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# R Programming Course And Certification

**What is R Programming?**

**R Programming** is a programming language that is used for carrying out statistical and data computing analysis which is developed and supported by the R software foundation.

**R Programming Language** is widely used among data analysts, miners and statisticians for developing statistical software and data analysis tools. Data mining surveys, polls, and studies of scholarly literature databases show a large increase in popularity.

**R Programming Language** is one of the most popular languages if not the most popular that is used by data scientists, statisticians, to get, tidy up, study, visualize and display data.

**Due to the expressive syntax and easy-to-use interface of R,** its popularity has grown in recent times. A GNU package, source code for the R software development environment is written basically in C, Fortran, and in R itself and is available for free under the GNU General Public License. Binary versions that are pre-compiled are made available for various software operating systems. Although R has a robust command-line interface, there are several graphical user interfaces, such as RStudio, which is an integrated development environment.

**R Programming Language** is basically an implementation of the S programming language joined together with lexical scoping semantics, that is inspired by Scheme. The S programming language was created by John Chambers in 1976 while working at Bell Labs. R was developed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is presently managed and developed by the R Development Core Team (of which Chambers is also a member). R was given its name, partly after the first names of the first two R authors and partly as a run on the name of S. The project idea was formed in 1992, with the release of its first version in 1995 and a stable beta version in 2000.

**R Programming Language** and all of its libraries were built with wide types of both statistical and graphical techniques and methods, including linear and nonlinear modeling, classical statistical tests, classification, clustering, among others. R is structured, modular, scalable and can be easily extended with the help of extensions and functions, and the R community is recognized for its very active contributions in the section of packages.

**Most of R's default functions are written in R,** this makes it quite easy for developers to know about the algorithmic choices that are made. For jobs that are computationally heavy, C, C++, and Fortran code can be linked together and ran at run time. More experienced users can write C, C++,.NET, Java, or Python code to play and manipulate objects in R directly. R is highly extensible by the use of user-developed packages for carrying out particular functions or in specific areas of study. Due to its origin from S, R has a much stronger object-oriented programming features than most statistical computing languages. Extending R is also made easy by its lexical scoping rules.

**R is extensible by the means of functions and extensions,** and the R community is noted and known for its regular contributions in terms of packages. Advanced users can write C, C++, Java, .NET or Python code to manipulate and use R objects directly. Due to its S heritage, R has stronger object-oriented programming facilities than most of these statistical computing languages. Extending R is also easy because of its lexical scoping rules.

**R Programming** is also used to generate publication-quality graphs, and also mathematical symbols. Dynamic and interactive graphics are available by means of additional packages.

**R also has Rd,** its own LaTeX-like documentation style, which is used to produce comprehensive documentation, both online in a number of styles and in hard copy.

**Features of R Programming**

**There are many features of R Programming** that makes it the most used development platform for large scale statistical computing, some of them are:

1. R is a fully-developed, simple and effective software programming language that includes conditionals, loops, user-defined recursive functions with input and output facilities.

2. R has a very effective facility for data handling and storage.

3. R provides a suite of operators for calculations on arrays, lists, vectors, and matrices.

4. R provides a large, coherent and integrated collection of tools for data analysis.

5. R provides graphical facilities for data analysis and display either directly at the computer or printing on papers.

6. R runs on all platforms: The R software that you are writing on one particular platform can easily be converted to another platform without any issues. Cross-platform compatibility is an essential feature to have in the computing world of this generation, even Microsoft is making its software cross-platform like the .NET platform which is currently available on all platforms after realizing the benefits of technology that runs on all systems.

**Benefits of R Programming Language**

**There are many benefits and advantages of R and some of them are:**

1. R is free and Open Source meaning that you don't have to pay to use.

2. R has exemplary support for Data Wrangling.

3. R comes with an array of Packages and extensions.

4. R allows you to Plot and Graph trends.

5. R allows you to carry out machine learning operations.

6. R is very popular and its popularity is still increasing.

7. Learning and Understanding R will triple your chances of getting a job since it's one of the most sort after languages for Data Analytics and science.

8. R is being used by the biggest giants of the tech world so it's going to be around for a very long time.

**Applications of R Programming**

1. Data Science.

2. Statistical computing.

3. Machine Learning.

6. Health Care.

7. Manufacturing.

8. E-Commerce.

9. Finance.

10. Banking.

11. R is mainly used for descriptive statistics like central tendency, measurement of variability.

12. R is widely applied to do exploration data analysis.

13. R is also applied for analyzing both discrete and continuous probability distribution.

14. R is also used for building and developing statistical software packages and to carry out analytical processing in other software suites.

**R Programming Course Outline**

**THEORY**

Chapter 1: Introducing R

Chapter 2: Exploring R

Chapter 3: The Fundamentals of R

Chapter 4: Getting Started with Arithmetic

Chapter 5: Getting Started with Reading and Writing

Chapter 6: Going on a Date with R

Chapter 7: Working in More Dimensions

Chapter 8: Putting the Fun in Functions

Chapter 9: Controlling the Logical Flow

Chapter 10: Debugging Your Code

Chapter 11: Getting Help

Chapter 12: Getting Data into and out of R

Chapter 13: Manipulating and Processing Data

Chapter 14: Summarizing Data

Chapter 15: Testing Differences and Relations

Chapter 16: Using Base Graphics

Chapter 17: Creating Faceted Graphics with Lattice

Chapter 18: Looking At ggplot2 Graphics

Chapter 19: Ten Things You Can Do in R That You

Chapter 20: Working with Packages

**PRACTICE**

R Programming - Course Overview

R Programming - Introduction

R Programming - Environment setup

R Programming - Basic Syntax

R Programming - Variables

R Programming - Datatypes

R Programming - Operators

R Programming - Arithmetic Operator

R Programming - Relational Operators

R Programming - Logical Operator

R Programming - Decision Making

R Programming - if Statement

R Programming - if else Statement

R Programming - Switch Statement

R Programming - Loops

R Programming - for Loop

R Programming - While Loop

R Programming - Repeat Loop

R Programming - Array

R Programming - Functions

R Programming - Strings

R Programming - Vectors

R Programming - List

R Programming - Matrices

R Programming - Factors

R Programming - Data Frames

R Programming - Packages

R Programming - Data Reshaping

R Programming - Opning Files

R Programming - Web Data

R Programming - Database

R Programming - Plotting

R Programming - Plotting Pie Chart

R Programming - Plotting Bar Chart

R Programming - Plotting Boxplots

R Programming - Plotting Histogram

R Programming - Plotting Line Graphs

R Programming - Plotting Scatterplots

R Programming - Mean, Median & Mode

R Programming - Linear Regression