Why R?

Author

Martin Schweinberger

Introduction

This page explains why LADAL focuses almost exclusively on R for data analysis, statistics, and text analytics. If you are new to quantitative research or are deciding which programming language to invest time in learning, this page is for you.

The Core Question

Why should I learn R when there are other tools available for the same tasks?

The short answer: R is free, powerful, beginner-friendly, scientifically rigorous, and increasingly indispensable in academic and industry research. The sections below explain each of these points in detail.

Learning any programming language takes time and effort. That investment is much easier to justify when you understand why the tool is worth learning. The arguments below are not about dismissing other tools — many are excellent for specific purposes — but about explaining why R is the right choice as a primary environment for language research, data science, and quantitative linguistics.

Full Flexibility

R is a fully-fledged programming environment with an extraordinarily broad range of applications. Within a single R session you can:

  • Load, clean, and process data of any size or format
  • Perform statistical analyses from simple t-tests to complex mixed-effects models
  • Conduct computational text analysis — concordancing, topic modelling, sentiment analysis, word embeddings
  • Create publication-quality static graphics and interactive visualisations
  • Build websites, dashboards, and Shiny apps
  • Design and run online experiments and questionnaires
  • Write and publish books, reports, and presentations

This breadth matters enormously in practice. Many researchers start out learning one specialised tool for each task — a concordancer for corpus work, a spreadsheet for data management, a statistical package for analysis, a graphics tool for visualisation. Each new tool requires its own learning curve, its own file format, and its own workflow. Over time, this fragmentation becomes a serious inefficiency.

With R, you learn one environment and use it for everything. The initial investment pays off repeatedly across every project you work on.

Beginner-Friendly

R has a reputation — mostly historical — for being difficult to learn. This reputation is no longer deserved. The tidyverse, a collection of R packages sharing a common design philosophy and grammar, has made R one of the most accessible programming environments available to beginners.

The tidyverse approach to data manipulation (via dplyr), visualisation (via ggplot2), and text processing (via stringr and tidytext) reads almost like plain English. Operations chain together in logical left-to-right sequences using the pipe operator (|>), and the consistent naming conventions across packages mean that learning one tidyverse package makes the others easier to pick up.

Google recognised this accessibility and now teaches R — specifically the tidyverse — as the primary language in its Data Analytics Certificate, one of the world’s most widely taken data science credentials. If it is accessible enough to be the first programming language for thousands of career changers worldwide, it is accessible enough for linguistics researchers with no prior coding experience.

The LADAL tutorials are designed with exactly this audience in mind: we assume no prior programming experience, and our Getting Started with R tutorial will have you running real analyses within a few hours.

Free and Open Source

R and RStudio (now called Posit) are completely free and open source. There are no licensing fees, no institutional subscriptions, no per-user costs, and no features locked behind a paywall. Anyone with a computer and internet access can download, install, and use R — with the full power of its 20,000+ packages — at no cost.

This has several important implications:

Equity. Researchers and students at well-funded institutions and those at under-resourced universities around the world have access to identical tools. Financial background does not limit what you can do.

Longevity. Analyses written in R do not become inaccessible when a software licence expires or a company discontinues a product. Your code from ten years ago will still run today.

No vendor lock-in. Unlike proprietary tools (SPSS, Stata, MATLAB, NVivo), R analyses are not tied to a company’s business decisions. The software cannot be discontinued, made unavailable to your institution, or changed in ways that break your existing code without community consensus.

Shareability. Because R is free, you can share your complete analysis — code and data — with any collaborator or reviewer anywhere in the world, and they can run it immediately without needing to purchase anything.

RStudio: A Powerful Working Environment

R on its own is a command-line environment. RStudio (now developed by Posit) transforms it into an integrated development environment (IDE) with a visual interface that makes working with R substantially more comfortable and productive.

RStudio provides:

  • A Script Editor for writing and saving code
  • A Console for running commands interactively
  • An Environment pane showing all loaded objects
  • A Files browser and Plot viewer
  • Integrated Git and GitHub support for version control
  • R Markdown and Quarto for combining code, output, and prose in one document
  • Project management with renv for reproducible package environments

The result is that an entire research project — data import, cleaning, analysis, visualisation, and write-up — can be conducted within one environment, with every step documented and version-controlled. This integration is a significant practical advantage over workflows that require switching between multiple tools.

Reproducibility and Transparency

Reproducibility is one of the most pressing issues in contemporary science. A finding is only as credible as the ability of others — or your future self — to verify and replicate it. R is perhaps the best available tool for achieving genuine reproducibility in quantitative research.

When you conduct an analysis in R:

  • Every step from raw data to final result is recorded in code
  • R Markdown and Quarto documents weave code, output, and interpretation into a single file that regenerates all results from scratch when rendered
  • R Projects keep data, scripts, and outputs in a self-contained folder
  • renv snapshots the exact package versions used, so the environment can be recreated identically on any machine years later
  • Git and GitHub provide version control and a permanent, citable record of the analysis

This is substantive reproducibility — not just a methods section description that someone might try to follow manually, but actual code that anyone can run and verify. Journals and funders are increasingly requiring this standard, and R is purpose-built to meet it.

