The Comprehensive Programming in R Course

The Comprehensive Programming in R Course, available at $54.99, has an average rating of 4.55, with 121 lectures, based on 260 reviews, and has 3348 subscribers.

You will learn about Acquire the skills needed to successfully develop general-purpose programming applications in the R environment Possess an in-depth understanding of the R programming environment and of the requirements for, and programming implications of, writing code using basic R objects: vectors, matrices, dataframes and lists. Understand the object-oriented characteristics of programming in R and know how to create S3 and S4 Class objects and functions that process these S3 and S4 objects. Know how to program mathematical functions, models and simulations in R. Know how to write R programs that effectively use and manipulate text and string variable objects. Know how to use the scan(), readline(), cat(), print() and readLines() functions in R for efficient data input and output and for effective user-prompting. Know how to 'tweak' R programs for maximum performance efficiency. This course is ideal for individuals who are Anyone interested in writing computer applications that execute in the R environment. or The common objective of students is common objective is to write R applications for diverse domains and purposes. or Students may already be skilled programmers (in other languages) or they may be complete novices to R programming or to programming in general, or Undergraduate or graduate students looking to acquire marketable job skills prior to graduation. or Analytics professionals looking to acquire additional job skills. It is particularly useful for Anyone interested in writing computer applications that execute in the R environment. or The common objective of students is common objective is to write R applications for diverse domains and purposes. or Students may already be skilled programmers (in other languages) or they may be complete novices to R programming or to programming in general, or Undergraduate or graduate students looking to acquire marketable job skills prior to graduation. or Analytics professionals looking to acquire additional job skills.

Enroll now: The Comprehensive Programming in R Course

Summary

Title: The Comprehensive Programming in R Course

Price: $54.99

Average Rating: 4.55

Number of Lectures: 121

Number of Published Lectures: 120

Number of Curriculum Items: 121

Number of Published Curriculum Objects: 120

Original Price: $159.99

Quality Status: approved

Status: Live

What You Will Learn

Acquire the skills needed to successfully develop general-purpose programming applications in the R environment
Possess an in-depth understanding of the R programming environment and of the requirements for, and programming implications of, writing code using basic R objects: vectors, matrices, dataframes and lists.
Understand the object-oriented characteristics of programming in R and know how to create S3 and S4 Class objects and functions that process these S3 and S4 objects.
Know how to program mathematical functions, models and simulations in R.
Know how to write R programs that effectively use and manipulate text and string variable objects.
Know how to use the scan(), readline(), cat(), print() and readLines() functions in R for efficient data input and output and for effective user-prompting.
Know how to 'tweak' R programs for maximum performance efficiency.

Who Should Attend

Anyone interested in writing computer applications that execute in the R environment.
The common objective of students is common objective is to write R applications for diverse domains and purposes.
Students may already be skilled programmers (in other languages) or they may be complete novices to R programming or to programming in general,
Undergraduate or graduate students looking to acquire marketable job skills prior to graduation.
Analytics professionals looking to acquire additional job skills.

Target Audiences

Anyone interested in writing computer applications that execute in the R environment.
The common objective of students is common objective is to write R applications for diverse domains and purposes.
Students may already be skilled programmers (in other languages) or they may be complete novices to R programming or to programming in general,
Undergraduate or graduate students looking to acquire marketable job skills prior to graduation.
Analytics professionals looking to acquire additional job skills.

The Comprehensive Programming in R Course is actually a combination of two R programming courses that together comprise a gentle, yet thorough introduction to the practice of general-purpose application development in the R environment. The original first course (Sections 1-8) consists of approximately 12 hours of video content and provides extensive example-based instruction on details for programming R data structures. The original second course (Sections 9-14), an additional 12 hours of video content, provides a comprehensive overview on the most important conceptual topics for writing efficient programs to execute in the unique R environment. Participants in this comprehensive course may already be skilled programmers (in other languages) or they may be complete novices to R programming or to programming in general, but their common objective is to write R applications for diverse domains and purposes. No statistical knowledge is necessary. These two courses, combined into one course here on Udemy, together comprise a thorough introduction to using the R environment and language for general-purpose application development.

The Comprehensive Programming in R Course (Sections 1-8) presents an detailed, in-depth overview of the R programming environment and of the nature and programming implications of basic R objects in the form of vectors, matrices, dataframes and lists. The Comprehensive Programming in R Course (Sections 9-14) then applies this understanding of these basic R object structures to instruct with respect to programming the structures; performing mathematical modeling and simulations; the specifics of object-oriented programming in R; input and output; string manipulation; and performance enhancement for computation speed and to optimize computer memory resources.

Course Curriculum

Chapter 1: Introduction and Overview of R

Lecture 1: Introduction to Comprehensive R Programming Course

Lecture 2: Introduction and Getting Started

Lecture 3: Getting Started and First R Session

Lecture 4: First R Session (part 2)

Lecture 5: First R Session (part 3)

Lecture 6: Matrices, Lists and Dataframes

Lecture 7: Introduction to Functions

Lecture 8: Functions and Default Arguments

Lecture 9: More Examples of Functions (part 1)

Lecture 10: More Functions Examples (part 2)

Lecture 11: More Functions Examples (part 3)

Lecture 12: More Functions Examples (part 4)

Lecture 13: More Functions Examples (part 5)

Lecture 14: More Functions Examples (part 6)

Chapter 2: What are Vector Data Structures in R ?

