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Practical Foundations of R Programming

By: Geoffrey Hubona

  • 5
  • (8)
  • 07:54:27
  • 102
  • 22
  • Language: English

Course Summary

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

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Target Audience

  • 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

Pre-Requisites

  • You should have previously installed and used R software and RStudio
  • It is helpful if you have already written some code in R or in other programming languages

Curriculum

  • Introduction to the Course
    01:56
  • A Word About the Course and Materials
    04:37
  • Exercise Files
  • Introduction to R Data Structures
    06:14
  • Atomic Vectors
    07:21
  • Testing Objects and Coercion
    08:31
  • List Data Structures (part 1)
    04:18
  • List Data Structures (part 2)
    05:26
  • Attributes (part 1)
    04:31
  • Attributes (part 2)
    04:33
  • Factors
    07:24
  • Matrices and Arrays (part 1)
    05:11
  • Matrices and Arrays (part 2)
    06:01
  • Matrices and Arrays (part 3)
    05:12
  • Matrices and Arrays Exercises
    02:08
  • Matrices and Arrays Exercise Solutions (part 1)
    05:22
  • Matrices and Arrays Exercise Solutions (part 2)
    05:31
  • Creating Data Frames
    06:51
  • Data Frame Testing and Coercion
    05:48
  • More about Data Frames
    03:43
  • Data Frame Exercises
    07:09
  • Data Frame Exercise Solutions (part 1)
    03:22
  • Data Frame Exercise Solutions (part 2)
    03:31
  • Data Frame Exercise Solutions (part 3)
    03:24
  • End-of-Section Data Structures Exercises
    02:08
  • Solutions to Data Structures Exercises (part 1)
    07:33
  • Solutions to Data Structures Exercises (part 2)
    04:19
  • Solutions to Data Structures Exercises (part 3)
    03:21
  • Solutions to Data Structures Exercises (part 4)
    05:55
  • Solutions to Data Structures Exercises (part 5)
    05:34
  • Solutions to Data Structures Exercises (part 6)
    04:33
  • Introduction to Subsetting
    03:10
  • Approaches to Subsetting R Objects (part 1)
    07:13
  • Approaches to Subsetting R Objects (part 2)
    05:30
  • Subsetting Matrices and Arrays (part 1)
    07:03
  • Subsetting Matrices and Arrays (part 2)
    06:07
  • Subsetting Data Frames
    05:44
  • Subsetting Exercises I
    03:33
  • Subsetting Exercises I Solutions (part 1)
    06:32
  • Subsetting Exercises I Solutions (part 2)
    05:48
  • Subsetting Exercises I Solutions (part 3)
    03:55
  • Subsetting Exercises I Solutions (part 4)
    03:18
  • Subsetting Exercises I Solutions (part 5)
    03:32
  • Subsetting Lists (part 1)
    03:52
  • Subsetting Lists (part 2)
    04:43
  • Preserving versus Simplifying Subsetting
    06:43
  • The Shorthand '$' Operator versus '[['
    02:18
  • Subsetting Missing / Out-of-Bounds
    02:00
  • Linear Regression Model Subsetting Exercise
    01:37
  • Linear Regression Model Subsetting Exercise Solution
    07:39
  • Subsetting and Assignment (part 1)
    04:15
  • Subsetting and Assignment (part 2)
    04:20
  • Character Subsetting
    02:43
  • Integer Subsetting
    04:07
  • Sampling Rows and Columns Randomly
    03:12
  • Ordering Rows and Columns
    05:02
  • Expanding Aggregated Counts
    02:51
  • Removing Columns from a Data Frame
    04:33
  • Selecting Rows Based on a Logical Condition
    05:34
  • Boolean Algebra versus Sets (part 1)
    03:56
  • Boolean Algebra versus Sets (part 2)
    06:20
  • Subsetting Exercises III
    02:45
  • Subsetting Exercises III with Solutions
    07:33
  • End-of-Section Exercises
    04:17
  • End-of-Section Exercise Solutions (part 1)
    04:09
  • End-of-Section Exercise Solutions (part 2)
    04:45
  • What are Functions in R ?
    04:40
  • Primitive Functions
    01:25
  • Functions Exercises I
    01:26
  • Functions Exercises I with Solutions
    05:15
  • Lexical Scoping
    03:23
  • Name Masking (part 1)
    04:12
  • Name Masking (part 2)
    03:17
  • Name Masking (part 3)
    03:58
  • Functions versus Variables
    05:32
  • A Fresh Start
    05:31
  • Dynamic Lookup (part 1)
    05:41
  • Dynamic Lookup (part 2)
    03:06
  • Functions Exercises II
    02:00
  • Functions Exercises II with Solutions
    03:09
  • More on Functions Calls (part 1)
    04:57
  • More on Function Calls (part 2)
    05:33
  • Function Arguments (part 1)
    05:05
  • Function Arguments (part 2)
    05:26
  • Calling Functions with a List of Arguments
    01:47
  • Default and Missing Arguments
    07:24
  • Lazy Evaluation (part 1)
    05:20
  • Lazy Evaluation (part 2)
    05:50
  • The " . . . " (Triple Dot) Function
    05:45
  • Functions Exercises III
    01:01
  • Functions Exercises III with Solutions
    07:16
  • Infix Operator Functions
    06:54
  • Replacement Functions
    06:46
  • Functions Exercises IV
    00:47
  • Functions Exercises IV with Solutions
    04:52
  • Return Values (part 1)
    05:14
  • Return Values (part 2)
    04:59
  • Functions End-of-Section Exercises
    06:07
  • Functions End-of-Section Exercises with Solutions (part 1)
    03:48
  • Functions End-of-Section Exercises with Solutions (part 2)
    05:01
  • Functions End-of-Section Exercises with Solutions (part 3)
    03:52
  • Functions End-of-Section Exercises with Solutions (part 4)
    06:02

