignore R?

Introduction: the puzzle and why it matters

If you ask a room of new data analysts today which language they’re learning first, most will say “Python.” That answer is so common it often comes without thought as if there were no real alternative. But it wasn’t always like that. R was once the lingua franca of statistical computing. For more than two decades, researchers and statisticians used R as the primary tool for data analysis, visualization, and reproducible research. So why, in the eyes of many newcomers, is R now a footnote an “academic” or “legacy” language rather than a must-learn skill?

This question matters because it helps explain how tools shape careers, research, and product development. If new analysts ignore R, we have to ask whether the omission is a practical necessity (for jobs and deployment), a misunderstanding (outdated impressions about R’s capabilities), or both. The answer touches on history, developer ecosystems, tooling, software lifecycle concerns, corporate culture, and pedagogy. In short: it’s not just a matter of syntax or speed; it’s a multi-layered shift in how organizations and educators think about data work.

This article unpacks that shift, explains where R still shines, clarifies common misconceptions, and gives practical guidance for analysts deciding what to learn. It’s an extended treatment long-form, pragmatic, and continuous so settle in.

Origins and trajectories: how R and Python got their identities

R: a language born in statistics

R emerged from an academic problem set. In the early 1990s, Ross Ihaka and Robert Gentleman built R as a free reimplementation of S, a language designed at Bell Labs for statistical computing. The guiding motive was explicit: make advanced statistical tools accessible and shareable. R quickly attracted an ecosystem of contributed packages (CRAN, the Comprehensive R Archive Network), and within a couple of decades it hosted thousands of packages implementing the latest statistical methods, tests, models, and plotting paradigms.

Key properties of R’s early identity:

Statistical-first design: R’s syntax and core functions assume a statistical workflow vectors, matrices, modeling frameworks, and formula interfaces are core.
Package-driven innovation: new methodologies are often released first as R packages (CRAN/Bioconductor), frequently accompanied by academic papers.
Visualization standard-bearer: ggplot2 built on the Grammar of Graphics changed how analysts express plots and became widely influential.

Because of these properties, R became the go-to language in many scientific disciplines (biostatistics, ecology, epidemiology, social sciences) where statistical rigor and reproducibility matter more than production deployment.

Python: a general-purpose language that moved into data

Python was created by Guido van Rossum in 1991 as a general-purpose programming language with readability and developer productivity at its core. For many years, Python's strength lay in web development, scripting, automation, and general application development. Then, in the late 2000s and 2010s, several libraries turned it into a data-science platform: NumPy introduced efficient numerical arrays, pandas brought data-frame operations, scikit-learn made machine learning accessible, and later TensorFlow and PyTorch dominated deep learning.

Key properties of Python’s rise:

General-purpose flexibility: works equally for backend services, web APIs, and script automation.
Unified ecosystem for ML and deployment: Python’s libraries cover prototyping, model training, and production deployment.
Developer-first community and tooling: VS Code, PyPI, and modern CI/CD workflows integrate tightly.

This generality made Python attractive to organizations that want an end-to-end skill set in a single language data prep, model building, and production deployment.

Practical divides: how workplace needs shape language choice

The software development lifecycle (SDLC) perspective

A crucial practical reason analysts gravitate toward Python is its fit with the software development lifecycle (SDLC). Typical enterprise projects move through stages: prototype -> package -> test -> deploy -> monitor -> maintain. Python spans that whole spectrum well:

Prototyping: Jupyter notebooks make quick data exploration easy.
Packaging: Python packages and pip/conda make dependency management straightforward.
Deployment: frameworks for building APIs (Flask, FastAPI), and easy containerization with Docker.
Production infrastructure: native integrations with cloud services, telemetry libraries, and established DevOps patterns.

R is brilliant at the prototyping stage, especially for exploratory statistics and plotting. But historically, R has been weaker on seamless productionization: R scripts often need to be wrapped or reimplemented as services to be production-ready. While tools like plumber (for creating APIs in R), RStudio Connect, and Dockerized R environments exist, they’re not as pervasive or as widely adopted in enterprise stacks where Python has already become the default. For example, if a team has Python-based microservices and a JVM-heavy backend, introducing R into the mix often requires extra expertise, monitoring solutions, and operational overhead.

The upshot: if you plan to move your analyses into production, Python often reduces friction. New analysts thinking about career prospects internalize this reality they want skills that will transfer to production systems.

Integration and interoperability concerns

Corporations hate polyglot complexity when they don’t have to. A single-language stack simplifies hiring, operations, security, and monitoring. When data analysts deliver artifacts that must be maintained by platform engineers, these engineers prefer languages they already use and support. Python checks that box far more often than R.

