R is what happens when a programming language grows up surrounded by statisticians, academics, and people who think “normal” is something you should test for.
At its core, R is a language and runtime built for statistical computing, data analysis, and graphics. If Python is the friendly golden retriever of programming, R is the brilliant house cat that can solve calculus but still knocks your coffee off the desk to remind you who’s in charge.
You can grab it straight from the source at https://www.r-project.org/ and live in the package wonderland that is https://cran.r-project.org/.
What is it?
R is a programming language plus an environment for analyzing data and making beautiful (or terrifying) charts. It’s also a culture.
When people say “I use R,” they usually mean some combination of:
- Crunching data
- Running statistical models
- Making plots that look like they belong in a peer-reviewed paper
- Writing reports that magically mix code and narrative (and occasionally passive-aggressive notes to their past selves)
Is it still relevant?
Yes. R is still a big deal in data-heavy worlds, especially where statistics is not a vibe but a legal requirement.
If you’re in healthcare, pharma, academia, public policy, economics, or anywhere a chart might be used as evidence in a meeting, R still shows up wearing a lab coat and carrying a clipboard.
And if you’re tracking what developers actually use, the Stack Overflow ecosystem keeps tabs on language usage and trends here: https://survey.stackoverflow.co/2025/technology
The personality of R (pros, cons, and mild emotional damage)
Pros
- Ridiculously strong statistics: A lot of methods show up in R first, especially in research.
- Visualization is first-class: Base plotting is fine, but the real party starts once you get into the broader plotting ecosystem.
- Packages for everything: If it exists, someone wrote an R package for it. If it doesn’t exist, someone wrote an R package anyway, just to be safe.
- Reporting workflows are excellent: R is great at “analysis + explanation + output” as a single artifact.
Cons
- The language has quirks: You’ll eventually have a “why is this doing that” moment. Possibly daily.
- Memory can get spicy: Large datasets can make R feel like it’s dragging a couch up the stairs by itself.
- Dependency chaos is real: Packages are amazing until your environment becomes a Jenga tower of versions.
- Base syntax can feel… historical: Like it was designed in a time when monitors were square and everyone wore cargo shorts unironically.
Strengths and weaknesses (the honest version)
R is strongest when:
- The work is statistical in nature
- You care about interpretability, models, and uncertainty
- You need publication-ready plots
- You want reproducible analysis
R is weaker when:
- You’re building general-purpose backend services
- You need high-performance production systems without careful tuning
- You want one consistent “way” to do everything across teams
R can absolutely be used in production. It’s just that production R is a more intentional lifestyle. Like sourdough.
What is it used for?
The classic list, with zero lies:
- Exploratory data analysis
- Statistical modeling (regression, time series, mixed models, Bayesian, you name it)
- Data visualization
- Bioinformatics and genomics workflows
- Social science, economics, and survey analysis
- Reporting and dashboards
- “We need this analysis by 3pm” miracles
History: where did R come from, and who invented it?
R is a descendant of the S language (from Bell Labs), and it was developed by Ross Ihaka and Robert Gentleman in the early 1990s.
Which means R has the pedigree of a serious research tool, but it also has the chaotic sparkle of something built by smart people who didn’t care whether the rest of us would someday have to debug it at 2 a.m.
How popular is it, and is it going up or down?
R remains widely used, but its popularity has shifted.
A bunch of developers who might have chosen R years ago now start with Python because Python is everywhere and plays nicely with general software development. Meanwhile, R keeps a loyal and very active base in statistics-heavy fields where it’s not just “good,” it’s the default.
So the honest answer is: R is not “dying.” It’s specializing. Like a brilliant professor who stopped attending random parties and now only goes to the ones with good snacks and peer review.
What are the alternatives?
If you’re choosing tools, here’s the real shortlist:
- Python: broader ecosystem, excellent for general-purpose development and ML.
- Julia: fast numeric computing, growing ecosystem, still smaller community.
- MATLAB: powerful, polished, expensive.
- SAS / SPSS / Stata: enterprise and academia favorites, sometimes mandated, often pricey.
- SQL: not a replacement, but the best friend R needs to stop reading CSV files like it’s 2009.
Does it work well with AI?
Yes, with a caveat.
