What Is R?
R is a free, open-source programming language and software environment purpose-built for statistical computing, data analysis, and graphics. It was created in 1993 by Ross Ihaka and Robert Gentleman at the University of Auckland as an open reimplementation of the S language originally developed at Bell Labs, and today it is maintained by the R Core Team with contributions distributed through the Comprehensive R Archive Network (CRAN).
Cricket analogy: Think of how Mumbai Indians built a squad specifically for T20 chases under lights rather than repurposing a Test-match roster; R was engineered from day one for statistics in 1993, not adapted afterward from a general-purpose tool.
Why R Exists: A Statistics-First Design
Unlike general-purpose languages that require importing a library to get basic tabular or statistical capability, R treats vectors and data frames as first-class citizens from the start. Core functions such as lm() for linear regression, t.test() for hypothesis testing, aov() for analysis of variance, and plot() for graphics ship in 'base R,' so an analyst can fit a regression model and inspect its residual plot in a handful of lines without adding any external dependency.
Cricket analogy: It's like the DRS (Decision Review System) being wired directly into how umpires officiate a match rather than bolted on afterward — R's lm() and t.test() are built into the umpiring of data the same way, no separate kit required.
R in the Real World
R is heavily used in biostatistics and pharmaceutical research, including clinical trial analysis submitted to regulators like the FDA, as well as in quantitative finance, academic social science, and data journalism outlets such as FiveThirtyEight and the BBC's data unit. Much of this reach comes from CRAN, which hosts more than 20,000 peer-reviewed packages covering everything from survival analysis (the survival package) to spatial statistics (sf) to Bayesian modeling (brms).
Cricket analogy: It's like how Cricinfo's Statsguru tool is trusted by broadcasters, journalists, and analysts worldwide for the same underlying data — CRAN's 20,000+ packages give R that same cross-industry trust, from pharma to journalism.
R vs Python: Choosing the Right Tool
R and Python both dominate data science, but they emphasize different strengths: R's ggplot2 produces publication-quality statistical graphics with a concise grammar-of-graphics syntax, and its base statistical modeling functions are deeply refined for classical inference, while Python tends to be favored for production software engineering, web backends, and deep learning frameworks like PyTorch. Many teams use both, calling Python from R via the reticulate package or calling R from Python, rather than treating the choice as exclusive.
Cricket analogy: It's like choosing between a specialist spinner such as Ravichandran Ashwin for a turning pitch in Chennai versus a pace all-rounder for Perth's bounce — R suits statistical 'pitches,' Python suits production 'conditions,' and many squads carry both.
# A first taste of R: create a numeric vector and summarize it
heights <- c(165, 172, 158, 180, 175, 169)
mean(heights) # 169.8333
sd(heights) # standard deviation
summary(heights) # min, quartiles, mean, max in one call
# base R already knows how to fit a model and plot it
weights <- c(59, 68, 54, 82, 75, 63)
model <- lm(weights ~ heights)
summary(model)
plot(heights, weights, main = "Height vs Weight")
abline(model, col = "red")CRAN (the Comprehensive R Archive Network) is R's central package repository. Every package on CRAN passes automated checks and a human review before publication, which is why R users trust CRAN packages more than arbitrary code copied from the internet.
R indexes vectors and data frames starting at 1, not 0. Programmers arriving from Python, JavaScript, or C will instinctively reach for element [0] and get an empty result instead of an error, which can silently produce wrong analyses if not caught early.
- R was created in 1993 by Ross Ihaka and Robert Gentleman as an open-source reimplementation of the S language from Bell Labs.
- R is statistics-first: vectors, data frames, and modeling functions like lm() and t.test() are built into base R, not add-ons.
- CRAN hosts over 20,000 peer-reviewed packages spanning survival analysis, spatial statistics, Bayesian modeling, and more.
- R is widely used in pharma/clinical trials, biostatistics, quantitative finance, academia, and data journalism.
- R and Python are complementary rather than strictly competing: R excels at statistical modeling and graphics, Python at production and deep learning.
- The reticulate package lets R and Python interoperate within the same project.
- R uses 1-based indexing, a common trip-up for programmers coming from 0-indexed languages.
Practice what you learned
1. Who created R, and in what year?
2. Which language did R originally reimplement as an open-source alternative?
3. What is CRAN?
4. Which of the following is TRUE about R's indexing?
5. What is a key reason data scientists often use R and Python together via the reticulate package?
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