Lecture 1: Homemade t-test Exercise Solution

Lecture 2: Section 2 Exercise and Package Demonstrations

Lecture 3: Begin Discussion of Vectors

Lecture 4: More Examples of Vectors

Lecture 5: Common Vector Operations and More

Lecture 6: Findruns Example and Vectors Exercises

Chapter 3: More Discussion of Vector Data Structures

Lecture 1: Vector-Based Programming Exercise Solution (part 1)

Lecture 2: Vector Exercise Solution (part 2) and Begin General Vector Discussion

Lecture 3: Continue General Vector Discussion

Lecture 4: More General Vector Examples

Lecture 5: More on Vectors and Vector Equality

Lecture 6: Extended Vector Example and Exercise

Chapter 4: Finish Vectors and Begin Matrices

Lecture 1: Finish Vector Discussion

Lecture 2: Vector-Maker Exercise Solutions

Lecture 3: Begin Discussion of Matrices and Arrays

Lecture 4: Filtering Matrices and More Examples

Lecture 5: Still More Matrices Examples

Chapter 5: Finish Matrices and Begin Lists Discussion

Lecture 1: Min-Merge Vector Exercise Solutions

Lecture 2: Game of Craps Exercise Solution

Lecture 3: Naming Matrix Rows and Columns

Lecture 4: Lists: General List Operations

Lecture 5: Processing Text with Lists

Lecture 6: Applying Functions to Lists

Lecture 7: Vector and Matrix Exercise

Chapter 6: Continue Lists Discussion

Lecture 1: Review Programming Exercises

Lecture 2: Finish Programming Exercise Review and Begin Discussing Lists

Lecture 3: List Data Structures General Discussion (part 2)

Lecture 4: List Data Structures General Discussion (part 3)

Lecture 5: Lists Data Structures General Discussion (part 4)

Chapter 7: Details About Dataframe Data Structures

Lecture 1: Dataframe-Maker Exercise

Lecture 2: List-Maker Exercise; Begin General Dataframe Discussion

Lecture 3: Extracting Subdata Frames

Lecture 4: A Salary Survey Extended Example

Lecture 5: Merging Dataframes

Lecture 6: End Dataframes Discussion; Matrix Exercise

Chapter 8: More Matrix and List Examples

Lecture 1: Covariance Matrix Exercise Solution

Lecture 2: List Example: Tree Growth (part 1)

Lecture 3: List Example: Tree Growth (part 2)

Lecture 4: Factor Data Types

Lecture 5: Factors: tapply() and split() Functions

Lecture 6: Factor Levels versus Values

Lecture 7: Pascal's Triangle Exercise

Chapter 9: Programming in R Environments

Lecture 1: Pascal's Triangle Exercise Solution

Lecture 2: Begin Programming Structures

Lecture 3: Environment and Scope Issues

Lecture 4: Nesting Multiple Environments

Lecture 5: Referencing Variables in Other Frames

Lecture 6: Writing to Global Variables and Recursion

Lecture 7: Replacement and Anonymous Functions

Lecture 8: Sorting Programs Exercise

Chapter 10: Performing Math and Simulations

Lecture 1: Sorting Programs Exercise Solution (part 1)

Lecture 2: Sorting Programs Exercise Solution (part 2)

Lecture 3: Calculating a Probability

Lecture 4: Linear Algebra Operations

Lecture 5: Set Operations and Simulation

Lecture 6: Combinatorial Simulations (part 1)

Lecture 7: Combinatorial Simulations (part 2)

Lecture 8: Winning at Roulette Exercise

Chapter 11: Object Oriented Programming (OOP) and S3 and S4 Classes

Lecture 1: Winning at Roulette Exercise solution

Lecture 2: Introduction to OOP in R

Lecture 3: OOP Example: lm() Function

Lecture 4: Writing S3 Classes

Lecture 5: Using Inheritance

Lecture 6: Compressing Matrices Example (part 1)

Lecture 7: Compressing Matrices Example (part 2)

Lecture 8: Writing S3 Classes Exercise

Lecture 9: Writing S4 Classes

Lecture 10: Implementing S4 Generic Functions

Lecture 11: Writing S4 Classes Exercise

Lecture 12: Live S3 and S4 Class Development

Lecture 13: Continue S3 Class Development

Lecture 14: Developing a Corresponding S4 Class

Chapter 12: Input and Output

Lecture 1: Writing S3 Classes Exercise Solution

Lecture 2: Writing S4 Classes Exercise Solution

Instructors

Geoffrey Hubona, Ph.D.
Associate Professor of MIS and Data Analytics

Rating Distribution

1 stars: 4 votes
2 stars: 15 votes
3 stars: 54 votes
4 stars: 89 votes
5 stars: 98 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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