R Programming Language Basic

● Statistical Computing

The R
Language

A powerful open-source language for statistical computing, data analysis, and visualization — loved by statisticians, researchers, and data scientists worldwide.

20K+

CRAN Packages

1993

Year Created

#1

In Data Science

Free

Open Source

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// about

What is R?

R is a free, open-source programming language and software environment for statistical computing, data analysis, and graphical visualization. It was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand.

Originally derived from the S programming language, R has grown into one of the world's most popular languages for data science, used extensively in academia, research, finance, healthcare, and the tech industry.

R runs on Windows, macOS, Linux, and is supported by an active community that has contributed over 20,000 packages to CRAN (Comprehensive R Archive Network).

1993

R development begins at University of Auckland by Ross Ihaka & Robert Gentleman

1995

First public release under GNU General Public License

1997

R Core Group formed; CRAN established for package distribution

2000

R 1.0.0 officially released — deemed stable for production

2011

RStudio IDE launched, revolutionizing the R development experience

2014

tidyverse ecosystem begins — ggplot2, dplyr, tidyr gain massive adoption

Today

Over 2 million users globally; 20,000+ CRAN packages available

// features

Why Choose R?

Statistical Analysis

Built from the ground up for statistics — linear models, hypothesis testing, time series, and more are first-class citizens.

Stunning Visualizations

ggplot2 and base R graphics produce publication-quality plots with minimal code. Interactive charts via plotly and shiny.

Rich Ecosystem

20,000+ packages on CRAN covering machine learning, bioinformatics, finance, NLP, spatial analysis, and more.

Reproducible Research

R Markdown and Quarto let you combine code, narrative, and output into dynamic, reproducible reports and notebooks.

Community & Support

Massive global community, Stack Overflow support, active mailing lists, and conferences (useR!, posit::conf).

Vectorized Operations

R is inherently vectorized — operations on entire vectors and matrices without loops, making code concise and fast.

Interoperability

Integrates with Python (reticulate), SQL, C++, Java, and cloud platforms including AWS, Google Cloud, and Azure.

Completely Free

R is free software under the GNU GPL — no licensing costs, no vendor lock-in, forever.

// code

R in Action

# R Basics — Variables, Vectors & Functions

# Assign variables using <- or="</span">
name <- "Data Scientist"
year <- 2024

# Vectors — the fundamental data type in R
scores <- c(85, 92, 78, 96, 88)

# Vectorized operations (no loops needed!)
scaled <- scores * 1.1

# Built-in functions
mean(scores)   # 87.8
sd(scores)     # standard deviation
summary(scores) # min, max, quartiles

# Define your own function
greet <- function(name, lang = "R") {
  paste("Hello,", name, "- Welcome to", lang)
}

greet("Alice")  # "Hello, Alice - Welcome to R"

# Data frame — R's table-like structure
df <- data.frame(
  name  = c("Alice", "Bob", "Carol"),
  score = c(90, 85, 92),
  pass  = c(TRUE, TRUE, TRUE)
)

# Data Visualization with ggplot2
library(ggplot2)

# Basic scatter plot
ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
  geom_point(size = 3, alpha = 0.8) +
  geom_smooth(method = "lm", se = FALSE) +
  labs(
    title   = "Car Weight vs Fuel Efficiency",
    x       = "Weight (1000 lbs)",
    y       = "Miles per Gallon",
    color   = "Cylinders"
  ) +
  theme_minimal()

# Bar chart
ggplot(diamonds, aes(x = cut, fill = clarity)) +
  geom_bar(position = "dodge") +
  scale_fill_brewer(palette = "Blues") +
  theme_classic()

# Histogram with density overlay
ggplot(iris, aes(x = Sepal.Length, fill = Species)) +
  geom_histogram(binwidth = 0.3, alpha = 0.6, position = "identity") +
  facet_wrap(~Species) +
  theme_bw()

# Statistical Analysis in R

# Linear regression
model <- lm(mpg ~ wt + hp + cyl, data = mtcars)
summary(model)  # coefficients, R², p-values

# T-test — comparing two groups
group_a <- rnorm(30, mean = 50, sd = 10)
group_b <- rnorm(30, mean = 55, sd = 10)
t.test(group_a, group_b)

# ANOVA
aov_model <- aov(Sepal.Length ~ Species, data = iris)
summary(aov_model)

# Chi-squared test
chisq.test(table(mtcars$cyl, mtcars$gear))

# Logistic regression
logit <- glm(am ~ wt + hp,
              data = mtcars,
              family = binomial())
summary(logit)

# Data Manipulation with dplyr (tidyverse)
library(dplyr)
library(tidyr)

# Pipe operator: |> chains operations cleanly
result <- starwars |>
  filter(!is.na(height), species == "Human") |>
  select(name, height, mass, gender) |>
  mutate(bmi = mass / (height/100)^2) |>
  arrange(desc(height))

# Group by and summarize
summary_df <- mtcars |>
  group_by(cyl) |>
  summarize(
    avg_mpg  = mean(mpg),
    avg_hp   = mean(hp),
    n        = n()
  )

# Join two data frames
joined <- left_join(orders, customers, by = "customer_id")

# Pivot data from wide to long
long_df <- wide_df |>
  pivot_longer(cols = starts_with("Q"),
               names_to = "quarter",
               values_to = "revenue")

// applications

Where R Shines

01

Data Science & ML

Build machine learning models with caret, tidymodels, xgboost, and randomForest. Handle feature engineering and model evaluation with ease.

02

Bioinformatics

Bioconductor provides 2,000+ packages for genomics, proteomics, and sequencing data analysis — R is the dominant language in life sciences research.

03

Finance & Econometrics

Risk modeling, portfolio optimization, time series forecasting with xts, quantmod, and PerformanceAnalytics.

04

Academic Research

Trusted by researchers worldwide for reproducible analysis. R Markdown and Quarto produce academic papers, reports, and presentations from one source.

05

Public Health & Epidemiology

Disease surveillance, clinical trial analysis, survival analysis, and spatial epidemiology — R was central to COVID-19 research globally.

06

Interactive Dashboards

Shiny lets you build interactive web applications directly from R with no HTML or JavaScript knowledge required.

// ecosystem

Essential Packages

ggplot2

Grammar of Graphics visualization

dplyr

Fast data manipulation verbs

tidyr

Tidy & reshape data frames

shiny

Interactive web applications

caret

Machine learning workflows

tidymodels

Modern ML framework

lubridate

Date & time handling

stringr

String manipulation tools

purrr

Functional programming

plotly

Interactive charts & plots

data.table

High-speed data operations

xgboost

Gradient boosting models

The R Language

r-project.org CRAN Tidyverse Shiny

Open Source · GNU GPL · Free Forever