- R - Data Reshaping
- R - Packages
- R - Data Frames
- R - Factors
- R - Arrays
- R - Matrices
- R - Lists
- R - Vectors
- R - Strings
- R - Functions
- R - Loops
- R - Decision Making
- R - Operators
- R - Variables
- R - Data Types
- R - Basic Syntax
- R - Environment Setup
- R - Overview
- R - Home

R Data Interfaces

- R - Database
- R - Web Data
- R - JSON Files
- R - XML Files
- R - Binary Files
- R - Excel Files
- R - CSV Files

R Charts & Graphs

R Statistics Examples

- R - Chi Square Tests
- R - Survival Analysis
- R - Random Forest
- R - Decision Tree
- R - Nonlinear Least Square
- R - Time Series Analysis
- R - Analysis of Covariance
- R - Poisson Regression
- R - Binomial Distribution
- R - Normal Distribution
- R - Logistic Regression
- R - Multiple Regression
- R - Linear Regression
- R - Mean, Median & Mode

R Useful Resources

Selected Reading

- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes

# R Tutorial

R is a programming language and software environment for statistical analysis, graphics representation and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R is freely available under the GNU General Pubpc License, and pre-compiled binary versions are provided for various operating systems pke Linux, Windows and Mac. This programming language was named **R**, based on the first letter of first name of the two R authors (Robert Gentleman and Ross Ihaka), and partly a play on the name of the Bell Labs Language **S**.

# Audience

This tutorial is designed for software programmers, statisticians and data miners who are looking forward for developing statistical software using R programming. If you are trying to understand the R programming language as a beginner, this tutorial will give you enough understanding on almost all the concepts of the language from where you can take yourself to higher levels of expertise.

# Prerequisites

Before proceeding with this tutorial, you should have a basic understanding of Computer Programming terminologies. A basic understanding of any of the programming languages will help you in understanding the R programming concepts and move fast on the learning track.

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