Programming Statistical Applications in R

Programming Statistical Applications in R, available at $19.99, has an average rating of 3.8, with 89 lectures, based on 57 reviews, and has 1704 subscribers.

You will learn about Understand how to create and manipulate R data structures used in scientific programming applications. Understand and use important statistical R programming concepts such as looping and control structures, interactive data input and formatting output, writing functions as programs, writing output to a file and plotting output. Understand and be able to use the R apply family of functions efficiently. Know how to debug programs and how to make programs run more efficiently. Understand and be able to implement various resampling methods effectively, including bootstrapping, jackknifing and N-fold cross validation. This course is ideal for individuals who are You do NOT need to be experienced with R, nor do you need to have experience with computer programming to successfully complete this course. or The course would be useful to anyone interested in learning more about statistical programming using the R language. or Course is good for undergraduate students seeking to acquire programming skills and knowledge of R software. or Course is useful for graduate students seeking to acquire and refine their skills relating to data analysis and manipulation. It is particularly useful for You do NOT need to be experienced with R, nor do you need to have experience with computer programming to successfully complete this course. or The course would be useful to anyone interested in learning more about statistical programming using the R language. or Course is good for undergraduate students seeking to acquire programming skills and knowledge of R software. or Course is useful for graduate students seeking to acquire and refine their skills relating to data analysis and manipulation.

Enroll now: Programming Statistical Applications in R

Summary

Title: Programming Statistical Applications in R

Price: $19.99

Average Rating: 3.8

Number of Lectures: 89

Number of Published Lectures: 89

Number of Curriculum Items: 89

Number of Published Curriculum Objects: 89

Original Price: $64.99

Quality Status: approved

Status: Live

What You Will Learn

Understand how to create and manipulate R data structures used in scientific programming applications.
Understand and use important statistical R programming concepts such as looping and control structures, interactive data input and formatting output, writing functions as programs, writing output to a file and plotting output.
Understand and be able to use the R apply family of functions efficiently.
Know how to debug programs and how to make programs run more efficiently.
Understand and be able to implement various resampling methods effectively, including bootstrapping, jackknifing and N-fold cross validation.

Who Should Attend

You do NOT need to be experienced with R, nor do you need to have experience with computer programming to successfully complete this course.
The course would be useful to anyone interested in learning more about statistical programming using the R language.
Course is good for undergraduate students seeking to acquire programming skills and knowledge of R software.
Course is useful for graduate students seeking to acquire and refine their skills relating to data analysis and manipulation.

Target Audiences

You do NOT need to be experienced with R, nor do you need to have experience with computer programming to successfully complete this course.
The course would be useful to anyone interested in learning more about statistical programming using the R language.
Course is good for undergraduate students seeking to acquire programming skills and knowledge of R software.
Course is useful for graduate students seeking to acquire and refine their skills relating to data analysis and manipulation.

Programming Statistical Applications in R is an introductory course teaching the basics of programming mathematical and statistical applications using the R language. The course makes extensive use of the Introduction to Scientific Programming and Simulation using R (spuRs) package from the Comprehensive R Archive Network (CRAN). The course is a scientific-programming foundations course and is a useful complement and precursor to the more simulation-application oriented R Programming for Simulation and Monte-Carlo Methods Udemy course. The two courses were originally developed as a two-course sequence (although they do share some exercises in common). Together, both courses provide a powerful set of unique and useful instruction about how to create your own mathematical and statistical functions and applications using R software.

Programming Statistical Applications in R is a "hands-on" course that comprehensively teaches fundamental R programming skills, concepts and techniques useful for developing statistical applications with R software. The course also uses dozens of "real-world" scientific function examples. It is not necessary for a student to be familiar with R, nor is it necessary to be knowledgeable about programming in general, to successfully complete this course. This course is 'self-contained' and includes all materials, slides, exercises (and solutions); in fact, everything that is seen in the course video lessons is included in zipped, downloadable materials files. The course is a great instructional resource for anyone interested in refining their skills and knowledge about statistical programming using the R language. It would be useful for practicing quantitative analysis professionals, and for undergraduate and graduate students seeking new job-related skills and/or skills applicable to the analysis of research data.

The course begins with basic instruction about installing and using the R console and the RStudio application and provides necessary instruction for creating and executing R scripts and R functions. Basic R data structures are explained, followed by instruction on data input and output and on basic R programming techniques and control structures. Detailed examples of creating new statistical R functions, and of using existing statistical R functions, are presented. Boostrap and Jackknife resampling methods are explained in detail, as are methods and techniques for estimating inference and for constructing confidence intervals, as well as of performing N-fold cross validation assessments of competing statistical models. Finally, detailed instructions and examples for debugging and for making R programs run more efficiently are demonstrated.