R works well alongside AI workflows when your AI work needs serious analysis, validation, and visualization. For example: analyzing model performance, doing statistical tests, generating explainability reports, and building reproducible research outputs.
If your AI workflow is mostly deep learning engineering and deployment, you’ll likely spend more time in Python. But R is excellent for “prove it” work. And in 2026, “prove it” is the new “wow.”
What tech stack does it work with?
R plays nicely with:
- Databases (especially when you behave and push heavy work into SQL)
- Data warehouses and CSV graveyards alike
- Notebooks and reporting workflows
- APIs (you can consume them, and you can serve them, but serving is where you should be deliberate)
If you want a friendly IDE experience, the Posit ecosystem is the common path, and RStudio is still the most recognizable home base: https://posit.co/downloads/
What tools work best with it?
- A good IDE (most people land on RStudio)
- A package manager mindset (yes, you will need one eventually)
- A reproducible workflow: scripts, projects, and outputs that don’t rely on “it worked on my laptop”
- Optional but fun: language tooling concepts like the Language Server Protocol can matter when editors get involved, and the spec lives here: https://microsoft.github.io/language-server-protocol/
How much is it going to cost me?
R itself is free.
Most of the ecosystem is free.
The costs usually show up as:
- Your time (because data is messy and reality hates you)
- Compute (big datasets, big models)
- Optional paid tooling and hosting if you go enterprise with managed platforms
So financially: cheap.
Spiritually: depends on how many missing values you have.
Can you give me an example?
Here’s a tiny R example that does three things people actually do: summarize data, fit a model, and look at the result.
# Simple example: fit a model and inspect results
# Sample data
set.seed(42)
df <- data.frame(
hours = runif(200, 0, 10),
score = 50 + 5 * runif(200, 0, 10) + rnorm(200, 0, 5)
)
# Quick summary
summary(df)
# Fit a linear model
model <- lm(score ~ hours, data = df)
# Inspect coefficients
summary(model)
# Basic plot
plot(df$hours, df$score, main = "Hours vs Score", xlab = "Hours", ylab = "Score")
abline(model)
Is this the fanciest thing R can do? No.
Is it exactly the kind of thing that turns into a 40-slide deck and a funding decision? Absolutely.
Is it the subject of any famous art?
Not in the “museum masterpiece” sense. But R has absolutely inspired a modern form of art: the data visualization flex.
If you’ve ever seen a chart so pretty you momentarily forgot it was describing a recession, that’s the spirit of R living in the world.
Any other interesting tidbits (because you deserve a little snack at the end)
- R has a thriving community that loves sharing knowledge, packages, and occasional mild outrage over best practices.
- The ecosystem can feel like “choose your own adventure,” which is great until your adventure becomes dependency hell.
- R is one of the best tools for turning raw data into an argument that other people can understand.
If you’ve made it this far, you are legally obligated to do one thing:
Follow me for more episodes in this series, and drop a comment with either:
- your favorite R package, or
- the weirdest dataset you’ve ever been handed with the words “should be simple” attached to it.
Art Prompt (Pointillism): A sunlit Mediterranean harbor scene built entirely from tiny, luminous dots of color. The water shimmers with dappled reflections in turquoise, cobalt, and mint, while small sailboats drift near a bright quay lined with pastel buildings. The sky is airy and radiant, constructed from soft stippled gradients of pale lemon, powder blue, and blush. The composition feels breezy and optimistic, with crisp silhouettes formed through dense clusters of dots and subtle complementary color vibrations. Emphasize rhythmic mark-making, sparkling light, and a clean, decorative structure that feels both scientific and joyful.
Video Prompt: Animate a bright pointillist harbor scene where thousands of tiny color-dots subtly pulse, drift, and rearrange like living pigment. The water sparkles with animated stipple highlights that ripple outward in rhythmic waves, while sailboats glide smoothly and leave shimmering dotted wakes behind them. The sky blooms with gentle dot-by-dot color transitions, like a time-lapse sunrise made of confetti. Use snappy micro-zooms, quick reframing cuts, and playful parallax between foreground boats and the pastel shoreline, keeping motion lively and upbeat with constant glittering detail.
Song Recommendations:
- Dopamine — Franc Moody
- Night Drive — The Midnight