This practical preference cascades into hiring: job postings list Python more frequently than R; teams advertise Python-first stacks; and analytic tooling vendors emphasize Python integrations. New analysts respond rationally.

Ecosystem convergence: Python ate some of R’s lunch

One of the telling developments of the last decade is that Python began incorporating many of the capabilities that once made R unique.

Data frames and data-wrangling semantics

R’s data.frame concept has been central since the early days; dplyr and the tidyverse brought a declarative, chainable syntax for common data transformations. Python’s pandas implemented data frames and vectorized operations; more recently, libraries like Polars and libraries that borrow tidy-like syntax have closed the expressivity gap. pandas can do what most analysts need for cleaning, reshaping, and aggregating.

Statistical modeling and ML

R historically had richer built-in statistical modeling tools (like lm, glm, advanced mixed models, and survival analysis packages). Python responded with statsmodels for traditional statistical modeling and scikit-learn (and later PyTorch/TensorFlow) for modern ML workflows. Today, if your goal is pragmatic ML for enterprise use, Python is typically first choice.

However and this is important R still leads in certain statistical niches. Packages for mixed models, survival analysis, certain Bayesian frameworks (brms, rstanarm), and domain-specific libraries are often more mature in R. Researchers still release new statistical methodologies in R first, because CRAN and the R community are deeply tied to academic workflows.

Visualization and declarative plotting

ggplot2’s influence is profound: it made it natural to think of plots as grammars. Python got plotnine (a ggplot2 port) and other high-level plotting tools like Altair and Bokeh. For many dashboards, interactive plotting is sufficient in Python, but publication-ready, layered plots with elegant grammar are still easiest in R/ggplot2 many researchers prefer ggplot2 for its expressive power.

Reproducible research and integrated reporting

RMarkdown and knitr created a convenient pattern: code + narrative + output in a single document, ideal for academic reproducibility and reporting. Jupyter notebooks filled a similar niche in Python for exploratory work and demos, but historically RMarkdown offered more robust static reporting (PDF/HTML/Word) workflows that researchers liked. That gap has narrowed somewhat, but the difference shaped adoption patterns for many years.

Pedagogy, perception, and habit: why students pick Python

Bootcamps, MOOCs, and the “job-ready” promise

The rise of bootcamps and online courses created a new customer for data education: career switchers looking for jobs. Bootcamps emphasize Python because it’s practical and marketable. The curriculum design choice is simple: teach the tool that maximizes employment prospects.

MOOCs and YouTube tutorials reinforce this: a quick search surfaces thousands of Python tutorials for data science, all designed around pandas, scikit-learn, and tensorflow. R tutorials exist, but they’re not as heavily marketed for job-seekers; many are tied to academic syllabi.

The “one language to rule them all” heuristic

For busy learners, the rational route is to pick one language and go deep. Given Python’s generality script automation, web APIs, ML, and data manipulation it often wins the “utility” contest. Even if R is better for a particular statistical technique, learners prioritize breadth. If a single language gets them most of the way to a job, they learn that language first.

Misconceptions and UI friction

Perception plays a role. Common (and sometimes outdated) impressions of R include:

“R is slow.” (Modern R and its packages rely on compiled backends; speed depends on implementation.)
“R is inconsistent.” (R’s historical design left rough edges; tidyverse standardized a lot of idioms.)
“R is for academics only.” (Partly true historically, but not a full truth; many corporations use R in analytics-heavy roles.)

New learners encounter these perceptions via blog posts, chilly GitHub comments, and casual workplace scuttlebutt. Perception is sticky: if teachers or peers say “learn Python,” beginners follow.

The tooling story in detail

Tooling is often underrated yet decisive in language choice. Humans prefer good tools.

IDEs and notebooks

RStudio is a mature, polished IDE built specifically for R; it integrates script editing, a visual data viewer, package management, plotting panes, and RMarkdown. It's a delightful environment for analyses.
Jupyter notebooks became the de facto exploratory environment for Python and are widely used in data science teams, crossover teaching, and ML research.
VS Code gives Python great language support and a rich extension ecosystem; increasingly it also supports R, but Python retains the stronger tooling tie-ins with debuggers, LSPs, and extensions.

Developers hired into engineering-heavy teams often learn VS Code/Jupyter/PyCharm workflows all of which feel more Python-native than RStudio. That tooling ecosystem nudges learners toward Python.

Packaging, dependency management, and deployment

Python: pip, virtualenv, conda, poetry and straightforward ways to create wheels or containers. Cloud vendors publish examples and SDKs in Python first.
R: install.packages(), renv, and packrat provide environments, but enterprise deployment patterns are less standardized. RStudio Connect and RStudio Package Manager fill enterprise needs, but they’re an extra cost in many contexts.