About the Author

Geoffrey Hubona, Professor of Information Systems

Dr. Geoffrey Hubona held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 3 major state universities in the Eastern United States from 1993-2010. In these positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL (1993); an MA in Economics (1990), also from USF; an MBA in Finance (1979) from George Mason University in Fairfax, VA; and a BA in Psychology (1972) from the University of Virginia in Charlottesville, VA. He was a full-time assistant professor at the University of Maryland Baltimore County (1993-1996) in Catonsville, MD; a tenured associate professor in the department of Information Systems in the Business College at Virginia Commonwealth University (1996-2001) in Richmond, VA; and an associate professor in the CIS department of the Robinson College of Business at Georgia State University (2001-2010). He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. Dr. Hubona is an expert of the analytical, open-source R software suite and of various PLS path modeling software packages, including SmartPLS. He has published dozens of research articles that explain and use these techniques for the analysis of data, and, with software co-development partner Dean Lim, has created a popular cloud-based PLS software application, PLS-GUI.

More From Author

Reviews

Raghav Raju
5

The depth of coverage is really excellent

Practical Foundations of R Programming

  • 07:54:27
  • 102
  • 22
  • Language: English
  • 15 days Money back Gurantee
  • Unlimited Access
  • Android, iPhone and iPad Access
  • Certificate of Completion

Course Summary

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

Read More

Target Audience

  • 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

Pre-Requisites

  • 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

About the Author

Geoffrey Hubona, Professor of Information Systems

Dr. Geoffrey Hubona held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 3 major state universities in the Eastern United States from 1993-2010. In these positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL (1993); an MA in Economics (1990), also from USF; an MBA in Finance (1979) from George Mason University in Fairfax, VA; and a BA in Psychology (1972) from the University of Virginia in Charlottesville, VA. He was a full-time assistant professor at the University of Maryland Baltimore County (1993-1996) in Catonsville, MD; a tenured associate professor in the department of Information Systems in the Business College at Virginia Commonwealth University (1996-2001) in Richmond, VA; and an associate professor in the CIS department of the Robinson College of Business at Georgia State University (2001-2010). He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. Dr. Hubona is an expert of the analytical, open-source R software suite and of various PLS path modeling software packages, including SmartPLS. He has published dozens of research articles that explain and use these techniques for the analysis of data, and, with software co-development partner Dean Lim, has created a popular cloud-based PLS software application, PLS-GUI.

More From Author

Review & Rating

Raghav Raju 5

The depth of coverage is really excellent