Practical Foundations of R Programming

Practical Foundations of R Programming, available at $39.99, has an average rating of 4.45, with 101 lectures, based on 63 reviews, and has 848 subscribers.

You will learn about Understand the most important concepts relating to data structures, subsetting, and writing functions in R This course is ideal for individuals who are Anyone who wishes to learn the fundamental basics of writing applications in R or Programmers in other languages who are learning R and wish to better understand the unique features of R It is particularly useful for Anyone who wishes to learn the fundamental basics of writing applications in R or Programmers in other languages who are learning R and wish to better understand the unique features of R.

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Summary

Title: Practical Foundations of R Programming

Price: $39.99

Average Rating: 4.45

Number of Lectures: 101

Number of Published Lectures: 101

Number of Curriculum Items: 101

Number of Published Curriculum Objects: 101

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the most important concepts relating to data structures, subsetting, and writing functions in R

Who Should Attend

Anyone who wishes to learn the fundamental basics of writing applications in R
Programmers in other languages who are learning R and wish to better understand the unique features of R

Target Audiences

Anyone who wishes to learn the fundamental basics of writing applications in R
Programmers in other languages who are learning R and wish to better understand the unique features of R

Practical Foundations of R Programming is the first course of a learning path that teaches critical foundation skills necessary to create quality code using the free and open-access R programming language. This course, and the courses that follow, are useful for both beginner and intermediate R programmers who want to understand the unique features of R and why “R works the way it does.” I have been using, teaching, and writing applications in R for 6 years and have come to appreciate that R is a beautiful and elegant language that is especially well-suited for writing applications for data analytics, and for mathematical and statistical applications. Furthermore, R is superior in terms of inherent graphical data presentation capabilities that go hand-in-hand with exploring and understanding data relationships.

Most introductory R courses, those that do not directly address sharpening one’s R programming skills, first teach the important R data structures, then the basics of R functions, and generally the use of base R graphics capabilities. However, these introductory R courses are not targeted at the R programmer population, but rather at the general R userpopulation. This course, Practical Foundations of R Programming, which contains all-unique material compared to my other Udemy R courses, addresses R data structures, R subsetting, and R functions, but from the focused perspective of someone who intends to write efficient higher-level applications using R. It is specifically intended to teach the most important foundation concepts and features of the R programming language which are necessary to understand to write efficient and effective applications in R.

This course, which is exclusively “hands-on,” demonstrates the construction and use of R code within the RStudio IDE, and focuses on the unique features of R that can make writing applications in R both a challenge and a delight. The course does not present a single power point slide and relies heavily on user exercises. In each of the three major sections of the course, (1) data structures, (2) subsetting, and (3) functions, there are multiple sets of within-section exercises, as well as a final end-of-section exercise set. Participants are encouraged to complete each set of exercises “on their own” before they view the videos that present the exercise solutions. All course videos, and all exercises, as well as their solutions, are presented within R scripts that are made accessible with the course materials. Anything and everything that you see me demonstrate and/or discuss in the 100+ course videos are available for you to download at the beginning of the course.

The second course in this learning path, which should be available to you by the time you complete this first course, will delve more deeply into functional programming in R per se. The second course will have a similar format to this first course: all “hands-on” with extensive use of practical and relevant in-section, and end-of-section, exercises.�

Course Curriculum

Chapter 1: Introduction to Practical Foundations of R Programming

Lecture 1: Introduction to the Course

Lecture 2: A Word About the Course and Materials

Chapter 2: Data Structures for R Programming

Lecture 1: Introduction to R Data Structures

Lecture 2: Atomic Vectors

Lecture 3: Testing Objects and Coercion

Lecture 4: List Data Structures (part 1)

Lecture 5: List Data Structures (part 2)

Lecture 6: Attributes (part 1)

Lecture 7: Attributes (part 2)

Lecture 8: Factors

Lecture 9: Matrices and Arrays (part 1)

Lecture 10: Matrices and Arrays (part 2)

Lecture 11: Matrices and Arrays (part 3)

Lecture 12: Matrices and Arrays Exercises

Lecture 13: Matrices and Arrays Exercise Solutions (part 1)

Lecture 14: Matrices and Arrays Exercise Solutions (part 2)

Lecture 15: Creating Data Frames

Lecture 16: Data Frame Testing and Coercion

Lecture 17: More about Data Frames

Lecture 18: Data Frame Exercises

Lecture 19: Data Frame Exercise Solutions (part 1)

