Learn R with Real Code Examples

R allows statisticians and data scientists to perform complex data analyses efficiently.

It integrates statistical techniques, machine learning algorithms, and high-quality graphics in a single platform.

Widely used in academia, research, and industry for data science, bioinformatics, finance, and social sciences.

Interpreted language with REPL interface

Functional programming paradigm

Rich data structures (vectors, matrices, lists, data frames)

Advanced statistical and machine learning libraries

Integration with C, C++, and Python for performance

Variables and basic data types (numeric, character, logical)

Vectors, matrices, lists, and data frames

Functions and functional programming

Control flow: loops, if-else, apply functions

Statistical modeling and plotting basics

data/ - raw and processed datasets

scripts/ - R scripts or notebooks

plots/ - output visualizations

docs/ - reports and R Markdown files

libs/ - custom or third-party packages

Load data using CSV, Excel, or database connectors

Perform data cleaning and transformation

Analyze data using statistical methods

Visualize results with plots and charts

Generate reproducible reports with R Markdown or Shiny apps

Beginner: descriptive statistics and plotting

Intermediate: regression and machine learning models

Advanced: big data analysis and optimization

Expert: custom package development and high-performance computing

Enterprise: production-grade pipelines and dashboards

Stronger in statistics than Python, though Python has broader general-purpose use

R excels at data visualization and reporting

CRAN offers thousands of specialized packages

RStudio IDE provides excellent data science workflow

Less performant for large-scale computational tasks than compiled languages

1993 – Initial release by Ihaka and Gentleman

1995 – R version 1.0 released

2000 – CRAN expands with contributed packages

2010 – Tidyverse ecosystem gains popularity

2025 – Active development with R 4.5+ and growing tools

Vector: basic data container for R

Data Frame: tabular data structure

List: collection of heterogeneous elements

Function: reusable block of code

Package: collection of functions and datasets