Course Curriculum

Chapter 1: Introduction to Course Materials, Installing Packages, and Executing Scripts

Lecture 1: Course Introduction

Lecture 2: Introduction to Course Materials

Lecture 3: Install R and RStudio

Lecture 4: General Discussion of R

Lecture 5: A Look at the R Console and RStudio

Lecture 6: Executing Script and Installing Packages in RStudio (part 1)

Lecture 7: Executing Script and Installing Packages in RStudio (part 2)

Lecture 8: R Script Demonstrations using RStudio

Lecture 9: Scripting Basic Data Structures (part 1)

Lecture 10: Scripting Basic Data Structures (part 2)

Lecture 11: R Functions (part 1)

Lecture 12: R Functions (part 2)

Lecture 13: R Functions (part 3)

Lecture 14: Manipulating Matrices (part 1)

Lecture 15: Manipulating Matrices (part 2)

Lecture 16: Manipulating Matrices (part 3)

Chapter 2: Basic R Programming Concepts and Techniques

Lecture 1: Basic R Programming Concepts and Examples (part 1)

Lecture 2: Basic R Programming Concepts and Examples (part 2)

Lecture 3: Basic R Programming Concepts and Examples (part 3)

Lecture 4: Looping Control Structure Examples (part 1)

Lecture 5: Looping Control Structure Examples (part 2)

Lecture 6: Looping and Control Structure Exercises

Lecture 7: Data Input and Output (part 1)

Lecture 8: Data Input and Output (part 2)

Lecture 9: Formatting Output (part 1)

Lecture 10: Formatting Output (part 2)

Lecture 11: Interactive Input and Output

Lecture 12: Looping and Control Structure Exercises (part 1)

Lecture 13: Looping and Control Structure Exercises (part 2)

Lecture 14: Looping and Control Structure Exercises (part 3)

Lecture 15: Writing Output to a File (part 1)

Lecture 16: Writing Output to a File (part 2)

Lecture 17: Plotting as Output (part 1)

Lecture 18: Plotting as Output (part 2)

Lecture 19: Exercise: Writing Statistical and Scientific Expressions

Lecture 20: Exercise Solution: Writing Statistical and Scientific Functions

Chapter 3: Writing User-Defined Functions in R

Lecture 1: Writing Functions as Programs (part 1)

Lecture 2: Writing Functions as Programs (part 2)

Lecture 3: Windsorized Means Example

Lecture 4: Writing Functions in R (part 1)

Lecture 5: Writing Functions in R (part 2)

Lecture 6: Writing Functions in R (part 3)

Lecture 7: Writing Functions in R (part 4)

Lecture 8: Apply Family of Functions (part 1)

Lecture 9: Apply Family of Functions (part 2)

Lecture 10: Apply Family of Functions (part 3)

Lecture 11: Apply Family of Functions (part 4)

Lecture 12: Apply Family of Functions (part 5)

Lecture 13: Making Programs Run Efficiently

Lecture 14: Exercise: Writing Functions and Programs

Lecture 15: Exercise Solutions: Writing Functions and Programs (part 1)

Lecture 16: Exercise Solutions: Writing Functions and Programs (part 2)

Lecture 17: Exercise: Vector Maker Functions

Chapter 4: Data Types and Structures: Factors, Dataframes and Lists

Lecture 1: Exercise Solutions: Vector Maker Functions (part 1)

Lecture 2: Exercise Solutions: Vector Maker Functions (part 2)

Lecture 3: Data Types: Factors (part 1)

Lecture 4: Data Types: Factors (part 2)

Lecture 5: Data Structures: Dataframes (part 1)

Lecture 6: Data Structures: Dataframes (part 2)

Lecture 7: Data Structures: Dataframes (part 3)

Lecture 8: Data Structures: Dataframes (part 4)

Lecture 9: Data Structures: Lists (part 1)

Lecture 10: Data Structures: Lists (part 2)

Chapter 5: Bootstrap and Jackknife Resampling Methods

Lecture 1: Bootstrap Estimate of Standard Error and Bias (part 1)

Lecture 2: Bootstrap Estimate of Standard Error and Bias (part 2)

Lecture 3: Bootstrapping a Ratio Statistic

Lecture 4: Jackknife Estimate of Bias and Standard Error

Lecture 5: Bootstrapping Confidence Intervals (part 1)

Lecture 6: Bootstrapping Confidence Intervals (part 2)

Lecture 7: Bootstrapping Confidence Intervals (part 3)

Lecture 8: N-Fold Cross Validation of Models (part 1)

Lecture 9: N-Fold Cross-Validation of Models (part 2)

Lecture 10: N-Fold Cross-Validation of Models (part 3)

Lecture 11: Bootstrap-Jackknife Resampling Exercise

Chapter 6: Debugging and Program Efficiency

Lecture 1: Bootstrap-Jackknife Resampling Exercise Solution

Lecture 2: Debugging R Programs

Lecture 3: Findruns Program Debugging Example (part 1)

Lecture 4: Findruns Program Debugging Example (part 2)

Lecture 5: Additional Programming Considerations

Lecture 6: Program Efficiencies and Scoping Rules

Lecture 7: Selecting Environment to Debug

Lecture 8: Creating S3 and S4 Classes (part 1)

Lecture 9: Creating S3 and S4 Classes (part 2)

Lecture 10: Creating S3 and S4 Classes (part 3)

Lecture 11: Numerical Accuracy and Program Efficiency (part 1)

Lecture 12: Numerical Accuracy and Program Efficiency (part 2)

Lecture 13: More on Program Efficiency (part 1)

Lecture 14: More on Program Efficiency (part 2)

Lecture 15: Selection Sort Exercise

Instructors

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

Rating Distribution

1 stars: 4 votes
2 stars: 4 votes
3 stars: 17 votes
4 stars: 16 votes
5 stars: 16 votes

Frequently Asked Questions

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

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