Because enterprise ops teams build playbooks for the most common cases and Python is the most common the path of least resistance favors Python. This affects hiring, onboarding, and long-term maintenance decisions, which in turn influence what newcomers learn.

Community, culture, and social proof

Language choice is social, not just technical.

Conferences and content

Large developer and data-science conferences PyCon, PyData, SciPy, TensorFlow summits generate enormous amounts of content and community activity. This content becomes both inspiration and tutorial base for newcomers. R has excellent conferences as well (useR!, RStudio conf), but the sheer volume of Python community content skews exposure.

Libraries and corporate backing

Big names in ML and tooling produced Python-first (or Python-dominant) libraries: TensorFlow, Keras, scikit-learn, Hugging Face Transformers, etc. Corporate backing by Google, Facebook, and others pushes Python forward and solidifies it as the language of industry research. R’s most influential packages often originate in academia, which affects commercial adoption.

Hiring and network effects

A positive feedback loop emerges: companies hire for Python, courses teach Python, online content favors Python, more learners adopt Python, and companies see a larger talent pool in Python making them even more likely to require it. Network effects are powerful; they can slowly (but decisively) marginalize an otherwise excellent alternative.

Where R still wins: concrete examples, niches, and strengths

It would be wrong to imply R is obsolete. R retains unique advantages and will remain critical in many domains.

Statistical modeling and cutting-edge methods

Many advanced statistical models and packages e.g., lme4 (mixed models), survival (time-to-event analysis), mgcv (generalized additive models), and brms (Bayesian regression via Stan) are mature, well-documented, and trusted in research communities. When a new statistical methodology appears in literature, researchers often implement and publish an R package first. Practitioners in disciplines relying on those methodologies continue to use R for that reason alone.

Visualization and publication-quality graphics

ggplot2 and its ecosystem (patchwork, ggsave, cowplot, etc.) make complex, layered, publication-quality figures more straightforward in R than in many Python ecosystems. For academic plotting and fine-grained aesthetic control, many analysts prefer ggplot2.

Reproducible, integrated reporting (RMarkdown)

RMarkdown serves as a powerful literate-programming tool enabling researchers to combine narrative, code, tables, and figures into a single reproducible document. RMarkdown’s output formats (PDF via LaTeX, HTML, Word docs) make it friendly for academic submissions and corporate reporting alike.

Domain-specific ecosystems

Bioconductor for genomics, CRAN’s many domain packages, and a mature set of R libraries for social science, ecology, and epidemiology create domain inertia. When a field standardizes on R tooling across labs and journals, new entrants are naturally trained in R.

Case studies (realistic, illustrative)

Below are stylized but realistic case studies to make the dynamics concrete.

Case: Clinical trials and biostatistics (R-first)

A team running clinical trials needs survival analysis, Cox proportional hazards models, and mixed-effects longitudinal models. They need to prepare reproducible reports for regulators and publish methods in journals. R offers mature packages (survival, coxme, nlme, lme4) and RMarkdown workflows for documentation. Translating those models to Python would be unnecessary overhead and risk differences in implementations. The team stays in R.

Case: Consumer product recommendation engine (Python-first)

An e-commerce company wants to deliver a real-time recommendation engine via an API. The model needs to be trained, packaged into a microservice, and monitored for drift. The operations team uses Kubernetes and prefers Python tooling. ML frameworks and model-serving patterns are Python-first. The analytics team prototypes in Python and productionizes without language hand-offs.

Case: A hybrid research lab (both)

A research lab develops new statistical methodology in R (because CRAN and academia are natural), but the product team wants a web demo and a production API. They implement the research in R and then either reimplement core parts in Python for the demo, or use rpy2/reticulate wrappers when performance and deployment constraints allow. This hybrid approach shows the practical middle-path many teams take.

Misconceptions and fair comparisons

There are common ways people compare R and Python unfairly. Addressing these helps newcomers make better choices.

“R is slow” the nuance

R’s base loops and some legacy functions can be slow in interpreted code, but modern R relies on compiled backend code (C/C++/Fortran) extensively. Many performance-critical R packages are implemented in compiled languages. Similarly, Python benefits from native bindings to C libraries (NumPy, pandas). Speed comparisons depend on algorithm implementation, not the language alone.

“Python absorbed R features” yes and no

Python replicated many of R’s conveniences, but several R idioms (e.g., tidy evaluation, non-standard evaluation for DSLs, formula interfaces) are still ergonomically distinct. Python libraries sometimes emulate tidyverse patterns (Polars, dfply), but the full expressive power, metaprogramming niceties, and domain-specific DSLs are often cleaner in R.