By contrast, analyses conducted in point-and-click tools (SPSS menus, Excel formulas, AntConc) are inherently difficult to reproduce: the exact sequence of operations is rarely recorded, and even the analyst themselves may struggle to reconstruct what they did six months later.

A Welcoming Community

One of R’s most underrated advantages is its community. The R community is large, active, and — unusually for a technical community — genuinely welcoming to newcomers.

Resources are everywhere. There are thousands of free tutorials, YouTube channels, online courses, and blog posts covering every aspect of R. The R for Data Science textbook is freely available online and is one of the best introductory data science books written in any language. Stack Overflow has answers to virtually every R question a beginner might have.

Questions are welcomed. Posting a question on the RStudio Community forum, Stack Overflow, or R-related Reddit and Facebook groups reliably produces helpful, respectful responses — even for very basic questions. This is not universal in technical communities, and it makes a real difference to people learning independently.

Anyone can contribute. R’s package ecosystem (CRAN, Bioconductor, GitHub) allows any researcher to contribute new tools. When a method is published, an R package implementing it often follows within months. This means R stays at the cutting edge of statistical and computational methodology faster than proprietary tools can.

Employability and Career Value

R skills have substantial market value beyond academia. Data scientist, research analyst, and quantitative researcher roles across industry, government, and the non-profit sector increasingly list R as either required or preferred.

There are practical reasons for this from an employer’s perspective. An employee who can do their analysis in R:

  • Requires no expensive software licences (unlike SPSS, Stata, or MATLAB users)
  • Produces analyses that are transparent, documented, and reproducible
  • Can work flexibly across a wide range of analytical tasks without additional tooling
  • Can adapt workflows as methods evolve, rather than waiting for a software vendor to update their product

For researchers considering careers outside academia, R proficiency — combined with statistical knowledge and domain expertise — is a genuinely competitive differentiator.

Scripts versus Point-and-Click Tools

It is entirely reasonable to start learning with point-and-click tools. Tools like AntConc, WordSmith, SPSS, or Excel are excellent for getting started, building intuitions about data, and performing straightforward analyses quickly. Many experienced researchers — including those who now use R fluently — began with exactly these tools.

However, point-and-click tools have structural limitations for research:

They are hard to reproduce. The sequence of menu clicks that produced your result is not automatically recorded. Reproducing the analysis — even by yourself — requires either a detailed manual log or redoing the work from scratch.

They are hard to scale. Processing one file manually is straightforward; processing 500 files requires automation that most point-and-click tools cannot provide.

They create external dependencies. Commercial tools require ongoing licences. If your institution stops paying, or the company is acquired or goes under, your workflows may break.

They limit methodological flexibility. If a new statistical method is published that would improve your analysis, you can only use it once the software vendor implements it — which may take years, or never happen.

R scripts address all of these limitations. A script is a permanent, shareable, executable record of exactly what was done to the data and why. The primary goal of LADAL is not just to show how to perform analyses, but how to perform them in a way that is sustainable, transparent, and reproducible — and scripted analysis in R is the most reliable path to that goal.

What About Python?

Python is an excellent programming language, and for some tasks it is genuinely the better choice. Python has traditionally been stronger in production-level natural language processing (NLP), deep learning, and web development. If your primary goal is building NLP pipelines at scale, training transformer models, or developing web applications, Python’s ecosystem (spaCy, Hugging Face, PyTorch, Django) is hard to beat.

That said, for the kinds of analyses most common in linguistics, corpus research, and the social sciences — statistical modelling, data wrangling, visualisation, and text analysis — R is at least as capable as Python and in several respects stronger:

  • Statistical modelling: R’s ecosystem of statistical packages (lme4, brms, emmeans, effectsize, lavaan, and hundreds more) is unmatched. Most cutting-edge statistical methods appear in R first.
  • Data visualisation: ggplot2 remains the gold standard for publication-quality statistical graphics. Python’s matplotlib and seaborn are capable but follow a different, less principled design philosophy.
  • Reproducible documents: R Markdown and Quarto originated in the R community and are most deeply integrated there, though Quarto now supports Python as well.
  • Ease for statistical beginners: R was designed by statisticians for statistics. Its data structures, built-in functions, and default behaviours reflect this heritage.

The choice between R and Python is less important than it might seem — both are free, both are widely used, and skills transfer readily between them. For researchers whose primary focus is quantitative analysis rather than software engineering, R is the natural starting point and remains the most productive environment for the kind of work LADAL supports.

Citation & Session Info

Schweinberger, Martin. 2026. Why R?. Brisbane: The University of Queensland. url: https://ladal.edu.au/tutorials/whyr/whyr.html (Version 2026.02.19).

@manual{schweinberger2026whyr,  
  author       = {Schweinberger, Martin},  
  title        = {Why R?},  
  note         = {https://ladal.edu.au/tutorials/whyr/whyr.html},  
  year         = {2026},  
  organization = {The University of Queensland, Australia. School of Languages and Cultures},  
  address      = {Brisbane},  
  edition      = {2026.02.19}  
}

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