## Description

R is one of the most widely used programming languages in the industry. It has a very steep learning curve since students get overwhelmed while learning.

You will be moving one step closer to mastering the R programming throughout this course.

R can also be used to analyze own datasets and bring out statistical outputs. The best thing about R is that you can bring up a powerful analysis with R vectors, arrays, matrices and lists. Combining these will bring the best analytic data.

This course will bring you the best knowledge on R programming which can be implemented during a real-time scenario when analyzing the data.

This course will also teach you how to create the best visualization with different datasets by using R programming.

**What is R Programming Language?**

The R Core Team and the R Foundation for Statistical Computing are two organizations that support the programming language R, which is used for statistical computing and graphics.

Ross Ihaka and Robert Gentleman created R in 1993, which also has time series, statistical inference, machine learning techniques, and more.

R is a widely used for

- Data analysis
- Statistical inference
- Machine learning methods.

R provides a helpful environment for statistical computing and design and offers a wide range of statistics-related libraries. Additionally, since it's helpful for importing and cleaning data, many quantitative analysts use the R programming language as a programming tool.

**Why should you learn R Programming?**

Data analysis powerhouse |
R is a great tool for managing and manipulating data because it was created specifically for statistical computing and analysis. |

Widely used in academia and industry |
R's widespread use and growing popularity across disciplines like data science, bioinformatics, finance, and the social sciences guarantee its continued success and growth. |

Rich ecosystem |
R's flexibility and adaptability come from its extensive package and library ecosystem, which allows it to do a wide variety of specialized tasks. |

Data visualization |
R is a powerful tool for producing professional-grade visualizations, which facilitates the discovery and sharing of insights from data. |

Reproducibility and collaboration |
R scripts facilitate reproducible research by facilitating the verification and replication of your results by other researchers. |

Open-source and free |
Since R is a free and open-source programming language, it can be used by anybody. |

Active community support |
The R community is active and helpful, with plenty of learning materials, discussion boards, and how-to guides available. |

Integration with other languages |
Integrating R with Python, for example, lets you take advantage of both languages' capabilities. |

Career opportunities |
Increase your employment opportunities in data analysis and data science by learning to code in R. |

**What will you require? **

- A Personal computer
- A compatible browser
- A Passion to Learn

**Syllabus**

**Hit the Ground and Running **

- Introduction to R Programming
- R studio installation in Mac and Windows
- Basics to datasets
- Additional resources

**Core Programming Principles **

- Variables and its types
- Using the variables
- What are Logical variables and operators?
- What is a “While” loop?
- How to use the console
- What is a “For” loop?
- If statement
- An Overview of the section
- Exercise for this section
- Quiz

**Fundamentals of R **

- Definition of Vector
- Creating some vectors
- How to use [] brackets
- What are vectorized operations?
- Power of Vectorized operations
- What are the functions in R?
- What are the packages in R?
- An Overview of the section
- Exercise for this section
- Quiz

**Matrices**

- Basketball Trends - Project
- What are Matrices?
- How to build your first matrix
- What are Naming dimensions?
- What are Colnames() and Rownames()?
- An overview of Matrix Operations
- Matplot() visualization
- What is subsetting?
- Subset visualization
- How to create your First Function
- Insights in Basketball
- An overview of the section
- Exercise for this section
- Matrices quiz

**Data Frames **

- Demographic Analysis - Project
- How to import data in R
- Dataset exploration
- How to use the “$” sign
- Data frame in basic operations
- How to filter Data frame
- Qplot introduction
- Qplot visualization - Part 1
- How to build Dataframes
- Merging of Data frames
- Qplot visualization - Part 2
- An Overview of the section
- Exercise for this section
- Quiz

**Advanced Visualization with GGPlot2 **

- Movie Ratings - Project
- Grammar Graphics - GGPlot2
- Explanation of Factor
- What are Aesthetics?
- How to plot with Layers?
- How to override with Aesthetics?
- Difference between Mapping and Setting
- Explanation of Histograms and Density Charts
- Introduction to Layer Tips
- What are Statistical Transformations?
- How to use Facets?
- What are Coordinates?
- How to perfect by adding themes?
- An overview of the section
- Exercise for this section
- Quiz

**Homework Solutions **

- Law of Large Numbers
- Financial Statement Analysis
- Basketball Free Throws
- World Trends
- Movie Domestic % Gross (Part 1)
- Movie Domestic % Gross (Part 2)
- Thanking the students

## What you’ll learn?

- R Programming at a good level
- Core Principles of Programming
- Creating variables
- Creating a while() loop and a for() loop in R
- Basics of matrix(), rbind() and cbind() functions
- Own Customization of R studio based on preferences
- Basic understanding of Normal distribution
- How to work with Financial data in R
- Using R studio
- Creating vectors in R
- Integer, double, logical, character and other data types in R
- Building and using matrices in R
- Installing packages in R
- Law of Large Numbers explanation
- Practical working with statistical data in R
- Practical working with sports data in R

**Who can enroll this course?**

- People who want to learn R programming
- People who are exhausted by searching R courses that are much more complicated
- People who want to learn R practically
- People who are excited about coding challenges
- People who are able to put extra work as homework in this course

**Course Duration**

- 285 Lectures and 32 Hours of on Demand HD Videos
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
- 7900+ students enrolled
- Complete Practical Training
- Download access
- Watch Videos in Android and iOS App