R is a programming language

environment specifically designed for statistical computing and graphics. Developed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, R has gained widespread popularity in the fields of data analysis, statistical modeling, and machine learning. It provides a comprehensive set of tools for data manipulation, statistical analysis, visualization, and the development of data-driven applications.

R is an open-source language, making it accessible to a large community of users and developers. Its syntax is user-friendly, allowing for easy implementation of statistical and mathematical operations. R also supports a vast number of packages, each providing specialized functions for various data analysis tasks.

Key features of R include its ability to handle and manipulate diverse data types, support for data visualization through packages like ggplot2, and its extensibility, allowing users to create and share their packages. Additionally, R has a strong community that contributes to its continuous development and improvement.

Overall, R is a powerful tool for statisticians, data scientists, and researchers who seek a flexible and versatile environment for data analysis and visualization. Its applications span various industries, including finance, healthcare, and academia.

Introduction

1. Overview of R Programming
2. Installation of R and R Studio
3. Understanding the interface of R Studio
4. Running the first program in R

Core Programming Principles

1. Types of variables
2. Assigning Values to Variables
3. Using the console
4. Working with Operators
5. The “If” statement
6. The “While” Loop
7. The “For” Loop
8. User-Defined Functions

Vectors

1. What is a Vector?
2. Working with vectors
3. Using the [] brackets
4. Vectorized operations
5. Functions
6. Packages

Matrices

1. Introduction to Matrices
2. Building the First Matrix
3. Naming Dimensions
4. Colnames() and Rownames()
5. Matrix Operations
6. Visualizing With Matplot()
7. Subsetting
8. Visualizing Subsets
9. Creating Your First Function
10. Working on a Project

Lists

1. Creating a List
2. Indexing Lists With Brackets
3. Indexing Lists Using Objects Names
4. Editing Values in Lists
5. Adding and Removing List Objects
6. Applying Functions to Lists

Factors

1. Working With Factors
2. Splitting a Vector By a Factor Levels
3. The tapply() Function
4. The by() Function

Dataframe

1. Introduction to Dataframe
2. Importing data into R
3. Exploring the dataset
4. Using the $ sign
5. Basic operations with a Data Frame
6. Filtering a Data Frame
7. Introduction to Data Visualization – qplot
8. Building Dataframes
9. Merging Data Frames
10. Visualizing With Qplot
11. Working on a Project

Advanced Data Visualization – GGPlot2

1. Overview of GGPlot2
2. Understanding the Grammar of  Graphics
3. What is a Factor?
4. Aesthetics
5. Plotting With Layers
6. Overriding Aesthetics
7. Mapping vs Setting
8. Histograms and Density Charts
9. Starting Layer Tips
10. Statistical Transformations
11. Using Facets
12. Coordinates
13. Perfecting By Adding Themes
14. Working on a Project