“R lacks modern ML” increasingly false

R’s ML ecosystem (caret, mlr3, tidymodels) has matured. That said, deep learning research communities and many production ML tools remain heavily Python-focused. If your target is deploying large-scale deep learning models, Python is still the pragmatic choice.

Practical guidance for new analysts

Given the landscape, what should a new analyst do? Here’s a pragmatic decision framework.

If you want a corporate product/engineering career → start with Python

Learn pandas, numpy, scikit-learn, matplotlib, and basic ML workflows.
Get comfortable with Jupyter, VS Code, and building simple APIs using Flask/FastAPI.
Learn containerization (Docker) and basic cloud deployment patterns.

Why? Because those skills map directly to many analytics and ML jobs and integrate with production systems.

If you want a research/statistics-heavy career → learn R early

Learn tidyverse (dplyr, tidyr, ggplot2), RMarkdown, and modeling packages relevant to your domain.
If you’ll publish, get comfortable with CRAN/Bioconductor packages and reproducible workflows.

Why? Because academics and many specialized domains still favor R’s statistical tooling and reproducibility workflows.

If you want maximum flexibility → learn both

Learn Python first if you have to pick one for employability, then add R for niche skills. Or do the reverse if you’re in a stats-heavy academic program.
Learn interoperability tools: reticulate (R <-> Python), rpy2.
The best pragmatic skill is not strict language allegiance, it’s the ability to pick the right tool for the problem and glue components together.

Learning path example

Start with data manipulation in Python (pandas) and exploratory plots (matplotlib/seaborn).
Build a few ML models with scikit-learn.
Learn the basics of Docker and serve a model as a simple API.
Add R to your toolkit: learn ggplot2, dplyr, and RMarkdown for reproducible reports.
Practice bridging: call R from Python or vice versa for workflows where both are useful.

Interoperability: using R and Python together

The reality is many teams use both languages. Good interoperability tools exist:

reticulate: R package that embeds Python in R sessions, letting you call Python from R.
rpy2: Python package that calls R from Python.
plumber: expose R code as an HTTP API, letting services call R functionality.
Docker: containerize R environments and call them as services.

This hybrid approach can deliver the best of both worlds: use R where its statistical packages offer unique benefits, and Python for deployment, automation, and ML infrastructure.

The role of new languages (Julia, Rust, etc.)

Julia has emerged with a promise: the convenience of a high-level language with near-C speed for numerical computing. It’s attractive for high-performance scientific computing and is gaining traction in optimization and certain modeling tasks. However, Julia’s ecosystem remains smaller, and corporate adoption is limited. As a result, it’s unlikely to displace Python or R in the near term, but it provides interesting options for specialized workloads.

Rust is making inroads as a systems language to power fast backends and libraries, and we’ll likely see more bindings to Rust for performance-critical data tasks. But these trends primarily affect library authors and systems programmers, not entry-level analysts.

Organizational and people dynamics (why corporate ops resist R)

Why do IT and Ops teams resist R? The answer is practical:

Support and maintainability: It's riskier to have multiple specialized languages if the company lacks in-house expertise.
Operational playbooks: Logging, monitoring, CI/CD, and security tooling are built around the most common languages.
Hiring market: More hires comfortable with Python than R means operational support favors Python.

This is not a statement about technical superiority; it’s about organizational friction and risk management.

The educational responsibility: how teachers shape choices

University and bootcamp curricula matter. If professors teach statistics and modeling through R, generations of students will start with R. If bootcamps teach web APIs and production ML using Python, their students will start with Python. Both decisions are rational for the instructors’ goals (research rigor vs job placement). But they produce different outcomes.

Ideally, curricula should teach concepts independently of the language where possible: teach data manipulation, model validation, reproducible workflows, and interpretability then show them in both Python and R. That’s a heavy lift, but it produces analysts who can genuinely choose the right tool for a task.

Final takeaways: a balanced view

Let’s close with crisp takeaways practical and honest.

New analysts often ignore R because of pragmatism, not because R is “bad.” The industry emphasis on production, deployability, and a unified engineering stack favors Python.
R still holds domain-specific power. If you need advanced statistics, publication-quality visualization, or reproducible academic workflows, R often remains the best tool.
Python has eaten into R’s domain by building broad, production-ready tooling. This makes it the most pragmatic single-language choice for many careers.
Interoperability is pragmatic. The smart teams use both languages as needed and rely on APIs or interop libraries where appropriate.
For career-minded learners: pick Python to maximize job opportunities, then add R for statistical depth if your work or interest requires it.
For researchers and statisticians: start with R if your field is R-dominant; otherwise learn both.