Lecture 20: Data Frame Exercise Solutions (part 2)

Lecture 21: Data Frame Exercise Solutions (part 3)

Lecture 22: End-of-Section Data Structures Exercises

Lecture 23: Solutions to Data Structures Exercises (part 1)

Lecture 24: Solutions to Data Structures Exercises (part 2)

Lecture 25: Solutions to Data Structures Exercises (part 3)

Lecture 26: Solutions to Data Structures Exercises (part 4)

Lecture 27: Solutions to Data Structures Exercises (part 5)

Lecture 28: Solutions to Data Structures Exercises (part 6)

Chapter 3: Subsetting R Objects

Lecture 1: Introduction to Subsetting

Lecture 2: Approaches to Subsetting R Objects (part 1)

Lecture 3: Approaches to Subsetting R Objects (part 2)

Lecture 4: Subsetting Matrices and Arrays (part 1)

Lecture 5: Subsetting Matrices and Arrays (part 2)

Lecture 6: Subsetting Data Frames

Lecture 7: Subsetting Exercises I

Lecture 8: Subsetting Exercises I Solutions (part 1)

Lecture 9: Subsetting Exercises I Solutions (part 2)

Lecture 10: Subsetting Exercises I Solutions (part 3)

Lecture 11: Subsetting Exercises I Solutions (part 4)

Lecture 12: Subsetting Exercises I Solutions (part 5)

Lecture 13: Subsetting Lists (part 1)

Lecture 14: Subsetting Lists (part 2)

Lecture 15: Preserving versus Simplifying Subsetting

Lecture 16: The Shorthand '$' Operator versus '[['

Lecture 17: Subsetting Missing / Out-of-Bounds

Lecture 18: Linear Regression Model Subsetting Exercise

Lecture 19: Linear Regression Model Subsetting Exercise Solution

Lecture 20: Subsetting and Assignment (part 1)

Lecture 21: Subsetting and Assignment (part 2)

Lecture 22: Character Subsetting

Lecture 23: Integer Subsetting

Lecture 24: Sampling Rows and Columns Randomly

Lecture 25: Ordering Rows and Columns

Lecture 26: Expanding Aggregated Counts

Lecture 27: Removing Columns from a Data Frame

Lecture 28: Selecting Rows Based on a Logical Condition

Lecture 29: Boolean Algebra versus Sets (part 1)

Lecture 30: Boolean Algebra versus Sets (part 2)

Lecture 31: Subsetting Exercises III

Lecture 32: Subsetting Exercises III with Solutions

Lecture 33: End-of-Section Exercises

Lecture 34: End-of-Section Exercise Solutions (part 1)

Lecture 35: End-of-Section Exercise Solutions (part 2)

Chapter 4: The Nature of R Functions

Lecture 1: What are Functions in R ?

Lecture 2: Primitive Functions

Lecture 3: Functions Exercises I

Lecture 4: Functions Exercises I with Solutions

Lecture 5: Lexical Scoping

Lecture 6: Name Masking (part 1)

Lecture 7: Name Masking (part 2)

Lecture 8: Name Masking (part 3)

Lecture 9: Functions versus Variables

Lecture 10: A Fresh Start

Lecture 11: Dynamic Lookup (part 1)

Lecture 12: Dynamic Lookup (part 2)

Lecture 13: Functions Exercises II

Lecture 14: Functions Exercises II with Solutions

Lecture 15: More on Functions Calls (part 1)

Lecture 16: More on Function Calls (part 2)

Lecture 17: Function Arguments (part 1)

Lecture 18: Function Arguments (part 2)

Lecture 19: Calling Functions with a List of Arguments

Lecture 20: Default and Missing Arguments

Lecture 21: Lazy Evaluation (part 1)

Lecture 22: Lazy Evaluation (part 2)

Lecture 23: The " . . . " (Triple Dot) Function

Lecture 24: Functions Exercises III

Lecture 25: Functions Exercises III with Solutions

Lecture 26: Infix Operator Functions

Lecture 27: Replacement Functions

Lecture 28: Functions Exercises IV

Lecture 29: Functions Exercises IV with Solutions

Lecture 30: Return Values (part 1)

Lecture 31: Return Values (part 2)

Instructors

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

Rating Distribution

1 stars: 0 votes
2 stars: 2 votes
3 stars: 6 votes
4 stars: 26 votes
5 stars: 29 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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