R Tutorial

R is an interpreted programming language widely used for statistical computing, data analysis and data visualization. It is especially popular among data scientists, statisticians and researchers for handling and analyzing structured data efficiently.

• Open-source language with strong community support
• Rich ecosystem of packages like dplyr, ggplot2, shiny and janitor
• Provides tools for data manipulation, analysis and visualization

Hello World Program in R Language

Here is an example of the first Hello World program in R Programming Language. To print in R language you just need to use a Print function.

Output

Hello World!

Installation and Setup

This section explains how to install R and RStudio and how to create and run your first R script.

• Installing R Studio on Windows & Linux
• Creation and Execution of R File in R Studio
• Introduction to R Studio

Fundamentals of R

This section covers the basic syntax, operators and core programming concepts required to start coding in R.

• Introduction
• Applications of R
• Jobs related to R
• Basic Syntax
• Comments
• Operators
• Keywords
• Data Types
• Variables

Data Structures

This section explains how R stores and organizes data using vectors, lists, matrices, data frames and other structures.

• Data Structures
• Vectors
• Lists
• Matrices
• Data Frames
• Arrays
• Factors
• Strings

Control Flow

This section covers conditional statements and loops used to control the execution of R programs.

• Decision Making
• Loops (for, while, repeat)

Functions & Object Oriented Programming

This section explains how to create reusable functions and introduces basic object-oriented concepts in R.

• Functions
• Object-Oriented Programming
• Classes
• Objects
• Encapsulation
• Polymorphism
• Inheritance
• Abstraction

File and Error Handling

This section covers reading and writing files and handling errors to make programs more robust.

• File Handling
• Data Handling
• Error Handling

Data Visualization

This section introduces plotting techniques and popular libraries used to create charts and graphs in R.

• Data Visualization
• Bar Charts
• Line Graphs
• Histograms
• Pie Charts
• Scatter plots
• Heatmap

Statistics, Data Science & Machine Learning

This section covers statistical methods and basic machine learning techniques available in R.

• Statistics
• Mean, Median and Mode
• Average, Variance & Standard Deviation
• Data Science with R
• Machine Learning with R

Popular Packages in R

This section highlights important R packages that enhance data analysis, visualization and modeling.

• Packages
• Top 15 Packages in R
• Tidyverse Packages

Projects In R

This section provides practical project ideas to apply R concepts in real-world scenarios.

• Beginner R Projects
• Advanced R Projects
• 30+ R projects

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• R Data Science Tutorial
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